Costa, Hugo, Benevides, Pedro, Moreira, Francisco D., Caetano, Mário, NOVA Information Management School (NOVA IMS), and Information Management Research Center (MagIC) - NOVA Information Management School
Costa, H., Benevides, P., Moreira, F. D., & Caetano, M. (2022). Detection and classification of changes in agriculture, forest, and shrublands for land cover map updating in Portugal. In C. M. U. Neale, & A. Maltese (Eds.), Proceedings of SPIE.Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIV (Vol. 12262, pp. 19). SPIE. Society of Photo-Optical Instrumentation Engineers. https://doi.org/10.1117/12.2636127 - ----Funding Information: The work has been supported by projects foRESTER (PCIF/SSI/0102/2017), and SCAPE FIRE (PCIF/MOS/0046/2017), and by Centro de Investigação em Gestão de Informação (MagIC), all funded by the Portuguese Foundation for Science and Technology (FCT). Value-added data processed by CNES for the Theia data centre www.theia-land.fr using Copernicus products. The processing uses algorithms developed by Theia's Scientific Expertise Centres. Portugal produced a land cover map for 2018 based on Sentinel-2 data and represents 13 classes, including agriculture, six tree forest species, and shrubland. The map was updated for 2020. The strategy focused on three strata where annual changes occur: S1 (agriculture) due to crop rotation, S2 (forest and shrubland) due to wildfires and clear-cuts, and S3 (fire scars and clear-cuts of previous years) where vegetation regeneration occurs. The methodology included i) change detection, ii) classification, and iii) knowledge-based rules. Stratum S1 was classified with images of the entire 2020 crop year and a training dataset extracted from the national Land Parcel Identification Systems (LPIS) of 2020. The land cover nomenclature was expanded and class agriculture was split in three distinct classes, hence resulting a map with 15 classes in total. Change detection, implemented in stratum S2, analyzed the profile of NDVI since 2018 to find potential loss of vegetation. S2 and S3 were classified through two stages. First, images of the entire 2020 crop year were used and then data of October 2020 (end of crop year) to capture late changes. The training points of the 2018 land cover map were used, but only if not associated with NDVI change. For all the three strata, knowledge-based rules corrected misclassifications and ensured consistency between the maps. A comparison between 2018 and 2020 reveal important land cover dynamics related to vegetation loss and regeneration on ~5% of the country. authorsversion published