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13 pages, 1217 KiB  
Article
The Use of Unmanned Aerial Systems for River Monitoring: A Bibliometric Analysis Covering the Last 25 Years
by Alonso Pizarro, Desirée Valera-Gran, Eva-María Navarrete-Muñoz and Silvano Fortunato Dal Sasso
Hydrology 2024, 11(6), 80; https://doi.org/10.3390/hydrology11060080 - 7 Jun 2024
Cited by 1 | Viewed by 1285
Abstract
Cutting-edge technology for fluvial monitoring has revolutionised the field, enabling more comprehensive data collection, analysis, and interpretation. Traditional monitoring methods were limited in their spatial and temporal resolutions, but advancements in remote sensing, unmanned aerial systems (UASs), and other innovative technologies have significantly [...] Read more.
Cutting-edge technology for fluvial monitoring has revolutionised the field, enabling more comprehensive data collection, analysis, and interpretation. Traditional monitoring methods were limited in their spatial and temporal resolutions, but advancements in remote sensing, unmanned aerial systems (UASs), and other innovative technologies have significantly enhanced the fluvial monitoring capabilities. UASs equipped with advanced sensors enable detailed and precise fluvial monitoring by capturing high-resolution topographic data, generate accurate digital elevation models, and provide imagery of river channels, banks, and riparian zones. These data enable the identification of erosion and deposition patterns, the quantification of sediment transport, the evaluation of habitat quality, and the monitoring of river flows. The latter allows us to understand the dynamics of rivers during various hydrological events, including floods, droughts, and seasonal variations. This manuscript aims to provide an update on the main research themes and topics in the literature on the use of UASs for river monitoring. The latter is achieved through a bibliometric analysis of the publication trends and identifies the field’s key themes and collaborative networks. The bibliometric analysis shows trends in the number of publications, number of citations, top contributing countries, top publishing journals, top contributing institutions, and top authors. A total of 1085 publications on UAS monitoring in rivers are identified, published between 1999 and 2023, showing a steady annual growth rate of 24.44%. Bibliographic records are exported from the Web of Science (WoS) database using a comprehensive set of keywords. The bibliometric analysis of the raw data obtained from the WoS database is performed using the R software. The results highlight important trends and valuable insights related to the use of UASs in river monitoring, particularly in the last decade. The most frequently used author keywords outline the core themes of UASs monitoring research and highlight the interdisciplinary nature and collaborative efforts within the field. “River”, “topography”, “photogrammetry”, and “Structure-from-Motion” are the core themes of UASs monitoring research. These findings can guide future research and promote new interdisciplinary collaborations. Full article
(This article belongs to the Section Surface Waters and Groundwaters)
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<p>Annual scientific production of UAS monitoring in rivers during the last 25 years.</p>
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<p>Geographical distribution map of countries’ scientific production in publishing papers on UAS monitoring in rivers, and country collaboration network map (brown lines) from 1999 to 2023. Darker blue colour means more scientific production.</p>
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<p>Network map of the 50 most frequently used author keywords in documents on UAS monitoring in rivers from 1999 to 2023. Different colours represent different clusters. The size of the box (and the keyword in question) represents the number of times that the keyword appeared within the database.</p>
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23 pages, 5067 KiB  
Article
A Multi-Sensor Approach to Characterize Winter Water-Level Drawdown Patterns in Lakes
by Abhishek Kumar, Allison H. Roy, Konstantinos M. Andreadis, Xinchen He and Caitlyn Butler
Remote Sens. 2024, 16(6), 947; https://doi.org/10.3390/rs16060947 - 8 Mar 2024
Viewed by 981
Abstract
Artificial manipulation of lake water levels through practices like winter water-level drawdown (WD) is prevalent across many regions, but the spatiotemporal patterns are not well documented due to limited in situ monitoring. Multi-sensor satellite remote sensing provides an opportunity to map and analyze [...] Read more.
