Search Results (9,901)

Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) products are essential data sources for global and regional climate change research. Currently, several versions of the MODIS LST product have been released, yet the performance differences and uncertainties they introduce in land surface studies remain insufficiently addressed. To bridge this gap, this study focuses on four distinct versions of the LST product: MxD11A1 Collection 5 (C5), Collection 6 (C6), Collection 6.1 (C6.1), and MxD21A1 Collection 6.1 (MxD21). The spatial resolution of all product generations is 1 km, and the temporal resolution is 0.5 days. This study provides a comprehensive analysis of the errors arising from different generations of these products in various land surface process studies. The error assessment includes cross-comparisons between product versions and evaluations of the absolute errors generated. Absolute errors in evaluation data were collected from 13 surface sites within the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project during the period 2013–2018. Cross-validation results show that the largest difference between C5 and C6.1 occurs over bare land, with an RMSE of approximately 1.45 K, while there is no significant change between C6 and C6.1. MOD21 shows considerable variation compared to C6.1 at night across different land cover types, with RMSE over cropland exceeding 2 K. The temperature difference between MOD21 and C6.1 is more pronounced at night (2.01 K) than during the day (0.30 K). Validation results based on temperature indicate that C5 has greater uncertainty compared to C6, especially over bare land, where errors are 2.06 K and 1.06 K, respectively. Furthermore, MxD21 demonstrates significant day–night performance discrepancies, with an average bias of 0.10 K at night, while daytime errors over bare land can reach 2 K, potentially influenced by atmospheric conditions. Based on the research in this paper, it is possible to clarify the performance of different versions of MODIS products, reflecting the appropriateness of their past applications; on the other hand, it is recommended to prioritize the use of the MxD11A1 C6 and C6.1 products for monitoring and applications in bare soil areas to ensure higher accuracy. Furthermore, for day and night monitoring, it may be beneficial to alternate between the MxD11A1 and MxD21A1 products to fully leverage their respective advantages and enhance overall monitoring effectiveness. Full article

As the climate crisis progresses, droughts and the seasonal availability of fresh water are becoming increasingly common in different regions of the world. One solution to tackle this problem is the reuse of treated wastewater in agriculture. This study was carried out in two significant hydrographic regions located in the southeast of Brazil (Mogi Guaçu River Water Management Unit—UGRHI-09 and Piracicaba River Basin—PRB) that have notable differences in terms of land use and land cover. The aim of this study was to carry out a multi-criteria analysis of a set of environmental attributes in order to classify the areas under study according to their levels of soil suitability and runoff potential. The integrated analysis made it possible to geospatialize prospective regions for reuse, under two specified conditions. In the UGRHI-09, condition 1 corresponds to 3373.24 km², while condition 2 comprises 286.07 km², located mainly in the north-western and central-eastern portions of the unit. In the PRB, condition 1 was also more expressive in occupational terms, corresponding to 1447.83 km²; and condition 2 was perceptible in 53.11 km², predominantly in the central region of the basin. The physical characteristics of the areas studied were decisive in delimiting the areas suitable for the reuse of treated wastewater. Full article

Long time series of annual land cover with fine spatio-temporal resolutions play a crucial role in studying environmental climate change, biophysical modeling, carbon cycling models, and land management. Despite a strong consistency exhibited by several publicly available medium to fine resolution global land cover datasets, significant discrepancies exist at the regional scale; moreover, only every 5/10 year land cover were available. Consequently, high-quality annual land cover datasets before 2000 are unavailable in China. In this study, we proposed a deep learning-based method by integrating multiple remote sensing data from different platforms with historical high spatial resolution land cover datasets (CNLUCC) to derive the 30 m annual land cover maps from 1980 to 1990 for Qilian Mountain. First, the super-resolution generative adversarial network models for upscaling the 5.5 km AVHRR NDVI to 250 m were established by employing the AVHRR and MODIS NDVI data with the same year as input, and the early time series AVHRR NDVI data were subsequently upscaled to 250 m through the above models. Second, the breaks for the additive seasonal and trend (BFAST) change detection algorithm was applied to the upscaled time series NDVI data to detect the change time of different land cover types. Third, the CNLUCC data in 1980 and 1990 were updated to annual land cover datasets from 1980 to 1990 and the annual mapping results provided insights into the dynamic processes of urbanization, deforestation, water bodies, and farmland from 1980 to 1990. Finally, comprehensive analysis and validation were carried out for evaluation and an overall accuracy of 77.26% for the land cover product in 1986 was achieved. Full article

