基于地块级时序NDVI的喀斯特山区撂荒地特征精准识别

doi:10.6046/gtzyyg.2020.03.04

摘要
图/表
参考文献
相关文章
Metrics

全文: PDF(4079 KB)

HTML
输出: BibTeX | EndNote (RIS)

摘要

撂荒地在全球呈扩散趋势,已成为土地利用的重要研究方向,喀斯特石漠化耕地的撂荒因多云多雨造成可利用的光学遥感影像十分有限,且因混合像元严重而不易被精确识别和提取。利用短期高空间分辨率影像和长时序中等空间分辨率影像的特性,以贵州省息烽县为例,首先,从高空间分辨率影像中提取的耕地地块数据,同时,利用最大值合成法基于Landsat数据得到2003—2018年间的时序NDVI数据; 然后,运用阈值法识别撂荒地NDVI特征; 最后,叠加喀斯特石漠化信息,分析撂荒与石漠化的关系。结果表明: ①耕地地块和时序NDVI组合数据能够精准地识别提取撂荒地,在95.56%的耕地提取精度下撂荒地识别精度达90.7%,该方法在光学数据缺乏和耕地破碎的多云雨山区有很好的应用效果; ②耕地撂荒归一化植被指数(normalized difference vegetation index,NDVI)曲线在形态上表现为“V”型特征,连续撂荒曲线形态呈“U”型特征,累计撂荒NDVI曲线形态呈不对称“W”型、“M”型或多个“W”和“M”组合型,其变化复杂多样; ③非喀斯特撂荒地NDVI总体水平比石漠化撂荒地高,石漠化等级高低与NDVI曲线整体水平呈反比关系、与曲线波动程度呈正相关关系,撂荒次数与地块NDVI值呈反比关系、与NDVI曲线离散程度呈正相关关系。研究结果为喀斯特多云雨山区撂荒地的精准识别提取提供了高效、可行的思路和方法。

	服务

	把本文推荐给朋友
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	王玲玉
	陈全
	吴跃
	周忠发
	但雨生

关键词 ：喀斯特, 石漠化耕地, 撂荒地, 时序分析, 地块

Abstract：

The abandoned land has been spread all over the world and has become an important research direction for land use. Due to the lack of optical remote sensing images and serious mixed pixels in Karst rocky desertification land, it is difficult to accurately identify and extract the abandoned land. Based on short-term high precision image and long temporal resolution data features, taking Xifeng County in Guizhou Province as an example, using the high precision image accurately, and aided by maximum value composite (MVC) method, the authors calculated Landsat data for 2003—2018 time-series NDVI data, identified characteristics of abandoned land NDVI, and analyzed the relationship between the abandoned land and karst rocky desertification. The results are as follows: ①The combination of land parcels and time series NDVI can accurately identify and extract abandoned land, with an accuracy of 90.7% under the condition of 95.56% of cultivated land extraction. This method has a good application effect in cloudy and rainy mountains areas where optical remote sensing data are lacking and cultivated land is broken. ②The curve shape of the NDVI of the abandoned land is of “V” type, the continuous abandonment of arable land curve shape is of “U” type, and the cumulative NDVI curve shape is of asymmetric “W”, “M” or several combinations. ③The overall level of NDVI in non-karst abandoned lands is higher than that in rocky desertification abandoned lands. The level of rocky desertification is inversely proportional to the overall level of the curve and positively correlated with the degree of curve fluctuation. The number of abandoned lands is inversely proportional to the value of NDVI of the plot and positively correlated with the degree of dispersion of the NDVI curve. The results provide an efficient and feasible method for accurate identification and extraction of uninhabited land in karst cloudy and rainy mountain areas.

Key words： Karst rocky desertification of cultivated land abandoned land time series analysis cultivated land parcels

收稿日期: 2019-09-04 出版日期: 2020-10-09

TP75

基金资助:国家自然科学基金地区项目“喀斯特石漠化地区生态资产与区域贫困耦合机制研究”(41661088);贵州省高层次创新型人才培养计划—“百”层次人才项目(黔科合平台人才〔2016〕5674);贵州省研究生科研基金项目(黔教合YJSCXJH[2020])

通讯作者: 周忠发

作者简介: 王玲玉(1988-),女,博士研究生。主要从事喀斯特山区耕地与遥感应用研究。Email: wly_yu@163.com。

引用本文:

王玲玉, 陈全, 吴跃, 周忠发, 但雨生. 基于地块级时序NDVI的喀斯特山区撂荒地特征精准识别[J]. 国土资源遥感, 2020, 32(3): 23-31.
WANG Lingyu, CHEN Quan, WU Yue, ZHOU Zhongfa, DAN Yusheng. Accurate recognition and extraction of karst abandoned land features based on cultivated land parcels and time series NDVI. Remote Sensing for Land & Resources, 2020, 32(3): 23-31.

