基于MODIS时序NDVI主要农作物种植信息提取研究

引用本文

郭昱杉, 刘庆生, 刘高焕, 黄翀. 基于MODIS时序NDVI主要农作物种植信息提取研究[J].自然资源学报, 2017,32(10): 1808-1818
GUO Yu-shan, LIU Qing-sheng, LIU Gao-huan, HUANG Chong. Extraction of Main Crops in Yellow River Delta Based on MODIS NDVI Time Series[J]. Journal of Natural Resources, 2017,32(10): 1808-1818 复制到剪切板

DOI:10.11849/zrzyxb.20160943

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基于MODIS时序NDVI主要农作物种植信息提取研究

郭昱杉^1,², 刘庆生^1,^*, 刘高焕¹, 黄翀¹

1. 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101

2. 中国科学院大学,北京 100049

第一作者简介：郭昱杉（1992- ）,女,山西晋城人,硕士研究生,研究方向为遥感与地理信息系统的应用。E-mail: guoyushan@lreis.ac.cn

*通信作者简介：刘庆生（1971- ）,男,山西忻州人,副研究员,主要研究方向为遥感与地理信息系统在资源环境评价中的应用研究。E-mail: liuqs@lreis.ac.cn

收稿日期:2016-08-31 网络出版日期:2017-10-20

基金:国家科技支撑计划项目（2013BAD05B03）; 国家自然科学基金项目（41671422,41271407,41471335）; 国家国际科技合作专项项目（2012DFG22050）

摘要

农业在黄河三角洲地区占有重要地位,及时、准确地掌握该地区农作物分布信息对政府有关部门制定农业政策、指导农业生产具有十分重要的意义。时序植被指数能够反映农作物物候特征,帮助识别农作物类型,在农作物种植信息提取方面具有明显优势。论文选取2014年MOD09Q1时序遥感数据集,以黄河三角洲主要农作物为研究对象,利用Harmonic Analysis of NDVI Time-Series（Hants）滤波重构NDVI时序曲线,通过对比待分像元NDVI时序曲线与参考时序曲线的相似性,实现农作物种植信息提取。对分类结果进行面积统计和空间分布精度检验：冬小麦、棉花、玉米的面积提取精度分别达到96.8%、95.5%、85.1%,空间匹配总体精度达86.9%。结果表明该方法有效可行,能够为该地区农业监测提供技术基础。

关键词: 遥感; 农作物种植信息提取; 时序植被指数; MODIS

中图分类号:S127 文献标志码:A 文章编号:1000-3037(2017)10-1808-11

Extraction of Main Crops in Yellow River Delta Based on MODIS NDVI Time Series

GUO Yu-shan^1,², LIU Qing-sheng¹, LIU Gao-huan¹, HUANG Chong¹

1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

2. University of Chinese Academy of Sciences, Beijing 100049, China

Fund:National Key Technology Support Program, No.2013BAD05B03; National Natural Science Foundation of China, No.41671422, 41271407 and 41471335; International S & T Cooperation Program of China, No.2012DFG22050

Abstract

Agriculture plays a key role in the Yellow River Delta, which is one of the greatest granaries of China. Timely and accurately understanding the crop distribution information is very important for related government departments to make reasonable decisions and guide agricultural production. Traditional methods based on field investigation and statistic data were time consuming and labor consuming. Time series of vegetation indices based on remote sensing images have obvious advantages and great application potentials in the extraction of crop planting information. This paper aimed at extracting the main crops in the Yellow River Delta, including winter wheat, maize and cotton. MODIS images with 250 m spatial resolution were used in this study. Dezhou City, Binzhou City and Dongying City were chosen as the study area for the convenience. The 250 m MOD09Q1 8 d time series remote sensing images in 2014 were acquired from the website of NASA. To avoid the disturbance from orchards and grasslands, firstly, the non-crop areas were masked out with the 1:100 000 land use map of the study area in 2014. Considering that there were some irregular fluctuations of the NDVI time series caused by the influence of clouds and atmosphere, we secondly reconstructed the NDVI time series with Hants filters. Then, the main crops planting information was extracted by comparing the NDVI time series with the reference NDVI time series which were the average NDVI of the sampling points collected in May, 2014 and November, 2014. Finally, the threshold value of each crop was determined and the planting information was extracted according to the thresholds. Two precision validation methods, spatial distribution and areal statistics, were adopted. The results showed that the accuracies of wheat and cotton in area were high (96.8%, 95.5%), while the accuracy of maize in area is a little lower (85.1%). The overall spatial consistency was 86.9% according to spatial distribution cooperation. The result suggests that the method in this paper is effective and practicable.

Keyword: remote sensing; crop planting information extraction; NDVI time series; MODIS

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黄河三角洲是我国主要的粮食产地之一, 该地区的农业生产情况对全国具有重要影响。及时、准确地获取农作物种植面积信息, 有利于农业部门指导农业生产, 调整农业结构, 为区域农作物的产量估计和预测提供可靠依据^[1]。同时, 对政策制定、粮食安全和农业结构调整等具有重要意义^[2]。传统的调查方法基于地面调查和统计数据, 不仅周期长, 而且费时费力; 遥感技术具有快速、准确、及时的特点, 为大面积农业信息提取和监测提供了有力手段^[3]。单一时相遥感影像只能反映农作物某一时刻的光谱特征, 容易导致同谱异物现象^[4]。植物的物候特征使其在不同生长发育阶段表现出不同的光谱特性, 多时相遥感数据能够描述同一作物在不同生长发育阶段的光谱差异^[5], 因此时间序列遥感影像在作物种植信息提取、产量预测、农情监测等方面具有巨大的应用潜力^[6]。而植被指数能够有效反映不同植被类型的物候差异和光谱特征, 将分类精度提高到作物层次^[7], 因此在作物种植信息提取研究中受到了广泛应用。其中, 归一化植被指数（NDVI）是农作物监测中应用最为广泛的光谱参数之一, 在农作物生产与管理中具有重要的实际指导意义^[8]。

目前已有不少学者基于遥感时序植被指数对农作物种植信息提取作了大量的研究, 并提出了很多技术和方法, 主要可分为以下3种类型：直接分类法、特征参量分类法、相似性度量分类法。直接分类是指将时序植被指数或经过变换（如主成分变换、傅里叶变换）的时序植被指数进行监督或非监督分类的方法。Panigrahy等^[9]利用时序SPOT VGT NDVI数据, 采用K-means算法对印度河平原地区进行非监督分类。李杨等^[10]利用3 a逐旬的 SPOT/VGT NDVI长时间序列数据集进行ISODATA非监督分类。张健康等^[11]运用多时相TM/ETM+影像和MODIS EVI 时序数据, 采取基于生态分类法的监督分类与决策树分类相结合的人机交互解译方法, 对黑龙港地区的主要作物进行遥感解译, 总体分类精度达到91.3%。许青云等^[6]基于MODIS NDVI长时间序列数据集, 利用ISO非监督分类方法并结合陕西省主要农作物的物候信息、农业气象台站点数据和TM土地覆盖分类数据对陕西省主要农作物和主要土地覆盖物的时序特征曲线进行提取, 总体精度达88.18%。特征参量分类法通过时序植被指数曲线提取不同地物类型的物候或其他特征参量, 通过分析这些特征参量完成分类。Hill等^[12]根据1991— 1999年时序AVHRR-NDVI曲线提取了表征时序NDVI形状的22个参量, 采用最大似然分类法对华盛顿州西南部进行土地覆盖分类。Colditz等^[13]在MODIS数据中选择纯净像元作为样本, 计算每个样本像元的时序NDVI特征参量, 通过决策树法对南非和德国的土地覆盖类型进行分类, 总体分类精度达80%以上。苗翠翠等^[14]利用NDVI时序数据, 确定水稻物候生长期的关键值, 利用关键值确定分类规则, 采取决策树法分类, 总体精度为87.5%。张荣群等^[4]依据样本点NDVI时序曲线物候特征, 设定分类规则, 结合决策树分类法和分层掩膜法, 对研究区内的冬小麦、夏玉米、春玉米、棉花等大宗农作物及小宗作物谷子进行分类研究, 总体精度达89.2%。平跃鹏等^[15]基于MODIS NDVI时序曲线及其他辅助数据, 提取作物的7个物候特征, 并结合归一化水体指数, 采用支持向量机分层分类法对松嫩平原作物进行了分类。张焕雪等^[16]利用多时相环境星HJ-1 A/B CCD数据及其多期平滑重构后的NDVI时间序列曲线特征, 在对象尺度上采用决策树算法开展了农作物分类研究, 总体精度为92.2%。相似性度量分类法的基本思路是先建立不同地物类型的参考植被指数曲线, 再比较待分类像元的时序植被指数曲线与参考曲线的相似程度确定其所属的地物类型。Evans等^[17]使用MODIS NDVI时序数据, 提出了基于傅里叶组分的波形相似度, 对叙利亚平原草地植被进行分类, 总体分类精度为72.67%。管续栋等^[18]利用MODIS时间序列数据, 提出一种基于动态时间弯曲（DTW）距离的相似性判别的土地覆盖分类方法, 对泰国东北部地区单、双季稻种植面积进行了遥感提取研究, 总体精度达83.4%。其中, 曲线相似性度量法简便易行, 能够实现作物种植信息的快速提取, 因而受到广泛应用。但是这种方法也有一定的局限性, 由于草地、果树等地物与农作物的时序曲线不易区分, 容易造成错分。为解决这一问题, 本文首先利用土地利用图将非耕地类型的区域去除, 在此基础上通过比较耕地像元的时序相似性提取农作物种植信息, 以提高精度。

