{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T21:13:37Z","timestamp":1784927617293,"version":"3.55.0"},"reference-count":46,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,26]],"date-time":"2022-06-26T00:00:00Z","timestamp":1656201600000},"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":["32071683"],"award-info":[{"award-number":["32071683"]}],"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>The continuous and extensive pinewood nematode disease has seriously threatened the sustainable development of forestry in China. At present, many studies have used high-resolution remote sensing images combined with a deep semantic segmentation algorithm to identify standing dead trees in the red attack period. However, due to the complex background, closely distributed detection scenes, and unbalanced training samples, it is difficult to detect standing dead trees (SDTs) in a variety of complex scenes by using conventional segmentation models. In order to further solve the above problems and improve the recognition accuracy, we proposed a new detection method called multi-scale spatial supervision convolutional network (MSSCN) to identify SDTs in a wide range of complex scenes based on airborne remote sensing imagery. In the method, a Gaussian kernel approach was used to generate a confidence map from SDTs marked as points for training samples, and a multi-scale spatial attention block was added into fully convolutional neural networks to reduce the loss of spatial information. Further, an augmentation strategy called copy\u2013pasting was used to overcome the lack of efficient samples in this research area. Validation at four different forest areas belonging to two forest types and two diseased outbreak intensities showed that (1) the copy\u2013pasting method helps to augment training samples and can improve the detecting accuracy with a suitable oversampling rate, and the best oversampling rate should be carefully determined by the input training samples and image data. (2) Based on the two-dimensional spatial Gaussian kernel distribution function and the multi-scale spatial attention structure, the MSSCN model can effectively find the dead tree extent in a confidence map, and by following this with maximum location searching we can easily locate the individual dead trees. The averaged precision, recall, and F1-score across different forest types and disease-outbreak-intensity areas can achieve 0.94, 0.84, and 0.89, respectively, which is the best performance among FCN8s and U-Net. (3) In terms of forest type and outbreak intensity, the MSSCN performs best in pure pine forest type and low-outbreak-intensity areas. Compared with FCN8s and U-Net, the MSSCN can achieve the best recall accuracy in all forest types and outbreak-intensity areas. Meanwhile, the precision metric is also maintained at a high level, which means that the proposed method provides a trade-off between the precision and recall in detection accuracy.<\/jats:p>","DOI":"10.3390\/rs14133075","type":"journal-article","created":{"date-parts":[[2022,6,26]],"date-time":"2022-06-26T22:50:23Z","timestamp":1656283823000},"page":"3075","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Detection of Standing Dead Trees after Pine Wilt Disease Outbreak with Airborne Remote Sensing Imagery by Multi-Scale Spatial Attention Deep Learning and Gaussian Kernel Approach"],"prefix":"10.3390","volume":"14","author":[{"given":"Zemin","family":"Han","sequence":"first","affiliation":[{"name":"College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjie","family":"Hu","sequence":"additional","affiliation":[{"name":"Hubei Academy of Forestry, Wuhan 430075, China"},{"name":"Shennongjia Forest Ecosystem Research Station, Shennongjia 442421, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shoulian","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoran","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9890-3598","authenticated-orcid":false,"given":"Jian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Macro Agriculture Research Institute, College of Resource and Environment, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan 430070, China"},{"name":"Hubei Engineering Technology Research Centre for Forestry Information, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengcheng","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan 430070, China"},{"name":"Hubei Engineering Technology Research Centre for Forestry Information, Huazhong Agricultural University, Wuhan 430070, China"},{"name":"Key Laboratory of Urban Agriculture in Central China, Ministry of Agriculture, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4957-3681","authenticated-orcid":false,"given":"Yuanyong","family":"Dian","sequence":"additional","affiliation":[{"name":"College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan 430070, China"},{"name":"Hubei Engineering Technology Research Centre for Forestry Information, Huazhong Agricultural University, Wuhan 430070, China"},{"name":"Key Laboratory of Urban Agriculture in Central China, Ministry of Agriculture, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,26]]},"reference":[{"key":"ref_1","first-page":"213","article-title":"Bursaphelenchus Xylophilus, the Pinewood Nematode: Its Significance and a Historical Review","volume":"55","year":"2011","journal-title":"Acta Biol. Szeged."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1080\/00049158.2018.1440467","article-title":"An Analysis of Pest Risk and Potential Economic Impact of Pine Wilt Disease to Pinus Plantations in Australia","volume":"81","author":"Carnegie","year":"2018","journal-title":"Aust. For."