Combining Multi-Source Remotely Sensed Data and a Process-Based Model for Forest Aboveground Biomass Updating

Abstract

Monitoring and understanding the spatio-temporal variations of forest aboveground biomass (AGB) is a key basis to quantitatively assess the carbon sequestration capacity of a forest ecosystem. To map and update forest AGB in the Greater Khingan Mountains (GKM) of China, this work proposes a physical-based approach. Based on the baseline forest AGB from Landsat Enhanced Thematic Mapper Plus (ETM+) images in 2008, we dynamically updated the annual forest AGB from 2009 to 2012 by adding the annual AGB increment (ABI) obtained from the simulated daily and annual net primary productivity (NPP) using the Boreal Ecosystem Productivity Simulator (BEPS) model. The 2012 result was validated by both field- and aerial laser scanning (ALS)-based AGBs. The predicted forest AGB for 2012 estimated from the process-based model can explain 31% (n = 35, p < 0.05, RMSE = 2.20 kg/m2) and 85% (n = 100, p < 0.01, RMSE = 1.71 kg/m2) of variation in field- and ALS-based forest AGBs, respectively. However, due to the saturation of optical remote sensing-based spectral signals and contribution of understory vegetation, the BEPS-based AGB tended to underestimate/overestimate the AGB for dense/sparse forests. Generally, our results showed that the remotely sensed forest AGB estimates could serve as the initial carbon pool to parameterize the process-based model for NPP simulation, and the combination of the baseline forest AGB and BEPS model could effectively update the spatiotemporal distribution of forest AGB.

Estimación de biomasa aérea de <i>Eucalyptus grandis</i> y <i>Pinus</i> spp. usando imágenes Sentinel1A y Sentinel2A en Colombia

“…El índice de vegetación de diferencia normalizada verde (GNDVI) es más sensible a la variación en el contenido de clorofila que el NDVI (Baloloy et al, 2018). El índice de vegetación ajustado al suelo (Savi) presenta un adecuado resultado cuando la cobertura vegetal es densa, ya que tienen rangos dinámicos amplios y menor susceptibilidad a las perturbaciones (Lu et al, 2017). Los índices se calcularon utilizando las expresiones presentadas en la tabla 2.…”

“…La evaluación del rendimiento de los modelos se efectuó con el 70 % de los datos de campo para ajustes (n = 50 y n = 57 en E. grandis y P. spp, respectivamente) y el 30 % para validación (n = 22 y n = 24 E. grandis y P. spp, respectivamente), aplicando la técnica de validación cruzada 10 veces para probar de manera sólida el rendimiento del algoritmo, tomando como criterio el menor valor de EMC (Ghosh y Behera, 2018). Finalmente, se mapeó la AGB forestal sobre cada lote usando el modelo AGB óptimo para cada especie con la interpolación Kriging (Lu et al, 2017). Los mapas resultantes fueron generados en el programa RStudio (versión 1.1.442; Team, 2015).…”

Quantitative Assessment of Factors Influencing the Spatiotemporal Variation in Carbon Dioxide Fluxes Simulated by Multi-Source Remote Sensing Data in Tropical Vegetation

“…Therefore, this study innovatively incorporated WSF based on daily LSWI calculations to estimate the maximum carboxylation rate (V m ). Furthermore, BEPS, driven by meteorological data and high-precision daily LAI to couple photosynthesis, respiration, and radiative transfer, has demonstrated accuracy and reliabil-ity at several stations as well as regional and global scales [55][56][57]. Details of the simulated NPP are described below.…”

Precise aboveground biomass estimation of plantation forest trees using the novel allometric model and UAV-borne LiDAR

“…the aboveground biomass (AGB) of a forest is vital for quantitatively estimating its carbon sequestration (Dubayah et al, 2010;Chen et al, 2015;Li et al, 2015;Dube and Mutanga, 2016;Lu et al, 2017;Su et al, 2020). Furthermore, the tree AGB is the primary component of the total biomass of a plantation forest (Ming et al, 2012;Yu et al, 2013;Zheng et al, 2014;Li, 2019;Guo et al, 2022).…”