Assessing the relationship between shire winter crop yield and seasonal variability of the MODIS NDVI and EVI images

Australian researchers have been developing robust yield estimation models, based mainly on the crop growth response to water availability during the crop season. However, knowledge of spatial distribution of yields within and across the production regions can be improved by the use of remote sensing techniques. Images of Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, available since 1999, have the potential to contribute to crop yield estimation. The objective of this study was to analyse the relationship between winter crop yields and the spectral information available in MODIS vegetation index images at the shire level. The study was carried out in the Jondaryan and Pittsworth shires, Queensland , Australia . Five years (2000 to 2004) of 250m resolution, 16-day composite of MODIS Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images were used during the winter crop season (April to November). Seasonal variability of the profiles of the vegetation index images for each crop season using different regions of interest (cropping mask) were displayed and analysed. Correlation analysis between wheat and barley yield data and MODIS image values were also conducted. The results showed high seasonal variability in the NDVI and EVI profiles, and the EVI values were consistently lower than those of the NDVI. The highest image values were observed in 2003 (in contrast to 2004), and were associated with rainfall amount and distribution. The seasonal variability of the profiles was similar in both shires, with minimum values in June and maximum values at the end of August. NDVI and EVI images showed sensitivity to seasonal variability of the vegetation and exhibited good association (e.g. r = 0.84, r = 0.77) with winter crop yields.

全球变化下中国东北样带植物多样性梯度分析及气候-NDVI-植被的关系研究

中国东北样带（NorthEast China Transect, NECT）是位于中纬度温带以降水量作为主要驱动因素的陆地样带。本文的工作以此作为研究平台，利用生态信息系统（Ecological Information System, EIS）以及Microsoft Excel 7.0软件包建立了样带的地理数据库和植物多样性数据库，包括气候数据库、植被数据库、遥感数据库和内蒙内C_4植物数据库以及样带内生态系统特征数据库。在此基础上，主要研究了以下四个方面的内容： 1. 利用Holdridge的生命地带方法对NECT内的生物群区进行了划分。 主要是确定了生物群区间过渡带的位置与宽度，并预测了在全球变化三种模式下NECT内生物群区，尤其是过渡带的变化图景。湿度升高2 ℃后，过渡带的面积都呈扩大化的趋势。森林区对于降水量的变化反应很敏感。荒漠灌丛（即荒漠草原类型）由于其水热条件处于样带内较极端类型，因而对于全球气候变化反应也比较敏感。 2. 研究了NECT内的α、β多样性以及包括生活型、水分生态型、区系地理成分等在内的植物群落特征多样性的梯度变化规律。 研究了样带内的多样性梯度，提出了在样带内存在的α多样性测度问题以及β多样性沿样带的变化规律：样带内由东到西，β多样性逐渐升高，群落内物种被替代的速率变慢;两种植被类型边界上的两个样地之间的相似程度由东到西呈上升趋势;同一类型群落之间的物种周转率比不同类型群落间的物种周转率相对要低。同时将各个环境因子与α、β多样性作了回归分析，找出样带内决定α、β多样性的主要环境因子指标。 样带内沿43.5°N一线附近植物群落的生活型共有17类，水分生态型8类，区系地理成分包括17类，以此为基础分析了群落特征沿样带的变化规律。并探讨了生活型分布的历史地理原因。 3. 对样带气候－NDVI间的关系以及植被－NDVI的关系进行了探讨。 利用来自气象卫星的遥感数据一归一化植被指数（NDVI），和数值化后的样带1:100万植被图进行叠加，找到NECT内每种植被类型对应的NDVI值。样带内共有植被类型147种，反映在NDVI变化上的植被类型有106类。其中，自然植被101种。 影响年均NDVI分布的因子主要有经度、辐射日照百分率及7月温度，与经度呈正相关，与辐射日照时数及7月温度呈负相关。回归方程如下： NDVI = -220.426 + 3.273Lon - 80.338Ratio - 1.962T_7 (R~2 = 0.9714, F = 521.52, p < 0.001) 4. 研究了NECT内的光合功能型。 主要包括内蒙古地区的C_4植物及其生态地理特性。揭标C_4植物的分类群特性、生活型、水分生态型与区系地理成分等生态学特性。C_4植物分布的科属极其集中。C_4光合型为维管植物某些分类群（科、属、种）的特性，为它们固有的遗传特性。推断C_4起源于草本的某些科属。C_4植物为喜热、耐旱的类群。世界种、泛热带种、泛地中海种C_4植物较集中。 样带内的C_3、C_4功能型及其与环境因子的相关性。样带内C_4和C_3光合型植物组成比例由东到西表现出两高两低的趋势。分布主要与年均温和降水量呈显著相关。 提出了一种新的C_3、C_4鉴别方法。即根据野外测定的光合数据建立了C_3、C_4的判别模型： f_1(x) = -1.5493 + 0.1427Pn + 0.1035Tr + 0.3768ΔT + 0.1000Gs f_2(x) = -15.6142 + 1.0542Pn - 0.2503Tr - 0.2957ΔT + 0.6491Gs 最后，综合7个GCMs模型（GFDL，GISS，LLNL，MPI，OSU，UKMOH，UKMOL）的输出结果，利用此结果和本文建立的回归模型，模拟了样带内生物多样性的窨分布格局，并预测了末来全球变化下归一化植被指数NDVI的空间分布格局的变化。

