{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T22:56:51Z","timestamp":1781996211278,"version":"3.54.5"},"reference-count":99,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,30]],"date-time":"2024-11-30T00:00:00Z","timestamp":1732924800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Program of Yunnan Provincial Science and Technology Department, China","award":["202303AC100009"],"award-info":[{"award-number":["202303AC100009"]}]},{"name":"Science and Technology Program of Yunnan Provincial Science and Technology Department, China","award":["XDYC-JYRC-2023-0083"],"award-info":[{"award-number":["XDYC-JYRC-2023-0083"]}]},{"name":"Education Talent of Xingdian Talent Support Program of Yunnan Province, China","award":["202303AC100009"],"award-info":[{"award-number":["202303AC100009"]}]},{"name":"Education Talent of Xingdian Talent Support Program of Yunnan Province, China","award":["XDYC-JYRC-2023-0083"],"award-info":[{"award-number":["XDYC-JYRC-2023-0083"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Estimation of forest above-ground biomass (AGB) using multi-source remote sensing data is an important method to improve the accuracy of the estimate. However, selecting remote sensing factors that can effectively improve the accuracy of forest AGB estimation from a large amount of data is a challenge when the sample size is small. In this regard, the Least Absolute Shrinkage and Selection Operator (Lasso) has advantages for extensive redundant variables but still has some drawbacks. To address this, the study introduces two Least Absolute Shrinkage and Selection Operator Lasso-based variable selection methods: Least Absolute Shrinkage and Selection Operator Genetic Algorithm (Lasso-GA) and Variance Inflation Factor Least Absolute Shrinkage and Selection Operator (VIF-Lasso). Sentinel 2, Sentinel 1, Landsat 8 OLI, ALOS-2 PALSAR-2, Light Detection and Ranging, and Digital Elevation Model (DEM) data were used in this study. In order to explore the variable selection capabilities of Lasso-GA and VIF-Lasso for remote sensing estimation of forest AGB. It compares Lasso-GA and VIF-Lasso with Boruta, Random Forest Importance Selection, Pearson Correlation, and Lasso for selecting remote sensing factors. Additionally, it employs eight machine learning models\u2014Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Bayesian Regression Neural Network (BRNN), Elastic Net (EN), K-Nearest Neighbors (KNN), Extremely Randomized Trees (ETR), and Stochastic Gradient Boosting (SGBoost)\u2014to estimate forest AGB in Wuyi Village, Zhenyuan County. The results showed that the optimized Lasso variable selection could improve the accuracy of forest biomass estimation. The VIF-Lasso method results in a BRNN model with an R2 of 0.75 and an RMSE of 16.48 Mg\/ha. The Lasso-GA method results in an ETR model with an R2 of 0.73 and an RMSE of 16.70 Mg\/ha. Compared to the optimal SGBoost model with the Lasso variable selection method (R2 of 0.69, RMSE of 18.63 Mg\/ha), the VIF-Lasso method improves R2 by 0.06 and reduces RMSE by 2.15 Mg\/ha, while the Lasso-GA method improves R2 by 0.04 and reduces RMSE by 1.93 Mg\/ha. From another perspective, they also demonstrated that the RX sample count and sensitivity provided by LiDAR, as well as the Horizontal Transmit, Vertical Receive provided by Microwave Radar, along with the feature variables (Mean, Contrast, and Correlation) calculated from the Green, Red, and NIR bands of optical remote sensing in 7 \u00d7 7 and 5 \u00d7 5 windows, play an important role in forest AGB estimation. Therefore, the optimized Lasso variable selection method shows strong potential for forest AGB estimation using multi-source remote sensing data.<\/jats:p>","DOI":"10.3390\/rs16234497","type":"journal-article","created":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T04:04:04Z","timestamp":1733198644000},"page":"4497","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Improving Forest Above-Ground Biomass Estimation Accuracy Using Multi-Source Remote Sensing and Optimized Least Absolute Shrinkage and Selection Operator Variable Selection Method"],"prefix":"10.3390","volume":"16","author":[{"given":"Er","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China"},{"name":"Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianbao","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China"},{"name":"Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China"},{"name":"Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9438-6608","authenticated-orcid":false,"given":"Lei","family":"Bao","sequence":"additional","affiliation":[{"name":"Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China"},{"name":"Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Binbing","family":"Guo","sequence":"additional","affiliation":[{"name":"Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China"},{"name":"Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhibo","family":"Yu","sequence":"additional","affiliation":[{"name":"Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China"},{"name":"Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zihang","family":"Feng","sequence":"additional","affiliation":[{"name":"Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China"},{"name":"Key Laboratory for Forest Resources 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