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Article

Comparison of Sentinel-2 and High-Resolution Imagery for Mapping Land Abandonment in Fragmented Areas

1
Instituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, C/ Paraninfo, 1, 46730 Grau de Gandia, Spain
2
Geo-Environmental Cartography and Remote Sensing Group, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Espana
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(12), 2062; https://doi.org/10.3390/rs12122062
Received: 22 May 2020 / Revised: 21 June 2020 / Accepted: 24 June 2020 / Published: 26 June 2020
Agricultural land abandonment is an important environmental issue in Europe. The proper management of agricultural areas has important implications for ecosystem services (food production, biodiversity, climate regulation and the landscape). In the coming years, an increase of abandoned areas is expected due to socio-economic changes. The identification and quantification of abandoned agricultural plots is key for monitoring this process and for applying management measures. The Valencian Region (Spain) is an important fruit and vegetable producing area in Europe, and it has the most important citrus industry. However, this agricultural sector is highly threatened by diverse factors, which have accelerated land abandonment. Landsat and MODIS satellite images have been used to map land abandonment. However, these images do not give good results in areas with high spatial fragmentation and small-sized agricultural plots. Sentinel-2 and airborne imagery shows unexplored potential to overcome this thanks to higher spatial resolutions. In this work, three models were compared for mapping abandoned plots using Sentinel-2 with 10 m bands, Sentinel-2 with 10 m and 20 m bands, and airborne imagery with 1 m visible and near-infrared bands. A pixel-based classification approach was used, applying the Random Forests algorithm. The algorithm was trained with 144 plots and 100 decision trees. The results were validated using the hold-out method with 96 independent plots. The most accurate map was obtained using airborne images, the Enhanced Vegetation Index (EVI) and Thiam’s Transformed Vegetation Index (TTVI), with an overall accuracy of 88.5%. The map generated from Sentinel-2 images (10 m bands and the EVI and TTVI spectral indices) had an overall accuracy of 77.1%. Adding 20 m Sentinel-2 bands and the Normalized Difference Moisture Index (NDMI) did not improve the classification accuracy. According to the most accurate map, 4310 abandoned plots were detected in our study area, representing 32.5% of its agricultural surface. The proposed methodology proved to be useful for mapping citrus in highly fragmented areas, and it can be adapted to other crops. View Full-Text
Keywords: citrus; land abandonment; high-resolution imagery; Sentinel-2; image classification; Random Forests algorithm; machine learning algorithms citrus; land abandonment; high-resolution imagery; Sentinel-2; image classification; Random Forests algorithm; machine learning algorithms
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MDPI and ACS Style

Morell-Monzó, S.; Estornell, J.; Sebastiá-Frasquet, M.-T. Comparison of Sentinel-2 and High-Resolution Imagery for Mapping Land Abandonment in Fragmented Areas. Remote Sens. 2020, 12, 2062. https://doi.org/10.3390/rs12122062

AMA Style

Morell-Monzó S, Estornell J, Sebastiá-Frasquet M-T. Comparison of Sentinel-2 and High-Resolution Imagery for Mapping Land Abandonment in Fragmented Areas. Remote Sensing. 2020; 12(12):2062. https://doi.org/10.3390/rs12122062

Chicago/Turabian Style

Morell-Monzó, Sergio, Javier Estornell, and María-Teresa Sebastiá-Frasquet. 2020. "Comparison of Sentinel-2 and High-Resolution Imagery for Mapping Land Abandonment in Fragmented Areas" Remote Sensing 12, no. 12: 2062. https://doi.org/10.3390/rs12122062

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