527 resultados para MODIS-NDVI


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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.

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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.

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Moderate resolution remote sensing data, as provided by MODIS, can be used to detect and map active or past wildfires from daily records of suitable combinations of reflectance bands. The objective of the present work was to develop and test simple algorithms and variations for automatic or semiautomatic detection of burnt areas from time series data of MODIS biweekly vegetation indices for a Mediterranean region. MODIS-derived NDVI 250m time series data for the Valencia region, East Spain, were subjected to a two-step process for the detection of candidate burnt areas, and the results compared with available fire event records from the Valencia Regional Government. For each pixel and date in the data series, a model was fitted to both the previous and posterior time series data. Combining drops between two consecutive points and 1-year average drops, we used discrepancies or jumps between the pre and post models to identify seed pixels, and then delimitated fire scars for each potential wildfire using an extension algorithm from the seed pixels. The resulting maps of the detected burnt areas showed a very good agreement with the perimeters registered in the database of fire records used as reference. Overall accuracies and indices of agreement were very high, and omission and commission errors were similar or lower than in previous studies that used automatic or semiautomatic fire scar detection based on remote sensing. This supports the effectiveness of the method for detecting and mapping burnt areas in the Mediterranean region.

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草地在广大的干旱、半干旱地区是重要的可更新资源,草地生产力是草地质量评价最重要的指标之一,不仅体现草地生态系统的稳定性及生物种群的多样性,而且在畜牧业的发展及生态建设方面都有极其重要的参考价值和指导意义。本文以内蒙古通辽市科尔沁半干旱草地生态系统为研究对象,通过生长季野外生物量调查,结合遥感植被指数建立该区域三类草地系统的经验估产模型;同时利用遥感植被指数建立的植被光合模型(VPM)模拟草甸草地区域总初级生产力(GPP),为区域畜牧业的发展、生态环境评价及生态恢复提供基础数据。主要结论有: (1)对2000—2006年MODIS的8 d合成归一化植被指数(NDVI)资料和逐日气象资料的分析结果显示:季节变化过程中,研究区水汽压与NDVI的相关程度明显大于降水量;积温和累积降水量共同控制着各年草地的返青速度,草地快速生长期(6、7月)的降水量对NDVI年最大值的影响比年总降水量更显著;时滞分析表明,水汽压对之后约12 d的NDVI有持续的显著影响,平均气温的时滞为11~15 d,降水对NDVI影响的累积和时滞双重效应可达36~52 d。 (2)根据野外生物量调查结果和MODIS数据,分别采用归一化植被指数(NDVI)、增强植被指数(EVI)和修正的土壤调节植被指数(MSAVI)对草甸草地、半退化草地以及沙丘草地三种类型的草地分别建立了生物量估算模型,其拟合优度系数分别为:0.704、0.537、0.723,经F检验其相关性均达到显著水平(P<0.01)。   (3)由于引入了草地生长阶段光合生理的季节动态,VPM模拟的总初级生产力(GPPVPM)和涡度相关法观测的总初级生产力(GPPobs)结果有很好的一致性,GPPVPM(1506.54C•m-2)略低于GPPobs(1615.38gC•m-2)。表明VPM可用于半干旱草地总初级生产力的模拟,从而实现对通量观测GPP的尺度扩展,为区域或全球生态系统总或净初级生产力的模拟提供高时间、空间分辨率的准确模拟。

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准确估算草地生物量对合理规划区域畜牧业、评估草地植被的生态效益有重要意义。目前,在常用的遥感估算模型中,采用的植被指数和模型函数形式多样。本文根据野外生物量调查结果和MODIS数据,分别采用归一化植被指数(NDVI)、增强植被指数(EVI)和修正的土壤调节植被指数(MSAVI)建立了内蒙古科尔沁左翼后旗草地地上生物量和地上地下总生物量估测的3种(线性、乘幂和指数)模型,并进行了比较。结果表明:3种模型能够对草地生物量进行较好的模拟,其中指数模型效果最佳;3个植被指数(NDVI,EVI和MSAVI)与草地生物量均有较高的相关性,可用于该草地产量估测,其中MSAVI对地上生物量拟合效果最好(R2=0.900);NDVI和EVI的线性模型对总生物量的模拟明显好于对地上生物量的模拟。

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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.

