Annual arable land and permanent agriculture extent in Southern Greece from 1986-2020, derived from Landsat and LUCAS data

This dataset consists of 30 m spatial resolution arable land and permanent agriculture maps for Southern Greece for the period 1986-2020. For this dataset, arable lands are defined as areas where seasonal crops are planted and are typically harvested and tilled at least once a year. Arable land includes cereal crops, root crops, non-permanent industrial crops, dry pulses, vegetables and flowers, and fodder crops. Permanent agriculture is defined as areas with permanent crops including fruit trees, olive groves and vineyards. Mapped regions include Attica, the Peloponnese peninsula, and Crete. Ground observations derived from the European Union statistical office (EUROSTAT) Land Use/Cover Area frame Survey (LUCAS) and Landsat spectral-temporal metrics were used to train a Random Forest classifier, which was used to classify arable land and permanent agriculture at annual time steps. A post-processing step was taken to reduce spurious landcover class transitions using a Hidden Markov Model that accounts for landcover class transition likelihoods and annual class membership likelihoods.

Data and Resources

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Cite this as

Sparks Aaron M, Bouhamed Imen, Bochetti Luigi, Gitas Ioannis Z, Kalaitzidis Chariton (2022). Dataset: Annual arable land and permanent agriculture extent in Southern Greece from 1986-2020, derived from Landsat and LUCAS data. https://doi.org/10.1594/PANGAEA.946950

DOI retrieved: 2022

Additional Info

Field Value
Imported on January 12, 2023
Last update August 4, 2023
License CC-BY-4.0
Source https://doi.org/10.1594/PANGAEA.946950
Author Sparks Aaron M
More Authors
Bouhamed Imen
Bochetti Luigi
Gitas Ioannis Z
Kalaitzidis Chariton
Source Creation 2022
Publication Year 2022
Subject Areas
Name: Agriculture

Name: LandSurface

Related Identifiers
Title: Mapping Arable Land and Permanent Agriculture Extent and Change in Southern Greece Using the European Union LUCAS Survey and a 35-Year Landsat Time Series Analysis
Identifier: https://doi.org/10.3390/rs14143369
Type: DOI
Relation: References
Year: 2022
Source: Remote Sensing
Authors: Sparks Aaron M , Bouhamed Imen , Boschetti Luigi , Gitas Ioannis Z , Kalaitzidis Chariton .