关键词：
Land cover change;Dongting Lake;support vector machine;Landsat TM/OLI
摘要：
Land cover in Dongting Lake region has been faced high variations during recent decades. Therefore , there has a strong need to investigate and understand the land cover changes in Dongting Lake region between land cover types. In this study, support vector machine (SVM) classification method was employed to detect changes in land cover dynamic in Dongting Lake region using Landsat images for the year 1995, 2006 and 2015. Land cover information was classified to five categories: waterbody, wetland, built-up, cropland and forestland. Quantitative analysis , change detection matrix and land cover dynamic degree were utilized for investigating and assessing the land cover changes in Dongting Lake region. The overall accuracy (OA) and kappa coefficient of the land cover classification results were over 96% and 0.9, respectively. The results indicated that in 1995 about 11.32% of study area was covered by water-body together with 13.31% of wetland. Nearly 50% of the area was covered by cropland and remaining 2.59% was covered by built-up. During the period 1995-2015, the change rate of the waterbody was evaluated at-0.29%, at-0.67% for the wetland and at-2.47% for the built-up. On the contrary, the for-estland and cropland increased by 0.72% and 0.03%, respectively. In addition, the results of this study can provide scientific information for government to formulate policy for sustainable land use management in Dongting Lake region. Land cover change, Dongting Lake, support vector machine , Landsat TM/OLI Land cover changes affect global climate, species diversity and ecosystem balance, which can accelerate land degradation and reduce ecosystem services [1, 2]. It has become a serious environmental problem. Over the past few decades, land cover in Dongting Lake region experienced tremendous changes by natural processes, as well as anthropo-genic activities [3]. In particular, anthropogenic activities , such as reclaiming cropland from lakes and returning cropland to lakes, have become a major concern of land cover changes in Dongting Lake region [4]. Therefore, a clear understanding of the spatial and temporal changes of land cover types in the Dongting Lake region in recent two decades is important. Remote sensing has been monitoring and capturing the earth land's surface every day and night by providing spatial and temporal images over large and inaccessible area for more than six decades [5]. Therefore, remote sensing became an acknowledged technology for monitoring the land cover changes. Some optical remote sensing products, such as Moderate Images Spectrometer (MODIS), Advanced Very High-Resolution Radiometer (AVHRR), and Satellite Pour 1'Obervation de la Terre (SPOT) with resolution at 250 m to 1 km, are the very suitable data resources for studying information of earth surface [1, 6-11]. Despite short revisiting cycle and large swath width, these low-resolution products are mainly available on the detecting of large scale coarse land cover changes, but the transformation details of land cover types and its ratio remains unknown which usually occurs at a small scale. In order to settle these problems and detail monitoring earth's land cover changes, medium remote sensing satellite data, such as Landsat Thematic Mapper (TM) [12, 13], Landsat Enhanced Thematic Mapper Plus (ETM+) [7, 14] and Landsat Operational Land Im-ager (OLI) [12, 15], with resolution of 30 m but re-visiting cycle of 16 day, have been widely utilized for mapping land cover and monitoring its changes. Numerous researches have been conducted and various algorithms have been developed for detecting land cover changes especially over Dongting Lake region using remote sensing satellite technologies. Li et al. [16] employed the Geographical Information System (GIS) and Remote Sensing (RS) technologies to study the characterized long-term land cover changes in Dongting Lake region using the Landsat images from 1978, 1989, 1998. Their results indicated that land cover patterns in Dongting Lake region had been greatly altered by empoldering. Three land type had changed remarkably. The cultivated land decreased,
作者机构：
[熊迎军] College of Information Science and Technology, Nanjing Agricultural University, Nanjing, 210095, China;[周俊; 韦玮; 沈明霞; 张保华] College of Engineering, Nanjing Agricultural University, Nanjing, 210031, China
通讯机构：
College of Engineering, Nanjing Agricultural University, Nanjing, China
作者机构：
[Zhang, Xike; Zhang, Qiuwen] Huazhong Univ Sci & Technol, Sch Hydropower & Informat Engn, Wuhan 430074, Hubei, Peoples R China.;[Zhang, Xike] Hunan City Univ, Sch Municipal & Mapping Engn, Yiyang 413000, Peoples R China.;[Zhang, Gui] Cent South Univ Forestry & Technol, Sch Sci, Changsha 410004, Hunan, Peoples R China.;[Gui, Zifan] Shenzhen Garden Management Ctr, Shenzhen 518000, Peoples R China.
会议名称：
2017 2nd International Conference on Frontiers of Sensors Technologies (ICFST)
会议时间：
April 2017
会议地点：
Shenzhen, China
会议主办单位：
[Zhang, Xike;Zhang, Qiuwen] Huazhong Univ Sci & Technol, Sch Hydropower & Informat Engn, Wuhan 430074, Hubei, Peoples R China.^[Zhang, Xike] Hunan City Univ, Sch Municipal & Mapping Engn, Yiyang 413000, Peoples R China.^[Zhang, Gui] Cent South Univ Forestry & Technol, Sch Sci, Changsha 410004, Hunan, Peoples R China.^[Gui, Zifan] Shenzhen Garden Management Ctr, Shenzhen 518000, Peoples R China.
会议论文集名称：
2017 2nd International Conference on Frontiers of Sensors Technologies (ICFST)
关键词：
river network extraction;NDWI;object-oriented classification method;Landsat-5
摘要：
Landsat data have the characteristics of high resolution and wide spectrum, and have been widely used to extract river network. Based on the feature analysis and pretreatment of Landsat-5 Thematic Mapper (TM) data, in this paper, we studied and realized the extraction of river network in Hunan Province, analyzed and dealt with the extraction results of river network. The main research works are as follows: The Landsat-5 TM images were pre-processed by using ENVI5.3 software over the study area in 2011. Radiometric correction, image mosaic and subset were carried out. The Normalized Difference Water Index (NDWI) and object-oriented classification method were used to extract the river network, and then the extraction results of the two methods of residential area, vegetation area and cloud-containing area were analyzed. The results showed that the extraction of river network based on object-oriented classification method was more complete, and the inaccuracy and omission extraction of river network were much less than that based on NDWI. And then, editor tool in ArcGIS was used to delete the non-river network region and connect the non-connected area of the river network.
