Grand Challenges in Satellite Remote Sensing

Frontiers in Remote Sensing, Journal Year: 2021, Volume and Issue: 2

Published: Feb. 24, 2021

SPECIALTY GRAND CHALLENGE article Front. Remote Sens., 24 February 2021 | https://doi.org/10.3389/frsen.2021.619818

Language: Английский

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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

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(16), P. 3926 - 3926

Published: Aug. 8, 2023

This study aimed to provide a systematic overview of the progress made in utilizing remote sensing for assessing impacts land use and cover (LULC) changes on water resources (quality quantity). review also addresses research gaps, challenges, opportunities associated with remotely sensed data assessment monitoring. The applications monitoring LULC, along their quality quantity, has advanced significantly. availability high-resolution satellite imagery, integration multiple sensors, classification techniques have improved accuracy mapping change detection. Furthermore, highlights vast potential providing detailed information relationship between LULC through advancements science analytics, drones, web-based platforms, balloons. It emphasizes importance promoting efforts, spatial patterns, ecosystem services, hydrological models enables more comprehensive evaluation quantity changes. Continued technology methodologies will further improve our ability assess monitor ultimately leading informed decision making effective resource management. Such endeavors are crucial achieving sustainable management quantity.

Language: Английский

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Vegetation Mapping with Random Forest Using Sentinel 2 and GLCM Texture Feature—A Case Study for Lousã Region, Portugal

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(18), P. 4585 - 4585

Published: Sept. 14, 2022

Vegetation mapping requires accurate information to allow its use in applications such as sustainable forest management against the effects of climate change and threat wildfires. Remote sensing provides a powerful resource fundamental data at different spatial resolutions spectral regions, making it an essential tool for vegetation biomass management. Due ever-increasing availability free software, satellites have been predominantly used map, analyze, monitor natural resources conservation purposes. This study aimed map from Sentinel-2 (S2) complex mixed cover Lousã district Portugal. We ten multispectral bands with resolution 10 m, four indices, including Normalized Difference Index (NDVI), Green (GNDVI), Enhanced (EVI), Soil Adjusted (SAVI). After applying principal component analysis (PCA) on S2A bands, texture features, mean (ME), homogeneity (HO), correlation (CO), entropy (EN), were derived first three components. Textures obtained using Gray-Level Co-Occurrence Matrix (GLCM). As result, 26 independent variables extracted S2. defining land classes object-based approach, Random Forest (RF) classifier was applied. The accuracy evaluated by confusion matrix, metrics overall (OA), producer (PA), user (UA), kappa coefficient (Kappa). described classification methodology showed high OA 90.5% 89% mapping. Using GLCM features indices increased up 2%; however, achieved highest (92%), indicating features′ capability detecting variability species stand level. ME CO contribution among textures. GNDVI outperformed other variable importance. Moreover, only especially 11, 12, 2, potential classify 88%. that adding least one feature index into may effectively increase tree discrimination.

Language: Английский

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Deep learning for urban land use category classification: A review and experimental assessment

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 311, P. 114290 - 114290

Published: July 14, 2024

Mapping the distribution, pattern, and composition of urban land use categories plays a valuable role in understanding environmental dynamics facilitating sustainable development. Decades effort mapping have accumulated series approaches products. New trends characterized by open big data advanced artificial intelligence, especially deep learning, offer unprecedented opportunities for patterns from regional to global scales. Combined with large amounts geospatial data, learning has potential promote higher levels scale, accuracy, efficiency, automation. Here, we comprehensively review advances based research practices aspects sources, classification units, approaches. More specifically, delving into different settings on learning-based mapping, design eight experiments Shenzhen, China investigate their impacts performance terms sample, model. For each investigated setting, provide quantitative evaluations discussed inform more convincing comparisons. Based historical retrospection experimental evaluation, identify prevailing limitations challenges suggest prospective directions that could further facilitate exploitation techniques using remote sensing other spatial across various

Language: Английский

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Comparison and relative quality assessment of the GLC2000, GLOBCOVER, MODIS and ECOCLIMAP land cover data sets at the African continental scale

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2010, Volume and Issue: 13(2), P. 207 - 219

Published: Dec. 22, 2010

Language: Английский

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