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Verlag: LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6139956447 ISBN 13: 9786139956449
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ISBN 10: 6139956447 ISBN 13: 9786139956449
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In den WarenkorbPaperback. Zustand: Brand New. 56 pages. 8.78x6.02x0.28 inches. In Stock.
Sprache: Englisch
Verlag: LAP LAMBERT Academic Publishing Jan 2019, 2019
ISBN 10: 6139956447 ISBN 13: 9786139956449
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Taschenbuch. Zustand: Neu. Neuware -Fires have been a major agent of environmental change and forest fires are a major cause of changes in forest structure and function. Among various floristic regions, the northeast region suffers maximum from the fires due to the age-old practice of shifting cultivation and spread of fires from jhum fields. For proper mitigation and management, an early warning of forest fires through risk modelling is required. The present study demonstrates the potential use of remote sensing and Geographic Information System (GIS) in identifying forest fire prone areas in Manipur, southeastern part of Northeast India. Land use land cover (LULC), vegetation type, Digital elevation model (DEM), slope, aspect and proximity to roads and settlements; factors that influence the behavior of fire, were used to model the forest fire risk zones. Each class of the layers was given weight according to their fire inducing capability and their sensitivity to fire. Weighted sum modelling and ISODATA clustering was used to classify the fire zones.Books on Demand GmbH, Überseering 33, 22297 Hamburg 56 pp. Englisch.
Sprache: Englisch
Verlag: LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6139956447 ISBN 13: 9786139956449
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Taschenbuch. Zustand: Neu. Mapping Forest Fire Risk Zones using Geospatial Tools | A risk assessment study in Manipur, India | Kanchan Puri (u. a.) | Taschenbuch | 56 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786139956449 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.
Sprache: Englisch
Verlag: LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6139956447 ISBN 13: 9786139956449
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Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 56 | Sprache: Englisch | Produktart: Bücher | Fires have been a major agent of environmental change and forest fires are a major cause of changes in forest structure and function. Among various floristic regions, the northeast region suffers maximum from the fires due to the age-old practice of shifting cultivation and spread of fires from jhum fields. For proper mitigation and management, an early warning of forest fires through risk modelling is required. The present study demonstrates the potential use of remote sensing and Geographic Information System (GIS) in identifying forest fire prone areas in Manipur, southeastern part of Northeast India. Land use land cover (LULC), vegetation type, Digital elevation model (DEM), slope, aspect and proximity to roads and settlements; factors that influence the behavior of fire, were used to model the forest fire risk zones. Each class of the layers was given weight according to their fire inducing capability and their sensitivity to fire. Weighted sum modelling and ISODATA clustering was used to classify the fire zones.
Sprache: Englisch
Verlag: LAP LAMBERT Academic Publishing Jan 2019, 2019
ISBN 10: 6139956447 ISBN 13: 9786139956449
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Fires have been a major agent of environmental change and forest fires are a major cause of changes in forest structure and function. Among various floristic regions, the northeast region suffers maximum from the fires due to the age-old practice of shifting cultivation and spread of fires from jhum fields. For proper mitigation and management, an early warning of forest fires through risk modelling is required. The present study demonstrates the potential use of remote sensing and Geographic Information System (GIS) in identifying forest fire prone areas in Manipur, southeastern part of Northeast India. Land use land cover (LULC), vegetation type, Digital elevation model (DEM), slope, aspect and proximity to roads and settlements; factors that influence the behavior of fire, were used to model the forest fire risk zones. Each class of the layers was given weight according to their fire inducing capability and their sensitivity to fire. Weighted sum modelling and ISODATA clustering was used to classify the fire zones. 56 pp. Englisch.
Sprache: Englisch
Verlag: LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6139956447 ISBN 13: 9786139956449
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Sprache: Englisch
Verlag: LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6139956447 ISBN 13: 9786139956449
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In den WarenkorbZustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Puri KanchanMs. Kanchan Puri is M.Sc in Biodiversity and Conservation with skills developed in Geospatial tools. She is working in Ministry of Environment, Forest and Climate Change (MoEF&CC), Government of India. Dr. Ritesh Joshi is.
Sprache: Englisch
Verlag: LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6139956447 ISBN 13: 9786139956449
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Fires have been a major agent of environmental change and forest fires are a major cause of changes in forest structure and function. Among various floristic regions, the northeast region suffers maximum from the fires due to the age-old practice of shifting cultivation and spread of fires from jhum fields. For proper mitigation and management, an early warning of forest fires through risk modelling is required. The present study demonstrates the potential use of remote sensing and Geographic Information System (GIS) in identifying forest fire prone areas in Manipur, southeastern part of Northeast India. Land use land cover (LULC), vegetation type, Digital elevation model (DEM), slope, aspect and proximity to roads and settlements; factors that influence the behavior of fire, were used to model the forest fire risk zones. Each class of the layers was given weight according to their fire inducing capability and their sensitivity to fire. Weighted sum modelling and ISODATA clustering was used to classify the fire zones.