链接本文:

https://www.gtzyyg.com/CN/10.6046/gtzyyg.2020.03.04 或 https://www.gtzyyg.com/CN/Y2020/V32/I3/23

Fig.1 基于面向对象的耕地地块信息提取结果

2003—2018年Landsat影像数据集

Fig.2 研究区影像及地面验证主要路线
(底图为Google Earth 影像)

息烽县石漠化撂荒地块统计

Fig.3 非撂荒地与单次撂荒地典型NDVI特征曲线

Fig.4 连续撂荒地的典型NDVI特征曲线

Fig.5 累计撂荒地的典型NDVI特征曲线

Fig.6 不同石漠化撂荒地的典型NDVI特征曲线

Fig.7 息烽县累计撂荒地数量趋势

Fig.8-1 不同石漠化等级撂荒地NDVI均值变化趋势

Fig.8-2 不同石漠化等级撂荒地NDVI均值变化趋势

Tab.3 息烽县撂荒地提取精度评价表

[1]	Meyfroidt P, Lambin E F. Global forest transition prospects for an end to deforestation[J]. The Annual Review of Environment and Resources, 2011,36:343-371. doi: 10.1146/annurev-environ-090710-143732
[2]	陈欣怡, 郑国全. 国内外耕地撂荒研究进展[J]. 中国人口·资源与环境, 2018,28(s2):37-41.
	Chen X Y, Zheng G Q. Research progress on arable land abandonment in China and abroad[J]. China Population,Resources and Environment, 2018,28(s2):37-41.
[3]	李升发, 李秀彬. 耕地撂荒研究进展与展望[J]. 地理学报, 2016,71(3):370-389.
	Li S F, Li X B. Progress and prospect on farmland abandonment[J]. Acta Geographica Sinica, 2016,71(3):370-389.
[4]	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.
[5]	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.
	Shi T C, Xu X H. 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.
[6]	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.
[7]	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.
[8]	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.
	Xiao G F, Zhu X F, Hou C Y, 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.
[9]	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):227-233.
	Ma S J, Pei Z Y, Wang F, 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):227-233.
[10]	李阳兵, 罗光杰, 黄娟. 茂兰喀斯特自然保护区撂荒地时空演变、机制及其植被恢复[J]. 中国岩溶, 2017,36(4):447-453.
	Li Y B, Luo G J, Huang J. Spatial-temporal evolution,mechanism and vegetation restoration of abandoned farmland in the Maolan National Nature Reserve[J]. Carsologica Sinica, 2017,36(4):447-453.
[11]	Wardlow B D, Egbert S L, Kastens J H. Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S.central Great Plains[J]. Remote Sensing of Environment, 2007,108(3):290-310.
[12]	王磊, 王贺, 卢艳丽, 等. NDVI在农作物监测中的研究与应用[J]. 中国农业资源与区划, 2013,34(4):43-50.
	Wang L, Wang H, Lu Y L, et al. The research and application of NDVI in crop monitoring[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2013,34(4):43-50.
[13]	郭昱杉, 刘庆生, 刘高焕, 等. 基于MODIS时序NDVI主要农作物种植信息提取研究[J]. 自然资源学报, 2017,32(10):1808-1818.
	Guo Y S, Liu Q S, Liu G H, et al. Extraction of main crops in Yellow River delta based on MODIS NDVI time series[J]. Journal of Natural Resources, 2017,32(10):1808-1818.
[14]	许青云, 杨贵军, 龙慧灵, 等. 基于MODIS NDVI多年时序数据的农作物种植识别[J]. 农业工程学报, 2014,30(11):134-144.
	Xu Q Y, Yang G J, Long H L, et al. Crop information identification based on MODIS NDVI time-series data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2014,30(11):134-144.
[15]	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.
	Cheng W F. Study on remote sensing survey methods of wasteland in southern China[D]. Beijing:University of Chinese Academy of Sciences, 2011.
[16]	Qiu B, Luo Y, Tang Z, et al. Winter wheat mapping combining variations before and after estimated heading dates[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 2017,123:35-46.
[17]	朱长明, 骆剑承, 沈占锋, 等. 基于地块特征基元与多时相遥感数据的冬小麦播种面积快速提取[J]. 农业工程学报, 2011,27(9):94-99.
	Zhu C M, Luo J C, Shen Z F, et al. Winter wheat planting area extraction using multi-temporal remote sensing data based on field parcel characteristic[J]. Transactions of the Chinese Society of Agricultural Engineering, 2011,27(9):94-99.
[18]	邓刘洋, 沈占锋, 柯映明, 等. 基于地块尺度多时相遥感影像的冬小麦种植面积提取[J]. 农业工程学报, 2018,34(21):157-164.
	Deng L Y, Shen Z F, Ke Y M, et al. Winter wheat planting area extraction using multi-temporal remote sensing images based on field parcel[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018,34(21):157-164.
[19]	苏维词. 贵州喀斯特山区的土壤侵蚀性退化及其防治[J].中国岩溶, 2001(3):51-57.
	Su W C. Soil erosive deterioration and its control in karst mountainous regions of Guizhou Province[J].Carsologica Sinica 2001(3):51-57.
[20]	李瑞玲, 王世杰, 熊康宁, 等. 贵州省岩溶地区坡度与土地石漠化空间相关分析[J].水土保持通报, 2006(4):82-86.
	Li R L, Wang S J, Xiong K N, et al. Correlation between rocky desertification and slope degree in Karst area of Guizhou[J].Bulletin of Soil and Water Conservation 2006(4):82-86.
[21]	贵州省发展和改革委员会, 贵州省科技厅, 贵州师范大学. 贵州省喀斯特石漠化综合防治图集(2006—2050)[M]. 贵阳: 贵州人民出版社, 2007.
	Guizhou Development and Reform Commission,Guizhou Science and Technology Department,Guizhou Normal University. Atlas of comprehensive control of karst rocky desertification in Guizhou Province(2006—2050)[M]. Guiyang: Guizhou People’s Publishing House, 2007.
[22]	周忠发, 闫利会, 陈全, 等. 人为干预下喀斯特石漠化演变机制与调控[M]. 北京: 科学出版社, 2016.
	Zhou Z F, Yan L H, Chen Q, et al. Evolution mechanism and regulation of karst rocky desertification under human intervention[M]. Beijing: Science Press, 2016.
[23]	赵英时. 遥感应用分析原理与方法[M]. 北京: 科学出版社
	Zhao Y S. Theory and method of remote sensing application and analysis[M]. Beijing: Science Press, 2003.
[24]	程维芳, 周艺, 王世新, 等. 基于多光谱遥感的撂荒地识别方法研究[J]. 光谱学与光谱分析, 2011,31(6):1615-1620. pmid: 21847945
	Cheng W F, Zhou Y, Wang S X, et al. Study on the method of recognizing abandoned farmlands based on multispectral remote sensing[J]. Spectroscopy and Spectral Analysis, 2011,31(6):1615-1620. pmid: 21847945
[25]	王利军, 郭燕, 贺佳, 等. 基于决策树和SVM的Sentinel-2A影像作物提取方法[J]. 农业机械学报, 2018,49(9):146-153.
	Wang L J, Guo Y, He J, et al. Classification method by fusion of decision tree and SVM based on Sentinel-2A image[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018,49(9):146-153.

