A series of five land cover maps, widely known as COS (Carta de Uso e Ocupação do Solo), have been produced since 1990 for mainland Portugal. Previous to 2015, all maps were produced through photo-interpretation of orthophotos. Land cover and land use changes were detected through comparison of previous and recent orthophotos, which were used for map updating, thereby producing a new map. The remaining areas of no change were preserved across the maps for consistency. Despite the value of the maps produced, the method is very time-consuming and limited to the single-date reference of the orthophotos. From 2015 onwards, a new approach was adopted for map production. Photo-interpretation of orthophoto maps is still the basis of mapping, but assisted by products derived from satellite data. The goals are three-fold: (i) cut time production, (ii) increase map accuracy, and (iii) further detail the nomenclature. The last map published (COS 2015) benefited from change detection and classification analyses of Landsat data, namely for guiding the photo-interpretation in forest, shrublands, and mapping annual agriculture. Time production and map error have been reduced comparing to previous maps. The new 2018 map, currently in production, further explores this approach. Landsat 8 time series of 2015-2018 are used for change detection in vegetation based on NDVI differencing, thresholding and clustering. Sentinel-2 time series of 2017-2018 are used to classify Autumn/Winter crops and Spring/Summer crops based on NDVI temporal profiles and classification rules. Benefits and pitfalls of the new mapping approach are presented and discussed.
|Number of pages||6|
|Journal||International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives|
|Publication status||Published - 14 Feb 2020|
|Event||Joint Meeting of the 21st William T. Pecora Memorial Remote Sensing Symposium, Pecora 2019 and the 38th International Symposium on Remote Sensing of Environment, ISRSE 2019 - Baltimore, United States|
Duration: 6 Oct 2019 → 11 Oct 2019
- Change detection
- Time series