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Spatial patterns of the canopy stress during 2005 drought in Amazonia

机译:亚马逊在2005年干旱期间冠层应力的空间模式

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In the last decades, the detection of drought occurrences and assessment of its severity using satellite data are becoming popular in disaster, desertification, crop production, phenology, land cover change and climate change studies. To detect the drought effects on different vegetation types, many methodologies have been developed, mostly relying on the use of vegetation indices. This communication reports the first attempt to assess the capability of MODIS NDVI, Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI) from 2000 to 2006 time-series to detect the 2005 drought in Amazonia. To reach this objective, monthly composites of the MOD13A2 product were generated from period. Then, monthly anomalies were calculated, considering anomalous values when lower than -1 standard deviation (sd) or higher than 1 sd. Rainfall data provided by the Tropical Rainfall Measuring Mission (TRMM) was also acquired for the same time-series with the objective of supporting the understanding of vegetation response with the precipitation. Water deficit data calculated based on the TRMM data were also used to guide the sampling scheme. A land cover map for South America updated with natural land cover changes detected by the Near Real Time Deforestation Detection Project (DETER) was used as a mask to avoid false anomalies in the Brazilian Amazon. In general, NDWI and EVI showed to be sensitive and consistent for the temporal series used. NDVI presented a high variability and though a difficult interpretation. Critical months in the NDWI and EVI series coincided with the months with higher water stress calculated based on the TRMM data. EVI also showed to detect changes in the canopy structure. These preliminary results suggest that this is a strong methodology to be used in the spatial analysis of the extent of the drought effects in the vegetation. Literfall data will be incorporate in this research for validation purposes.
机译:在过去的几十年中,使用卫星数据检测干旱发生和对其严重程度的评估正在灾害,荒漠化,作物生产,候选,土地覆盖变化和气候变化研究中变得流行。为了检测对不同植被类型的干旱影响,已经开发了许多方法,主要是依赖于植被指数的使用。该通信报告了第一次评估2000年至2006年时间序列从2006年到2006年时间序列评估Modis NDVI,增强植被指数(EVI)和标准化差异水指数(NDWI)的能力,以检测亚马逊的2005年干旱。为了达到此目标,从期间产生Mod13A2产品的月度复合材料。然后,计算每月异常,考虑低于-1标准偏差(SD)或高于1 SD时的异常值。通过热带降雨测量使命(TRMM)提供的降雨数据也被收购了同一时间序列,其目的是支持通过降水的理解理解植被响应。基于TRMM数据计算的水赤字数据也用于指导采样方案。南美洲的陆地覆盖地图更新了近实时砍伐检测项目(防止)检测到的自然土地覆盖变化(防止)作为面具,以避免巴西亚马逊的假异常。通常,NDWI和EVI显示出对使用的时间系列的敏感性和一致。 NDVI呈现出高度的变化,虽然难以解释。 NDWI和EVI系列中的关键月份与基于TRMM数据计算的水压力较高的月份恰逢。 EVI还显示出检测冠层结构的变化。这些初步结果表明,这是一种强有力的方法,可用于植被中的干旱效应程度的空间分析。 Longlefall数据将在本研究中包含验证目的。

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