基于GF-1与MODIS时空融合的南方丘陵区水稻提取研究
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摘要
[目的]水稻在我国粮食生产中占有重要的地位,针对南方低山丘陵区地形复杂多样,地块破碎的环境特点,利用遥感数据时空融合方法对水稻分布进行提取研究对作物精准估产和政府决策具有重要意义。[方法]以衡阳县为研究区,基于增强型时空数据融合模型(Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model,ESTARFM)将MODIS数据分别和GF-1WFV、Landsat 8影像进行数据融合,构建高时空分辨率的NDVI时间序列集,对NDVI序列进行滤波并获取各地物物候参数,采用决策树分类方法提取水稻种植面积。[结果](1)ESTARFM能有效融合MODIS和GF-1WFV、Landsat 8影像,其中Landsat 8数据的单波段融合效果(R_(NIR)=0.92,R_R=0.82)优于GF-1WFV融合结果(R_(NIR)=0.82,R_R=0.75),但NDVI结果相关性差异不大(Landsat:R_(NDVI)=0.93,GF-1WFV:R_(NDVI)=0.89);(2)利用决策树分类法进行地物分类,基于GF-1WFV融合数据的分类结果优于Landsat融合数据分类结果,其中GF-1WFV数据得到的分类结果总精度达到86.37%,单、双季稻的精度分别为62.26%与86.70%,Kappa系数达到0.80;而Landsat数据的分类总体精度为80.96%,单、双季稻精度分别为56.87%和70.74%,Kappa系数为0.72。[结论]GF-1WFV融合数据可用于获取精细的水稻种植分布;针对南方低山丘陵区,特别是地形复杂度较高的区域,GF-1 WFV数据比Landsat数据在水稻提取方面更具有优势。
Paddy rice plays an important role in grain production in China. In view of the complex and diverse topography and fragmented land in hilly area of south China, this research aims to extract rice distribution by using the spatiotemporal fusion model of remote sensing data, so as to provide the reference for the accurate estimation of crop yield and government decision-making. The research took Hengyang county of Hunan province as the research region. By utilizing the Enhanced Spatial and Temporal the Adaptive Reflectance Fusion Model(ESTARFM) with combination of pairs of MODIS-GF1 and MODIS-Landsat 8 image, it constructed the NDVI time-series with high spatiotemporal resolution. Moreover, it obtained phenological information through filtering NDVI series. In addition, it extracted the rice area by adopting the decision tree classification method. The results showed that(1) ESTARFM could effectively fuse MODIS and GF-1 WFV and Landsat-8 images, the fusion of single-band using Landsat 8 data(RNIR=0.92,RR=0.82) was better than GF-1 WFV(RNIR = 0.82, RR = 0.75), the fusion of NDVI both achieved high accuracy(Landsat: RNDVI = 0.93,GF-1 WFV: RNDVI =0.89).(2) The classification of using GF-1 WFV fusion data were superior to Landsat fusion data. The overall accuracy of using GF-1 WFV for rice identification was 86.37%, the Kappa index was 0.80. The accuracy of single-rice and double-rice was 62.26% and 86.70% respectively. For Landsat fusion data, the overall accuracy for rice identification was 80.96%, the Kappa index was 0.72. The precision of single and double cropping rice was 56.87% and 70.74%. The extraction method of GF-1 WFV fusion data has a good extraction performance in obtainning accurate rice planting distribution. For hilly area of south China, especially in the area with complex and diverse topography, the rice extraction area obtained by GF-1 WFV data has more advantages than by Landsat data.
Paddy rice plays an important role in grain production in China. In view of the complex and diverse topography and fragmented land in hilly area of south China, this research aims to extract rice distribution by using the spatiotemporal fusion model of remote sensing data, so as to provide the reference for the accurate estimation of crop yield and government decision-making. The research took Hengyang county of Hunan province as the research region. By utilizing the Enhanced Spatial and Temporal the Adaptive Reflectance Fusion Model(ESTARFM) with combination of pairs of MODIS-GF1 and MODIS-Landsat 8 image, it constructed the NDVI time-series with high spatiotemporal resolution. Moreover, it obtained phenological information through filtering NDVI series. In addition, it extracted the rice area by adopting the decision tree classification method. The results showed that(1) ESTARFM could effectively fuse MODIS and GF-1 WFV and Landsat-8 images, the fusion of single-band using Landsat 8 data(RNIR=0.92,RR=0.82) was better than GF-1 WFV(RNIR = 0.82, RR = 0.75), the fusion of NDVI both achieved high accuracy(Landsat: RNDVI = 0.93,GF-1 WFV: RNDVI =0.89).(2) The classification of using GF-1 WFV fusion data were superior to Landsat fusion data. The overall accuracy of using GF-1 WFV for rice identification was 86.37%, the Kappa index was 0.80. The accuracy of single-rice and double-rice was 62.26% and 86.70% respectively. For Landsat fusion data, the overall accuracy for rice identification was 80.96%, the Kappa index was 0.72. The precision of single and double cropping rice was 56.87% and 70.74%. The extraction method of GF-1 WFV fusion data has a good extraction performance in obtainning accurate rice planting distribution. For hilly area of south China, especially in the area with complex and diverse topography, the rice extraction area obtained by GF-1 WFV data has more advantages than by Landsat data.
