Extraction of land cover information of Karst based on landform

doi:10.6046/gtzyyg.2016.03.22

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Abstract

Landscape limited forms of land use and land use type influence the formation and evolution of the landscape. In view of that relationship, this paper proposes an object-oriented land use/land cover classification method. Taking Anshun City with diverse karst landform types as an example and using 2010 TM images as the basic data source, the authors divided the test data according to different scale response characteristics of different karst landforms combinations and with multi-scale segmentation technology. Using DEM data and the derivative features such as slope, relief amplitude, and surface incision and establishing membership functions determining the threshold, the authors extracted the karst landform information. Then, on the basis of landform types and according to the landuse characteristics of different landform types, the authors divided landuse types by using nearest neighbor analysis. Overall classification precision reached 87.35%. Finally, the distribution characteristics of landform and land cover were discussed on the basis of the classification results. The results show that the landform types in the study area are mainly peak cluster valley and peak cluster depression, with a few karst pimple plains and hoodoo basins. Land cover types are mainly shrubs and dry land, which account for 25.58% and 20.74% of total area respectively.

Keywords automated fault model discretization DInSAR inversion of slip parameters mixed resampling

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Issue Date: 01 July 2016

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	CHEN Danlei
	LIU Guoxiang
	WANG Xiaowen
	WANG Lei
	PU Huilong

Cite this article:

CHEN Danlei,LIU Guoxiang,WANG Xiaowen, et al. Extraction of land cover information of Karst based on landform[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(3): 138-145.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2016.03.22 OR https://www.gtzyyg.com/EN/Y2016/V28/I3/138

