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Volume 10
Issue 5
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ISPRS Int. J. Geo-Inf., Volume 10, Issue 5 (May 2021) – 85 articles

Cover Story (view full-size image): As human beings, we are prone to taking for granted information that stems from our domain knowledge, instead of being properly formalized. On the other hand, automated agents are typically not apt to correctly model information that is not crisp but involves blurred membership degrees. Finally, even when fully formalized, knowledge may strive for consensus and widespread adoption in the specific application domain. Applying appropriate “semantics” to data and metadata objects is thus of paramount importance, especially for geoinformation, as input data frequently have blurred contours and output data may depend on judgment by the researcher. This paper applies a renowned classification of semantics to geosemantics in order to pinpoint elective approaches for the individual categories. View this paper
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29 pages, 6359 KiB  
Article
A GIS Assessment of the Suitability of Tilapia and Clarias Pond Farming in Tanzania
by Håkan Berg, Deogratias Mulokozi and Lars Udikas
ISPRS Int. J. Geo-Inf. 2021, 10(5), 354; https://doi.org/10.3390/ijgi10050354 - 20 May 2021
Cited by 2 | Viewed by 4035
Abstract
Aquaculture production in Tanzania has increased in recent years, responding to an increased demand for fish, but the scale and productivity of smallholder aquaculture remains below the level needed to support significant sector growth in Tanzania. This study assesses, through geospatial analyses, the [...] Read more.
Aquaculture production in Tanzania has increased in recent years, responding to an increased demand for fish, but the scale and productivity of smallholder aquaculture remains below the level needed to support significant sector growth in Tanzania. This study assesses, through geospatial analyses, the suitability for freshwater pond farming of Oreochromis niloticus and Clarias gariepinus in Tanzania, by assessing the geographical distribution of seven criteria (water availability, water temperature, soil texture, terrain slope, availability of farm inputs, potential farm-gate sales, and access to local markets) identified as important for fish pond farming. The criteria were developed and standardized from 15 sub-criteria, which were classified into a four-level suitability scale based on physical scores. The individual weights of the different criteria in the overall GIS suitability assessment were determined through a multi-criteria evaluation. The final results were validated and compared through field observations, interviews with 89 rural and 11 urban aquaculture farmers, and a questionnaire survey with 16 regional fisheries officers. Our results indicate that there is a good potential for aquaculture in Tanzania. Almost 60% of Tanzania is assessed as being suitable and 40% as moderately suitable for small-scale subsistence pond farming, which is the dominating fish farming practice currently. The corresponding figures for medium-scale commercial farming, which many regions expect to be the dominating farming method within ten-years, were 52% and 47% respectively. The availability of water was the most limiting factor for fish pond farming, which was confirmed by both farmers and regional fisheries officers, and assessed as being “suitable” in only 28% of the country. The availability of farm-gate sales and local markets were “moderate suitable” to “suitable” and were seen as a constraint for commercial farms in rural areas. The availability of farm inputs (agriculture waste and manure) was overall good (26% very suitable and 32% suitable), but high-quality fish feed was seen as a constraint to aquaculture development, both by farmers and regional fisheries officers. Soil, terrain, and water temperature conditions were assessed as good, especially at low altitudes and in regions close to the sea and south of Lake Victoria. Full article
(This article belongs to the Special Issue GIS-Based Analysis for Quality of Life and Environmental Monitoring)
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<p>(<b>a</b>) Map of Tanzania showing constraint areas (major cities, protected areas and water bodies), that not were included in the analysis, and ponds observed in the field (84). (<b>b</b>) Distribution of fish ponds and main hatcheries in Tanzania in 2016 (pers. com. Ministry of Livestock and Fisheries development).</p> Full article ">Figure 2
<p>Schematic overview of the procedures integrating GIS and MCE to assess the suitability of small-scale and commercial fish pond farming in Tanzania.</p> Full article ">Figure 3
<p>Structure of the GIS spatial analysis.</p> Full article ">Figure 4
<p>The relative importance of factors influencing on fish pond farming as ranked by 16 regional fisheries officers in Tanzania.</p> Full article ">Figure 5
<p>The distribution of suitability scores for; (<b>a</b>) annual water availability and (<b>b</b>) water temperature.</p> Full article ">Figure 6
<p>Spearman’s rank-order correlations showing: (<b>a</b>) A positive correlation between the log number of fish ponds per region in 2016 and the suitability score for water availability, which was statistically significant (rs(22) = 0.623, <span class="html-italic">p</span> = 0.001); (<b>b</b>) and a negative correlation between the log number of fish ponds per region in 2016 and the suitability score for engineering capabilities (soil and terrain), which was statistically significant (rs(22) = −0.746, <span class="html-italic">p</span> &lt; 0.001).</p> Full article ">Figure 7
<p>The relative importance of different constraints to development of fish pond farming as ranked by 16 regional fisheries officers in Tanzania.</p> Full article ">Figure 8
<p>The distribution of suitability scores for; (<b>a</b>) engineering capabilities (soil texture and terrain) and; (<b>b</b>) availability of farm inputs (agriculture waste and manure).</p> Full article ">Figure 9
<p>The relative importance of factors for expanding fish pond farming as ranked by 16 regional fisheries officers in Tanzania.</p> Full article ">Figure 10
<p>The distribution of suitability scores for (<b>a</b>) farm-gate sales and; (<b>b</b>) market accessibility.</p> Full article ">Figure 11
<p>The relative importance of benefits from fish pond farming as ranked by 16 regional fisheries officers in Tanzania.</p> Full article ">Figure 12
<p>Regional fisheries officers perception of the dominant aquaculture system in their region now and in the 10-years time in Tanzania.</p> Full article ">Figure 13
<p>The distribution of suitability scores for; (<b>a</b>) small-scale subsistence fish pond farming and; (<b>b</b>) medium-scale commercial fish pond farming in Tanzania.</p> Full article ">
31 pages, 9866 KiB  
Article
Automatic Delineation of Urban Growth Boundaries Based on Topographic Data Using Germany as a Case Study
by Oliver Harig, Robert Hecht, Dirk Burghardt and Gotthard Meinel
ISPRS Int. J. Geo-Inf. 2021, 10(5), 353; https://doi.org/10.3390/ijgi10050353 - 20 May 2021
Cited by 16 | Viewed by 5485
Abstract
Urban Growth Boundary (UGB) is a growth management policy that designates specific areas where growth should be concentrated in order to avoid urban sprawl. The objective of such a boundary is to protect agricultural land, open spaces and the natural environment, as well [...] Read more.
Urban Growth Boundary (UGB) is a growth management policy that designates specific areas where growth should be concentrated in order to avoid urban sprawl. The objective of such a boundary is to protect agricultural land, open spaces and the natural environment, as well as to use existing infrastructure and public services more efficiently. Due to the inherent heterogeneity and complexity of settlements, UGBs in Germany are currently created manually by experts. Therefore, every dataset is linked to a specific area, investigation period and dedicated use. Clearly, up-to-date, homogeneous, meaningful and cost-efficient delineations created automatically are needed to avoid this reliance on manually or semi-automatically generated delineations. Here, we present an aggregative method to produce UGBs using building footprints and generally available topographic data as inputs. It was applied to study areas in Frankfurt/Main, the Hanover region and rural Brandenburg while taking full account of Germany’s planning and legal framework for spatial development. Our method is able to compensate for most of the weaknesses of available UGB data and to significantly raise the accuracy of UGBs in Germany. Therefore, it represents a valuable tool for generating basic data for future studies. Application elsewhere is also conceivable by regionalising the employed parameters. Full article
(This article belongs to the Special Issue Geo-Information Science in Planning and Development of Smart Cities)
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<p>Using the municipality of Herzberg (Elster) in Germany as an example, the illustration shows the differences between the delineation of the settlement body (ATKIS<sup>®</sup>-Ortslage) according to the official digital landscape models (DLM) and an expert delineation (ED) of an inner zone by statute. (<span class="html-italic">source</span>: own illustration based on data from official building polygons, ATKIS<sup>®</sup>-Ortslage Base-DLM © Geobasis-DE/BKG (2017) and Planning Information System of the Joint State Planning Department Berlin/Brandenburg).</p> Full article ">Figure 2
<p>Workflow for delineation and assessment of urban growth boundaries based on building footprints (<span class="html-italic">source</span>: own illustration).</p> Full article ">Figure 3
<p>Cartographic partitioning process: (<b>a</b>) point raster with different density values represented by shades of grey from white = 0 to black = 1; (<b>b</b>) Voronoi diagram of selected points; and (<b>c</b>) original buildings (dark grey) and boundaries of the final cartographic partitions (light grey). Scale 1:100,000 (<span class="html-italic">source</span>: own illustration based on data from official building polygons, ATKIS<sup>®</sup>-Base-DLM © Geobasis-DE/BKG (2017)).</p> Full article ">Figure 4
<p>The figure shows how the reference area is determined for calculating the building coverage ratio, here using as an example the settlement of Ortrand in Brandenburg. The light grey areas with dark grey borders are the street blocks derived from the road network. The majority of the street blocks are very large at the edge of the settlement and only a small share of them can be assigned to the actual settlement area. In order to consider only those street blocks that lie within the settlement area, all buildings are therefore enclosed by a 100-metre buffer (hatched areas). Street blocks that lie within this buffer area and contain at least 20 buildings or parts of buildings are used for the calculation (dark grey areas). (<span class="html-italic">source</span>: own illustration based on data from ATKIS<sup>®</sup> Base-DLM © Geobasis-DE/BKG (2017)).</p> Full article ">Figure 5
<p>The figure shows how the filter algorithm works using the example of the settlement of Hirschfeld in Brandenburg. The buildings removed by the negative filter (highlighted in red) can be found both inside and outside the expert boundary. In contrast, the majority of the buildings of the positive filter (highlighted in blue) only occur within the expert boundary. Very few buildings remain unassigned to either of the two filters. The application of the density function ensures that not every single blue building contributes to the formation of the positive buffer polygon. The red buildings outside the buffer area are removed. The assignment was made on the basis of explicit statements in the commentary literature or the legal text of the BauGB. (<span class="html-italic">Source</span>: own compilation based on data from official building polygons, ATKIS<sup>®</sup> Base-DLM © Geobasis-DE/BKG (2017)).</p> Full article ">Figure 6
<p>The figure shows the delineation results of (<b>a</b>) an area minimising and (<b>b</b>) an edge-weighted algorithm for creating minimum enclosing rectangles. (<span class="html-italic">Source</span>: own compilation based on data from official building polygons, ATKIS<sup>®</sup> Base-DLM © Geobasis-DE/BKG (2017)).</p> Full article ">Figure 7
<p>Visualisation of the refinement process: In a first step, areas between dense blocks, grouped buildings and single buildings are closed. This is followed by the closing of gaps and holes. Finally, all geometries are merged into a single geometry. (<span class="html-italic">source</span>: own illustration based on data from ATKIS<sup>®</sup> Base-DLM © Geobasis-DE/BKG (2017)).</p> Full article ">Figure 8
<p>Types of area-positive deviations (<span class="html-italic">source</span>: own compilation based on data from official building polygons, ATKIS<sup>®</sup> Base-DLM © Geobasis-DE/BKG (2017), Planning Information System of the Joint State Planning Department Berlin/Brandenburg).</p> Full article ">Figure 9
<p>Classes of area-negative deviations (<span class="html-italic">source</span>: own compilation based on data from official building polygons, ATKIS<sup>®</sup> Base-DLM © Geobasis-DE/BKG (2017), Planning Information System of the Joint State Planning Department Berlin/Brandenburg).</p> Full article ">Figure 10
<p>Study areas within the three analysed regions: (<b>a</b>) Brandenburg; (<b>b</b>) Hanover Region; and (<b>c</b>) Frankfurt/Main; and (<b>d</b>) Germany. The administrative borders of the regions are shown as black lines, the study areas as grey areas. Since the layout is a result of the settlement structure (partitioning), these do not coincide with the administrative boundaries. White areas between partitions result from missing expert delineation data. (<span class="html-italic">source</span>: own compilation based on data from ATKIS<sup>®</sup> Base-DLM © Geobasis-DE/BKG (2017)).</p> Full article ">Figure 11
<p>Results for (<b>a</b>) Isernhagen, (<b>b</b>) Nordgoltern and Grossgoltern in the Hanover region, (<b>c</b>) Rietz in Brandenburg and (<b>d</b>) Frankfurt/Main. Delineated areas determined by our method are highlighted in light grey. (<span class="html-italic">source</span>: own compilation based on data from official building polygons, ATKIS<sup>®</sup> Base-DLM © Geobasis-DE/BKG (2017), Planning Information System of the Joint State Planning Department Berlin/Brandenburg).</p> Full article ">
23 pages, 4599 KiB  
Article
Public Bike Trip Purpose Inference Using Point-of-Interest Data
by Jiwon Lee, Kiyun Yu and Jiyoung Kim
ISPRS Int. J. Geo-Inf. 2021, 10(5), 352; https://doi.org/10.3390/ijgi10050352 - 20 May 2021
Cited by 12 | Viewed by 3739
Abstract
Public bike-sharing is eco-friendly, connects excellently with other transportation modes, and provides a means of mobility that is highly suitable in the current era of climate change. This study proposes a methodology for inferring the bike trip purpose based on bike-share and point-of-interest [...] Read more.
Public bike-sharing is eco-friendly, connects excellently with other transportation modes, and provides a means of mobility that is highly suitable in the current era of climate change. This study proposes a methodology for inferring the bike trip purpose based on bike-share and point-of-interest (POI) data. Because the purpose of a trip involves decision-making, its inference necessitates an understanding of the spatiotemporal complexity of human activities. Thus, the spatiotemporal features affecting bike trips were selected from the bike-share data, and the land uses at the origin and destination of the trips were extracted from the POI data. During POI type embedding, the data were augmented considering the geographical distance between the POIs and the number of bike rentals at each bike station. We further developed a ground truth data construction method that uses temporal mobile and POI data. The inference model was built using machine learning and applied to experiments involving bike stations in Seocho-gu, Seoul, Korea. The experimental results revealed that optimal performance was achieved with the use of decision tree algorithms, as demonstrated by a 78.95% overall accuracy and 66.43% F1-score. The proposed method contributes to a better understanding of the causes of movement within cities. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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<p>Flow chart of the proposed bike trip purpose inference method.</p> Full article ">Figure 2
<p>Word embedding vs. POI type embedding.</p> Full article ">Figure 3
<p>Composition of a POI tuple pair at a bike station.</p> Full article ">Figure 4
<p>Standardization of different space units into mesh data.</p> Full article ">Figure 5
<p>Standardization method using building floor area data.</p> Full article ">Figure 6
<p>Method for extracting the bike trip purpose using mobile data and POI data.</p> Full article ">Figure 7
<p>Results of the elbow technique for determining the optimal number of clusters.</p> Full article ">Figure 8
<p>Results of the final clustering.</p> Full article ">Figure 9
<p>Visualization of POI distributions within a cluster.</p> Full article ">
20 pages, 7020 KiB  
Article
Development of an Integrated BIM-3D GIS Approach for 3D Cadastre in Morocco
by Rafika Hajji, Reda Yaagoubi, Imane Meliana, Imane Laafou and Ahmed El Gholabzouri
ISPRS Int. J. Geo-Inf. 2021, 10(5), 351; https://doi.org/10.3390/ijgi10050351 - 20 May 2021
Cited by 27 | Viewed by 5255
Abstract
With rapid population growth, there is an increasing demand for vertical use of space. The wide spread of complex and high-rise buildings, as well as the increasing number of infrastructure above or underground, requires new methods for efficient management of land property. 3D [...] Read more.
