基于MODIS时序影像的湖南省油菜种植面积遥感监测分析

基于时间序列影像数据的提取方法可实现快速监测大面积农作物的种植分布和面积估算.以湖南省为研究区,利用2017年500 m空间分辨率的MODIS NDVI时序数据,结合湖南省耕地分布数据和实地样点数据得到油菜物候标准曲线,采用最小二乘法与阈值法提取得到湖南省油菜种植分布.结果显示,遥感提取得到的湖南省油菜种植面积主要分布...     

MODIS和HJ-1 CCD数据时空融合重构NDVI时间序列

遥感数据反演高时空分辨率NDVI对监测植被动态变化过程具有重要意义,然而受天气影响,单颗卫星难以提供时间连续的高空间分辨率NDVI数据。以华北平原中东部为实验区,联合HJ-1 CCD数据和MODIS数据,对STARFM算法进行了改进,(1)考虑了不同地物对光谱响应的差异,为减少分类错误利用统计学上()对分类数据进行筛选,按照不同地物类型分别利用线性拟合方法修改光谱距离权重;(2)定义了预测半径,对HJ-1 CCD数据因外界影响而缺失的影像进行了预测。结果表明,与真实影像相比,预测结果呈现了较好的空间一致性,相关系数均达到了极显著相关,改进算法的预测精度要高于原算法。利用该方法将HJ-1 CCD NDVI的空间变化信息与MODIS NDVI时间变化信息有机结合重构了高时空分辨率NDVI序列,有效补充了HJ-1CCD NDVI的缺失数据集。     

基于地形调节植被指数估算长汀县植被覆盖度

植被覆盖度遥感估算最常用的方法是基于植被指数构建模型,但大部分的植被指数没有考虑地形的影响。以福建省长汀县作为研究区,引入能消除地形影响的地形调节植被指数(topography adjusted vegetation index,TAVI),利用像元二分模型估算植被覆盖度,旨在研究TAVI对植被覆盖度估算结果的影响,并与基于归一化差值植被指数(normalized difference vegetation index,NDVI)估算的结果进行比较。根据目视效果和统计指标的分析表明:基于TAVI估算的植被覆盖度精度高于基于NDVI的估算结果,并能有效降低阴坡阳坡间的差异,提高阴坡区域植被覆盖度的估算精度。     

基于遥感与GIS技术对城市绿地信息提取、统计方法研究

本文结合新疆石河子市国家人居城市申报项目,基于遥感技术,利用QUICKBIRD卫星影像,运用归一化植被指数(NDVI)法提取绿地信息,建立解译标志,采用人机交互方式,提高解译精度,该方法目前在植被监测、土地覆盖变化、荒漠化研究中有广泛的应用。     

基于生态参数的岩溶峰丛区石灰岩基岩表面溶蚀率遥感反演

为了探索岩溶峰丛区生态参数与石灰岩基岩表面溶蚀率的相关性,用相关生态参数反演土层下石灰岩基岩表面的溶蚀率,从而间接估算其变形。选择桂林丫吉村岩溶峰丛区为研究区,以Landsat5 TM多光谱数据为信息源,提取归一化差值植被指数(normalized difference vegetation index,NDVI)、地面温度及土壤湿度等遥感参数;运用SPSS统计软件对这3种参数分别与石灰岩溶蚀率进行了相关分析,确定其相关系数分别为-0.91,0.85及0.93;在此基础上,通过逐步回归分析,建立了运用NDVI估算植被覆盖下石灰岩表面溶蚀率的遥感反演模型。结果表明:NDVI与石灰岩溶蚀率相关性最大,所以植被信息是石灰岩表层基岩溶蚀的主要间接标志;溶蚀率与NDVI指数存在线性关系,因此只要已知研究区其他地区的NDVI指数,即可估算出该地区的石灰岩基岩表面溶蚀率。     

基于MODIS数据的浒苔信息提取方法研究

浒苔是一种大型绿藻,大量浒苔爆发会严重影响景观,干扰旅游观光和水上运动的进行。本文利用NASA提供的MODIS02的250m分辨率影像,分别采用了比值法、归一化植被指数法和监督分类中的最小距离分类,提取了浒苔的信息,实验结果表明,三者都可以提取浒苔信息,但是比值法提取的结果最好,最小距离分类很难与薄云区分,必须加入人工目视解译,归一化植被指数可以区分出薄云,但是遗漏较多。     

