基于无人机遥感多光谱影像的棉花倒伏信息提取
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摘要
为在棉花发生倒伏灾害后快速获取田块尺度下的受灾信息,该文以2017年8月21日强风暴雨导致大面积棉花倒伏的新疆生产建设兵团第八师135团的部分田块作为研究区,由无人机遥感试验获取倒伏后的多光谱影像,通过分析倒伏和正常棉花的光谱反射率差异提取了多种植被指数和主成分纹理特征,结合地面调查样本建立了3种花铃期倒伏棉花的Logistic二分类模型并进行了精度评价和验证。结果表明:棉花倒伏前后在可见光波段的反射率差异微小,而在红边和近红外波段的反射率明显降低0.12～0.20;以第一主成分均值(PCA1_mean)建立的Logistic二分类纹理模型效果最优,在测试集上分类结果的准确率为91.30%,ROC(receiver operating characteristic)曲线距左上角点最近,AUC(area under the roc curve)值为0.80。通过将该模型应用于试验区影像,分类制图效果良好且符合棉田倒伏症状特点。该研究可为无人机多光谱遥感棉花灾损评估提供参考。
Extracting crop lodging information, such as spatial location and area, is very critical to agricultural disaster assessment and agricultural insurance claim. It is hard work to measure the lodging information using traditional methods such as a ground survey. A survey method using remote sensing techniques can quickly and efficiently obtain crop lodging information, but it is limited by the lack of timely and available satellite remote sensing data. In recent years, the application of unmanned aerial vehicles(UAV) develop rapidly in the agricultural field, which makes UAV equipped with image sensors become a portable, stable and efficient crop survey tool with the characteristics of low cost, high timeliness, and small weather impact. A few scholars have measured the lodging area of wheat and corn crops using visible or multispectral images. However, studies using UAV multispectral images to survey cotton lodging information have not been published. Therefore, a survey method of cotton lodging using multi-spectral image was derived from UAV remote sensing experiment which was carried out in the 135 th Regiment of the 8 th Division of Xinjiang Production and Construction Corps on August 23 of 2017. In this study, the spectral characteristics of lodging and normal cotton were first analyzed and summarized, and a series of vegetation indices were calculated. 16 texture features of the first two components were calculated according to gray level co-occurrence matrix(GLCM) after principal component analysis(PCA), and the optimal texture features were selected in terms of the coefficient of variation(CV) and the relative difference(RD). The result showed that it was apparently different between lodging and normal cotton in spectral curves and texture features. Compared with normal cotton, the difference in reflectance of the lodging cotton in the visible wavebands was small, while was significant in the red and near-infrared bands, in which the reflectance dropped about 0.12-0.20. The main reason for this phenomenon might be the collapse of the cotton canopy structure. Mean of the first principal component(PCA1_mean), PCA1_entropy, PCA1_homogeneity, PCA2_mean, and PCA2_homogeneity texture features had the lower CV and higher RD, which were very suitable for classification of normal and lodging cotton. Then, 10 vegetation indices and 5 texture features of the measured samples were calculated as characteristics index, and the training set and test set were divided. Forward stepwise was used to select the best features on the data set. Binary Logistic models on lodging and normal cotton classification were constructed with different features combination, including spectral model, texture model, and spectral-texture model. The prediction accuracies of the classification models were evaluated by ground survey samples. All classification models had a good classification effect on lodging and normal cotton. Among them, the texture model constructed with the PCA1_mean had the highest precision, and the classification accuracy on the test set was 91.30%. The classification accuracies of spectral-texture model and spectral model were following, but the classification accuracy was also more than 85%. Finally, the classification models were applied to the multi-spectral image at the pixel level, and 3 thematic classification maps were created. Compared with the visual interpretation results, the texture model has the best classification effect. The "salt-and-pepper plaque" of the thematic map was the least, and the lodging crop had the characteristic of aggregation occurring in space. The ROC(receiver operating characteristic) curve was closest to the upper left corner and the calculated AUC(area under the ROC curve) value was 0.80. According to the results of the study, we may safely draw the conclusion that the method to extract lodging cotton information using the multi-spectral image of UAV remote sensing based on optimum texture features is accurate. The lodging classification has a high accuracy of mapping, which is basically consistent with the actual lodging in the field.
