Gradual and abrupt vegetation changes between 2017 and 2024 around the Neusiedl Lake, Austria

Many consequences of land use and land cover changes (LULCC) can be captured by remote sensing imagery, and here in particular by estimates of photosynthetic activity. For example, the harvesting of trees or reed will cause an abrupt drop in vegetation cover, while planting crops and fertilization and watering will result in gradual increase in vegetation cover over time. Vegetation can be captured by the Normalized Difference Vegetation Index (NDVI), a widely used remote sensing metric that quantifies the health and density of vegetation.Provided here are two intermediate analysis results showing abrupt and gradual vegetation change in the study region of Neusiedl in the INSPIRE project (https://www.inspire-biodiversa.com/). Vegetation cover was estimated as monthly NDVI Sentinel 2 Level-2A data. Input satellite imagery were pre-processed by removing clouds and gaps and constructing atmospherically corrected surface reflectance estimates at monthly time steps (arithmetric average aggregation).More detailed description below. These are gridded geoTiff files that can be opened in any standard GIS software.File: Neusiedl_NDVI_gradual_2017-2024.tif

Contains a single band with the coefficients of a robust linear regression calculated on the pixel level. Positive values indicate gradual linear increase in NDVI per month between 2017 and end of 2024.  Only statistically 'significant' coefficients are shown (at a 5% significance level).

File: Neusiedl_NDVI_largestchange_2017-2024.tif

Contains two bands with the direction (Band 1) and the numeric date (Band 2) of the largest change in mean or variance of vegetation cover over the reference period. Opposed to a gradual change this allows to capture both the direction (gain or loss) and timing of vegetation change as captured by NDVI.---Data properties:

Spatial grain
10 m

Geographic projection
WGS 84

Temporal grain
Whole time period

Spatial extent
Study region of the INSPIRE casestudy

Temporal extent
2017 March to December 2024

Number of variables/entities
3

All files are provided as is and the author takes no responsibility for errors or misuse and misinterpretation.