Green infrastructure (GI) is increasingly prioritised in landscape policy and planning due to its potential to benefit ecosystems and enhance wildlife conservation. However, due to the uneven distribution of protected areas (PAs) and the fragmentation of habitats more generally, multi-level policy strategies are needed to create an integrated GI network bridging national, regional and local scales. In the province of Almeria, southeastern Spain, protected areas are mainly threatened by two land use/land cover changes. On the one hand, there is the advance of intensive greenhouse agriculture, which, between 1984 and 2007, increased in surface area by more than 58%. On the other hand, there is the growth of artificial surfaces, including urban areas (+64%), construction sites (+194%) and road infrastructures (+135%). To address this challenge, we present a proposal for green infrastructure deployment in the province of Almeria. We combine Geographic Information Systems (GISs) and multi-criteria evaluation (MCE) techniques to identify and evaluate suitability for key elements to be included in GI in two key ways. First, we identify the most suitable areas to form part of the GI in order to address vulnerability to degradation and fragmentation. Second, we propose 15 ecological corridors connecting the 35 protected areas of the province that act as core areas. The proposed GI network would extend along the western coast of the province and occupy the valleys of the main rivers. The river Almanzora plays a leading role. Due to its remoteness from the coast and its climatic conditions, it has not attracted intensive greenhouse agriculture and urban development, the main drivers of the transformation and fragmentation of traditional land uses. Around 50% of the area occupied by the proposed corridors would be located in places of medium and high suitability for the movement of species between core areas. Full article

Since the Wenchuan earthquake in 2008, Old Beichuan County-town has experienced significant subsidence due to the disruption of the geological environment and the concurrent increase in precipitation. The ongoing land surface deformation poses a threat to the preservation and utilization of the Beichuan National Earthquake Ruins Museum (BNERM), as well as to the safety of urban residents’ lives. However, the evolutionary characteristics of surface deformation in these areas remain largely unexplored. Here, we focused on the BNERM control zone and employed the small-baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique to accurately measure land surface deformation and its spatiotemporal changes. Subsequently, we integrated this data with land cover types and precipitation to investigate the driving factors of deformation. The results indicate a slight overall elevation increase in the study area from June 2015 to May 2023, with deformation rates varying between −35.2 mm/year and 22.9 mm/year. Additionally, four unstable slopes were identified within the BNERM control zone. Our analysis indicates that surface deformation in the study area is closely linked to changes in land cover types and precipitation, exhibiting a seasonal cumulative pattern, and active geological activity may also be a cause of deformation. This study provides invaluable insights into the surface deformation characteristics of the BNERM and can serve as a scientific foundation for the protection of earthquake ruins, risk assessment, early warning, and disaster prevention measures. Full article

Urban tree canopy (UTC) coverage, or area, is an important metric for monitoring changes in UTC over large areas within a municipality. Several methods have been used to obtain these data, but remote sensing image classification is one of the fastest and most reliable over large areas. However, most studies have tested only one or two classification methods to accomplish this while using costly satellite imagery or LiDAR data. This study seeks to compare three urban tree canopy cover classifiers by testing a deep learning U-Net convolutional neural network (CNN), support vector machine learning classifier (SVM) and a random forests machine learning classifier (RF) on cost-free 2012 aerial imagery over a small southern USA city and midsize, growing southern USA city. The results of the experiment are then used to decide the best classifier and apply it to more recent aerial imagery to determine canopy changes over a 10-year period. The changes are subsequently compared visually and statistically with recent urban heat maps derived from thermal Landsat 9 satellite data to compare the means of temperatures within areas of UTC loss and no change. The U-Net CNN classifier proved to provide the best overall accuracy for both cities (89.8% and 91.4%), while also requiring the most training and classification time. When compared spatially with city heat maps, city periphery regions were most impacted by substantial changes in UTC area as cities grow and the outer regions get warmer. Furthermore, areas of UTC loss had higher temperatures than those areas with no canopy change. The broader impacts of this study reach the urban forestry managers at the local, state/province, and national levels as they seek to provide data-driven decisions for policy makers. Full article

