基于GF-1卫星数据监测灌区灌溉面积方法研究——以东雷二期抽黄灌区为例
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摘要
由于田块破碎、灌区信息化水平不高、土壤墒情反演困难等原因,在我国开展较高精度灌溉面积遥感监测依然面临很多困难。基于GF-1较高空间分辨率卫星数据,通过光谱匹配方法像元尺度应用,并引入OTSU自适应阈值算法,构建了高分辨率灌溉面积遥感监测新方法。选择我国西北干旱半干旱区典型渠灌灌区即东雷二期抽黄灌区为研究区,对其2018年的主要粮食作物种植强度及其灌溉面积开展了遥感识别提取研究。结果表明,东雷二期抽黄灌区灌溉面积为81 571.58 hm~2,其中双季轮作(小麦与玉米轮作)灌溉面积为40 335.88 hm~2,单季小麦灌溉面积为15 276.94 hm~2,单季玉米灌溉面积为14 059.14 hm~2;各灌溉子系统灌溉面积由大到小排序依次是流曲、孙镇、兴镇、荆姚、刘集、蒲城和大荔;通过野外采样精度验证,结果总体精度为88.27%(Kappa系数为0.8308),与国际水管理研究所灌溉数据产品相比,能更有效识别小田块灌溉分布及建设用地信息,在作物种植强度及其灌溉面积分布方面更符合我国实际情况,可为干旱监测预警、灌溉面积监测、灌溉用水效益评估等提供技术保障。
Due to the fragmentation of the field, the low level of informatization in the irrigation area, and the difficulty in inversion of soil moisture, the remote sensing monitoring of high precision irrigation area in China still faces many difficulties. Based on the GF-1 satellite data with higher spatial resolution, a new remote sensing monitoring method for irrigation area with high-resolution was constructed through the application of spectral matching method in pixel scale and the introduction of OTSU adaptive threshold algorithm. A typical canal irrigation area, Donglei Phase Ⅱ Irrigation District pumping water from the Yellow River, in the arid and semi-arid in Northwest China, was selected as the research area, and the planting intensity and irrigation area of wheat and corn in 2018 were studied by remote sensing identification and extraction. The Donglei Phase Ⅱ Irrigation District is 81571.58 hm~2, of which the irrigation area of double-season rotation(wheat and corn rotation) is 40335.88 hm~2,the irrigated area of single-season wheat is15276.94 hm~2, and that of single-season maize is 14059.14 hm~2. The areas of each irrigation subsystem from large to small is listed as Liuqu, Sunzhen, Xingzhen, Jingyao, Liuji, Pucheng and Daxie. The accuracy is verified by field sampling, which show that the overall accuracy is 88.27%(Kappa=0.8308). Compared with the irrigation data products of the International Water Management Institute, it can more effectively identify the irrigation distribution and construction land information of small fields, and is more in line with the actual situation in China in terms of crop planting intensity and irrigation area distribution,which can provide technical support for drought monitoring and early warning, irrigated area monitoring, irrigation water benefit evaluation and so on.
Due to the fragmentation of the field, the low level of informatization in the irrigation area, and the difficulty in inversion of soil moisture, the remote sensing monitoring of high precision irrigation area in China still faces many difficulties. Based on the GF-1 satellite data with higher spatial resolution, a new remote sensing monitoring method for irrigation area with high-resolution was constructed through the application of spectral matching method in pixel scale and the introduction of OTSU adaptive threshold algorithm. A typical canal irrigation area, Donglei Phase Ⅱ Irrigation District pumping water from the Yellow River, in the arid and semi-arid in Northwest China, was selected as the research area, and the planting intensity and irrigation area of wheat and corn in 2018 were studied by remote sensing identification and extraction. The Donglei Phase Ⅱ Irrigation District is 81571.58 hm~2, of which the irrigation area of double-season rotation(wheat and corn rotation) is 40335.88 hm~2,the irrigated area of single-season wheat is15276.94 hm~2, and that of single-season maize is 14059.14 hm~2. The areas of each irrigation subsystem from large to small is listed as Liuqu, Sunzhen, Xingzhen, Jingyao, Liuji, Pucheng and Daxie. The accuracy is verified by field sampling, which show that the overall accuracy is 88.27%(Kappa=0.8308). Compared with the irrigation data products of the International Water Management Institute, it can more effectively identify the irrigation distribution and construction land information of small fields, and is more in line with the actual situation in China in terms of crop planting intensity and irrigation area distribution,which can provide technical support for drought monitoring and early warning, irrigated area monitoring, irrigation water benefit evaluation and so on.
引文
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[18] TELUGUNTLA P,THENKABAIL P S,XIONG J,et al. Spectral matching techniques(SMTs)and automated cropland classification algorithms(ACCAs)for mapping croplands of Australia using MODIS 250-m time-series(2000—2015)data[J]. International Journal of Digital Earth,2017,10(9):1-34.
