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

[EN] 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.Morell-Monzó, S.; Estornell Cremades, J.; Sebastiá-Frasquet, M. (2020). Comparison of Sentinel-2 and High-Resolution Imagery for Mapping Land Abandonment in Fragmented Areas. Remote Sensing. 12(12):1-18. https://doi.org/10.3390/rs121220621181212MacDonald, D., Crabtree, J. ., Wiesinger, G., Dax, T., Stamou, N., Fleury, P., … Gibon, A. (2000). Agricultural abandonment in mountain areas of Europe: Environmental consequences and policy response. Journal of Environmental Management, 59(1), 47-69. doi:10.1006/jema.1999.0335Kosmas, C., Kairis, O., Karavitis, C., Acikalin, S., Alcalá, M., Alfama, P., … Solé-Benet, A. (2015). An exploratory analysis of land abandonment drivers in areas prone to desertification. CATENA, 128, 252-261. doi:10.1016/j.catena.2014.02.006Corbelle Rico, E., & Crecente Maseda, R. (2018). Estudio da evolución da superficie agrícola na comarca da Terra Chá a partir de fotografía aérea histórica e mapas de usos, 1956-2004. Recursos Rurais, (4), 57-65. doi:10.15304/rr.id5312Gellrich, M., Baur, P., Koch, B., & Zimmermann, N. E. (2007). Agricultural land abandonment and natural forest re-growth in the Swiss mountains: A spatially explicit economic analysis. Agriculture, Ecosystems & Environment, 118(1-4), 93-108. doi:10.1016/j.agee.2006.05.001Rey Benayas, J. M. (2007). Abandonment of agricultural land: an overview of drivers and consequences. CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources, 2(057). doi:10.1079/pavsnnr20072057Árgyelán, T. (2015). Abandonment phenomenon in Europe. Acta Universitatis Sapientiae, Agriculture and Environment, 7(1), 89-97. doi:10.1515/ausae-2015-0008Citricultura Valenciana: Gestión Integrada de Plagas y Enfermedades en Cítricoshttp://gipcitricos.ivia.es/citricultura-valencianaRounsevell, M. D. A., Reginster, I., Araújo, M. B., Carter, T. R., Dendoncker, N., Ewert, F., … Tuck, G. (2006). A coherent set of future land use change scenarios for Europe. Agriculture, Ecosystems & Environment, 114(1), 57-68. doi:10.1016/j.agee.2005.11.027Verburg, P. H., Schulp, C. J. E., Witte, N., & Veldkamp, A. (2006). Downscaling of land use change scenarios to assess the dynamics of European landscapes. Agriculture, Ecosystems & Environment, 114(1), 39-56. doi:10.1016/j.agee.2005.11.024Dubinin, M., Potapov, P., Lushchekina, A., & Radeloff, V. C. (2010). Reconstructing long time series of burned areas in arid grasslands of southern Russia by satellite remote sensing. Remote Sensing of Environment, 114(8), 1638-1648. doi:10.1016/j.rse.2010.02.010Ruiz-Flan˜o, P., Garci´a-Ruiz, J. M., & Ortigosa, L. (1992). Geomorphological evolution of abandoned fields. A case study in the Central Pyrenees. CATENA, 19(3-4), 301-308. doi:10.1016/0341-8162(92)90004-uFischer, J., Hartel, T., & Kuemmerle, T. (2012). Conservation policy in traditional farming landscapes. Conservation Letters, 5(3), 167-175. doi:10.1111/j.1755-263x.2012.00227.xPenov, I. (2004). The Use of Irrigation Water in Bulgaria’s Plovdiv Region During Transition. Environmental Management, 34(2), 304-313. doi:10.1007/s00267-004-0019-8Novara, A., Gristina, L., Sala, G., Galati, A., Crescimanno, M., Cerdà, A., … La Mantia, T. (2017). Agricultural land abandonment in Mediterranean environment provides ecosystem services via soil carbon sequestration. Science of The Total Environment, 576, 420-429. doi:10.1016/j.scitotenv.2016.10.123Cerdà, A., Ackermann, O., Terol, E., & Rodrigo-Comino, J. (2019). Impact of Farmland Abandonment on Water Resources and Soil Conservation in Citrus Plantations in Eastern Spain. Water, 11(4), 824. doi:10.3390/w11040824Rey Benayas, J. M., & Bullock, J. M. (2012). Restoration of Biodiversity and Ecosystem Services on Agricultural Land. Ecosystems, 15(6), 883-899. doi:10.1007/s10021-012-9552-0Shrivastava, R. J., & Gebelein, J. L. (2007). Land cover classification and economic assessment of citrus groves using remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 61(5), 341-353. doi:10.1016/j.isprsjprs.2006.10.003Löw, F., Prishchepov, A., Waldner, F., Dubovyk, O., Akramkhanov, A., Biradar, C., & Lamers, J. (2018). Mapping Cropland Abandonment in the Aral Sea Basin with MODIS Time Series. Remote Sensing, 10(2), 159. doi:10.3390/rs10020159Alcantara, C., Kuemmerle, T., Prishchepov, A. V., & Radeloff, V. C. (2012). Mapping abandoned agriculture with multi-temporal MODIS satellite data. Remote Sensing of Environment, 124, 334-347. doi:10.1016/j.rse.2012.05.019Estel, S., Kuemmerle, T., Alcántara, C., Levers, C., Prishchepov, A., & Hostert, P. (2015). Mapping farmland abandonment and recultivation across Europe using MODIS NDVI time series. Remote Sensing of Environment, 163, 312-325. doi:10.1016/j.rse.2015.03.028Dara, A., Baumann, M., Kuemmerle, T., Pflugmacher, D., Rabe, A., Griffiths, P., … Hostert, P. (2018). Mapping the timing of cropland abandonment and