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Hardback or Cased Book. Condizione: New. Remote Sensing of Snow and Its Applications. Book.

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Hardback. Condizione: New.

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Condizione: As New. Unread book in perfect condition.

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Condizione: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | The reprint book of the "Remote Sensing of Snow and Its Applications" Special Issue provides recent studies on all aspects of remote sensing of snow, from retrieving the data to the application. These studies mainly address the following: (a) New opportunities (Copernicus Sentinels) and emerging remote sensing methods, (b) use of snow data in modeling, and (c) characterization of snowpack.…

Condizione: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | The reprint book of the "Remote Sensing of Snow and Its Applications" Special Issue provides recent studies on all aspects of remote sensing of snow, from retrieving the data to the application. These studies mainly address the following: (a) New opportunities (Copernicus Sentinels) and emerging remote sensing methods, (b) use of snow data in modeling, and (c) characterization of snowpack.…

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HRD. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condizione: New. Print on Demand pp. 190.

Condizione: New. Print on Demand pp. 190.

HRD. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condizione: New. PRINT ON DEMAND pp. 190.

Hardcover. Condizione: 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.…

Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The reprint book of the 'Remote Sensing of Snow and Its Applications' Special Issue provides recent studies on all aspects of remote sensing of snow, from retrieving the data to the application. These studies mainly address the following: (a) New opportunities (Copernicus Sentinels) and emerging remote sensing methods, (b) use of snow data in modeling, and (c) characterization of snowpack.…

Hardcover. Condizione: 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.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. KlappentextrnrnThe reprint book of the Remote Sensing of Snow and Its Applications Special Issue provides recent studies on all aspects of remote sensing of snow, from retrieving the data to the application. These studies mainly address the fo.…

Hardcover. Condizione: 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.…

Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering.

Buch. Condizione: Neu. Remote Sensing of Snow and Its Applications | Buch | Gebunden | Englisch | 2021 | MDPI AG | EAN 9783036500706 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Buch. Condizione: 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.