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of Inner Mongolia Normal University","award":["2022YFSH0027"],"award-info":[{"award-number":["2022YFSH0027"]}]},{"name":"Project for the introduction of high-level talents of Inner Mongolia Normal University","award":["2020ZD0028"],"award-info":[{"award-number":["2020ZD0028"]}]},{"name":"Project for the introduction of high-level talents of Inner Mongolia Normal University","award":["42201374"],"award-info":[{"award-number":["42201374"]}]},{"name":"Project for the introduction of high-level talents of Inner Mongolia Normal University","award":["2022LHQN04001"],"award-info":[{"award-number":["2022LHQN04001"]}]},{"name":"Project for the introduction of high-level talents of Inner Mongolia Normal University","award":["022YFSH0027"],"award-info":[{"award-number":["022YFSH0027"]}]},{"name":"Project for the introduction of high-level talents of Inner Mongolia Normal University","award":["2020YJRC050"],"award-info":[{"award-number":["2020YJRC050"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The technology of remote sensing-assisted tree species classification is increasingly developing, but the rapid refinement of tree species classification on a large scale is still challenging. As one of the treasures of ecological resources in China, Arxan has 80% forest cover, and tree species classification surveys guarantee ecological environment management and sustainable development. In this study, we identified tree species in three samples within the Arxan Duraer Forestry Zone based on the spectral, textural, and topographic features of unmanned aerial vehicle (UAV) multispectral remote sensing imagery and light detection and ranging (LiDAR) point cloud data as classification variables to distinguish among birch, larch, and nonforest areas. The best extracted classification variables were combined to compare the accuracy of the random forest (RF), support vector machine (SVM), and classification and regression tree (CART) methodologies for classifying species into three sample strips in the Arxan Duraer Forestry Zone. Furthermore, the effect on the overall classification results of adding a canopy height model (CHM) was investigated based on spectral and texture feature classification combined with field measurement data to improve the accuracy. The results showed that the overall accuracy of the RF was 79%, and the kappa coefficient was 0.63. After adding the CHM extracted from the point cloud data, the overall accuracy was improved by 7%, and the kappa coefficient increased to 0.75. The overall accuracy of the CART model was 78%, and the kappa coefficient was 0.63; the overall accuracy of the SVM was 81%, and the kappa coefficient was 0.67; and the overall accuracy of the RF was 86%, and the kappa coefficient was 0.75. To verify whether the above results can be applied to a large area, Google Earth Engine was used to write code to extract the features required for classification from Sentinel-2 multispectral and radar topographic data (create equivalent conditions), and six tree species and one nonforest in the study area were classified using RF, with an overall accuracy of 0.98, and a kappa coefficient of 0.97. In this paper, we mainly integrate active and passive remote sensing data for forest surveying and add vertical data to a two-dimensional image to form a three-dimensional scene. The main goal of the research is not only to find schemes to improve the accuracy of tree species classification, but also to apply the results to large-scale areas. This is necessary to improve the time-consuming and labor-intensive traditional forest survey methods and to ensure the accuracy and reliability of survey data.<\/jats:p>","DOI":"10.3390\/rs15102596","type":"journal-article","created":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T01:58:06Z","timestamp":1684288686000},"page":"2596","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Application of Machine Learning to Tree Species Classification Using Active and Passive Remote Sensing: A Case Study of the Duraer Forestry Zone"],"prefix":"10.3390","volume":"15","author":[{"given":"Su","family":"Rina","sequence":"first","affiliation":[{"name":"College of Geographic Science, Inner Mongolia Normal University, Hohhot 010022, China"},{"name":"Inner Mongolia Key Laboratory of Remote Sensing and Geographic Information Systems, Inner Mongolia Normal University, Hohhot 010022, China"}]},{"given":"Hong","family":"Ying","sequence":"additional","affiliation":[{"name":"College of Geographic Science, Inner Mongolia Normal University, Hohhot 010022, China"},{"name":"Inner Mongolia Key Laboratory of Remote Sensing and Geographic Information Systems, Inner Mongolia Normal University, Hohhot 010022, China"}]},{"given":"Yu","family":"Shan","sequence":"additional","affiliation":[{"name":"College of Geographic Science, Inner Mongolia Normal University, Hohhot 010022, China"},{"name":"Inner Mongolia Key Laboratory of Remote Sensing and Geographic Information Systems, Inner Mongolia Normal University, Hohhot 010022, China"}]},{"given":"Wala","family":"Du","sequence":"additional","affiliation":[{"name":"Chinese Academy of Agricultural Sciences Grassland Research Institute, Hohhot 010022, China"},{"name":"Arxan Forest and Grassland Disaster Prevention and Mitigation Research Station of Inner Mongolia Autonomous Region, Alxan 137400, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6177-9124","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Geographic Science, Inner Mongolia Normal University, Hohhot 010022, China"},{"name":"Inner Mongolia Key Laboratory of Remote Sensing and Geographic Information Systems, Inner Mongolia Normal University, Hohhot 010022, China"}]},{"given":"Rong","family":"Li","sequence":"additional","affiliation":[{"name":"College of Geographic Science, Inner Mongolia Normal University, Hohhot 010022, China"},{"name":"Inner Mongolia Key Laboratory of Remote Sensing and Geographic Information Systems, Inner Mongolia Normal University, Hohhot 010022, China"}]},{"given":"Dingzhu","family":"Deng","sequence":"additional","affiliation":[{"name":"Inner Mongolia Autonomous Region Surveying, Mapping and Geographic Information Center, Hohhot 010022, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1126\/science.263.5144.185","article-title":"Carbon Pools and Flux of Global Forest Ecosystems","volume":"263","author":"Dixon","year":"1994","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-Resolution Global Maps of 21st-Century Forest Cover Change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"103542","DOI":"10.1016\/j.gloplacha.2021.103542","article-title":"Integrated remote sensing and model approach for impact assessment of future climate change on the carbon budget of global forest ecosystems","volume":"203","author":"Zhao","year":"2021","journal-title":"Glob. 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