[1] 程维明,周成虎.多尺度数字地貌等级分类方法[J].地理科学进展,2014,33(1):23-33. Cheng W M,Zhou C H.Methodology on hierarchical classification of multi-scale digital geomorphology[J].Progress in Geography,2014,33(1):23-33.
[2] 宋乃平,陈忠祥.地貌与土地利用关系之探讨[J].宁夏大学学报:自然科学版,1993,14(3):27-31. Song N P,Chen Z X.A study on the relationship of landform and land use[J].Journal of Ningxia University:Natural Science Edition,1993,14(3):27-31.
[3] 周成虎,程维明,钱金凯,等.中国陆地1:100万数字地貌分类体系研究[J].地球信息科学学报,2009,11(6):707-724. Zhou C H,Cheng W M,Qian J K,et al.Research on the classification system of digital land geomorphology of 1:1000000 in China[J].Journal of Geo-Information Science,2009,11(6):707-724.
[4] 李宗发.贵州喀斯特地貌分区[J].贵州地质,2011,28(3):177-181,234. Li Z F.Division of karst landform in Guizhou[J].Guizhou Geology,2011,28(3):177-181,234.
[5] 盈斌,熊康宁,陈起伟,等.喀斯特地区土地利用与石漠化分布特征研究——以贵州省毕节市鸭池示范区为例[J].水土保持通报,2012,32(2):190-193. Ying B,Xiong K N,Chen Q W,et al.Distribution features of rocky desertification and land use in karst area:With special reference to Yachi demonstration base in Bijie Area of Guizhou Province[J].Bulletin of Soil and Water Conservation,2012,32(2):190-193.
[6] 熊康宁,池永宽.中国南方喀斯特生态系统面临的问题及对策[J].生态经济,2015,31(1):23-30. Xiong K N,Chi Y K.The problems in southern China karst ecosystem in southern of China and its countermeasures[J].Ecological Economy,2015,31(1):23-30.
[7] 宋同清,彭晚霞,杜虎,等.中国西南喀斯特石漠化时空演变特征, 发生机制与调控对策[J].生态学报,2014,34(18):5328-5341. Song T Q,Peng W X,Du H,et al.Occurrence, spatial-temporal dynamics and regulation strategies of karst rocky desertification in southwest China[J].Acta Ecologica Sinica,2014,34(18):5328-5341.
[8] 陈述彭.西南地区的喀斯特地貌[J].地理知识,1954(3):74-76. Chen S P.The karst geomorphology in southwest China[J].Geographical Knowledges,1954(3):74-76.
[9] 曾昭璇.中国南部石灰岩地貌类型若干问题[J].地质学报,1964,44(1):119-129. Zeng Z X.Some questions on the classification of relief types in karst region in south China[J].Journal of Geology,1964,44(1):119-129.
[10] 中国科学院地质研究所岩溶研究组.中国岩溶研究[M].北京:科学出版社,1979. Research group of Chinese Academy of Sciences Institute of Karst Geology Research.Karst Research in China[M].Beijing:Science Press,1979.
[11] 李雪冬,杨广斌,张旭亚,等.利用影像尺度响应进行城市扩张变化检测[J].山地学报,2014,32(3):293-299. Li X D,Yang G B,Zhang X Y,et al.Application of multiscal segmentation to the detection of urban expansion[J].Mountain Research,2014,32(3):293-299.
[12] 鲁恒,李永树,唐敏.面向对象的山地区域多源遥感影像分割尺度选择及评价[J].山地学报,2011,29(6):688-694. Lu H,Li Y S,Tang M.Evaluation and selection of segmentation scale with object-oriented method in multiple source remote sensing image of mountain areas[J].Journal of Mountain Science,2011,29(6):688-694.
[13] Martha T R,Kerle N,van Westen C J,et al.Object-oriented analysis of multi-temporal panchromatic images for creation of historical landslide inventories[J].ISPRS Journal of Photogrammetry and Remote Sensing,2012,67:105-119.
[14] Sulong I,Mohd-Lokman H,Mohd-Tarmizi K,et al.Mangrove mapping using Landsat imagery and aerial photographs:Kemaman District,Terengganu,Malaysia[J].Environment,Development and Sustainability,2002,4(2):135-152.
[15] 张学儒,刘林山,张镱锂,等.基于ENVI ZOOM面向对象的高海拔灌丛植被提取——以定日县为例[J].地理与地理信息科学,2010,26(4):104-108. Zhang X R,Liu L S,Zhang Y L,et al.Extraction of shrub vegetation by object-oriented classification method based on ENVI ZOOM in high-altitude area:A case of Dingri County[J].Geography and Geo-Information Science,2010,26(4):104-108.
[16] 苑全治,吴炳方,张磊,等.辅助数据在面向对象分类方法中的应用——以密云水库上游为例[J].生态学报,2014,34(24):7202-7209. Yuan Q Z,Wu B F,Zhang L,et al.Application of auxiliary data in the object-based classification method:A case study on the Miyun Reservoir area[J].Acta Ecologica Sinica,2014,34(24):7202-7209.
[17] 贾明明,任春颖,刘殿伟,等.基于环境星与MODIS时序数据的面向对象森林植被分类[J].生态学报,2014,34(24):7167-7174. Jia M M,Ren C Y,Liu D W,et al.Object-oriented forest classification based on combination of HJ-1 CCD and MODIS NDVI data[J].Acta Ecologica Sinica,2014,34(24):7167-7174.
[18] 李雪冬,杨广斌,李蔓,等.面向对象的喀斯特地区土地利用遥感分类信息提取——以贵州毕节地区为例[J].中国岩溶,2013,32(2):231-237. Li X D,Yang G B,Li M,et al.RS classification information extraction of landuse in karst area by means of object oriented approach:A case in Bijie,Guizhou[J]. Carsologica Sinica,2013,32(2):231-237.
[19] Xu E Q,Zhang H Q,Li M X.Object-based mapping of karst rocky desertification using a support vector machine[J].Land Degradation & Development,2015,26(2):158-167.
[20] Zhang C Y,Xie Z X.Combining object-based texture measures with a neural network for vegetation mapping in the Everglades from hyperspectral imagery[J].Remote Sensing of Environment,2012,124:310-320.
[21] 吴田军,胡晓东,夏列钢,等.基于对象级分类的土地覆盖动态变化及趋势分析[J].遥感技术与应用,2014,29(4):600-606. Wu T J,Hu X D,Xia L G,et al.Analysis on dynamic change and tendency of land-cover based on object-oriented classification[J].Remote Sensing Technology and Application,2014,29(4):600-606.
[22] 盛茂银,熊康宁,崔高仰,等.贵州喀斯特石漠化地区植物多样性与土壤理化性质研究[J].生态学报,2015,35(2):434-448. Sheng M Y,Xiong K N,Cui G Y,et al.Plant diversity and soil physical-chemical properties in karst rocky desertification ecosystem of Guizhou,China[J].Acta Ecologica Sinica,2015,35(2):434-448.
[23] 吴秀芹,蔡运龙,蒙吉军.喀斯特山区土壤侵蚀与土地利用关系研究——以贵州省关岭县石板桥流域为例[J].水土保持研究,2005,12(4):46-48,77. Wu X Q,Cai Y L,Meng J J.Impacts of land use on soil erosion in karst mountainous area:A case study in Shibanqiao catchment in Guanling County,Guizhou Province[J].Research of Soil and Water Conservation,2005,12(4):46-48,77.

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