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Viser: Remote Sensing Applications in Environmental Research
Remote Sensing Applications in Environmental Research
Prashant K. Srivastava, Saumitra Mukherjee, Manika Gupta og Tanvir Islam
(2016)
Sprog: Engelsk
Springer International Publishing AG
1.163,00 kr.
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Detaljer om varen
- Paperback
- Udgiver: Springer International Publishing AG (September 2016)
- Forfattere: Prashant K. Srivastava, Saumitra Mukherjee, Manika Gupta og Tanvir Islam
- ISBN: 9783319359410
Remote Sensing Applications in Environmental Research is the basis for advanced Earth Observation (EO) datasets used in environmental monitoring and research. Now that there are a number of satellites in orbit, EO has become imperative in today's sciences, weather and natural disaster prediction. This highly interdisciplinary reference work brings together diverse studies on remote sensing and GIS, from a theoretical background to its applications, represented through various case studies and the findings of new models. The book offers a comprehensive range of contributions by well-known scientists from around the world and opens a new window for students in presenting interdisciplinary and methodological resources on the latest research. It explores various key aspects and offers state-of-the-art research in a simplified form, describing remote sensing and GIS studies for those who are new to the field, as well as for established researchers.
Introduction.- Remote sensing-based determination of conifer needle flushing phenology over boreal-dominant regions.- Information System for Integrated Watershed Management using Remote Sensing and GIS.- Sensitivity Exploration of SimSphere Land Surface Model Towards its Use for Operational Products Development from Earth Observation Data.- Remote estimation of land surface temperature for different LULC features of a moist deciduous tropical forest region.- Geospatial Strategy for Estimation of Soil Organic Carbon in Tropical Wildlife Reserve.- A Comparative Assessment Between the Application of Fuzzy Unordered Rules Induction Algorithm and J48 Decision Tree Models in spatial Prediction of Shallow Landslides at Lang Son City, Vietnam.
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