With rapid population growth, there is an increasing demand for vertical use of space. The wide spread of complex and high-rise buildings, as well as the increasing number of infrastructure above or underground, requires new methods for efficient management of land property. 3D cadastre has, thus, become a necessity for land administration. However, the success of 3D cadastral systems relies on the definition of legal and institutional frameworks and requires implementing performant technical solutions. The potential of BIM and 3D GIS in this field has been demonstrated by several authors. However, cadastral development is strongly related to the national context of each country in terms of laws, institutions, etc. In this paper, an integrated approach based on BIM and 3D GIS for the implementation of a 3D cadastre in Morocco is presented. This approach demonstrates the relevance of such integration for the efficient management of cadastral information. First, a Conceptual Data Model (CDM) based on an extension of CityGML, was proposed for the management of cadastral information in Morocco. Then, a BIM modeling process was developed according to the model’s specifications and then translated to CityGML format. After that, a 3D Geodatabase was implemented in ArcGIS based on the proposed CDM. Our method was applied to a case of co-ownership building, showing several difficulties and limits in terms of 2D representation. The results show several advantages in terms of representation and management of 3D cadastral objects. In addition, some improvements are proposed to extend the concept of co-owner share to a volumetric calculation. Full article
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<p>The “Oudaya” Tunnel in Rabat (Morocco).</p> Full article ">Figure 2
<p>Verbal description of the right of “houa” in a sketch of demarcation.</p> Full article ">Figure 3
<p>IFC extension model for 3D cadastre [<a href="#B7-ijgi-10-00351" class="html-bibr">7</a>].</p> Full article ">Figure 4
<p>Relevant IFC spatial and physical entities for 3D Cadastre (inspired from [<a href="#B25-ijgi-10-00351" class="html-bibr">25</a>]).</p> Full article ">Figure 5
<p>Main stages of the methodology.</p> Full article ">Figure 6
<p>CityGML extension for the Cadastre Data Model.</p> Full article ">Figure 7
<p>The building co-ownership, object of the case study.</p> Full article ">Figure 8
<p>(<b>a</b>) A 2D representation of the ramp and the parking space; (<b>b</b>) the associated 3D model.</p> Full article ">Figure 9
<p>(<b>a</b>) The representation of commerce in 2D drawings (ground floor); (<b>b</b>) The representation of the void of commerce in 2D drawings (first floor).</p> Full article ">Figure 10
<p>The 3D extent of: (<b>a</b>) the right-of-way easement; (<b>b</b>) the right of usage.</p> Full article ">Figure 11
<p>A vertical graphic of the building.</p> Full article ">Figure 12
<p>(<b>a</b>) Representation of legal objects within BIM; (<b>b</b>) The resulting BIM textured in Revit.</p> Full article ">Figure 13
<p>FME algorithm for IFC To CityGML conversion.</p> Full article ">Figure 14
<p>Available information on a selected private area.</p> Full article ">Figure 15
<p>An extracted 2D plan according the formal specifications of ANCFCC.</p> Full article ">
21 pages, 4236 KiB  
Article
Comparison of Ecohydrological and Climatological Zoning of the Cities: Case Study of the City of Pilsen
by Jan Kopp, Jindřich Frajer, Marie Novotná, Jiří Preis and Martin Dolejš
ISPRS Int. J. Geo-Inf. 2021, 10(5), 350; https://doi.org/10.3390/ijgi10050350 - 19 May 2021
Cited by 8 | Viewed by 2561
Abstract
Standardized delimiting of local climate zones (LCZ) will be better applicable to the urban adaptation to climate change when the ecohydrological properties of LCZ units are known. Therefore, the properties of LCZ units based on the methodology of ecohydrological zoning of the urban [...] Read more.
Standardized delimiting of local climate zones (LCZ) will be better applicable to the urban adaptation to climate change when the ecohydrological properties of LCZ units are known. Therefore, the properties of LCZ units based on the methodology of ecohydrological zoning of the urban landscape, which was created in GIS as a basis for planning blue-green infrastructure of cities in the Czech Republic, are presented in the paper. The goal of this study is to compare approaches and results of our own ecohydrological zonation and standardized LCZ delimiting in the city of Pilsen. Both methodological approaches differ in input data, resolution details and parameters used. The results showed that the areas of the individual LCZ classes show different levels of ecohydrological qualities. Internal heterogeneity of LCZ classes demonstrated by variance of ecohydrological parameters’ values can be partly explained by different techniques and data sources for delimitation of both zonations, but by different sets of delimitation criteria. The discussion is held on the importance of terrain slope for supplementing the LCZ classification. A case study can be a stimulus for further development of holistic urban zoning methodologies that would take into account both climatological and ecohydrological conditions. Full article
(This article belongs to the Special Issue Application of GIS-Based Mapping of Local Climate Zones in Urban Areas)
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<p>Geographical conditions of the territory of the city of Pilsen. Data sources: ArcČR500 (2016); Czech Office for Surveying, Mapping and Cadaster (2020); Openstreet maps and Geofabrik (2020).</p> Full article ">Figure 2
<p>Flow-chart of the ecohydrological zoning of the city of Pilsen.</p> Full article ">Figure 3
<p>Delimiting local climate zones (LCZ) in the cadastral area of Pilsen. Source: own processing based on classification algorithm [<a href="#B61-ijgi-10-00350" class="html-bibr">61</a>].</p> Full article ">Figure 4
<p>Ecohydrological parameterization of LCZ raster on the basis of microstructures.</p> Full article ">Figure 5
<p>Relative representation of microstructure types in LCZ 5–LCZ 9 (%).</p> Full article ">Figure 6
<p>Box plots of LCZ classes according to ecohydrological parameters ((<b>A</b>) runoff coefficient, (<b>B</b>) evapotranspiration coefficient, (<b>C</b>) biotope area factor) and slope (<b>D</b>). The lower part of the box is the first quartile, and the upper is the third quartile. Whiskers indicate the lowest value still within 1.5 IQR (IQR = third quartile−first quartile) and the highest value still within 1.5 IQR. Points indicate outliers.</p> Full article ">Figure 7
<p>Distributions of LCZ 5–LCZ 9 cells according to ecohydrological parameters ((<b>A</b>) runoff coefficient, (<b>B</b>) evapotranspiration coefficient, (<b>C</b>) biotope area factor).</p> Full article ">
17 pages, 9393 KiB  
Article
Coupling Historical Maps and LiDAR Data to Identify Man-Made Landforms in Urban Areas
by Martino Terrone, Pietro Piana, Guido Paliaga, Marco D’Orazi and Francesco Faccini
ISPRS Int. J. Geo-Inf. 2021, 10(5), 349; https://doi.org/10.3390/ijgi10050349 - 18 May 2021
Cited by 21 | Viewed by 4503
Abstract
In recent years, there has been growing interest in urban geomorphology both for its applications in terms of landscape planning, and its historical, cultural, and scientific interest. Due to recent urban growth, the identification of landforms in cities is difficult, particularly in Mediterranean [...] Read more.
In recent years, there has been growing interest in urban geomorphology both for its applications in terms of landscape planning, and its historical, cultural, and scientific interest. Due to recent urban growth, the identification of landforms in cities is difficult, particularly in Mediterranean and central European cities, characterized by more than 1000 years of urban stratification. By comparing and overlapping 19th-century cartography and modern topography from remote sensing data, this research aims to assess the morphological evolution of the city of Genoa (Liguria, NW Italy). The analysis focuses on a highly detailed 1:2’000 scale map produced by Eng. Ignazio Porro in the mid-19th century. The methodology, developed in QGIS, was applied on five case studies of both hillside and valley floor areas of the city of Genoa. Through map overlay and digitalization of elevation data and contour lines, it was possible to identify with great accuracy the most significant morphological transformations that have occurred in the city since the mid-19th century. In addition, the results were validated by direct observation and by drills data of the regional database. The results allowed the identification and quantification of the main anthropic landforms. The paper suggests that the same methodology can be applied to other historical urban contexts characterized by urban and architectural stratification. Full article
(This article belongs to the Special Issue Geographic Information Science (GIScience) and Geospatial Approaches for the Analysis of Historical Visual Sources and Cartographic Material)
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<p>(<b>A</b>) Study area and areas of interest. (<b>B</b>) The <span class="html-italic">geological sketch map</span> box: (1) waterfront embankments; (2) alluvial deposits; (3) slope deposits; (4) stiff fissured clays; (5) shales; (6) marly limestone with clayey shales interlayers.</p> Full article ">Figure 2
<p>Urban sprawl in western Genoa (<b>a</b>) Vinzoni (1773) “Il dominio della Serenissima Repubblica di Genova in terraferma (Riviera di Ponente)”; (<b>b</b>) Carta degli Stati Sardi in Terraferma di S. M. il Re di Sardegna “Minute di campagna scale”, scale 1:9,450 period 1815–1823; 1:25,000 scale topographic map of the Italian Military Geographic Institute of 1878 (<b>c</b>) and 1934 (<b>d</b>).</p> Full article ">Figure 3
<p>(<b>a</b>) Porro Plate n°52. (<b>b</b>) The same area today, overlay of Regional Technical Map and DTM LiDAR.</p> Full article ">Figure 4
<p>Methodology workflow.</p> Full article ">Figure 5
<p>An example of a borehole with translated stratigraphy used for this research.</p> Full article ">Figure 6
<p>Sant’Agata Old Bridge: (<b>a</b>) Old surface; (<b>b</b>) DoD with the medieval and modern trace.</p> Full article ">Figure 7
<p>Polcevera Viaduct: (<b>a</b>) Old surface; (<b>b</b>) DoD.</p> Full article ">Figure 8
<p>San Benigno Promontory and Via Digione: (<b>a</b>) Old surface; (<b>b</b>) DoD; (<b>c</b>) Via Digione cross-section; (<b>d</b>) San Benigno Promontory cross-section.</p> Full article ">Figure 9
<p>Circonvallazione a Monte: (<b>a</b>) Old surface; (<b>b</b>) DoD.</p> Full article ">Figure 10
<p>(<b>a</b>) The output of Polcevera viaduct, in purple dotted line the Campasso area; (<b>b</b>) Left bank, elevated area with embankment sustaining wall in the Campasso area (view by Carlo Bossoli, 1853).</p> Full article ">Figure 11
<p>(<b>a</b>) Sant’Agata Bridge in the background (painting of Giuseppe Bisi, c. 1825); (<b>b</b>) Ancient Genoa: outside the city walls, over the hills, the site where Circonvallazione a Monte currently lies was characterized by a rural landscape (painting, unknown author, c. XV-XVII century); (<b>c</b>) San Benigno Promontory not yet excavated and the Lighthouse (<span class="html-italic">La Lanterna</span>) in the background; (<b>d</b>) Via Digione landslide (1968).</p> Full article ">
15 pages, 1790 KiB  
Article
A Quantitative Analysis of Factors Influencing Organic Matter Concentration in the Topsoil of Black Soil in Northeast China Based on Spatial Heterogeneous Patterns
by Zhenbo Du, Bingbo Gao, Cong Ou, Zhenrong Du, Jianyu Yang, Bayartungalag Batsaikhan, Battogtokh Dorjgotov, Wenju Yun and Dehai Zhu
ISPRS Int. J. Geo-Inf. 2021, 10(5), 348; https://doi.org/10.3390/ijgi10050348 - 18 May 2021
Cited by 36 | Viewed by 3493
Abstract
Black soil is fertile, abundant with organic matter (OM) and is exceptional for farming. The black soil zone in northeast China is the third-largest black soil zone globally and produces a quarter of China’s commodity grain. However, the soil organic matter (SOM) in [...] Read more.
Black soil is fertile, abundant with organic matter (OM) and is exceptional for farming. The black soil zone in northeast China is the third-largest black soil zone globally and produces a quarter of China’s commodity grain. However, the soil organic matter (SOM) in this zone is declining, and the quality of cultivated land is falling off rapidly due to overexploitation and unsustainable management practices. To help develop an integrated protection strategy for black soil, this study aimed to identify the primary factors contributing to SOM degradation. The geographic detector, which can detect both linear and nonlinear relationships and the interactions based on spatial heterogeneous patterns, was used to quantitatively analyze the natural and anthropogenic factors affecting SOM concentration in northeast China. In descending order, the nine factors affecting SOM are temperature, gross domestic product (GDP), elevation, population, soil type, precipitation, soil erosion, land use, and geomorphology. The influence of all factors is significant, and the interaction of any two factors enhances their impact. The SOM concentration decreases with increased temperature, population, soil erosion, elevation and terrain undulation. SOM rises with increased precipitation, initially decreases with increasing GDP but then increases, and varies by soil type and land use. Conclusions about detailed impacts are presented in this paper. For example, wind erosion has a more significant effect than water erosion, and irrigated land has a lower SOM content than dry land. Based on the study results, protection measures, including conservation tillage, farmland shelterbelts, cross-slope ridges, terraces, and rainfed farming are recommended. The conversion of high-quality farmland to non-farm uses should be prohibited. Full article
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<p>The location of the study area and SOM concentration.</p> Full article ">Figure 2
<p>Transferring the spatial support of SOM.</p> Full article ">Figure 3
<p>The spatial distribution of influencing factors in the study area. (<b>a</b>) The spatial distribution of mean annual temperature; (<b>b</b>) the spatial distribution of mean annual precipitation; (<b>c</b>) the spatial distribution of digital elevation model; (<b>d</b>) the spatial distribution of pixelized population map; (<b>e</b>) the spatial distribution of pixelized gross domestic product map; (<b>f</b>) the spatial distribution of geomorphic types; (<b>g</b>) the spatial distribution of soil erosion data; (<b>h</b>) the spatial distribution of soil type; (<b>i</b>) the spatial distribution of land use type.</p> Full article ">Figure 3 Cont.
<p>The spatial distribution of influencing factors in the study area. (<b>a</b>) The spatial distribution of mean annual temperature; (<b>b</b>) the spatial distribution of mean annual precipitation; (<b>c</b>) the spatial distribution of digital elevation model; (<b>d</b>) the spatial distribution of pixelized population map; (<b>e</b>) the spatial distribution of pixelized gross domestic product map; (<b>f</b>) the spatial distribution of geomorphic types; (<b>g</b>) the spatial distribution of soil erosion data; (<b>h</b>) the spatial distribution of soil type; (<b>i</b>) the spatial distribution of land use type.</p> Full article ">Figure 4
<p>Results of the interaction detector. The size of the circle represents the relative size of the interaction <span class="html-italic">q</span>-value of the two factors.</p> Full article ">Figure 5
<p>Average SOM values with different grades for all factors. (<b>a</b>) Average SOM values with different grades for pixelized gross domestic product map; (<b>b</b>) average SOM values with different grades for pixelized population map; (<b>c</b>) average SOM values with different grades for land use type; (<b>d</b>) average SOM values with different grades for mean annual temperature; (<b>e</b>) average SOM values with different grades for mean annual precipitation; (<b>f</b>) average SOM values with different grades for digital elevation model; (<b>g</b>) average SOM values with different grades for soil type; (<b>h</b>) average SOM values with different grades for soil erosion data; (<b>i</b>) average SOM values with different grades for geomorphic types.</p> Full article ">
18 pages, 7947 KiB  
Article
Urban Quality of Life: Spatial Modeling and Indexing in Athens Metropolitan Area, Greece
by Antigoni Faka, Kleomenis Kalogeropoulos, Thomas Maloutas and Christos Chalkias
ISPRS Int. J. Geo-Inf. 2021, 10(5), 347; https://doi.org/10.3390/ijgi10050347 - 18 May 2021
Cited by 14 | Viewed by 4611
Abstract
The purpose of this study is to assess and visualize the Quality of Life provided by urban space as a place of residence. The proposed methodology, after its theoretical documentation, is implemented in Athens Metropolitan Area, Greece. For the evaluation of Urban Quality [...] Read more.
