{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:02:51Z","timestamp":1760230971689,"version":"build-2065373602"},"reference-count":82,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T00:00:00Z","timestamp":1661212800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41871198"],"award-info":[{"award-number":["41871198"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Unmanned aerial vehicle (UAV) remote sensing technology is gradually playing a role alternative to traditional field survey methods in monitoring plant functional traits of forest ecology. Few studies focused on monitoring functional trait ecology of underground forests of inaccessible negative terrain with UAV. The underground forests of tiankeng were discovered and are known as the inaccessible precious ecological refugia of extreme negative terrain. The aim of this research proposal is to explore the suitability of UAV technology for extracting the stand parameters of underground forests\u2019 functional traits in karst tiankeng. Based on the multi-scale segmentation algorithm and object-oriented classification method, the canopy parameters (crown width and densities) of underground forests in degraded karst tiankeng were extracted by UAV remote sensing image data and appropriate features collection. First, a multi-scale segmentation algorithm was applied to attain the optimal segmentation scale to obtain the single wood canopy. Second, feature space optimization was used to construct the optimal feature space set for the image and then the k-nearest neighbor(k-NN) classifier was used to classify the image features. The features were classified into five types: canopy, grassland, road, gap, and bare land. Finally, both the crown densities and average crown width of the trees were calculated, and their accuracy were verified. The results showed that overall accuracy of object-oriented image feature classification was 85.60%, with 0.72 of kappa coefficient. The accuracy of tree canopy density extraction was 82.34%, for which kappa coefficient reached 0.91. The average canopy width of trees in the samples from the tiankeng-inside was 5.38 m, while that of the outside samples was 4.83 m. In conclusion, the canopy parameters in karst tiankeng were higher than those outside the tiankeng. Stand parameters extraction of karst tiankeng underground forests based on UAV remote sensing was relatively satisfactory. Thus, UAV technology provides a new approach to explore forest resources in inaccessible negative terrain such as karst tiankengs. In the future, we need to consider UAVs with more bands of cameras to extract more plant functional traits to promote the application of UAV for underground forest ecology research of more inaccessible negative terrain.<\/jats:p>","DOI":"10.3390\/rs14174128","type":"journal-article","created":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T02:55:34Z","timestamp":1661309734000},"page":"4128","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Is an Unmanned Aerial Vehicle (UAV) Suitable for Extracting the Stand Parameters of Inaccessible Underground Forests of Karst Tiankeng?"],"prefix":"10.3390","volume":"14","author":[{"given":"Wei","family":"Shui","sequence":"first","affiliation":[{"name":"College of Environment and Safety Engineering, Fuzhou University, Fuzhou 350116, China"}]},{"given":"Hui","family":"Li","sequence":"additional","affiliation":[{"name":"College of Environment and Safety Engineering, Fuzhou University, Fuzhou 350116, China"}]},{"given":"Yongyong","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Environment and Safety Engineering, Fuzhou University, Fuzhou 350116, China"}]},{"given":"Cong","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Urban and Environmental Sciences, Peking University, Beijing 100871, China"}]},{"given":"Sufeng","family":"Zhu","sequence":"additional","affiliation":[{"name":"Chinese Research Academy of Environmental Sciences, Beijing 100020, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8460-6821","authenticated-orcid":false,"given":"Qianfeng","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Environment and Safety Engineering, Fuzhou University, Fuzhou 350116, China"}]},{"given":"Yuanmeng","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Environment and Safety Engineering, Fuzhou University, Fuzhou 350116, China"}]},{"given":"Sili","family":"Zong","sequence":"additional","affiliation":[{"name":"College of Environment and Safety Engineering, Fuzhou University, Fuzhou 350116, China"}]},{"given":"Yunhui","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Environment and Safety Engineering, Fuzhou University, Fuzhou 350116, China"}]},{"given":"Meiqi","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Environment and Safety Engineering, Fuzhou University, Fuzhou 350116, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,23]]},"reference":[{"key":"ref_1","first-page":"2","article-title":"Tiankeng: Definition and description","volume":"1","author":"Zhu","year":"2006","journal-title":"Speleogenesis Evol. Karst Aquifers"},{"key":"ref_2","unstructured":"Zhu, X., and Chen, W. (2006). Tiankengs in the karst of China. Carsologica Sin., 7\u201324."