Journal of Geo-information Science >
Land Cover Classification in Mongolian Plateau Based on Decision Tree Method： A Case Study in Tov Province, Mongolia
Received date: 2013-07-29
Revised date: 2013-10-29
Online published: 2014-05-10
Global and regional land use/cover data processed by remote sensing images is a supportive part for the researches related to environment and so on. Mongolia Plateau includes all of Mongolia, parts of southern Russia and northern China. Study on land use/cover changes in Mongolia Plateau has important practical and scientific significance to discover the resources, environmental and ecological characteristics of this area and even in northeast Asia area, and also it is beneficial to enhance international cooperation in regional resources utilization, ecological and environmental protection and sustainable development. According to the land use/cover mapping requirement in Mongolia Plateau, a land cover classification approach was designed based on QUEST (Quick Unbiased and Efficient Statistical Tree) decision tree method in the representative area of Mongolia Plateau, where includes Tov Province and Ulaanbaatar City in Mongolia. Land cover classification dataset in this area was captured using Landsat TM images, through QUEST automatic classification and visual interpretation. Results show that the total area of steppe occupies 70.88%, followed by forest accounting for 14.83%, barren 10.73%, cropland 2.98%, water 0.31%, built area 0.27%, and wetland 0.02%. 139 GPS verification points for accuracy assessment were collected by fieldwork, which was held in August 2013. Accuracy assessment found that land cover overall accuracy is up to 72.66% in class I, while the overall accuracy in class II is obviously descent, mainly due to the confusion of real steppe and desert steppe. This was caused by the different understanding of these two steppe types between scientists from China and Mongolia. In general, the QUEST decision tree method used in this paper is proved to be feasible for higher accuracy land cover classification mapping in this area.
Key words： remote sensing; Mongolian Plateau; QUEST; land cover
TIAN Jing, WANG Juanle, LI Yifan, ZHOU Yujie, GUO Haihui, ZHU Junxiang . Land Cover Classification in Mongolian Plateau Based on Decision Tree Method： A Case Study in Tov Province, Mongolia[J]. Journal of Geo-information Science, 2014 , 16(3) : 460 -469 . DOI: 10.3724/SP.J.1047.2014.00460
[1] 陈军,陈晋,宫鹏.全球地表覆盖遥感制图的发展动向[J]. 国际摄影测量与遥感动态,2010(2): 1-5.
[2] Townshend J R, Masek J G, et al. Global characterization and monitoring of forest cover using Landsat data: Opportunities and challenges[J]. International Journal of Digital Earth, 2012,5(5):373-397.
[3] Loveland T R, Reed B C, Brown J F, et al. Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data[J]. International Journal of Remote Sensing, 2000,21(6):1303-1330.
[4] Hansen M C, Reed B. A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products[J]. International Journal of Remote Sensing, 2000,21(6-7):1365-1373.
[5] Bartholome E, Belward A. GLC2000: A new approach to global land cover mapping from Earth observation data[J]. International Journal of Remote Sensing, 2005,26(9): 1959-1977.
[6] Muchoney D, Borak J, Chi H, et al. Application of theMODIS global supervised classification model to vegetation and land cover mapping of Central America[J]. International Journal of Remote Sensing, 2000,21(6-7):1115-1138.
[7] Arino O, Gross D, Ranera F, et al. GlobCover: ESA service for Global land cover from MERIS[J]. International Geoscience and Remote Sensing, 2007:2412-2415.
[8] 岳瑞红.基于MODIS数据的蒙古高原土地覆盖分类研究[D].呼和浩特:内蒙古师范大学,2010.
[9] 吴俊杰.基于MODIS植被指数时序数据的东北亚地区土地覆盖分类[D].吉林:东北师范大学,2010.
[10] 赵萍,冯学智,林广发.SPOT 卫星影像居民地信息自动提取的决策树方法研究[J].遥感学报,2003,7(4):309-315.
[11] 曹丽琴,李平湘,张良培,等.基于多地表特征参数的遥感影像分类研究[J].遥感技术与应用,2010,25(1):38-44.
[12] 吴健生,潘况一,彭建,等.基于QUEST决策树的遥感影像土地利用分类——以云南省丽江市为例[J].地理研究, 2012,31(11):1973-1980.
[13] 朱晓荣.基于决策树的洞庭湖湿地信息提取技术研究[D].北京:中国林业科学研究院,2012.
[14] Ranchin T, Aiazzi B, Alperone L, et al. Image fusion-the ARSIS concept and successful implementation schemes[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2003,58(2):391-399.
[15] 袁路.基于像元分解与不确定性分析的遥感分类评估方法[D].北京:清华大学,2005.
[16] 孙枢.地球数据是地球科学创新的重要源泉——从地球科学谈科学数据共享[J]. 地球科学进展,2003,18(3): 334-337.
/
| 〈 |
|
〉 |