The purpose of this study is to assess and visualize the Quality of Life provided by urban space as a place of residence. The proposed methodology, after its theoretical documentation, is implemented in Athens Metropolitan Area, Greece. For the evaluation of Urban Quality of Life, a complex index is constructed by using multicriteria analysis. For this purpose, Quality of Life controlling factors such as built space, natural, socioeconomic, and cultural environment, infrastructure and services, and the quality of housing were analyzed within a GIS environment. The mapping of this index led to the identification of areas with different levels of Quality of Life. The results of the research can lead to more effective decision making regarding the planning of targeted actions and the distribution of financial resources to improve the Quality of Life of the residents in urban areas. Full article
(This article belongs to the Special Issue GIS-Based Analysis for Quality of Life and Environmental Monitoring)
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<p>The study area of Athens Metropolitan Area, in Attica, Greece.</p> Full article ">Figure 2
<p>Workflow of evaluating and mapping UQoL.</p> Full article ">Figure 3
<p>Indicators for the Built environment, Socioeconomic environment, and Housing conditions, across municipalities of AMA, Greece. (<b>a</b>) Population density, (<b>b</b>) open spaces, (<b>c</b>) unemployment, (<b>d</b>) high-educated population, (<b>e</b>) illiterate population, (<b>f</b>) income, (<b>g</b>) houses without basic facilities, (<b>h</b>) detached houses, (<b>i</b>) new buildings, (<b>j</b>) size of houses. (Red color in each map indicates low QoL and blue color high QoL values).</p> Full article ">Figure 4
<p>Indicators for the Natural environment, Public services and infrastructures, and Cultural and recreational facilities, across municipalities of AMA, Greece. (<b>a</b>) Mean distance to industrial units, (<b>b</b>) density of high-traffic roads and highways, (<b>c</b>) percentage of green urban areas, (<b>d</b>) proximity to medical services, (<b>e</b>) schools per 10,000 population, (<b>f</b>) proximity to sport facilities, (<b>g</b>) proximity recreational facilities, (<b>h</b>) proximity to cultural facilities. (Red color in each map indicates low QoL and blue color high QoL values).</p> Full article ">Figure 5
<p>The composite criteria of QoL in AMA, Greece. (<b>a</b>) Built environment, (<b>b</b>) natural environment, (<b>c</b>) socioeconomic environment, (<b>d</b>) public services and infrastructures, (<b>e</b>) housing conditions, (<b>f</b>) cultural and recreational facilities.</p> Full article ">Figure 6
<p>The final QoL map for AMA, Greece.</p> Full article ">Figure 7
<p>The urban background of disadvantageous municipalities. (<b>A</b>) Perama municipality, (<b>B</b>) Keratsini-Drapetsona municipality, (<b>C</b>) Korydallos municipality, (<b>D</b>) Peristeri municipality.</p> Full article ">Figure 8
<p>The urban background of advantageous municipalities. (<b>A</b>) Filothei-Psichiko municipality, (<b>B</b>) Aghia Paraskevi municipality, (<b>C</b>) Penteli municipality, (<b>D</b>) Glyfada municipality.</p> Full article ">
19 pages, 4699 KiB  
Article
Simulation of Land-Use Changes Using the Partitioned ANN-CA Model and Considering the Influence of Land-Use Change Frequency
by Quanli Xu, Qing Wang, Jing Liu and Hong Liang
ISPRS Int. J. Geo-Inf. 2021, 10(5), 346; https://doi.org/10.3390/ijgi10050346 - 18 May 2021
Cited by 37 | Viewed by 3684
Abstract
Land-use change is a typical geographic evolutionary process characterized by spatial heterogeneity. As such, the driving factors, conversion rules, and rate of change vary for different regions around the world. However, most cellular automata (CA) models use the same transition rules for all [...] Read more.
Land-use change is a typical geographic evolutionary process characterized by spatial heterogeneity. As such, the driving factors, conversion rules, and rate of change vary for different regions around the world. However, most cellular automata (CA) models use the same transition rules for all cells in the model space when simulating land-use change. Thus, spatial heterogeneity change is ignored in the model, which means that these models are prone to over- or under simulation, resulting in a large deviation from reality. An effective means of accounting for the influence of spatial heterogeneity on the quality of the CA model is to establish a partitioned model based on cellular space partitioning. This study established a partitioned, dual-constrained CA model using the area-weighted frequency of land-use change (AWFLUC) to capture its spatial heterogeneity. This model was used to simulate the land-use evolution of the Dianchi Lake watershed. First, the CA space was divided into subzones using a dual-constrained spatial clustering method. Second, an artificial neural network (ANN) was used to automatically acquire conversion rules to construct an ANN-CA model of land-use change. Finally, land-use changes were simulated using the ANN-CA model based on data from 2006 to 2016, and model reliability was validated. The experimental results showed that compared with the non-partitioned CA model, the partitioned counterpart was able to improve the accuracy of land-use change simulation significantly. Furthermore, AWFLUC is an important indicator of the spatial heterogeneity of land-use change. The shapes of the division spaces were more similar to reality and the simulation accuracy was higher when AWFLUC was considered as a land-use change characteristic. Full article
(This article belongs to the Special Issue Spatial Data Management in Geosciences, Urban Planning, Land Management, and Cultural Heritage)
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<p>The geographical location of the Dianchi Lake watershed.</p> Full article ">Figure 2
<p>The computing flow of the area-weighted frequency of land-use change (AWFLUC). (<b>a</b>) The classifications of different periods and the consequences of overlay analysis; (<b>b</b>) The statisti-cal counting of change from one type to another during any adjacent time; (<b>c</b>) The cumulative process of the frequency and area-weighted smoothing consequences from the beginning to the end of land use.</p> Full article ">Figure 3
<p>ANN-CA Model Flow Chart.</p> Full article ">Figure 4
<p>Land-use changes from 2006 to 2016, partition with different weights. (<b>a</b>) The frequency of land-use change (AWFLUC); (<b>b</b>) Partition based on spatial distance; (<b>c</b>) Dual-constrained partition without considering AWFLUC; (<b>d</b>) Dual-constrained partition with considering AWFLUC.</p> Full article ">Figure 5
<p>The probability maps (x1–x7) are the transition probabilities of seven land-use types, which are, respectively, IS, forest, cultivated land, grassland, garden, water area and bare land. Here, x is a, b or c, which refers to, respectively, 2006–2009, 2009–2013 or 2013–2016.</p> Full article ">Figure 6
<p>Comparison of simulation results with different CA partition. (x1) is the actual land use, and (x2–x4) are the simulation results of Model I, Model II and Model III. Here, “x” is a, b or c, which means the period of 2006–2009, 2009–2013 and 2013–2016, respectively.</p> Full article ">Figure 7
<p>Three testing areas to analyze the sensitivity of AWFLUC.</p> Full article ">Figure 8
<p>The simulation results of using three different CA models in three testing areas. (<b>a1</b>) Initial land use in Area 1; (<b>a2</b>) Simulation result by Model I in Area 1; (<b>a3</b>) Simulation result by Model II in Area 1; (<b>a4</b>) Simulation result by Model III in Area 1; (<b>b1</b>) Initial land use in Area 2; (<b>b2</b>) Simulation result by Model I in Area 2; (<b>b3</b>) Simulation result by Model II in Area 2; (<b>b4</b>) Simulation result by Model III in Area 2; (<b>c1</b>) Initial land use in Area 3; (<b>c2</b>) Simulation result by Model I in Area 3; (<b>c3</b>) Simulation result by Model II in Area 3; (<b>c4</b>) Simulation result by Model III in Area 3.</p> Full article ">
20 pages, 9906 KiB  
Article
Seismic Damage Semantics on Post-Earthquake LOD3 Building Models Generated by UAS
by Konstantinos Chaidas, George Tataris and Nikolaos Soulakellis
ISPRS Int. J. Geo-Inf. 2021, 10(5), 345; https://doi.org/10.3390/ijgi10050345 - 18 May 2021
Cited by 6 | Viewed by 2742
Abstract
In a post-earthquake scenario, the semantic enrichment of 3D building models with seismic damage is crucial from the perspective of disaster management. This paper aims to present the methodology and the results for the Level of Detail 3 (LOD3) building modelling (after an [...] Read more.
In a post-earthquake scenario, the semantic enrichment of 3D building models with seismic damage is crucial from the perspective of disaster management. This paper aims to present the methodology and the results for the Level of Detail 3 (LOD3) building modelling (after an earthquake) with the enrichment of the semantics of the seismic damage based on the European Macroseismic Scale (EMS-98). The study area is the Vrisa traditional settlement on the island of Lesvos, Greece, which was affected by a devastating earthquake of Mw = 6.3 on 12 June 2017. The applied methodology consists of the following steps: (a) unmanned aircraft systems (UAS) nadir and oblique images are acquired and photogrammetrically processed for 3D point cloud generation, (b) 3D building models are created based on 3D point clouds and (c) 3D building models are transformed into a LOD3 City Geography Markup Language (CityGML) standard with enriched semantics of the related seismic damage of every part of the building (walls, roof, etc.). The results show that in following this methodology, CityGML LOD3 models can be generated and enriched with buildings’ seismic damage. These models can assist in the decision-making process during the recovery phase of a settlement as well as be the basis for its monitoring over time. Finally, these models can contribute to the estimation of the reconstruction cost of the buildings. Full article
(This article belongs to the Special Issue Geospatial Semantics Applications)
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<p>The study area of the Vrisa settlement with LOD2 buildings based on the categorization of EPPO in 3 classes: i. red—dangerous for use; ii. yellow—unsafe for use; iii. green—safe for use. Up until today, the majority (&gt;90%) of “red buildings” have been demolished while more than 80% of the “yellow buildings” have been repaired.</p> Full article ">Figure 2
<p>The workflow of the methodology implemented consists of five steps.</p> Full article ">Figure 3
<p>Point cloud density: (<b>a</b>) Building A, the majority of points present a range of neighbors 1300–1900, (<b>b</b>) histogram of density (mean = 1595 and standard deviation = 249.89), (<b>c</b>) building B, the majority of points present a range of neighbors 500–700, (<b>d</b>) histogram of density (mean = 559 and standard deviation = 145.47).</p> Full article ">Figure 3 Cont.
<p>Point cloud density: (<b>a</b>) Building A, the majority of points present a range of neighbors 1300–1900, (<b>b</b>) histogram of density (mean = 1595 and standard deviation = 249.89), (<b>c</b>) building B, the majority of points present a range of neighbors 500–700, (<b>d</b>) histogram of density (mean = 559 and standard deviation = 145.47).</p> Full article ">Figure 4
<p>Three-dimensional building model generation: (<b>a</b>) point cloud of building A, (<b>b</b>) LOD3 model of building A designed in SketchUp, (<b>c</b>) point cloud of building B and (<b>d</b>) LOD3 model of building B designed in SketchUp.</p> Full article ">Figure 5
<p>FME Workbench, transformation of SketchUp models into the CityGML format.</p> Full article ">Figure 6
<p>Categorization of seismic damage at the level of buildings, walls and roofs according to EMS-98 as shown in several 3D models created by processing UAS images. The subcategories of these grades on the walls: 2.1 Cracks in many walls, 3.1 Large and extensive cracks in most walls, 3.2 Diagonal large and extensive cracks in most walls, 3.3 Failure of individual non-structural elements (partitions, gable walls), 4.1 Serious failure of walls and 5.1 Total collapse. The subcategories of these on the roofs are: 1.4 Fall of roof tiles, 2.4 Partial collapse of chimneys, 3.4 Roof tiles detach, 4.3 Partial structural failure of roofs and 5.1 Total collapse.</p> Full article ">Figure 7
<p>UML class diagram of the CityGML feature structure of basic semantic LOD3 building model, extended with DamageGrades.</p> Full article ">Figure 8
<p>Cloud-to-cloud distance method results: (<b>a</b>) mean distance is 5 cm for building A, (<b>b</b>) histogram of the results (standard deviation = 0.04), (<b>c</b>) mean distance is 9 cm for building B, (<b>d</b>) histogram of the results (standard deviation = 0.05).</p> Full article ">Figure 9
<p>(<b>a</b>) Wall damage grades for building A were 1.1. (<b>b</b>) For building B, one wall was characterized with Wall damage 4.1 and the rest with grade 2.2, and the roof was characterized with grade 4.3.</p> Full article ">Figure 10
<p>LOD3 CityGML building models with semantic layers rendered in FZK viewer for (<b>a</b>) building A and (<b>b</b>) building B.</p> Full article ">Figure 10 Cont.
<p>LOD3 CityGML building models with semantic layers rendered in FZK viewer for (<b>a</b>) building A and (<b>b</b>) building B.</p> Full article ">
18 pages, 4866 KiB  
Article
Spatiotemporal Patterns of Human Mobility and Its Association with Land Use Types during COVID-19 in New York City
by Yuqin Jiang, Xiao Huang and Zhenlong Li
ISPRS Int. J. Geo-Inf. 2021, 10(5), 344; https://doi.org/10.3390/ijgi10050344 - 18 May 2021
Cited by 26 | Viewed by 4177
Abstract
The novel coronavirus disease (COVID-19) pandemic has impacted every facet of society. One of the non-pharmacological measures to contain the COVID-19 infection is social distancing. Federal, state, and local governments have placed multiple executive orders for human mobility reduction to slow down the [...] Read more.
The novel coronavirus disease (COVID-19) pandemic has impacted every facet of society. One of the non-pharmacological measures to contain the COVID-19 infection is social distancing. Federal, state, and local governments have placed multiple executive orders for human mobility reduction to slow down the spread of COVID-19. This paper uses geotagged tweets data to reveal the spatiotemporal human mobility patterns during this COVID-19 pandemic in New York City. With New York City open data, human mobility pattern changes were detected by different categories of land use, including residential, parks, transportation facilities, and workplaces. This study further compares human mobility patterns by land use types based on an open social media platform (Twitter) and the human mobility patterns revealed by Google Community Mobility Report cell phone location, indicating that in some applications, open-access social media data can generate similar results to private data. The results of this study can be further used for human mobility analysis and the battle against COVID-19. Full article
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<p>Human mobility changing patterns at parcel-level in week 1–week 9.</p> Full article ">Figure 2
<p>Human mobility changing patterns at parcel-level in weeks 10–15.</p> Full article ">Figure 3
<p>Mobility changing patterns for six land use types based on Twitter data.</p> Full article ">Figure 3 Cont.
<p>Mobility changing patterns for six land use types based on Twitter data.</p> Full article ">Figure 4
<p>Twitter- and Google-based human mobility changing patterns in Bronx County.</p> Full article ">Figure 5
<p>Twitter- and Google-based human mobility changing patterns in Kings County.</p> Full article ">Figure 6
<p>Twitter- and Google-based human mobility changing patterns in New York County.</p> Full article ">Figure 6 Cont.
<p>Twitter- and Google-based human mobility changing patterns in New York County.</p> Full article ">Figure 7
<p>Twitter- and Google-based human mobility changing patterns in Queens County.</p> Full article ">
13 pages, 3275 KiB  
Article
A Spatial Approach for Modeling Amphibian Road-Kills: Comparison of Regression Techniques
by Diana Sousa-Guedes, Marc Franch and Neftalí Sillero
ISPRS Int. J. Geo-Inf. 2021, 10(5), 343; https://doi.org/10.3390/ijgi10050343 - 18 May 2021
Cited by 3 | Viewed by 3456
Abstract
Road networks are the main source of mortality for many species. Amphibians, which are in global decline, are the most road-killed fauna group, due to their activity patterns and preferred habitats. Many different methodologies have been applied in modeling the relationship between environment [...] Read more.
