Pixel-wise regression between two raster time series (e.g. NDVI and rainfall)

Doing a pixel-wise regression between two raster time series can be useful for several reasons, for example: find the relation between vegetation and rainfall for each pixel, e.g. a low correlation could be a sign of degradation derive regression coefficients to model the depending variable using the independend variable (e.g. model NDVI with rainfall data) … Continue reading Pixel-wise regression between two raster time series (e.g. NDVI and rainfall)

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Renaming, converting, clipping: script based raster time series processing

When working with remotely sensed time series data (e.g. MODIS, AVHRR, GIMMS, etc.), bulk processing can save a lot of time. Using a terminal in a Linux environment, simple scripts can process thousends of files in short time. Here are some basic hints on how to start, gdal has to be installed. For Windows users … Continue reading Renaming, converting, clipping: script based raster time series processing