Artificial manipulation of lake water levels through practices like winter water-level drawdown (WD) is prevalent across many regions, but the spatiotemporal patterns are not well documented due to limited in situ monitoring. Multi-sensor satellite remote sensing provides an opportunity to map and analyze drawdown frequency and metrics (timing, magnitude, duration) at broad scales. This study developed a cloud computing framework to process time series of synthetic aperture radar (Sentinel 1-SAR) and optical sensor (Landsat 8, Sentinel 2) data to characterize WD in 166 lakes across Massachusetts, USA, during 2016–2021. Comparisons with in situ logger data showed that the Sentinel 1-derived surface water area captured relative water-level fluctuations indicative of WD. A machine learning approach classified lakes as WD versus non-WD based on seasonal water-level fluctuations derived from Sentinel 1-SAR data. The framework mapped WD lakes statewide, revealing prevalence throughout Massachusetts with interannual variability. Results showed WDs occurred in over 75% of lakes during the study period, with high interannual variability in the number of lakes conducting WD. Mean WD magnitude was highest in the wettest year (2018) but % lake area exposure did not show any association with precipitation and varied between 8% to 12% over the 5-year period. WD start date was later and duration was longer in wet years, indicating climate mediation of WD implementation driven by management decisions. The data and tools developed provide an objective information resource to evaluate ecological impacts and guide management of this prevalent but understudied phenomenon. Overall, the results and interactive web tool developed as part of this study provide new hydrologic intelligence to inform water management and policies related to WD practices. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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<p>Study area map showing lakes &gt;0.3 km<sup>2</sup> in Massachusetts where water-level fluctuations were evaluated. The lakes highlighted in orange colors are those with available in situ water-level data; blue dots are lakes with satellite data only.</p>
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<p>Overall study framework including data and processing levels to characterize winter drawdown lakes in Massachusetts (MA). Shapes used in the above flowchart are as follows: tilted rectangles represent data; regular rectangles represent processing and analysis; and small circles are used as connectors and for comparisons. WD = winter drawdown.</p>
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<p>Raw images (<b>a</b>–<b>l</b>) and corresponding surface water area maps (<b>a’</b>–<b>l’</b>) derived from three satellite sensors (Landsat 8-Operational Land Imager (OLI), Sentinel 2-MultiSpectral Instrument (MSI), and Sentinel 1-synthetic aperture radar (SAR)) for Lake Onota, Massachusetts. Dates were selected where environmental conditions presented potential issues: sun glint (<b>a</b>–<b>c</b>; 21 June 2017), clouds (<b>d</b>–<b>f</b>; 30 July 2017), cloud shadow (<b>g</b>–<b>i</b>; 14 December 2017), and ice (<b>j</b>–<b>l</b>; 31 January 2018).</p>
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<p>Surface water area time-series of Lake Otis, a winter drawdown (WD) lake (<b>a</b>), and Leverett Pond, a non-winter drawdown lake (ND) (<b>b</b>) in Massachusetts derived from three sensors, namely Sentinel 1-synthetic aperture radar (SAR), Landsat 8-Operational Land Imager (OLI), and Sentinel 2-MultiSpectral Instrument (MSI). Cloud cover and revisit frequency resulted in less frequent observations derived from optical sensors (Landsat 8OLI and Sentinel 2-MSI) than Sentinel 1-SAR. Red circles highlight outliers caused by ice and cloud cover.</p>
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<p>(<b>a</b>) Example of spatiotemporal variability in surface water area showing drawdown regions (dark regions compared to blue-colored water regions) matching with shallow regions (orange and red colors) in respective bathymetry maps for Lake Onota, Massachusetts (MA), and (<b>b</b>) comparison between water area derived from three sensors (red circle: Landsat 8-Operational Land Imager (OLI); blue square: Sentinel 1-synthetic aperture radar (SAR); and green triangle: Sentinel 2-MultiSpectral Instrument (MSI)) and water level derived from in situ loggers (purple diamond) for Lake Onota.</p>
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<p>(<b>a</b>) Winter drawdown (WD) frequency for 166 lakes across Massachusetts for five years (2016–2020); bubble size and color reflect the frequency. (<b>b</b>) Total number of WD lakes (blue bars) and total annual precipitation (orange bar) and total precipitation between September to December (green bar) for contiguous Massachusetts between 2016–2020.</p>
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<p>Boxplots reporting the minimum and maximum (whiskers), 25th–75th%iles (boxes), medians (horizontal line and label), and outliers (circles) of winter drawdown (WD) metrics: (<b>a</b>) start date, (<b>b</b>) duration, (<b>c</b>) relative magnitude, and (<b>d</b>) % lake area exposed for five years (2016–2020). Start dates range from 1 September to 1 February.</p>
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<p>(<b>a</b>) Comparison between relative winter drawdown (WD) magnitude (Km<sup>2</sup>) and % lake area exposed for five years (2016–2020) with linear regression R<sup>2</sup> reported for each year. Each year’s data (points and trendline) are shown in a different color. The combined data trendline for all five years is indicated by a dashed black line (<b>a</b>). Comparison between WD start date and WD duration for five years (2016–2020) (<b>b</b>). Each year data is shown in different color with different symbols (<b>b</b>).</p>
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29 pages, 1507 KiB  
Review
A Systematic Review on Advancements in Remote Sensing for Assessing and Monitoring Land Use and Land Cover Changes Impacts on Surface Water Resources in Semi-Arid Tropical Environments
by Makgabo Johanna Mashala, Timothy Dube, Bester Tawona Mudereri, Kingsley Kwabena Ayisi and Marubini Reuben Ramudzuli
Remote Sens. 2023, 15(16), 3926; https://doi.org/10.3390/rs15163926 - 8 Aug 2023
Cited by 35 | Viewed by 8341
Abstract
This study aimed to provide a systematic overview of the progress made in utilizing remote sensing for assessing the impacts of land use and land cover (LULC) changes on water resources (quality and quantity). This review also addresses research gaps, challenges, and opportunities [...] Read more.