冬小麦、玉米、棉花作为黄河三角洲三大粮食作物, 其种植信息对地区农业生产具有重要意义。很多学者对黄河三角洲地区农作物种植信息提取作了大量研究, 目前大部分研究以冬小麦提取为主, 对其他大宗作物如玉米、棉花等的提取还不多见, 并且大多基于县级尺度开展。秦元伟等^[19]采用陆地卫星和中巴资源卫星（CBERS）中高分辨率遥感图像, 采用决策树分类方法提取了山东省广饶县冬小麦种植面积。贾吉超等^[20]基于ETM影像, 通过分析典型地物光谱曲线生成决策树模型, 提取冬小麦分布信息。刘庆生等^[21]以山东省无棣县为研究区, 根据该县作物栽培技术月历为参考选择关键期HJ星遥感数据, 采用支持向量机（SVM）方法对该县主要作物种植空间分布进行了信息提取。

MODIS NDVI时序数据具有质量高、易获取的特点, 能够反映农作物物候特征和生长规律, 对于大尺度农作物种植信息提取具有显著的优势。通过对比参考地物和待分像元的MODIS NDVI时序曲线, 可以快速、准确地获得一定范围内农作物种植分布信息。本文基于MODIS 8 d合成数据, 首先利用2014年1:10万土地利用数据, 掩膜去除非耕地类型。然后利用时间序列谐波分析法（Hants）滤波重构NDVI时序曲线。选取欧式距离作为相似性度量的指标, 通过对比待分像元时序NDVI曲线与参考曲线的相似性, 对黄河三角洲地区主要农作物— — 冬小麦、玉米、棉花进行分类。

1 研究区及数据来源

1.1 研究区概况

黄河三角洲位于山东省的东北部, 由黄河携带的大量泥沙在入海口处沉积所形成, 是我国三大河口三角洲之一^[22]。受到太平洋和欧亚大陆的共同作用, 该地区四季分明、雨热同期, 属于温带大陆性季风气候区^[23]。黄河三角洲内无地带性植被类型, 植被类型少、结构简单^[24]。该地区自然资源丰富, 土地资源优势突出, 产业发展基础好, 受到了国家和山东省的高度重视。这里先后被国家列为黄淮海农业开发区、黄河三角洲农业综合开发试验区、国家八大农业开发区之一, 同时也是全国“ 五大” 粮仓之一^[25]。为研究方便, 本文将研究区的范围限定在“ 十二五” 国家科技计划“ 渤海粮仓科技示范工程” 项目山东片区的研究范围, 即德州、滨州和东营三市（图1）。

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	图1 研究区位置示意图Fig. 1 The geographic location of the study area

该地区农作物种类主要为冬小麦、玉米、棉花等, 多分布于土壤含盐量较低的河滩地和河成高地。该地区冬小麦一般于9月底到10月初开始播种; 3月中旬, 冬小麦开始进入返青期; 4月上旬进入拔节期, 冬小麦快速生长; 5月冬小麦进入抽穗期; 5月中下旬进入灌浆期; 5月下旬开始进入乳熟期; 6月上旬成熟。春玉米从4月底至5月初开始播种; 5月中旬出苗; 6月中旬为玉米大喇叭口期和抽雄期; 8月上旬进入成熟期。夏玉米从6月中下旬开始播种, 6月下旬至7月上旬陆续出苗; 8月上旬为夏玉米大喇叭口期和抽雄期; 9月下旬夏玉米进入成熟期。棉花一般在4月中下旬播种, 5月至6月上旬为出苗期, 6月上旬至7月上旬为蕾期, 7月中旬至8月中旬进入花铃期, 9月中下旬进入吐絮期, 10月中下旬进入收获期^[26]。

1.2 数据来源及预处理

1.2.1 MODIS数据

数据使用NASA网站（http://modis.gsfc.nasa.gov/）提供的2014年8 d合成的MOD09Q1数据产品, 共计46个时相。空间分辨率为250 m, 行列号为h27v05。利用MODIS产品批处理工具MRT（MODIS Reprojection Tool）, 将投影统一转换成Albers等积投影, 地理坐标系为WGS-84。以研究区范围为裁剪框对转换后的数据进行裁剪, 减少后续数据处理量。并利用红光波段数据和近红外波段反射率数据计算归一化植被指数（NDVI）, 最终获得2014年研究区MODIS NDVI时序数据。

1.2.2 验证数据集

为更好地验证分类结果, 本文通过定量分析和空间分析两方面进行精度验证。其中, 定量分析数据来源于德州、滨州、东营统计年报中的农作物播种面积。空间分析主要利用野外采样点数据, 分别于2014年5月、11月和2016年9月在研究区开展调查获得。其中, 2014年5月以6 km× 6 km规则格网设计采样点位, 实际采样中根据点位的可到达性及样点土地利用类型的代表性（该土地利用类型面积大于250 m× 250 m）, 适当调整位置, 在设计点位附近进行替代采样, 采样时用GPS记录各样点的坐标位置和高程, 以及土地利用类型及农作物种植等信息。2014年11月和2016年9月通过走访农户进行调查, 收集地块种植作物类型等信息, 并用GPS记录点位。

2 研究方法

根据不同农作物植被指数时序曲线特性及研究区特征, 本文首先利用Hants滤波对时序NDVI进行重构, 去除噪声; 然后利用土地利用类型图掩膜剔除非耕地类型, 减少其他地物类型的干扰; 然后通过比较参考时序NDVI和待分像元时序NDVI的相似性, 设置阈值实现主要农作物种植信息提取, 主要过程如图2所示。

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	图2 农作物种植信息提取流程Fig. 2 The flowchart of crop planting information extraction

2.1 时序植被指数重构

由于云、气溶胶以及地物的双向性反射造成NDVI值降低, 这些变化噪声极大地影响了对地表覆盖及陆地生态系统的监测^[27]。特别是云层覆盖的地区, 像元近红外和红外反射率值会下降。这些干扰使得植被指数时间序列曲线出现不规则波动, 趋势变化不明显, 直接影响了植被指数时间序列分析。因此在进行农作物植被指数时间序列分析前, 需要对植被指数时间序列进行平滑, 即重构时序植被指数。

本文采用Hants进行植被指数时间序列重构。该方法的核心是傅里叶变换和最小二乘法拟合, 即把时间波谱数据分解成许多不同频率的正弦曲线和余弦曲线, 从中选取若干个能够反映时间序列特征的曲线进行叠加, 从而达到时间序列数据的重建目的^[28]。其表达式为：

$\begin{matrix} y_{i} = A_{0} + \overset{m}{\sum_{j = 1}} A_{j} \sin (i ω_{j} + θ_{j}) (i = 0, 1, \dots, N) \end{matrix}$ （1）

式中：谐波的余项A₀等于序列的平均值; A_j为各谐波的振幅; ω =2jπ /N为各谐波的频率; N为序列的长度; θ _j为各谐波的初相位; m为谐波的个数, m=N-1。这些正弦函数叠加构成傅里叶序列。

Hants工作过程是：首先依据所有的离散数据量生成最小平方拟合曲线。然后检查每一个数据值, 与曲线进行对比。图像上受云干扰的点, NDVI值很低。因此, 应剔除明显低于拟合NDVI变化曲线的点, 并赋予零值。其中偏离量超过阈值最大的点最先被剔除, 然后根据剩余的采样点重新生成拟合曲线。再检查每个数据值。循环此过程, 最后生成光滑曲线^[29]。经Hants滤波后的曲线如图3所示, 与未经处理的时间序列对比, 表明该方法可以有效去除噪声, 得到光滑曲线。

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	图3 Hants滤波前后NDVI时间序列对比Fig. 3 NDVI time series before and after Hants filtering

2.2 耕地信息提取

为了提高农作物分类精度, 减少其他非耕地地物类型的干扰, 本文将首先提取出耕地地物类型, 在耕地基础上再进行农作物分类。本文利用研究区2014年1:10万土地利用类型图, 掩膜提取出MODIS影像中的耕地类型。该土地利用类型图是基于2014年Landsat 8 OLI影像, 辅以研究区2010年1:10万土地利用类型图和Google Earth高分辨率影像目视解译获得, 经2014年野外调查数据检验, 分类精度为89.3%, 基本满足本文研究的需要。

2.3 相似性度量

目前常用的相似性度量方法包括距离测量和相关系数法, 其中相关系数法即计算曲线的线性相关性, 由于这种方法度量的是线性关系, 未计算时间序列值间的差异, 所以该度量方法不考虑时间序列曲线平移和振幅缩放的影响。距离测量法通过计算各时刻时间序列值的差异来度量曲线相似性, 易于计算和实现^[30]。本文采用距离测量方法来度量时间序列曲线相似性。

明科夫斯基距离（Minkowski Distance）是最常见的距离测量方法, 其表达式为：

$\begin{matrix} D_{Mink} = {({\overset{N}{\sum_{i = 1}} |f_{t}^{p} - f_{t}^{q}|}^{r})}^{\frac{1}{r}} \end{matrix}$ （2）