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Context: Variation in photosynthetic activity of trees induced by climatic stress can be effectively evaluated using remote sensing data. Although adverse effects of climate on temperate forests have been subjected to increased scrutiny, the suitability of remote sensing imagery for identification of drought stress in such forests has not been explored fully. Aim: To evaluate the sensitivity of MODIS-based vegetation index to heat and drought stress in temperate forests, and explore the differences in stress response of oaks and beech. Methods: We identified 8 oak and 13 beech pure and mature stands, each covering between 4 and 13 MODIS pixels. For each pixel, we extracted a time series of MODIS NDVI from 2000 to 2010. We identified all sequences of continuous unseasonal NDVI decline to be used as the response variable indicative of environmental stress. Neural Networks-based regression modelling was then applied to identify the climatic variables that best explain observed NDVI declines. Results: Tested variables explained 84–97% of the variation in NDVI, whilst air temperature-related climate extremes were found to be the most influential. Beech showed a linear response to the most influential climatic predictors, while oak responded in a unimodal pattern suggesting a better coping mechanism. Conclusions: MODIS NDVI has proved sufficiently sensitive as a stand-level indicator of climatic stress acting upon temperate broadleaf forests, leading to its potential use in predicting drought stress from meteorological observations and improving parameterisation of forest stress indices.

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克隆植物存在于自然界几乎所有类型生态系统,在群落和生态系统中起着重要作用。作为植物群落的重要组分,克隆植物势必深刻地影响群落的结构和功能。揭示克隆植物在不同类型生态系统中的重要性及其与环境因子的关系是克隆植物生态学研究的重要内容。 本研究以东北样带为平台,通过采用野外植物和土壤调查,结合2006年23期500 m MODIS NDVI数据,重点分析环境因素和群落生产力与克隆植物丰富度和重要性的关系。主要结论如下: 1.沿东北样带自西向东的不同植被类型中,克隆植物出现的频率和重要值呈现降低的趋势,具体体现为:典型草原 > 荒漠草原 > 草甸草原 > 农田 > 森林。克隆植物丰富度、重要值和相对物种数均与海拔呈显著相关,但这种关系随克隆构型发生变化。 2.群落中克隆植物物种丰富度与土壤有机碳、全氮和全磷均有显著的相关关系,但与全钾并不呈现显著相关关系。通径分析表明,在三种草原植被中,土壤成分对克隆植物重要性的影响强度随草原类型变化而变化。 3.植被生产力与年均降水量和年均日照时数显著相关;虽然植被生产力与年均温度没有显著的相关性,但与温度季节性变化呈显著相关;植被生产力与降水和温度季节性的相关性随植被类型发生变化。 4. 就整个东北样带而言,植被生产力与群落中总的物种丰富度和克隆植物丰富度呈显著正相关,但与不同克隆构型克隆植物的丰富度相关关系不一致。在不同植被类型中,生产力与克隆植物丰富度没有相关关系,但与克隆植物重要值呈现不同相关关系。具体而言,克隆植物重要值与植被生产力的相关性在荒漠草原表现为正相关,在典型草原和草甸草原呈负相关,而在农田和森林没有显著相关性。 在土壤环境相对贫瘠和植被生产力水平较低的条件下,克隆植物可能比非克隆植物具有更强的适应能力,并在群落次生演替过程中起重要作用。在高海拔、养分贫瘠的群落中,克隆植物出现频率较高。在荒漠草原,由于土壤贫瘠、扰动频繁,因此克隆植物在群落中的重要性较高,在生产力水平高的植物群落中克隆植物重要性较高;在典型草原和草甸草原,由于土壤养分等条件的改善,克隆植物在群落中的重要性降低,在生产力水平高的植物群落中克隆植物重要性较低;在农田和森林群落中,环境质量最好,克隆植物在群落中的重要性低,对群落的结构和生产力不构成显著影响,因此克隆植物重要性与生产力相关性不显著。

