Abstract
Forest biomass is a crucial component of the global carbon budget in climate change studies. Therefore, it is essential to develop a credible way to estimate forest biomass as carbon stock. Our study used PALSAR-2 (ALOS-2) and Sentinel-2 images to drive the Random Forest regression model, which we trained with airborne lidar data. We used the model to estimate forest aboveground biomass (AGB) of two significant coniferous trees, Japanese cedar and Japanese cypress, in Ibaraki Prefecture, Japan. We used 48 variables derived from the two remote sensing datasets to predict forest AGB under the Random Forest algorithm, and found that the model that combined the two datasets performed better than models based on only one dataset, with R2 = 0.31, root-mean-square error (RMSE) = 54.38 Mg ha−1, mean absolute error (MAE) = 40.98 Mg ha−1, and relative RMSE (rRMSE) of 0.35 for Japanese cedar, and R2 = 0.37, RMSE = 98.63 Mg ha−1, MAE = 76.97 Mg ha−1, and rRMSE of 0.33 for Japanese cypress, over the whole AGB range. In the satellite AGB map, the total AGB of Japanese cedar in 17 targeted cities in Ibaraki Prefecture was 5.27 Pg, with a mean of 146.50 Mg ha−1 and a standard deviation of 44.37 Mg ha−1. The total AGB of Japanese cypress was 3.56 Pg, with a mean of 293.12 Mg ha−1 and a standard deviation of 78.48 Mg ha−1. We also found a strong linear relationship with between the model estimates and Japanese government data, with R2 = 0.99 for both species and found the government information underestimates the AGB for cypress but overestimates it for cedar. Our results reveal that combining information from multiple sensors can predict forest AGB with increased accuracy and robustness.
Highlights
Forests play a significant role in the global carbon budget, as they store a large share of terrestrial carbon in their biomass [1]
It is important to note that only cedar and cypress were considered in the aboveground biomass (AGB) calculation; this is acceptable because we focused on plantations, which are essentially single-species forests
We developed robust and effective models to estimate the AGB of Japanese cedar and cypress by a machine learning approach
Summary
Forests play a significant role in the global carbon budget, as they store a large share of terrestrial carbon in their biomass [1]. About 90% of the total carbon in the world’s vegetation stock comprises forests, which cover 65% of the land area [2]. The forest aboveground biomass (AGB) is considered one of the most important factors in evaluating forest carbon pools [3]. To better understand the amount of stored carbon in forest, spatially explicit and temporally consistent estimates of AGB are urgently needed [4]. Tree height as inputs for allometries based on destructive sampling, have provided simple and useful models, but constructing reliable allometric relationships over large areas is difficult, time-consuming, and expensive [5].
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