式中： $\begin{matrix} f_{t}^{p} \end{matrix}$ 和 $\begin{matrix} f_{t}^{q} \end{matrix}$ 代表t时刻的时间序列值; N为样元个数; r为系数。r=1时, 公式表示曼哈顿空间距离; r=2时, 公式表示对差异更为敏感的欧式距离^[31]。本文采用欧式距离来度量NDVI时间序列相似性。

参考时序NDVI来源于各样本点对应的2014年内植被指数时间序列。经剔除异常曲线后, 计算各种作物类型植被指数时间序列的平均值, 获得冬小麦-夏玉米、春玉米、棉花NDVI时间序列参考曲线, 如图4所示。

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	图4 各作物NDVI时序参考曲线Fig. 4 Reference NDVI time series of three crops

逐一计算待分像元NDVI时间序列与各作物类型参考曲线的欧式距离作为相似性度量指标, 用于后续分类。

2.4 农作物分类

基于野外调查样本和Google Earth, 从中抽取冬小麦-夏玉米、棉花、春玉米样本各15个, 分别提取所有样本的时序NDVI, 并求各类农作物样本的时序植被指数与参考库中各类作物的时序植被指数的欧氏距离。根据样本与参考时序曲线的欧氏距离, 确定提取每类农作物的初始阈值。对于不同作物类型样本时序曲线的最小距离明显大于同种作物时序曲线最大距离的作物, 初始阈值的取值为同种作物样本时序曲线最大距离的1.5倍。个别作物出现了不同作物间样本时序曲线最小距离小于同种作物样本时序曲线最大距离的现象, 初始阈值的取值为同种作物样本时序曲线的最小距离。确定初始阈值后, 再通过试凑法^[18]确定冬小麦-夏玉米NDVI时序相似性阈值为0.72, 棉花NDVI时序相似性阈值为0.48, 春玉米NDVI时序相似性阈值为0.60。根据设置的阈值进行农作物分类。

3 结果与分析

3.1 农作物种植信息提取精度验证

精度验证从定量分析和空间分析两方面进行。定量分析通过与2014年德州市、滨州市和东营市统计年报中提供的各作物播种面积进行对比验证分类精度。其中, 将冬小麦-夏玉米与春玉米种植面积之和作为玉米的播种面积。结果如表1所示：在城市尺度上, 小麦分类精度为96.8%, 玉米分类精度为85.1%, 棉花精度为95.5%。

表1 基于统计数据的精度验证 Table 1 Accuracy verification based on statistic data

空间分布精度验证通过综合野外调查样点和Google Earth, 尽量选取纯像元点, 对比分类结果与验证样点空间一致性获得。一共获得245个有效样点, 覆盖整个研究区。结果表明：183个冬小麦-夏玉米样点中, 164个被正确分类, 样点的匹配精度为89.6%; 53个棉花样点中, 42个在影像中被正确分类为棉花, 空间匹配精度为79.2%; 9个春玉米样点中, 7个被正确分类, 空间匹配精度为77.8%。空间匹配总体精度达86.9%。

Chu等^[32]通过基于MODIS时序NDVI的决策树方法提取了黄河三角洲冬小麦种植信息, 本文在剔除非耕地类型的基础上, 借鉴他们的方法, 使用决策树分类方法对该地区主要农作物种植信息进行了提取。结果表明：冬小麦提取面积为973 275 hm², 精度为77.8%; 玉米提取面积为994 850 hm² , 精度为74.7%; 棉花提取面积为552 987 hm², 精度为13.2%。说明直接使用基于时序NDVI的决策树分类法对冬小麦和玉米种植信息提取较为有效, 而棉花提取的结果精度不高。这是因为棉花和春玉米的植被指数时序曲线形状十分近似, 直接运用决策树法不易表示二者的特征, 导致春玉米被误分为棉花。相比之下, 在表达不同农作物时序曲线特征时, 直接利用决策树分类法在建立决策树时容易受到主观因素的影响, 并且不同区域需建立不同的决策分类树, 区域推广性差; 而本文利用的时序NDVI相似性度量法能够完整客观地保留所有特征, 简单有效, 可以实现黄河三角洲地区主要农作物— — 小麦、玉米、棉花种植信息的快速准确提取, 为该地区实现粮食估产、高中低产田划分等提供了技术基础。

本文误差的主要来源包括：利用土地利用图掩膜提取耕地区域时土地利用图本身的误差以及掩膜提取时带来的误差; 另外, 由于各作物的参考植被指数时序曲线数据主要来自于野外抽样调查, 也是本文误差的可能来源; 再者, 本研究采用的数据是250 m空间分辨率的MODIS数据, 存在混合像元的问题, 以及“ 同物异谱” 、“ 异物同谱” 的现象, 造成空间精度低于面积精度。

3.2 农作物种植信息空间分布特征

本文主要农作物种植信息提取结果如图5所示。

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	图5 主要农作物提取结果Fig. 5 Extraction result of main crops

总体来看, “ 渤海粮仓” 山东省内区域中, 冬小麦的种植面积最大, 棉花次之, 春玉米最少。其中, 冬小麦-夏玉米的种植面积最广, 且主要分布于德州和滨州, 少量位于东营市西部的利津县以及南部的广饶县。棉花主要集中于研究区东北部的沿海地区。这与东北沿海地区土壤盐分含量较高, 不适宜种植小麦和玉米, 而较适宜种植棉花有关, 另有少部分棉花位于德州西部夏津县和滨州北部无棣县、沾化县。研究区内主要种植模式为冬小麦-夏玉米, 相比较而言, 春玉米种植量较少, 且零星分布于研究区东北部和少量分布于滨州市南部如邹平县、博兴县等。

由此可以看出, 土壤盐分对农作物种植分布有一定的影响。东北部沿海地区土壤含盐量高, 盐渍化土壤不适宜种植农作物, 因此农作物种植面积较少。棉花作为盐碱地的先锋作物, 具有较强的耐盐性, 但是盐分含量过高也对棉花生长有一定影响。东营市作为“ 渤海粮仓” 重点粮食增产区, 应积极采取措施进行控水调盐, 加强微生物菌肥的使用, 培育耐盐品种等。同时, 结合土壤和气候条件, 因地适宜开展农作物种植。

4 结论

本文基于MODIS 8 d NDVI时序数据, 对黄河三角洲地区主要农作物种植信息进行了提取。结果表明：通过已有土地利用类型图掩膜去除非耕地信息, 能够减少草地、果园对农作物提取的干扰, 提高分类精度。运用作物完整生育期的时序植被指数数据, 能够更全面地反映作物植被指数物候特征, 实现作物信息的高效提取。另外, 通过计算待分像元与样本中提取的NDVI时序参考曲线的欧式距离, 能够方便快捷地区分不同作物, 且作物提取面积达到了较高的精度, 满足监测管理需求, 能够为本地区农业政策制定、结构调整、产量预测、农田改良提供技术基础。

2014年研究区冬小麦-夏玉米、春玉米和棉花种植面积共2 003 119 hm², 其中冬小麦-夏玉米823 019 hm², 春玉米109 450 hm², 棉花247 631 hm²。冬小麦-夏玉米的种植面积最广, 且主要分布于德州和滨州, 少量位于东营市西部的利津县以及南部的广饶县。棉花主要集中于研究区东北部的沿海地区。春玉米种植量较少, 且零星分布于研究区北部和少量分布于滨州市南部。

本研究对于大面积种植的农作物提取效果较好, 而研究区东北部河流附近存在一些小面积种植的农作物, 由于混合像元的问题, 在250 m分辨率的MODIS影像中不易被识别, 提取精度有待进一步提高。混合像元一直是遥感影像分类中的难点, 目前已有人提出一些图像融合的方法以解决混合像元的影响, 在以后的工作中可以进一步结合中高分辨率遥感影像进行混合像元分解, 提高农作物种植面积提取的精度, 特别是空间匹配的精度。另外, 由于棉花和春玉米的植被指数时序曲线十分近似, 只比较曲线的欧氏距离, 二者不易区分, 以后的工作中可以考虑融合图像的纹理或作物高度等其他特征, 或者尝试其他曲线比较的方法, 如动态时间弯曲距离、相关系数等方法开展进一步研究。

The authors have declared that no competing interests exist.

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[31]	郝鹏宇, 牛铮, 王力, 等. 基于时序MODISEVI匹配的棉花信息提取——以新疆博乐市为例[J]. 遥感技术与应用, 2013, 28(2): 309-314. [HAO P Y, NIU Z, WANG L, et al. Planting information extraction of cotton based on MODIS EVI time-series Matching: A case study of Bole County in Xinjiang. Remote Sensing Technology and Application, 2013, 28(2): 309-314. ] [本文引用:1]
[32]	CHU L, LIU G H, HUANG C, et al. Phenology detection of winter wheat in the Yellow River delta using MODIS NDVI time-series data [R]. International Conference on Agro-Geoinformatics. IEEE, 2014: 1-5. [本文引用:1]

2014

0.0

玉苏普江·艾麦提, 买合皮热提·吾拉木, 玉苏甫·买买提, 等. 基于多时相HJ卫星的渭干河-库车河绿洲主要农作物种植信息提取[J]. 中国农业资源与区划, 2014, 35(5): 38-43.

