|
|
|
|
| LEADER |
04840nma a2201105 u 4500 |
| 001 |
EB001963748 |
| 003 |
EBX01000000000000001126650 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
210512 ||| eng |
| 020 |
|
|
|a 9783039284719
|
| 020 |
|
|
|a 9783039284702
|
| 020 |
|
|
|a books978-3-03928-471-9
|
| 100 |
1 |
|
|a Vastaranta, Mikko
|
| 245 |
0 |
0 |
|a Remote Sensing Technology Applications in Forestry and REDD+
|h Elektronische Ressource
|
| 260 |
|
|
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2020
|
| 300 |
|
|
|a 1 electronic resource (244 p.)
|
| 653 |
|
|
|a machine learning
|
| 653 |
|
|
|a Landsat
|
| 653 |
|
|
|a gray level co-occurrence matrix (GLCM)
|
| 653 |
|
|
|a tree mapping
|
| 653 |
|
|
|a random forest
|
| 653 |
|
|
|a above-ground biomass
|
| 653 |
|
|
|a Cameroon
|
| 653 |
|
|
|a forest canopy
|
| 653 |
|
|
|a low-accuracy estimation
|
| 653 |
|
|
|a forestry
|
| 653 |
|
|
|a silviculture
|
| 653 |
|
|
|a crown density
|
| 653 |
|
|
|a tall trees
|
| 653 |
|
|
|a subtropical forest
|
| 653 |
|
|
|a forest inventory
|
| 653 |
|
|
|a destructive sampling
|
| 653 |
|
|
|a predictive mapping
|
| 653 |
|
|
|a environment effects
|
| 653 |
|
|
|a human activity
|
| 653 |
|
|
|a quantitative structural model
|
| 653 |
|
|
|a old-growth forest
|
| 653 |
|
|
|a airborne laser scanning
|
| 653 |
|
|
|a 3D tree modelling
|
| 653 |
|
|
|a forest classification
|
| 653 |
|
|
|a Pinus massoniana
|
| 653 |
|
|
|a leaf area
|
| 653 |
|
|
|a random forest (RF)
|
| 653 |
|
|
|a full polarimetric SAR
|
| 653 |
|
|
|a agriculture
|
| 653 |
|
|
|a overstory trees
|
| 653 |
|
|
|a Guyana
|
| 653 |
|
|
|a forest growing stock volume (GSV)
|
| 653 |
|
|
|a topographic effects
|
| 653 |
|
|
|a reference level
|
| 653 |
|
|
|a voxelization
|
| 653 |
|
|
|a model comparison
|
| 653 |
|
|
|a specific leaf area
|
| 653 |
|
|
|a spectral
|
| 653 |
|
|
|a multispectral satellite imagery
|
| 653 |
|
|
|a model evaluation
|
| 653 |
|
|
|a geographic information system
|
| 653 |
|
|
|a digital hemispherical photograph (DHP)
|
| 653 |
|
|
|a REDD+
|
| 653 |
|
|
|a support vector machine
|
| 653 |
|
|
|a LiDAR
|
| 653 |
|
|
|a terrestrial laser scanning
|
| 653 |
|
|
|a remote sensing
|
| 653 |
|
|
|a phenology
|
| 653 |
|
|
|a crown delineation
|
| 653 |
|
|
|a geographically weighted regression
|
| 653 |
|
|
|a hazard mapping
|
| 653 |
|
|
|a aboveground biomass
|
| 653 |
|
|
|a texture
|
| 653 |
|
|
|a local tree allometry
|
| 653 |
|
|
|a Environmental science, engineering & technology / bicssc
|
| 653 |
|
|
|a sentinel imagery
|
| 653 |
|
|
|a topography
|
| 653 |
|
|
|a canopy cover (CC)
|
| 653 |
|
|
|a ensemble model
|
| 653 |
|
|
|a aboveground biomass estimation
|
| 653 |
|
|
|a forest baseline
|
| 653 |
|
|
|a deforestation
|
| 700 |
1 |
|
|a Calders, Kim
|
| 700 |
1 |
|
|a Jonckheere, Inge
|
| 700 |
1 |
|
|a Nightingale, Joanne
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b DOAB
|a Directory of Open Access Books
|
| 500 |
|
|
|a Creative Commons (cc), https://creativecommons.org/licenses/by-nc-nd/4.0/
|
| 028 |
5 |
0 |
|a 10.3390/books978-3-03928-471-9
|
| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/2103
|7 0
|x Verlag
|3 Volltext
|
| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/58179
|z DOAB: description of the publication
|
| 082 |
0 |
|
|a 363
|
| 082 |
0 |
|
|a 000
|
| 082 |
0 |
|
|a 630
|
| 082 |
0 |
|
|a 140
|
| 082 |
0 |
|
|a 600
|
| 082 |
0 |
|
|a 620
|
| 520 |
|
|
|a 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.
|