Road networks are the main source of mortality for many species. Amphibians, which are in global decline, are the most road-killed fauna group, due to their activity patterns and preferred habitats. Many different methodologies have been applied in modeling the relationship between environment and road-kills events, such as logistic regression. Here, we compared the performance of five regression techniques to relate amphibians’ road-kill frequency to environmental variables. For this, we surveyed three country roads in northern Portugal in search of road-killed amphibians. To explain the presence of road-kills, we selected a set of environmental variables important for the presence of amphibians and the occurrence of road-kills. We compared the performances of five modeling techniques: (i) generalized linear models, (ii) generalized additive models, (iii) random forest, (iv) boosted regression trees, and (v) geographically weighted regression. The boosted regression trees and geographically weighted regression techniques performed the best, with a percentage of deviance explained between 61.8% and 76.6% and between 55.3% and 66.7%, respectively. Moreover, the geographically weighted regression showed a great advantage over the other techniques, as it allows mapping local parameter coefficients as well as local model performance (pseudo-R2). The results suggest that geographically weighted regression is a useful tool for road-kill modeling, as well as to better visualize and map the spatial variability of the models. Full article
(This article belongs to the Special Issue Application of GIS for Biodiversity Research)
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<p>Main steps of the research process for comparing the performance of five regression techniques in modeling amphibian road-kills. GLM: generalized linear models; GAM: generalized additive models; RF: random forest; BRT: boosted regression trees; GWR: geographically weighted regression; AUC: area under the curve.</p> Full article ">Figure 2
<p>Study area with the three surveyed roads (R1, R2 and R3) in northern Portugal.</p> Full article ">Figure 3
<p>Spatial distribution of the mean model standardized residuals in each surveyed road of the two techniques with the best performance: BRT and GWR. Red squares are the locations with negative residuals, yellow squares are the locations with correct predictions (residuals close to zero) and blue squares the locations with positive residuals.</p> Full article ">Figure 4
<p>Local pseudo-R<sup>2</sup> of the GWR analysis in each surveyed road. In a gradient from red to green, the green points represent higher values of the local pseudo-R<sup>2</sup>.</p> Full article ">Figure 5
<p>Estimated significant coefficients (<span class="html-italic">p</span> &lt; 0.05) of two variables obtained with the GWR technique in the three roads: distance to urban areas (at the <b>top</b>) and distance to broadleaved forests (at the <b>bottom</b>). White points represent the locations with negative coefficients and black points represent the locations with positive coefficients.</p> Full article ">
42 pages, 10969 KiB  
Article
The Land-Use Change Dynamics Based on the CORINE Data in the Period 1990–2018 in the European Archipelagos of the Macaronesia Region: Azores, Canary Islands, and Madeira
by Rui Alexandre Castanho, José Manuel Naranjo Gomez, Ana Vulevic and Gualter Couto
ISPRS Int. J. Geo-Inf. 2021, 10(5), 342; https://doi.org/10.3390/ijgi10050342 - 17 May 2021
Cited by 12 | Viewed by 3548
Abstract
Islands as peripheral and ultra-peripheral are typically highlighted as ecologically sensitive areas to human activities due to the tremendous biological diversity of beings and the future possibility of habitat loss. In this regard, the comprehension of the land occupation dynamics and trends in [...] Read more.
Islands as peripheral and ultra-peripheral are typically highlighted as ecologically sensitive areas to human activities due to the tremendous biological diversity of beings and the future possibility of habitat loss. In this regard, the comprehension of the land occupation dynamics and trends in the ultra-peripheral territories is crucial to attempt long-lasting regional sustainability, as is the island region’s case. Therefore, the present article aims to analyze the trends and dynamics of the land-use changes on the European Archipelagos of the Macaronesia Region over the last three decades, using the CORINE (Coordination of Information on the Environment) data. Some of the obtained results show that about 3.4% of the Azores’ surface is characterized mainly by discontinuous urban fabric, representing 67% of the total urban fabric of the Azores over the last thirty years. Additionally, in Madeira Archipelago, the land is mainly occupied by forest and semi-natural areas, representing almost three-thirds of the territory. A similar scenario is verified in the Canary Islands, where forests and semi-natural areas represent approximately three-quarters of the territory. Once more, this study shows the relevance of the island areas’ unique character, which should be preserved and protected. Therefore, the priorities must be defined and established management strategies that are significant for the well-being of these highly valued areas. Moreover, the study showed that notable changes had occurred in the period 1990–2018 in this landscape. Hence there is a need for appropriate measures to mitigate these negative impacts on the environment. Full article
(This article belongs to the Special Issue Geo-Information Technology and Its Applications)
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<p>Macaronesia Region: The Canarias.</p> Full article ">Figure 2
<p>Macaronesia Region: The Azores.</p> Full article ">Figure 3
<p>Macaronesia Region: Madeira.</p> Full article ">Figure 4
<p>Macaronesia Region location map (adapted from [<a href="#B52-ijgi-10-00342" class="html-bibr">52</a>]).</p> Full article ">Figure 5
<p>Percentage of land-uses according to level 1 of CLC nomenclature in the Autonomous Region of the Azores.</p> Full article ">Figure 6
<p>Percentage of land-uses according to CLC nomenclature in the Autonomous Region of the Azores.</p> Full article ">Figure 7
<p>Thematic cartography regarding the land-use changes in the Azores Archipelago Eastern group in the year 1990.</p> Full article ">Figure 8
<p>Thematic cartography regarding the land-use changes in the Azores Archipelago Eastern group in the year 2018.</p> Full article ">Figure 9
<p>Percentage of land-uses according to level 1 of CLC nomenclature in the Autonomous Region of Madeira.</p> Full article ">Figure 10
<p>Percentage of land-uses according to CLC nomenclature in the Autonomous Region of Madeira.</p> Full article ">Figure 11
<p>Thematic cartography regarding the land-use changes in the Madeira Archipelago in the year 1990.</p> Full article ">Figure 12
<p>Thematic cartography regarding the land-use changes in the Madeira Archipelago in the year 2018.</p> Full article ">Figure 13
<p>Percentage of land-uses according to level 1 of CLC nomenclature in the Autonomous Community of the Canary Islands.</p> Full article ">Figure 14
<p>Percentage of land-uses according to CLC nomenclature in the Autonomous Community of the Canary Islands.</p> Full article ">Figure 15
<p>Thematic cartography regarding the land-use changes in the Canary Archipelago in the year 1990.</p> Full article ">Figure 16
<p>Thematic cartography regarding the land-use changes in the Canary Archipelago in the year 2018.</p> Full article ">Figure A1
<p>Thematic cartography regarding the land-use changes in the Western Group of Azores Archipelago in the year 1990.</p> Full article ">Figure A2
<p>Thematic cartography regarding the land-use changes in the Western Group of Azores Archipelago in the year 2018.</p> Full article ">Figure A3
<p>Thematic cartography regarding the land-use changes in the Central Group of Azores Archipelago in the year 1990.</p> Full article ">Figure A4
<p>Thematic cartography regarding the land-use changes in the Central Group of Azores Archipelago in the year 2018.</p> Full article ">Figure A5
<p>Thematic cartography regarding the land-use changes in the Eastern Group of Azores Archipelago in the year 1990.</p> Full article ">Figure A6
<p>Thematic cartography regarding the land-use changes in the Eastern Group of Azores Archipelago in the year 2018.</p> Full article ">Figure A7
<p>Thematic cartography regarding the land-use changes in the Madeira Archipelago in the year 1990.</p> Full article ">Figure A8
<p>Thematic cartography regarding the land-use changes in the Madeira Archipelago in the year 2018.</p> Full article ">Figure A9
<p>Thematic cartography regarding the land-use changes in the North of Canary Archipelago in the year 1990.</p> Full article ">Figure A10
<p>Thematic cartography regarding the land-use changes in the North of Canary Archipelago in the year 2018.</p> Full article ">Figure A11
<p>Thematic cartography regarding the land-use changes in the South of Canary Archipelago in the year 1990.</p> Full article ">Figure A12
<p>Thematic cartography regarding the land-use changes in the South of Canary Archipelago in the year 2018.</p> Full article ">
14 pages, 5601 KiB  
Article
Subsurface Topographic Modeling Using Geospatial and Data Driven Algorithm
by Abbas Abbaszadeh Shahri, Ali Kheiri and Aliakbar Hamzeh
ISPRS Int. J. Geo-Inf. 2021, 10(5), 341; https://doi.org/10.3390/ijgi10050341 - 17 May 2021
Cited by 28 | Viewed by 3454
Abstract
Infrastructures play an important role in urbanization and economic activities but are vulnerable. Due to unavailability of accurate subsurface infrastructure maps, ensuring the sustainability and resilience often are poorly recognized. In the current paper a 3D topographical predictive model using distributed geospatial data [...] Read more.
Infrastructures play an important role in urbanization and economic activities but are vulnerable. Due to unavailability of accurate subsurface infrastructure maps, ensuring the sustainability and resilience often are poorly recognized. In the current paper a 3D topographical predictive model using distributed geospatial data incorporated with evolutionary gene expression programming (GEP) was developed and applied on a concrete-face rockfill dam (CFRD) in Guilan province- northern to generate spatial variation of the subsurface bedrock topography. The compared proficiency of the GEP model with geostatistical ordinary kriging (OK) using different analytical indexes showed 82.53% accuracy performance and 9.61% improvement in precisely labeled data. The achievements imply that the retrieved GEP model efficiently can provide accurate enough prediction and consequently meliorate the visualization insights linking the natural and engineering concerns. Accordingly, the generated subsurface bedrock model dedicates great information on stability of structures and hydrogeological properties, thus adopting appropriate foundations. Full article
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<p>Toward decision platform using geospatial database.</p> Full article ">Figure 2
<p>Applied terminology in <span class="html-italic">DTB</span> definition.</p> Full article ">Figure 3
<p>Flow diagram of <span class="html-italic">GEP</span> algorithm.</p> Full article ">Figure 4
<p><span class="html-italic">ETs</span> expression and coded chromosome for (<b>A</b>) three and (<b>B</b>) two genes denoting the head (<span class="html-italic">H</span>) and tail (<span class="html-italic">T</span>). The vertical arrow corresponding to bolded characters show the termination point of each gene (<b>A</b>).</p> Full article ">Figure 5
<p>Location of studied area in Iran (<b>A</b>) and Guilan province (<b>B</b>), generated DEM and overlaid spatial data (<b>C</b>), and distribution of geospatial surveyed drilled boreholes (<b>D</b>).</p> Full article ">Figure 6
<p>(<b>A</b>) Variation of <span class="html-italic">RMSE</span> for series parametric analyses using different head size and genes based on the number of chromosomes, (<b>B</b>) evaluated candidate models using <span class="html-italic">RMSE</span> and <span class="html-italic">R</span><sup>2</sup>.</p> Full article ">Figure 7
<p>The <span class="html-italic">ETs</span> expression of selected optimum <span class="html-italic">GEP</span> model.</p> Full article ">Figure 8
<p>Predictability of developed <span class="html-italic">GEP</span> model using employed datasets.</p> Full article ">Figure 9
<p>Predicted spatial <span class="html-italic">DTB</span> using optimum <span class="html-italic">GEP</span> topology.</p> Full article ">Figure 10
<p>Results of <span class="html-italic">OK</span> (<b>A</b>), predictability of <span class="html-italic">OK</span> and <span class="html-italic">GEP</span> using <span class="html-italic">CI</span> and <span class="html-italic">PI</span> (<b>B</b>) and compared residuals (<b>C</b>).</p> Full article ">Figure 11
<p>Visualized insights into predicted spatial <span class="html-italic">DTB</span> models using optimum <span class="html-italic">GEP</span> and <span class="html-italic">OK</span>.</p> Full article ">Figure 12
<p>Compared performance of models using statistical error metrics for whole datasets (delete the dot line).</p> Full article ">
17 pages, 6084 KiB  
Article
What Is the Shape of Geographical Time-Space? A Three-Dimensional Model Made of Curves and Cones
by Alain L’Hostis and Farouk Abdou
ISPRS Int. J. Geo-Inf. 2021, 10(5), 340; https://doi.org/10.3390/ijgi10050340 - 17 May 2021
Cited by 3 | Viewed by 4332
Abstract
Geographical time-spaces exhibit a series of properties, including space inversion, that turns any representation effort into a complex task. In order to improve the legibility of the representation and leveraging the advances of three-dimensional computer graphics, the aim of the study is to [...] Read more.
Geographical time-spaces exhibit a series of properties, including space inversion, that turns any representation effort into a complex task. In order to improve the legibility of the representation and leveraging the advances of three-dimensional computer graphics, the aim of the study is to propose a new method extending time-space relief cartography introduced by Mathis and L’Hostis. The novelty of the model resides in the use of cones to describing the terrestrial surface instead of graph faces, and in the use of curves instead of broken segments for edges. We implement the model on the Chinese space. The Chinese geographical time-space of reference year 2006 is produced by the combination and the confrontation of the fast air transport system and of the 7.5-times slower road transport system. Slower, short range flights are represented as curved lines above the earth surface with longer length than the geodesic, in order to account for a slower speed. The very steep slope of cones expresses the relative difficulty of crossing terrestrial time-space, as well as the comparably extreme efficiency of long-range flights for moving between cities. Finally, the whole image proposes a coherent representation of the geographical time-space where fast city-to-city transport is combined with slow terrestrial systems that allow one to reach any location. Full article
(This article belongs to the Special Issue Spatio-Temporal Models and Geo-Technologies)
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Figure 1
<p>Space inversion between three places located in geographical space (kilometres) and in geographical time-space (duration).</p> Full article ">Figure 2
<p>The commercial speed of aircraft services on a sample of origin destination pairs (data from <a href="http://www.flightglobal.com" target="_blank">www.flightglobal.com</a> in 2016) and a linear approximation.</p> Full article ">Figure 3
<p>Cones and edges in three dimensions as a basic structure for time-space representation.</p> Full article ">Figure 4
<p>A representation of the Chinese geographical time-space in 2006. View of a model generated by the <span class="html-italic">Shriveling world</span> software, unprojected Chinese cities, flight information from openflights.org, UN WUP cities data.</p> Full article ">Figure A1
<p>Cones and edges in projected geometry.</p> Full article ">Figure A2
<p>Drawing edges and cones with different speed in the spherical geometry.</p> Full article ">Figure A3
<p>Several graphical solutions to the constraint of drawing an edge with a given length between cities <span class="html-italic">a</span> and <span class="html-italic">b</span>: Two straight segments (dotted) and different Bézier curves (red with one control point <math display="inline"><semantics> <mrow> <mi>Q</mi> <mi>r</mi> </mrow> </semantics></math>; blue with two control points <math display="inline"><semantics> <mrow> <mi>Q</mi> <mi>b</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>b</mi> </mrow> </semantics></math>; yellow with two control points <math display="inline"><semantics> <mrow> <mi>Q</mi> <mi>y</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>y</mi> </mrow> </semantics></math>; and green with four control points <math display="inline"><semantics> <mrow> <mi>Q</mi> <mi>g</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>g</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>S</mi> <mi>g</mi> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>T</mi> <mi>g</mi> </mrow> </semantics></math>). The position of control points was adjusted to constrain the curve length.</p> Full article ">
19 pages, 5949 KiB  
Article
Block2vec: An Approach for Identifying Urban Functional Regions by Integrating Sentence Embedding Model and Points of Interest
by Zhihao Sun, Hongzan Jiao, Hao Wu, Zhenghong Peng and Lingbo Liu
ISPRS Int. J. Geo-Inf. 2021, 10(5), 339; https://doi.org/10.3390/ijgi10050339 - 17 May 2021
Cited by 30 | Viewed by 3571
Abstract
Urban functional regions are essential information in parsing urban spatial structure. The rapid and accurate identification of urban functional regions is important for improving urban planning and management. Thanks to its low cost and fast data update characteristics, the Point of Interest (POI) [...] Read more.
