热带地理 ›› 2020, Vol. 40 ›› Issue (2): 289-302.doi: 10.13284/j.cnki.rddl.003233

• “地理空间智能技术及应用”专题 • 上一篇    下一篇


赵馨1, 周忠发1(), 王玲玉1, 骆剑承2, 孙营伟2, 刘巍2, 吴田军3   

  1. 1.贵州师范大学 喀斯特研究院//地理与环境科学学院,贵阳 550001
    2.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
    3.长安大学 地质工程与测绘学院,西安 710064
  • 收稿日期:2019-11-29 修回日期:2020-03-16 出版日期:2020-03-10 发布日期:2020-05-15
  • 通讯作者: 周忠发 E-mail:fa6897@163.com
  • 作者简介:赵馨(1996—),女,贵州人,硕士研究生,主要研究方向为地理信息系统与遥感,(E-mail) 1303873722@qq.com。
  • 基金资助:

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

Zhao Xin1, Zhou Zhongfa1(), Wang Lingyu1, Luo Jiancheng2, Sun Yingwei2, Liu Wei2, Wu Tianjun3   

  1. 1.Institute of Karst Science, Guizhou Normal University//School of Geography and Environmental Science, Guiyang 550001, China
    2.Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
    3.School of Geology Engineering and Geomatics, Chang’an University, Xi’an 710064, China
  • Received:2019-11-29 Revised:2020-03-16 Online:2020-03-10 Published:2020-05-15
  • Contact: Zhou Zhongfa E-mail:fa6897@163.com


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。

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


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.

Key words: karst mountainous area, rocky desertified cultivated land, deep learning, cultivated land mapping, cultivated land extraction


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