This dataset contains Sentinel 2 satellite images clipped to 5 high-altitude lakes in the Sierra Nevada Mountain Range, Spain. The images were processed with the following atmospheric correction algorithms: - C2RCC (Brockmann et al. 2016) - SIAC (Yin et al. 2022) - ACOLITE (Vanhellemont & Ruddick, 2018) - 6SV (Vermote et al. 2006)

This dataset contains chlorophyll-a (ug/L) predictions for 4 high-altitude lakes in the Sierra Nevada Mountain Range, Spain. Predictions were made using a simple linear regression model with field sample chlorophyll-a as the dependent variable, and the following Sentinel-2 derived spectral index as the independent variable: B3 - (B4+((B2-B4)*((665-560)/(665-490))) Prediction maps are included as GeoTiffs and NetCDF files. Sentinel-2 data were atmospherically corrected using the following algorithms: - ACOLITE (Vanhellemont & Ruddick, 2018) - 6SV (Vermote et al. 2006)

The dataset contains 6 layers with functional attributes derived from time series of EVI (Enhanced Vegetation Index) MODIS 2001-2015. The layers contains pixel-level information about the mean, the coefficient of variation (sCv), the maximum value, the minimum, the date of the maximum value and minimum for the EVI. The EVI layers were obtained from the time series for the time period 2001-2015 MOD13Q1 product.

Conservation Biology faces the challenge of safeguarding the ecological processes that sustain biodiversity. Characterization and evaluation of these processes can be carried out through attributes or functional traits related, for example, to the exchanges of matter and energy between vegetation and the atmosphere. Nowadays, the use of satellite imagery provides useful methods to produce a spatially continuous characterization of ecosystem functioning and processes at regional scales. Our dataset characterizes the patterns of ecosystem functioning in the Sierra Nevada Biosphere Reserve (SE Spain) from the vegetation dynamics captured through the spectral vegetation index EVI (Enhanced Vegetation Index) since 2001 to 2018 (product MOD13Q1.006 from MODIS sensor). First, we provided three Ecosystem Functional Attributes (EFAs) (i.e., annual primary production, seasonality and phenology of carbon gains), as well as their integration into a synthetic mapping of Ecosystem Functional Types (EFTs). Second, we provided two measures of functional diversity, EFT richness and EFT rarity. Finally, to show which are the most stable and variable zones between year in terms of ecosystem functioning, we delivered the interannual variability in ecosystem functioning from two measures, EFTs interannual variability and EFTs interannual similarity. For each variable there are two groups of data (two subfolders): one containing the yearly variables, and another one containing the summaries for the 18-year period. A dataset description is avaiable. Data layers are in .TIF format with its associated metadata in .TFW (with an aditional Data Management Plan). Furthermore, we have incorporated rendered versions of all layers as required by Google Earth Pro, and we have also developed an ad-hoc visualization platform for all the layers (http://obsnev.es/apps/efts_SN.html).

This dataset includes maps with NDSI values that come from Landsat 5 Surface Reflectance collection images available in Google Eath Engine. TIFF images cropped to a region of Lanjaron where different post-fire treatments were established are presented. This area was affected by a forest fire in September 2005. That is why NDSI values and binary layers are presented for two periods, before and after the fire (hese binary layers are generated based on a threshold of 0.35). Comparisons are made in three forest management treatments after the fire. These treatments were No Intervention (NI), Partial Court (PCL) and Salvage Logging (SL). This dataset also includes a grid with the sizes of the Landsat pixels that includes as information the treatment to which it belongs (column Trat_1). And a folder of outputs where each cell has an associated value of ancillary variables (such as elevation, slope, shadows) and another where they present the NDSI values extracted from the images of the dataset