Urban functional regions are essential information in parsing urban spatial structure. The rapid and accurate identification of urban functional regions is important for improving urban planning and management. Thanks to its low cost and fast data update characteristics, the Point of Interest (POI) is one of the most common types of open access data. It mainly identifies urban functional regions by analyzing the potential correlation between POI data and the regions. Even though this is an important manifestation of the functional region, the spatial correlation between regions is rarely considered in previous studies. In order to extract the spatial semantic information among regions, a new model, called the Block2vec, is proposed by using the idea of the Skip-gram framework. The Block2vec model maps the spatial correlation between the POIs, as well as the regions, to a high-dimensional vector, in which classification of urban functional regions can be better performed. The results from cluster analysis showed that the high-dimensional vector extracted can well distinguish the regions with different functions. The random forests classification result (Overall accuracy = 0.7186, Kappa = 0.6429) illustrated the effectiveness of the proposed method. This study also verified the potential of the sentence embedding model in the semantic information extraction of POIs. Full article
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<p>Location, administrative districts and POIs distribution of Wuhan. The main urban area was obtained from the Wuhan Natural Resources and Planning Bureau website (<a href="http://zrzyhgh.wuhan.gov.cn/zwgk_18/fdzdgk/ghjh/zzqgh/202001/t20200107_602858.shtml" target="_blank">http://zrzyhgh.wuhan.gov.cn/zwgk_18/fdzdgk/ghjh/zzqgh/202001/t20200107_602858.shtml</a>, accessed on 12 May 2017).</p> Full article ">Figure 2
<p>The workflow of the urban functional region classification.</p> Full article ">Figure 3
<p>Semantic sequence group for the center parcel <span class="html-italic">S<sub>i</sub></span>.</p> Full article ">Figure 4
<p>Latent semantic feature extraction model.</p> Full article ">Figure 5
<p>Classification diagram based on the trained encoder.</p> Full article ">Figure 6
<p>Distribution of POI sequence length of parcel. The POI sequence length is the number of POIs in the parcel.</p> Full article ">Figure 7
<p>Silhouette score of K-Means clustering with different k values (k presents the number of clusters in cluster analysis).</p> Full article ">Figure 8
<p>Results of K-Means clustering analysis of blocks using POI latent semantic features (k = 2,3,4).</p> Full article ">Figure 9
<p>Urban functional region classification results via different methods.</p> Full article ">Figure 9 Cont.
<p>Urban functional region classification results via different methods.</p> Full article ">Figure 10
<p>Confusion matrixes of classification results via (<b>a</b>) Word2Vec, (<b>b</b>) TF-IDF, (<b>c</b>) LDA and (<b>d</b>) proposed method.</p> Full article ">Figure 11
<p>Comparison of local regions classification results. (<b>a</b>) The central area in Wuchang; (<b>b</b>) the central area in Hanyang; (<b>c</b>) the area southeast of Wuchang (The Google online maps are on the left; the planning maps are in the middle and our classification results are on the right).</p> Full article ">Figure 12
<p>Changes in the accuracy of urban functional region classification under different latent semantic features.</p> Full article ">
27 pages, 7739 KiB  
Article
Autonomous Flight Trajectory Control System for Drones in Smart City Traffic Management
by Dinh Dung Nguyen, Jozsef Rohacs and Daniel Rohacs
ISPRS Int. J. Geo-Inf. 2021, 10(5), 338; https://doi.org/10.3390/ijgi10050338 - 17 May 2021
Cited by 49 | Viewed by 14804
Abstract
With the exponential growth of numerous drone operations ranging from infrastructure monitoring to even package delivery services, the integration of UAS in the smart city transportation systems is an actual task that requires radically new, sustainable (safe, secure, with minimum environmental impact and [...] Read more.
With the exponential growth of numerous drone operations ranging from infrastructure monitoring to even package delivery services, the integration of UAS in the smart city transportation systems is an actual task that requires radically new, sustainable (safe, secure, with minimum environmental impact and life cycle cost) solutions. The primary objective of this proposed option is the definition of routes as desired and commanded trajectories and their autonomous execution. The airspace structure and fixed routes are given in the global GPS reference system with supporting GIS mapping. The concept application requires a series of further studies and solutions as drone trajectory (or corridor) following by an autonomous trajectory tracking control system, coupled with autonomous conflict detection, resolution, safe drone following, and formation flight options. The second part of the paper introduces such possible models and shows some results of their verification tests. Drones will be connected with the agency, designed trajectories to support them with factual information on trajectories and corridors. While the agency will use trajectory elements to design fixed or desired trajectories, drones may use the conventional GPS, infrared, acoustic, and visual sensors for positioning and advanced navigation. The accuracy can be improved by unique markers integrated into the infrastructure. Full article
(This article belongs to the Special Issue UAV in Smart City and Smart Region)
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<p>Recommended concept (following to initial idea of Kin Huat Low [<a href="#B19-ijgi-10-00338" class="html-bibr">19</a>]).</p> Full article ">Figure 2
<p>Development of cockpit tools to support precision landing.</p> Full article ">Figure 3
<p>General concept and system layout of the proposed autonomous drone management system.</p> Full article ">Figure 4
<p>Geographical sector and restricted areas.</p> Full article ">Figure 5
<p>Sector for “vertical motion”, changing the flight altitude.</p> Full article ">Figure 6
<p>One way: (<b>a</b>) vertical view, (<b>b</b>) 3D view.</p> Full article ">Figure 7
<p>Two ways: (<b>a</b>) vertical view, (<b>b</b>) 3D view.</p> Full article ">Figure 8
<p>Multi-lanes in one direction: (<b>a</b>) vertical view, (<b>b</b>) 3D view: v—vertical safe distance, h—horizontal safe distance</p> Full article ">Figure 9
<p>Multi-lanes in two ways: (<b>a</b>) vertical view, (<b>b</b>) 3D view.</p> Full article ">Figure 10
<p>Turning: (<b>a</b>) in one way at the same altitude, (<b>b</b>) in two ways at the same height.</p> Full article ">Figure 11
<p>Changing altitude in the same direction: (<b>a</b>) with a straight flight, (<b>b</b>) with coordinated turn.</p> Full article ">Figure 12
<p>Crossing: (<b>a</b>) changing lane, (<b>b</b>) changing heading (in top view).</p> Full article ">Figure 13
<p>Changing heading at different altitude: 1—changing to a new lane, 2—flying in the new lane, 3—increasing/decreasing the altitude, 4—turning on the same altitude, 5—flying at the new lane in the desired heading, 6—merging in the lane at the same altitude and at the desired new heading.</p> Full article ">Figure 14
<p>Urban total transportation system: I—industrial area (factories), II—Forest area, III—urban area, IV—airport area, 1—underground, 2—road, 3—upper ground, 4—path, 5—railway, 6—highway, 7—freight transport, 8—urban air transport, 9—water transport.</p> Full article ">Figure 15
<p>The proposed trajectory following model.</p> Full article ">Figure 16
<p>Obstacle representation and safe distance calculation.</p> Full article ">Figure 17
<p>The proposed UAV landing zones.</p> Full article ">Figure 18
<p>Acceleration, deceleration of the first drone applied in verification tests.</p> Full article ">Figure 19
<p>Verification results for comparison of the SD and Markov drone following models.</p> Full article ">Figure 20
<p>The difference between desired and real trajectories (pink line—desired trajectory, blue line—real trajectory).</p> Full article ">Figure 21
<p>The difference between desired and actual altitude of drone, green line—desired altitude, red line—actual altitude.</p> Full article ">Figure 22
<p>The desired trajectory for UAV landing in the given direction.</p> Full article ">Figure 23
<p>The altitude, vertical velocity, and roll angle when the UAV must land in the given direction.</p> Full article ">
22 pages, 7075 KiB  
Article
Multifractal Characteristics Analysis Based on Slope Distribution Probability in the Yellow River Basin, China
by Zilong Qin, Jinxin Wang and Yan Lu
ISPRS Int. J. Geo-Inf. 2021, 10(5), 337; https://doi.org/10.3390/ijgi10050337 - 16 May 2021
Cited by 8 | Viewed by 3758
Abstract
Multifractal theory provides a reliable method for the scientific quantification of the geomorphological features of basins. However, most of the existing research has investigated small and medium-sized basins rather than complex and large basins. In this study, the Yellow River Basin and its [...] Read more.
Multifractal theory provides a reliable method for the scientific quantification of the geomorphological features of basins. However, most of the existing research has investigated small and medium-sized basins rather than complex and large basins. In this study, the Yellow River Basin and its sub-basins were selected as the research areas, and the generalized fractal dimension and multifractal spectrum were computed and analyzed with a multifractal technique based on the slope distribution probability. The results showed that the Yellow River Basin and its sub-basins exhibit clear multifractal characteristics, which indicates that the multifractal theory can be applied well to the analysis of large-scale basin geomorphological features. We also concluded that the region with the most uneven terrain is the Yellow River Downstream Basin with the “overhanging river”, followed by the Weihe River Basin, the Yellow River Mainstream Basin, and the Fenhe River Basin. Multifractal analysis can reflect the geomorphological feature information of the basins comprehensively with the generalized fractal dimension and the multifractal spectrum. There is a strong correlation between some common topographic parameters and multifractal parameters, and the correlation coefficients between them are greater than 0.8. The results provide a scientific basis for analyzing the geomorphic characteristics of large-scale basins and for the further research of the morphogenesis of the forms. Full article
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<p>Study Area: (<b>a</b>) Location of the Yellow River Basin in China; (<b>b</b>) Location of the Yellow River sub-basins; (<b>c</b>) Digital elevation model of the Yellow River Basin and its sub-basins; (<b>d</b>) Slope of the Yellow River Basin and its sub-basins. YRM, Mainstream Basin; WH, Weihe River Basin; FH, Fenhe River Basin; YRD, Yellow River Downstream Basin.</p> Full article ">Figure 2
<p>3 × 3 local moving window in the digital elevation model (DEM) with a grid size of 30 m.</p> Full article ">Figure 3
<p>Workflow diagram of the multifractal analysis in this study.</p> Full article ">Figure 4
<p>The <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>M</mi> <mrow> <mo>(</mo> <mrow> <mi>e</mi> <mo>,</mo> <mi>q</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>~<math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>e</mi> </mrow> </semantics></math> relationships of (<b>a</b>) The Yellow River Mainstream Basin (YRM); (<b>b</b>) The Fenhe River Basin (FH); (<b>c</b>) The Weihe River Basin (WH); (<b>d</b>) The Yellow River Downstream Basin (YRD); and (<b>e</b>) The Yellow River Basin (YR).</p> Full article ">Figure 4 Cont.
<p>The <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>M</mi> <mrow> <mo>(</mo> <mrow> <mi>e</mi> <mo>,</mo> <mi>q</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>~<math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>e</mi> </mrow> </semantics></math> relationships of (<b>a</b>) The Yellow River Mainstream Basin (YRM); (<b>b</b>) The Fenhe River Basin (FH); (<b>c</b>) The Weihe River Basin (WH); (<b>d</b>) The Yellow River Downstream Basin (YRD); and (<b>e</b>) The Yellow River Basin (YR).</p> Full article ">Figure 5
<p>The relationships between the mass exponent <math display="inline"><semantics> <mrow> <mi>τ</mi> <mrow> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and the moment order <span class="html-italic">q</span> of (<b>a</b>) The Yellow River Mainstream Basin (YRM), the Fenhe River Basin (FH), the Weihe River Basin (WH), the Yellow River Downstream Basin (YRD); and (<b>b</b>) The Yellow River Basin (YR).</p> Full article ">Figure 6
<p>The relationships between the generalized multifractal dimension <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>q</mi> </msub> </mrow> </semantics></math> and the moment order <span class="html-italic">q</span> of (<b>a</b>) The Yellow River Mainstream Basin (YRM), the Fenhe River Basin (FH), the Weihe River Basin (WH), the Yellow River Downstream Basin (YRD), and (<b>b</b>) The Yellow River Basin (YR).</p> Full article ">Figure 7
<p>The multifractal spectra of (<b>a</b>) The Yellow River Mainstream Basin (YRM), (<b>b</b>) The Fenhe River Basin (FH), (<b>c</b>) The Weihe River Basin (WH), (<b>d</b>) The Yellow River Downstream Basin (YRD), and (<b>e</b>) The Yellow River Basin (YR).</p> Full article ">Figure 7 Cont.
<p>The multifractal spectra of (<b>a</b>) The Yellow River Mainstream Basin (YRM), (<b>b</b>) The Fenhe River Basin (FH), (<b>c</b>) The Weihe River Basin (WH), (<b>d</b>) The Yellow River Downstream Basin (YRD), and (<b>e</b>) The Yellow River Basin (YR).</p> Full article ">Figure 8
<p>The relationship between <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>M</mi> <mrow> <mo>(</mo> <mrow> <mi>e</mi> <mo>,</mo> <mi>q</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>e</mi> </mrow> </semantics></math> in the Yellow River Basin. The relationship between <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>M</mi> <mrow> <mo>(</mo> <mrow> <mi>e</mi> <mo>,</mo> <mi>q</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>e</mi> </mrow> </semantics></math> (<b>a</b>) When <span class="html-italic">e</span> is [500, 50,000]; (<b>b</b>) When <span class="html-italic">e</span> is [40,000, 50,000] and <span class="html-italic">q</span> is −30; (<b>c</b>) When <span class="html-italic">e</span> is [40,000, 50,000] and <span class="html-italic">q</span> is −15.</p> Full article ">Figure 9
<p>The correlation between (<b>a</b>) Altitude (m a.s.l.) and simple fractal dimension; (<b>b</b>) Altitude and topographic roughness; (<b>c</b>) Slope and information dimension; (<b>d</b>) Altitude and information dimension; and (<b>e</b>) Topographic roughness and information dimension.</p> Full article ">Figure A1
<p>The relationship of <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>M</mi> <mrow> <mo>(</mo> <mrow> <mi>e</mi> <mo>,</mo> <mi>q</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>e</mi> </mrow> </semantics></math> in the Yellow River Mainstream Basin. (<b>a</b>) The relationship of <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>M</mi> <mrow> <mo>(</mo> <mrow> <mi>e</mi> <mo>,</mo> <mi>q</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>e</mi> </mrow> </semantics></math> when <span class="html-italic">e</span> is [40,000, 50,000] and <span class="html-italic">q</span> is −30; (<b>b</b>) The relationship of <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>M</mi> <mrow> <mo>(</mo> <mrow> <mi>e</mi> <mo>,</mo> <mi>q</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>e</mi> </mrow> </semantics></math> when <span class="html-italic">e</span> is [40,000, 50,000] and <span class="html-italic">q</span> is −15.</p> Full article ">Figure A2
<p>The relationship of <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>M</mi> <mrow> <mo>(</mo> <mrow> <mi>e</mi> <mo>,</mo> <mi>q</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>e</mi> </mrow> </semantics></math> in the Fenhe River Basin. (<b>a</b>) The relationship of <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>M</mi> <mrow> <mo>(</mo> <mrow> <mi>e</mi> <mo>,</mo> <mi>q</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>e</mi> </mrow> </semantics></math> when <span class="html-italic">e</span> is [40,000, 50,000] and <span class="html-italic">q</span> is −30; (<b>b</b>) The relationship of <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>M</mi> <mrow> <mo>(</mo> <mrow> <mi>e</mi> <mo>,</mo> <mi>q</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>e</mi> </mrow> </semantics></math> when <span class="html-italic">e</span> is [40,000, 50,000] and <span class="html-italic">q</span> is −15.</p> Full article ">Figure A3
<p>The relationship of <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>M</mi> <mrow> <mo>(</mo> <mrow> <mi>e</mi> <mo>,</mo> <mi>q</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>e</mi> </mrow> </semantics></math> in the Weihe River Basin. (<b>a</b>) The relationship of <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>M</mi> <mrow> <mo>(</mo> <mrow> <mi>e</mi> <mo>,</mo> <mi>q</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>e</mi> </mrow> </semantics></math> when <span class="html-italic">e</span> is [40,000, 50,000] and <span class="html-italic">q</span> is −30; (<b>b</b>) The relationship of <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>M</mi> <mrow> <mo>(</mo> <mrow> <mi>e</mi> <mo>,</mo> <mi>q</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>e</mi> </mrow> </semantics></math> when <span class="html-italic">e</span> is [40,000, 50,000] and <span class="html-italic">q</span> is −15.</p> Full article ">Figure A4
<p>The relationship of <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>M</mi> <mrow> <mo>(</mo> <mrow> <mi>e</mi> <mo>,</mo> <mi>q</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>e</mi> </mrow> </semantics></math> in the Yellow River Downstream Basin. (<b>a</b>) The relationship of <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>M</mi> <mrow> <mo>(</mo> <mrow> <mi>e</mi> <mo>,</mo> <mi>q</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>e</mi> </mrow> </semantics></math> when <span class="html-italic">e</span> is [40,000, 50,000] and <span class="html-italic">q</span> is −30; (<b>b</b>) The relationship of <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>M</mi> <mrow> <mo>(</mo> <mrow> <mi>e</mi> <mo>,</mo> <mi>q</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ln</mi> <mi>e</mi> </mrow> </semantics></math> when <span class="html-italic">e</span> is [40,000, 50,000] and <span class="html-italic">q</span> is 15.</p> Full article ">
11 pages, 717 KiB  
Article
A Dynamic and Static Context-Aware Attention Network for Trajectory Prediction
by Jian Yu, Meng Zhou, Xin Wang, Guoliang Pu, Chengqi Cheng and Bo Chen
ISPRS Int. J. Geo-Inf. 2021, 10(5), 336; https://doi.org/10.3390/ijgi10050336 - 16 May 2021
Cited by 17 | Viewed by 3245
Abstract
Forecasting the motion of surrounding vehicles is necessary for an autonomous driving system applied in complex traffic. Trajectory prediction helps vehicles make more sensible decisions, which provides vehicles with foresight. However, traditional models consider the trajectory prediction as a simple sequence prediction task. [...] Read more.