This study aimed to provide a systematic overview of the progress made in utilizing remote sensing for assessing the impacts of land use and land cover (LULC) changes on water resources (quality and quantity). This review also addresses research gaps, challenges, and opportunities associated with the use of remotely sensed data in assessment and monitoring. The progress of remote sensing applications in the assessment and monitoring of LULC, along with their impacts on water quality and quantity, has advanced significantly. The availability of high-resolution satellite imagery, the integration of multiple sensors, and advanced classification techniques have improved the accuracy of land cover mapping and change detection. Furthermore, the study highlights the vast potential for providing detailed information on the monitoring and assessment of the relationship between LULC and water resources through advancements in data science analytics, drones, web-based platforms, and balloons. It emphasizes the importance of promoting research efforts, and the integration of remote sensing data with spatial patterns, ecosystem services, and hydrological models enables a more comprehensive evaluation of water quantity and quality changes. Continued advancements in remote sensing technology and methodologies will further improve our ability to assess and monitor the impacts of LULC changes on water quality and quantity, ultimately leading to more informed decision making and effective water resource management. Such research endeavors are crucial for achieving the effective and sustainable management of water quality and quantity. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Function and Traits)
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<p>Methodology undertaken for selection of articles considered in the review.</p>
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<p>Classification algorithms (%) used to detect and map LULC using multi-sensors.</p>
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<p>(<b>a</b>) Progress of remote sensing publications, and (<b>b</b>) sensors that were used in monitoring the effect of land use and land cover change on water quantity and quality.</p>
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<p>Algorithms used to assess and monitor the relationship between impacts of LULC and water quality.</p>
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32 pages, 15948 KiB  
Article
Comparison of Deterministic and Statistical Models for Water Quality Compliance Forecasting in the San Joaquin River Basin, California
by Nigel W. T. Quinn, Michael K. Tansey and James Lu
Water 2021, 13(19), 2661; https://doi.org/10.3390/w13192661 - 27 Sep 2021
Cited by 5 | Viewed by 2960
Abstract
Model selection for water quality forecasting depends on many factors including analyst expertise and cost, stakeholder involvement and expected performance. Water quality forecasting in arid river basins is especially challenging given the importance of protecting beneficial uses in these environments and the livelihood [...] Read more.
Model selection for water quality forecasting depends on many factors including analyst expertise and cost, stakeholder involvement and expected performance. Water quality forecasting in arid river basins is especially challenging given the importance of protecting beneficial uses in these environments and the livelihood of agricultural communities. In the agriculture-dominated San Joaquin River Basin of California, real-time salinity management (RTSM) is a state-sanctioned program that helps to maximize allowable salt export while protecting existing basin beneficial uses of water supply. The RTSM strategy supplants the federal total maximum daily load (TMDL) approach that could impose fines associated with exceedances of monthly and annual salt load allocations of up to $1 million per year based on average year hydrology and salt load export limits. The essential components of the current program include the establishment of telemetered sensor networks, a web-based information system for sharing data, a basin-scale salt load assimilative capacity forecasting model and institutional entities tasked with performing weekly forecasts of river salt assimilative capacity and scheduling west-side drainage export of salt loads. Web-based information portals have been developed to share model input data and salt assimilative capacity forecasts together with increasing stakeholder awareness and involvement in water quality resource management activities in the river basin. Two modeling approaches have been developed simultaneously. The first relies on a statistical analysis of the relationship between flow and salt concentration at three compliance monitoring sites and the use of these regression relationships for forecasting. The second salt load forecasting approach is a customized application of the Watershed Analysis Risk Management Framework (WARMF), a watershed water quality simulation model that has been configured to estimate daily river salt assimilative capacity and to provide decision support for real-time salinity management at the watershed level. Analysis of the results from both model-based forecasting approaches over a period of five years shows that the regression-based forecasting model, run daily Monday to Friday each week, provided marginally better performance. However, the regression-based forecasting model assumes the same general relationship between flow and salinity which breaks down during extreme weather events such as droughts when water allocation cutbacks among stakeholders are not evenly distributed across the basin. A recent test case shows the utility of both models in dealing with an exceedance event at one compliance monitoring site recently introduced in 2020. Full article
(This article belongs to the Special Issue Decision Support Tools for Water Quality Management)
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<p>Major subareas within the SJR Basin that drain to the SJR as defined in the salinity TMDL [<a href="#B13-water-13-02661" class="html-bibr">13</a>]. Reach 83 shown in the figure is the reach for which water quality (salinity) is regulated through the recognition of three compliance monitoring stations at Crows Landing, Maze Road Bridge and Vernalis. The most salient feature of the SJR Basin is that drainage from sources to the west of the SJR are elevated in salinity by virtue of native salts in alluvial sediments deposited from the coastal range mountains west of the Valley floor and the importation of irrigation water supply from the Sacramento-San Joaquin Delta that is also salt impacted. Tributary inflow from land areas to the east of the SJR are of high quality, derived from snowmelt from the Sierra Nevada mountains. Real-time management is essentially a scheduling activity—coordinating salt load assimilative capacity consumed by west-side saline drainage with salt load assimilative capacity supplied by east-side reservoir releases along the major tributaries.</p>
