Dataset - Identification of early abandonment in cropland through radar-based coherence data and application of a Random-Forest model

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 Meijninger¹, Berien Elbersen¹, Michiel van Eupen¹, Stephan Mantel², Pilar Ciria Ciria³, Andrea Parenti⁴, Marina Sanz Gallego³ and Paloma Perez Ortiz³, Marco Acciai⁴,and Andrea Monti⁴
Institutes: 1) Wageningen University & Research, 2) ISRIC, 3) CIEMAT, 4) Bologna University,

Abstract (Manuscript)

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.

Readme data files

Coherence_quarterly_statisitcs_2017_to_2020.zip

Radar coherence quarterly statistics - Albacete (Spain)

Radar coherence data is based on Sentinel-1B
Period: 2017 to 2020

File naming (.tif files) per year (YYYY):

• Mean coherence: mean_YYYY_1to4.tif
• Standard deviation coherence: std_YYYY_1to4.tif
• Range coherence: range_YYYY_1to4.tif
• Mean delta coherence: mean_delta_YYYY_1to4.tif
• Standard deviation delta coherence: std_delta_YYYY_1to4.tif
• Maximum delta coherence: max_delta_YYYY_1to4.tif

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 >0 and 1
No data: 0-values

Projection:
EPSG:32630 - WGS 84 / UTM zone 30N
Pixel size: 20m

SIGPAC_data_Albacete_2018_to_2020.zip

• More than 5 year fallow (20m raster files)
• Land Use Land Cover LULC (20m raster files)

More than 5 year fallow (according to SIGPAC)
Period: 2018 to 2020
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:
0: Not fallow
1: Fallow more than 5 years

Projection:
EPSG:32630 - WGS 84 / UTM zone 30N
Pixel size: 20m

Land Use Land Cover LULC (according to SIGPAC)
Period: 2018 to 2020
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:
EPSG:32630 - WGS 84 / UTM zone 30N
Pixel size: 20m

Annual_unused_used_land_maps_Albacete_2017_to_2020.zip

Derived annual unused/used land maps - Albacete (Spain), based on Random-Forest model
Period: 2017-2020
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:
0 - Used (and/or Nan)
1 - Unused

Projection:
EPSG:32630 - WGS 84 / UTM zone 30N
Pixel size: 20m

Four_year_abandoned_land_Albacete_2017_to_2020.zip

Four-year abandonment map is based on the 4 annual unused/used land maps
File naming (ENVI):

• Four_year_abandoned_land_Albacete_2017_to_2020.dat (+ Four_year_abandoned_land_Albacete_2017_to_2020.hdr)

Pixel values:
0 - (Nan)
1 - Used (1 year unused in period 2017 - 2020)
2 - Used (2 year unused in a row in period 2017 - 2020)
3 - Abandoned (3 year unused in a row in period 2017 - 2020)
4 - Abandoned (4 year unused in a row in period 2017 - 2020)

Projection:
EPSG:32630 - WGS 84 / UTM zone 30N
Pixel size: 20m