Forecasting the motion of surrounding vehicles is necessary for an autonomous driving system applied in complex traffic. Trajectory prediction helps vehicles make more sensible decisions, which provides vehicles with foresight. However, traditional models consider the trajectory prediction as a simple sequence prediction task. The ignorance of inter-vehicle interaction and environment influence degrades these models in real-world datasets. To address this issue, we propose a novel Dynamic and Static Context-aware Attention Network named DSCAN in this paper. The DSCAN utilizes an attention mechanism to dynamically decide which surrounding vehicles are more important at the moment. We also equip the DSCAN with a constraint network to consider the static environment information. We conducted a series of experiments on a real-world dataset, and the experimental results demonstrated the effectiveness of our model. Moreover, the present study suggests that the attention mechanism and static constraints enhance the prediction results. Full article
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<p>Architecture of the proposed dynamic and static context-aware attention network (DSCAN). Track history and environmental constraint are considered by the modules, and the attentional decoder uses the concatenated representation to predict the vehicle’s trajectory.</p> Full article ">Figure 2
<p>Process of the constraint net: this network is designed for extracting environmental constraints. The embedded features are concatenated and activated to form a tensor for next time-step.</p> Full article ">Figure 3
<p>Comparison before and after data preprocessing: (<b>a</b>) instantaneous velocity comparison of No. 1882 vehicle in I-80 and (<b>b</b>) instantaneous acceleration comparison of No. 1882 vehicle in I-80.</p> Full article ">Figure 4
<p>Visualization results of average weights of test samples: (<b>a</b>) attention distribution when prediction time is 1 s, 3 s, and 5 s (the darker grid indicates the greater weight); (<b>b</b>) attention weight (middle lane) in the horizon of 5 s (the black dotted line is the research object’s position).</p> Full article ">Figure 5
<p>Attention weight distribution under different scenarios. Rows 1, 2, and 3 correspond to three different driving scenarios. Column “a” presents the groundtruth trajectories, while columns “b”, “c”, and “d”, respectively, visualize the attention distribution of 1 s, 3 s, and 5 s in the future.</p> Full article ">
17 pages, 2922 KiB  
Article
Geocoding Freeform Placenames: An Example of Deciphering the Czech National Immigration Database
by Jan Šimbera, Dušan Drbohlav and Přemysl Štych
ISPRS Int. J. Geo-Inf. 2021, 10(5), 335; https://doi.org/10.3390/ijgi10050335 - 15 May 2021
Cited by 1 | Viewed by 2509
Abstract
The growth of international migration and its societal and political impacts bring a greater need for accurate data to measure, understand and control migration flows. However, in the Czech immigration database, the birthplaces of immigrants are only kept in freeform text fields, a [...] Read more.
The growth of international migration and its societal and political impacts bring a greater need for accurate data to measure, understand and control migration flows. However, in the Czech immigration database, the birthplaces of immigrants are only kept in freeform text fields, a substantial obstacle to their further processing due to numerous errors in transcription and spelling. This study overcomes this obstacle by deploying a custom geocoding engine based on GeoNames, tailored transcription rules and fuzzy matching in order to achieve good accuracy even for noisy data while not depending on third-party services, resulting in lower costs than the comparable approaches. The results are presented on a subnational level for the immigrants coming to Czechia from the USA, Ukraine, Moldova and Vietnam, revealing important spatial patterns that are invisible on the national level. Full article
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<p>Transcription of an example placename using the developed system. The transcription rules are applied in the specified order from left to right; solid black arrows indicate where the specific rule matched; if that results in variant expansions, the arrows bifurcate. The output variants at the right side (after applying all of the rules from the ruleset) are tried top to bottom until one of them—in red—produces a match to the gazetteer. Duplicate variants from the bottom of the variant list are eliminated. Source: our own research.</p> Full article ">Figure 2
<p>Immigrants’ places of birth by states: those who migrated from the USA to Czechia between 2008 and 2017, including both permanent and temporary residence permits. Source: our own research.</p> Full article ">Figure 3
<p>Immigrants’ places of birth by provinces – those who migrated from Vietnam to Czechia between 2008 and 2017, including both permanent and temporary residence permits. Source: our own research.</p> Full article ">Figure 4
<p>Spatial distribution of migrants into Czechia from Ukraine and Moldova between 2013 and 2017, including both permanent and temporary residence permits. Source: our own research.</p> Full article ">Figure 5
<p>Fraction of temporary residence applications in the immigrant applications for Czechia from Ukraine (by oblast) and Moldova (by raion). Source: our own research.</p> Full article ">Figure 6
<p>Fraction of female immigrants into Czechia from Ukraine (by oblast) and Moldova (by raion), including both permanent and temporary residence permits. Source: our own research.</p> Full article ">Figure 7
<p>Difference in the count of migrants into Czechia from Ukraine (by oblast) and Moldova (by raion) in 2013–2017 compared to 2008–2012, including both permanent and temporary residence permits. Source: our own research.</p> Full article ">
17 pages, 1495 KiB  
Article
A Trajectory Ensemble-Compression Algorithm Based on Finite Element Method
by Haibo Chen and Xin Chen
ISPRS Int. J. Geo-Inf. 2021, 10(5), 334; https://doi.org/10.3390/ijgi10050334 - 14 May 2021
Cited by 5 | Viewed by 2288
Abstract
Trajectory compression is an efficient way of removing noise and preserving key features in location-based applications. This paper focuses on the dynamic compression of trajectory in memory, where the compression accuracy of trajectory changes dynamically with the different application scenarios. Existing methods can [...] Read more.
Trajectory compression is an efficient way of removing noise and preserving key features in location-based applications. This paper focuses on the dynamic compression of trajectory in memory, where the compression accuracy of trajectory changes dynamically with the different application scenarios. Existing methods can achieve this by adjusting the compression parameters. However, the relationship between the parameters and compression accuracy of most of these algorithms is considerably complex and varies with different trajectories, which makes it difficult to provide reasonable accuracy. We propose a novel trajectory compression algorithm that is based on the finite element method, in which the trajectory is taken as an elastomer to compress as a whole by elasticity theory, and trajectory compression can be thought of as deformation under stress. The compression accuracy can be determined by the stress size that is applied to the elastomer. When compared with the existing methods, the experimental results show that our method can provide more stable, data-independent compression accuracy under the given stress parameters, and with reasonable performance. Full article
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<p>Simplified trajectory by intuitive feeling.</p> Full article ">Figure 2
<p>Two types of trajectory compression tasks</p> Full article ">Figure 3
<p>Triangular element.</p> Full article ">Figure 4
<p>Standard deviations of compression accuracy.</p> Full article ">Figure 5
<p>Compression ratio at different parameters.</p> Full article ">Figure 6
<p>Compression rate at different parameters.</p> Full article ">Figure 7
<p>Error ratio at different parameters.</p> Full article ">Figure 8
<p>Correlation between error ratio and compression ratio.</p> Full article ">Figure 9
<p>The correlation between length ratio, curvature ratio, and compression ratio.</p> Full article ">Figure 10
<p>Application System Overview.</p> Full article ">Figure 11
<p>Compressed trajectoy in two applications.</p> Full article ">
19 pages, 5427 KiB  
Article
Filtering Link Outliers in Vehicle Trajectories by Spatial Reasoning
by Junli Liu, Miaomiao Pan, Xianfeng Song, Jing Wang, Kemin Zhu, Runkui Li, Xiaoping Rui, Weifeng Wang, Jinghao Hu and Venkatesh Raghavan
ISPRS Int. J. Geo-Inf. 2021, 10(5), 333; https://doi.org/10.3390/ijgi10050333 - 14 May 2021
Viewed by 2281
Abstract
Vehicle trajectories derived from Global Navigation Satellite Systems (GNSS) are used in various traffic applications based on trajectory quality analysis for the development of successful traffic models. A trajectory consists of points and links that are connected, where both the points and links [...] Read more.
Vehicle trajectories derived from Global Navigation Satellite Systems (GNSS) are used in various traffic applications based on trajectory quality analysis for the development of successful traffic models. A trajectory consists of points and links that are connected, where both the points and links are subject to positioning errors in the GNSS. Existing trajectory filters focus on point outliers, but neglect link outliers on tracks caused by a long sampling interval. In this study, four categories of link outliers are defined, i.e., radial, drift, clustered, and shortcut; current available algorithms are applied to filter apparent point outliers for the first three categories, and a novel filtering approach is proposed for link outliers of the fourth category in urban areas using spatial reasoning rules without ancillary data. The proposed approach first measures specific geometric properties of links from trajectory databases and then evaluates the similarities of geometric measures among the links, following a set of spatial reasoning rules to determine link outliers. We tested this approach using taxi trajectory datasets for Beijing with a built-in sampling interval of 50 to 65 s. The results show that clustered links (27.14%) account for the majority of link outliers, followed by shortcut (6.53%), radial (3.91%), and drift (0.62%) outliers. Full article
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<p>Four types of outlier tracking links (tracking points, tracking links, radial outliers, drift outliers, clustered outliers, and shortcut outliers are shown in (<b>a</b>), (<b>b</b>), (<b>c</b>), (<b>d</b>), (<b>e</b>), and (<b>f</b>), respectively).</p> Full article ">Figure 2
<p>An integrated approach for the detection of outlier tracking links.</p> Full article ">Figure 3
<p>Spatial patterns of the tracking links, with normal links, i.e., I<sub>1</sub>, II<sub>11</sub>, II<sub>21</sub>, and III<sub>11</sub>, and shortcut outlier links, i.e., I<sub>2</sub>, II<sub>12</sub>, II<sub>22</sub>, III<sub>12</sub>, and III<sub>20</sub>.</p> Full article ">Figure 4
<p>A schematic diagram showing the spatial reasoning workflow.</p> Full article ">Figure 5
<p>Sampling time intervals of the dataset.</p> Full article ">Figure 6
<p>Plot of the labelled tracking links in Beijing.</p> Full article ">Figure 7
<p>High-quality tracking points versus poor-quality tracking links at the Mudanyuan site: (<b>a</b>) shows that on-crossroads and on-road tracking points are well separated in space and (<b>b</b>) shows the complex intercross pattern of poor-quality tracking links at the intersection area.</p> Full article ">Figure 8
<p>Three-dimensional map of the information entropy values for the tracking points in the Mudanyuan area, where red represents the on-crossroad points and green is the on-road points.</p> Full article ">Figure 9
<p>Differences between on-site headings and roadway directions: (<b>a</b>) shows the histogram and cumulative curve of headings differences and (<b>b</b>) shows the spatial distribution of on-road points with errors larger than 7°.</p> Full article ">Figure 10
<p>Agreement between two directions, i.e., <math display="inline"><semantics> <mrow> <mi>max</mi> <mrow> <mo>(</mo> <mrow> <mo>|</mo> <msub> <mi>α</mi> <mrow> <mi>l</mi> <mi>i</mi> <mi>n</mi> <mi>k</mi> </mrow> </msub> <mo>−</mo> <msub> <mi>β</mi> <mn>0</mn> </msub> <mrow> <mo>|</mo> <mrow> <mo>,</mo> <mtext> </mtext> <mo>|</mo> <msub> <mi>α</mi> <mrow> <mi>l</mi> <mi>i</mi> <mi>n</mi> <mi>k</mi> </mrow> </msub> <mo>−</mo> <msub> <mi>β</mi> <mn>1</mn> </msub> </mrow> <mo>|</mo> </mrow> </mrow> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </semantics></math> is defined to measure the difference between the on-site headings and link direction. (<b>a1</b>) and (<b>b1</b>) respectively show the measured values of the normal and abnormal links and (<b>a2</b>) and (<b>b2</b>) for the cumulative curve, respectively.</p> Full article ">Figure 11
<p>Changes in the fractile line densities, i.e., <math display="inline"><semantics> <mrow> <mi>min</mi> <mrow> <mo>(</mo> <mrow> <msub> <mi>ρ</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>…</mo> <mi>m</mi> </mrow> </msub> </mrow> <mo>)</mo> </mrow> <mo>/</mo> <mi>min</mi> <mrow> <mo>(</mo> <mrow> <msub> <mi>ρ</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>ρ</mi> <mi>m</mi> </msub> </mrow> <mo>)</mo> </mrow> <mo stretchy="false">)</mo> <mo>,</mo> <mo> </mo> </mrow> </semantics></math>is defined to describe the change in the fractile line densities along a link: (<b>a1</b>) and (<b>b1</b>), respectively, show the measured values of the normal and abnormal links and (<b>a2</b>) and (<b>b2</b>) for the cumulative curve, respectively.</p> Full article ">Figure 12
<p>Effects of sample size on four parameters: (<b>a1</b>) and (<b>a2</b>) are about the entropy and density gradient; (<b>b1</b>) and (<b>b2</b>) are related to on-site heading and fractile line-density.</p> Full article ">
16 pages, 3737 KiB  
Article
Geospatial Management and Analysis of Microstructural Data from San Andreas Fault Observatory at Depth (SAFOD) Core Samples
by Elliott M. Holmes, Andrea E. Gaughan, Donald J. Biddle, Forrest R. Stevens and Jafar Hadizadeh
ISPRS Int. J. Geo-Inf. 2021, 10(5), 332; https://doi.org/10.3390/ijgi10050332 - 14 May 2021
Cited by 1 | Viewed by 3311
Abstract
Core samples obtained from scientific drilling could provide large volumes of direct microstructural and compositional data, but generating results via the traditional treatment of such data is often time-consuming and inefficient. Unifying microstructural data within a spatially referenced Geographic Information System (GIS) environment [...] Read more.