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<p>Map of the SJR Basin represented as major contributing watersheds within the WARMF model. The WARMF model allows further disaggregation of these watersheds into small contributing subareas and allows the substitution of available data at the major outlets of these subareas for model-derived flow and water quality estimates.</p>
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<p>A unique feature of the WARMF model is the availability of customized model outputs such as the “Gowdy” output (named after its developer) shown here. This depicts a Lagrangian view of the SJR at any point in time showing the major inflow to and diversions from the river approximately every ½ mile (800 m) along its main reach as well as the incremental flow and EC concentration from the origin at Lander Avenue to the EC compliance monitoring station at Vernalis [<a href="#B23-water-13-02661" class="html-bibr">23</a>,<a href="#B25-water-13-02661" class="html-bibr">25</a>].</p>
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<p>Flow and EC observations at Vernalis compliance monitoring station on the SJR for the period 2000–2018.</p>
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<p>Means of the observed (OBS) EC and Forecast (FC) EC for the Regression and WARMF models for all forecast lead times between 22 February 2018 and 22 May 2020.</p>
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<p>Comparison of mean differences in forecasted EC and observed EC for the Regression and WARMF models for the period between 22 February 2018 and 22 May 2020.</p>
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<p>(<b>a</b>,<b>b</b>). Comparison of the standard deviations of forecasted EC and observed EC and standard deviations of differences between EC forecasts and EC observations for the Regression and WARMF models by lead time in the period between 22 February 2018 and 22 May 2020.</p>
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<p>Comparison of means of forecasted EC and observed EC for the Regression and WARMF models for the period between 22 February 2018 and 22 May 2020. Data censored to include only over (positive) predictions.</p>
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<p>Comparison of means of forecasted and observed EC for the Regression and WARMF models for the period between 22 February 2018 and 22 May 2020. Data censored to include only under (negative)-predictions.</p>
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<p>Comparison of the percentages of higher (positive bias) EC forecasts for the Regression and WARMF models for the period between 22 February 2018 and 22 May 2020.</p>
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<p>Comparison of Regression model forecasts and observations of EC at various lead times for the period between 22 February 2018 and 22 May 2020.</p>
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<p>Comparison of WARMF model forecasts and observations of EC at various lead times for the period between 22 February 2018 and 22 May 2020.</p>
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<p>(<b>a</b>,<b>b</b>). Boxplots of observed EC and forecast EC by the Regression (<b>a</b>) and WARMF (<b>b</b>) models are shown for forecast lead time ∆ Day + 12. Fligner–Killeen variance <span class="html-italic">p</span> values are 0.6244 and 0.2703 for the Regression and WARMF models, respectively.</p>
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<p>(<b>a</b>,<b>b</b>). Calculated linear regression relationship (solid blue line) for the Regression (<b>a</b>) and WARMF (<b>b</b>) models together with a scatterplot of the underlying observed EC data and model forecast EC for lead time ∆ Day + 12.</p>
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<p>(<b>a</b>,<b>b</b>). Histograms of the mean differences between observed EC and model forecast EC for the Regression (<b>a</b>) and WARMF (<b>b</b>) models for model forecast lead time ∆ Day + 12.</p>
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<p>(<b>a</b>,<b>b</b>). Histograms of the mean differences between observed EC and model forecast EC for the Regression (<b>a</b>) and WARMF (<b>b</b>) models for model forecast lead time ∆ Day + 12.</p>
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<p>Adjusted R-squared values for the Regression and WARMF models for all EC forecast lead times.</p>
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<p>Comparison of daily WARMF and Regression model forecasts for EC at the Crows Landing compliance monitoring station on 22 February 2021 (<b>a,b</b>); 26 April 2021 (<b>c,d,e,f</b>); and 1 June 2021 (<b>g,h</b>). Graphs (<b>e,f</b>) show the 30-day running average EC forecast on 26 April 2021 relative to the the 30-day running average EC compliance objective. Conversion of flow in cfs to m<sup>3</sup>/s: 100 cfs = 2.83 m<sup>3</sup>/s.</p>
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<p>Comparison of daily WARMF and Regression model forecasts for EC at the Crows Land-ing compliance monitoring station on 22 February 2021 (<b>a,b</b>); 26 April 2021 (<b>c,d,e,f</b>); and 1 June 2021 (<b>g,h</b>). Graphs (<b>e,f</b>) show the 30-day running average EC forecast on 26 April 2021 relative to the the 30-day running average EC compliance objective. Conversion of flow in cfs to m3/s: 100 cfs = 2.83 m<sup>3</sup>/s.</p>
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<p>Comparison of daily WARMF and Regression model forecasts for EC at the Crows Landing compliance monitoring station on 1 June 2021. Figures (<b>a</b>,<b>b</b>) show the 30 day running average EC and forecast for 1 June 2021. Figure (<b>c</b>) shows the SLAC at the Crows landing station. By early May wetland drainage no longer dominates Mud and Salt Sloughs and daily SLAC in the river increases. The 30 day running average SLAC crosses the zero line around 28 May 2021. Breaks in the plot are the result of temporary EC sensor malfunction at the Crows Landing station. Conversion of flow in cfs to m<sup>3</sup>/s: 100 cfs = 2.83 m<sup>3</sup>/s.</p>
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19 pages, 13256 KiB  
Article
An Open-Source Web Platform to Share Multisource, Multisensor Geospatial Data and Measurements of Ground Deformation in Mountain Areas
by Martina Cignetti, Diego Guenzi, Francesca Ardizzone, Paolo Allasia and Daniele Giordan
ISPRS Int. J. Geo-Inf. 2020, 9(1), 4; https://doi.org/10.3390/ijgi9010004 - 18 Dec 2019
Cited by 10 | Viewed by 3422
Abstract
Nowadays, the increasing demand to collect, manage and share archives of data supporting geo-hydrological processes investigations requires the development of spatial data infrastructure able to store geospatial data and ground deformation measurements, also considering multisource and heterogeneous data. We exploited the GeoNetwork open-source [...] Read more.
