{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T14:07:33Z","timestamp":1773842853522,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T00:00:00Z","timestamp":1673222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Third Xinjiang Scientific Expedition Program","award":["2022xjkk0402"],"award-info":[{"award-number":["2022xjkk0402"]}]},{"name":"Third Xinjiang Scientific Expedition Program","award":["41571105"],"award-info":[{"award-number":["41571105"]}]},{"name":"Third Xinjiang Scientific Expedition Program","award":["41861019"],"award-info":[{"award-number":["41861019"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022xjkk0402"],"award-info":[{"award-number":["2022xjkk0402"]}],"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":["41571105"],"award-info":[{"award-number":["41571105"]}],"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":["41861019"],"award-info":[{"award-number":["41861019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Grassland aboveground biomass (AGB) is an important indicator for studying the change in grassland ecological quality and carbon cycle. The rapid development of high-resolution remote sensing and unmanned aerial vehicles (UAV) provides a new opportunity for accurate estimation of grassland AGB on the plot scale. In this study, the mountain grassland was taken as the research object. Using UAV Light Detection and Ranging (LiDAR) data and multispectral satellite images, the influence of topographic correction methods on AGB estimation was compared and a series of LiDAR metrics and vegetation indices were extracted. On this basis, a comprehensive indicator, the vegetation index-height-intensity model (VHI), was proposed to estimate AGB quickly. The results show that: (1) Among the four topographic correction methods, the Teillet regression has the best effect, and can effectively improve the accuracy of AGB estimation in mountain grassland. The correlation between corrected ratio vegetation index and AGB was the highest (correlation coefficient: 0.682). (2) Among the height and intensity metrics, median height and max intensity yielded the higher accuracy in estimating AGB, with Root Mean Square Error (RMSE) of 322 g\/m2 and 333 g\/m2, respectively. (3) The VHI integrated spectrum and LiDAR information, and its accuracy for AGB estimation for mountain grassland, was obviously better than other indicators, with an RMSE of 272 g\/m2. We also found that the accuracy of VHI in univariate models was comparable to that of complex multivariate models such as stepwise regression, support vector machine, and random forest. This study provides a new approach for estimating grassland AGB with multi-source data. As a simple and effective indicator, VHI has shown strong application potential for grassland AGB estimating in mountainous areas, and can be further applied to grassland carbon cycle research and fine management.<\/jats:p>","DOI":"10.3390\/rs15020405","type":"journal-article","created":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T01:57:48Z","timestamp":1673315868000},"page":"405","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Fusion of LiDAR and Multispectral Data for Aboveground Biomass Estimation in Mountain Grassland"],"prefix":"10.3390","volume":"15","author":[{"given":"Ang","family":"Chen","sequence":"first","affiliation":[{"name":"School of Grassland Science, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Xing","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Grassland Science, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Min","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Grassland Science, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Jian","family":"Guo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Xiaoyu","family":"Xing","sequence":"additional","affiliation":[{"name":"School of Grassland Science, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Dong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Grassland Science, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Huilong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Grassland Science, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Zhiyan","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Grassland Science, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Ze","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Grassland Science, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Xiuchun","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Grassland Science, Beijing Forestry University, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Reinermann, S., Asam, S., and Kuenzer, C. 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