},{"key":"ref_3","first-page":"51","article-title":"A brief study on karst tiankeng","volume":"22","author":"Zhu","year":"2003","journal-title":"Carsologica Sin."},{"key":"ref_4","first-page":"431","article-title":"Origination, study progress and prospect of karst tiankeng research in China","volume":"70","author":"Shui","year":"2015","journal-title":"Acta Geogr. Sin."},{"key":"ref_5","first-page":"751","article-title":"Research on flora of seed plants in Dashiwei Karst Tiankeng Group of Leye, Guangxi","volume":"40","author":"Shen","year":"2020","journal-title":"Guihaia"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"9408","DOI":"10.1038\/s41598-022-13678-0","article-title":"Original karst tiankeng with underground virgin forest as an inaccessible refugia originated from a degraded surface flora in Yunnan, China","volume":"12","author":"Shui","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s12229-017-9179-0","article-title":"Research progress on karst tiankeng ecosystems","volume":"83","author":"Pu","year":"2017","journal-title":"Bot. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Huang, L., Yang, H., An, X., Yu, Y., Yu, L., Huang, G., Liu, X., Chen, M., and Xue, Y. (2022). Species abundance distributions patterns between tiankeng forests and nearby non-tiankeng forests in southwest China. Diversity, 14.","DOI":"10.3390\/d14020064"},{"key":"ref_9","first-page":"1496","article-title":"Vertical distribution characteristics of plant community in shady slope of degraded tiankeng talus: A case study of Zhanyi Shenxiantang in Yunnan, China","volume":"31","author":"Zhu","year":"2020","journal-title":"Carsologica Sin."},{"key":"ref_10","first-page":"114","article-title":"Review on the architecture of tropical trees","volume":"5","author":"Zang","year":"1998","journal-title":"Sci. Silvae Sin."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1890\/0012-9658(2006)87[1289:AOMTST]2.0.CO;2","article-title":"Architecture of 54 moist-forest tree species: Traits, trade-offs, and functional groups","volume":"87","author":"Poorter","year":"2006","journal-title":"Ecology"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"399","DOI":"10.2307\/2389797","article-title":"Significance of architecture and allometry in saplings","volume":"1","author":"Kohyama","year":"1987","journal-title":"Funct. Ecol."},{"key":"ref_13","first-page":"2614","article-title":"Tree architecture variation of plant communities along altitude and impact factors in Maoer Mountain, Guangxi, China","volume":"30","author":"Tan","year":"2019","journal-title":"Chin. J. Appl. Ecol."},{"key":"ref_14","first-page":"228","article-title":"Extraction method of tree crown using high-resolution satellite image","volume":"2","author":"Qin","year":"2005","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_15","first-page":"82","article-title":"Subalpine coniferous forest crown information automatic extraction based on optical UAV remote sensing","volume":"4","author":"Wang","year":"2017","journal-title":"For. Resour. Manag."},{"key":"ref_16","first-page":"20","article-title":"Extraction of tree crown parameters from high-density pure Chinese fir plantations based on UAV images","volume":"42","author":"Sun","year":"2020","journal-title":"J. Beijing For. Univ."},{"key":"ref_17","first-page":"476","article-title":"The impact of UAV remote sensing technology on the industrial development of China: A review","volume":"21","author":"Yan","year":"2019","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhu, X., Meng, L., Zhang, Y., Weng, Q., and Morris, J. (2019). Tidal and meteorological influences on the growth of invasive spartina alterniflora: Evidence from UAV remote sensing. Remote Sens., 11.","DOI":"10.3390\/rs11101208"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, Y., Hou, C., Tang, Y., Zhuang, J., Lin, J., He, Y., Guo, Q., Zhong, Z., Lei, H., and Luo, S. (2019). Citrus tree segmentation from UAV images based on monocular machine vision in a natural orchard environment. Sensors, 19.","DOI":"10.3390\/s19245558"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yan, W., Guan, H., Cao, L., Yu, Y., Li, C., and Lu, J. (2020). A self-adaptive mean shift tree-segmentation method using UAV LiDAR data. Remote Sens., 12.","DOI":"10.3390\/rs12030515"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wu, X., Shen, X., Cao, L., Wang, G., and Cao, F. (2019). Assessment of individual tree detection and canopy cover estimation using Unmanned Aerial Vehicle based Light Detection and Ranging (UAV-LiDAR) data in planted forests. Remote Sens., 11.","DOI":"10.3390\/rs11080908"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yan, D., and Liu, Z. (2019). Application of UAV-Based Multi-Angle hyperspectral remote sensing in fine vegetation classification. Remote Sens., 