Nowadays, the increasing demand to collect, manage and share archives of data supporting geo-hydrological processes investigations requires the development of spatial data infrastructure able to store geospatial data and ground deformation measurements, also considering multisource and heterogeneous data. We exploited the GeoNetwork open-source software to simultaneously organize in-situ measurements and radar sensor observations, collected in the framework of the HAMMER project study areas, all located in high mountain regions distributed in the Alpines, Apennines, Pyrenees and Andes mountain chains, mainly focusing on active landslides. Taking advantage of this free and internationally recognized platform based on standard protocols, we present a valuable instrument to manage data and metadata, both in-situ surface measurements, typically acquired at local scale for short periods (e.g., during emergency), and satellite observations, usually exploited for regional scale analysis of surface displacement. Using a dedicated web-interface, all the results derived by instrumental acquisitions and by processing of remote sensing images can be queried, analyzed and downloaded from both expert users and stakeholders. This leads to a useful instrument able to share various information within the scientific community, including the opportunity of reprocessing the raw data for other purposes and in other contexts. Full article
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
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<p>Areas of interest and their geographical location: (<b>a</b>) European study areas; (<b>b</b>) South America study area (orthoimages from BING—<a href="https://www.bing.com/maps" target="_blank">https://www.bing.com/maps</a>).</p>
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<p>HAMMER spatial data infrastructure (H-SDI) architecture based on the GeoNetwork open-source software.</p>
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<p>Data and metadata hierarchy structure with Parent/Child link, following the reiterated pattern “Test site (rectangular box)—Instrument/Sensor (triangles)—Raw Data (drops)”.</p>
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<p>Web-interface data query [<a href="#B35-ijgi-09-00004" class="html-bibr">35</a>].</p>
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<p>Ivancich landslide inclinometer data (I103). Inclinometer displacements refer to the ground level (cumulative displacement).</p>
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<p>Example of planimetric ground deformation time-series graph (dxy), relative to the prism PR01 belonging to the Grange Orgiera topographic network.</p>
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<p>H-SDI metadata hierarchy scheme based on “Parent/Child” links; in dark blue are the test sites of the Hammer project; in blue are the available sensors and/or in situ instruments for each test site; in light grey are the raw data of ground deformation measurements.</p>
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<p>Metadata configuration in the H-SDI GeoNetwork platform relative to the Salar de Atacama test site.</p>
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<p>SBAS targets visualization in KML format on Google Earth for the Grange Orgiera test site.</p>
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<p>QGIS platform connected to the WMS offered by the GeoServer showing Grange Orgiera SBAS targets.</p>
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<p>OpenLayers preview of the SBAS targets of the Grange Orgiera test site.</p>
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<p>Example of data usage: (<b>a</b>) map of the SBAS targets, processed by G-POD, generated in GIS environment for the Salar de Atacama test site; (<b>b</b>) plot of the ground deformation time-series relative to some SBAS target of the Salar de Incauasi, located in the Salar de Atacama AOI.</p>
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31 pages, 16418 KiB  
Article
The RainBO Platform for Enhancing Urban Resilience to Floods: An Efficient Tool for Planning and Emergency Phases
by Giulia Villani, Stefania Nanni, Fausto Tomei, Stefania Pasetti, Rita Mangiaracina, Alberto Agnetti, Paolo Leoni, Marco Folegani, Gianluca Mazzini, Lucio Botarelli and Sergio Castellari
Climate 2019, 7(12), 145; https://doi.org/10.3390/cli7120145 - 17 Dec 2019
Cited by 6 | Viewed by 3874
Abstract
Many urban areas face an increasing flood risk, which includes the risk of flash floods. Increasing extreme precipitation events will likely lead to greater human and economic losses unless reliable and efficient early warning systems (EWS) along with other adaptation actions are put [...] Read more.