[Yusupujiang

Aimaiti

, Yusuf mamat, Alimujiang kasimu, et al. Crop extraction based on multi-temporal HJ satellite in the Weigan-Kuqa River Oasis. Chinese Journal of Agricultural Resources and Regional Planning, 2014, 35(5): 38-43. ]

... 及时、准确地获取农作物种植面积信息,有利于农业部门指导农业生产,调整农业结构,为区域农作物的产量估计和预测提供可靠依据^[1] ...

2004

0.0

吴炳方. 中国农情遥感速报系统[J]. 遥感学报, 2004, 8(6): 481-497.

[WU B

China crop watch system with remote sensing.

Journal of Remote Sensing, 2004, 8(6): 481-497. ]

The paper introduces China Crop Watch System with Remote Sensing (CropWatch),including its contents, technical innovation and operational development, validation and future activities. Crop Watch contents 5 components, consisting of crop condition monitoring, crop acreage estimation and yield prediction, grain production estimation, cropping index and crop structure monitoring, and drought monitoring. Each component is described briefly in this paper and detailed in following papers. Since 1998, the CropWatch has been put in operation. Each year, there are 7 monthly bulletins released, covering crop condition, crop acreage and yield, drought, agro-meteorology, cropping index and crop structure, and also grain production. And there are 21 10-days announcements released, covering drought and crop condition. During the operational period since 1998, the technology used by the CropWatch are consistently improved. All AVHRR image from 1991 are processed for calibration and correction precisely which began in 2000, including sensor calibration, zenith correction, cloudy detection and masking, geometric correction, atmospheric correction, BRDF correction,and NDVI and surface temperature calculation. The CropWatch uses both different image and growing profile of NDVI to monitor the crop condition, estimates crop acreage using remote sensing and sampling technique based on stratification. The cluster-sample framework is used to estimate plant proportion with TM and Radarsat image as samples, and transect-sampling framework is used to survey crop type proportion in planted-area. At the same time, The Crop Watch continuously expands its monitoring area. From 1998 to 2003, its monitoring area has been expanded from east China to national wide, to north and south America, to Australia and Thailand. Meanwhile, the Crop Watch continuously expands its monitoring contents, from crop condition monitoring, crop acreage estimation and crop yield prediction, to grain production, crop structure, and cropping index. The Crop Watch has its own validation programme. Each year, a few validation sites have been selected to do the field measurement for verifying specific results. For example, in 1999, Jiangning in South China were Dehui in North China were two sites. The crop LAI every 10 days and crop type proportion have been measured to verify the crop condition and crop acreage. Validation shows that the accuracy are up to 97% for plant proportion and 95 crop type proportion.

介绍了中国农情遥感速报系统的建设情况 ,系统内容包括农作物长势监测、农作物种植面积监测、农作物单产预测与粮食产量估算、作物时空结构监测和粮食供需平衡预警等。简要介绍了 1998年以来中国农情遥感速报系统在监测内容与监测范围、监测频率、技术发展以及质量控制与过程检验体系建立等方面的进展 ,并就中国农情遥感速报系统的发展方向提出了展望。

... 同时,对政策制定、粮食安全和农业结构调整等具有重要意义^[2] ...

2003

0.0

潘志强, 刘高焕, 周成虎. 黄河三角洲农作物种植分区的遥感研究[J]. 地理研究, 2003, 22(6): 799-806.

[PAN Z

, LIU G

, ZHOU C

The crop distribution of Yellow River Delta using remote sensing method.

Geographical Research, 2003, 22(6): 799-806. ]

本文选取了一年中三个不同时相的TM影像 ,分别求出了三幅影像的NDVI分布图 ,将其合成为一幅影像图。由于不同区域种植的作物在三个时相中的NDVI变化是不同的 ,因此在NDVI合成图上会呈现不同的颜色区域。通过对不同颜色区域进行采样分析 ,可以确定桃红色区域为冬小麦、玉米 (大豆 )轮作区 ,蓝紫色区域为棉花、春玉米、杂粮种植区 ,亮蓝区域为水稻种植区 ,亮绿色区域为林地、草地。最后 ,根据不同颜色区域的NDVI变化特征用非监督分类和监督分类相结合的方法对影像进行了分类提取。这样便可对黄河三角洲农作物的种植情况进行宏观的了解 ,为农作物种植合理布局及农业可持续发展提供依据。

... 遥感技术具有快速、准确、及时的特点,为大面积农业信息提取和监测提供了有力手段^[3] ...

2015

0.0

张荣群, 王盛安, 高万林, 等. 基于时序植被指数的县域作物遥感分类方法研究[J]. 农业机械学报, 2015, 46(S1): 246-252.

[ZHANG R

, WANG S

, GAO W

, et al.

Remote-sensing classification method of county-level agricultural crops using time-series NDVI.

Transactions of the CSAM, 2015, 46(S1): 246-252. ]

Abstract: Getting all kinds of crop planting area information accurately is the Agricultural Information Management Department’s main responsibility in order to master the basis of crop production information in an efficient manner. A remote sensing classification method was used based on time-series NDVI that is gathered by using Landsat8 satellite equipped with remote sensing technology. This remote sensing technology possessed a short cycle, performed its analysis in a very speedy manner and used a strong microscope to closely analyze the area it has been assigned to. Based on the analysis of the time-series spectrum character curve, crop type identification and acreage extraction can be effectively achieved. This helped to overcome the confusing agricultural crops classification problem caused by “same object with different spectra” and “foreign body with spectrum” by using a single temporary remote sensing image. In order to accurately ascertain the planting area for the various kinds of crops for providing technical support, the best NDVI threshold range for the crops was studied and the various crop classification rules were explored. The Quzhou County, Hebei Province was taken as the study area, and a distribution map of the study area was made based on this information which was gathered in 2014. Throughout five time phases of Landsat satellite data gathered in 2014, a study on the classification of remote sensing for planting area of winter wheat, summer maize, spring corn, cotton, and millet in the study area was conducted. Classification results can be shown for 2014 with all kinds of crops in the study area, respectively: winter wheat is 27 776.61 hm 2, summer corn is 27 776.61 hm 2, spring corn is 2 582.73 hm 2, cotton is 6 485.94 hm 2, and millet is 277.65 hm 2. Using the Kappa coefficient and statistical data to verify the accuracy of this classification, the result shows that the winter wheat, summer corn, spring corn, cotton and millet can be effectively identified, with an overall classification accuracy of 89.166 7%, along with a Kappa coefficient of 0.857 4. Compared with the statistical data, the relative margin of error for individual crops is as follows: winter wheat -0.80%, summer corn -0.32%, spring corn -3.15%, cotton -2.71%, millet 4.12%. This paper proves that mass crop planting areas can be precisely obtained from analyzing the time-series data of remote sensing images with a medium spatial resolution. It also proves that this method can provide a technical basis for using remote sensing to investigate crop planting areas at a county level.

准确地获取农作物种植面积信息是农业管理部门及时掌握农作物生产信息的基础。基于时序植被指数的作物遥感分类方法，可以充分发挥遥感技术周期短、速度快和宏观性强的特点，克服单时相遥感数据的“同物异谱”和“异物同谱”导致的混分问题。以河北省曲周县作物遥感分类为例，在研究待分类作物的最佳NDVI阈值区间的基础上，探讨了基于时序植被指数的农作物分类知识规则建立方法。分类结果显示研究区2014年各类作物的种植面积分别为：冬小麦27 776.61 hm 2、夏玉米27 776.61 hm 2、春玉米2 582.73 hm 2、棉花6 485.94 hm 2、谷子 277.65 hm 2。用总体分类精度、Kappa系数和统计数据对分类精度进行了验证，总体分类精度为89.166 7%，Kappa系数为0.857 4，与统计数据的相对误差分别为冬小麦-0.80%、夏玉米-0.32%、春玉米-3.15%、棉花-2.71%、谷子4.12%。研究结果表明该方法可为县域农作物种植面积遥感调查提供技术依据。

... 单一时相遥感影像只能反映农作物某一时刻的光谱特征,容易导致同谱异物现象^[4] ...

... 张荣群等^[4]依据样本点NDVI时序曲线物候特征,设定分类规则,结合决策树分类法和分层掩膜法,对研究区内的冬小麦、夏玉米、春玉米、棉花等大宗农作物及小宗作物谷子进行分类研究,总体精度达89 ...

2003

0.0

... 植物的物候特征使其在不同生长发育阶段表现出不同的光谱特性,多时相遥感数据能够描述同一作物在不同生长发育阶段的光谱差异^[5],因此时间序列遥感影像在作物种植信息提取、产量预测、农情监测等方面具有巨大的应用潜力^[6] ...

2014

0.0

许青云, 杨贵军, 龙慧灵, 等. 基于MODIS NDVI多年时序数据的农作物种植识别[J]. 农业工程学报, 2014, 30(11): 134-144.

[XU Q

, YANG G

, LONG H

, et al.

Crop information identification based on MODIS NDVI time-series data.

Transactions of the CSAE, 2014, 30(11): 134-144. ]

... 许青云等^[6]基于MODIS NDVI长时间序列数据集,利用ISO非监督分类方法并结合陕西省主要农作物的物候信息、农业气象台站点数据和TM土地覆盖分类数据对陕西省主要农作物和主要土地覆盖物的时序特征曲线进行提取,总体精度达88 ...