Core samples obtained from scientific drilling could provide large volumes of direct microstructural and compositional data, but generating results via the traditional treatment of such data is often time-consuming and inefficient. Unifying microstructural data within a spatially referenced Geographic Information System (GIS) environment provides an opportunity to readily locate, visualize, correlate, and apply remote sensing techniques to the data. Using 26 core billet samples from the San Andreas Fault Observatory at Depth (SAFOD), this study developed GIS-based procedures for: 1. Spatially referenced visualization and storage of various microstructural data from core billets; 2. 3D modeling of billets and thin section positions within each billet, which serve as a digital record after irreversible fragmentation of the physical billets; and 3. Vector feature creation and unsupervised classification of a multi-generation calcite vein network from cathodluminescence (CL) imagery. Building on existing work which is predominantly limited to the 2D space of single thin sections, our results indicate that a GIS can facilitate spatial treatment of data even at centimeter to nanometer scales, but also revealed challenges involving intensive 3D representations and complex matrix transformations required to create geographically translated forms of the within-billet coordinate systems, which are suggested for consideration in future studies. Full article
(This article belongs to the Special Issue Application of Geology and GIS)
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<p>Workflow diagram of in situ data collection, processes, and resulting outputs under a GIS-based framework. Starred items indicate procedures carried out by third parties (see Acknowledgements).</p> Full article ">Figure 2
<p>(<b>A</b>) General lithological characteristics of SAFOD core sections with a legend for units (measured depth-MD-meters). Black and white circles represent the distribution of sample billets (adapted from [<a href="#B26-ijgi-10-00332" class="html-bibr">26</a>]). (<b>B</b>) Color photo of Hole E, Run 1, <a href="#sec1-ijgi-10-00332" class="html-sec">Section 1</a> including red and black core orientation lines (adapted from [<a href="#B27-ijgi-10-00332" class="html-bibr">27</a>]).</p> Full article ">Figure 3
<p>Procedure for processing 2D images to generate 3D billet models depicting: (<b>A</b>) camera positions; (<b>B</b>) initial tie points; (<b>C</b>) dense tie points cloud; and (<b>D</b>) processed 3D billet model.</p> Full article ">Figure 4
<p>CL image containing two visually distinct generations of calcite vein growth and predefined regions of interest for spectral sampling and vector data creation. Because of significant spectral variation and superimposition among growth features, we infer that there are two distinct generations of calcite and that generation 1 (G1) is relatively younger than G2.2.4.2. CL Image Classification and Assessment.</p> Full article ">Figure 5
<p>Intermediate data from the classification procedure including: (<b>A</b>) the raw CL image; (<b>B</b>) the raster band generated via the FM function, with pixel values indication strength of membership; (<b>C</b>) the binary mask band produced by thresholding the original FM image; and (<b>D</b>) the clipped raster containing RGB pixel values only within the calcite region defined by the mask band. Images B and D serve as the input for the unsupervised classification.</p> Full article ">Figure 6
<p>Flow diagram illustrating how project microstructural data are structured, visualized, and explored interactively within the micro-GIS. The figure depicts (<b>A</b>) the SAFOD core overview map showing sample billet locations and attributes; (<b>B</b>) An example of the HTML pop-up window containing attribute information, results from XRD analysis, and links to additional data associated with sample billet G24; (<b>C</b>) the ArcScene workspace containing billet G24′s 3D solid surface model; and (<b>D</b>) the spatially referenced 2D image data from a thin section extracted from the XY plane in G24.</p> Full article ">Figure 7
<p>ArcGIS map view of sample points within the local coordinates of the CL image, labeled with custom symbology and relevant attribute information regarding spectral wavelength and calcite generation association.</p> Full article ">Figure 8
<p>Results of unsupervised classification showing (<b>A</b>) the input CL image, randomly generated accuracy assessment points (n = 100), and (<b>B</b>) the classified output with counts of pixels assigned as either calcite generation 1 or 2.</p> Full article ">Figure 9
<p>(<b>A</b>) Map view of SAF bearing 137° SE through California, core sections G123 and G456 cutting across the SAF with compass bearings of 027° NE (plunge of 67°) and 035° NE (plunge of 68°), respectively. (<b>B</b>) Schematic depiction of borehole depicting billet clockwise angle with respect to red and black core orientation lines.</p> Full article ">Figure 10
<p>Schematic representation of thin section orientation framework depicting: (<b>A</b>) Definition of billet sectioning planes XY, XZ, and YZ with respect to within-billet foliation plane; and (<b>B</b>) Suggested markings to be placed on physical thin sections for spatial referencing within the local coordinates of billet models (2D section markings in B were adapted after Tickoff et al. [<a href="#B4-ijgi-10-00332" class="html-bibr">4</a>]).</p> Full article ">
20 pages, 8751 KiB  
Article
An Evaluation Model for Analyzing Robustness and Spatial Closeness of 3D Indoor Evacuation Networks
by Lei Niu, Zhiyong Wang, Yiquan Song and Yi Li
ISPRS Int. J. Geo-Inf. 2021, 10(5), 331; https://doi.org/10.3390/ijgi10050331 - 13 May 2021
Cited by 4 | Viewed by 2073
Abstract
Indoor evacuation efficiency heavily relies on the connectivity status of navigation networks. During disastrous situations, the spreading of hazards (e.g., fires, plumes) significantly influences indoor navigation networks’ status. Nevertheless, current research concentrates on utilizing classical statistical methods to analyze this status and lacks [...] Read more.
Indoor evacuation efficiency heavily relies on the connectivity status of navigation networks. During disastrous situations, the spreading of hazards (e.g., fires, plumes) significantly influences indoor navigation networks’ status. Nevertheless, current research concentrates on utilizing classical statistical methods to analyze this status and lacks the flexibility to evaluate the increasingly disastrous scope’s influence. We propose an evaluation method combining 3D spatial geometric distance and topology for emergency evacuations to address this issue. Within this method, we offer a set of indices to describe the nodes’ status and the entire network under emergencies. These indices can help emergency responders quickly identify vulnerable nodes and areas in the network, facilitating the generation of evacuation plans and improving evacuation efficiency. We apply this method to analyze the fire evacuation efficiency and resilience of two experiment buildings’ indoor networks. Experimental results show a strong influence on the network’s spatial connectivity on the evacuation efficiency under disaster situations. Full article
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<p>An example of spatial grouping mechanism for an evacuation network.</p> Full article ">Figure 2
<p>An example of spatial grouping result for an evacuation network. The dotted lines indicate the isolated networks that are directly influenced by the fire in I1 (in gray); the dashed lines indicate the isolated networks that will be potentially influenced by the fire in I1 (in gray).</p> Full article ">Figure 3
<p>South view of the 3D model for the Henan University of Urban Construction (HUUC) building.</p> Full article ">Figure 4
<p>A graphic demonstration of the 3D model for the Meiluocheng (MLC) building.</p> Full article ">Figure 5
<p>East view of the navigation graph for the HUUC building.</p> Full article ">Figure 6
<p>Navigation network demonstration for the MLC building.</p> Full article ">Figure 7
<p>The scope of the average node spatial distance for the HUUC building.</p> Full article ">Figure 8
<p>The scope of the average node spatial distance for the MLC building.</p> Full article ">Figure 9
<p>Numbers of valid paths for two studied buildings with different percentage settings of using nodes.</p> Full article ">Figure 10
<p>Average spatial distance values for two studied buildings with different percentage settings of using nodes.</p> Full article ">Figure 11
<p>The search time costs of the HUUC building with different node usage percentage settings.</p> Full article ">Figure 12
<p>The search time costs of the MLC building with different node usage percentage settings.</p> Full article ">Figure 13
<p>Average path spatial distances of the two studied buildings with different node usage percentage settings.</p> Full article ">Figure 14
<p>Distance reduction ratios of the two studied buildings with different node usage percentage settings.</p> Full article ">
32 pages, 1284 KiB  
Article
Implicit, Formal, and Powerful Semantics in Geoinformation
by Gloria Bordogna, Cristiano Fugazza, Paolo Tagliolato Acquaviva d’Aragona and Paola Carrara
ISPRS Int. J. Geo-Inf. 2021, 10(5), 330; https://doi.org/10.3390/ijgi10050330 - 13 May 2021
Cited by 5 | Viewed by 3627
Abstract
Distinct, alternative forms of geosemantics, whose classification is often ill-defined, emerge in the management of geospatial information. This paper proposes a workflow to identify patterns in the different practices and methods dealing with geoinformation. From a meta-review of the state of the art [...] Read more.
Distinct, alternative forms of geosemantics, whose classification is often ill-defined, emerge in the management of geospatial information. This paper proposes a workflow to identify patterns in the different practices and methods dealing with geoinformation. From a meta-review of the state of the art in geosemantics, this paper first pinpoints “keywords” representing key concepts, challenges, methods, and technologies. Then, we illustrate several case studies, following the categorization into implicit, formal, and powerful (i.e., soft) semantics depending on the kind of their input. Finally, we associate the case studies with the previously identified keywords and compute their similarities in order to ascertain if distinguishing methodologies, techniques, and challenges can be related to the three distinct forms of semantics. The outcomes of the analysis sheds some light on the diverse methods and technologies that are more suited to model and deal with specific forms of geosemantics. Full article
(This article belongs to the Special Issue Artificial Intelligence for Multisource Geospatial Information)
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<p>Depiction of the workflow followed.</p> Full article ">Figure 2
<p>Diagram connecting keywords in geosemantics (<b>right</b>) and their categorization (<b>left</b>), as found in the reviews taken into consideration.</p> Full article ">Figure 3
<p>Case studies and the keywords representing their main activities and technologies.</p> Full article ">Figure 4
<p>Jaccard similarity between study cases represented as fuzzy sets of keywords.</p> Full article ">Figure 5
<p>Comparison between the grouping of keywords in <a href="#ijgi-10-00330-f002" class="html-fig">Figure 2</a> (on the right-hand side) and the grouping induced by the three forms of geosemantics (via the case studies) makes it apparent their greater distinguishing power.</p> Full article ">
20 pages, 12669 KiB  
Article
Cascaded Attention DenseUNet (CADUNet) for Road Extraction from Very-High-Resolution Images
by Jing Li, Yong Liu, Yindan Zhang and Yang Zhang
ISPRS Int. J. Geo-Inf. 2021, 10(5), 329; https://doi.org/10.3390/ijgi10050329 - 13 May 2021
Cited by 49 | Viewed by 3078
Abstract
The use of very-high-resolution images to extract urban, suburban and rural roads has important application value. However, it is still a problem to effectively extract the road area occluded by roadside tree canopy or high-rise buildings to maintain the integrity of the extracted [...] Read more.
The use of very-high-resolution images to extract urban, suburban and rural roads has important application value. However, it is still a problem to effectively extract the road area occluded by roadside tree canopy or high-rise buildings to maintain the integrity of the extracted road area, the smoothness of the sideline and the connectivity of the road network. This paper proposes an innovative Cascaded Attention DenseUNet (CADUNet) semantic segmentation model by embedding two attention modules, such as global attention and core attention modules, in the DenseUNet framework. First, a set of cascaded global attention modules are introduced to obtain the contextual information of the road; secondly, a set of cascaded core attention modules are embedded to ensure that the road information is transmitted to the greatest extent among the dense blocks in the network, and further assist the global attention module in acquiring multi-scale road information, thereby improving the connectivity of the road network while restoring the integrity of the road area shaded by the tree canopy and high-rise buildings. Based on binary cross entropy, an adaptive loss function is proposed for network parameter tuning. Experiments on the Massachusetts road dataset and the DeepGlobe-CVPR 2018 road dataset show that this semantic segmentation model can effectively extract the road area shaded by tree canopy and improve the connectivity of the road network. Full article
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<p>Architecture of CADUNet (The parameters include: <span class="html-italic">k</span>, the kernel size; <span class="html-italic">n</span>, the number of output channels; <span class="html-italic">s</span>, the stride size; <span class="html-italic">p</span>, the padding size).</p> Full article ">Figure 2
<p>Structure of dense block.</p> Full article ">Figure 3
<p>Core attention module.</p> Full article ">Figure 4
<p>Global attention module (FC means a fully connected layer).</p> Full article ">Figure 5
<p>Typical images with roads in rural, suburban and urban areas in the Massachusetts roads dataset. (<b>a</b>) The rural roads; (<b>b</b>) The suburban roads; (<b>c</b>) The urban roads.</p> Full article ">Figure 6
<p>Pavement integrity and sideline smoothness of the extracted roads in the Massachusetts dataset. (<b>1</b>–<b>3</b>) The partial roads occluded by tree canopies in rural areas; (<b>4</b>,<b>5</b>) The partial roads occluded by tree canopies in suburbs; (<b>6</b>) The partial roads occluded by urban high-rise buildings in urban areas.</p> Full article ">Figure 7
<p>Pavement integrity and sideline smoothness of the extracted roads in the CVPR dataset. (<b>1</b>,<b>2</b>) The partial roads occluded by tree canopies in rural areas; (<b>3</b>,<b>4</b>) The partial roads occluded by tree canopies in suburbs; (<b>5</b>,<b>6</b>) The partial roads occluded by urban high-rise buildings in urban areas.</p> Full article ">Figure 8
<p>Connectivity of the road networks extracted from the Massachusetts roads dataset. (<b>1</b>–<b>3</b>) The rural roads; (<b>4</b>,<b>5</b>) The suburban roads; (<b>6</b>) The urban roads; (<b>7</b>,<b>8</b>) The transportation hub roads.</p> Full article ">Figure 9
<p>Connectivity of the road networks extracted from the CVPR roads dataset. (<b>1</b>–<b>3</b>) The rural roads; (<b>4</b>,<b>5</b>) The suburban roads; (<b>6</b>) The urban roads.</p> Full article ">Figure 10
<p>Training and validating curves.</p> Full article ">Figure 11
<p>Visual accuracy assessment of road extraction based on the Massachusetts dataset. (<b>1</b>) The loop road; (<b>2</b>) The road sheltered by the elevated railway; (<b>3</b>) The intersection of the main road and the minor road; (<b>4</b>) The road occluded by dense tree canopies on the roadside; (<b>5</b>) The minor road connected to residential houses; (<b>6</b>) The main and minor road intersection area.</p> Full article ">Figure 12
<p>Visual accuracy assessment of road extraction based on the CVPR dataset. (<b>1</b>,<b>2</b>) The rural roads; (<b>3</b>) The parallel roads; (<b>4</b>) Intersection roads; (<b>5</b>,<b>6</b>) The urban road occluded by the shadow of the buildings.</p> Full article ">
21 pages, 957 KiB  
Article
Evaluating the Effect of the Financial Status to the Mobility Customs
by Gergő Pintér and Imre Felde
ISPRS Int. J. Geo-Inf. 2021, 10(5), 328; https://doi.org/10.3390/ijgi10050328 - 13 May 2021
Cited by 5 | Viewed by 2555
Abstract
In this article, we explore the relationship between cellular phone data and housing prices in Budapest, Hungary. We determine mobility indicators from one months of Call Detail Records (CDR) data, while the property price data are used to characterize the socioeconomic status at [...] Read more.
