Please wait a minute...
img

Wechat

Remote Sensing Technology and Application  2020, Vol. 35 Issue (3): 596-605    DOI: 10.11873/j.issn.1004-0323.2020.3.0596
    
Study on Extraction Method of Abandoned Farmland based on the Seasonal Variation Characteristics of Remotely Sensed Images
Hongyan Wang1,2(),Xiaofan Wang1,Liang Gao2,Qiangzi Li2(),Longcai Zhao2,Xin Du2,Yuan Zhang2
1.China Land Surveying and Planning Institute, Key Labouratory of Land Use, Ministry of Natural Resources, Beijing 100035, China
2.Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences,Beijing 100101, China
Download:  HTML  PDF (5046KB) 
Export:  BibTeX | EndNote (RIS)      
Abstract  

In southwestern China, the cultivation conditions are poor, the plots are relatively fragmented, and the types of plots are complex. Therefore, the use of low and medium resolution remote sensing data is not able to satisfy the needs of abandoned farmland extraction. This paper explored the ability of single or multi-phased high resolution remotely sensed images in detecting abandoned farmland in southwest China, using Xiuwen County, Guizhou Province, China as a case study area. Remote sensing based monitoring methods for abandoned farmland were developed, providing a reference for the statistical survey of abandoned farmland in southwest China.The extraction method of abandoned farmland was proposed based on the field survey data, considering different types of abandoned farmland. Sensitive feature sets of different types of abandoned farmland were identified from a series of features including the spectral characteristics, vegetation indices and multi-temporal difference vegetation indices. The CART decision tree classification method was applied on the selected sensitive features to extract abandoned farmland. The results showed that:(1) There was a significant difference in the recognition ability of single-phase image in extracting different types of abandoned farmland, so it was difficult to use only single-phase image to extract abandoned farmland with high accuracy; (2) The vegetation index change characteristics of different time phases had strong recognition ability for abandoned farmland, and the ratio vegetation index was better than the difference vegetation index and normalized vegetation index; (3) The spatial distribution map of abandoned farmland and the statistical analysis of abandoned farmland area were carried out in Xiuwen County, Guizhou Province. The area of abandoned farmland in Xiuwen County was about 6,460 hectares, accounting for 13% of the cultivated land area.(4)Based on multi-temporal high-resolution remote sensing data, the method of detecting abandoned farmland using seasonal variation characteristics can meet the requirements of high-precision extraction of abandoned farmland in southwest China, and the results provided technical reference for remote sensing survey and mapping of abandoned farmland in large-scale.

Key words:  Abandoned Farmland      CART      Multi-temporal difference vegetation index      Sentinel-2A     
Received:  18 March 2019      Published:  10 July 2020
ZTFLH:  TP79  
Corresponding Authors:  Qiangzi Li     E-mail:  wanghy@radi.ac.cn;liqz@radi.ac.cn
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Hongyan Wang
Xiaofan Wang
Liang Gao
Qiangzi Li
Longcai Zhao
Xin Du
Yuan Zhang

Cite this article: 

Hongyan Wang,Xiaofan Wang,Liang Gao,Qiangzi Li,Longcai Zhao,Xin Du,Yuan Zhang. Study on Extraction Method of Abandoned Farmland based on the Seasonal Variation Characteristics of Remotely Sensed Images. Remote Sensing Technology and Application, 2020, 35(3): 596-605.

URL: 

http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2020.3.0596     OR     http://www.rsta.ac.cn/EN/Y2020/V35/I3/596