珠江流域NDVI和气象因子的变化及相关分析

对于一个特定的区域特定的时间段来说影响植被覆盖变化的主要因素是气候因素，主要包括降水、温度和光照。珠江流域地形复杂，东西狭长，气象因子差异较大，地表植被覆盖较好，研究珠江流域植被覆盖变化与气象因子之间的关系具有重要意义。 本文利用1982-1999年月平均NDVI和气象因子资料，分析了珠江流域植被和气象因子的时空分布特征，采用奇异值分解（SVD）方法和联合EOF方法研究了珠江流域NDVI和降水在年和年际尺度上的异常关系，利用相关系数、多元线性回归方法分析了NDVI与降水及其他一些气象因子的年际相关及滞后相关。 研究发现，珠江流域植被和气象因子在空间分布上具有明显的东西差异性，除温度以外均具有较好的经度方向一致性、纬度方向差异性。植被和气象因子均存在较大的季节变化和明显的年际变化。在1982-1999年期间，流域整体的NDVI在春季和秋季呈增加趋势，夏季和冬季呈下降趋势，整体呈下降趋势。降水在夏季呈明显的增加趋势，其他季节呈下降趋势，整体呈增加趋势，温度夏季呈下降趋势，其他季节呈上升趋势，整体呈升高趋势，光照在春、秋两季呈增强趋势，夏、冬两季呈减弱趋势，整体呈增强趋势。 方差分析发现，珠江流域NDVI和气象因子的年际方差均呈明显东西差异性，在季节上也有较大差异，且NDVI方差较大的季节基本会对应出现一些气象因子方差较大的现象。6-7月NDVI变化较大，降水变化也较大，说明该流域6-7月的植被有可能受当地降水的变化较大影响。早春季节NDVI变化较大，而早春的温度及光照变化也较大，说明早春的植被生长有可能受温度及光照影响较大。 SVD分析发现，NDVI和降水在年内异常上具有较好的空间一致性，在时间上具有1—2个月的滞后；年际尺度上两者异常在空间上存在明显的差异，流域东部(下游)异常为负相关，西部（上游）异常为正相关。NDVI和温度年内异常呈空间一致性，时间上滞后温度一个月，年际异常也表现为空间一致性。NDVI和光照在年内异常具有空间差异性，西北高原地区NDVI和光照年内异常反向，其他地区年内异常同向。 NDVI和各气象因子在整个区域上的年际相关分析发现，NDVI和同期降水呈负相 I 珠江流域 NDVI 和气候因子的变化及相关分析 关，值为-0.2017，NDVI和温度及短波辐射强度呈正相关，相关系数分别为：0.45和0.4，但是在不同的时间段也有很大的不同。流域NDVI和各气象因子年际相关存在明显的空间和季节差异：空间上，流域东部NDVI和降水负相关明显，和温度及太阳短波辐射正相关明显；流域西部NDVI和降水呈弱的正相关，滞后正相关明显，和温度相关不明显；季节上，NDVI在夏季和降水呈显著负相关，在春、秋季节滞后降水、成明显正相关且滞后三个月正相关最为明显。

基于NDVI的不同火烧强度下大兴安岭林火迹地森林景观恢复

利用LANDSATTM影像,通过分类、提取森林景观类型及NDVI值,在较大尺度上探讨了火烧区火烧强度与森林景观格局、功能恢复的关系。结果表明,火烧区森林总体恢复情况较好。恢复状况与火烧强度具有明显的相关性。火烧强度越高,恢复状况越差。重度火烧区的针叶林景观所占比重低且生长状况较差;沼泽面积高于未火烧对照区,这一现象应引起足够重视,特别是在全球变化气温升高的背景下,应防止寒温带针叶林的退化以及林地沼泽化。在三种主要森林类型(针叶林、阔叶林、针阔叶混交林)中,针阔叶混交林是生长状况最好的,标志着火烧迹地正由演替的初期阶段向中期阶段过渡

