Abstract
Accurate estimation of aboveground forest biomass (AGB) at a large scale is important in global carbon cycle, forest productivity, and climate change. Coarse resolution remote sensing data of long time series are often used to estimate large scale AGB, but the result is inaccurate due to the scaling effect caused by nonlinearity in data representation and the existence of mixed pixels containing different forest types and land uses. Improvement in the accuracy of AGB estimated from coarse resolution remote sensing data is urgently needed. Research on spatial scaling of AGB is still lacking, therefore, this article proposed an approach based on structural analysis of mixed pixels and the Random Forest model (SMPRF) to increase the accuracy of AGB estimated from coarse resolution data. MODIS and SPOT 5 data were used to create forest biomass distribution maps of the study area at two scales. The scaling effect on estimating forest biomass based on remote sensing was analyzed by comparing data from these two datasets. SMPRF, which included a correction factor for the scaling effect on AGB estimated from coarse resolution MODIS data, was used to create a model that scaled from the fine resolution data (SPOT 5) to the coarse resolution data (MODIS). The results showed that the accuracy of AGB estimated from MODIS data was increased using this method. The Pearson correlation coefficient ( $r$ ) for data verification increased from 0.63 to 0.89 and the root mean squared error decreased from 51.6 Mg $\cdot $ ha−1 to 26.8 Mg $\cdot $ ha−1. The difference tests showed that the changes were extremely significant ( $p = 0$ ). Thus, SMPRF can significantly improve the accuracy of large scale AGB estimation based on coarse resolution remote sensing data and the feasibility of applying the method proposed in this study to related fields is verified.
Highlights
Aboveground forest biomass (AGB) is a measure of carbon sequestration and forest productivity in a forest ecosystem [1]–[3]
The results show that 98.19% of the predictions and 99.01% of the validation results were within the range [−3, 3], which indicates that any outliers that may lead to changes in the regression models are controlled within in a very small range
In this study, we constructed models to predict aboveground forest biomass (AGB) from data obtained at two scales, SPOT 5 (10 m) and MODIS (250 m) data
Summary
Aboveground forest biomass (AGB) is a measure of carbon sequestration and forest productivity in a forest ecosystem [1]–[3]. Accurate scientific estimation of regional forest biomass is fundamental to studies of global carbon cycle, forest productivity, and climate change [4]. Remote sensing technology provides regional or global forest coverage and is a data source for estimating AGB at a large scale. Optical remote sensing data [7], microwave radar data [8], and lidar data [9], [10] are increasingly used to estimate forest parameters [9], [11]. Multispectral imaging is the most widely used technique to take advantage of remote sensing data for large scale AGB observations [12]; it offers little information concerning the vertical structure of a forest [13]. Lidar penetrates forest canopy better than optical remote sensing or SAR, and lidar data can be used to accurately predict vertical forest
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