Many urban areas face an increasing flood risk, which includes the risk of flash floods. Increasing extreme precipitation events will likely lead to greater human and economic losses unless reliable and efficient early warning systems (EWS) along with other adaptation actions are put in place in urban areas. The challenge is in the integration and analysis in time and space of the environmental, meteorological, and territorial data from multiple sources needed to build up EWS able to provide efficient contribution to increase the resilience of vulnerable and exposed urban communities to flooding. Efficient EWS contribute to the preparedness phase of the disaster cycle but could also be relevant in the planning of the emergency phase. The RainBO Life project addressed this matter, focusing on the improvement of knowledge, methods, and tools for the monitoring and forecast of extreme precipitation events and the assessment of the associated flood risk for small and medium watercourses in urban areas. To put this into practice, RainBO developed a webGIS platform, which contributes to the “planning” of the management of river flood events through the use of detailed data and flood risk/vulnerability maps, and the “event management” with real-time monitoring/forecast of the events through the collection of observed data from real sensors, estimated/forecasted data from hydrologic models as well as qualitative data collected through a crowdsourcing app. Full article
(This article belongs to the Special Issue Urban Climate and Adaptation Tools)
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<p>Study areas of the RainBO project.</p>
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<p>Map of existing monitoring network on the Ravone area. The circles are the rain gauges, the square is the water level gauge.</p>
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<p>The entry of the culvert of the Ravone creek where the water level gauge is installed and the alarm thresholds are highlighted.</p>
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<p>Baganza river in Parma after the October 2014 event.</p>
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<p>Parma-Baganza basin and monitoring network.</p>
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<p>The hydro-pluviometric network of Emilia-Romagna.</p>
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<p>Random Forest tree generated for the main Parma River section (Ponte Verdi).</p>
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<p>Random Forest model schematization. P<sub>obs</sub> = observed mean hourly rainfall (unit: mm), Q = discharge (unit: m<sup>3</sup>/s).</p>
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<p>Quantitative precipitation estimates accumulated from 11/05/2016 00 UTC to 12/05/2016 00 UTC. (top left) Radar quantitative precipitation estimates (QPE), (top right) radar adjusted QPE; (bottom left) ERG5; (bottom right) microwave links QPE.</p>
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<p>CRITERIA-3D simulation of the surface water flow on the Ravone catchment during a rainfall event. The color scale represents the surface water level (unit: m).</p>
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<p>The opening screen of the RainBO platform interface in the planning support mode. On the left bar the thematic maps can be selected and displayed, on the bottom time bar the blue circles, corresponding to historical events, can be clicked and displayed on the map. The two buttons (green and gray) on the upper right corner allows switching from planning support mode to event management mode.</p>
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<p>(<b>a</b>) Parma River hazard map from Floods Directive; (<b>b</b>) Parma River hazard map integrated with the flooded area of October 2014. The color from light blue to dark blue shows three different level of hydraulic hazard: P1—L (Low probability of floods or extreme event scenarios), P2—M (infrequent floods: return time between 100 and 200 years—medium probability), P3—H (frequent floods: return time between 20 and 50 years—high probability).</p>
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<p>Reno River and Ravone creek hazard map. The color from light blue to dark blue shows three different level of hydraulic hazard: P1—L (low probability of floods or extreme event scenarios), P2—M (infrequent floods: return time between 100 and 200 years—medium probability), P3—H (frequent floods: return time between 20 and 50 years—high probability).</p>
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<p>Vulnerability map at 04:00 of a working day. In the color legend, green (“vulnerabilità 1”) is low vulnerability, yellow (“vulnerabilità 2”) is medium vulnerability, orange (“vulnerabilità 3”) is medium-high vulnerability, red (“vulnerabilità 3”) is high vulnerability.</p>
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<p>Risk map at 04:00 of a working day referred to the Ravone creek. In the color legend, green (“rischio 1”) is low risk, yellow (“rischio2”) is a medium risk, orange (“rischio3”) is medium-high vulnerability, red (“rischio3”) is high risk.</p>
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<p>Past flood event on the Parma catchment. The colored squares represent past events where the red (alarm) and orange (pre-alarm) thresholds were exceeded.</p>
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<p>Map of critical points referred to an extreme event occurred on 2015, March 25th in Ravone catchment. The house icons represent the points where damages due to extreme events are recorded.</p>
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<p>RainBO sensors of observed data: water level gauge (rectangles), rain gauges (drops), and traffic data (cars).</p>
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<p>Map of the COSMO-LAMI precipitation forecast. The left-sided color legend represents the quantity of precipitation forecast (unit: mm).</p>
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<p>Map of radar reflectivity, a proxy variable to estimate precipitation. The left-sided color legend shows the scale of reflectivity (unit: dBZ).</p>
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<p>Virtual sensors of precipitation estimated by commercial microwave links (CMLs). The radio wave icons represent the midpoint of radio links on the Lepida wireless network.</p>
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<p>(<b>a</b>) Operational forecast of the maximum water level (m) at the culvert entry of Ravone delivered by the platform on the morning of May 17th, 2019. The green line is the moment of the forecast, on the left, the red dots are the observed data, on the right, the boxplot is the forecast distribution. The box of boxplot represents the interval between the 25° and 75° percentile, the tails are the 5° and 95° percentiles; (<b>b</b>) water level (m) observed at the culvert entry of Ravone, as displayed on the RainBO platform in the event management mode.</p>
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<p>Mean rainfall, discharge, and water level during the December 2017 event.</p>
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<p>RainBO platform RF results for the Parma River during the event of December 2017.</p>
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<p>Alert of an ongoing event. The icon on the right side of the platform is highlighted and the dashboard displays the information connected with the event.</p>
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<p>Example of charts of water level gauge and its alarm thresholds.</p>
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<p>Critical sites list and corresponding information displayed by selecting an area of interest.</p>
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1936 KiB  
Article
A Hydrological Sensor Web Ontology Based on the SSN Ontology: A Case Study for a Flood
by Chao Wang, Nengcheng Chen, Wei Wang and Zeqiang Chen
ISPRS Int. J. Geo-Inf. 2018, 7(1), 2; https://doi.org/10.3390/ijgi7010002 - 24 Dec 2017
Cited by 32 | Viewed by 6067
Abstract
Accompanying the continuous development of sensor network technology, sensors worldwide are constantly producing observation data. However, the sensors and their data from different observation platforms are sometimes difficult to use collaboratively in response to natural disasters such as floods for the lack of [...] Read more.
