Please wait a minute...
img

官方微信

遥感技术与应用  2019, Vol. 34 Issue (4): 785-792    DOI: 10.11873/j.issn.1004-0323.2019.4.0785
作物信息提取专栏     
基于GF-1影像的耕地地块破碎区水稻遥感提取
张海东1(),田婷1,张青1(),陆洲2,石春林3,谭昌伟4,罗明4,钱春花5
1. 江苏太湖地区农业科学研究所,江苏 苏州 215155
2. 中国科学院地理科学与资源研究所,北京 100101
3. 江苏省农业科学院,江苏 南京 210014
4. 扬州大学,江苏 扬州 225009
5. 苏州农业职业技术学院,江苏 苏州 215008
Study on Extraction of Paddy Rice Planting Area in Low Fragmented Regions based on GF-1 WFV Images
Haidong Zhang1(),Ting Tian1,Qing Zhang1(),Zhou Lu2,Chunlin Shi3,Changwei Tan4,Ming Luo4,Chunhua Qian5
1. Institute of Agricultural Sciences in Taihu Area of Jiangsu,Suzhou 215155,China
2. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy Sciences, Beijing 100101, China
3. Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
4. Yangzhou University, Yangzhou 225009, China, 5. Suzhou Polytechnic Institute of Agriculture,Suzhou 215008, China
5. Suzhou Polytechnic Institute of Agriculture,Suzhou 215008,China
 全文: PDF(2690 KB)   HTML
摘要:

耕地地块破碎区水稻遥感提取是作物监测研究的热点问题之一。以苏州市高新区为例,通过挖掘关键物候期水稻与下垫面水体光谱特征组合差异,基于分蘖期与齐穗期两景16 m分辨率的GF-1 WFV数据,构建归一化差值植被指数(NDVI)差值法、归一化水体指数和比值植被指数(NDWI-RVI)差值法提取水稻分布,并深入探究了水稻面积提取精度及空间重合度影响因素。结果显示:与非监督分类和监督分类方法相比,植被指数差值法水稻识别精度贡献率可提升30%以上,NDVI差值法提取水稻种植面积的精度、空间重合度、制图总体精度和Kappa系数分别为86.2%、66.1%、92.2%和0.72;NDWI-RVI差值法上述指标分别高达95.5%、78.4%、93.5%和0.846,实现了利用少量中高分辨率遥感影像精确提取耕地地块破碎区水稻分布的目的,可实际服务于太湖地区农业生产及相关决策支持。

关键词: 时空分辨率GF?1植被指数差值提取精度空间重合度    
Abstract:

The extraction of paddy rice planting area in low fragmented regions based on remote sensing images is a hotspot in crop monitoring. Taking Gaoxin district of Suzhou city in Taihu Lake region as a case study, the rice and underlying water spectral characteristics in critical phenophase were studied in-depth to reduce the demand of remote sensing images, and only two GF-1 WFV images with resolution of 16 m during rice tillering and full heading stages were employed to extract the paddy rice planting area. Two vegetation index methods, including difference of Normalized Differential Vegetation Index (NDVI) and the combination of difference of Normalized Differential Water Index (NDWI) and Ratio Vegetation Index (RVI) were studied. The results suggested that both the methods effectively promoted the extraction precision, comparing with the results of supervised classification and unsupervised classification methods. The area recognition accuracy, space consistency mapping accuracy and kappa coefficient of NDVI method were 86.2%, 66.1%, 92.2% and 0.72, while those of NDWI-RVI method were up to 95.5%, 78.4%, 93.5% and 0.85, respectively. The two methods realized the purpose of accurately extracting rice area in low fragmented regions by using a few medium and high resolution remote sensing images, and can be effectively serviced for actual production and relevant decision support in Taihu Lake region.

Key words: Temporal-spatial resolution    GF-1    Difference of vegetation index    Extraction of paddy rice    Space consistency
收稿日期: 2018-08-21 出版日期: 2019-10-16
ZTFLH:  S127  
基金资助: 江苏省农业科技自主创新资金项目(CX(16)1042);苏州市农业科学院科研项目(8111722);苏州市科技计划项目(SNG201643);江苏省农业三新工程项目(SXGC[2017]245)
通讯作者: 张青     E-mail: zhdsznky@163.com;qzhangsaas@163.com
作者简介: 张海东(1984—),男,江苏东台人,博士,副研究员,主要从事农业信息化、资源遥感研究。E?mail:zhdsznky@163.com
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  
张海东
田婷
张青
陆洲
石春林
谭昌伟
罗明
钱春花

引用本文:

张海东,田婷,张青,陆洲,石春林,谭昌伟,罗明,钱春花. 基于GF-1影像的耕地地块破碎区水稻遥感提取[J]. 遥感技术与应用, 2019, 34(4): 785-792.

