基于NDVI加权指数的冬小麦种植面积遥感监测
发布时间：2018-10-30 16:20
【摘要】：该文针对农业信息服务中冬小麦种植面积调查业务的现状与需求,提出了一种基于NDVI(normal difference vegetation index)时间序列的冬小麦NDVI加权指数(WNDVI,weighted NDVI index)影像算法,可在训练样本、验证样本选择的基础上实现冬小麦面积的自动提取,并以河北省安平县及周边地区2013-2014年度冬小麦面积提取为例,采用GF-1/WFV(wide field view)数据进行了算法实现。算法的主要思路是在时序影像基础上,通过冬小麦NDVI加权指数影像的构建,扩大冬小麦地类与其他地类的差异,结合自适应的阈值获取方法,区分冬小麦地类,获取冬小麦作物面积。算法包括冬小麦时间序列影像的获取、基于网格的样本点设置、构建冬小麦NDVI加权指数影像、迭代确定冬小麦NDVI加权指数提取阈值、精度验证这5个部分。影像的获取根据冬小麦的生长时间确定,保证每月1景GF-1/WFV无云影像,并进行预处理及NDVI计算;同时将研究区划分为一定数量的网格,每个网格再等分为2×2个子网格,根据目视解译、专家知识、实地调查等方法,确定左上网格中心点及右下网格中心点的地物类型。统计该期所有左上网格点冬小麦及其他地物的NDVI均值,冬小麦NDVI大于其他地物的将该期影像的权值设置为1,否则设置为-1,将所有时相NDVI影像进行加权平均,即可获取冬小麦NDVI加权指数影像。获取冬小麦NDVI加权指数影像后,还需设置合适的阈值提取冬小麦。该文选用右下网格点目视解译分类结果作为阈值提取依据,具体方法是将冬小麦指数从小到大按照一定间隔划分,作为冬小麦NDVI加权指数提取阈值,将各阈值二值法运用,与右下网格点的冬小麦提取的目视解译结果对比,精度最高的就是最优冬小麦NDVI加权指数分割阈值。在所有网格中,以初始识别获取的冬小麦面积为准,等概率选择10个样方作为精度验证样方进行验证。精度验证结果表明分类总体精度达到94.4%,Kappa系数达0.88。该文通过构建冬小麦NDVI加权指数,将比较复杂的多个参数转换为一个参数,并且农学意义明确,相比传统的NDVI时序影像进行冬小麦面积的提取,具有自动化程度高、面积提取精度高、分类结果稳定的特点,已经在全国农作物面积遥感监测业务中进行了应用。
[Abstract]:In view of the present situation and demand of winter wheat planting area survey in agricultural information service, a NDVI weighted index (WNDVI,weighted NDVI index) image algorithm based on NDVI (normal difference vegetation index) time series was proposed, which can be used in training samples. The automatic extraction of winter wheat area is realized on the basis of sample selection. Taking Anping County of Hebei Province and its surrounding area as an example, the GF-1/WFV (wide field view) data are used to implement the algorithm. The main idea of the algorithm is to expand the difference between winter wheat and other ground types through the construction of winter wheat NDVI weighted index image on the basis of time series image, and to distinguish winter wheat ground type with adaptive threshold acquisition method. To obtain the crop area of winter wheat. The algorithm includes the acquisition of winter wheat time series image, the construction of winter wheat NDVI weighted index image based on the grid sample point setting, the iterative determination of winter wheat NDVI weighted index extraction threshold, and the accuracy verification of the five parts. According to the growth time of winter wheat, the acquisition of image was determined to ensure 1 GF-1/WFV cloud-free image per month, and the preprocessing and NDVI calculation were carried out. At the same time, the study area is divided into a certain number of meshes. Each grid is divided into 2 脳 2 subgrids. According to the methods of visual interpretation, expert knowledge and field investigation, the types of ground objects at the center point of the upper left grid and the center point of the lower right grid are determined. The NDVI mean value of all the left upper grid points of winter wheat and other ground objects in this period is counted. The NDVI of winter wheat is larger than that of other ground objects, and the weight of the image of this period is set to 1, otherwise it is set to -1, and all temporal NDVI images are weighted to average. The NDVI weighted index image of winter wheat can be obtained. After obtaining NDVI weighted index image of winter wheat, it is necessary to set appropriate threshold to extract winter wheat. In this paper, the classification results of visual interpretation of the lower right grid points are selected as the basis of threshold extraction. The specific method is to divide the winter wheat index from small to large according to a certain interval, as the NDVI weighted index of winter wheat to extract the threshold, and apply each threshold binary method. Compared with the visual interpretation results of winter wheat extracted from the lower right grid point, the best NDVI weighted index segmentation threshold is the most accurate. In all meshes, the area of winter wheat obtained by initial identification is taken as the standard, and 10 samples are chosen as precision verification samples to verify the accuracy. The accuracy verification results show that the overall accuracy of the classification is 94. 4% and the Kappa coefficient is 0. 88. In this paper, the NDVI weighted index of winter wheat is constructed, the more complex parameters are converted into one parameter, and the agronomic significance is clear. Compared with the traditional NDVI sequential image, the extraction of winter wheat area has a high degree of automation. The feature of high precision of area extraction and stable classification results has been applied in the field of remote sensing monitoring of crop area in China.