NDVI spatial pattern and its differentiation on the Mongolian Plateau

GIMMS NDVI database and geo-statistics were used to depict the spatial distribution and temporal stability of NDVI on the Mongolian Plateau. The results demonstrated that: (1) Regions of interest with high NDVI indices were distributed primarily in forested mountainous regions of the east and the north, areas with low NDVI indices were primarily distributed in the Gobi desert regions of the west and the southwest, and areas with moderate NDVI values were mainly distributed in a middle steppe strap from northwest to southeast. (2) The maximum NDVI values maintained for the past 22 years showed little variation. The average NDVI variance coefficient for the 22-year period was 15.2%. (3) NDVI distribution and vegetation cover showed spatial autocorrelations on a global scale. NDVI patterns from the vegetation cover also demonstrated anisotropy; a higher positive spatial correlation was indicated in a NW-SE direction, which suggested that vegetation cover in a NW-SE direction maintained increased integrity, and vegetation assemblage was mainly distributed in the same specific direction. (4) The NDVI spatial distribution was mainly controlled by structural factors, 88.7% of the total spatial variation was influenced by structural and 11.3% by random factors. And the global autocorrelation distance was 1178 km, and the average vegetation patch length (NW-SE) to width (NE-SW) ratio was approximately 2.4:1.0.

Impacto del uso del suelo sobre el Índice de Vegetación Normalizado (NDVI) en el noroeste argentino

En este trabajo se evalúan los impactos del cambio del uso del suelo en la región Chaqueña noroccidental de Argentina abordando cuestiones como la relación entre factores ambientales y edáficos y la dinámica del Índice de Vegetación Normalizado (NDVI) en áreas donde la vegetación ha sido ligeramente modificada y cómo afecta a la dinámica del NDVI la intensificación del uso de la tierra. El NDVI constituye una cuantificación de la fracción de energía absorbida por la vegetación y, bajos ciertas condiciones, de la productividad primaria neta (PPN). Fue obtenido desde el sensor MODIS (Moderate Resolution Imaging Spectroradiometer) ubicado a bordo de las misiones satelitales de la NASA Aqua y Terra. Se incorporó información de uso de suelo, clima, suelo y NDVI en un sistema de información geográfica con una resolución espacial coincidente con la grilla de la serie temporal de NDVI denominada LTDR (Long Term Data Record). El uso del suelo fue caracterizado por la proporción de cultivos en cada celda de la grilla. Tres atributos fueron derivados de la dinámica estacional del NDVI: la integral anual (NDVI-I), el rango relativo (RREL) y la fecha del máximo NDVI (DMAX). La influencia de los factores ambientales para las celdas con menor proporción de cultivos se analizó mediante regresiones individuales con los tres atributos de NDVI como variables dependientes y las variables de clima y suelo como variables independientes. Para los tres atributos de NDVI persiste en la variabilidad observada un porcentaje importante que no es explicado por las variables consideradas. Se aplicaron los modelos obtenidos a las celdas con mayor proporción de cultivos y se analizaron las diferencias entre los valores observados y predichos de los atributos derivados del NDVI. Ninguno de los modelos ajustados explica la mayor parte de la variabilidad observada cuando se aplican a entornos modificados. En líneas generales, para el NDVI-I los valores observados son menores que los estimados; para RREL, los valores observados son mayores que los estimados y para DMAX no hay evidencias claras de diferencias entre ambos valores. Se analizaron los desvíos entre valores observados y estimados y su relación con el uso de suelo. La magnitud de los cambios observados en la radiación absorbida y la estacionalidad están vinculados a la proporción del paisaje agriculturizado. Por un lado, se observa una disminución del NDVI-I. Por el otro el efecto más relevante de la agriculturización del paisaje sobre la dinámica del carbono es el incremento significativo de la estacionalidad evidenciado por un aumento del RREL. Con respecto a la fecha de máximo NDVI no surgen evidencias claras acerca de la influencia de la agriculturización sobre la misma.