[1]	李梦梦, 范雪婷, 陈超, 李倩楠, 杨锦. 徐州矿区2016—2018年地面沉降监测与分析[J]. 自然资源遥感, 2021, 33(4): 43-54.
[2]	安全, 贺中华, 赵翠薇, 梁虹, 焦树林, 杨朝晖. 基于地貌视角的喀斯特流域水系分维估算方法适应性分析[J]. 国土资源遥感, 2019, 31(4): 104-111.
[3]	吴林霖, 官云兰, 李嘉伟, 袁晨鑫, 李睿. 基于MODIS影像喀斯特石漠化状况研究——以贵州省为例[J]. 国土资源遥感, 2019, 31(4): 235-242.
[4]	韩衍欣, 蒙继华. 面向地块的农作物遥感分类研究进展[J]. 国土资源遥感, 2019, 31(2): 1-9.
[5]	李雪冬, 张洪岩, 杨广斌, 李晓松, 周越. 辅以地貌类型的喀斯特地区土地利用信息提取[J]. 国土资源遥感, 2016, 28(3): 138-145.
[6]	梁若飞, 杨风暴, 王毅敏, 孟颖晨, 卫红. 一种类矩形引导的玉米田遥感图像分割算法[J]. 国土资源遥感, 2016, 28(3): 53-59.
[7]	董士伟, 孙丹峰, 张微微, 李红, 周连第. 农村土地承包经营地块现场测量方法与应用[J]. 国土资源遥感, 2013, 25(4): 155-159.
[8]	李苓苓, 朱文泉, 潘耀忠, 曹森, 朱再春. 基于高精度历史耕地地块的农区多光谱影像分割方法研究[J]. 国土资源遥感, 2011, 23(4): 20-25.

Viewed

Full text

Abstract

Cited

Shared

Discussed