Fig.1  Location of the study area
波段中心波长/μm空间分辨率/m波段中心波长/μm空间分辨率/m
B1-Coastal aerosol0.44360B8-NIR0.84210
B2-Blue0.49010B8A- Vegetation Red Edge0.86520
B3-Green0.56010B9-Water vapour0.94560
B4-Red0.66510B10-SWIR-Cirrus1.37560
B5-Vegetation Red Edge0.70520B11-SWIR1.61020
B6- Vegetation Red Edge0.74020B12-SWIR2.19020
B7- Vegetation Red Edge0.78320
Table 1  The bands of Sentinel-2A satellite
Fig. 2  The distribution map of field survey sample point
Fig. 3  Field survey photos of different types of abandoned farmland
Fig. 4  Technical roadmap
Fig. 5  Schematic diagram of box plot
Fig. 6  Single phase spectrum curve of typical feature (2018-6-8)
Fig. 7  The box plots of main typical class feature
Fig. 8  Decision tree model for abandoned farmland extraction
Fig. 9  The distribution map of abandoned farmland in Xiuwen county, Guizhou province
类别撂荒地其他总计
撂荒地13220152
其他25152177
总计157172329
Table 2  The confusion matrix of precision verification
1 Xie H, Wang P, Yao G. Exploring the Dynamic Mechanisms of Cropland Abandonment based on a Spatially Explicit Economic Model for Environmental Sustainability: A Case Study in Jiangxi Province China[J]. Sustainability, 2014, 6(3): 1260-1282.
2 Ding Guangping, Liu Chengwu, Huang Limin. A Theoretical Analysis and Empirical Research of Marginalization of Agriculture Land in Hilly-mountainous Area under Farmer-benefiting Policy: A Case Study of Tongcheng County in Hubei Province[J]. Geographical Research, 2009, 28(1): 109-117.
2 定光平, 刘成武, 黄利民. 惠农政策下丘陵山区农地边际化的理论分析与实证—以湖北省通城县为例[J]. 地理研究, 2009, 28(1): 109-117.
3 Shao Jingan, Zhang Shichao, Li Xiubin. Farmland Marginalization in the Mountainous Sreas:Characteristics, Influencing Factors and Policy Implications[J]. Acta Gegraphica Sinica, 2014, 69(2): 227-242.
3 邵景安, 张仕超, 李秀彬. 山区耕地边际化特征及其动因与政策含义[J]. 地理学报, 2014, 69(2): 227-242.
4 Liu Chengwu, Li Xiubin. Regional Differences in the Changes of the Agricultural Land Use in China during 1980~2002[J].Acta Gegraphica Sinica, 2006, 61(2): 139-145.
4 刘成武, 李秀彬. 1980年以来中国农地利用变化的区域差异[J]. 地理学报, 2006, 61(2): 139-145.
5 Shi Tiechou. Research on Farmland Abandonment Scale and Influencing Factors in Chongqing Mountain Area[D]. Beijing: University of Chinese Academy of Sciences, 2015.
5 史铁丑. 重庆山区耕地撂荒的规模及影响因素研究[D].北京: 中国科学院大学, 2015.
6 Zhang Y, Li X, Song W. Determinants of Cropland Abandoned at the Parcel, Household and Village Levels in Mountain Areas of China: A Multi-level Analysis[J]. Land Use Policy, 2014, 41(4): 186-192.
7 Shi Tiechou, Xu Xiaohong. Extraction and Validation of Abandoned Farmland Parcel in Typical Counties of Chongqing[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(24): 261-267.
7 史铁丑, 徐晓红. 重庆市典型县撂荒耕地图斑的提取与验证[J]. 农业工程学报, 2016, 32(24):261-267.
8 Queiroz C, Beilin R, Folke C, et al. Farmland Abandoned: Threat or Opportunity for Biodiversity Conservation?A Global Review[J]. Frontiers in Ecology and the Environment, 2014, 12(5): 288-296.
9 Molinillo M, Lasanta T, Garcia- Ruiz J M. Research: Managing Mountainous Degraded Landscapes after Farmland Abandonment in the Central Spanish Pyrenees[J]. Environmental Management, 1997, 21(4): 587-598.
10 Bakker M M, Govers G, Doorn A V, et al. The Response of Soil Erosion and Sediment Export to Land-use Change in Four Areas of Europe: The Importance of Landscape Pattern[J]. Geomorphology, 2008, 98(3/4): 213-226.
11 Batllebayer L, Batjes N H, Bindraban P S. Changes in Organic Carbon Stocks Upon Land Use Conversion in the Brazilian Cerrado: A Review[J]. Agriculture Ecosystems & Environment, 2010, 137(1): 47-58.
12 Vuichard N, Ciais P, Belelli L, et al. Carbon Sequestration due to the Abandoned of Agriculture in the Former USSR Since 1990[J]. Global Biogeochemical Cycles, 2008, 22(4): 1417-1430.
13 Macdonald D, Crabtree J R, Wiesinger G, et al. Agricultural Abandonment in Mountain Areas of Europe: Environmental Consequences and Policy Response[J]. Journal of Environmental Management, 2000, 59(1): 47-69.
14 Peng Yang, Bai Yanfeng, Jiang Chunqian, et al. Carbon Storage Differences for Farmland with Two Rehabilitation Approaches[J]. Journal of Zhejiang A & F University, 2018, 35(2):235-242.
14 彭阳, 白彦锋, 姜春前,等. 耕地造林和撂荒2种植被恢复方式碳储量差异[J]. 浙江农林大学学报, 2018, 35(2):235-242.
15 Estel S, Kuemmerle T, Alcántara C, et al. Mapping Farmland Abandonment and Recultivation Across Europe Using MODIS NDVI Time Series[J]. Remote Sensing of Environment, 2015, 163: 312-325.
