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240202 ||| eng |
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|a 9783036594781
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|a 9783036594798
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|a books978-3-0365-9478-1
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|a Ganga, Antonio
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245 |
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|a Applications of GIS and Remote Sensing in Soil Environment Monitoring
|h Elektronische Ressource
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260 |
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2023
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300 |
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|a 1 electronic resource (202 p.)
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653 |
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|a indices
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653 |
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|a machine learning
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653 |
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|a sediment monitoring
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653 |
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|a logistic regression
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653 |
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|a analytical hierarchical process
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|a soil erosion
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|a random forest
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|a Andean region
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653 |
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|a soil salinization
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653 |
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|a land degradation vulnerability
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653 |
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|a erosion risk assessment
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653 |
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|a spatial mapping
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|a n/a
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|a different depths
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653 |
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|a MMF
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|a RUSLE
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|a interpolation methods
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|a soil salinity
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|a regional scale
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|a drought
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|a water stress
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653 |
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|a soil properties
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|a cohesion
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653 |
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|a GIS
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653 |
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|a Beichuan
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653 |
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|a soil quality
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653 |
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|a risk assessment
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653 |
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|a remote sensing monitoring
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|a fine sediments
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653 |
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|a sonar
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|a Research & information: general / bicssc
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|a environmental hazard
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|a ecosystem services
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|a USLE
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|a landslide
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|a soil moisture dynamics
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|a aquatic drone
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653 |
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|a debris flow
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653 |
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|a vegetation indices
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653 |
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|a saline soil treatment
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653 |
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|a lakes
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653 |
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|a soil mapping
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653 |
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|a remote sensing
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653 |
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|a DVI
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653 |
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|a land surface temperature
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653 |
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|a climate change
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653 |
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|a model construction
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653 |
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|a salinity indices
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653 |
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|a water capacity
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653 |
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|a land use
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653 |
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|a NDVI
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700 |
1 |
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|a Repe, Blaž
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700 |
1 |
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|a Elia, Mario
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700 |
1 |
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|a Ganga, Antonio
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041 |
0 |
7 |
|a eng
|2 ISO 639-2
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989 |
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|b DOAB
|a Directory of Open Access Books
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500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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028 |
5 |
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|a 10.3390/books978-3-0365-9478-1
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856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/128806
|z DOAB: description of the publication
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856 |
4 |
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|u https://www.mdpi.com/books/pdfview/book/8273
|7 0
|x Verlag
|3 Volltext
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082 |
0 |
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|a 551.6
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082 |
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|a 363
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|a 000
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|a 658
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|a 320
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|a The reprint focuses on environmental monitoring as a key issue in the sustainable management of land resources, where the increasing availability of temporal and spatial soil data plays a fundamental role. Remote sensing and GIS (Geographic Information System) applications allow the efficient handling of these data with the aim of developing predictive and robust models to reduce land degradation and soil erosion.
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