2007

0.0

... 而植被指数能够有效反映不同植被类型的物候差异和光谱特征,将分类精度提高到作物层次^[7],因此在作物种植信息提取研究中受到了广泛应用 ...

2013

0.0

王磊, 王贺, 卢艳丽, 等. NDVI在农作物监测中的研究与应用[J]. 中国农业资源与区划, 2013, 34(4): 43-50.

[WANG

, WANG

, LU Y

, et al.

The research and application of NDVI in crop monitoring.

Chinese Journal of Agricultural Resources and Regional Planning, 2013, 34(4): 43-50. ]

Normalized difference vegetation index（NDVI） is one of the most widely used spectrum parameters in crop monitoring， which has important practical guiding significance in crop production and management. In this pa- per， the application of NDVI in crop yield， biomass， harvest index and leaf area index， nutritional status， crop ad- justment was discussed， the existing problems and deficiencies in current research were also summarized， and the research focus of NDVI in crop monitoring in future were put forward.

归一化差值植被指数（normalized difference vegetation index，NDVI）是农作物监测中应用最为广泛的光谱参数之一，在农作物生产与管理中具有重要的实际指导意义。该文就NDVI在作物产量、生物量、收获指数、叶面积指数、营养状况、作物冻害等方面的研究与应用作一综述，并总结当前研究中存在的问题与不足，提出了NDVI在今后作物监测中进一步的研究重点。

... 其中,归一化植被指数（NDVI）是农作物监测中应用最为广泛的光谱参数之一,在农作物生产与管理中具有重要的实际指导意义^[8] ...

2010

0.0

... Panigrahy等^[9]利用时序SPOT VGT NDVI数据,采用K-means算法对印度河平原地区进行非监督分类 ...

2010

0.0

李杨, 江南, 侍昊, 等. 基于SPOT/VGT NDVI的大区域农作物空间分布[J]. 农业工程学报, 2010, 26(12): 242-247.

[LI

, JIANG

, SHI

, et al.

Crops distribution in large scale based on SPOT/VGT NDVI.

Transactions of the CSAE, 2010, 26(12): 242-247. ]

... 李杨等^[10]利用3 a逐旬的 SPOT/VGT NDVI长时间序列数据集进行ISODATA非监督分类 ...

2012

0.0

张健康, 程彦培, 张发旺, 等. 基于多时相遥感影像的作物种植信息提取[J]. 农业工程学报, 2012, 28(2): 134-141.

[ZHANG J

, CHENG Y

, ZHANG F

Crops planting information extraction based on multi-temporal remote sensing images.

Transactions of the CSAE, 2012, 28(2): 134-141. ]

The multi-temporal remote sensing data were used to extract crops planting information quickly and accurately from TM/ETM+ remote sensing images and thirteen MODIS time series remote sensing images, together with the supervised classification and decision tree classification system to interpret major crops in the Heilonggang area. Overall, classification accuracy was up to 91.3%. Compared with one simple supervised classification of TM images, the relative errors of cotton, maize, wheat and vegetables reduced by 1.3%, 20.5%, 2.0% and 13.8% respectively. It proved that this method has high accuracy and it is a good index for the crop planting distribution. The data can provide important scientific information for the adjustment of the major crops planting structure in Heilonggang area and application references for crops classification and crop planting extraction in other area.

为了快速、准确地在遥感影像上对作物种植信息进行提取，该研究运用多时相的 TM/ETM+遥感影像数据和13幅时间序列的MODIS EVI遥感影像数据，采取基于生态分类法的监督分类与决策树分类相结合的人机交互解译方法，建立决策树识别模型，对黑龙港地区的主要作物进行遥感解译，总体分类精度达到了91.3%，与单纯对TM影像进行监督分类相比，棉花、玉米、小麦、蔬菜4类作物的相对误差的绝对值分别降低了1.3%、20.5%、2.0%、13.8%。结果表明该方法的分类精度高，能较好的反映作物的分布状况，可为该地区主要作物种植结构调整提供科学依据，还可为其他区域尺度作物分布信息的提取提供参考。

... 张健康等^[11]运用多时相TM/ETM+影像和MODIS EVI 时序数据,采取基于生态分类法的监督分类与决策树分类相结合的人机交互解译方法,对黑龙港地区的主要作物进行遥感解译,总体分类精度达到91 ...

2003

0.0

... Hill等^[12]根据1991#cod#x02014 ...

2011

0.0

... Colditz等^[13]在MODIS数据中选择纯净像元作为样本,计算每个样本像元的时序NDVI特征参量,通过决策树法对南非和德国的土地覆盖类型进行分类,总体分类精度达80%以上 ...

2011

0.0

苗翠翠, 江南, 彭世揆, 等. 基于NDVI时序数据的水稻种植面积遥感监测分析——以江苏省为例[J]. 地球信息科学学报, 2011, 13(2): 273-280.

[MIAO C

, JIANG

, PENG S

, et al.

Extraction of paddy land area based on NDVI time-series data: Taking Jiangsu Province as an example. Journal of

Geo-Information Science, 2011, 13(2): 273-280. ]

As a kind of crucial data which was able to reflect the continuous coverage of vegetation on the earth surface, the long time-series MODIS NDVI products have already become a reliable data source used in remote sensing for crop measurement. In this paper, a long time-series and high-resolution (250m) MODIS data in 2008 was used to extract the area of the paddy land in Jiangsu Province, China. In the beginning, an annual time-series NDVI dataset of the study area was built up by pre-processing,and a reconstruction of time-series based on Savitzky-Golay filter provided this study excellent images with the better visual effect and NDVI temporal profile. Considering the paddy phenological calendar, the identifiers of paddy were parameterized as starting time, range, extent, and maximum of NDVI during the growth period. In the end, through comparing the phenological key values, a reasonable classification rule was generated based on the thresholds of phenological key values, and a decision tree classifier was constructed to extract Paddy land area, whose precision was 87.5%. It turned out the feasibilities of long time-series MODIS NDVI data and our classification strategy adapted to the extraction of crop land area.

1. Wuxi Water Corporation, Wuxi 214031, China; 2. School of Geography Science, Nanjing Normal University, Nanjing 210097, China; 3. College of Forest Resources and Environment, Nanjing Forestry University, Nanjing 210037, China; 4. Wuxi City & Country Water-Supply & Drainage Engineering Design Instrtute, Wuxi 214031, China

MODIS植被指数时间序列产品能够连续反映植被的覆盖情况,是农作物遥感测量的重要数据源。本文选取江苏省为研究区,利用2008年23个时相的MODIS NDVI数据,采用S-G滤波法进行时间序列的重构,提高NDVI时间序列信息的真实性。另结合农作物物候历、种植结构、地面调查样本等辅助资料,将水稻植被指数时间序列曲线参量化为水稻物候生长期的关键值——生长周期的起始时间、生长幅度、生长长度以及生长过程的NDVI最大值。最后,利用这些关键值确定分类规则,采取决策树分类器,建立区域水稻种植面积提取模型,总体提取精度为87.5%,其表明MODIS植被指数时序数据及本文研究方法在农作物信息提取中的有效性。

... 苗翠翠等^[14]利用NDVI时序数据,确定水稻物候生长期的关键值,利用关键值确定分类规则,采取决策树法分类,总体精度为87 ...

2016

0.0

平跃鹏, 臧淑英. 基于MODIS时间序列及物候特征的农作物分类[J]. 自然资源学报, 2016, 31(3): 503-513.

[PING Y

, ZANG S

Crop identification based on MODIS NDVI time-series dataand phenological characteristics.

Journal of Natural Resources, 2016, 31(3): 503-513. ]

摘　要：论文以2012年6月至2014年6月期间的MOD09Q1及2013年四五月的MOD09A1为数据源,合成归一化植被指数（NDVI）和归一化水体指数（NDWI）,利用TIMESAT软件对NDVI时间序列数据应用分段高斯函数拟合方法重构NDVI时序曲线,并获取7个物候特征（Phenology,以下简称PH,包括生长季始期、生长季末期、生长季长度、NDVI振幅、NDVI左导数、NDVI右导数、生长季期间的NDVI积分）。结合Landsat 8 OLI遥感影像、中国第二次土地调查数据和实地采样样本数据,根据2013年多种地物平滑后的NDVI曲线特征,将年NDVI最大值低于0.5的水体和建设用地掩膜去除。为了获取研究区农作物的最优分类方法,采用分层分类：首先对平滑后的46个NDVI时序数据进行支持向量机（SVM）分类,得到农用地等分类信息;其次利用平滑后的46个NDVI波段、7个物候参数及6期归一化水体指数相互组合,对农用地进行支持向量机分类提取3种农作物的分布信息。经不同波段组合分类对比可知,分类总体精度及Kappa系数的关系为：NDVI＋NDWI〉NDVI＋PH＋NDWI〉PH＋NDWI〉NDVI＋PH〉NDVI〉PH。研究结果表明,遥感数据波段的增加不一定带来较高的分类精度;论文中归一化水体指数有效地提高了水稻的分类精度。此外,辅以物候特征对农作物分类也具有一定的可行性。

... 平跃鹏等^[15]基于MODIS NDVI时序曲线及其他辅助数据,提取作物的7个物候特征,并结合归一化水体指数,采用支持向量机分层分类法对松嫩平原作物进行了分类 ...

