Geo-Informatics in Resource Management
The management of natural resources can be approached using different data sources and techniques, from images registered by sensors of onboard satellites to UAV platforms, using remote sensing techniques and geographic information systems, among others. The variability of problems and projects to b...
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| Year of Publication: | 2020 |
| Language: | English |
| Physical Description: | 1 electronic resource (206 p.) |
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| 100 | 1 | |a Mesas Carrascosa, Francisco Javier |4 edt | |
| 245 | 1 | 0 | |a Geo-Informatics in Resource Management |
| 260 | |a Basel, Switzerland |b MDPI - Multidisciplinary Digital Publishing Institute |c 2020 | ||
| 300 | |a 1 electronic resource (206 p.) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 520 | |a The management of natural resources can be approached using different data sources and techniques, from images registered by sensors of onboard satellites to UAV platforms, using remote sensing techniques and geographic information systems, among others. The variability of problems and projects to be analyzed, studied, and solved is very wide. This book presents a collection of different experiences, ranging from the location of areas of interest to the simulation of future scenarios of a territory at local and regional scales, considering spatial resolutions ranging from centimeters to hundreds of meters. The common objective of all the works compiled in this book is to support decision-making in environmental management. | ||
| 546 | |a English | ||
| 650 | 7 | |a Research & information: general |2 bicssc | |
| 653 | |a secondary succession monitoring | ||
| 653 | |a Natura 2000 threats | ||
| 653 | |a tree detection | ||
| 653 | |a archival photographs | ||
| 653 | |a spectro-textural classification | ||
| 653 | |a granulometric analysis | ||
| 653 | |a GLCM | ||
| 653 | |a alpine grassland | ||
| 653 | |a fractional vegetation cover | ||
| 653 | |a ground survey | ||
| 653 | |a precision evaluation | ||
| 653 | |a multi-scale LAI product validation | ||
| 653 | |a PROSAIL model | ||
| 653 | |a EBK | ||
| 653 | |a crop growth period | ||
| 653 | |a adaptive K-means algorithm | ||
| 653 | |a heavy industry heat sources | ||
| 653 | |a NPP-VIIRS | ||
| 653 | |a active fire data | ||
| 653 | |a night-time light data | ||
| 653 | |a spatial autocorrelation | ||
| 653 | |a spatial pattern | ||
| 653 | |a spatial relationship | ||
| 653 | |a natural wetlands changes | ||
| 653 | |a associated influencing factors | ||
| 653 | |a mainland China | ||
| 653 | |a farmland abandonment mapping | ||
| 653 | |a textural segmentation | ||
| 653 | |a aerial imagery | ||
| 653 | |a land use | ||
| 653 | |a Poznań | ||
| 653 | |a agent based modeling | ||
| 653 | |a disaster management | ||
| 653 | |a resource allocation | ||
| 653 | |a high severity level | ||
| 653 | |a first come first serve | ||
| 653 | |a geographical information system | ||
| 653 | |a bearing capacity | ||
| 653 | |a analytic hierarchy process | ||
| 653 | |a geographical survey of national conditions | ||
| 653 | |a hotspot analysis | ||
| 653 | |a topsis algorithm | ||
| 653 | |a automatic identification system data | ||
| 653 | |a 21st Century Maritime Silk Road region | ||
| 653 | |a oil flow analysis | ||
| 653 | |a maritime oil chokepoint | ||
| 653 | |a Middle East Respiratory Syndrome | ||
| 653 | |a seismic parameters | ||
| 653 | |a GIS | ||
| 653 | |a seismicity | ||
| 653 | |a spatial analysis | ||
| 653 | |a b-value | ||
| 653 | |a earthquake catalog | ||
| 653 | |a future scenarios | ||
| 653 | |a prelude | ||
| 653 | |a dynamic of land use | ||
| 653 | |a Spatial Decision Support System, CORINE Land Cover | ||
| 653 | |a remote sensing | ||
| 653 | |a geographic information system | ||
| 776 | |z 3-03943-739-9 | ||
| 776 | |z 3-03943-740-2 | ||
| 700 | 1 | |a Mesas Carrascosa, Francisco Javier |4 oth | |
| 906 | |a BOOK | ||
| ADM | |b 2023-12-15 05:36:34 Europe/Vienna |f system |c marc21 |a 2022-04-04 09:22:53 Europe/Vienna |g false | ||
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