THE APPLICATION OF THE KNOWLEDGE-BASED DECISION TREE CLASSIFICATION METHOD TO THE EXTRACTION OF LAND TYPES IN MINING AREAS: A CASE STUDY OF DAYE AREA, HUBEI PROVINCE

doi:10.6046/gtzyyg.2004.03.12

Download: PDF(424 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Simple and clear, the knowledge-based decision tree classification method can choose the best band composition and characteristic parameters of different ground objects so as to get the highly accurate classification results. Based on the spectral characteristics and the spatial knowledge of the ground objects in Daye which served as a training area, the authors used image composite, iron oxide index, normalized difference vegetation index (NDVI) and digital elevation model and employed the decision tree classification method with multitemporal Landsat TM (ETM) images. The classification algorithm was applied to all the Landsat TM (ETM) data so as to detect temporal and spatial changes in the mining areas, which, in turn, were divided into ten classes. The characteristics of the highly accurate classification results enable us to perform highly accurate change detection and quantitative analysis of such features in different mining areas as waste,water bodies, change of land use, reclamation process and estimation of vegetation cover in affected places. From the change detection results, it is observed that the decreasing vegetation and land degradation caused by mining activities in the study area are serious, and that only about 35% of the abandoned mining area was reclaimed from 1986 to 2002.

Issue Date: 02 August 2011

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

Cite this article:

CHEN Hua-li, CHEN Zhi-hua, DING Guo-ping . THE APPLICATION OF THE KNOWLEDGE-BASED DECISION TREE CLASSIFICATION METHOD TO THE EXTRACTION OF LAND TYPES IN MINING AREAS: A CASE STUDY OF DAYE AREA, HUBEI PROVINCE[J]. REMOTE SENSING FOR LAND & RESOURCES,2004, 16(3): 49-53.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2004.03.12 OR https://www.gtzyyg.com/EN/Y2004/V16/I3/49

[1] 郭达志,盛业华,胡明星,等.矿区环境灾害动态监测与分析评价［M］.徐州:中国矿业大学出版社,1998.

[2] 张银辉,赵庚星.土地利用/土地覆盖遥感分类方法的研究综述［J］.中国农业资源与区划,2002,23(3):21-25.

[3] 雷利卿,岳燕珍,孙九林,等.遥感技术在矿区环境污染监测中的应用研究［J］.环境保护,2002,(2):33-36.

[4] Llorens J F,Fernandez-Turiel J L,Banninger C,et al..A remote sensing based approach for the restoration of an open-cast coal mine site in Spain
[A].Remote Sensing in the 21st Century:Economic and Environmental Applications［C］.Barcelon Spain:Casanova(ed.),2000,491-496.

[5] Schmidt H,Glaesser C.Multitemporal analysis of satellite data and their use in the monitoring of environmental impacts of open cast lignite mining areas in Eastern Germany［J］.International Journal of Remote Sensing,1998,19(12):2245-2260.

[6] Raimundo Almeida-Filho,Yosio E Shimabukuro.Digital processing of a Landsat-TM time series for mapping and monitoring degraded areas caused by independent gold miners,Roraima State,Brazilian Amazon［J］.Remote Sensing of Environment,2002,79(1):42-50.

[7] Venkataraman G,Kumar S P,Ratha D S,et al..Open cast mine monitoring and environmental impact studies through remote sensing-a case study from Goa,India［J］.Geocarto-International,1997,12(2):39-53.

[8] 王建,董光荣,等.利用遥感信息决策树方法分层提取荒漠化土地类型的研究探讨［J］.中国沙漠,2000,20(3):243-247.

[9] Swain P H,Hauska H.The decision tree classifier:design and potential［J］.IEEE Transactions on Geoscience and Remote Sensing,1977,15(1):142-147.

[10] Mark A Friedl,Carla E Brodley,Alan H Strahler.Maximizing land cover classification accuracies produced by decision trees at continental to global scales［J］.IEEE Transactions on geoscience and remote sensing,1999,37(2):969-977.

[11] Hansen M C,Defries R S,Townshend J R G,et al..Global land cover classification at 1 km spatial resolution using a classification tree approach［J］.International Journal of Remote Sensing,2000,21(6):1331-1364.

[12] Gahegan M,West G.The classification of complex geographic datasets:an operational comparison of artificial neural networks and decision tree classifiers
[A].Proceedings of the 3rd International Conference on GeoComputation［C］.Bristol UK:University of Bristol,1998,61:17-19.

[13] Evans F.An investigation into the use of maximum likelihood classifiers,decision trees,neural networks and conditional probabilistic networks for mapping and predicting salinity［D］.M.Sc. thesis,Department of Computer science,Curtin University of Technology,Australia,1998.

[14] Muchoney D,Borak J,Chi C,et al..Application of the MODIS global supervised classification model to vegetation and land cover mapping of central America［J］.International Journal of Remote Sensing,2000,21(6):1115-1138.

[15] Brandt Tso,Paul M Mather.Classification Methods for Remotely Sensed Data［M］.London:Taylor & Francis,2001.

[16] 陆家驹.长江南京江段水质遥感分析［J］.国土资源遥感,2002,(3):33-36.

[17] 周成虎,骆剑承,杨晓梅,等.遥感影像地学理解与分析［M］.北京:科学出版社,2001.

[18] 谭永杰.西部矿产资源开发状况的动态监测方法探讨［J］.西安科技学院学报,2000,20(增刊):59-62.

[19] 富融科技有限公司.ERDAS(IMAGINE)——遥感领域专业分析软件的排头兵［J］.国土资源遥感,2002,(1):69.