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Monitoring Forest Carbon Sequestration with Remote Sensing
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The forest, as the main body of the terrestrial ecosystem, has a huge carbon sink function and plays an important role in coping with global climate change. This reprint on “Monitoring forest carbon sequestration with remote sensing” mainly focuses on new remote sensing theories, methods, and technologies for monitoring carbon sinks in forest ecosystems (including urban forest ecosystems).
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Keywords
- 3-PG model
- 3D green volume
- above-ground biomass (AGB)
- aboveground biomass
- aboveground biomass (AGB)
- aboveground carbon density
- airborne hyperspectral
- airborne LiDAR
- artificial neural network
- backscatter coefficients
- bamboo forest
- Bayesian hierarchical modelling
- BEPS model
- canopy closure
- canopy volume
- carbon budget
- carbon storage
- classifying forest types
- Climate Change
- clumping index
- coherence
- coherence optimization
- collinearity
- complex coherence amplitude inversion
- deep learning
- diameter at breast height (DBH)
- driving factors
- dynamic change
- dynamic model
- dynamic threshold method
- estimation
- Eucalyptus
- Eucalyptus camaldulensis
- Eucalyptus grandis
- extinction coefficient
- feature fusion
- feature selection
- field measurement
- fisheye camera photos
- forest aboveground biomass
- forest aboveground biomass (AGB)
- forest age
- forest carbon stocks
- forest density
- forest height
- forest height inversion
- forest structure
- FY-3C VIRR
- geostatistics
- gross primary productivity
- habitat dataset
- Hainan Island
- impact analysis
- influential mechanism
- Interferometry
- InVEST model
- L-band PolInSAR
- LAI
- land use/cover change
- Landsat
- Landsat 8-OLI images
- Landsat time-series
- LiDAR
- LUCC
- machine learning algorithm
- mangrove forests
- Mathematics
- Mathematics & science
- model comparison
- MODIS
- multi-source data
- near-infrared reflectance of vegetation
- net primary productivity
- Northeast China
- PCA
- phenology
- Pine Forest
- Pinus densata
- Pinus densata forests
- Pinus patula
- PLUS model
- polarization decomposition
- Probability & statistics
- quantile regression neural network
- random forest
- random forest (RF)
- random forest Kriging
- random forest model
- random forests
- random volume over ground (RVoG) model
- Reference, information & interdisciplinary subjects
- Remote sensing
- remote sensing inversion
- Research & information: general
- RF
- ridge regression
- rubber plantation
- RVoG model
- scale correction
- scale effect
- scenario simulation
- sensitivity
- SENTINEL-2 images
- Shaoguan City
- shapelet
- signal penetration
- simulation
- spatial random effects
- spatially varying coefficient
- spatiotemporal evolution
- SRTM
- stem volume (V)
- stochastic gradient boosting
- stratified estimation
- Synthetic Aperture Radar (SAR)
- terrain niche index
- terrain slope
- the GOST model
- thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general
- thema EDItEUR::P Mathematics and Science
- thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics
- three-stage algorithm
- three-stage inversion method
- time series
- TIMESAT
- transects
- UAV-Lidar
- urban forest
- VCT model
- wavelet analysis
- wavelet transform
- Yunnan province
Links
DOI: 10.3390/books978-3-0365-7209-3Editions