1.State Key Laboratory of Geodesy and Earth’s Dynamics, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China; 2.University of Chinese Academy of Sciences, Beijing 100049, China
Global climate warming and human activities have caused large areas of permafrost degradation and thermal erosion gully in the Tibetan Plateau, seriously affecting the engineering construction and the ecological environment in permafrost regions. In this study, high resolution unmanned aerial vehicle (UAV) images and object-oriented classification approaches were applied to extracting the thermal erosion gullies in Eboling Mountain of Heihe River. Five kinds of object-oriented supervised learning algorithms, namely nearest neighbor, K-nearest neighbor, decision tree, support vector machine (SVM), and random forest, were analyzed for the capability and accuracy of the extraction of thermal erosion gullies in detail. The field GPS data were used for evaluating the classification accuracy. The results show that, in the object-oriented image analysis, the segmentation scale parameters have little effect on the extraction of thermal erosion gullies, wheres classification features have a greater impact, so it is important to select the appropriate classification features. The overall accuracies of the five machine learning methods are all over 90%, among which the Kappa coefficient of the SVM is higher than the other four classification methods. This means that SVM is more suitable for the thermal erosion gullies boundary extraction of UAV images in this study. The combination of high resolution UAV images and object-oriented classification methods has broad application prospects in the extraction of the thermal erosion gullies.
Zhou Y W, Guo D X, Qiu G Q , et al. Chinese Frozen Soil[M]. Beijing: Science Press, 2000.
[2]
Zhang T, Barry R G, Knowles K , et al. Statistics and characteristics of permafrost and ground ice distribution in the Northern Hemisphere[J]. Polar Geography, 1999,23(2):147-169.
[3]
Williams P J, Smith M W. The Frozen Earth [M]. New York: Cambridge University Press, 1989.
[4]
周幼吾, 郭东信 . 我国多年冻土的主要特征[J]. 冰川冻土, 1982,4(1):1-19.
Zhou Y W, Guo D X . Principal characteristics of permafrost in China[J]. Journal of Glaciology and Cryopedology, 1982,4(1):1-9.
Mu C C, Zhang T J, Cao B , et al. Study of the organic carbon storage in the active layer of permafrost over the Eboling Mountain in the upper reaches of the Heihe River in the eastern Qilian Mountains[J]. Journal of Glaciology and Geocryology, 2013,35(1):1-9.
Ma L F . Study on Thaw Slump and Its Hazardness to Engineering in Permafrost Regions[D].Lanzhou:Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, 2006.
[7]
Daniel H, Petra F, Francesco A , et al. A semi-automated object-based approach for landslide detection validated by persistent scatterer interferometry measures and landslide inventories[J]. Remote Sensing, 2012,4(5):1310-1336.
doi: 10.3390/rs4051310
Gan T, Li J P, Li X Q , et al. Object-oriented method of building damage extraction from high-resolution images[J]. Engineering of Surveying and Mapping, 2015,24(4):11-15.
Sun N, Gao Z Q, Wang X J , et al. High-precise extraction for water on the Loess Plateau region from high resolution satellite image[J]. Remote Sensing for Land and Resources, 2017,29(4):173-178.doi: 10.6046/gtzyyg.2017.04.26.
Wang Y, Wang X Q, Dou A X . Building damage detection of the 2008 Wenchuan,China earthquake based on object-oriented classification method[J]. Earthquake, 2009,29(3):54-60.
[11]
Kraaijenbrink P D A, Shea J M, Pellicciotti F , et al. Object-based analysis of unmanned aerial vehicle imagery to map and characterise surface features on a debris-covered glacier[J]. Remote Sensing of Environment, 2016,186:581-595.
doi: 10.1016/j.rse.2016.09.013
[12]
Colomina I, Molina P . Unmanned aerial systems for photogrammetry and remote sensing:A review[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014,92(2):79-97.
doi: 10.1016/j.isprsjprs.2014.02.013
Li D R, Li M . Research advance and application prospect of unmanned aerial vehicle remote sensing system[J]. Geomatics and Information Science of Wuhan University, 2014,39(5):505-513.
[14]
Baatz M,Schäpe A .An optimization approach for high quality multi-scale image segmentation[EB/OL]. http://test.ecognition.com/sites/default/files/405_baatz_fp_12.pdf.
Wang X D, Duan F Z, Qu X Y , et al. Building extraction based on UAV imagery data with the synergistic use of objected-based method and SVM classifier[J]. Remote Sensing for Land and Resources, 2017,29(1):97-103.doi: 10.6046/gtzyyg.2017.01.15.
Benz U C, Hofmann P, Willhauck G , et al. Multi-resolution,object-oriented fuzzy analysis of remote sensing data for GIS-ready information[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2004,58(3):239-258.
doi: 10.1016/j.isprsjprs.2003.10.002
2019, Vol. 31 
), 周志伟1, 陈玉兴1,2, 孙亚飞1,2
