{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:53:00Z","timestamp":1774630380793,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T00:00:00Z","timestamp":1668729600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42061072"],"award-info":[{"award-number":["42061072"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["202002AA100007-015"],"award-info":[{"award-number":["202002AA100007-015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022Y579"],"award-info":[{"award-number":["2022Y579"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Major Science and Technology Special Project of Yunnan Provincial Science and Technology Department","award":["42061072"],"award-info":[{"award-number":["42061072"]}]},{"name":"Major Science and Technology Special Project of Yunnan Provincial Science and Technology Department","award":["202002AA100007-015"],"award-info":[{"award-number":["202002AA100007-015"]}]},{"name":"Major Science and Technology Special Project of Yunnan Provincial Science and Technology Department","award":["2022Y579"],"award-info":[{"award-number":["2022Y579"]}]},{"name":"Scientific Research Fund Project of Yunnan Provincial Education Department","award":["42061072"],"award-info":[{"award-number":["42061072"]}]},{"name":"Scientific Research Fund Project of Yunnan Provincial Education Department","award":["202002AA100007-015"],"award-info":[{"award-number":["202002AA100007-015"]}]},{"name":"Scientific Research Fund Project of Yunnan Provincial Education Department","award":["2022Y579"],"award-info":[{"award-number":["2022Y579"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The mapping of tropical rainforest forest structure parameters plays an important role in biodiversity and carbon stock estimation. The current mechanism models based on PolInSAR for forest height inversion (e.g., the RVoG model) are physical process models, and realistic conditions for model parameterization are often difficult to establish for practical applications, resulting in large forest height estimation errors. As an alternative, machine learning approaches offer the benefit of model simplicity, but these tools provide limited capabilities for interpretation and generalization. To explore the forest height estimation method combining the mechanism model and the empirical model, we utilized UAVSAR multi-baseline PolInSAR L-band data from the AfriSAR project and propose a solution of a mechanism model combined with machine learning. In this paper, two mechanism models were used as controls, the RVoG three-phase method and the RVoG phase-coherence amplitude method. The vertical structure parameters of the forest obtained from the mechanism model were used as the independent variables of the machine learning model. Random forest (RF) and partial least squares (PLS) regression models were used to invert the forest canopy height. Results show that the inversion accuracy of the machine learning method, combined with the mechanism model, is significantly better than that of the single-mechanism model method. The most influential independent variables were penetration depth, volume coherence phase center height, coherence separation, and baseline selection. With the precondition that the cumulative contribution of the independent variables was greater than 90%, the number of independent variables in the two study areas was reduced from 19 to 4, and the accuracy of the RF-RVoG-DEP model was higher than that of the PLS-RVoG-DEP model. For the Lope test area, the R2 of the RVoG phase coherence amplitude method is 0.723, the RMSE is 8.583 m, and the model bias is \u22122.431 m; the R2 of the RVoG three-stage method is 0.775, the RMSE is 7.748, and the bias is 1.120 m, the R2 of the PLS-RVoG-DEP model is 0.850, the RMSE is 6.320 m, and the bias is 0.002 m; and the R2 of the RF-RVoG-DEP model is 0.900, the RMSE is 5.154 m, and the bias is \u22120.061 m. The results for the Pongara test area are consistent with the pattern for the Lope test area. The combined \u201cfusion model\u201d offers a substantial improvement in forest height estimation from the traditional mechanism modeling method.<\/jats:p>","DOI":"10.3390\/rs14225849","type":"journal-article","created":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T04:13:36Z","timestamp":1669004016000},"page":"5849","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Method for Forest Canopy Height Inversion Based on Machine Learning and Feature Mining Using UAVSAR"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9885-3014","authenticated-orcid":false,"given":"Hongbin","family":"Luo","sequence":"first","affiliation":[{"name":"College of Forestry, Southwest Forestry University, Kunming 650224, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cairong","family":"Yue","sequence":"additional","affiliation":[{"name":"College of Forestry, Southwest Forestry University, Kunming 650224, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1935-121X","authenticated-orcid":false,"given":"Fuming","family":"Xie","sequence":"additional","affiliation":[{"name":"Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bodong","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Forestry, Southwest Forestry University, Kunming 650224, China"},{"name":"College of Forestry, Northeastern Forestry University, Harbin 150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Si","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Forestry, Southwest Forestry University, Kunming 650224, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"231","DOI":"10.4155\/cmt.11.18","article-title":"Advances in remote sensing technology and implications for measuring and monitoring forest carbon stocks and change","volume":"2","author":"Goetz","year":"2011","journal-title":"Carbon Manag."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhao, P., Lu, D., Wang, G., Wu, C., Huang, Y., and Yu, S. 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