This study analyzed land use and land cover (LULC) changes to identify the levels of deforestation and forest degradation in two locations in the Amazon rainforest and their conservation units. Using Sentinel-2 satellite imagery and object-based image classification, yearly LULC maps were created from 2018 to 2023. Disturbances were then quantified by Primary Forest conversions. This study revealed a gain of around 22,362 ha in Secondary Forest areas in Manaus and 29,088 ha in Agriculture/Pastureland in Porto Velho within the study period. Differing yearly rates of deforestation and degradation were detected between the areas, with agriculture/pastureland expansion being observed as the primary driver of forest loss. State and federal units showed the largest conversion of primary to Secondary Forest, while state units experienced the most conversion to non-forest areas. Sustainable use units and buffer zones were particularly impacted by these disturbances. These findings suggest that factors beyond environmental policies contribute to these outcomes, highlighting the importance of understanding local contexts. Comparing areas with varying degradation levels provides insights into the effectiveness of restoration and conservation efforts. Full article

The destruction of arable land caused by coal mining in coal grain compound areas is a major bottleneck restricting grain production increase. The spatiotemporal correlation between the decline in cultivated land quality and crop growth deterioration due to mining subsidence still needs to be clarified. This study employed the CDR AVHRR NDVI dataset and applied correlation and trend analysis methods to extract vegetation cover information from 1982 to 2022. It also explored the relationships between vegetation cover and temperature and precipitation. The study found the following: (1) Over the past 41 years, the NDVI in the study area showed a significant upward trend. Specifically, the average annual NDVI growth rate in the mining area was 51.85%, while the corresponding growth rates for the 10 km buffer area, 20 km buffer area, and check area (CK) were 65.91%, 65.86%, and 68.09%, respectively. The start of the growing season (SOS) for winter wheat in the mining area and control area advanced by 49 ± 1.5 days and 65 ± 1.5 days, respectively, while the length of the growing season (LOS) extended by 59 ± 1.5 days and 72 ± 1.5 days, respectively. For summer maize, the SOS advanced by 11 ± 1.5 days and 15 ± 1.5 days, respectively, and the LOS extended by 17 ± 1.5 days and 19 ± 1.5 days, respectively. The study area exhibited a significant positive correlation between the NDVI and temperature. Specifically, the correlation coefficient for the mining area was 0.6865 (p < 0.01); for the 10 km buffer zone, it was 0.5937 (p < 0.01), for the 20 km buffer zone, it was 0.6775 (p < 0.01), and for the control check area (CK), it was 0.6591 (p < 0.01). The results of this study can provide data support for the collaborative rehabilitation of and source reduction in coal grain compound areas, as well as for the restoration of damaged farmland. Full article

Land cover is a crucial factor in controlling rainfall–runoff processes in mountain basins. However, various anthropogenic activities, such as converting natural vegetation to agricultural or urban areas, can affect this cover, thereby increasing the risk of flooding in cities. This study evaluates the hydrological behavior of two mountain basins in Loja, Ecuador, under varying land use scenarios. El Carmen small basin (B1), located outside the urban perimeter, and Las Pavas small basin (B2), within the urban area, were modeled using HEC-HMS 4.3 software. The results highlight the significant influence of vegetation degradation and restoration on hydrological processes. In degraded vegetation scenarios, peak flows increase due to reduced soil infiltration capacity, while baseflows decrease. Conversely, the conserved and restored vegetation scenarios show lower peak flows and higher baseflows, which are attributed to enhanced evapotranspiration, interception, and soil water storage. The study underscores the importance of ecosystem management and restoration in mitigating extreme hydrological events and improving water resilience. These findings provide a foundation for decision-making in urban planning and basin management, emphasizing the need for comprehensive and multidisciplinary approaches to develop effective public policies. Full article

