Characterization of pasture biophysical properties and the impact of grazing intensity using remotely sensed data
Characterization of pasture biophysical properties and the impact of grazing intensity using remotely sensed data
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DOI：
10.1016/j.rse.2007.01.013
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发表时间：
2007-08
影响因子：
13.5
通讯作者：
Izaya Numata;D. Roberts;O. Chadwick;J. Schimel;F. A. A. Sampaio-F.-A.-A.-Sampaio-15545438;Francisco das Chagas Leonidas;J. V. Soares
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作者：
Izaya Numata;D. Roberts;O. Chadwick;J. Schimel;F. A. A. Sampaio-F.-A.-A.-Sampaio-15545438;Francisco das Chagas Leonidas;J. V. Soares
遥感有可能提高我们绘制和监测牧场退化的能力。牧场退化是亚马逊地区最重要的问题之一，但放牧强度、土壤条件和土地使用年限对牧场生物物理特性的影响方式，以及我们使用遥感监测牧场生物物理特性的能力，却知之甚少。我们评估了草地生物物理措施和遥感之间的联系，并调查了放牧强度对巴西亚马逊地区朗多尼亚牧场生物物理措施的影响。在旱季，在不同的草地上测量了地上生物量，冠层含水量和高度。利用陆地卫星专题制图仪（TM）数据，从光谱混合分析中获得了四个光谱植被指数和分数，即，计算了非光合植被（NPV）、绿色植被（GV）、土壤、遮荫和NPV+土壤，并与田间草地测量值进行了比较。对于干燥条件下的放牧牧场，归一化差异红外指数（NDII 5和NDII 7）与生物物理测量的相关性高于归一化差异植被指数（NDVI）和土壤调整植被指数（SAVI）。净现值与所有田间措施的相关性最高，这表明这一部分是一个很好的指标，在朗多尼亚牧场的特点。动脉高度与阴影分数相关。建立了草地生物物理变化的概念模型，NPV、Shade和GV用于表征Rondônia可能的牧草退化过程。基于实地测量，放牧强度对牧草生物物理性质的影响最显着相比，土壤秩序和土地利用年龄。在旱季放牧对牧场的影响可以通过使用净现值等遥感测量方法来测量。
Remote sensing has the potential of improving our ability to map and monitor pasture degradation. Pasture degradation is one of the most important problems in the Amazon, yet the manner in which grazing intensity, edaphic conditions and land‐use age impact pasture biophysical properties, and our ability to monitor them using remote sensing is poorly known. We evaluate the connection between field grass biophysical measures and remote sensing, and investigate the impact of grazing intensity on pasture biophysical measures in Rondônia, in the Brazilian Amazon. Above ground biomass, canopy water content and height were measured in different pasture sites during the dry season. Using Landsat Thematic Mapper (TM) data, four spectral vegetation indices and fractions derived from spectral mixture analysis, i.e., Non‐Photosynthetic Vegetation (NPV), Green Vegetation (GV), Soil, Shade, and NPV+Soil, were calculated and compared to field grass measures. For grazed pastures under dry conditions, the Normalized Difference Infrared Index (NDII5 and NDII7), had higher correlations with the biophysical measures than the Normalized Difference Vegetation Index (NDVI) and the Soil‐Adjusted Vegetation Index (SAVI). NPV had the highest correlations with all field measures, suggesting this fraction is a good indicator of pasture characteristics in Rondônia. Pasture height was correlated to the Shade fraction. A conceptual model was built for pasture biophysical change using three fractions, i.e., NPV, Shade and GV to characterize possible pasture degradation processes in Rondônia. Based upon field measures, grazing intensity had the most significant impact on pasture biophysical properties compared to soil order and land‐use age. The impact of grazing on pastures in the dry season could be potentially measured by using remotely sensed measures such as NPV.