MODIS NDVI时间序列在三江平原湿地植被信息提取中的应用

以三江平原为研究区,利用多时相的中分辨率成像光谱仪(Moderate Resolution Imaging Spectroradiometer,MODIS)影像数据,采用一种基于归一化植被指数(Normalized Difference Vegetation Index,NDVI)时间序列的监督分类方法获取了研究区湿地植被的分布数据。监督分类以NDVI时间序列的波形所反映出的植被物候特征作为分类器,将离散的傅立叶变换应用于NDVI时间序列以减少高频噪声对分类的影响,并运用傅立叶变换后波形幅度和相位的相似性来确定像素的归属类别。根据研究区植被的物候特征的差异,区分出7种地表(沼泽、沼泽化草甸、滩地、水田、旱地、灌木和林地)的植被类型,得到三江平原2005年湿地植被的分布数据。该方法的总体分类精度达到79.67%,Kappa系数为0.752 5。研究表明,基于MODIS多时相NDVI数据,采用基于傅立叶组分的相似度分类方法可以客观、经济、快速的提取湿地植被分布数据。     

An assessment of land degradation in the Save catchment of Zimbabwe

The lack of reliable baseline information on land degradation is a hindrance towards its monitoring and mitigation. Of particular interest is the identification of areas susceptible to degradation. In this study, remote sensing and GIS technologies were applied to detect and map susceptibility to land degradation in Buhera district, in Save catchment, Zimbabwe. Data used included Landsat TM and ETM imagery for 1992 and 2002, agro-ecological zones, vegetation cover and population density. The study identified five preliminary categories of degradation susceptibility ranging from very high to low.     

利用NOAA NDVI数据集监测冬小麦生育期的研究

探索了利用NDVI研究作物生育期的方法，对黄淮海冬麦区的返青期、抽穗期、成熟期进行了估测，并利用地面实际观测资料进行了验证。结果表明，NDVI数据对大范围农作物生育期监测是可行的。冬小麦遥感反青期由南到北依次推迟，符合春季绿波由南到北推移规律。对冬小麦遥感生育期年际变化分析表明，黄淮海平原返青期变化相对较大，而抽穗期和成熟期变化较小。根据历年月平均温度与返青期分析，冬小麦返青日期与2月份平均温度密切相关。对于局部地区，利用5d合成1km分辨率数据，且按农业生态分区分别制定生育期判别标准，估测效果将更好。     

应用遥感数据研究中国植被生态系统与气候的关系

应用1982-1994年NOAA/AVHRR的归一化植被指数（NDVI）资料和587个气象台站的数据对我国不同类型植被生态系统和气候的关系进行研究，首先将我国的植被类型划分为21类，在此基础上分别研究了不同时间尺度下我国不同区域，不同植被类型和气候的关系。结果表明：在多年平均状态下，植被生态系统NDVI水平主要受水分条件的影响；年内变化上，温度对植被生态系统季相变化化起着比降水略大的作用，年降水量造成了植被季相响应的差异，在年际变化上，分别研究了4个季节和整个生长期尺度上的关系，一般情形为温度和降水对植被的年际波动起着大致相反的作用，不同植被类型在不同的生长时期（季节）对气候的变化响应方式也不同，发现在植被的生长期，我国南方和北方的植被生态系统对温度和降水的响应方式相反；同时存在2个植被-气候敏感区，分别为我国北方的典型草原到森林的过渡区和云南中部部分区域。     

Spatiotemporal characteristics of white mold and impacts on yield in soybean fields in South Dakota

ABSTRACT

White mold of soybeans is one of the most important fungal diseases that affect soybean production in South Dakota. However, there is a lack of information on the spatial characteristics of the disease and relationship with soybean yield. This relationship can be explored with the Normalized Difference Vegetation Index (NDVI) derived from Landsat 8 and a fusion of Landsat 8 and the Moderate Resolution Imaging Spectroradiometer (MODIS) images. This study investigated the patterns of yield in two soybean fields infected with white mold between 2016 and 2017, and estimated yield loss caused by white mold. Results show evidence of clustering in the spatial distribution of yield (Moran’s I = 0.38; p < 0.05 in 2016 and Moran’s I = 0.45; p < 0.05 in 2017) that can be explained by the spatial distribution of white mold in the observed fields. Yield loss caused by white mold was estimated at 36% in 2016 and 56% in 2017 for the worse disease pixels, with the most accurate period for estimating this loss on 21 August and 8 September for 2016 field and 2017 field, respectively. This study shows the potential of free remotely sensed satellite data in estimating yield loss caused by white mold.     

