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遥感技术与应用  2020, Vol. 35 Issue (3): 596-605    DOI: 10.11873/j.issn.1004-0323.2020.3.0596
LUCC专栏     
基于季相变化特征的撂荒地遥感提取方法研究
王红岩1,2(),汪晓帆1,高亮2,李强子2(),赵龙才2,杜鑫2,张源2
1.中国国土勘测规划院自然资源部土地利用重点实验室,北京 100035
2.中国科学院遥感与数字地球研究所,北京 100101
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
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摘要:

在我国西南地区耕种条件差,地块比较破碎,地块类型比较复杂,中低分辨率遥感数据难以满足撂荒地提取的需要。选取贵州修文县为试验区,基于高分辨率卫星遥感数据(哨兵2号),探索单期或多期影像在中国西南地区的撂荒地检测能力,构建撂荒地遥感监测方法,为今后我国西南地区撂荒地统计调查提供参考。结合野外调查数据,在划分不同撂荒地类型基础上,综合遥感影像的光谱特征、植被指数特征以及多时相植被指数变化特征分析,优选不同类别撂荒地遥感提取敏感特征集,利用CART决策树分类方法,提取不同类型的撂荒地。结果表明:①单个时相对不同类型的撂荒地识别能力差异显著,基于单时相影像,难以开展撂荒地高精度遥感监测提取;②不同时相的植被指数变化特征对撂荒地的识别能力较强,其中比值植被指数优于差值植被指数和归一化植被指数;③以贵州修文县为例,开展了撂荒地空间分布制图及撂荒面积统计分析,修文县撂荒地面积约为6 460 hm2,占修文县耕地面积的13%;④基于多时相高分辨遥感数据,通过季相变化特征构建的撂荒地检测方法,能够满足我国西南地区撂荒地高精度遥感监测提取,为大范围撂荒地遥感调查和制图提供技术参考。

关键词: 撂荒地CART多时相差值植被指数哨兵2号    
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
收稿日期: 2019-03-18 出版日期: 2020-07-10
ZTFLH:  TP79  
基金资助: 国土资源部土地利用重点实验室开放基金项目(KLLU201804);国家重点研发计划课题(2016YFB0501505);国家自然科学青年基金项目(41501467)
通讯作者: 李强子     E-mail: wanghy@radi.ac.cn;liqz@radi.ac.cn
作者简介: 王红岩(1986-),女,助理研究员,主要从事生态遥感、农业遥感、土地退化遥感监测方面的研究。E?mail: wanghy@radi.ac.cn
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引用本文:

王红岩,汪晓帆,高亮,李强子,赵龙才,杜鑫,张源. 基于季相变化特征的撂荒地遥感提取方法研究[J]. 遥感技术与应用, 2020, 35(3): 596-605.

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.

链接本文:

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

图1  研究区地理位置
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
表1  Sentinel-2A卫星数据波段
图2  野外调查样本点分布图
图3  不同撂荒类型野外调查照片
图4  技术路线图
图5  箱型图的结构示意图
图6  典型地物单时相光谱曲线(2018-6-8)
图7  主要典型地物特征箱型图
图8  撂荒地检测决策树模型
图9  贵州修文县撂荒地空间分布图
类别撂荒地其他总计
撂荒地13220152
其他25152177
总计157172329
表2  精度验证混淆矩阵表
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