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhao, J., Huang, J., Yan, J., and Fang, G. (2020). Economic Loss of Pine Wood Nematode Disease in Mainland China from 1998 to 2017. Forests, 11.","DOI":"10.3390\/f11101042"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cha, D., Kim, D., Choi, W., Park, S., and Han, H. (2020). Point-of-care diagnostic (POCD) method for detecting Bursaphelenchus xylophilus in pinewood using recombinase polymerase amplification (RPA) with the portable optical isothermal device (POID). PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0227476"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1016\/j.compag.2018.12.018","article-title":"A Remote Sensing Technique for Detecting Laurel Wilt Disease in Avocado in Presence of Other Biotic and Abiotic Stresses","volume":"156","author":"Abdulridha","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e00415","DOI":"10.1002\/mbo3.415","article-title":"Understanding pine wilt disease: Roles of the pine endophytic bacteria and of the bacteria carried by the disease-causing pinewood nematode","volume":"6","author":"Grass","year":"2017","journal-title":"MicrobiologyOpen"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/s40725-017-0056-1","article-title":"Application of Remote Sensing Technologies for Assessing Planted Forests Damaged by Insect Pests and Fungal Pathogens: A Review","volume":"3","author":"Stone","year":"2017","journal-title":"Curr. For. Rep."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1111\/efp.12147","article-title":"Development of Two Alternative Loop-Mediated Isothermal Amplification Tools for Detecting Pathogenic Pine Wood Nematodes","volume":"45","author":"Kang","year":"2015","journal-title":"For. Pathol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, X., Tong, T., Luo, T., Wang, J., Rao, Y., Li, L., Jin, D., Wu, D., and Huang, H. (2022). Retrieving the Infected Area of Pine Wilt Disease-Disturbed Pine Forests from Medium-Resolution Satellite Images Using the Stochastic Radiative Transfer Theory. Remote Sens., 14.","DOI":"10.3390\/rs14061526"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Dian, Y., Zhou, J., Peng, S., Hu, Y., Hu, L., Han, Z., Fang, X., and Cui, H. (2021). Characterizing Spatial Patterns of Pine Wood Nematode Outbreaks in Subtropical Zone in China. Remote Sens., 13.","DOI":"10.3390\/rs13224682"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, B., Ye, H., Lu, W., Huang, W., Wu, B., Hao, Z., and Sun, H. (2021). A Spatiotemporal Change Detection Method for Monitoring Pine Wilt Disease in a Complex Landscape Using High-Resolution Remote Sensing Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13112083"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.rse.2015.06.015","article-title":"Detection of Spruce Beetle-Induced Tree Mortality Using High- and Medium-Resolution Remotely Sensed Imagery","volume":"168","author":"Hart","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"24","DOI":"10.2747\/1548-1603.44.1.24","article-title":"An Object-Based Classification Approach in Mapping Tree Mortality Using High Spatial Resolution Imagery","volume":"44","author":"Guo","year":"2007","journal-title":"GIScience Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Iordache, M.-D., Mantas, V., Baltazar, E., Pauly, K., and Lewyckyj, N. (2020). A Machine Learning Approach to Detecting Pine Wilt Disease Using Airborne Spectral Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12142280"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.rse.2013.01.002","article-title":"Evaluating Methods to Detect Bark Beetle-Caused Tree Mortality Using Single-Date and Multi-Date Landsat Imagery","volume":"132","author":"Meddens","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/S0034-4257(03)00112-3","article-title":"Sensitivity of the Thematic Mapper Enhanced Wetness Difference Index to Detect Mountain Pine Beetle Red-Attack Damage","volume":"86","author":"Skakun","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.rse.2013.09.014","article-title":"Assessing the Potential of Hyperspectral Imagery to Map Bark Beetle-Induced Tree Mortality","volume":"140","author":"Fassnacht","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"S296","DOI":"10.4039\/tce.2016.11","article-title":"Remote Sensing of Forest Pest Damage: A Review and Lessons Learned from a Canadian Perspective","volume":"148","author":"Hall","year":"2016","journal-title":"Can. Entomol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"32","DOI":"10.5558\/tfc85032-1","article-title":"Challenges for the Operational Detection of Mountain Pine Beetle Green Attack with Remote Sensing","volume":"85","author":"Wulder","year":"2009","journal-title":"For. Chron."