Semantic segmentation of remote sensing images is a fundamental task in computer vision, holding substantial relevance in applications such as land cover surveys, environmental protection, and urban building planning. In recent years, multi-modal fusion-based models have garnered considerable attention, exhibiting superior segmentation performance when compared with traditional single-modal techniques. Nonetheless, the majority of these multi-modal models, which rely on Convolutional Neural Networks (CNNs) or Vision Transformers (ViTs) for feature fusion, face limitations in terms of remote modeling capabilities or computational complexity. This paper presents a novel Mamba-based multi-modal fusion network called MFMamba for semantic segmentation of remote sensing images. Specifically, the network employs a dual-branch encoding structure, consisting of a CNN-based main encoder for extracting local features from high-resolution remote sensing images (HRRSIs) and of a Mamba-based auxiliary encoder for capturing global features on its corresponding digital surface model (DSM). To capitalize on the distinct attributes of the multi-modal remote sensing data from both branches, a feature fusion block (FFB) is designed to synergistically enhance and integrate the features extracted from the dual-branch structure at each stage. Extensive experiments on the Vaihingen and the Potsdam datasets have verified the effectiveness and superiority of MFMamba in semantic segmentation of remote sensing images. Compared with state-of-the-art methods, MFMamba achieves higher overall accuracy (OA) and a higher mean F1 score (mF1) and mean intersection over union (mIoU), while maintaining low computational complexity. Full article

An Urban Heat Island (UHI) is an important variable in climate and environmental studies. Nowadays, population growth and urbanization development are the most important factors affecting the temperature increase in urban areas, which cause the creation of heat islands in urban areas. (1) Background: This study explores the influence of major land uses on the creation of Urban Heat Islands in Urmia city, Iran. (2) Methods: To achieve the aim of this study, Landsat satellite data including Landsat 5 and 8 imageries from the time periods of 1990 and 2023 were used. With the series of data processing and analyses on vegetation cover and land surface temperature, the impact of main land uses on the creation of Urban Heat Islands and the intensification of their effects have been investigated. (3) Results: The analysis reveals that barren lands consistently exhibit the highest temperature, while garden lands show the lowest temperature across both periods. In addition, the spatial distribution of Urban Heat Islands demonstrates a clustered pattern throughout the study period, with hot spots mainly located in the northwestern and southwestern areas. (4) Conclusions: This study’s findings can be helpful for urban policymakers and planners to develop practical strategies to mitigate UHIs and improve climate resilience in cities. Full article

Mountainous land types are characterized by a scarcity of observational data, particularly in remote areas such as the Kunlun Mountains, where conventional Automatic Weather Stations (AWSs) typically do not record land surface temperature (LST) data. This study aims to develop and evaluate models for converting air temperature (TA) to LST using newly established meteorological station data from the Kunlun Mountain Gradient Observation System, thereby providing time-continuous LST data for AWSs. We constructed a conceptual model to explore the relationship between 1.5 m TA and LST and instantiated it using three machine learning algorithms: Support Vector Machine (SVR), Convolutional Neural Network (CNN), and CatBoost. The results demonstrated that the CatBoost algorithm outperformed the others under complex terrain and climatic conditions, achieving a coefficient of determination (R²) of 0.997 and the lowest root mean square error (RMSE) of 0.627 °C, indicating superior robustness and accuracy. Consequently, CatBoost was selected as the optimal model. Additionally, this study analyzed the spatiotemporal distribution characteristics of cloud cover in the Kunlun Mountain region using the MOD11A1 product and assessed the uncertainties introduced by the 8-day average compositing method of the MOD11A2 product. The results revealed significant discrepancies between the monthly average LST derived from polar-orbiting satellites and the hourly composite monthly LST measured on-site or under ideal cloud-free conditions. These differences were particularly pronounced in high-altitude regions (4000 m and above), with the greatest differences occurring in winter, reaching up to 10.2 °C. These findings emphasize the importance of hourly LST calculations based on AWSs for accurately assessing the spatiotemporal characteristics of LST in the Kunlun Mountains, thus providing more precise spatiotemporal support for remote sensing applications in high-altitude regions. Full article