引文
[1] 王一杰,邸菲,辛岭.我国粮食主产区粮食生产现状、存在问题及政策建议.农业现代化研究,2018(1):37-47.
[2] 刘珍环,李正国,唐鹏钦,等.近30年中国水稻种植区域与产量时空变化分析.地理学报,2013(5):680-693.
[3] 李志鹏,刘珍怀,李正国,等.水稻空间分布遥感提取研究进展与展望.中国农业资源与区划,2014,35(6):9-18.
[4] 李根,景元书,王琳,等.基于MODIS时序植被指数和线性光谱混合模型的水稻面积提取.大气科学学报,2014,37(1):119-126.
[5] 张莉,吴文斌,左丽君,等.基于EOS/MODIS数据的南方水稻面积提取研究.中国农业资源与区划,2011,32(4):39-44.
[6] 于文颖,冯锐,纪瑞鹏,等.利用MODIS数据提取水稻种植面积及精度分析.中国农学通报,2013,29(33):32-36.
[7] Xiao X M,Boles S,Frolking S,et al.Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images.REMOTE SENSINg of ENVIRONMENT,2006,100(1):95-113.
[8] Xiao X M,Boles S,Liu J Y,et al.Mapping paddy rice agriculture in southern China using multi-temporal MODIS images.REMOTE SENSINg of ENVIRONMENT,2005,95(4):480-492.
[9] 黄振国,陈仲新,刘芳清,等.利用SPOT5影像提取水稻种植面积的研究——以湖南株洲市区为例.湖南农业大学学报,2013,39(2):137-140.
[10] Masuoka P M,Claborn D M,Andre R G,et al.Use of IKONOs and Landsat for malaria control in the Republic of Korea.REMOTE SENSINg of ENVIRONMENT,2003,88(1-2):187-194.
[11] 孙佩军,张锦水,潘耀忠,等.构建时空融合模型进行水稻遥感识别.遥感学报,2016,20(2):328-343.
[12] 邬明权,牛铮,王长耀.利用遥感数据时空融合技术提取水稻种植面积.农业工程学报,2010,26(S2):48-52.
[13] Gao F,Masek J,Schwaller M,et al.On the blending of the Landsat and MODIS surface reflectance:Predicting daily Landsat surface reflectance.IEEE TRANSACTIONS ON GEOSCIENCe and REMOTE SENSING,2006,44(8):2207-2218.
[14] Tian F,Wang Y,Fensholt R,et al.Mapping and Evaluation of NDVI Trends from Synthetic Time Series Obtained by Blending Landsat and MODIS Data around a Coalfield on the Loess Plateau.REMOTE SENSING,2013,5(9):4255-4279.
[15] Singh D.Evaluation of long-term NDVI time series derived from Landsat data through blending with MODIS data.ATMOSFERA,2012,25(1):43-63.
[16] Zhu X,Chen J,Gao F,et al.An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions.REMOTE SENSINg of ENVIRONMENT,2010,114(11):2610-2623.
[17] Jonsson P,Eklundh L.TIMESAT-a program for analyzing time-series of satellite sensor data.comPUTERS & GEOSCIENCES,2004,30(8):833-845.
[18] 杨闫君,占玉林,田庆久,等.基于GF-1/WFVNDVI时间序列数据的作物分类.农业工程学报,2015,31(24):155-161.
[19] 王昆,张丽,王志勇,等.基于半方差函数的STARFM改进模型.测绘科学,2013,38(3):140-142.
[20] 陆佩玲,于强,贺庆棠.植物物候对气候变化的响应.生态学报,2006(3):923-929.
[21] 于文婧,刘晓娜,孙丹峰,等.基于HJ-CCD数据和决策树法的干旱半干旱灌区土地利用分类.农业工程学报,2016,32(2):212-219.
[22] 康峻,侯学会,牛铮,等.基于拟合物候参数的植被遥感决策树分类.农业工程学报,2014,30(9):148-156.
[23] 刘晓娜,封志明,姜鲁光.基于决策树分类的橡胶林地遥感识别.农业工程学报,2013,29(24):163-172.
[24] 陈君颖,田庆久.高分辨率遥感植被分类研究.遥感学报,2007(2):221-227.