11.","DOI":"10.3390\/rs11232753"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Avtar, R., Suab, S.A., Syukur, M.S., Korom, A., Umarhadi, D.A., and Yunus, A.P. (2020). Assessing the influence of UAV altitude on extracted biophysical parameters of Young Oil Palm. Remote Sens., 12.","DOI":"10.3390\/rs12183030"},{"key":"ref_24","first-page":"1897","article-title":"Research on recognition methods of elm sparse forest based on object-based image analysis and deep learning","volume":"22","author":"Chen","year":"2020","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"549","DOI":"10.11834\/jrs.20219347","article-title":"Object-oriented crop classification for GF-6 WFV remote sensing images based on Convolutional Neural","volume":"25","author":"Li","year":"2021","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_26","first-page":"312","article-title":"Land-use information of object-oriented classification by UAV image","volume":"39","author":"Ma","year":"2021","journal-title":"J. Appl. Sci."},{"key":"ref_27","first-page":"258","article-title":"Extract of land use\/cover information based on HJ satellites data and object-oriented classification","volume":"33","author":"Zhu","year":"2017","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_28","first-page":"226","article-title":"Land use classification based on RS object-oriented method in coastal spectral confusion region","volume":"28","author":"Chang","year":"2012","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1007\/s11430-010-4133-6","article-title":"Object-oriented land cover classification using HJ-1 remote sensing imagery","volume":"53","author":"Sun","year":"2010","journal-title":"Sci. China Earth Sci."},{"key":"ref_30","first-page":"126","article-title":"An object-oriented and variogram based method of automatic extraction of tea planting area from high resolution remote sensing imagery","volume":"36","author":"Zhang","year":"2021","journal-title":"Remote Sens. Inf."},{"key":"ref_31","first-page":"492","article-title":"Comparison of mangrove remote sensing classification based on multi-type UAV data","volume":"39","author":"Liu","year":"2019","journal-title":"Trop. Geogr."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1109\/TPAMI.2016.2537320","article-title":"Multiscale combinatorial grouping for image segmentation and object proposal generation","volume":"39","author":"Pont","year":"2017","journal-title":"IEEE T. Pattern Anal."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1701","DOI":"10.1080\/0143116031000150095","article-title":"Mapping subalpine forest types using networks of nearest neighbour classifiers","volume":"25","author":"Collins","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1126","DOI":"10.1080\/01431161.2013.875634","article-title":"Object-based classification using SPOT-5 imagery for Moso bamboo forest mapping","volume":"35","author":"Han","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., and Yan, G. (2016). An easy-to-use airborne LiDAR data filtering method based on cloth simulation. Remote Sens., 8.","DOI":"10.3390\/rs8060501"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.isprsjprs.2003.10.002","article-title":"Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information","volume":"58","author":"Benz","year":"2004","journal-title":"ISPRS J. Photogramm."},{"key":"ref_37","first-page":"316","article-title":"Classification of remote sensing image based on object-oriented and class rules","volume":"4","author":"Chen","year":"2006","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_38","first-page":"521","article-title":"Object-oriented urban land-cover classification of multi-scale image segmentation method-a case study in Kuala Lumpur city center, Malaysia","volume":"4","author":"Su","year":"2007","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_39","first-page":"566","article-title":"Scale estimation of object-oriented image analysis based on spectral-spatial statistics","volume":"21","author":"Ma","year":"2017","journal-title":"J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1080\/13658810903174803","article-title":"ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data","volume":"24","author":"Dragut","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_41","first-page":"87","article-title":"Extraction of tobacco planting areas from UAV remote sensing imagery by object-oriented classification method","volume":"39","author":"Dong","year":"2014","journal-title":"Sci. Surv. Mapp."},{"key":"ref_42","unstructured":"Liu, Y., Yu, X., Fan, J., Zhou, J., Cheng, H., Yao, G., Meng, F., and Jin, F. (2022). Rapid estimation of rural homestead area in Western China based on UVA imagery and object-oriented method. Bull. Surv. Mapp., 125\u2013129."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","article-title":"An introduction to kernel and nearest-neighbor nonparametric regression","volume":"46","author":"Altman","year":"1992","journal-title":"Am. Stat."