Chapter Above‐Ground Biomass Estimation with High Spatial Resolution Satellite Images
Cristina Gon�alves, Ana
Chapter Above‐Ground Biomass Estimation with High Spatial Resolution Satellite Images - InTechOpen 2017 - 1 online resource
Open Access
Assessment and monitoring of forest biomass are frequently done with allometric functions per species for inventory plots. The estimation per area unit is carried out with an extrapolation method. In this chapter, a review of the recent methods to estimate forest above‐ground biomass (AGB) using remote sensing data is presented. A case study is given with an innovative methodology to estimate above‐ground biomass based on crown horizontal projection obtained with high spatial resolution satellite images for two evergreen oak species. The linear functions fitted for pure, mixed and both compositions showed a good performance. Also, the functions with dummy variables to distinguish species and compositions adjusted had the best performance. An error threshold of 5% corresponds to stand areas of 8.7 and 5.5 ha for the functions of all species and compositions without and with dummy variables. This method enables the overall area evaluation, and it is easily implemented in a geographic information system environment.
Creative Commons
English
65665
10.5772/65665 doi
Alternative & renewable energy sources & technology
QuickBird, multi‐resolution segmentation, crown horizontal projection, forest inventory, regressions
Chapter Above‐Ground Biomass Estimation with High Spatial Resolution Satellite Images - InTechOpen 2017 - 1 online resource
Open Access
Assessment and monitoring of forest biomass are frequently done with allometric functions per species for inventory plots. The estimation per area unit is carried out with an extrapolation method. In this chapter, a review of the recent methods to estimate forest above‐ground biomass (AGB) using remote sensing data is presented. A case study is given with an innovative methodology to estimate above‐ground biomass based on crown horizontal projection obtained with high spatial resolution satellite images for two evergreen oak species. The linear functions fitted for pure, mixed and both compositions showed a good performance. Also, the functions with dummy variables to distinguish species and compositions adjusted had the best performance. An error threshold of 5% corresponds to stand areas of 8.7 and 5.5 ha for the functions of all species and compositions without and with dummy variables. This method enables the overall area evaluation, and it is easily implemented in a geographic information system environment.
Creative Commons
English
65665
10.5772/65665 doi
Alternative & renewable energy sources & technology
QuickBird, multi‐resolution segmentation, crown horizontal projection, forest inventory, regressions