Extracting crop lodging information, such as spatial location and area, is very critical to agricultural disaster assessment and agricultural insurance claim. It is hard work to measure the lodging information using traditional methods such as a ground survey. A survey method using remote sensing techniques can quickly and efficiently obtain crop lodging information, but it is limited by the lack of timely and available satellite remote sensing data. In recent years, the application of unmanned aerial vehicles(UAV) develop rapidly in the agricultural field, which makes UAV equipped with image sensors become a portable, stable and efficient crop survey tool with the characteristics of low cost, high timeliness, and small weather impact. A few scholars have measured the lodging area of wheat and corn crops using visible or multispectral images. However, studies using UAV multispectral images to survey cotton lodging information have not been published. Therefore, a survey method of cotton lodging using multi-spectral image was derived from UAV remote sensing experiment which was carried out in the 135 th Regiment of the 8 th Division of Xinjiang Production and Construction Corps on August 23 of 2017. In this study, the spectral characteristics of lodging and normal cotton were first analyzed and summarized, and a series of vegetation indices were calculated. 16 texture features of the first two components were calculated according to gray level co-occurrence matrix(GLCM) after principal component analysis(PCA), and the optimal texture features were selected in terms of the coefficient of variation(CV) and the relative difference(RD). The result showed that it was apparently different between lodging and normal cotton in spectral curves and texture features. Compared with normal cotton, the difference in reflectance of the lodging cotton in the visible wavebands was small, while was significant in the red and near-infrared bands, in which the reflectance dropped about 0.12-0.20. The main reason for this phenomenon might be the collapse of the cotton canopy structure. Mean of the first principal component(PCA1_mean), PCA1_entropy, PCA1_homogeneity, PCA2_mean, and PCA2_homogeneity texture features had the lower CV and higher RD, which were very suitable for classification of normal and lodging cotton. Then, 10 vegetation indices and 5 texture features of the measured samples were calculated as characteristics index, and the training set and test set were divided. Forward stepwise was used to select the best features on the data set. Binary Logistic models on lodging and normal cotton classification were constructed with different features combination, including spectral model, texture model, and spectral-texture model. The prediction accuracies of the classification models were evaluated by ground survey samples. All classification models had a good classification effect on lodging and normal cotton. Among them, the texture model constructed with the PCA1_mean had the highest precision, and the classification accuracy on the test set was 91.30%. The classification accuracies of spectral-texture model and spectral model were following, but the classification accuracy was also more than 85%. Finally, the classification models were applied to the multi-spectral image at the pixel level, and 3 thematic classification maps were created. Compared with the visual interpretation results, the texture model has the best classification effect. The "salt-and-pepper plaque" of the thematic map was the least, and the lodging crop had the characteristic of aggregation occurring in space. The ROC(receiver operating characteristic) curve was closest to the upper left corner and the calculated AUC(area under the ROC curve) value was 0.80. According to the results of the study, we may safely draw the conclusion that the method to extract lodging cotton information using the multi-spectral image of UAV remote sensing based on optimum texture features is accurate. The lodging classification has a high accuracy of mapping, which is basically consistent with the actual lodging in the field.