Accompanying the continuous development of sensor network technology, sensors worldwide are constantly producing observation data. However, the sensors and their data from different observation platforms are sometimes difficult to use collaboratively in response to natural disasters such as floods for the lack of semantics. In this paper, a hydrological sensor web ontology based on SSN ontology is proposed to describe the heterogeneous hydrological sensor web resources by importing the time and space ontology, instantiating the hydrological classes, and establishing reasoning rules. This work has been validated by semantic querying and knowledge acquiring experiments. The results demonstrate the feasibility and effectiveness of the proposed ontology and its potential to grow into a more comprehensive ontology for hydrological monitoring collaboratively. In addition, this method of ontology modeling is generally applicable to other applications and domains. Full article
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<p>The core classes and properties in the hydrological/flood ontology based on Semantic Sensor Network (SSN).</p>
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<p>Main subclasses and some instances in the hydrological sensor web ontology.</p>
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<p>Class hierarchy, object properties, and individual definitions of the flood ontology in Protégé. (<b>a</b>) Class hierarchy of the proposed ontology. (<b>b</b>) Object properties of the proposed ontology. (<b>c</b>) Data properties of the proposed ontology.</p>
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<p>The search result of sensors observing precipitation and the platforms on which these sensors are deployed.</p>
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<p>The search result of all sensors and platforms in the Jinsha River.</p>
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<p>The result dates when the precipitation &gt;10 mm and the water level &gt;19 m at Liangzi Lake.</p>
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<p>The result dates when the precipitation &gt;10 mm and the water level &gt;19 m at Liangzi Lake.</p>
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<p>Stage divisions using the reasoning rules with data of the daily precipitation and water level at Liangzi Lake during the flood period in 2010.</p>
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5832 KiB  
Article
A Sensor Web and Web Service-Based Approach for Active Hydrological Disaster Monitoring
by Xi Zhai, Peng Yue and Mingda Zhang
ISPRS Int. J. Geo-Inf. 2016, 5(10), 171; https://doi.org/10.3390/ijgi5100171 - 24 Sep 2016
Cited by 12 | Viewed by 6358
Abstract
Rapid advancements in Earth-observing sensor systems have led to the generation of large amounts of remote sensing data that can be used for the dynamic monitoring and analysis of hydrological disasters. The management and analysis of these data could take advantage of distributed [...] Read more.
Rapid advancements in Earth-observing sensor systems have led to the generation of large amounts of remote sensing data that can be used for the dynamic monitoring and analysis of hydrological disasters. The management and analysis of these data could take advantage of distributed information infrastructure technologies such as Web service and Sensor Web technologies, which have shown great potential in facilitating the use of observed big data in an interoperable, flexible and on-demand way. However, it remains a challenge to achieve timely response to hydrological disaster events and to automate the geoprocessing of hydrological disaster observations. This article proposes a Sensor Web and Web service-based approach to support active hydrological disaster monitoring. This approach integrates an event-driven mechanism, Web services, and a Sensor Web and coordinates them using workflow technologies to facilitate the Web-based sharing and processing of hydrological hazard information. The design and implementation of hydrological Web services for conducting various hydrological analysis tasks on the Web using dynamically updating sensor observation data are presented. An application example is provided to demonstrate the benefits of the proposed approach over the traditional approach. The results confirm the effectiveness and practicality of the proposed approach in cases of hydrological disaster. Full article
(This article belongs to the Special Issue Geosensor Networks and Sensor Web)
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<p>Using Sensor Web services for hydrological disaster monitoring. SOS1 makes in situ sensor observations available for active monitoring and event detection. SOS2 is a service for providing observations planned by the SPS, which later can be sent to the WPS for geoprocessing.</p>
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<p>The event processing flow in an event-driven mechanism for hydrological disaster monitoring.</p>
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<p>The architecture of the proposed Sensor Web-enabled hydrological Web service system.</p>
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<p>A wrapper for hydrological analysis programs.</p>
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<p>Flowchart diagram illustrating the process of workflow-based hydrological service chaining.</p>
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<p>The process of data monitoring and processing for the detection of excessive sediment concentrations.</p>
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<p>The graphical user interface for event subscription in the turbidity extraction case.</p>
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<p>An instance of the Observation class.</p>
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<p>Performance tests of turbidity extraction achieved via manual operations and the proposed approach.</p>
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<p>Performance tests of turbidity extraction on different servers.</p>
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<p>The execution and monitoring of the processes executed as part of the turbidity extraction geoprocessing workflow.</p>
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<p>A thematic image of the sediment concentrations in Poyang Lake.</p>
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857 KiB  
Article
Using Open Geographic Data to Generate Natural Language Descriptions for Hydrological Sensor Networks
by Martin Molina, Javier Sanchez-Soriano and Oscar Corcho
Sensors 2015, 15(7), 16009-16026; https://doi.org/10.3390/s150716009 - 3 Jul 2015
Cited by 5 | Viewed by 5685
Abstract
Providing descriptions of isolated sensors and sensor networks in natural language, understandable by the general public, is useful to help users find relevant sensors and analyze sensor data. In this paper, we discuss the feasibility of using geographic knowledge from public databases available [...] Read more.
