Remote Sensing of Above Ground Biomass

• Main Author: Mutanga, Onisimo (auth)
• Other Authors: Kumar, Lalit (auth)
• Format: Electronic Book Chapter
• Language: English
• Published: MDPI - Multidisciplinary Digital Publishing Institute 2019
• Subjects: History of engineering & technology; NDLMA; n/a; multi-angle remote sensing; TerraSAR-X; above ground biomass; stem volume; regression analysis; ground-based remote sensing; sensor fusion; pasture biomass; grazing management; livestock; mixed forest; SPLSR; estimation accuracy; Bidirectional Reflectance Distribution Factor; forage crops; Land Surface Phenology; climate change; vegetation index; dry biomass; mapping; rangeland productivity; vegetation indices; error analysis; broadleaves; remote sensing; applicability evaluation; ultrasonic sensor; chlorophyll index; alpine meadow grassland; forest biomass; anthropogenic disturbance; fractional vegetation cover; alpine grassland conservation; carbon mitigation; conifer; short grass; grazing exclusion; MODIS time series; random forest; aboveground biomass; NDVI; AquaCrop model; inversion model; wetlands; field spectrometry; spectral index; yield; foliage projective cover; lidar; correlation coefficient; Sahel; biomass; dry matter index; Niger; Landsat; grass biomass; particle swarm optimization; winter wheat; carbon inventory; rice; forest structure information; MODIS; light detection and ranging (LiDAR); ALOS2; ecological policies; above-ground biomass; Wambiana grazing trial; food security; forest above ground biomass (AGB); Atriplex nummularia; regional sustainability; CIRed-edge
• Online Access: DOAB: download the publication; DOAB: description of the publication

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.