2015

0.0

张焕雪, 曹新, 李强子, 等. 基于多时相环境星NDVI时间序列的农作物分类研究[J]. 遥感技术与应用, 2015, 30(2): 304-311.

[ZHANG H

, CAO

, LI Q

, et al.

Research on crop identification using multi-temporal HJ NDVI images.

Remote Sensing Technology and Application, 2015, 30(2): 304-311. ]

... 张焕雪等^[16]利用多时相环境星HJ-1 A/B CCD数据及其多期平滑重构后的NDVI时间序列曲线特征,在对象尺度上采用决策树算法开展了农作物分类研究,总体精度为92 ...

2006

0.0

... Evans等^[17]使用MODIS NDVI时序数据,提出了基于傅里叶组分的波形相似度,对叙利亚平原草地植被进行分类,总体分类精度为72 ...

2014

0.0

... 管续栋等^[18]利用MODIS时间序列数据,提出一种基于动态时间弯曲（DTW）距离的相似性判别的土地覆盖分类方法,对泰国东北部地区单、双季稻种植面积进行了遥感提取研究,总体精度达83 ...

... 确定初始阈值后,再通过试凑法^[18]确定冬小麦-夏玉米NDVI时序相似性阈值为0 ...

2009

0.0

秦元伟, 赵庚星, 姜曙千, 等. 基于中高分辨率卫星遥感数据的县域冬小麦估产[J]. 农业工程学报, 2009, 25(7): 118-123.

[QIN Y

, ZHAO G

, JIANG S

, et al.

Winter wheat yield estimation based on high and moderate resolutionremote sensing data at county level.

Transactions of the CSAE, 2009, 25(7): 118-123. ]

Grain safety has been attracting much more attention as the population increasing and cultivated land decreasing continually. Taking Guangrao County, Shandong Province as the study area, winter wheat yield estimation techniques were explored based on high and moderate resolution remote sensing data at county level. Selecting four medium and high resolution images of Landsat and China-Brazil Earth Resources Satellite(CBERS), which hare the similar temporal characteristics with obvious winter wheat information, the area of winter wheat was acquired by a decision tree classification approach through image pre-processing, including geometric accurate correction, masking and relative radiometric calibration, based on the analysis of spectrum characteristics of typical objects in the study area. Vegetation indexes were calibrated according to their changing trends, and winter wheat yield estimation models were established based on the sum of vegetation index with pixel ratio (∑RVI) and normalized difference vegetation index (NDVI) in different growing areas, respectively, according to their relations to wheat yields. The results showed high precision for winter wheat area extraction of more than 96%, and precision for yield estimation of two models were better than 96% and 94.74%, respectively. The study provides an effective method for winter wheat yield prediction, which is favorable for winter wheat production and grain policy-making and has significance for regional agriculture sustainable development and grain safety.

随着人口的增加和耕地面积的不断减少，粮食安全问题一直备受关注。该文以山东省广饶县为研究区，探讨基于中高分辨率卫星遥感数据的县域冬小麦估产技术。采用陆地卫星和中巴资源卫星（CBERS）中高分辨率遥感图像，选择冬小麦信息较为突出的4个相似时相，经几何精校正、掩膜、相对辐射校正等预处理，在分析研究区典型地物光谱特征的基础上，采用决策树分类方法提取冬小麦种植面积。同时，利用植被指数变化规律对各时相植被指数进行修正，根据植被指数分布情况及其与产量的关系，分别构建了基于像元比值植被指数之和（∑RVI）及不同长势区归一化植被指数（NDVI）的冬小麦估产模型。结果显示：各时相冬小麦种植面积的提取精度均在96%以上，且具有较好的空间精度；二种估产模型的估产精度分别达到了96%以上和94.74%，效果较好。研究为县域冬小麦产量的预测提供了有效方法，能更好地指导冬小麦生产和粮食政策的制定，对区域农业可持续发展和粮食安全有积极价值。

... 秦元伟等^[19]采用陆地卫星和中巴资源卫星（CBERS）中高分辨率遥感图像,采用决策树分类方法提取了山东省广饶县冬小麦种植面积 ...

2015

0.0

贾吉超, 赵庚星, 高明秀, 等. 黄河三角洲典型区域冬小麦播种面积变化与土壤盐分关系研究[J]. 植物营养与肥料学报, 2015, 21(5): 1200-1208.

[JIA J

, ZHAO G

, GAO M

, et al.

Study on the relationship between winter wheat sowing area changes and soil salinity in the typical area of the Yellow River Delta.

Journal of Plant Nutrition and Fertilizer, 2015, 21(5): 1200-1208. ]

【Objectives】 This paper tried to analyze temporal and spatial relationship between soil salinity and distribution of winter wheat, to explore soil salinity effect on winter wheat planting, and to supply scientific advice for winter wheat production decision. 【Methods】 Taking Kenli County,located in the Yellow River Delta, as study area, the winter wheat distribution information was extracted by decision tree model that was generated by analysis of the typical spectral curves with ETM images in April 2003, April 2008 and March 2013. Through spatial overlay of winter wheat planting distribution at each phase, the variation of winter wheat area and the distribution in recent 10 years were analyzed. Based on investigation and analysis of field soil salinity data, the relationship between winter wheat planting area changes and the soil salinity was analyzed. The distribution of winter wheat and the corresponding phase soil salinity distribution were spatially overlaid to compare and analyze the relationship of winter wheat and soil salinity distribution. Related attribute statistic was conducted correspondingly. At the same fince soil salinity distribution in 2008 and 2013 was overlaid, salinity changes have divided into salt reduced and increased area. Soil salinity distribution and the winter wheat planting area at same period were spatially overlaid to analyze the influence of soil salt content on the winter wheat distribution changes.【Results】 1)Winter wheat distribution has obvious spatial characteristics in Kenli County, mainly distributed in the southwest of Kenli and northeast coast of the Yellow River, with a consistent spatial distribution characteristics to low soil salinity zone. 2)The winter wheat planting area significantly reduced from 2003 to 2008 and slightly increased from 2008 to 2013 in Kenli County. 3)The winter wheat planting area change and soil salinity was extremely well correlated. In constant winter wheat planting area, soil salinity was concentrated in the 1.5-2.5 g/kg, in increasing planting area soil salinity focused on the 2-3 g/kg, and in winter wheat planting reduced area, the soil salinitywas more than 3 g/kg. 4)The distribution change of winter wheat in 2008-2013 has a very high consistency with the otants of soil salinity. The planting areas of winter wheat in increased and unchanged area account for 98.07% in soil salinity reduced wheat area, and in the soil salinity increasing wheat area, winter wheat planting reduced area accounts for 84.54%.【Conclusion】 Winter wheat planting area is highly aftected by the change of the soil salinity. With increasing soil salinity, the winter wheat planting areas were rapidly reduced.The upper limit for winter wheat normal growth is 3 g/kg. The control of soil salinity is the key method to maintain and expand the scope of the winter wheat planting.

【目的】将土壤盐分含量与冬小麦分布变化结合,分析两者之间的时空关系,旨在探索土壤盐碱化对冬小麦种植的影响,为冬小麦生产决策提供科学依据。【方法】以黄河三角洲垦利县为研究区,采用2003年4月、2008年4月和2013年3月三期ETM影像,通过分析典型地物光谱曲线生成决策树模型,提取冬小麦分布信息,将各时相冬小麦种植分布提取结果做空间叠加,分析了近10年来冬小麦面积与分布的变化规律;并结合实地土壤盐分调查分析数据,分析了冬小麦种植面积变化与土壤盐分的关系。一方面,将垦利县冬小麦分布图分别与相应时相的土壤含盐量分布图进行空间叠加分析,并对叠加图的属性进行统计,对比分析冬小麦分布与土壤含盐量分布的关系。另一方面,通过叠加2008和2013年土壤盐分含量分布图,将盐分变化分为盐分升高区和盐分降低区,将其与同时段的冬小麦种植范围变化图进行叠加,分析土壤盐分含量变化对冬小麦分布变化的影响。【结果】1)垦利县冬小麦的分布具有明显的空间特征,主要分布在垦利县域西南部和东北部黄河沿岸两个区域,与土壤低含盐量区具有一致的空间分布特征。2)垦利县冬小麦种植面积呈现2003~2008时段大幅减少和2008~2013时段的少许增加趋势。3)冬小麦种植范围变化与土壤盐分含量的相关性极高,冬小麦种植无变化区域土壤含盐量都集中在1.5~2.5 g/kg之间,冬小麦种植增加区域的土壤盐分含量集中在2~3 g/kg,而冬小麦种植减少区的土壤盐分含量都在3 g/kg以上,即超过3 g/kg的土壤含盐量已不再适合冬小麦的生长。4)2008~2013年垦利县冬小麦分布区域变化显著受到土壤含盐量的变化。在土壤含盐量降低的小麦区域中,冬小麦种植增加区和不变区的面积占98.07%,而在土壤含盐量升高的小麦区域中,冬小麦种植减少的面积占84.54%。【结论】冬小麦种植范围及其变化显著受到土壤盐分状况及其变化的影响,冬小麦种植减少区的土壤盐分含量都在3 g/kg以上,且随着土壤含盐量的升高冬小麦种植面积骤减,3 g/kg的土壤含盐量是适合冬小麦生长的上限,土壤含盐量调控是维持和扩大冬小麦种植范围的关键手段。

... 贾吉超等^[20]基于ETM影像,通过分析典型地物光谱曲线生成决策树模型,提取冬小麦分布信息 ...

