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Remote Sensing Technology Applications in Forestry and REDD+


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Produktinformationen
cover
cover
Artikel-Nr.:
     5667A-9783039284702
Hersteller:
     MDPI
Herst.-Nr.:
     9783039284702
EAN/GTIN:
     9783039284702
Suchbegriffe:
Geowissenschaften-Bücher
Geowissenschaftsbücher
geowissenschaftliche Bücher - engli...
geowissenschaften bücher
Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion.
Weitere Informationen:
Author:
Kim Calders; Inge Jonckheere; Mikko Vastaranta; Joanne Nightingale
Verlag:
MDPI
Sprache:
eng
Weitere Suchbegriffe: above-ground biomass; predictive mapping; sentinel imagery, sentinel imagery, above-ground biomass, predictive mapping, machine learning, geographically weighted regression, canopy cover (CC), spectral, texture, digital hemispherical photograph (DHP), random forest (RF)
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