recultivation in northern Kazakhstan using annual Landsat time series. Remote Sensing of Environment, 213, 49-60. doi:10.1016/j.rse.2018.05.005Müller, D., Leitão, P. J., & Sikor, T. (2013). Comparing the determinants of cropland abandonment in Albania and Romania using boosted regression trees. Agricultural Systems, 117, 66-77. doi:10.1016/j.agsy.2012.12.010Yin, H., Prishchepov, A. V., Kuemmerle, T., Bleyhl, B., Buchner, J., & Radeloff, V. C. (2018). Mapping agricultural land abandonment from spatial and temporal segmentation of Landsat time series. Remote Sensing of Environment, 210, 12-24. doi:10.1016/j.rse.2018.02.050Kuemmerle, T., Hostert, P., Radeloff, V. C., van der Linden, S., Perzanowski, K., & Kruhlov, I. (2008). Cross-border Comparison of Post-socialist Farmland Abandonment in the Carpathians. Ecosystems, 11(4), 614-628. doi:10.1007/s10021-008-9146-zGrădinaru, S. R., Kienast, F., & Psomas, A. (2019). Using multi-seasonal Landsat imagery for rapid identification of abandoned land in areas affected by urban sprawl. Ecological Indicators, 96, 79-86. doi:10.1016/j.ecolind.2017.06.022Prishchepov, A. V., Radeloff, V. C., Dubinin, M., & Alcantara, C. (2012). The effect of Landsat ETM/ETM + image acquisition dates on the detection of agricultural land abandonment in Eastern Europe. Remote Sensing of Environment, 126, 195-209. doi:10.1016/j.rse.2012.08.017Baumann, M., Kuemmerle, T., Elbakidze, M., Ozdogan, M., Radeloff, V. C., Keuler, N. S., … Hostert, P. (2011). Patterns and drivers of post-socialist farmland abandonment in Western Ukraine. Land Use Policy, 28(3), 552-562. doi:10.1016/j.landusepol.2010.11.003Szostak, M., Hawryło, P., & Piela, D. (2017). Using of Sentinel-2 images for automation of the forest succession detection. European Journal of Remote Sensing, 51(1), 142-149. doi:10.1080/22797254.2017.1412272Kanjir, U., Đurić, N., & Veljanovski, T. (2018). Sentinel-2 Based Temporal Detection of Agricultural Land Use Anomalies in Support of Common Agricultural Policy Monitoring. ISPRS International Journal of Geo-Information, 7(10), 405. doi:10.3390/ijgi7100405Proisy, C., Viennois, G., Sidik, F., Andayani, A., Enright, J. A., Guitet, S., … Suhardjono. (2018). Monitoring mangrove forests after aquaculture abandonment using time series of very high spatial resolution satellite images: A case study from the Perancak estuary, Bali, Indonesia. Marine Pollution Bulletin, 131, 61-71. doi:10.1016/j.marpolbul.2017.05.056Thanh Noi, P., & Kappas, M. (2017). Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery. Sensors, 18(2), 18. doi:10.3390/s18010018Maxwell, A. E., Warner, T. A., & Fang, F. (2018). Implementation of machine-learning classification in remote sensing: an applied review. International Journal of Remote Sensing, 39(9), 2784-2817. doi:10.1080/01431161.2018.1433343https://rdrr.io/cran/raster/https://cran.r-project.org/web/packages/rgdal/index.htmlHuete, A., Justice, C., & Liu, H. (1994). Development of vegetation and soil indices for MODIS-EOS. Remote Sensing of Environment, 49(3), 224-234. doi:10.1016/0034-4257(94)90018-3Wilson, E. H., & Sader, S. A. (2002). Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing of Environment, 80(3), 385-396. doi:10.1016/s0034-4257(01)00318-2Silleos, N. G., Alexandridis, T. K., Gitas, I. Z., & Perakis, K. (2006). Vegetation Indices: Advances Made in Biomass Estimation and Vegetation Monitoring in the Last 30 Years. Geocarto International, 21(4), 21-28. doi:10.1080/10106040608542399Huete, A. . (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295-309. doi:10.1016/0034-4257(88)90106-xGitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58(3), 289-298. doi:10.1016/s0034-4257(96)00072-7Breiman, L. (2001). Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324Gislason, P. O., Benediktsson, J. A., & Sveinsson, J. R. (2006). Random Forests for land cover classification. Pattern Recognition Letters, 27(4), 294-300. doi:10.1016/j.patrec.2005.08.011Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140. doi:10.1007/bf00058655Pal, M. (2005). Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26(1), 217-222. doi:10.1080/01431160412331269698Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42-57. doi:10.1016/j.rse.2014.02.015Whiteside, T. G., Maier, S. W., & Boggs, G. S. (2014). Area-based and location-based validation of classified image objects. International Journal of Applied Earth Observation and Geoinformation, 28, 117-130. doi:10.1016/j.jag.2013.11.009Morell Monzó, S., & Membrado-Tena, J. C. (2019). Causas y consecuencias del crecimiento urbanístico en el litoral valenciano a través de la evolución de los usos del suelo. El caso de Oliva. Cuadernos de Turismo, (44), 303-326. doi:10.6018/turismo.44.404861Smith, P., House, J. I., Bustamante, M., Sobocká, J., Harper, R., Pan, G., … Pugh, T. A. M. (2015). Global change pressures on soils from land use and management. Global Change Biology, 22(3), 1008-1028. doi:10.1111/gcb.1306