Advances of Remote Sensing in Land Cover and Land Use Mapping
The reprint of this Special Issue of Remote Sensing highlights recent advancements in the mapping of land use and land cover (LULC), emphasizing methodological innovation and scientific rigor. In response to growing challenges from climate variability, territorial dynamics, and societal transformati...
Збережено в:
| Формат: | Online |
|---|---|
| Мова: | Англійська |
| Опубліковано: |
MDPI - Multidisciplinary Digital Publishing Institute
2026
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| Предмети: | |
| Онлайн доступ: | ONIX_20260416T142754_9783725859191_19 |
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| _version_ | 1869520482378711040 |
|---|---|
| collection | Directory of Open Access Books |
| description | The reprint of this Special Issue of Remote Sensing highlights recent advancements in the mapping of land use and land cover (LULC), emphasizing methodological innovation and scientific rigor. In response to growing challenges from climate variability, territorial dynamics, and societal transformations, the contributions aim to enhance the accuracy, consistency, and transparency of LULC assessments. It showcases cutting-edge applications of remote sensing technologies, including developments in artificial intelligence, the increasing availability of satellite data, and the integration of large geospatial databases. These tools facilitate improved observation, monitoring, and modelling of land-related processes. The articles included explore novel approaches to understanding the spatial and temporal dynamics of land systems, addressing multi-scale patterns and changes in land use and cover. This Special Issue serves as a comprehensive overview of how contemporary remote sensing techniques can inform spatial planning and environmental management, grounded in robust methodological frameworks and operational case studies across various disciplines. |
| format | Online |
| id | doab-20.500.12854ir-175014 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1750142026-04-16T18:19:20Z Advances of Remote Sensing in Land Cover and Land Use Mapping Gadal, Sébastien Mozgeris, Gintautas Land use Land cover Machine learning Remote sensing Random forest MOLUSCE plugin Artificial neural network Cellular automata Spatial dependence Temporal variability Sequential Gaussian simulation Kriging Cokriging CHIRPS High-performance computing Workflow Automation MODIS NDVI time series product Contaminated areas Reconstruction LULC classification LULC changes CA-Markov model LULC projection Land-use planning GIS IDRISI UMngeni river catchment LULC Landsat 8 South Africa Semantic segmentation Land cover mapping Deep learning Land cover classification Climate change Cover change GeoWEPP Sediment yield Flow Fine-tuning CNN U-net Training data Validation data Alaska Land use land cover changes Google Earth Engine Environmental impact Future projection Neural network Quaternary geology Bedrock mapping Aerial photos Otsu thresholding Combined model Seasonal dynamics Hyperspectral image Hyperspectral image classification N A thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general The reprint of this Special Issue of Remote Sensing highlights recent advancements in the mapping of land use and land cover (LULC), emphasizing methodological innovation and scientific rigor. In response to growing challenges from climate variability, territorial dynamics, and societal transformations, the contributions aim to enhance the accuracy, consistency, and transparency of LULC assessments. It showcases cutting-edge applications of remote sensing technologies, including developments in artificial intelligence, the increasing availability of satellite data, and the integration of large geospatial databases. These tools facilitate improved observation, monitoring, and modelling of land-related processes. The articles included explore novel approaches to understanding the spatial and temporal dynamics of land systems, addressing multi-scale patterns and changes in land use and cover. This Special Issue serves as a comprehensive overview of how contemporary remote sensing techniques can inform spatial planning and environmental management, grounded in robust methodological frameworks and operational case studies across various disciplines. 2026-04-16T18:19:14Z 2026-04-16T18:19:14Z 2025 book ONIX_20260416T142754_9783725859191_19 9783725859191 9783725859207 https://directory.doabooks.org/handle/20.500.12854/175014 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/ https://mdpi.com/books/pdfview/book/11914 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-5920-7 10.3390/books978-3-7258-5920-7 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725859191 9783725859207 330 CH open access |
| spellingShingle | Land use Land cover Machine learning Remote sensing Random forest MOLUSCE plugin Artificial neural network Cellular automata Spatial dependence Temporal variability Sequential Gaussian simulation Kriging Cokriging CHIRPS High-performance computing Workflow Automation MODIS NDVI time series product Contaminated areas Reconstruction LULC classification LULC changes CA-Markov model LULC projection Land-use planning GIS IDRISI UMngeni river catchment LULC Landsat 8 South Africa Semantic segmentation Land cover mapping Deep learning Land cover classification Climate change Cover change GeoWEPP Sediment yield Flow Fine-tuning CNN U-net Training data Validation data Alaska Land use land cover changes Google Earth Engine Environmental impact Future projection Neural network Quaternary geology Bedrock mapping Aerial photos Otsu thresholding Combined model Seasonal dynamics Hyperspectral image Hyperspectral image classification N A thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general Advances of Remote Sensing in Land Cover and Land Use Mapping |
| title | Advances of Remote Sensing in Land Cover and Land Use Mapping |
| title_full | Advances of Remote Sensing in Land Cover and Land Use Mapping |
| title_fullStr | Advances of Remote Sensing in Land Cover and Land Use Mapping |
| title_full_unstemmed | Advances of Remote Sensing in Land Cover and Land Use Mapping |
| title_short | Advances of Remote Sensing in Land Cover and Land Use Mapping |
| title_sort | advances of remote sensing in land cover and land use mapping |
| topic | Land use Land cover Machine learning Remote sensing Random forest MOLUSCE plugin Artificial neural network Cellular automata Spatial dependence Temporal variability Sequential Gaussian simulation Kriging Cokriging CHIRPS High-performance computing Workflow Automation MODIS NDVI time series product Contaminated areas Reconstruction LULC classification LULC changes CA-Markov model LULC projection Land-use planning GIS IDRISI UMngeni river catchment LULC Landsat 8 South Africa Semantic segmentation Land cover mapping Deep learning Land cover classification Climate change Cover change GeoWEPP Sediment yield Flow Fine-tuning CNN U-net Training data Validation data Alaska Land use land cover changes Google Earth Engine Environmental impact Future projection Neural network Quaternary geology Bedrock mapping Aerial photos Otsu thresholding Combined model Seasonal dynamics Hyperspectral image Hyperspectral image classification N A thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general |
| topic_facet | Land use Land cover Machine learning Remote sensing Random forest MOLUSCE plugin Artificial neural network Cellular automata Spatial dependence Temporal variability Sequential Gaussian simulation Kriging Cokriging CHIRPS High-performance computing Workflow Automation MODIS NDVI time series product Contaminated areas Reconstruction LULC classification LULC changes CA-Markov model LULC projection Land-use planning GIS IDRISI UMngeni river catchment LULC Landsat 8 South Africa Semantic segmentation Land cover mapping Deep learning Land cover classification Climate change Cover change GeoWEPP Sediment yield Flow Fine-tuning CNN U-net Training data Validation data Alaska Land use land cover changes Google Earth Engine Environmental impact Future projection Neural network Quaternary geology Bedrock mapping Aerial photos Otsu thresholding Combined model Seasonal dynamics Hyperspectral image Hyperspectral image classification N A thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general |
| url | ONIX_20260416T142754_9783725859191_19 |