|
Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Agriculture / Faculty of Agriculture >
Peer-reviewed Journal Articles, etc >
Using multisource satellite products to estimate forest aboveground biomass in Oita prefecture : a novel approach with improved accuracy and computational efficiency
| Title: | Using multisource satellite products to estimate forest aboveground biomass in Oita prefecture : a novel approach with improved accuracy and computational efficiency |
| Authors: | Li, Hantao Browse this author | | Kato, Tomomichi Browse this author →KAKEN DB | | Hayashi, Masato Browse this author | | Liu, Jianhong Browse this author |
| Keywords: | Aboveground biomass | | deciduous broadleaf forest | | random forest | | recursive feature elimination | | principal component analysis | | GLCM-Textures |
| Issue Date: | 20-Oct-2022 |
| Publisher: | Taylor & Francis |
| Journal Title: | Geocarto international |
| Volume: | 38 |
| Issue: | 1 |
| Start Page: | 1 |
| End Page: | 20 |
| Publisher DOI: | 10.1080/10106049.2022.2134466 |
| Abstract: | Accurate estimation of forest aboveground biomass (AGB) using satellite information is crucial for quantitatively evaluating forest carbon stock for climate change mitigation. However, with the emergence of an enormous number of identical data, selecting the most valuable dataset for estimation is essential to improve model efficiency and accuracy. Here, we present a novel framework using principal component analysis (PCA)-derived textures to tackle redundant variables and the integration of multisource satellite images using the random forest regression algorithm with recursive feature elimination to map the AGB of deciduous broadleaf forests in Oita prefecture, Japan. Our results highlighted that the model using PCA and derivative features had the best performance [R-2 = 0.69, root mean square error (RMSE) = 33.59 Mg/ha, mean absolute error = 25.01 Mg/ha, relative RMSE = 0.27]. In this case, of the 34 variables selected by RFE for the model, 19 were derived from principal components and corresponding textures, indicating a considerable improvement in using our novel framework to estimate AGB. Together, this study provides a valuable strategy for feature selection to lessen multicollinearity and redundancy. |
| Type: | article |
| URI: | http://hdl.handle.net/2115/91038 |
| Appears in Collections: | 農学院・農学研究院 (Graduate School of Agriculture / Faculty of Agriculture) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
|
|