2014

0.0

刘庆生, 黄翀, 刘高焕, 等. 基于关键期HJ卫星数据提取无棣县作物种植面积[J]. 中国农学通报, 2014, 30(26): 284-290.

[LIU Q

, HUANG

, LIU G

, et al.

Planting area extraction of acrop key growth period in Wudi County based on HJ satellite data.

Chinese Agricultural Science Bulletin, 2014, 30(26): 284-290. ]

To explore the feasibility of quickly extracting planting area using HJ satellite CCD data acquired at the main crop key growth periods, a quick and easy crop planting area extraction method was presented. The author took Wudi County of Shandong Province as a study area, 3 HJ remote sensing images acquired at the main crop key growth periods were selected based on the main crop cultivation technique calendar. Through the EVI time-series data analysis and regions of interesting (ROI) creations, the support vector machine (SVM) was used for extracting the main crop planting area. The results showed that: the EVI data of the crop key growth periods in combination with the SVM method could more accurately estimate the winter wheat, corn and cotton planting area of Wudi County, and overall accuracy of total planting area extraction and spatial distribution location was more than 93% and 75% respectively. Through using the remote sensing data acquired at a crop key growth period, time and labor for remote sensing data processing was reduced greatly, and it was easy to visualize the choice of ROI. Only used SVM could guarantee the regional crop planting area extraction accuracy, which greatly simplified the operation process and provided a reference for other regions to use HJ satellite data to extract crop planting area.

探讨利用作物关键生育期HJ卫星遥感数据，进行区域主要作物种植面积快速监测的可行性，旨在建立一种方便快捷的作物种植面积提取技术。以山东省无棣县为研究区，根据该县作物栽培技术月历为参考选择关键期HJ星遥感数据，通过EVI指数时序数据分析，采用支持向量机(SVM)方法进行了该县主要作物种植空间分布信息提取。结果表明：关键期HJ星EVI指数数据结合SVM方法，能较为准确地获取该县冬小麦、玉米和棉花的种植面积，总体的面积总量精度超过93%，空间分布精度大于75%；利用关键期影像大大减少了数据处理量，便于可视化感兴趣区的选择，仅利用SVM方法就获得了满足精度要求的作物面积提取结果，大大简化了操作流程，为其他区域利用HJ卫星数据提取作物种植面积提供了参考。

... 刘庆生等^[21]以山东省无棣县为研究区,根据该县作物栽培技术月历为参考选择关键期HJ星遥感数据,采用支持向量机（SVM）方法对该县主要作物种植空间分布进行了信息提取 ...

2012

0.0

... 1 研究区概况黄河三角洲位于山东省的东北部,由黄河携带的大量泥沙在入海口处沉积所形成,是我国三大河口三角洲之一^[22] ...

2009

0.0

宗秀影, 刘高焕, 乔玉良, 等. 黄河三角洲湿地景观格局动态变化分析[J]. 地球信息科学学报, 2009, 11(1): 91-97.

[ZONG X

, LIU G

, QIAO Y

, et al.

Study on dynamic changes of wetland land scape pattern in Yellow River Delta. Journal of

Geo-Information Science, 2009, 11(1): 91-97. ]

摘　要：在RS和GIS技术的支持下，以黄河三角洲1986，1996，2006年的三期TM影像为数据源，利用遥感图像处理软件对近代黄河三角洲湿地信息进行了提取。借助于地理信息系统软件和景观生态学软件，我们对20年来黄河三角洲湿地景观格局的动态变化，以及影响其格局动态变化的驱动力进行了分析。结果表明：（1）近20年来，人工湿地面积有了大幅度的增加，而天然湿地的比重在降低。其中，滩涂和柽柳灌草丛景观明显萎缩。（2）一些重要的景观格局指数的计算结果表明，20世纪90年代以来，黄河三角洲湿地景观斑块个数、香农多样性指数、香农均匀度指数一直在增加。黄河三角洲湿地景观的破碎化程度在加剧，斑块类型更加多样化，湿地景观中没有明显的优势类型且各斑块类型在景观中均匀分布。（3）湿地与湿地、湿地与非湿地之间发生着类型转化。其中，18．1％的芦苇草甸转化为农田；26．6％的翅碱莲草甸转化为盐田；11．9％的芦苇沼泽转化为芦苇草甸。（4）在黄河三角洲湿地演化的过程中，受到自然和人为方面演化驱动力的共同作用。其中，自然因素主要有：黄河断流、泥沙淤积和自身演替动力等。人为驱动力主要为农田开垦、滩涂的开发与围垦、油田开发和人工建筑等。

... 受到太平洋和欧亚大陆的共同作用,该地区四季分明、雨热同期,属于温带大陆性季风气候区^[23] ...

2011

0.0

... 黄河三角洲内无地带性植被类型,植被类型少、结构简单^[24] ...

2003

0.0

毛汉英, 赵千钧, 高群. 生态环境约束下的黄河三角洲资源开发的思路与模式[J]. 自然资源学报, 2003, 18(4): 459-466.

[MAO H

, ZHAO Q

, GAO

New thought and pattern under the eco-environmental restriction in the Yellow River Delta.

Journal of Natural Resources, 2003, 18(4): 459-466. ]

The Yellow River Delta is a region abundant in land resources,oil,natural gas,water and sand and ocean resources.But because of large scale exploitation in recent 20years,a series of ecologically environmental problems appear,such as soil salinization-alkalization,vegetation system degradation,seacoast eroding,biodiversity destruction.Based on the thought of old"extension exploitation"model and the request of sustainable development,the paper brings forward a new thought in order to harmonize the relationship between the exploitation of resources and ecological protection in the Yellow River Delta.The new thought is that the exploitation should be moderate and the ecologically environmental protection should be the first.The goal is to construct the ecological economic region in the Yellow River Delta.Based on the high effective utility of resources,this region should develop a new pattern.The pattern is characterized by circulation economy and chain economy.And then the paper discusses the main exploitation patterns of natural resources,such as water,land,energy sources and ocean bio-resources in the Yellow River Delta.

黄河三角洲拥有十分丰富的土地资源、石油天然气资源、水沙资源与海洋资源。经过近20年来的大规模开发,出现了诸如土壤盐碱化加重、植被生态系统成逆向演化、海岸蚀退、生物多样性受到破坏等生态环境问题。根据实施可持续发展战略的要求,在对以往资源开发“外延式”模式进行反思的基础上,提出了协调好资源开发与生态环境保护的新思路,即:在保护生态环境的前提下,进行资源的适度开发,在资源开发过程中搞好生态环境保护;以建设生态经济区为目标,以资源高效利用为基础,以循环经济和链式经济为特色的区域经济发展新模式。并探讨了水土资源、能源等矿产资源和海洋生物资源可持续开发利用的途径与模式。

... 粮仓之一^[25] ...

2007

0.0

陈健. 基于遥感和作物模型的冬小麦水肥生产力及产量差研究 [D]. 北京: 中国农业大学, 2007.

[CHEN

Study on Water and Nutrition Production of Winter Wheat Based on Remote Sensing and Crop Growth Simulation Model.

Beijing: China Agricultural University, 2007. ]

目前作物实际产量与潜在产量之间存在较大的差距，不同地区或者同一地区不同田块间作物产量差异显著，这种现象在世界范围内的农业生产中广泛存在。缩小这个差距对于提高粮食产量，满足日益增加的粮食需求具有重要意义，特别是针对我国人多地少，水资源短缺的情况，其意义更为重要。本文利用多年农户调查数据，采用回归树方法，充分考虑因子间交互作用，对农户经营田块尺度上冬小麦主要产量限制因子进行分析，围绕土壤肥力和养分平衡的时空变化指标，利用土壤采样数据、农户调查数据和遥感数据，结合DNDC模型，分析评价土壤养分空间变化的驱动机制以及农户管理...

... 棉花一般在4月中下旬播种,5月至6月上旬为出苗期,6月上旬至7月上旬为蕾期,7月中旬至8月中旬进入花铃期,9月中下旬进入吐絮期,10月中下旬进入收获期^[26] ...

2008

0.0

张明伟, 周清波, 陈仲新, 等. 基于MODIS时序数据分析的作物识别方法[J]. 中国农业资源与区划, 2008, 29(1): 31-35.

[ZHANG M

, ZHOU Q

, CHEN Z

, et al.

Method for identifying plants analyzed by time order data based on MODIS.

Chinese Journal of Agricultural Resources and Regional Planning, 2008, 29(1): 31-35. ]

... 1 时序植被指数重构由于云、气溶胶以及地物的双向性反射造成NDVI值降低,这些变化噪声极大地影响了对地表覆盖及陆地生态系统的监测^[27] ...

2010

0.0

侯光雷, 张洪岩, 王野乔, 等. 基于时间序列谐波分析的东北地区耕地资源提取[J]. 自然资源学报, 2010, 25(9): 1607-1617.

[HOU G

, ZHANG H

, WANG Y

, et al.

Application of harmonic analysis of time series to exrtracting the cropland and resource in northeast China.