Haidong Zhang,Ting Tian,Qing Zhang,Zhou Lu,Chunlin Shi,Changwei Tan,Ming Luo,Chunhua Qian. Study on Extraction of Paddy Rice Planting Area in Low Fragmented Regions based on GF-1 WFV Images. Remote Sensing Technology and Application, 2019, 34(4): 785-792.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2019.4.0785        http://www.rsta.ac.cn/CN/Y2019/V34/I4/785

图1  研究区域图
图2  高新区水稻分蘖期与齐穗期GF-1影像图
5月 6月 7月 8月 9月 10月 11月
播种 出苗 移栽 分蘖 拔节 孕穗 齐穗 乳熟 蜡熟 完熟
表1  研究区水稻主要物候期
图3  水田与其他地物各波段像元亮度值
图4  不同方法提取水稻面积对比
检验样点(个) NDVI差值法 NDWI-RVI差值法 监督分类法 非监督分类法
水稻 其他 水稻 其他 水稻 其他 水稻 其他
水稻 361 139 436 64 321 179 171 329
其他 77 2 201 97 2 181 275 2 003 201 2 077
总样点数 438 2 340 533 2 245 596 2 182 372 2 406
生产精度/% 82.4 81.8 53.9 46.0
用户精度/% 72.2 87.2 64.2 34.2
总体精度/% 92.2 93.5 83.7 80.9
Kappa系数 0.723 0.809 0.485 0.282
表2  不同遥感提取方法水稻识别精度
图5  植被指数差值法水稻种植区识别效果的空间差异
1 Liu Lihua , Yin Changbin . Ecosystem Service Value of Rice Paddies based on Contingent Valuation Method——A Case Study at Suzhou City of Jiangsu Province [J]. Bulletin of Soil and Water Conservation, 2015, 35(2): 355-360.
1 刘利花, 尹昌斌 . 基于意愿调查法的水田生态服务价值研究——以江苏省苏州市为例[J]. 水土保持通报, 2015, 35(2): 355-360.
2 Dong J , Xiao X . Evolution of Regional to Global Paddy Rice Mapping Methods: A Review[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 2016, 119: 214-227.
3 Huang Qing , Wu Wenbin , Deng Hui , et al . Study on Planting Areas Extraction of Remote Sensing and Monitoring of Crop Growth of Winter Wheat and Rice in Jiangsu Province in 2009 [J]. Jiangsu Agricultural Sciences, 2010(6): 508-511.
3 黄青, 吴文斌, 邓辉, 等 . 2009年江苏省冬小麦和水稻种植面积信息遥感提取及长势监测[J]. 江苏农业科学, 2010(6): 508-511.
4 Azar R , Villa P , Stroppiana D , et al . Assessing in Season Crop Classification Performance Using Satellite Data: A Test Case in Northern Italy [J]. European Journal of Remote Sensing, 2016, 49: 361-380.
5 Liu Guodong , Wu Mingquan , Niu Zheng , et al . Investigation Method for Crop Area Using Remote Sensing Sampling based on GF-1 Satellite Data [J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(5): 160-166.
5 刘国栋, 邬明权, 牛铮, 等 . 基于GF-1卫星数据的农作物种植面积遥感抽样调查方法[J]. 农业工程学报, 2015, 31(5): 160-166.
6 Ji Zhonglin , Zhang Yueping , Li Qiaoxuan , et al . Planting Information Extraction of Winter Wheat and Rape based on GF-1 Images [J]. Remote Sensing Technology and Application, 2017, 32(4):760-765.
6 姬忠林, 张月平, 李乔玄, 等 . 基于GF-1影像的冬小麦和油菜种植信息提取[J]. 遥感技术与应用, 2017, 32(4):760-765.
7 Qiu Lin , Lu Bihui , Sun Ling , et al . Effect on Rice Identification Accuracy based on Different Spatial Resolution Images of GF-1 [J]. Jiangsu Journal of Agricultural Sciences, 2019, 35(1): 75-80.
7 邱琳, 卢必慧, 孙玲, 等 . GF-1卫星影像的空间分辨率对水稻识别精度的影响[J]. 江苏农业学报, 2019, 35(1): 75-80.
8 Singha M , Wu B , Zhang M . Object-based Paddy Rice Mapping Using HJ-1A/B Data and Temporal Features Extracted from Time Series MODIS NDVI Data[J]. Sensors, 2016, 17(10):1-17.
9 Qiu B , Li W , Tang Z , et al . Mapping Paddy Rice Areas based on Vegetation Phenology and Surface Moisture Conditions [J]. Ecological Indicators, 2015, 56: 79-86.
10 Lu Zhou , Lu Jiancheng , Huang Qiting , et al . Investigation On Crop Planting Structure based on Remote Sensing Big Data——A Case Study at Laibin City of Guangxi Province [J]. Chinese Agricultural Science Bulletin, 2014, 30: 24-29.
