Remote Sensing of Above Ground Biomass

Above ground biomass has been listed by the Intergovernmental Panel on Climate Change as one of the five most prominent, visible, and dynamic terrestrial carbon pools. The increased awareness of the impacts of climate change has seen a burgeoning need to consistently assess carbon stocks to combat c...

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Year of Publication:2019
Language:English
Physical Description:1 electronic resource (264 p.)
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520 |a Above ground biomass has been listed by the Intergovernmental Panel on Climate Change as one of the five most prominent, visible, and dynamic terrestrial carbon pools. The increased awareness of the impacts of climate change has seen a burgeoning need to consistently assess carbon stocks to combat carbon sequestration. An accurate estimation of carbon stocks and an understanding of the carbon sources and sinks can aid the improvement and accuracy of carbon flux models, an important pre-requisite of climate change impact projections. Based on 15 research topics, this book demonstrates the role of remote sensing in quantifying above ground biomass (forest, grass, woodlands) across varying spatial and temporal scales. The innovative application areas of the book include algorithm development and implementation, accuracy assessment, scaling issues (local–regional–global biomass mapping), and the integration of microwaves (i.e. LiDAR), along with optical sensors, forest biomass mapping, rangeland productivity and abundance (grass biomass, density, cover), bush encroachment biomass, and seasonal and long-term biomass monitoring. 
546 |a English 
653 |a NDLMA 
653 |a multi-angle remote sensing 
653 |a TerraSAR-X 
653 |a above ground biomass 
653 |a stem volume 
653 |a regression analysis 
653 |a ground-based remote sensing 
653 |a sensor fusion 
653 |a pasture biomass 
653 |a grazing management 
653 |a livestock 
653 |a mixed forest 
653 |a SPLSR 
653 |a estimation accuracy 
653 |a Bidirectional Reflectance Distribution Factor 
653 |a forage crops 
653 |a Land Surface Phenology 
653 |a climate change 
653 |a vegetation index 
653 |a dry biomass 
653 |a mapping 
653 |a rangeland productivity 
653 |a vegetation indices 
653 |a error analysis 
653 |a broadleaves 
653 |a remote sensing 
653 |a applicability evaluation 
653 |a ultrasonic sensor 
653 |a chlorophyll index 
653 |a alpine meadow grassland 
653 |a forest biomass 
653 |a anthropogenic disturbance 
653 |a fractional vegetation cover 
653 |a alpine grassland conservation 
653 |a carbon mitigation 
653 |a conifer 
653 |a short grass 
653 |a grazing exclusion 
653 |a MODIS time series 
653 |a random forest 
653 |a aboveground biomass 
653 |a NDVI 
653 |a AquaCrop model 
653 |a inversion model 
653 |a wetlands 
653 |a field spectrometry 
653 |a spectral index 
653 |a yield 
653 |a foliage projective cover 
653 |a lidar 
653 |a correlation coefficient 
653 |a Sahel 
653 |a biomass 
653 |a dry matter index 
653 |a Niger 
653 |a Landsat 
653 |a grass biomass 
653 |a particle swarm optimization 
653 |a winter wheat 
653 |a carbon inventory 
653 |a rice 
653 |a forest structure information 
653 |a MODIS 
653 |a light detection and ranging (LiDAR) 
653 |a ALOS2 
653 |a ecological policies 
653 |a above-ground biomass 
653 |a Wambiana grazing trial 
653 |a food security 
653 |a forest above ground biomass (AGB) 
653 |a Atriplex nummularia 
653 |a regional sustainability 
653 |a CIRed-edge 
776 |z 3-03921-209-5 
700 1 |a Kumar, Lalit  |4 auth 
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