16 Alcantara C, Kuemmerle T, Baumann M, et al. Mapping the Extent of Abandoned Farmland in Central and Eastern Europe Using MODIS Time Series Satellite Data[J]. Environmental Research Letters, 2013, 8(3): 1345-1346.
17 Cheng Weifang. Study on Remote Sensing Survey Method of Abandoned Farmland in South China[D]. Beijing: University of Chinese Academy of Sciences, 2011.
17 程维芳. 南方撂荒地遥感调查方法研究[D]. 北京: 中国科学院大学, 2011.
18 Yusoff N M, Muharam F M, Khairunniza-Bejo S. Towards the Use of Remote-sensing Data for Monitoring of Abandoned Oil Palm Lands in Malaysia: A Semi-automatic Approach[J]. International Journal of Remote Sensing, 2017, 38(2): 432-449.
19 Baumann M, Kuemmerle T, Elbakidze M, et al. Patterns and Drivers of Post-socialist Farmland Abandonment in Western Ukraine[J]. Land Use Policy, 2011, 28(3): 552-562.
20 Kuemmerle T, Müller D, Griffiths P, et al. Land Use Change in Southern Romania after the Collapse of Socialism[J]. Regional Environmental Change, 2009, 9(1): 1-12.
21 Niu Jiqiang, Lin Hao, Niu Yingnan, et al. Analysis of Spatial Pattern and Driving Factors for Abandoned Arable Lands in Underdevelopment Region[J]. Transactions of the Chinese Society for Agricultural Machinery, 2017, 48(2): 141-149.
21 牛继强, 林昊, 牛樱楠, 等. 经济欠发达地区撂荒耕地空间格局与驱动因素分析[J]. 农业机械学报, 2017, 48(2): 141-149.
22 Xiao Guofeng, Zhu Xiufang, Hou Chenyao, et al. Extraction and Analysis of Abandoned Farmland: A Case Study of Qingyun and Wudi Counties in Shandong Province [J]. Acta Geographica Sinica, 2018, 73(9): 1658-1673.
22 肖国峰, 朱秀芳, 侯陈瑶,等. 撂荒耕地的提取与分析——以山东省庆云县和无棣县为例[J]. 地理学报, 2018, 73(9): 1658-1673.
23 Ding Rui, Zhang Ermei, Xie zijing, et al. Extraction and Analysis of Abandoned Farmland in the Boundary Region of Hebei, Shandong and Henan Province[J]. Hubei Agriculture Sciences, 2019, 58(13):122-128.
23 丁锐,张二梅,谢紫菁,等. 冀鲁豫省际边界区域撂荒地提取与分析[J]. 湖北农业科学,2019, 58(13):122-128.
24 Ma Shangjie, Pei Zhiyuan, Wang Fei, et al. Application on Remote Sensing Survey of Abandoned Farmlands in Winter Along the Huaihe River based on GF-1 Image[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(1):235-241.
24 马尚杰,裴志远,王飞,等. 基于GF-1影像的沿淮地区冬季耕地撂荒遥感调查应用[J]. 农业工程学报, 2019, 35(1):235-241.
25 Yang Tong, Guo Xudong, Yue Dengpeng, et al. Information Extraction and Driving Factor Assessment of Farmland Abandonment based on Joint Change Detection[J]. Transaction of the Chinese Society of Agriculture Machinery, 2019, 50(6):201-208.
25 杨通,郭旭东,岳德鹏,等. 基于联合变化检测的耕地撂荒信息提取与驱动因素分析[J]. 农业机械学报,2019, 50(6):201-208.
26 Li Shengfa, Li Xiubin, Xin Liangjie, et al. Extent and Distribution of Cropland Abandonment in Chinese Mountainous Areas[J]. Resources Science, 2017,39(10): 1801-1811.
26 李升发, 李秀彬, 辛良杰,等. 中国山区耕地撂荒程度及空间分布—基于全国山区抽样调查结果[J]. 资源科学, 2017,39(10): 1801-1811.
27 Chen Yun, Dai Jinfang, Li Junjie. CART-based Decision Tree Classifier Using Multi-feature of Image and Its Application[J]. Geography and Geo-Information Science, 2008, 24(2): 33-36.
27 陈云, 戴锦芳, 李俊杰. 基于影像多种特征的CART决策树分类方法及其应用[J]. 地理与地理信息科学, 2008, 24(2): 33-36.
28 Ma Xin, Wang Xiyuan, Hu Bo. The Cart Automatic Decision Tree to Multi-source Remote Sensing Image Classification based on ENVI: Taking Beijing as An Example[J]. Ningxia Engineering Technology, 2017, 16(1): 63-66.
28 马鑫, 汪西原, 胡博. 基于ENVI的CART自动决策树多源遥感影像分类: 以北京市为例[J]. 宁夏工程技术, 2017, 16(1): 63-66.
29 Breiman L,Friedman J H, Stone C J, et al. Classification and Regression Trees[M].UK:Chapman and Hall/CRC, 1984.
[1] Rui YANG Su Yang. U-Net neural networks and its application in high resolution satellite image classification[J]. Remote Sensing Technology and Application, 0, (): 0 .
[2] . [J]. Remote Sensing Technology and Application, 1986, 1(1): 11 -12 .
[3] . [J]. Remote Sensing Technology and Application, 1986, 1(1): 8 -10 .
[4] . [J]. Remote Sensing Technology and Application, 1986, 1(1): 65 -66 .
[5] . [J]. Remote Sensing Technology and Application, 1986, 1(2): 18 -21 .
[6] . [J]. Remote Sensing Technology and Application, 1987, 2(1): 31 -39 .
[7] . [J]. Remote Sensing Technology and Application, 1987, 2(1): 40 -50 .
[8] . [J]. Remote Sensing Technology and Application, 1987, 2(1): 53 -62 .
[9] . [J]. Remote Sensing Technology and Application, 1987, 2(2): 62 -63 .
[10] . [J]. Remote Sensing Technology and Application, 1987, 2(3): 67 .