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of machine-learning classification in remote sensing: An applied review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3781","DOI":"10.1080\/01431160500166433","article-title":"Estimation of mediterranean forest attributes by the application of k-NN procedures to multitemporal landsat ETM+Images","volume":"26","author":"Maselli","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10115-007-0114-2","article-title":"Top 10 algorithms in data mining","volume":"14","author":"Wu","year":"2008","journal-title":"Knowl. Inf. Syst."},{"key":"ref_47","first-page":"170","article-title":"A new KNN classification approach","volume":"35","author":"Zhang","year":"2008","journal-title":"Comput. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.isprsjprs.2015.08.005","article-title":"An object-based approach to delineate wetlands across landscapes of varied disturbance with high spatial resolution satellite imagery","volume":"109","author":"Mui","year":"2015","journal-title":"ISPRS J. Photogramm."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3587","DOI":"10.1016\/j.eswa.2008.02.003","article-title":"Pseudo nearest neighbor rule for pattern classification","volume":"36","author":"Zeng","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_50","first-page":"2436","article-title":"The spatial distribution pattern of rock desertification area based on Unmanned Aerial Vehicle imagery and object-oriented classification method","volume":"22","author":"Zhang","year":"2020","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_51","first-page":"2565","article-title":"Combining Textures and Spatial Features to Extract Tea Plantations Based on Object-Oriented Method by Using Multispectral Image","volume":"41","author":"Huang","year":"2021","journal-title":"Spectrosc. Spect. Anal."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"259","DOI":"10.13031\/2013.27838","article-title":"Color indices for weed identification under various soil, residue, and lighting conditions","volume":"38","author":"Woebbecke","year":"1995","journal-title":"Trans. ASAE"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.compag.2015.01.008","article-title":"Detecting creeping thistle in sugar beet fields using vegetation indices","volume":"112","author":"Kazmi","year":"2015","journal-title":"Comput. Electron. Agr."},{"key":"ref_54","first-page":"208","article-title":"Plant species identification, size, and enumeration using machine vision techniques on near-binary images","volume":"1836","author":"Woebbecke","year":"1993","journal-title":"Proc. SPIE-Int. Soc. Opt. Eng."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1007\/s11119-005-2324-5","article-title":"Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status","volume":"6","author":"Hunt","year":"2005","journal-title":"Precis. Agric."},{"key":"ref_56","first-page":"72","article-title":"A new estimation method for fractional vegetation cover based on UVA visual light spectrum","volume":"45","author":"Xie","year":"2020","journal-title":"Sci. Surv. Mapp."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2341","DOI":"10.1016\/j.rse.2007.11.001","article-title":"Angular sensitivity analysis of vegetation indices derived from CHRIS\/PROBA data","volume":"112","author":"Verrelst","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1046\/j.1469-8137.1999.00424.x","article-title":"Assessing Leaf pigment content and activity with a reflectometer","volume":"143","author":"Gamon","year":"1999","journal-title":"New Phytol."},{"key":"ref_59","first-page":"152","article-title":"Extraction of vegetation information from visible unmanned aerial vehicle images","volume":"31","author":"Wang","year":"2015","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Kwak, G.H., and Park, N.W. (2019). Impact of texture information on crop classification with machine learning and UAV images. Appl. Sci., 9.","DOI":"10.3390\/app9040643"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Castro, A., Pe\u00f1a, J., Torres-S\u00e1nchez, J., Jim\u00e9nez-Brenes, F., and L\u00f3pez-Granados, F. (2019). Mapping cynodon dactylon infesting cover crops with an automatic decision tree-OBIA procedure and UAV imagery for precision viticulture. Remote Sens., 12.","DOI":"10.3390\/rs12010056"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","article-title":"Multiresolution gray-scale and rotation invariant texture classification with local binary patterns","volume":"24","author":"Ojala","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_63","first-page":"40","article-title":"Review of research and application of forest canopy closure and its measuring methods","volume":"21","author":"Li","year":"2008","journal-title":"World For. Res."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.foreco.2006.05.069","article-title":"Comparison of five canopy cover estimation techniques in the western Oregon Cascades","volume":"232","author":"Fiala","year":"2006","journal-title":"Forest Ecol. Manag."