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This Special Issue highlights the evolving role of remote sensing in monitoring land use, land-use change, and forestry (LULUCF), a critical component of greenhouse gas (GHG) accounting and climate policy. As demands for transparent and spatially explicit reporting increase, Earth observation (EO) data-particularly from programmes such as Copernicus-are becoming central to national inventories and monitoring, reporting, and verification (MRV) systems. The contributions demonstrate a clear shift from experimental research to operational applications. High-resolution satellite data (e.g., Sentinel-2), cloud-based platforms such as Google Earth Engine, and machine learning methods-especially Random Forest-are widely used to produce consistent, large-scale land-use classifications. A recurring theme is the integration of multi-source datasets, including spectral, topographic, and ancillary information, to improve accuracy and better represent complex land-use categories. The papers also emphasize validation, methodological harmonization, and alignment with LULUCF reporting frameworks. Overall, this collection underlines the growing maturity and policy relevance of EO-based approaches for robust, scalable, and transparent LULUCF monitoring.
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Hardcover. Zustand: new. Hardcover. This Special Issue highlights the evolving role of remote sensing in monitoring land use, land-use change, and forestry (LULUCF), a critical component of greenhouse gas (GHG) accounting and climate policy. As demands for transparent and spatially explicit reporting increase, Earth observation (EO) data-particularly from programmes such as Copernicus-are becoming central to national inventories and monitoring, reporting, and verification (MRV) systems. The contributions demonstrate a clear shift from experimental research to operational applications. High-resolution satellite data (e.g., Sentinel-2), cloud-based platforms such as Google Earth Engine, and machine learning methods-especially Random Forest-are widely used to produce consistent, large-scale land-use classifications. A recurring theme is the integration of multi-source datasets, including spectral, topographic, and ancillary information, to improve accuracy and better represent complex land-use categories. The papers also emphasize validation, methodological harmonization, and alignment with LULUCF reporting frameworks. Overall, this collection underlines the growing maturity and policy relevance of EO-based approaches for robust, scalable, and transparent LULUCF monitoring. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9783725891139
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Hardcover. Zustand: new. Hardcover. This Special Issue highlights the evolving role of remote sensing in monitoring land use, land-use change, and forestry (LULUCF), a critical component of greenhouse gas (GHG) accounting and climate policy. As demands for transparent and spatially explicit reporting increase, Earth observation (EO) data-particularly from programmes such as Copernicus-are becoming central to national inventories and monitoring, reporting, and verification (MRV) systems. The contributions demonstrate a clear shift from experimental research to operational applications. High-resolution satellite data (e.g., Sentinel-2), cloud-based platforms such as Google Earth Engine, and machine learning methods-especially Random Forest-are widely used to produce consistent, large-scale land-use classifications. A recurring theme is the integration of multi-source datasets, including spectral, topographic, and ancillary information, to improve accuracy and better represent complex land-use categories. The papers also emphasize validation, methodological harmonization, and alignment with LULUCF reporting frameworks. Overall, this collection underlines the growing maturity and policy relevance of EO-based approaches for robust, scalable, and transparent LULUCF monitoring. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9783725891139
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Hardcover. Zustand: new. Hardcover. This Special Issue highlights the evolving role of remote sensing in monitoring land use, land-use change, and forestry (LULUCF), a critical component of greenhouse gas (GHG) accounting and climate policy. As demands for transparent and spatially explicit reporting increase, Earth observation (EO) data-particularly from programmes such as Copernicus-are becoming central to national inventories and monitoring, reporting, and verification (MRV) systems. The contributions demonstrate a clear shift from experimental research to operational applications. High-resolution satellite data (e.g., Sentinel-2), cloud-based platforms such as Google Earth Engine, and machine learning methods-especially Random Forest-are widely used to produce consistent, large-scale land-use classifications. A recurring theme is the integration of multi-source datasets, including spectral, topographic, and ancillary information, to improve accuracy and better represent complex land-use categories. The papers also emphasize validation, methodological harmonization, and alignment with LULUCF reporting frameworks. Overall, this collection underlines the growing maturity and policy relevance of EO-based approaches for robust, scalable, and transparent LULUCF monitoring. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Bestandsnummer des Verkäufers 9783725891139
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Buch. Zustand: Neu. Remote Sensing Applications in Land Use, Land-Use Change and Forestry (LULUCF) | Buch | Englisch | 2026 | MDPI AG | EAN 9783725891139 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Bestandsnummer des Verkäufers 138256075
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