Providing descriptions of isolated sensors and sensor networks in natural language, understandable by the general public, is useful to help users find relevant sensors and analyze sensor data. In this paper, we discuss the feasibility of using geographic knowledge from public databases available on the Web (such as OpenStreetMap, Geonames, or DBpedia) to automatically construct such descriptions. We present a general method that uses such information to generate sensor descriptions in natural language. The results of the evaluation of our method in a hydrologic national sensor network showed that this approach is feasible and capable of generating adequate sensor descriptions with a lower development effort compared to other approaches. In the paper we also analyze certain problems that we found in public databases (e.g., heterogeneity, non-standard use of labels, or rigid search methods) and their impact in the generation of sensor descriptions. Full article
(This article belongs to the Section Sensor Networks)
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<p>Geographic distribution of the national hydrological sensor network SAIH (part of the network shown by the website of the Ministry of Environment of Spain).</p>
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<p>Example of a presentation generated by the VSAIH application.</p>
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<p>Generated text describing a hydrologic situation (translated from <a href="#sensors-15-16009-f002" class="html-fig">Figure 2</a>).</p>
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<p>Main components of the method.</p>
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<p>Number of sensors in the evaluation dataset for each geographic area; (<b>a</b>) number of sensors; (<b>b</b>) geographic areas.</p>
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<p>Evaluation results for geographic areas. The graphic shows the values obtained for the baseline accuracy (orange) and for our method’s accuracy (blue).</p>
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<p>Main causes of incorrect descriptions ((<b>a</b>) average and (<b>b</b>) best geographic area).</p>
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809 KiB  
Article
Environmental Studies with the Sensor Web: Principles and Practice
by Kevin A. Delin, Shannon P. Jackson, David W. Johnson, Scott C. Burleigh, Richard R. Woodrow, J. Michael McAuley, James M. Dohm, Felipe Ip, Ty P.A. Ferré, Dale F. Rucker and Victor R. Baker
Sensors 2005, 5(1), 103-117; https://doi.org/10.3390/s5010103 - 28 Feb 2005
Cited by 66 | Viewed by 13900
Abstract
In 1997, the Sensor Web was conceived at the NASA/Jet Propulsion Laboratory (JPL)to take advantage of the increasingly inexpensive, yet sophisticated, mass consumer-marketchips for the computer and telecommunication industries and use them to create platforms thatshare information among themselves and act in concert [...] Read more.
In 1997, the Sensor Web was conceived at the NASA/Jet Propulsion Laboratory (JPL)to take advantage of the increasingly inexpensive, yet sophisticated, mass consumer-marketchips for the computer and telecommunication industries and use them to create platforms thatshare information among themselves and act in concert as a single instrument. This instrumentwould be embedded into an environment to monitor and even control it. The Sensor Web’spurpose is to extract knowledge from the data it collects and use this information to intelligentlyreact and adapt to its surroundings. It links a remote end-user's cognizance with the observedenvironment. Here, we examine not only current progress in the Sensor Web technology, butalso its recent application to problems in hydrology to illustrate the general concepts involved. Full article
(This article belongs to the Special Issue Sensors for Environmental Monitoring)
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<p>Generalized concept of the Sensor Web, including both orbital and terrestrial platforms.</p>
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<p>The Sensor Web forms an informational backbone that creates a dynamic infrastructure for the sensors in the Sensor Web pods.</p>
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<p>Various Sensor Web pods. Top: Functioning Sensor Web 1.0 pod, circa 1998. Note the small size which includes antenna, battery, and temperature and light sensors. Bottom Left: Sensor Web 3.1 pod deployed at the Huntington Botanical Gardens, circa 2002. It is about the size of two decks of playing cards. The pod is mud spattered from rain and watering and has a chewed antenna. Subterranean sensors (soil moisture and temperature) can be seen going into the ground. Bottom Right: A Sensor Web 5.0 pod, circa 2004. This new generation of Sensor Web pods is more compact and more power efficient than previous ones, a direct result of exploiting Moore's Law in its design.</p>
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<p>Aerial view of a portion of CAVSARP facility showing the location of the Sensor Web pods in recharge basin 102. The portal pod (pod 0) is connected to a computer which transfers the data to the Internet. In this photograph, recharge basin 103 is drying out, with the wetter soil in the northern portion of the basin. In contrast, basin 102 is being flooded with the moving water advancing southward. The basins immediately north of 103 and south of 102 are fully flooded.</p>
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<p>NASA/JPL team members deploy a Sensor Web 3.2 pod in recharge basin. Extended stands allow the pods to stay operational above water during a flooding event. Note polygon patterns in soil, indicative of previous flooding/drying cycles.</p>
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<p>View looking north with recharge basin 102 fully inundated. Pod 11 is clearly visible above the rising water.</p>
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<p>Screen-capture of Internet data from CAVSARP facility. Graphs (top to bottom): surface temperature (°C), surface moisture, and soil moisture at 0.5 m depth (relative units; lower values imply wetter soil). Diurnal cycles in soil water potential measurements are largely artifacts that can be corrected using the soil temperature at the same location and depth. Sensor Web pod 1 (southwest basin corner) is in blue, pod 6 (basin inlet, northwest corner) in red, pod 10 (basin center) in green, pod 11 (southeast basin corner, diagonal from pod 6) in light blue. Data correlate with water discharge into basin, inundation, infiltration, drying, and the beginning of another cycle.</p>
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<p>Soil water potential at 0.5 m deep along the west basin border during the first two flooding events after deployment. Traveling from north to south, the pods are positioned: 6 (inlet), 15, and then 1. Also shown is the inlet water rate.</p>
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<p>Soil water potential at 0.5 m deep along the west basin border. Note that the third flooding event did not last long enough to affect pod 1.</p>
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