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"159","DOI":"10.2307\/2529310","article-title":"The measurement of observer agreement for categorical data","volume":"33","author":"Landis","year":"1977","journal-title":"Biometrics"},{"key":"ref_66","first-page":"133","article-title":"Research on individual tree crown detection and segmentation using UAV imaging and mask R-CNN","volume":"6","author":"Huang","year":"2021","journal-title":"J. For. Eng."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Guo, X., Liu, Q., Sharma, R.P., Chen, Q., Ye, Q., Tang, S., and Fu, L. (2021). Tree recognition on the plantation using UAV images with ultrahigh spatial resolution in a complex environment. Remote Sens., 13.","DOI":"10.3390\/rs13204122"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Yang, K., Zhang, H., Wang, F., and Lai, R. (2022). Extraction of Broad-Leaved tree crown based on UAV visible images and OBIA-RF model: A case study for Chinese Olive Trees. Remote Sens., 14.","DOI":"10.3390\/rs14102469"},{"key":"ref_69","first-page":"180","article-title":"Extraction of stand factors in UAV image based on FCM and watershed algorithm","volume":"55","author":"Li","year":"2019","journal-title":"Sci. Silvae Sin."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1007\/s12524-020-01240-2","article-title":"An integrated object and machine learning approach for tree canopy extraction from UAV datasets","volume":"49","author":"Adhikari","year":"2022","journal-title":"J. Indian Soc. Remote"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Liu, L., Xie, Y., Gao, X., Cheng, X., Huang, H., and Zhang, J. (2021). A new threshold-based method for extracting canopy temperature from thermal infrared images of Cork Oak Plantations. Remote Sens., 13.","DOI":"10.3390\/rs13245028"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Fraser, B., and Congalton, R. (2019). Evaluating the effectiveness of Unmanned Aerial Systems (UAS) for collecting thematic map accuracy assessment reference data in New England Forest. Forests, 10.","DOI":"10.3390\/f10010024"},{"key":"ref_73","first-page":"3635","article-title":"Niche characteristics of understory dominant species of talus slope in degraded tiankeng","volume":"30","author":"Guo","year":"2019","journal-title":"Chin. J. Appl. Ecol."},{"key":"ref_74","first-page":"4","article-title":"Spatial pattern of plant community in original karst tiankeng: A case study of Zhanyi tiankeng in Yunnan, China","volume":"29","author":"Shui","year":"2018","journal-title":"Chin. J. Appl. Ecol."},{"key":"ref_75","first-page":"4704","article-title":"Species diversity and stability of grassland plant community in heavily-degraded karst tiankeng: A case study of Zhanyi tiankeng in Yunnan, China","volume":"38","author":"Jian","year":"2018","journal-title":"Acta Ecol. Sin."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"119446","DOI":"10.1016\/j.foreco.2021.119446","article-title":"Managing climate change microrefugia for vascular plants in forested karst landscapes","volume":"496","author":"Barta","year":"2021","journal-title":"Forest Ecol. Manag."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1007\/s12665-018-8033-y","article-title":"Tiankeng: An ideal place for cli mate warming research on forest ecosystems","volume":"78","author":"Yang","year":"2019","journal-title":"Environ. Earth Sci."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"4249","DOI":"10.1038\/s41598-017-04592-x","article-title":"Karst tiankengs as refugia for indigenous tree flora amidst a degraded landscape in southwestern China","volume":"7","author":"Su","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_79","unstructured":"Jin, Z., Cao, S., Wang, L., and Sun, W. (2020). Study on extraction of tree crown information from UVA visible light image of Piceaschrenkiana var. Tianschanica forest. For. Resour. Manag., 125\u2013135."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Chung, C., Wang, J., Deng, S., and Huang, C. (2022). Analysis of canopy gaps of Coastal broadleaf forest plantations in Northeast Taiwan using UAV Lidar and the Weibull distribution. Remote Sens., 14.","DOI":"10.3390\/rs14030667"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Jin, C., Oh, C., Shin, S., Njungwi, N.W., and Choi, C. (2020). A Comparative study to evaluate accuracy on canopy height and density using UAV, ALS, and fieldwork. Remote Sens., 11.","DOI":"10.3390\/f11020241"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"4650","DOI":"10.1109\/TGRS.2013.2283272","article-title":"Forest Canopy Height extraction in rugged areas with ICESat\/GLAS data","volume":"52","author":"Wang","year":"2014","journal-title":"IEEE T. Geosci. Remote"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4128\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:13:51Z","timestamp":1760141631000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4128"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,23]]},"references-count":82,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14174128"],"URL":"https:\/\/doi.org\/10.3390\/rs14174128","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,8,23]]}}}