Abstract
Abstract. Sustainable forest management is a critical topic which contributes to ecological, economical, and socio-cultural aspect of the environment. Providing accurate AGB maps is of paramount importance for sustainable forest management, carbon accounting, and climate change monitoring. The main goal of this study was to leverage the potential of two machine learning algorithms for predicting AGB using optical and synthetic aperture radar (SAR) datasets. To achieve this goal random forest (RF) and light gradient boosting machine (LightGBM) models were deployed to predict AGB values in Huntington Wild Forest (HWF) in Essex County, NY using continuous forest inventory (CFI) plots. Both models were trained and evaluated based on airborne light detection and ranging (LiDAR) data, Landsat imagery, advanced land observing satellite (ALOS) phased array type L-band Synthetic Aperture Radar (PALSAR), and their combination. The integration of airborne LiDAR, optic, and SAR datasets provided the best results in terms of root mean square error (RMSE) and mean bias error (MBE). The RF model outperformed the LightGBM in all scenarios (LiDAR, Landsat 5, ALOS PALSAR, and their combination). The RF model was able to predict AGB values with the RMSE of 51.90 Mg/ha and MBE of −0.189 Mg/ha for the combination of LiDAR, optic, and SAR data, while LightGBM estimated the AGB values with the RMSE of 52.78 Mg/ha and MBE of −0.253 Mg/ha. LightGBM is more sensitive to noise and there are lots of hyperparameters that need to be tuned which highly affect its performance.
Highlights
In today’s world, global deforestation is expanding and accelerating, resulting in the release of a quarter of carbon into the atmosphere (Li, Quackenbush, and Im 2019)
This section describes the results of applied regression models for above-ground biomass (AGB) estimation for 4 different scenarios: airborne light detection and ranging (LiDAR), Landsat 5, advanced land observing satellite (ALOS) phased array type L-band Synthetic Aperture Radar (PALSAR), and the combination of them
random forest (RF) regression model outperformed LightGBM. Both RF and LightGBM are capable of handling over-fitting issue
Summary
In today’s world, global deforestation is expanding and accelerating, resulting in the release of a quarter of carbon into the atmosphere (Li, Quackenbush, and Im 2019). Forest monitoring and more accurate estimates of forest above-ground biomass (AGB) is of significance to clarify the contribution of forests in global climate change. Some countries have developed a campaign which is known as Reducing Emissions from Deforestation and Degradation (REDD) to mitigate the effects of the climate change (Bellassen and Gitz 2008). A key question for REDD effort is how much AGB is available at national and global scale. According to the requirements of this effort, participating countries are supposed to report verified estimates of AGB at national level which is a key indicator of carbon pools in forest systems (Chen et al 2018). One major problem in accurate carbon estimation is to find an efficient method for the determination of the forest AGB. The increasing availability of remote sensing data paves the road for cost-effective and large scale AGB estimation
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