[25] Murthy S K.Automatic construction of decision trees from data:A multi-disciplinary survey.DATA MININg and KNOWLEDGE DISCOVERY,1998,2(4):345-389.
[26] Myles A J,Feudale R N,Liu Y,et al.An introduction to decision tree modeling.Journal of Chemometrics,2010,18(6):-.
[27] 衡阳市统计局.衡阳县2016年国民经济和社会发展统计公报.2016.
[2] 刘珍环,李正国,唐鹏钦,等.近30年中国水稻种植区域与产量时空变化分析.地理学报,2013(5):680-693.
[3] 李志鹏,刘珍怀,李正国,等.水稻空间分布遥感提取研究进展与展望.中国农业资源与区划,2014,35(6):9-18.
[4] 李根,景元书,王琳,等.基于MODIS时序植被指数和线性光谱混合模型的水稻面积提取.大气科学学报,2014,37(1):119-126.
[5] 张莉,吴文斌,左丽君,等.基于EOS/MODIS数据的南方水稻面积提取研究.中国农业资源与区划,2011,32(4):39-44.
[6] 于文颖,冯锐,纪瑞鹏,等.利用MODIS数据提取水稻种植面积及精度分析.中国农学通报,2013,29(33):32-36.
[7] Xiao X M,Boles S,Frolking S,et al.Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images.REMOTE SENSINg of ENVIRONMENT,2006,100(1):95-113.
[8] Xiao X M,Boles S,Liu J Y,et al.Mapping paddy rice agriculture in southern China using multi-temporal MODIS images.REMOTE SENSINg of ENVIRONMENT,2005,95(4):480-492.
[9] 黄振国,陈仲新,刘芳清,等.利用SPOT5影像提取水稻种植面积的研究——以湖南株洲市区为例.湖南农业大学学报,2013,39(2):137-140.
[10] Masuoka P M,Claborn D M,Andre R G,et al.Use of IKONOs and Landsat for malaria control in the Republic of Korea.REMOTE SENSINg of ENVIRONMENT,2003,88(1-2):187-194.
[11] 孙佩军,张锦水,潘耀忠,等.构建时空融合模型进行水稻遥感识别.遥感学报,2016,20(2):328-343.
[12] 邬明权,牛铮,王长耀.利用遥感数据时空融合技术提取水稻种植面积.农业工程学报,2010,26(S2):48-52.
[13] Gao F,Masek J,Schwaller M,et al.On the blending of the Landsat and MODIS surface reflectance:Predicting daily Landsat surface reflectance.IEEE TRANSACTIONS ON GEOSCIENCe and REMOTE SENSING,2006,44(8):2207-2218.
[14] Tian F,Wang Y,Fensholt R,et al.Mapping and Evaluation of NDVI Trends from Synthetic Time Series Obtained by Blending Landsat and MODIS Data around a Coalfield on the Loess Plateau.REMOTE SENSING,2013,5(9):4255-4279.
[15] Singh D.Evaluation of long-term NDVI time series derived from Landsat data through blending with MODIS data.ATMOSFERA,2012,25(1):43-63.
[16] Zhu X,Chen J,Gao F,et al.An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions.REMOTE SENSINg of ENVIRONMENT,2010,114(11):2610-2623.
[17] Jonsson P,Eklundh L.TIMESAT-a program for analyzing time-series of satellite sensor data.comPUTERS & GEOSCIENCES,2004,30(8):833-845.
[18] 杨闫君,占玉林,田庆久,等.基于GF-1/WFVNDVI时间序列数据的作物分类.农业工程学报,2015,31(24):155-161.
[19] 王昆,张丽,王志勇,等.基于半方差函数的STARFM改进模型.测绘科学,2013,38(3):140-142.
[20] 陆佩玲,于强,贺庆棠.植物物候对气候变化的响应.生态学报,2006(3):923-929.
[21] 于文婧,刘晓娜,孙丹峰,等.基于HJ-CCD数据和决策树法的干旱半干旱灌区土地利用分类.农业工程学报,2016,32(2):212-219.
[22] 康峻,侯学会,牛铮,等.基于拟合物候参数的植被遥感决策树分类.农业工程学报,2014,30(9):148-156.
[23] 刘晓娜,封志明,姜鲁光.基于决策树分类的橡胶林地遥感识别.农业工程学报,2013,29(24):163-172.
[24] 陈君颖,田庆久.高分辨率遥感植被分类研究.遥感学报,2007(2):221-227.
[25] Murthy S K.Automatic construction of decision trees from data:A multi-disciplinary survey.DATA MININg and KNOWLEDGE DISCOVERY,1998,2(4):345-389.
[26] Myles A J,Feudale R N,Liu Y,et al.An introduction to decision tree modeling.Journal of Chemometrics,2010,18(6):-.
[27] 衡阳市统计局.衡阳县2016年国民经济和社会发展统计公报.2016.
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