基于MODIS EVI时序数据的江汉平原油菜种植分布信息提取

油菜是我国主要的食用油料作物。及时、准确地获取油菜种植分布信息对油菜长势监测、估产以及灾情评估具有十分重要的意义。以江汉平原为研究区,利用250 m空间分辨率的MODIS EVI时序数据,以TM数据作为野外采样数据与MODIS EVI数据之间的过渡数据,间接提取MODIS EVI数据农作物的训练样本;通过分析江汉平原油菜和冬小麦的EVI光谱特征及物候信息,建立油菜种植面积提取模型;采用多次阈值比较法提取2014—2015年间江汉平原油菜种植分布信息。研究结果表明,2014年和2015年油菜面积遥感提取结果与农业局统计数据相比,总体提取精度分别达到95.22%和91.29%;2014年MODIS数据与TM数据提取的油菜面积一致性为88.61%;基于时间序列MODIS EVI数据,结合EVI光谱特征和物候信息,利用该方法可以有效提取江汉平原油菜种植分布信息。     

基于MODIS数据的植被指数分析

针对宏观土地覆盖遥感分类的现状,充分利用MODIS相对于AVHRR数据具有的多光谱和分辨率优势,提出了利用MODIS数据进行分类特征选择与提取并结合多时相特征进行宏观土地覆盖分类的分类方法,并进行了分类试验。     

基于MODIS NDVI的科尔沁沙地荒漠化动态监测

以MODIS植被指数产品为数据源,利用归一化植被指数(NDVI)与植被盖度的高相关性,以NDVI为荒漠化评价的定量依据进行荒漠化程度划分,得出科尔沁沙地2000年与2007年的NDVI分级分布图,并分别统计出相应的不同程度荒漠化土地面积所占比例; 最后,利用不同等级荒漠化土地面积所占比例和NDVI分级分布图所提供的空间分配状况来评价研究区荒漠化程度及动态变化状况.研究结果表明,2000年至2007年科尔沁沙地荒漠化程度总体上呈减少趋势.     

基于GPS-IR的归一化植被指数反演

针对利用GPS接收机在接收L波段信号时对周围植被水分含量较为敏感的特性,使用GPS反射信号的变化,进行测站归一化植被指数(NDVI)反演.利用2个GPS参考站近5年的连续观测数据计算的归一化微波反射指数(NMRI),构建了反演NDVI的一元线性模型.NMRI整体变化趋势与同时间段内中分辨率成像光谱仪(MODIS)NDV...     

基于TM数据的植被覆盖度反演

本文首先对TM影像进行了几何纠正、辐射校正、大气校正;然后根据混合像元的结构特征,利用TM数据从植被指数(NDVI)中采用“等密度模型”和“非密度模型”提取了宜昌南部地区的植被覆盖度。在用“非密度模型”反演植被覆盖度的过程中,叶面积指数(LAI)是一个必要的参数,本文提出了一种改进的借助可见光波段和近红外波段反射值来提取叶面积指数(LAI)的方法。通过和MODIS数据反演结果比较表明:“非密度模型”的估算精度要高于“等密度模型”;利用“等密度模型”和“非密度模型”反演植被覆盖度是可行。     

中国西部地区基于经验模型的土壤水分反演

地表土壤水分含量的时空分布信息是十分重要的,常常作为水文模型、气候模型、生态模型的输入参数,同时,也是干旱预报、农作物估产等工作的重要指标。被动微波遥感是监测土壤含水量最有效的手段之一。相比红外与可见光,它具有波长长,穿透能力强的优势。相比主动微波雷达,被动微波辐射计具有监测面积大、周期短,受粗糙度影响小,对土壤水分更为敏感,算法更为成熟的优势。目前,已研究出许多反演土壤水分的方法.本课题的主要内容是借助AMSR-E土壤水分影像数据、MODIS归一化植被指数(NDVI)影像数据和MODIS分类影像数据,利用ENVI软件进行遥感图像数据处理,运用统计分析方法建立NDVI与土壤水分的经验模型,研究中国西部地区稀疏植被覆盖区土壤水分的反演。     

基于MODIS遥感数据的水体提取模型及算法改进

针对MODIS遥感数据采用多波段谱间关系模型提取水体容易与阴影混淆,产生提取不精确的问题,作者改进了模型中的水体提取算法,然后对鄱阳湖的MODIS遥感数据进行水体提取实验。最终,实验结果表明,改进后的水体提取算法能够有效地提升水体提取精度。该方法现已运用到一些采用MODIS数据进行水体监测与提取的实践作业中。     

Seasonal trend analysis (STA) of MODIS vegetation index time series for the mangrove canopy of the Teacapan-Agua Brava lagoon system,Mexico