引文
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[15]李宗南,陈仲新,王利民,等.基于小型无人机遥感的玉米倒伏面积提取[J].农业工程学报,2014,30(19):207-213.Li Zongnan,Chen Zhongxin,Wang Limin,et al.Area extraction of maize lodging based on remote sensing by small unmanned aerial vehicle[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2014,30(19):207-213.(in Chinese with English abstract)
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[2]吴美华,王怀军,孙桂丽,等.新疆农业气象灾害成因及其风险分析[J].干旱区地理,2016,39(6):1212-1220.Wu Meihua,Wang Huaijun,Sun Guili,et al.Formation and risk analysis of meteorological disasters in Xinjiang[J].Arid Land Geography,2016,39(6):1212-1220.(in Chinese with English abstract)
[3]邹陈,李新建,杨举芳,等.石河子棉区棉株倒伏的气象因子分析[J].中国沙漠,2008,28(5):891-895.Zou Chen,Li Xinjian,Yang Jufang,et al.Meteorological factors effecting cotton lodge in Shihezi cotton region of Northern Xinjiang[J].Journal of Desert Research,2008,28(5):891-895.(in Chinese with English abstract)
[4]李明,黄愉淇,李绪孟,等.基于无人机遥感影像的水稻种植信息提取[J].农业工程学报,2018,34(4):108-114.Li Ming,Huang Yuqi,Li Xumeng,et al.Extraction of rice planting information based on remote sensing image from UAV[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(4):108-114.(in Chinese with English abstract)
[5]陈雯,武威,孙成明,等.无人机遥感在作物监测中的应用与展望[J].上海农业学报,2016,32(2):138-143.Chen Wen,Wu Wei,Sun Chengming,et al.Application and prospect of UAV remote sensing in crop monitoring[J].Acta Agriculturae Shanghai,2016,32(2):138-143.(in Chinese with English abstract)
[6]李宗南,陈仲新,任国业,等.基于Worldview-2影像的玉米倒伏面积估算[J].农业工程学报,2016,32(2):1-5.Li Zongnan,Chen Zhongxin,Ren Guoye,et al.Estimation of maize lodging area based on Worldview-2 image[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2016,32(2):1-5.(in Chinese with English abstract)
[7]王立志,顾晓鹤,胡圣武,等.基于多时相HJ-1B CCD影像的玉米倒伏灾情遥感监测[J].中国农业科学,2016,49(21):4120-4129.Wang Lizhi,Gu Xiaohe,Hu Shengwu,et al.Remote sensing monitoring of maize lodging disaster with multi-temporal HJ-1B CCD image[J].Scientia Agricultura Sinica,2016,49(21):4120-4129.(in Chinese with English abstract)
[8]刘婷婷,张惊雷.基于ORB特征的无人机遥感图像拼接改进算法[J].计算机工程与应用,2018,54(2):193-197.Liu Tingting,Zhang Jinglei.Improved image stitching algorithm based on ORB features by UAV remote sensing[J].Computer Engineering and Applications,2018,54(2):193-197.(in Chinese with English abstract)
[9]郑庆河,杨明强,蒋楠,等.无人机航空遥感图像的多分辨率拼接方法[J].西安邮电大学学报,2017,22(2):53-59.Zheng Qinghe,Yang Mingqiang,Jiang Nan,et al.Amulti-resolution mosaic method used for unmanned aerial vehicle(UAV)remote sensing image[J].Journal of Xi’an University of Posts and Telecommunications,2017,22(2):53-59.(in Chinese with English abstract)
[10]金鼎坚,支晓栋,王建超,等.面向地质灾害调查的无人机遥感影像处理软件比较[J].国土资源遥感,2016,28(1):183-189.Jin Dingjian,Zhi Xiaodong,Wang Jianchao,et al.Comparison of UAV remote sensing imagery processing software for geological disasters monitoring[J].Remote Sensing For Land&Resources,2016,28(1):183-189.(in Chinese with English abstract)
[11]宋建辉,闫蓓蕾.基于SIFT的无人机航拍图像快速拼接技术研究[J].计算机应用与软件,2018,35(2):230-234.Song Jianhui,Yan Beilei.Research on rapid mosaic technology of UAV aerial image based on SIFT[J].Computer Applications and Software,2018,35(2):230-234.(in Chinese with English abstract)