In this article, we explore the relationship between cellular phone data and housing prices in Budapest, Hungary. We determine mobility indicators from one months of Call Detail Records (CDR) data, while the property price data are used to characterize the socioeconomic status at the Capital of Hungary. First, we validated the proposed methodology by comparing the Home and Work locations estimation and the commuting patterns derived from the cellular network dataset with reports of the national mini census. We investigated the statistical relationships between mobile phone indicators, such as Radius of Gyration, the distance between Home and Work locations or the Entropy of visited cells, and measures of economic status based on housing prices. Our findings show that the mobility correlates significantly with the socioeconomic status. We performed Principal Component Analysis (PCA) on combined vectors of mobility indicators in order to characterize the dependence of mobility habits on socioeconomic status. The results of the PCA investigation showed remarkable correlation of housing prices and mobility customs. Full article
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<p>SIM cards in the 2017-04 data set categorized by the number of activity records. The categories are: only 1 record, 1 to 10 records, 10 to 100 records, 100 to 1000 records and greater than 1000 records. The SIM cards in the last category (17.7% of the SIM cards) provide the majority (75.48%) of the activity.</p> Full article ">Figure 2
<p>SIM card distribution in the 2017-04 data set by the number of active days.</p> Full article ">Figure 3
<p>The simplified processing the Mobile Phone data along with the Cell information and the Estate Price data used to determine the financial status of the subscribers.</p> Full article ">Figure 4
<p>The mobile phone activity distribution by days of week and hours based on the 2017 data set. Mondays and Fridays are not so bright as the other workdays, probably because of Easter Monday and Good Friday that are holidays in Hungary.</p> Full article ">Figure 5
<p>Comparision between the CDR and the census based (Data from [<a href="#B40-ijgi-10-00328" class="html-bibr">40</a>], Figure 1) commuting ratios.</p> Full article ">Figure 6
<p>Comparision of commuting to Budapest fromthe sectors of the agglomeration by age categories.</p> Full article ">Figure 7
<p>Indicators by financial category.</p> Full article ">Figure 8
<p>Voronoi polygons of the merged mobile phone cells colored by the estate prices and the Radius of Gyration. River Danube is represented with white, and the cells without enough data colored with light gray.</p> Full article ">Figure 9
<p>Distributions of the estate price: (<b>a</b>) shows the distribution of the estate price samples distinguished by the Buda and Pest of the city, and (<b>b</b>) shows the distribution of the home location prices of the individuals.</p> Full article ">Figure 10
<p>Budapest districts and the settlements of the agglomeration colored by the mean Radius of Gyration (km) for workdays. The tendency is that the farther lives someone from the city center the more one travels. The white border denotes the administrative border of Budapest and the Danube is displayed by light blue.</p> Full article ">Figure 11
<p>Histogram of the Radius of Gyration (<b>a</b>), Entropy (<b>b</b>) and the Home–Work location distance (<b>c</b>).</p> Full article ">Figure 12
<p>The differences between workdays (blue) and holidays (orange) are clear. Orange columns represent the holidays, 14 April 2017 was Good Friday and 17 April 2017 was Easter Monday that are holidays in Hungary.</p> Full article ">Figure 13
<p>The Pareto histogram for the 60 components of the Principal Component Analysis.</p> Full article ">Figure 14
<p>Scatter plot of the 2-component PCA. Marker sizes indicates the HomePrice category, the color/type WorkPrice category and also the day type (Weekday/Weekend).</p> Full article ">
18 pages, 3762 KiB  
Article
Natural and Political Determinants of Ecological Vulnerability in the Qinghai–Tibet Plateau: A Case Study of Shannan, China
by Yunxiao Jiang, Rong Li, Yu Shi and Luo Guo
ISPRS Int. J. Geo-Inf. 2021, 10(5), 327; https://doi.org/10.3390/ijgi10050327 - 12 May 2021
Cited by 11 | Viewed by 2522
Abstract
Changing land-use patterns in the Qinghai–Tibet Plateau (QTP) due to natural factors and human interference have led to higher ecological vulnerability and even more underlying issues related to time and space in this alpine area. Ecological vulnerability assessment provides not only a solution [...] Read more.
Changing land-use patterns in the Qinghai–Tibet Plateau (QTP) due to natural factors and human interference have led to higher ecological vulnerability and even more underlying issues related to time and space in this alpine area. Ecological vulnerability assessment provides not only a solution to surface-feature-related problems but also insight into sustainable eco-environmental planning and resource management as a response to potential climate changes if driving factors are known. In this study, the ecological vulnerability index (EVI) of Shannan City in the core area of the QTP was assessed using a selected set of ecological, social, and economic indicators and spatial principal component analysis (SPCA) to calculate their weights. The data included Landsat images and socio-economic data from 1990 to 2015, at five-year intervals. The results showed that the total EVI remains at a medium vulnerability level, with minor fluctuations over 25 years (peaks in 2000, when there was a sudden increase in slight vulnerability, which switched to extreme vulnerability), and gradually increases from east to west. In addition, spatial analysis showed a distinct positive correlation between the EVI and land-use degree, livestock husbandry output, desertification area, and grassland area. The artificial afforestation program (AAP) has a positive effect by preventing the environment from becoming more vulnerable. The results provide practical information and suggestions for planners to take measures to improve the land-use degree in urban and pastoral areas in the QTP based on spatial-temporal heterogeneity patterns of the EVI of Shannan City. Full article
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<p>Location of study area.</p> Full article ">Figure 2
<p>The technique flowchart of the study.</p> Full article ">Figure 3
<p>Proportion of ecological vulnerability of Shannan in 1990–2015.</p> Full article ">Figure 4
<p>The EVI distribution of Shannan in 1990–2015.</p> Full article ">Figure 5
<p>EVI Tupu changes of Shannan in 1990–2015.</p> Full article ">Figure 6
<p>Analysis of cold-hot spots of EVI changes in Shannan from 1990 to 2015.</p> Full article ">Figure 7
<p>Correlation analysis between principal components 1–5 and various indicators. (P—average annual precipitation; T—average annual temperature; Sun—hours of sunshine; POP—population density; LO—livestock husbandry output; S—slope; NDVI; GDP; G—grassland area; PPA—plateau permafrost area; W—wind speed; SRD—surface relief degree; SRI—solar radiation intensity; LUD—land use degree; DA—desertification area; Sh—Shannon index; RH—relative humidity; WRA—water resources amount).</p> Full article ">Figure 8
<p>The changes of EVI and afforestation area from 1990 to 2015.</p> Full article ">
20 pages, 4447 KiB  
Article
Geospatial Decision-Making Framework Based on the Concept of Satisficing
by Goran Milutinović, Stefan Seipel and Ulla Ahonen-Jonnarth
ISPRS Int. J. Geo-Inf. 2021, 10(5), 326; https://doi.org/10.3390/ijgi10050326 - 12 May 2021
Cited by 3 | Viewed by 3062
Abstract
Decision-making methods used in geospatial decision making are computationally complex prescriptive methods, the details of which are rarely transparent to the decision maker. However, having a deep understanding of the details and mechanisms of the applied method is a prerequisite for the efficient [...] Read more.
Decision-making methods used in geospatial decision making are computationally complex prescriptive methods, the details of which are rarely transparent to the decision maker. However, having a deep understanding of the details and mechanisms of the applied method is a prerequisite for the efficient use thereof. In this paper, we present a novel decision-making framework that emanates from the need for intuitive and easy-to-use decision support systems for geospatial multi-criteria decision making. The framework consists of two parts: the decision-making model Even Swaps on Reduced Data Sets (ESRDS), and the interactive visualization framework. The decision-making model is based on the concept of satisficing, and as such, it is intuitive and easy to understand and apply. It integrates even swaps, a prescriptive decision-making method, with the findings of behavioural decision-making theories. Providing visual feedback and interaction opportunities throughout the decision-making process, the interactive visualization part of the framework helps the decision maker gain better insight into the decision space and attribute dependencies. Furthermore, it provides the means to analyse and compare the outcomes of different scenarios and decision paths. Full article
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<p>The process model for the framework.</p> Full article ">Figure 2
<p>The interaction path between different units of the framework.</p> Full article ">Figure 3
<p>The main window of GISAnalyzer; initial setup, after the data for all criteria are loaded.</p> Full article ">Figure 4
<p>Different views of the threshold adjustment panel: (<b>a</b>) non-filtered view, where, for each criterion, all alternatives conforming to the threshold value for that particular criterion are drawn; (<b>b</b>) filtered view, where only alternatives conforming to all the thresholds are drawn, with one criterion (<span class="html-italic">Power distance</span>) discarded (equivalent to setting the threshold for that criterion to <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> </semantics></math>) and another criterion (<span class="html-italic">Distance to cities</span>) selected as the criterion to be automatically adjusted, <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>s</mi> <mi>p</mi> </mrow> </msub> </semantics></math>. Red numbers in the upper right corner of each histogram denote the number of alternatives with a given value.</p> Full article ">Figure 5
<p>The main application window during the threshold adjustment process. The initial set of 15,555 alternatives is reduced to 4898, by adjusting the thresholds values for three criteria. The threshold for <span class="html-italic">Distance to cities</span> is increased to approximately 8500 m, the threshold for <span class="html-italic">Power distance</span> to approximately 1100 m, and the threshold for <span class="html-italic">Road distance</span> to approximately 14,000 m. The remaining acceptable alternatives are colour-coded based on the value in terms of <span class="html-italic">Road distance</span>, as it happens to be the criterion currently being adjusted.</p> Full article ">Figure 6
<p>Geomap in default view (<b>a</b>) and in desaturated view (<b>b</b>).</p> Full article ">Figure 7
<p>Each of the five coloured axes in the parallel coordinates plot holds the values of the remaining acceptable alternatives in terms of one of the criteria. The last two axes represent longitude and latitude of the alternatives in the geographical space. Polylines representing the alternatives are colour-coded from green (best) to red (worst).</p> Full article ">Figure 8
<p>The interaction between the parallel coordinates plot and the geomap. When an alternative is selected in the plot, it is highlighted in the geomap. Selecting the alternative in the geomap opens a “detail-on-demand” window (<b>a</b>). When an area is selected in the geomap, all acceptable alternatives within the selected area are highlighted white in the parallel coordinates plot (<b>b</b>).</p> Full article ">Figure 9
<p>AHP window. The “Apply” button is enabled if the relative importance values for the criteria are consistent, i.e., if the consistency index <math display="inline"><semantics> <mrow> <mi>C</mi> <mi>I</mi> <mo>≤</mo> <mn>0.1</mn> </mrow> </semantics></math>.</p> Full article ">Figure 10
<p>The parallel coordinates plot and the complementary diagram after applying comparison with AHP.</p> Full article ">Figure 11
<p>The status after three of the criteria were previously dismissed from further process and seven dominated alternatives were removed. In the current step, <span class="html-italic">Distance to main roads</span> is chosen as the reference criterion, and <span class="html-italic">Slope</span> is chosen as the response criterion. The values in terms of the response criterion need to be adjusted in order to compensate for the adjustments needed to render all three remaining alternatives equal (at the value of 1300) in terms of the reference criterion. This is done by increasing the value in terms of <span class="html-italic">Slope</span> from four to five degrees for alternative A2, and from two to six degrees for alternative A10. After this swap is performed, the reference criterion, <span class="html-italic">Distance to main roads</span>, will be dismissed. The values in terms of the only remaining criterion, <span class="html-italic">Slope</span>, will be five degrees for A2, ten degrees for A3, and six degrees for A10. A2 is chosen as the most preferred alternative, as it has the best value on the only remaining criterion.</p> Full article ">Figure 12
<p>The full view of the GISAnalyzer main window after applying even swaps to obtain the most preferred alternative.</p> Full article ">
21 pages, 6320 KiB  
Article
An Automatic and Operational Method for Land Cover Change Detection Using Spatiotemporal Analysis of MODIS Data: A Northern Ontario (Canada) Case Study
by Ima Ituen and Baoxin Hu
ISPRS Int. J. Geo-Inf. 2021, 10(5), 325; https://doi.org/10.3390/ijgi10050325 - 11 May 2021
Cited by 4 | Viewed by 2124
Abstract
Mapping and understanding the differences in land cover and land use over time is an essential component of decision-making in sectors such as resource management, urban planning, and forest fire management, as well as in tracking of the impacts of climate change. Existing [...] Read more.
Mapping and understanding the differences in land cover and land use over time is an essential component of decision-making in sectors such as resource management, urban planning, and forest fire management, as well as in tracking of the impacts of climate change. Existing methods sometimes pose a barrier to the effective monitoring of changes in land cover and land use, since a threshold parameter is often needed and determined based on trial and error. This study aimed to develop an automatic and operational method for change detection on a large scale from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Super pixels were the basic unit of analysis instead of traditional individual pixels. T2 tests based on the feature vectors of temporal Normalized Difference Vegetation Index (NDVI) and land surface temperature were used for change detection. The developed method was applied to data over a predominantly vegetated area in northern Ontario, Canada spanning 120,000 sq. km from 2001–2016. The accuracies ranged between 78% and 88% for the NDVI-based test, from 74% to 86% for the LST-based test, and from 70% to 86% for the joint method compared with manual interpretation. Our proposed method for detecting land cover change provides a functional and viable alternative to existing methods of land cover change detection as it is reliable, repeatable, and free from uncertainty in establishing a threshold for change. Full article
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<p>Study areas of Cochrane and Hearst in the Great Clay Belt of Ontario.</p> Full article ">Figure 2
<p>Example of land use map for the Great Claybelt area.</p> Full article ">Figure 3
<p>Example of smoothing performed on NDVI data.</p> Full article ">Figure 4
<p>Example of a semivariogram.</p> Full article ">Figure 5
<p>Result of image segmentation on Hearst area in 2005.</p> Full article ">Figure 6
<p>Changed areas (colored) detected from 2001 to 2005 in (<b>a</b>) NDVI; (<b>b</b>) LST overlaid on the map of the study area.</p> Full article ">Figure 7
<p>Changed areas (colored) detected from 2005 to 2009 in (<b>a</b>) NDVI; (<b>b</b>) LST overlaid on the map of the study area.</p> Full article ">Figure 8
<p>Changed areas (colored) detected from 2010 to 2016 in (<b>a</b>) NDVI; (<b>b</b>) LST overlaid on the map of the study area.</p> Full article ">Figure 9
<p>NDVI maps of Hearst. From left to right: January, March, May, and November in 2001 (<b>top</b>) and 2010 (<b>bottom</b>).</p> Full article ">Figure 10
<p>NDVI maps of Cochrane. From left to right: January, March, May, and November in 2001 (<b>top</b>) and 2010 (<b>bottom</b>).</p> Full article ">Figure 11
<p>LST maps of Hearst. From left to right: January, March, May, and November 2001 (<b>top</b>) and 2010 (<b>bottom</b>).</p> Full article ">Figure 12
<p>LST map of Cochrane. From left to right: January, March, May, and November 2001 (<b>top</b>) and 2010 (<b>bottom</b>).</p> Full article ">Figure 13
<p>Change magnitude (<b>a</b>) and direction (<b>b</b>) in Cochrane between April 2005 and April 2006 and between November 2005 and November 2006.</p> Full article ">Figure 14
<p>Change magnitude and direction in the Heart forest area from 2001 to 2005.</p> Full article ">Figure 15
<p>Change magnitude and direction in the Hearst forest area from 2009 to 2016.</p> Full article ">Figure 16
<p>Illustrating the disturbances which occurred in the Claybelt from 2001 to 2009.</p> Full article ">
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