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Landnutzungsänderungen sind eine wesentliche Ursache von Treibhausgasemissionen. Die Umwandlung von Ökosystemen mit permanenter natürlicher Vegetation hin zu Ackerbau mit zeitweise vegetationslosem Boden (z.B. nach der Bodenbearbeitung vor der Aussaat) führt häufig zu gesteigerten Treibhausgasemissionen und verminderter Kohlenstoffbindung. Weltweit dehnt sich Ackerbau sowohl in kleinbäuerlichen als auch in agro-industriellen Systemen aus, häufig in benachbarte semiaride bis subhumide Rangeland Ökosysteme. Die vorliegende Arbeit untersucht Trends der Landnutzungsänderung im Borana Rangeland Südäthiopiens. Bevölkerungswachstum, Landprivatisierung und damit einhergehende Einzäunung, veränderte Landnutzungspolitik und zunehmende Klimavariabilität führen zu raschen Veränderungen der traditionell auf Tierhaltung basierten, pastoralen Systeme. Mittels einer Literaturanalyse von Fallstudien in ostafrikanischen Rangelands wurde im Rahmen dieser Studie ein schematisches Modell der Zusammenhänge von Landnutzung, Treibhausgasemissionen und Kohlenstofffixierung entwickelt. Anhand von Satellitendaten und Daten aus Haushaltsbefragungen wurden Art und Umfang von Landnutzungsänderungen und Vegetationsveränderungen an fünf Untersuchungsstandorten (Darito/Yabelo Distrikt, Soda, Samaro, Haralo, Did Mega/alle Dire Distrikt) zwischen 1985 und 2011 analysiert. In Darito dehnte sich die Ackerbaufläche um 12% aus, überwiegend auf Kosten von Buschland. An den übrigen Standorten blieb die Ackerbaufläche relativ konstant, jedoch nahm Graslandvegetation um zwischen 16 und 28% zu, während Buschland um zwischen 23 und 31% abnahm. Lediglich am Standort Haralo nahm auch „bare land“, vegetationslose Flächen, um 13% zu. Faktoren, die zur Ausdehnung des Ackerbaus führen, wurden am Standort Darito detaillierter untersucht. GPS Daten und anbaugeschichtlichen Daten von 108 Feldern auf 54 Betrieben wurden in einem Geographischen Informationssystem (GIS) mit thematischen Boden-, Niederschlags-, und Hangneigungskarten sowie einem Digitales Höhenmodell überlagert. Multiple lineare Regression ermittelte Hangneigung und geographische Höhe als signifikante Erklärungsvariablen für die Ausdehnung von Ackerbau in niedrigere Lagen. Bodenart, Entfernung zum saisonalen Flusslauf und Niederschlag waren hingegen nicht signifikant. Das niedrige Bestimmtheitsmaß (R²=0,154) weist darauf hin, dass es weitere, hier nicht erfasste Erklärungsvariablen für die Richtung der räumlichen Ausweitung von Ackerland gibt. Streudiagramme zu Ackergröße und Anbaujahren in Relation zu geographischer Höhe zeigen seit dem Jahr 2000 eine Ausdehnung des Ackerbaus in Lagen unter 1620 müNN und eine Zunahme der Schlaggröße (>3ha). Die Analyse der phänologischen Entwicklung von Feldfrüchten im Jahresverlauf in Kombination mit Niederschlagsdaten und normalized difference vegetation index (NDVI) Zeitreihendaten dienten dazu, Zeitpunkte besonders hoher (Begrünung vor der Ernte) oder niedriger (nach der Bodenbearbeitung) Pflanzenbiomasse auf Ackerland zu identifizieren, um Ackerland und seine Ausdehnung von anderen Vegetationsformen fernerkundlich unterscheiden zu können. Anhand der NDVI Spektralprofile konnte Ackerland gut Wald, jedoch weniger gut von Gras- und Buschland unterschieden werden. Die geringe Auflösung (250m) der Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI Daten führte zu einem Mixed Pixel Effect, d.h. die Fläche eines Pixels beinhaltete häufig verschiedene Vegetationsformen in unterschiedlichen Anteilen, was deren Unterscheidung beeinträchtigte. Für die Entwicklung eines Echtzeit Monitoring Systems für die Ausdehnung des Ackerbaus wären höher auflösende NDVI Daten (z.B. Multispektralband, Hyperion EO-1 Sensor) notwendig, um kleinräumig eine bessere Differenzierung von Ackerland und natürlicher Rangeland-Vegetation zu erhalten. Die Entwicklung und der Einsatz solcher Methoden als Entscheidungshilfen für Land- und Ressourcennutzungsplanung könnte dazu beitragen, Produktions- und Entwicklungsziele der Borana Landnutzer mit nationalen Anstrengungen zur Eindämmung des Klimawandels durch Steigerung der Kohlenstofffixierung in Rangelands in Einklang zu bringen.

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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.