Estimation of Forest Aboveground Biomass in Changbai Mountain Region Using ICESat/GLAS and Landsat/TM Data

Abstract

Mapping the magnitude and spatial distribution of forest aboveground biomass (AGB, in Mg•ha −1) is crucial to improve our understanding of the terrestrial carbon cycle. Landsat/TM (Thematic Mapper) and ICESat/GLAS (Ice, Cloud, and land Elevation Satellite, Geoscience Laser Altimeter System) data were integrated to estimate the AGB in the Changbai Mountain area. Firstly, four forest types were delineated according to TM data classification. Secondly, different models for prediction of the AGB at the GLAS footprint level were developed from GLAS waveform metrics and the AGB was derived from field observations using multiple stepwise regression. Lastly, GLAS-derived AGB, in combination with vegetation indices, leaf area index (LAI), canopy closure, and digital elevation model (DEM), were used to drive a data fusion model based on the random forest approach for extrapolating the GLAS footprint AGB to a continuous AGB map. The classification result showed that the Changbai Mountain region was characterized as forest-rich in altitudinal vegetation zones. The contribution of remote sensing variables in modeling the AGB was evaluated. Vegetation index metrics account for large amount of contribution in AGB ranges <150 Mg•ha −1 , while canopy closure has the largest contribution in AGB ranges ≥150 Mg•ha −1. Our study revealed that spatial information from two sensors and DEM could be combined to estimate the AGB with an R 2 of 0.72 and an RMSE of 25.24 Mg•ha −1 in validation at stand level (size varied from~0.3 ha tõ 3 ha).

Estimation and Mapping of Forest Structure Parameters from Open Access Satellite Images: Development of a Generic Method with a Study Case on Coniferous Plantation

“…To overcome the limitations and related uncertainties of their surveys, national bodies have researched two main areas of improvement: (1) additional airborne/spaceborne remote sensing instruments (e.g., lidar, tomography or photogrammetry) with the view to generate better data [3][4][5][6][7][8], and (2) smarter use of existing data through statistical or machine learning techniques that mutually enrich unrelated data sources (multi-source approaches [9][10][11][12]). Our study fits into this second field, considering the use of freely available satellite images as a promising way to build a cost-effective method for mapping forest structure parameters [13][14][15][16][17][18][19].…”

Transectos de datos LiDAR: una estrategia de muestreo para estimar biomasa aérea en áreas forestales

“…Aunque el problema tampoco se resolverá inmediatamente con los datos de LiDAR, debido a su limitada cobertura espacial, es necesario hacer uso de pequeñas muestras de datos que incrementan la precisión de la variable de interés evaluada, y a través de la conexión con los datos ópticos de satélite es posible realizar estimaciones en superficies mayores sin tener que invertir el tiempo y el dinero que implica realizar el inventario forestal sobre esa misma gran superficie (Chi et al, 2017).…”

Iterative Pointing Angle Calibration Method for the Spaceborne Photon-Counting Laser Altimeter Based on Small-Range Terrain Matching

“…The spaceborne laser altimeter, as an important instrument for earth observation, is of great significance for global ecosystem observation and for glacier and lake research [1][2][3][4][5][6]. Following the Geoscience Laser Altimeter System (GLAS) of the Ice, Cloud and Land Elevation Satellite (ICESat) [7], which was a single-beam instrument that recorded the received laser energy as a waveform, the National Aeronautics and Space Administration (NASA) launched the Ice, Cloud and Land Elevation Satellite-2 ICESat-2 satellite on 15 September 2018 [8,9].…”