【作者单位】： 中国农业科学院农业资源与农业区划研究所;
【基金】：“十二五”国家科技重大专项(高分辨率对地观测系统重大专项“高分农业遥感监测与评估示范系统(一期)”)
【分类号】：S512.11;S127
，
本文编号：2300515
[Abstract]:In view of the present situation and demand of winter wheat planting area survey in agricultural information service, a NDVI weighted index (WNDVI,weighted NDVI index) image algorithm based on NDVI (normal difference vegetation index) time series was proposed, which can be used in training samples. The automatic extraction of winter wheat area is realized on the basis of sample selection. Taking Anping County of Hebei Province and its surrounding area as an example, the GF-1/WFV (wide field view) data are used to implement the algorithm. The main idea of the algorithm is to expand the difference between winter wheat and other ground types through the construction of winter wheat NDVI weighted index image on the basis of time series image, and to distinguish winter wheat ground type with adaptive threshold acquisition method. To obtain the crop area of winter wheat. The algorithm includes the acquisition of winter wheat time series image, the construction of winter wheat NDVI weighted index image based on the grid sample point setting, the iterative determination of winter wheat NDVI weighted index extraction threshold, and the accuracy verification of the five parts. According to the growth time of winter wheat, the acquisition of image was determined to ensure 1 GF-1/WFV cloud-free image per month, and the preprocessing and NDVI calculation were carried out. At the same time, the study area is divided into a certain number of meshes. Each grid is divided into 2 脳 2 subgrids. According to the methods of visual interpretation, expert knowledge and field investigation, the types of ground objects at the center point of the upper left grid and the center point of the lower right grid are determined. The NDVI mean value of all the left upper grid points of winter wheat and other ground objects in this period is counted. The NDVI of winter wheat is larger than that of other ground objects, and the weight of the image of this period is set to 1, otherwise it is set to -1, and all temporal NDVI images are weighted to average. The NDVI weighted index image of winter wheat can be obtained. After obtaining NDVI weighted index image of winter wheat, it is necessary to set appropriate threshold to extract winter wheat. In this paper, the classification results of visual interpretation of the lower right grid points are selected as the basis of threshold extraction. The specific method is to divide the winter wheat index from small to large according to a certain interval, as the NDVI weighted index of winter wheat to extract the threshold, and apply each threshold binary method. Compared with the visual interpretation results of winter wheat extracted from the lower right grid point, the best NDVI weighted index segmentation threshold is the most accurate. In all meshes, the area of winter wheat obtained by initial identification is taken as the standard, and 10 samples are chosen as precision verification samples to verify the accuracy. The accuracy verification results show that the overall accuracy of the classification is 94. 4% and the Kappa coefficient is 0. 88. In this paper, the NDVI weighted index of winter wheat is constructed, the more complex parameters are converted into one parameter, and the agronomic significance is clear. Compared with the traditional NDVI sequential image, the extraction of winter wheat area has a high degree of automation. The feature of high precision of area extraction and stable classification results has been applied in the field of remote sensing monitoring of crop area in China.
【作者单位】： 中国农业科学院农业资源与农业区划研究所;
【基金】：“十二五”国家科技重大专项(高分辨率对地观测系统重大专项“高分农业遥感监测与评估示范系统(一期)”)
【分类号】：S512.11;S127
，
本文编号：2300515
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