DOI: 10.5281/zenodo.6334938
title: Dataset - Identification of early abandonment in cropland through radar-based coherence data and application of a Random-Forest model

description: This dataset accompanies the manuscript titled "Identification of early abandonment in cropland through radar-based coherence data and application of a Random-Forest model", submitted by co-authors to the journal Global Change Biology (GCB) Bioenergy. Wouter Meijninger<sup>1</sup>, Berien Elbersen<sup>1</sup>, Michiel van Eupen<sup>1</sup>, Stephan Mantel<sup>2</sup>, Pilar Ciria Ciria<sup>3</sup>, Andrea Parenti<sup>4</sup>, Marina Sanz Gallego<sup>3</sup> and Paloma Perez Ortiz<sup>3</sup>, Marco Acciai<sup>4</sup>,and Andrea Monti<sup>4</sup><br> Institutes: 1) Wageningen University & Research, 2) ISRIC, 3) CIEMAT, 4) Bologna University, <strong>Abstract (Manuscript)</strong> In the context of increased pressures on land for food and non-food production it is relevant to understand better, which land resources have become unused and abandoned and where these lands are. Data on where these lands are and what their extend is are not collected in regular statistics. In this paper we present an approach to detect signs of abandonment in cropping land using radar coherence data. The methodology was tested in the Spanish regions of Albacete and Soria where agricultural land abandonment is a common process. The results show that land abandonment detection using radar coherence data works well for the region of Albacete in arable lands. The radar-based analysis is a relatively simple method to detect land abandonment in an early to longer-term state and can therefore be applied once developed and tested further in other regions to larger areas of the EU where land abandonment is serious and needs monitoring and policy response. The applicability of the method to Soria and Emilia Romagna (Italy) regions show that there are still challenges to overcome to make the method more widely applicable for detecting land abandonment in other environmental zones of Europe. Lack of reliable training and validation data, like LPIS data, in regions is one of the challenges in this respect. <strong>Readme data files</strong> <em>Coherence_quarterly_statisitcs_2017_to_2020.zip</em> Radar coherence quarterly statistics - Albacete (Spain) Radar coherence data is based on Sentinel-1B<br> Period: 2017 to 2020 File naming (.tif files) per year (<em>YYYY</em>): Mean coherence: <em>mean_YYYY_1to4.tif</em> Standard deviation coherence: <em>std_YYYY_1to4.tif</em> Range coherence: <em>range_YYYY_1to4.tif</em> Mean delta coherence: <em>mean_delta_YYYY_1to4.tif</em> Standard deviation delta coherence: <em>std_delta_YYYY_1to4.tif</em> Maximum delta coherence: <em>max_delta_YYYY_1to4.tif</em> Each file consists of 4 bands: band 1: 1st quarter [Jan-Feb-March] band 2: 2nd quarter [April-May-June] band 3: 3rd quarter [July-Aug-Sept] band 4: 4th quarter [Oct-Nov-Dec] Statistics are based on radar coherence data, which is scaled between &gt;0 and 1<br> No data: 0-values Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m <em>SIGPAC_data_Albacete_2018_to_2020.zip</em> More than 5 year fallow (20m raster files) Land Use Land Cover LULC (20m raster files) More than 5 year fallow (according to SIGPAC)<br> Period: 2018 to 2020<br> File naming (ENVI files): Albacete_SIGPAC_MoreThan5YrsFallowAreas_2018_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2018_20m.hdr) Albacete_SIGPAC_MoreThan5YrsFallowAreas_2019_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2019_20m.hdr) Albacete_SIGPAC_MoreThan5YrsFallowAreas_2020_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2020_20m.hdr) Pixel values:<br> 0: Not fallow<br> 1: Fallow more than 5 years Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m Land Use Land Cover LULC (according to SIGPAC)<br> Period: 2018 to 2020<br> File naming (ENVI files): LULC_SIGPAC_Albacete_2018_20m.dat (+ LULC_SIGPAC_Albacete_2018_20m.hdr) LULC_SIGPAC_Albacete_2019_20m.dat (+ LULC_SIGPAC_Albacete_2019_20m.hdr) LULC_SIGPAC_Albacete_2020_20m.dat (+ LULC_SIGPAC_Albacete_2020_20m.hdr) Pixel values: 0 - Nan 1 - Arable land 2 - Vineyards 3 - Olives 4 - Fruits 5 - Nuts 6 - Citrus 7 - Permanent grassland 8 - Forest 9 - Rest, small elements 10 - Built-up areas 11 - Water 12 - Roads 13 - Unproductive land Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m <em>Annual_unused_used_land_maps_Albacete_2017_to_2020.zip</em> Derived annual unused/used land maps - Albacete (Spain), based on Random-Forest model<br> Period: 2017-2020<br> File naming (ENVI files): predict_RF_Albacete_2017_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2017_quarterly_stats_LU1_v181920.hdr) predict_RF_Albacete_2018_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2018_quarterly_stats_LU1_v181920.hdr) predict_RF_Albacete_2019_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2019_quarterly_stats_LU1_v181920.hdr) predict_RF_Albacete_2020_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2020_quarterly_stats_LU1_v181920.hdr) Pixel values:<br> 0 - Used (and/or Nan)<br> 1 - Unused Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m <em>Four_year_abandoned_land_Albacete_2017_to_2020.zip</em> Four-year abandonment map is based on the 4 annual unused/used land maps<br> File naming (ENVI): Four_year_abandoned_land_Albacete_2017_to_2020.dat (+ Four_year_abandoned_land_Albacete_2017_to_2020.hdr) Pixel values:<br> 0 - (Nan)<br> 1 - Used (1 year unused in period 2017 - 2020)<br> 2 - Used (2 year unused in a row in period 2017 - 2020)<br> 3 - Abandoned (3 year unused in a row in period 2017 - 2020)<br> 4 - Abandoned (4 year unused in a row in period 2017 - 2020) Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m