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4427","DOI":"10.1080\/01431160802566439","article-title":"Mapping Whitebark Pine Mortality Caused by a Mountain Pine Beetle Outbreak with High Spatial Resolution Satellite Imagery","volume":"30","author":"Hicke","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.rse.2006.03.012","article-title":"Assessment of QuickBird High Spatial Resolution Imagery to Detect Red Attack Damage Due to Mountain Pine Beetle Infestation","volume":"103","author":"Coops","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2236","DOI":"10.1080\/01431161.2012.743694","article-title":"Using WorldView-2 Bands and Indices to Predict Bronze Bug (Thaumastocoris Peregrinus) Damage in Plantation Forests","volume":"34","author":"Oumar","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.rse.2016.08.013","article-title":"Review of Studies on Tree Species Classification from Remotely Sensed Data","volume":"186","author":"Fassnacht","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1080\/2150704X.2020.1752410","article-title":"Simple Weakly Supervised Deep Learning Pipeline for Detecting Individual Red-Attacked Trees in VHR Remote Sensing Images","volume":"11","author":"Qiao","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"163","DOI":"10.5194\/isprs-annals-IV-1-163-2018","article-title":"A study of using fully convolutional network for treetop detection on remote sensing data","volume":"IV\u20131","author":"Xiao","year":"2018","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Qin, J., Wang, B., Wu, Y., Lu, Q., and Zhu, H. (2021). Identifying Pine Wood Nematode Disease Using Uav Images and Deep Learning Algorithms. Remote Sens., 13.","DOI":"10.3390\/rs13020162"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","article-title":"A Systematic Study of the Class Imbalance Problem in Convolutional Neural Networks","volume":"106","author":"Buda","year":"2018","journal-title":"Neural Netw."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kisantal, M., Wojna, Z., Murawski, J., Naruniec, J., and Cho, K. (2019). Augmentation for small object detection. arXiv.","DOI":"10.5121\/csit.2019.91713"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1002\/rse2.109","article-title":"How Canopy Shadow Affects Invasive Plant Species Classification in High Spatial Resolution Remote Sensing","volume":"5","author":"Lopatin","year":"2019","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"108438","DOI":"10.1016\/j.ecolind.2021.108438","article-title":"Mapping Standing Dead Trees in Temperate Montane Forests Using a Pixel- and Object-Based Image Fusion Method and Stereo WorldView-3 Imagery","volume":"133","author":"Liu","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.isprsjprs.2019.12.010","article-title":"A Convolutional Neural Network Approach for Counting and Geolocating Citrus-Trees in UAV Multispectral Imagery","volume":"160","author":"Osco","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Pfister, T., Charles, J., and Zisserman, A. (2015, January 7\u201313). Flowing Convnets for Human Pose Estimation in Videos. Proceedings of the IEEE international conference on computer vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.222"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"112307","DOI":"10.1016\/j.rse.2021.112307","article-title":"Individual Tree Crown Segmentation from Airborne LiDAR Data Using a Novel Gaussian Filter and Energy Function Minimization-Based Approach","volume":"256","author":"Yun","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.rse.2005.03.007","article-title":"Detection of Red Attack Stage Mountain Pine Beetle Infestation with High Spatial Resolution Satellite Imagery","volume":"96","author":"White","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Mai, Z., Hu, X., Peng, S., and Wei, Y. (2019, January 19\u201321). Human Pose Estimation via Multi-Scale Intermediate Supervision Convolution Network. Proceedings of the 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Suzhou, China.","DOI":"10.1109\/CISP-BMEI48845.2019.8965911"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Han, Z., Dian, Y., Xia, H., Zhou, J., Jian, Y., Yao, C., Wang, X., and Li, Y. (2020). Comparing Fully Deep Convolutional Neural Networks for Land Cover Classification with High-Spatial-Resolution Gaofen-2 Images. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9080478"},{"key":"ref_39","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015\u2014Conference Track Proceedings, San Diego, CA, USA."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE conference on computer vision and pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yu, F., Koltun, V., and Funkhouser, T. (2017, January 21\u201326). Dilated Residual Networks. Proceedings of the Proceedings, 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.75"},{"key":"ref_42","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018). CBAM: Convolutional Block Attention Module. arXiv.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3583","DOI":"10.1080\/01431161.2021.1876272","article-title":"Dual Attention Deep Fusion Semantic Segmentation Networks of Large-Scale Satellite Remote-Sensing Images","volume":"42","author":"Li","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Chadwick, A.J., Goodbody, T.R.H., Coops, N.C., Hervieux, A., Bater, C.W., Martens, L.A., White, B., and R\u00f6eser, D. (2020). Automatic Delineation and Height Measurement of Regenerating Conifer Crowns under Leaf-off Conditions Using UAV Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12244104"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.foreco.2014.02.037","article-title":"Spatial and Temporal Patterns of Landsat-Based Detection of Tree Mortality Caused by a Mountain Pine Beetle Outbreak in Colorado, USA","volume":"322","author":"Meddens","year":"2014","journal-title":"For. Ecol. Manag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3075\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:38:46Z","timestamp":1760139526000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3075"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,26]]},"references-count":46,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["rs14133075"],"URL":"https:\/\/doi.org\/10.3390\/rs14133075","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,26]]}}}