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Articles | Volume XLVIII-4/W1-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4/W1-2022, 553–560, 2022
https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-553-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4/W1-2022, 553–560, 2022
https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-553-2022
 
06 Aug 2022
06 Aug 2022

MODELING OF FOREST LANDSCAPE EVOLUTION AT REGIONAL LEVEL: A FOSS4G APPROACH

P. Zatelli1, C. Tattoni2, S. Gobbi1,3, M. G. Cantiani1, N. La Porta3, and M. Ciolli1,4 P. Zatelli et al.
  • 1Department of Civil, Environmental and Mechanical Engineering, University of Trento, 38123 Trento, Italy
  • 2Department of Theoretical and Applied sciences, Università degli Studi dell’Insubria, 21100 Varese, Italy
  • 3Centro di Ricerca e Innovazione, Fondazione Edmund Mach, 38010 S. Michele all’Adige (TN), Italy
  • 4Center Agriculture Food Environment, Fondazione Edmund Mach, 38010 S. Michele all’Adige (TN), Italy

Keywords: Landscape, Aereal Imagery, Historical maps, OBIA, Land use/Land cover, Time series

Abstract. In the last decades the Alpine landscape has dramatically changed due to social and economic factors. The most visible impact has been the reduction of the population for mid and high altitude villages and the shrinking of the part of the land used for agriculture and grazing, with a progressive reduction of pastures and meadows and the expansion of the forested areas. For these reasons, a dataset describing the forest, meadows and pasture coverage for the Trentino region, in the eastern Italian Alps, has been created. A set of heterogeneous sources has been selected so that maps and images cover the longest possible time span on the whole Trentino region with comparable quality, creating a Land Use/Land Cover (LULC) map based on historical maps from 1859 to 1936 and aerial images from 1954 to 2015. The achieved accuracy ranges from 98% for historical maps to 94% for aereal imagery. The analysis of selected landscape metrics provided preliminary results about the forest distribution and patterns of recolonization during the last 155 years. It has been possible to create future scenarios for the forest evolution for the next 85 years. Given the large number of maps involved, the great flexibility provided by FOSS for spatial analysis, such as GRASS, R, QGIS and GAMA and the possibility of scripting all the operations have played a pivotal role in the success both in the creation of the dataset and in the extraction and modeling of land use changes.