Abstract
Remote sensing–based forest aboveground biomass (AGB) estimation has been extensively explored in the past three decades, but how to effectively combine different sensor data and modeling algorithms is still poorly understood. This research conducted a comparative analysis of different datasets (e.g., Landsat Thematic Mapper (TM), ALOS PALSAR L-band data, and their combinations) and modeling algorithms (e.g., artificial neural network (ANN), support vector regression (SVR), Random Forest (RF), k-nearest neighbor (kNN), and linear regression (LR)) for AGB estimation in a subtropical region under non-stratification and stratification of forest types. The results show the following: (1) Landsat TM imagery provides more accurate AGB estimates (root mean squared error (RMSE) values in 27.7–29.3 Mg/ha) than ALOS PALSAR (RMSE values in 30.3–33.7 Mg/ha). The combination of TM and PALSAR data has similar performance for ANN and SVR, worse performance for RF and KNN, and slightly improved performance for LR. (2) Overestimation for small AGB values and underestimation for large AGB values are major problems when using the optical (e.g., Landsat) or radar (e.g., ALOS PALSAR) data. (3) LR is still an important tool for AGB modeling, especially for the AGB range of 40–120 Mg/ha. Machine learning algorithms have limited effects on improving AGB estimation overall, but ANN can improve AGB modeling when AGB values are greater than 120 Mg/ha. (4) Forest type and AGB range are important factors that influence AGB modeling performance. (5) Stratification based on forest types improved AGB estimation, especially when AGB was greater than 160 Mg/ha, using the LR approach. This research provides new insight for remote sensing-based AGB modeling for the subtropical forest ecosystem through a comprehensive analysis of different source data, modeling algorithms, and forest types. It is critical to develop an optimal AGB modeling procedure, including the collection of a sufficient number of sample plots, extraction of suitable variables and modeling algorithms, and evaluation of the AGB estimates.
Highlights
Forest biomass is one of the important variables needed to quantify the structure and function of forest ecosystems [1]
The selected variables (Table 4) using Random Forest (RF) were used for the aboveground biomass (AGB) model using artificial neural network (ANN), support vector regression (SVR), and k-nearest neighbor (kNN)
The results indicate the following: (1) Landsat TM imagery provided more accurate estimates of AGB than ALOS PALSAR, and the combination of TM and PALSAR had limited effects on improving AGB estimation
Summary
Forest biomass is one of the important variables needed to quantify the structure and function of forest ecosystems [1]. Many previous studies have examined the importance of selecting suitable variables (e.g., spectral bands, vegetation indices, texture measures, and subpixel features) in improving AGB estimation [4,5,6]. Previous research has indicated that the combination of spectral responses and texture variables can improve AGB estimation compared with using a single kind of imagery alone, especially in the tropical and subtropical regions with complex forest stand structure and composition of tree species [3,4]. Texture measures are especially valuable for sites with complex forest stand structures [4] Another common approach to identify variables for AGB modeling is Random Forest (RF) because it can provide the ranks of importance of variables [7,8,9,10]. The stepwise regression is simple and easy to use but the identified variables are only those having a linear relationship with AGB, while the selected variables using RF can have nonlinear relationships with AGB
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