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[2] TELUGUNTLA P,RYU D,GEORGE B,et al. Mapping flooded rice paddies using time series of modis imagery in the Krishna River Basin,India[J]. Remote Sensing,2015,7(7):8858-8882.
[3] AMPRAKO J L. The United Nations World Water Development Report 2015:Water for a Sustainable World[R].2015.
[4] FRIES R S D,HANSEN M,TOWNSHEND J R G,et al. Global land cover classifications at 8 km spatial resolution:The use of training data derived from Landsat imagery in decision tree classifiers[J]. International Journal of Remote Sensing,1998,19(16):3141-3168.
[5] WALDNER F,CANTO G S,DEFOURNY P. Automated annual cropland mapping using knowledge-based temporal features[J]. Isprs Journal of Photogrammetry&Remote Sensing,2015,110:1-13.
[6] THENKABAIL P S,SCHULL M,TURRAL H. Ganges and Indus river basin land use/land cover(LULC)and irrigated area mapping using continuous streams of MODIS data[J]. Remote Sensing of Environment,2005,95(3):317-341.
[7] ZHOU Y,XIAO X,QIN Y,et al. Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images[J]. Int. J. Appl. Earth Obs. Geoinf.,2016,46:1-12.
[8] SHAO Y,LUNETTA R S. Comparison of support vector machine,neural network,and CART algorithms for the land-cover classification using limited training data points[J]. Isprs Journal of Photogrammetry&Remote Sensing,2012,70:78-87.
[9] THENKABAIL P S,WU Z. An automated cropland classification algorithm(ACCA)for Tajikistan by combining landsat,MODIS,and secondary data[J]. Remote Sensing,2012,4(10):2890-2918.
[10] THENKABAIL P S,GANGADHARARAO P,BIGGS T W,et al. Spectral matching techniques to determine historical land-use/land-cover(LULC)and irrigated areas using time-series 0. 1-degree AVHRR Pathfinder9[J].Photogrammetric Engineering&Remote Sensing,2007,73(9):1029-1040.
[11] DUVEILLER G,LOPEZ-LOZANO R,CESCATTI A. Exploiting the multi-angularity of the MODIS temporal signal to identify spatially homogeneous vegetation cover:A demonstration for agricultural monitoring applications[J]. Remote Sensing of Environment,2015,166:61-77.
[12] PANTAZI X E,MOSHOU D,ALEXANDRIDIS T,et al. Wheat yield prediction using machine learning and advanced sensing techniques[J]. Computers&Electronics in Agriculture,2016,121:57-65.
[13] XU T,DEINES J M,KENDALL A D,et al. Addressing challenges for mapping irrigated fields in subhumid temperate regions by integrating remote sensing and hydroclimatic data[J]. Remote Sensing,2019,11(3):370.
[14] GHULAM A,QIN Q,ZHAN Z. Designing of the perpendicular drought index[J]. Environmental Geology,2007,52(6):1045-1052.
[15] THENKABAIL P S,BIRADAR C M,NOOJIPADY P,et al. Global irrigated area map(GIAM),derived from remote sensing,for the end of the last millennium[J]. International Journal of Remote Sensing,2009,30(14):3679-3733.
[16] THENKABAIL P S,BIRADAR C M,NOOJIPADY P,et al. Global irrigated area map(GIAM),derived from remote sensing,for the end of the last millennium[J]. International Journal of Remote Sensing,2009,30(14):3679-3733.
[17] AMBIKA A K,WARDLOW B,MISHRA V. Remotely sensed high resolution irrigated area mapping in India for2000 to 2015[J]. Scientific Data,2016,3:160118.
[18] TELUGUNTLA P,THENKABAIL P S,XIONG J,et al. Spectral matching techniques(SMTs)and automated cropland classification algorithms(ACCAs)for mapping croplands of Australia using MODIS 250-m time-series(2000—2015)data[J]. International Journal of Digital Earth,2017,10(9):1-34.
[19] OZDOGAN,MUTLU,WOODCOCK,et al. Resolution dependent errors in remote sensing of cultivated areas[J]. Remote Sensing of Environment,2006,103(2):203-217.
[20] DHEERAVATH V,THENKABAIL P S,CHANDRAKANTHA G,et al. Irrigated areas of India derived using MODIS 500 m time series for the years 2001—2003[J]. Isprs Journal of Photogrammetry&Remote Sensing,2010,65(1):42-59.
[21] GUMMA M K,NELSON A,THENKABAIL P S,et al. Mapping rice areas of South Asia using MODIS multitemporal data[J]. Journal of Applied Remote Sensing,2011,5(4):863-871.
[22] OHTSU N. A threshold selection method from Gray-Level histograms[J]. IEEE Transactions on Systems Man&Cybernetics,2007,9(1):62-66.