Multi-scale remote sensing to support insurance policies in agriculture: from mid-term to instantaneous deductions

ABSTRACT

Climate change is today one of the biggest issues for farmers. The increasing number of natural disasters and change of seasonal trends is making insurance companies more interested in new technologies that can somehow support them in quantifying and mapping risks. Remotely sensed data, with special focus on free ones, can certainly provide the most of information they need, making possible to better calibrate insurance fees in space and time. In this work, a prototype of service based on free remotely sensed data is proposed with the aim of supporting insurance companies’ strategies. The service is thought to calibrate annual insurance rates, longing for their reduction at such level that new customers could be attracted. The study moves from the entire Piemonte region (NW Italy), to specifically focus onto the Cuneo province (Southern Piemonte), which is mainly devoted to agriculture. MODIS MOD13Q1-v6 and Sentinel-2 L2A image time series were jointly used. NDVI maps from MODIS data were useful to describe the midterm phenological trends of main crops at regional level in the period 2000–2018; differently, Sentinel-2 data permitted to map local crop differences at field level in 2016 and 2017 years. With reference to MODIS data, the average phenological behavior of main crop classes in the area, obtained from the CORINE Land Cover map Level 3, was considered using a time series decomposition approach. Trend analyses showed that the most of the crop classes alternated three phases (about 7 years) suggesting that, presently, this is probably the time horizon to be considered to tune mid-term algorithms for risk estimates in the agricultural context. Crop classes trends were consequently split into three phases and each of them modeled by a first-order polynomial function used to update correspondent insurance risk rate. Sentinel-2 data were used to map phenological anomalies at field level for the 2016 and 2017 growing seasons; shifts from class average behavior were considered to locally and temporarily tune insurance premium around its average trend as described at the previous step. Synthesizing, one can say that this approach, integrating MODIS and Sentnel-2 data, makes possible to locally and temporarily calibrate premiums of indexed insurance policies by describing the average trends of crop performance (NDVI) at regional level by MODIS data and refining it at field and specific crop level by Sentinel-2 data.     

基于谱间关系的MODIS遥感影像水体提取研究

水是地球上万物的命脉所在,水体提取对于水资源调查、洪水灾害预测评估和环境监测等有着重要作用。而可见光和近红外波段水体与植被、城市和土壤光谱反射率的差异是利用遥感手段提取水体的基本原理。本文利用谱间关系法进行MOD IS图像的水体提取。结果表明,用于提取水体的谱间关系法比归一化植被指数(NDVI)法快速,但谱间关系法有把云错误地当作水体提取的缺点。     

辽宁省耕地植被指数变化特征分析

利用NOAA/AVHRR资料通道1(0.58~0.68μm)、通道2(0.725~1.1μm)数据计算归一化植被指数(NDVI),对辽宁省5个气候区内1999~2004年208个旱田监测点和84个水田监测点的作物生长状况进行连续监测,分析NDVI年际、旬际变化特征。结果表明:辽宁耕地(旱田、水田)植被指数年际间差异明显,造成旱田年际变化以及地区差异的主要原因是降水时空分布不均匀的结果,因此旱田2004年NDVI达到近年最大,2001年较低;而造成水田年际差异以及地区差异的原因是受水田用水状况以及光温条件的影响,水田2003年NDVI最大,2001年较低。在整个生长季内(5~9月份),辽宁耕地植被指数呈单峰型变化,从5月上旬开始到6月中旬是缓慢增长阶段,6月下旬到8月下旬快速增长并达到最大,之后又迅速降低;2004年耕地植被指数旬变化趋势与6 a平均植被指数旬变化趋势基本相同,但作物生长前期NDVI较6 a平均值低,主要原因是受到2004年春夏之交严重干旱的影响。     

Study on NDVI- T s space by combining LAI and evapotranspiration

This paper focuses on interpreting the different spatial relationships between NDVI and T _s, a triangular or a trapezoid, and on analyzing transformation conditions, the physical and ecological meanings of the vegetation index-surface temperature space as well. Further, we use the Temperature-Vegetation Dryness Index (TVDI) to explain the existent meaning of a triangular space after NDVI reaches its saturated state by employing the relationships between NDVI, LAI and evapotranspiration. The specific relations between NDVI and T _s are useful for describing, validating and updating land surface models.