Estado de la vegetación y rendimientos de maíz en la provincia de Córdoba evaluados mediante datos NOAA - NDVI

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Assessment of NDVI, land surface temperature and precipitation anomalies for drought monitoring in Bayankhongor province, Mongolia

During the last decade Mongolia’s region was characterized by a rapid increase of both severity and frequency of drought events, leading to pasture reduction. Drought monitoring and assessment plays an important role in the region’s early warning systems as a way to mitigate the negative impacts in social, economic and environmental sectors. Nowadays it is possible to access information related to the hydrologic cycle through remote sensing, which provides a continuous monitoring of variables over very large areas where the weather stations are sparse. The present thesis aimed to explore the possibility of using NDVI as a potential drought indicator by studying anomaly patterns and correlations with other two climate variables, LST and precipitation. The study covered the growing season (March to September) of a fifteen year period, between 2000 and 2014, for Bayankhongor province in southwest Mongolia. The datasets used were MODIS NDVI, LST and TRMM Precipitation, which processing and analysis was supported by QGIS software and Python programming language. Monthly anomaly correlations between NDVI-LST and NDVI-Precipitation were generated as well as temporal correlations for the growing season for known drought years (2001, 2002 and 2009). The results show that the three variables follow a seasonal pattern expected for a northern hemisphere region, with occurrence of the rainy season in the summer months. The values of both NDVI and precipitation are remarkably low while LST values are high, which is explained by the region’s climate and ecosystems. The NDVI average, generally, reached higher values with high precipitation values and low LST values. The year of 2001 was the driest year of the time-series, while 2003 was the wet year with healthier vegetation. Monthly correlations registered weak results with low significance, with exception of NDVI-LST and NDVI-Precipitation correlations for June, July and August of 2002. The temporal correlations for the growing season also revealed weak results. The overall relationship between the variables anomalies showed weak correlation results with low significance, which suggests that an accurate answer for predicting drought using the relation between NDVI, LST and Precipitation cannot be given. Additional research should take place in order to achieve more conclusive results. However the NDVI anomaly images show that NDVI is a suitable drought index for Bayankhongor province.

Multiscale geostatistical analysis of AVHRR, SPOT-VGT, and MODIS global NDVI products

Global NDVI data are routinely derived from the AVHRR, SPOT-VGT, and MODIS/Terra earth observation records for a range of applications from terrestrial vegetation monitoring to climate change modeling. This has led to a substantial interest in the harmonization of multisensor records. Most evaluations of the internal consistency and continuity of global multisensor NDVI products have focused on time-series harmonization in the spectral domain, often neglecting the spatial domain. We fill this void by applying variogram modeling (a) to evaluate the differences in spatial variability between 8-km AVHRR, 1-km SPOT-VGT, and 1-km, 500-m, and 250-m MODIS NDVI products over eight EOS (Earth Observing System) validation sites, and (b) to characterize the decay of spatial variability as a function of pixel size (i.e. data regularization) for spatially aggregated Landsat ETM+ NDVI products and a real multisensor dataset. First, we demonstrate that the conjunctive analysis of two variogram properties – the sill and the mean length scale metric – provides a robust assessment of the differences in spatial variability between multiscale NDVI products that are due to spatial (nominal pixel size, point spread function, and view angle) and non-spatial (sensor calibration, cloud clearing, atmospheric corrections, and length of multi-day compositing period) factors. Next, we show that as the nominal pixel size increases, the decay of spatial information content follows a logarithmic relationship with stronger fit value for the spatially aggregated NDVI products (R2 = 0.9321) than for the native-resolution AVHRR, SPOT-VGT, and MODIS NDVI products (R2 = 0.5064). This relationship serves as a reference for evaluation of the differences in spatial variability and length scales in multiscale datasets at native or aggregated spatial resolutions. The outcomes of this study suggest that multisensor NDVI records cannot be integrated into a long-term data record without proper consideration of all factors affecting their spatial consistency. Hence, we propose an approach for selecting the spatial resolution, at which differences in spatial variability between NDVI products from multiple sensors are minimized. This approach provides practical guidance for the harmonization of long-term multisensor datasets.

NDVI With Artificial Neural Networks For SRTM Elevation Model Improvement – Hydrological Model Application

Digital elevation model (DEM) plays a substantial role in hydrological study, from understanding the catchment characteristics, setting up a hydrological model to mapping the flood risk in the region. Depending on the nature of study and its objectives, high resolution and reliable DEM is often desired to set up a sound hydrological model. However, such source of good DEM is not always available and it is generally high-priced. Obtained through radar based remote sensing, Shuttle Radar Topography Mission (SRTM) is a publicly available DEM with resolution of 92m outside US. It is a great source of DEM where no surveyed DEM is available. However, apart from the coarse resolution, SRTM suffers from inaccuracy especially on area with dense vegetation coverage due to the limitation of radar signals not penetrating through canopy. This will lead to the improper setup of the model as well as the erroneous mapping of flood risk. This paper attempts on improving SRTM dataset, using Normalised Difference Vegetation Index (NDVI), derived from Visible Red and Near Infra-Red band obtained from Landsat with resolution of 30m, and Artificial Neural Networks (ANN). The assessment of the improvement and the applicability of this method in hydrology would be highlighted and discussed.