干旱区生态环境知识资源中心(Arid): 喀斯特山区石漠化耕地遥感精准提取与分析以贵州省北盘江镇与花江镇为例

Arid

	喀斯特山区石漠化耕地遥感精准提取与分析以贵州省北盘江镇与花江镇为例
其他题名	Extraction and Analysis of Cultivated Land Experiencing Rocky Desertification in Karst Mountain Areas Based on Remote Sensing-A Case Study of Beipanjiang Town and Huajiang Town in Guizhou Province
	赵馨; 周忠发; 王玲玉; 骆剑承; 孙营伟; 刘巍; 吴田军
来源期刊	热带地理
ISSN	1001-5221
出版年	2020
卷号	40 期号:2
中文摘要	选取北盘江镇与花江镇作为研究区,利用谷歌高精度遥感影像,结合分区分层分级思想,基于深度学习与传统约束方法对研究区耕地进行精准提取。结果表明:1) 在数量精度上,以视觉形态差异对研究区进行分区并选取不同模型训练获得的精细地块,面积约为9 867 hm~2,与实际数量基本一致,F-Measure主要分布在[0.82,0.98]之间,受到地形和岩石裸露率的影响,石漠化严重地区耕地提取精度较低。2)在形态精度上,预测耕地与实际耕地的GIOU主要分布在[0.7,1]之间,分割正确率>0.85,表明预测耕地边界与实际地块边界吻合度高,提取结果符合研究区实际情况。3)利用地貌坡度等约束条件对耕地进行划分发现,研究区以喀斯特耕地为主,占比74%,并且石漠化程度较严重,其中轻度石漠化耕地与中度石漠化耕地占总耕地的32%。在石漠化地区,耕地的狭长程度、破碎程度受人类影响较大;距离居民地越近、可达性越高的地区,其土地利用率越高,斑块越破碎。文章所提出方法可用于耕地破碎、地形复杂地区的耕地提取,能够为地区的发展、耕地治理与研究、环境保护与决策等提供精准数据支持。
英文摘要	Determining the accurate distribution of cultivated land is a prerequisite and foundation for the development of precise, modernized agricultural practices, and it is an important factor in land policies and agricultural production at the local scale. However, in karst areas, because of the complex terrain and cloudy and rainy weather conditions, the images often show repeated patterns of "identical foreign matter"; i.e., it is difficult to obtain accurate farmland information using traditional image-based farmland extraction. To solve this problem, Beipanjiang Town and Huajiang Town in Guizhou Province were selected as target areas in this study. Using Google's high-resolution remote sensing images and combining the concepts of zoning and grading, accurate extraction and evaluation of cultivated land in the study area could be performed based on deep learning methods and traditional constraints. Firstly, based on visual data, farmland was divided into three types: farmland in gentle slopes), Relatively slender and unevenly distributed farmland, and farmland with blurred edges. Then, the Holistically-Nested Edge Detection (HED) model, Richer Convolutional Features (RCF) network model, and D LinkNet semantic segmentation model were used to extract cultivated land. After confirming that the prediction data were sufficiently accurate, cultivated land was further classified based on the slope and topographical data of the test area, and the distribution characteristics of cultivated land within various terrains were studied. The results showed that: 1) The area of cultivated land in the study area classified via visual morphological differences and various models was 9 867 ha, which is basically consistent with the actual area. The F-measure was mainly distributed between [0.82,0.98] and was affected by the topography and the exposure rate of rocks. The precision of arable land extraction in areas with severe rocky desertification was low. 2) In terms of morphological accuracy, the Generalized Intersection Over Union (GIOU) of the predicted and actual area of cultivated land was mainly distributed between [0.7,1], The correct segmentation rate was more than 85%,which indicates that the predicted farmland boundaries generally coincided with the actual plot boundaries, and the extraction results reflected the actual situation in the study area. The research area mainly consists of karst cultivated land, which accounted for 74% of the total arable land area in the study area. Further, the degree of rocky desertification is relatively serious, of which light and moderate rocky desertified arable land accounts for about 32% of the total cultivated land. The narrowness and fragmentation of cultivated land in rocky desertified areas are greatly affected by anthropogenic activities. Land closer to residential areas is more accessible, and the tendency of the plots to be fragmented increases as the land utilization rate increases. In summary, the method proposed in this study can effectively extract cultivated land in areas with fragmented cultivated land and complex terrain, thus providing accurate data to support regional development, cultivated land management and research, and environmental protection and decision-making.
中文关键词	喀斯特山区 ; 石漠化耕地 ; 深度学习 ; 耕地制图 ; 耕地提取
英文关键词	karst mountainous area rocky desertified cultivated land deep learning cultivated land mapping cultivated land extraction
类型	Article
语种	中文
收录类别	CSCD
WOS类目	Agriculture
CSCD记录号	CSCD:6706932
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/353716
作者单位	赵馨, 贵州师范大学喀斯特研究院//地理与环境科学学院, 贵阳, 贵州 550001, 中国. 周忠发, 贵州师范大学喀斯特研究院//地理与环境科学学院, 贵阳, 贵州 550001, 中国. 王玲玉, 贵州师范大学喀斯特研究院//地理与环境科学学院, 贵阳, 贵州 550001, 中国. 骆剑承, 中国科学院遥感与数字地球研究所, 遥感科学国家重点实验室, 北京 100101, 中国. 孙营伟, 中国科学院遥感与数字地球研究所, 遥感科学国家重点实验室, 北京 100101, 中国. 刘巍, 中国科学院遥感与数字地球研究所, 遥感科学国家重点实验室, 北京 100101, 中国. 吴田军, 长安大学地质工程与测绘学院, 西安, 陕西 710064, 中国.
推荐引用方式 GB/T 7714	赵馨,周忠发,王玲玉,等. 喀斯特山区石漠化耕地遥感精准提取与分析以贵州省北盘江镇与花江镇为例[J],2020,40(2).
APA	赵馨.,周忠发.,王玲玉.,骆剑承.,孙营伟.,...&吴田军.(2020).喀斯特山区石漠化耕地遥感精准提取与分析以贵州省北盘江镇与花江镇为例.热带地理,40(2).
MLA	赵馨,et al."喀斯特山区石漠化耕地遥感精准提取与分析以贵州省北盘江镇与花江镇为例".热带地理 40.2(2020).