10 陆洲, 路剑承, 黄启厅, 等 . 基于遥感大数据的精细化种植结构调查——以广西来宾为例[J]. 中国农学通报, 2014, 30: 24-29.
11 Shan Jie , Sun Ling , Yu Kun , et al . Rice Planting Area Monitoring based on GF-1 Satellite Images of Different Time Phases [J]. Jiangsu Agricultural Sciences, 2017, 45(22): 229-232.
11 单捷, 孙玲, 于堃, 等 . 基于不同时相高分一号卫星影像的水稻种植面积监测研究 [J]. 江苏农业科学, 2017, 45(22): 229-232.
12 Song Panpan , Du Xin , Wu Liangcai , et al . Research on the Method of Rice Remote Sensing Identification based on Spectral Time-series Fitting in Southern China [J]. Geo-Information Science, 2017, 19(1): 117-124.
12 宋盼盼, 杜鑫, 吴良才, 等 . 基于光谱时间序列拟合的中国南方水稻遥感识别方法研究[J].地球信息科学学报, 2017, 19(1): 117-124.
13 Kontgis C , Schneider A , Ozdogan M . Mapping Rice Paddy Extent and Intensification in the Vietnamese Mekong River Delta with Dense Time Stacks of Landsat Data[J]. Remote Sensing of Environment, 2015, 169: 255-269.
14 Qiu B , Lu D , Tang Z , et al . Automatic and Adaptive Paddy Rice Mapping Using Landsat Images: Case Study in Songnen Plain in Northeast China[J]. Science of the Total Environment, 2017, 598: 581-592.
15 Sakamoto T , Sprague D S , Okamoto K , et al . Semi-automatic Classification Method for Mapping the Rice-planted Areas of Japan Using Multi-temporal Landsat Images[J]. Remote Sensing Applications: Society and Environment, 2018, 10: 7-17.
16 Xie Dengfeng , Zhang Jinshui , Pan Yaozhong , et al . Fusion of MODIS and Landsat 8 Images to Generate High Spatial-temporal Resolution Data for Mapping Autumn Crop Distribution [J]. Journal of Remote Sensing, 2015, 19(5): 791-805.
16 谢登峰, 张锦水, 潘耀忠, 等 . Landsat 8和MODIS融合构建高时空分辨率数据识别秋粮作物[J].遥感学报, 2015, 19(5): 791-805.
17 Zhu Tong , Zhang Xuexia , Wang Shiyuan , et al . Extraction of Spring Maize Planting Area by Combined Phenological Feature with Mixed Spectral Information [J]. Journal of Shenyang Agricultural University, 2017, 48(3): 328-337.
17 朱彤, 张学霞, 王士远,等 . 基于物候特征和混合光谱信息的春玉米种植面积提取[J]. 沈阳农业大学学报, 2017, 48(3): 328-337.
18 Yang Y J , Huang Y , Tian Q J , et al . The Extraction Model of Paddy Rice Information based on GF-1 Satellite WFV Images [J]. Spectroscopy and Spectral Analysis, 2015, 35(11): 3255-3261.
19 Zhang Ming , Huang Shuangyan . Remote Sensing Image Classification based on Landsat-8 [J]. Geomatics & Spatial Information Technology, 2019, 42(1): 177-180.
19 张明, 黄双燕 . 基于Landsat-8的遥感影像分类研究[J]. 测绘与空间地理信息, 2019, 42(1): 177-180.
20 Fang Hongliang . Analysis of Two Remote Sensing Methods of Rice Planting Areas Extraction[J]. Acta Geographica Sinica, 1998,53(1): 58-65.
20 方红亮 . 两种水稻种植面积遥感提取方案的分析[J]. 地理学报, 1998,53(1): 58-65.
21 Li Xiaohui , Wang Hong , Li Xiaobing , et al . Study on Crops Remote Sensing Classification based on Multi-temporal Landsat 8 OLI Images[J]. 2019, 34(2): 389-397.
21 李晓慧, 王宏, 李晓兵, 等 . 基于多时相Landsat 8 OLI影像的农作物遥感分类研究[J]. 遥感技术与应用, 2019, 34(2): 389-397.
22 Liu Zhenbo , Zou Xian , Ge Yunjian , et al . Retrieval Rice Leaf Area Index Using Random Forest Algorithm based on GF-1 WFV Remote Sensing Data[J]. 2018, 33(3): 458-464.
22 刘振波, 邹娴, 葛云健, 等 . 基于高分一号WFV影像的随机森林算法反演水稻LAI[J]. 遥感技术与应用, 2018, 33(3): 458-464.
[1] 陈阳,范建容,张云,李胜,甘泉,应国伟,曹伟超. 多云雾地区高时空分辨率植被覆盖度构建方法研究[J]. 遥感技术与应用, 2016, 31(3): 518-529.
[2] 郭文静,李爱农,赵志强,王继燕. 基于AVHRR和TM数据的时间序列较高分辨率NDVI数据集重构方法[J]. 遥感技术与应用, 2015, 30(2): 267-276.