[12]孙刚,黄文江,陈鹏飞,等.轻小型无人机多光谱遥感技术应用进展[J].农业机械学报,2018,49(3):1-17.Sun Gang,Huang Wenjiang,Chen Pengfei,et al.Advances in UAV-based multispectral remote sensing applications[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(3):1-17.(in Chinese with English abstract)
[13]叶伟林,宿星,魏万鸿,等.无人机航测系统在滑坡应急中的应用[J].测绘通报,2017(9):70-74.Ye Weilin,Su Xing,Wei Wanhong,et al.Application of uav aerial photograph system in emergency rescue and relief for landslide[J].Bulletin of Surveying and Mapping,2017(9):70-74.(in Chinese with English abstract)
[14]吴永亮,陈建平,姚书朋,等.无人机低空遥感技术应用[J].国土资源遥感,2017,29(4):120-125.Wu Yongliang,Chen Jianping,Yao Shupeng,et al.Application of UAV low-altitude remote sensing[J].Remote Sensing For Land&Resources,2017,29(4):120-125.(in Chinese with English abstract)
[15]李宗南,陈仲新,王利民,等.基于小型无人机遥感的玉米倒伏面积提取[J].农业工程学报,2014,30(19):207-213.Li Zongnan,Chen Zhongxin,Wang Limin,et al.Area extraction of maize lodging based on remote sensing by small unmanned aerial vehicle[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2014,30(19):207-213.(in Chinese with English abstract)
[16]董锦绘,杨小冬,高林,等.基于无人机遥感影像的冬小麦倒伏面积信息提取[J].黑龙江农业科学,2016(10):147-152.Dong Jinhui,Yang Xiaodong,Gao Lin,et al.Information extraction of winter wheat lodging area based on UAVremote sensing image[J].Heilongjiang Agricultural Sciences,2016(10):147-152.(in Chinese with English abstract)
[17]李忠强,王瀚宇,刘婷婷,等.基于Pix4Dmapper的无人机数据自动化处理技术探讨[J].海洋科学,2018,42(1):39-44.Li Zhongqiang,Wang Hanyu,Liu Tingting,et al.Investigation of Pix4Dmapper automatic data-processing technology in unmanned aerial vehicles[J].Marine Sciences,2018,42(1):39-44.(in Chinese with English abstract)
[18]Pearson R L,Miller L D.Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie[J].Remote Sensing of Environment,VIII,1972,45(2)7-12.
[19]Bouman B A M.Accuracy of estimating the leaf area index from vegetation indices derived from crop reflectance characteristics,a simulation study[J].International Journal of Remote Sensing,1992,13(16):3069-3084.
[20]Rouse J W.Monitoring the vernal advancement and retrogradation(greenwave effect)of natural vegetation[R].USA:NTRS,1972.
[21]Gitelson A A,Kaufman Y J,Merzlyak M N.Use of a green channel in remote sensing of global vegetation from EOS-MODIS[J].Remote Sensing of Environment,1996,58(3):289-298.
[22]Richardson A J.Distinguishing vegetation from soil background information[J].Photogrammetric Engineering&Remote Sensing,1977,43(12):1541-1552.
[23]Huete A R.A soil-adjusted vegetation index(SAVI)[J].Remote Sensing of Environment,1988,25(3):295-309.
[24]Genevieve Rondeaux,Steven M,Frederic Baret.Optimization of soil-adjusted vegetation indices[J].Remote Sensing of Environment,1996,55(2):95-107.
[25]An V D,Gulinck Hubert.Classification and quantification of green in the expanding urban and semi-urban complex:Application of detailed field data and IKONOS-imagery[J].Ecological Indicators,2011,11(1):52-60.
[26]Broge N H,Leblanc E.Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density[J].Remote Sensing of Environment,2001,76(2):156-172.
[27]Gamon J A,Penuelas J,Field C B.A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency[J].Remote Sensing of Environment,1992,41(1):35-44.
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