{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T11:01:31Z","timestamp":1780570891565,"version":"3.54.1"},"reference-count":33,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,15]],"date-time":"2023-01-15T00:00:00Z","timestamp":1673740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Foundation Research Funds of Institute of Forest Resource Information Techniques (IFRIT)","award":["CAFYBB2019SZ004"],"award-info":[{"award-number":["CAFYBB2019SZ004"]}]},{"name":"Foundation Research Funds of Institute of Forest Resource Information Techniques (IFRIT)","award":["32071681"],"award-info":[{"award-number":["32071681"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["CAFYBB2019SZ004"],"award-info":[{"award-number":["CAFYBB2019SZ004"]}],"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":["32071681"],"award-info":[{"award-number":["32071681"]}],"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>Urban tree canopy (UTC) area is an important index for evaluating the urban ecological environment; the very high resolution (VHR) images are essential for improving urban tree canopy survey efficiency. However, the traditional image classification methods often show low robustness when extracting complex objects from VHR images, with insufficient feature learning, object edge blur and noise. Our objective was to develop a repeatable method\u2014superpixel-enhanced deep neural forests (SDNF)\u2014to detect the UTC distribution from VHR images. Eight data expansion methods was used to construct the UTC training sample sets, four sample size gradients were set to test the optimal sample size selection of SDNF method, and the best training times with the shortest model convergence and time-consumption was selected. The accuracy performance of SDNF was tested by three indexes: F1 score (F1), intersection over union (IoU) and overall accuracy (OA). To compare the detection accuracy of SDNF, the random forest (RF) was used to conduct a control experiment with synchronization. Compared with the RF model, SDNF always performed better in OA under the same training sample size. SDNF had more epoch times than RF, converged at the 200 and 160 epoch, respectively. When SDNF and RF are kept in a convergence state, the training accuracy is 95.16% and 83.16%, and the verification accuracy is 94.87% and 87.73%, respectively. The OA of SDNF improved 10.00%, reaching 89.00% compared with the RF model. This study proves the effectiveness of SDNF in UTC detection based on VHR images. It can provide a more accurate solution for UTC detection in urban environmental monitoring, urban forest resource survey, and national forest city assessment.<\/jats:p>","DOI":"10.3390\/rs15020519","type":"journal-article","created":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T04:31:32Z","timestamp":1673843492000},"page":"519","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Very High Resolution Images and Superpixel-Enhanced Deep Neural Forest Promote Urban Tree Canopy Detection"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2554-1770","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"first","affiliation":[{"name":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Key Laboratory of Forestry Remote Sensing and Information System, The National Forestry and Grassland Administration, Beijing 100091, China"},{"name":"Dongting Lake Remote Sensing Product Validation Station, Beijing 100091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huaiqing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Key Laboratory of Forestry Remote Sensing and Information System, The National Forestry and Grassland Administration, Beijing 100091, China"},{"name":"Dongting Lake Remote Sensing Product Validation Station, Beijing 100091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeyu","family":"Cui","sequence":"additional","affiliation":[{"name":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Key Laboratory of Forestry Remote Sensing and Information System, The National Forestry and Grassland Administration, Beijing 100091, China"},{"name":"Dongting Lake Remote Sensing Product Validation Station, Beijing 100091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kexin","family":"Lei","sequence":"additional","affiliation":[{"name":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Key Laboratory of Forestry Remote Sensing and Information System, The National Forestry and Grassland Administration, Beijing 100091, China"},{"name":"Dongting Lake Remote Sensing Product Validation Station, Beijing 100091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanqing","family":"Zuo","sequence":"additional","affiliation":[{"name":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Key Laboratory of Forestry Remote Sensing and Information System, The National Forestry and Grassland Administration, Beijing 100091, China"},{"name":"Dongting Lake Remote Sensing Product Validation Station, Beijing 100091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiansen","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Key Laboratory of Forestry Remote Sensing and Information System, The National Forestry and Grassland Administration, Beijing 100091, China"},{"name":"Dongting Lake Remote Sensing Product Validation Station, Beijing 100091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingtao","family":"Hu","sequence":"additional","affiliation":[{"name":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Key Laboratory of Forestry Remote Sensing and Information System, The National Forestry and Grassland Administration, Beijing 100091, China"},{"name":"Dongting Lake Remote Sensing Product Validation Station, Beijing 100091, China"},{"name":"School of Geography and Environmental Sciences, Guizhou Normal University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanqing","family":"Qiu","sequence":"additional","affiliation":[{"name":"Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Key Laboratory of Forestry Remote Sensing and Information System, The National Forestry and Grassland Administration, Beijing 100091, China"},{"name":"Dongting Lake Remote Sensing Product Validation Station, Beijing 100091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1038\/s41467-018-03622-0","article-title":"Strategically Growing the Urban Forest Will Improve Our World","volume":"9","author":"Endreny","year":"2018","journal-title":"Nat. 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