Downloading and processing MODIS with fast and free tools

Here's a simple way of processing MODIS: downloading, extracting the bands from HDF as GEOTIFF, merging tiles and reprojecting. Area extraction, NDVI calculation, cloud filtering and more can be done in GRASS for example. It's Linux based, but can easily used in Windows. My way might not be the most elegant, but it's fast and … Continue reading Downloading and processing MODIS with fast and free tools

Remote sensing of vegetation in drylands: Evaluating vegetation optical depth (VOD) using NDVI and in situ data over Sahel

Tian, F.; Brandt, M.; Liu, Y. Y.; Verger, A.; Tagesson, T.; Diouf, A. A.; Rasmussen, K.; Mbow, C.; Wang, Y.; Fensholt, R. Remote sensing of vegetation dynamics in drylands: Evaluating vegetation optical depth (VOD) using AVHRR NDVI and in situ green biomass data over West African Sahel. Remote Sensing of Environment 2016, 177, 265–276.   … Continue reading Remote sensing of vegetation in drylands: Evaluating vegetation optical depth (VOD) using NDVI and in situ data over Sahel

Be cautious using GIMMS3g v0 for trend analysis!

GIMMS3g NDVI is widely used to assess vegetation trends from local to global scale. And even though it is the best long term dataset available right now (July 2015), people should be aware that a serious error affects the dataset in semi arid areas, like the Sahel, which has severe impacts on trend analysis. The … Continue reading Be cautious using GIMMS3g v0 for trend analysis!

Smoothing/Filtering a NDVI time series using a Savitzky Golay filter and R

Coarse scaled NDVI (or FAPAR, EVI...) images have a high temporal frequency and are delivered as Maximum Value Composites (MVC) of several days, which means the highest value is taken, assuming that clouds and other contaminated pixels have low values. However, especially in areas with a rainy season, the composites over 10-16 days still contain … Continue reading Smoothing/Filtering a NDVI time series using a Savitzky Golay filter and R

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)

Environmental change in time series – An interdisciplinary study in the Sahel of Mali and Senegal

Our next article is now online in the Journal of Arid Environments: Brandt, M., Romankiewicz, C., Spiekermann, R. & C. Samimi: Environmental change in time series – An interdisciplinary study in the Sahel of Mali and Senegal. Journal of Arid Environments 105, June 2014, Pages 52-63. It is available here: http://www.sciencedirect.com/science/article/pii/S0140196314000536 Abstract: Climatic changes and human activities have caused … Continue reading Environmental change in time series – An interdisciplinary study in the Sahel of Mali and Senegal

Pixel-wise time series trend anaylsis with NDVI (GIMMS) and R

The GIMMS dataset is currently offline and the new GIMMS3g will soon be released, but it does not really matter which type of data is used for this purpose. It can also be SPOT VGT, MODIS or anything else as long as the temporal resolution is high and the time frame is long enough to … Continue reading Pixel-wise time series trend anaylsis with NDVI (GIMMS) and R