Monthly time series, from 2001 to 2016, of the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) from MOD13Q1 products were analyzed with Seasonal Trend Analysis (STA), assessing seasonal and long-term changes in the mangrove canopy of the Teacapan-Agua Brava lagoon system, the largest mangrove ecosystem in the Mexican Pacific coast. Profiles from both vegetation indices described similar phenological trends, but the EVI was more sensitive in detecting intra-annual changes. We identified a seasonal cycle dominated by Laguncularia racemosa and Rhizophora mangle mixed patches, with the more closed canopy occurring in the early autumn, and the maximum opening in the dry season. Mangrove patches dominated by Avicennia germinans displayed seasonal peaks in the winter. Curves fitted for the seasonal vegetation indices were better correlated with accumulated precipitation and solar radiation among the assessed climate variables (Pearson’s correlation coefficients, estimated for most of the variables, were r ≥ 0.58 p < 0.0001), driving seasonality for tidal basins with mangroves dominated by L. racemosa and R. mangle. For tidal basins dominated by A. germinans, the maximum and minimum temperatures and monthly precipitation fit better seasonally with the vegetation indices (r ≥ 0.58, p < 0.0001). Significant mangrove canopy reductions were identified in all the analyzed tidal basins (z values for the Mann-Kendall test ≤ ?1.96), but positive change trends were recorded in four of the basins, while most of the mangrove canopy (approximately 87%) displayed only seasonal canopy changes or canopy recovery (z > ?1.96). The most resilient mangrove forests were distributed in tidal basins dominated by L. racemosa and R. mangle (Mann-Kendal Tau t ≥ 0.4, p ≤ 0.03), while basins dominated by A. germinans showed the most evidence of disturbance.     

小麦倒伏的光谱特征及遥感监测

小麦倒伏后茎秆和叶片在探测视场中的比例及植株组分的受光条件发生了变化,其冠层光谱特性也随之发生改变,所以利用遥感监测倒伏是可能的。首先,分析了叶片和茎秆组分的光谱特点,解释了倒伏后冠层光谱的变化特点,即光谱反射率随倒伏角度的增加而增加。其次,利用倒伏后冠层光谱反射率在可见光波段的相对增幅高于近红外波段的特点,利用实测数据分析和建立了归一化差异植被指数NDVI随倒伏角度的增加而降低的规律及模型。最后,采用2003年4月7日和5月9日倒伏发生前后的2景LandsatETM卫星影像,利用倒伏前后的NDVI值的变化,成功监测了小麦倒伏的发生程度。     

Spatiotemporal analysis of vegetation variability and its relationship with climate change in China

This paper investigated spatiotemporal dynamic pattern of vegetation, climate factor, and their complex relationships from seasonal to inter-annual scale in China during the period 1982–1998 through wavelet transform method based on GIMMS data-sets. First, most vegetation canopies demonstrated obvious seasonality, increasing with latitudinal gradient. Second, obvious dynamic trends were observed in both vegetation and climate change, especially the positive trends. Over 70% areas were observed with obvious vegetation greening up, with vegetation degradation principally in the Pearl River Delta, Yangtze River Delta, and desert. Overall warming trend was observed across the whole country (>98% area), stronger in Northern China. Although over half of area (58.2%) obtained increasing rainfall trend, around a quarter of area (24.5%), especially the Central China and most northern portion of China, exhibited significantly negative rainfall trend. Third, significantly positive normalized difference vegetation index (NDVI)–climate relationship was generally observed on the de-noised time series in most vegetated regions, corresponding to their synchronous stronger seasonal pattern. Finally, at inter-annual level, the NDVI–climate relationship differed with climatic regions and their long-term trends: in humid regions, positive coefficients were observed except in regions with vegetation degradation; in arid, semiarid, and semihumid regions, positive relationships would be examined on the condition that increasing rainfall could compensate the increasing water requirement along with increasing temperature. This study provided valuable insights into the long-term vegetation–climate relationship in China with consideration of their spatiotemporal variability and overall trend in the global change process.     

Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method

Normalized difference vegetation index (NDVI) of highly dense vegetation (NDVI_v) and bare soil (NDVI_s), identified as the key parameters for Fractional Vegetation Cover (FVC) estimation, are usually obtained with empirical statistical methods However, it is often difficult to obtain reasonable values of NDVI_v and NDVI_s at a coarse resolution (e.g., 1 km), or in arid, semiarid, and evergreen areas. The uncertainty of estimated NDVI_s and NDVI_v can cause substantial errors in FVC estimations when a simple linear mixture model is used. To address this problem, this paper proposes a physically based method. The leaf area index (LAI) and directional NDVI are introduced in a gap fraction model and a linear mixture model for FVC estimation to calculate NDVI_v and NDVI_s. The model incorporates the Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) model parameters product (MCD43B1) and LAI product, which are convenient to acquire. Two types of evaluation experiments are designed 1) with data simulated by a canopy radiative transfer model and 2) with satellite observations. The root-mean-square deviation (RMSD) for simulated data is less than 0.117, depending on the type of noise added on the data. In the real data experiment, the RMSD for cropland is 0.127, for grassland is 0.075, and for forest is 0.107. The experimental areas respectively lack fully vegetated and non-vegetated pixels at 1 km resolution. Consequently, a relatively large uncertainty is found while using the statistical methods and the RMSD ranges from 0.110 to 0.363 based on the real data. The proposed method is convenient to produce NDVI_v and NDVI_s maps for FVC estimation on regional and global scales.