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Remote Sensing Technology Applications in Forestry and REDD+
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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.
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Keywords
- 3D tree modelling
- above-ground biomass
- aboveground biomass
- aboveground biomass estimation
- Agriculture
- airborne laser scanning
- Cameroon
- canopy cover (CC)
- crown delineation
- crown density
- Deforestation
- destructive sampling
- digital hemispherical photograph (DHP)
- ensemble model
- environment effects
- forest baseline
- forest canopy
- forest classification
- forest growing stock volume (GSV)
- forest inventory
- forestry
- full polarimetric SAR
- geographic information system
- geographically weighted regression
- gray level co-occurrence matrix (GLCM)
- Guyana
- hazard mapping
- human activity
- Landsat
- leaf area
- LiDAR
- local tree allometry
- low-accuracy estimation
- Machine learning
- model comparison
- model evaluation
- multispectral satellite imagery
- old-growth forest
- overstory trees
- phenology
- Pinus massoniana
- predictive mapping
- quantitative structural model
- random forest
- random forest (RF)
- REDD+
- reference level
- Remote sensing
- sentinel imagery
- Silviculture
- specific leaf area
- spectral
- subtropical forest
- support vector machine
- tall trees
- terrestrial laser scanning
- Texture
- topographic effects
- topography
- tree mapping
- voxelization
Links
DOI: 10.3390/books978-3-03928-471-9Editions
