{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T19:08:21Z","timestamp":1774379301596,"version":"3.50.1"},"reference-count":62,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T00:00:00Z","timestamp":1673049600000},"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":["42171367"],"award-info":[{"award-number":["42171367"]}],"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":["2022FY101902"],"award-info":[{"award-number":["2022FY101902"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science &amp; Technology Fundamental Resources Investigation Program","award":["42171367"],"award-info":[{"award-number":["42171367"]}]},{"name":"Science &amp; Technology Fundamental Resources Investigation Program","award":["2022FY101902"],"award-info":[{"award-number":["2022FY101902"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remotely sensed estimates of forest diversity have become increasingly important in assessing anthropogenic and natural disturbances and their effects on biodiversity under limited resources. Whereas field inventories and optical images are generally used to estimate forest diversity, studies that combine vertical structure information and multi-temporal phenological characteristics to accurately quantify diversity in large, heterogeneous forest areas are still lacking. In this study, combined with regression models, three different diversity indices, namely Simpson (\u03bb), Shannon (H\u2032), and Pielou (J\u2032), were applied to characterize forest tree species diversity by using GEDI LiDAR data and Sentinel-2 imagery in temperate natural forest, northeast China. We used Mean Decrease Gini (MDG) and Boosted Regression Tree (BRT) to assess the importance of certain variables including monthly spectral bands, vegetation indices, foliage height diversity (FHD), and plant area index (PAI) of growing season and non-growing seasons (68 variables in total). We produced 12 forest diversity maps on three different diversity indices using four regression algorithms: Support Vector Machines (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Lasso Regression (LR). Our study concluded that the most important variables are FHD, NDVI, NDWI, EVI, short-wave infrared (SWIR) and red-edge (RE) bands, especially in the growing season (May and June). In terms of algorithms, the estimation accuracies of the RF (averaged R2 = 0.79) and SVM (averaged R2 = 0.76) models outperformed the other models (R2 of KNN and LR are 0.68 and 0.57, respectively). The study demonstrates the accuracy of GEDI LiDAR data and multi-temporal Sentinel-2 images in estimating forest diversity over large areas, advancing the capacity to monitor and manage forest ecosystems.<\/jats:p>","DOI":"10.3390\/rs15020375","type":"journal-article","created":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T04:47:08Z","timestamp":1673239628000},"page":"375","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Quantifying Temperate Forest Diversity by Integrating GEDI LiDAR and Multi-Temporal Sentinel-2 Imagery"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8798-3449","authenticated-orcid":false,"given":"Chunying","family":"Ren","sequence":"first","affiliation":[{"name":"Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"given":"Hailing","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Tourism and Geographic Sciences, Jilin Normal University, Siping 136000, China"}]},{"given":"Yanbiao","family":"Xi","sequence":"additional","affiliation":[{"name":"Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"given":"Pan","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"given":"Huiying","family":"Li","sequence":"additional","affiliation":[{"name":"School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266520, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111283","DOI":"10.1016\/j.rse.2019.111283","article-title":"Forest biomass estimation over three distinct forest types using TanDEM-X InSAR data and simulated GEDI lidar data","volume":"232","author":"Qi","year":"2019","journal-title":"Remote Sens. 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