Wildfire represents a significant threat to ecosystems and communities in the Department of Amazonas, Peru, causing losses in biodiversity and land degradation and affecting socioeconomic security. The objective of this study was to analyze the spatial and temporal distribution of burned areas (BAs) from 1986 to 2023 to identify recurrence patterns and their impact on different types of land use and land cover (LULC). Landsat 5, 7, and 8 satellite images, processed by Google Earth Engine (GEE) using a decision tree approach, were used to map and quantify the affected areas. The results showed that the BAs were mainly concentrated in the provinces of Utcubamba, Luya, and Rodríguez de Mendoza, with a total of 1208.85 km² burned in 38 years. The most affected land covers were pasture/grassland (38.25%), natural cover (forest, dry forest, and shrubland) (29.55%) and agricultural areas (14.74%). Fires were most frequent between June and November, with the highest peaks in September and August. This study provides crucial evidence for the implementation of sustainable management strategies, fire prevention, and restoration of degraded areas, contributing to the protection and resilience of Amazonian ecosystems against future wildfire threats. Full article

Driven by the widespread adoption of deep learning (DL) in crop mapping with satellite image time series (SITS), this study was motivated by the recent success of temporal attention-based approaches in crop mapping. To meet the needs of beekeepers, this study aimed to develop DL-based classification models for mapping five essential crops in pollination services in Quebec province, Canada, by using Sentinel-2 SITS. Due to the challenging task of crop mapping using SITS, this study employed three DL-based models, namely one-dimensional temporal convolutional neural networks (CNNs) (1DTempCNNs), one-dimensional spectral CNNs (1DSpecCNNs), and long short-term memory (LSTM). Accordingly, this study aimed to capture expert-free temporal and spectral features, specifically targeting temporal features using 1DTempCNN and LSTM models, and spectral features using the 1DSpecCNN model. Our findings indicated that the LSTM model (macro-averaged recall of 0.80, precision of 0.80, F1-score of 0.80, and ROC of 0.89) outperformed both 1DTempCNNs (macro-averaged recall of 0.73, precision of 0.74, F1-score of 0.73, and ROC of 0.85) and 1DSpecCNNs (macro-averaged recall of 0.78, precision of 0.77, F1-score of 0.77, and ROC of 0.88) models, underscoring its effectiveness in capturing temporal features and highlighting its suitability for crop mapping using Sentinel-2 SITS. Furthermore, applying one-dimensional convolution (Conv1D) across the spectral domain demonstrated greater potential in distinguishing land covers and crop types than applying it across the temporal domain. This study contributes to providing insights into the capabilities and limitations of various DL-based classification models for crop mapping using Sentinel-2 SITS. Full article

Land scarcity in most cases hampers development and encourages the misuse of land. The suitability of land must be considered before appropriating or allocating land for any use. Land supports the livelihood of every being on the Earth and therefore determines survival, success, and sustainability (sustainable living). This study aimed at integrating remote sensing and GIS-based analysis to determine the rate at which built-up areas have spread across the terrain of Onitsha Metropolis, Anambra State, Nigeria, and the dynamics of other land uses. This research involved both primary and secondary data. The primary data included measurements, direct field observations, and key informant interviews to understand people’s perceptions of the land use in the area. The secondary data included satellite images of the area obtained from USGS and analyzed using ArcGIS 10.2 for variations in the terrain of the Onitsha Metropolis; to determine the land use and land cover change (LULCC) of the Onitsha Metropolis over 40 years, published and unpublished articles and books were also consulted. The geological analysis of the study showed that the area of the Ogwashi/Asaba formation is 318.57 km²; the areas of the Nanka sands and Bende-Ameke are 423.07 km² and 259.42 km², respectively. The Nanka sands and Bende-Ameke formations are best suited for engineering construction purposes, while the Ogwashi/Asaba formation is suitable for agriculture and should be designated as a buffer zone or park. However, due to the unavailability of land as a result of the growing population and the proximity of the area to the city center, the area is being encroached upon, and a large area (about 30.40%) has been converted to built-up areas as of 2022. Forecast analysis showed that if the trend continues, 158.28 km² (49.68%) of the alluvium soils of the Ogwashi/Asaba formation will be covered with buildings by 2072. The geology and the terrain of the Onitsha Metropolis determine the soil characteristics and the land use suitability; mapping the geological formations and overlaying these with the land use and land cover change of the area revealed the extent of the encroachment on the Ogwashi/Asaba formation, which must be discouraged. Full article