Journal of Natural Resources, 2010, 25(9): 1607-1617. ]

As one of the most important agricultural resources, the cropland is the basic survival condition for human being. Accurate information on cropland area is of critical importance for assessing food security. The Northeast China includes provinces of Liaoning, Jilin, Heilongjiang and eastern part of Inner Mongolia Autonomous Region. It is one of the most important marketable production bases and output regions with rich water resources, fertile soil and vast cultivated land. With the unprecedented combination of economic and population growth, a dramatic land transformation has caused across this region, and the cropland degradation is increasingly serious. In order to preserve and manage cropland resources, it is essential to investigate and monitor cropland dynamics. Compared to traditional observations in the field, the principal advantage of remote sensing data is the possibility that they offer to gather synoptic information at regular time intervals over large areas. Especially for the muti-temporal images, repeated observations can be used to monitor characteristics of phonological dynamics at regional level. The normalized difference vegetation index (NDVI) which derived from the remote sensed data, is one of the most important parameters for the vegetation growth and was widely used in the land cover classification. In recent years, with the development of the theory about Artificial Neural Network (ANN) system, the neural network technology is becoming increasingly an effective means of classification processing of remote sensor digital images. Therefore, on the basis of the muti-period NDVI, the cropland can be identified and separated from the other land cover types by means of the neural network technology.In this paper, Harmonic Analysis of a Time Series of SPOT/VGT NDVI data was used to develop an innovative technique for cropland identification in Northeast China based on temporal variations of NDVI values during 2007. Different vegetation classes (forest, cropland, grassland, water body) exhibiting distinctive seasonal patterns of NDVI variation have strong periodic characteristics. A Discrete Fourier Filter was applied to NDVI time-series data in order to minimize the influence of high-frequency noise on class assignment. Because of the different phonology and periodicity in various land use types, the amplitude, phase and annual NDVI mean value in the studying area are acquired and integrated for an image. According to the training sample size, the neural network classification measure is introduced to extract cropland. The total accuracy is 83.26% while Kappa coefficient is 0.7324. The accuracy of measurement to extract cropland information is much higher by comparing the four current products (GLC2000 land cover data, UMD land cover data, IGBP land cover data and CAS land cover data). The study indicates that it is feasible for cropland extraction utilization of time-series analysis and neural network classification, and can provide accurate, scientific cropland information for agricultural administrators and land management decision making.

耕地是人类社会赖以生存发展最重要的资源之一,及时获取其空间分布是国家农业决策的基础。论文利用2007年多时相的SPOT/VGT NDVI数据提取东北地区耕地资源信息。以NDVI时间序列数据年内变化振幅和周期差异性作为分类的依据,采用时间序列谐波分析法对全年时间谱NDVI数据进行重构,减少高频噪声对信息提取的影响,获得研究区地物信息在时间维度上的振幅、相位以及年均NDVI值影像图,然后将三者合成。应用神经网络分类方法,对合成后的影像选择训练样本,获取东北地区耕地资源的空间分布。实验中提取耕地的精度为83.26%,Kappa系数为0.732 4;该方法获取耕地资源空间分布的精度均高于GLC2000、UMD、IGBP和中科院1∶100万土地利用数据4种分类产品。研究表明,基于时间序列谐波分析法对NDVI数据重建,利用不同类型植被NDVI曲线在一年内振幅、相位特征的差异,采用神经网络分类的方法,可以精确地提取耕地资源信息,及时为农业和土地管理部门管理决策提供科学依据。

... 该方法的核心是傅里叶变换和最小二乘法拟合,即把时间波谱数据分解成许多不同频率的正弦曲线和余弦曲线,从中选取若干个能够反映时间序列特征的曲线进行叠加,从而达到时间序列数据的重建目的^[28] ...

2005

0.0

王丹, 姜小光, 唐伶俐, 等. 利用时间序列傅立叶分析重构无云NDVI图像[J]. 国土资源遥感, 2005(2): 29-32.

[WANG

, JIANG X

, TANG L

, et al. The application of time-series fourier analysis to reconstructing cloud-free NDVI images. Remote Sensing for Land & Resources, 2005(2): 29-32. ]

This paper applied the Harmonic Analysis of Time Series (HANTS) algorithm based on Fourier transformation to predigest and compress the NOAA/AVHRR Normalized Difference Vegetation Index (NDVI) time-series images, and at the same time, wipe off and fill in the data where cloud cover and misdata are examined. The HANTS algorithm outputs such Fourier components as amplitude components and phase components. A fit curve is built according to the Fourier components. Temporal interpolation according to the curve makes it possible to reconstruct the sequential time-series images.

1.China Remote Sensing Satellite Ground Station, Beijing 100086, China; 2.Graduate School of Chinese Academy of Science, Beijing 100039, China

利用基于傅立叶变换的HANTS算法，对中国地区（不包括南海诸岛）AVHRR NDVI时间序列数据进行简化和压缩，将植被的动态变化情况通过NDVI在时间和空间上量化，实现了时间序列图像中云和错误信息的检测、去除和替代。利用HANTS算法提取时间序列的傅立叶分量（幅值分量、频率分量），并由这些分量得出NDVI时间序列拟合曲线，依照曲线进行时间上的插值，从而重构无缝的时间序列图像。

... 循环此过程,最后生成光滑曲线^[29] ...

2013

0.0

张浚哲, 朱文泉, 郑周涛, 等. 高光谱数据的相似性测度对比研究[J]. 测绘科学, 2013, 38(6): 33-36.

[ZHANG J

, ZHU W

, ZHENG Z

, et al.

Comparative study on similarity measures of hyperspectral remote sensing data.

Sciences of Surveying and Mapping, 2013, 38(6): 33-36. ]

光谱相似性测度是高光谱分类的基础,实践证明多种相似性测度的结合能更加全面地表现光谱特征.本文将反应光谱曲线形状的相关系数和反映光谱曲线幅度的欧氏距离进行有效组合,在统一的测试框架下采用ChrisElvidge植被光谱库数据和Hyperion高光谱影像,对欧氏距离、光谱角余弦、光谱信息离散度、相关系数、光谱角余弦—欧氏距离、欧氏距离—相关系数这6种相似性测度方法进行综合评价,并对每种相似性测度的适用范围进行比较;研究结果显示欧氏距离—相关系数方法的分类精度要高于其他5种方法.

... 距离测量法通过计算各时刻时间序列值的差异来度量曲线相似性,易于计算和实现^[30] ...

2013

0.0

郝鹏宇, 牛铮, 王力, 等. 基于时序MODISEVI匹配的棉花信息提取——以新疆博乐市为例[J]. 遥感技术与应用, 2013, 28(2): 309-314.

[HAO P

, NIU

, WANG

, et al.

Planting information extraction of cotton based on MODIS EVI time-series Matching: A case study of Bole County in Xinjiang.

Remote Sensing Technology and Application, 2013, 28(2): 309-314. ]

Vegetation mapping based on EVI time series matching have been widely used due to the usefulness of EVI datasets in distinguishing phenological differences,and many scholars have made great results.However,most studies just made the ideal time series profile from the unclassified image,and research on constructing the ideal profile from datasets of previous years is even less.This study used the time series Moderate Resolution Imaging Spectroradiometer (MODIS) EVI datasets with intermediate spatial resolution(250 m),high temporal resolution ( 16 day composite period) ,and cost free status,made use of Savitzky-Golay method to reconstruct the EVI time series image,odopted the dataset between 2006 and 2010 to create an ideal EVI time series profile of cotton and collected the plant distribution of cotton in 2011 by comparing the Euclidean distance between the ideal profile and the time series EVI profile of unclassified pixels.This study also used the cotton plot surveyed in 2011 as region of interest,checked out the accuracy of the plant distribution of cotton in 2011 using confusion matrix.The result is that the overall accuracy is 82.31%,demonstrating that collecting the plant distribution of cotton using the ideal profile created by the datasets in pervious years is feasible.

(1.The State Key Laboratory of Remote Sensing Science,Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing 100101,China; 2.University of Chinese Academy of Sciences,Beijing 100049,China; 3.The Academy of Forestry,Beijing Forestry University,Beijing 100083,China; 4.Xinjiang Survey Organization,National Bureau of Statistic of China,Urumqi 830001,China)

增强植被指数(EVI)是植被生长状态及植被覆盖度的指示因子,其时序数据也已成为基于物候特征开展大区域植被和土地覆盖分类的基本手段。利用Savitzky-Golay滤波对MODIS EVI 16 d合成时间序列数据进行重建,并利用2006~2010年重建得到的时序数据建立棉花理想时序EVI曲线,通过比较待分类像元与理想曲线欧氏距离的方法,提取新疆博乐市棉花种植分布。使用2011年实地调查的棉花地块为感兴趣区,利用混淆矩阵对2011年棉花种植分布的提取结果进行精度检验,总体精度为82.31%。结果表明:利用多年数据建立的理想时序EVI曲线提取棉花种植分布有效可行。

... r=2时,公式表示对差异更为敏感的欧式距离^[31] ...

2014

0.0

... Chu等^[32]通过基于MODIS时序NDVI的决策树方法提取了黄河三角洲冬小麦种植信息,本文在剔除非耕地类型的基础上,借鉴他们的方法,使用决策树分类方法对该地区主要农作物种植信息进行了提取 ...