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MODIS based continuous fields of tree cover using generalized linear models

机译:基于MODIS基于树盖的连续字段,使用广义线性模型

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Knowledge about the land cover over large area is important for monitoring and modeling of ecological and environmental processes. Considerable efforts have recently resulted in the development of global continuous fields for different land cover types at small spatial scales based on NOAA-AVHRR and TERRA-MODIS data. Researchers have applied a range of techniques to depict the sub-pixel fraction of land cover types from remotely sensed data. As a result, such products have a high potential to accurately monitor land cover change over tune. In this study, a methodology is described for deriving and optimizing continuous fields of tree cover for complex topography at the regional scale of the European Alps using generalized linear models (GLM) which are commonly used in ecological research. The presented study is carried out in Switzerland a central part of the European Alps with a complex topography and a highly fragmented landscape. MODIS data (MOD09) at a spatial resolution of 500m are used as explanatory variables and Landsat TM based data of fractional tree-cover are used as target variable to calibrate the GLM. For purpose of evaluation we calculated different accuracy measures of the resulting continuous fields of tree cover, and we compared the model output with two available global data sets: (1) TERRA-MODIS Vegetation Continuous Fields product (MOD44), and (2) the NOAA-AVHRR vegetation continuous fields. Our regionally optimized GLM model produces higher accuracies compared to MOD44- and AVHRR-products. The GLM model achieve an average mean absolute error (MAE) of 0.093, while the average MAE of the MOD44 and the AVHRR products are 0.205 and 0.200, respectively. We conclude that GLM's are appropriate for deriving fractional tree cover for complex topography at the scale of the European Alps. Regional calibrations of vegetation continuous fields may offer significantly improved predictions compared to globally calibrated models. Such regionally calibrated and optimized models may serve as valuable tools for regional monitoring of land cover pattern and its temporal change.
机译:关于大面积的土地覆盖的知识对于监测和建模生态和环境流程非常重要。最近努力最终导致基于NOAA-AVHRR和Terra-Modis数据的小空间尺度的不同土地覆盖类型的全局连续领域的开发。研究人员已经应用了一系列技术,以描绘从远程感测数据中的陆地覆盖类型的子像素分数。因此,这种产品具有高潜力,可以精确地监测陆地覆盖变化通过曲调。在本研究中,描述了一种方法,用于使用通常用于生态研究的广义线性模型(GLM)在欧洲阿尔卑斯州的区域规模中获得和优化树木覆盖的连续领域的连续领域。本研究在瑞士进行了欧洲阿尔卑斯山的中央部分,具有复杂的地形和高度分散的景观。 Modis数据(MOD09)在500m的空间分辨率下用作解释变量,并且基于地图的分数树覆盖数据用作校准GLM的目标变量。出于评估目的,我们计算了由此产生的连续字段的树木封面的不同精度测量,并将模型输出与两个可用的全局数据集进行比较:(1)Terra-Modis植被连续字段产品(MOD44),(2) NOAA-AVHRR植被连续田地。与Mod44和AVHRR-Products相比,我们的区域优化GLM模型会产生更高的精度。 GLM模型实现了0.093的平均平均值(MAE),而MOD44和AVHRR产品的平均MAE分别为0.205和0.200。我们得出结论,GLM适用于在欧洲阿尔卑斯山的规模上导出复杂地形的分数树盖。与全球校准的模型相比,植被连续领域的区域校准可能会提供显着改善的预测。这种区域校准和优化的模型可以作为区域监测土地覆盖模式及其时间变化的有价值的工具。

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