Spatiotemporal patterns of land-use changes in Lithuania
LTReikšminiai žodžiai: Žemės naudojimas; Žemės paskirties keitimas; Miškai; Dirbama žemė; Pieva; Erdvinė autokoreliacija; Regresija. Keywords: Land use; Land-use change; Forests; Producing land; Grassland; Spatial autocorrelation; Regression.Reikšminiai žodžiai: Žemės naudojimas; Žemės paskirtis; Keitimas; Miškai; Pievos; Erdvinė autokoreliacija; Regresija; Land use; Forests; Spatial autocorrelation; Regression.
ENThe spatially explicit assessment of land use and land-use change patterns can identify critical areas and provide insights to improve land management policies and associated decisions. This study mapped the land uses and land-use changes in Lithuanian municipalities since 1971. Additionally, an analysis was conducted of three shorter periods, corresponding to major national land-use policy epochs. Data on land uses, available from the Lithuanian National Forest Inventory (NFI) and collected on an annual basis with the primary objective of conducting greenhouse gas (GHG) accounting and reporting for the land use, land-use change, and forestry (LULUCF) sectors, were explored. The overall trend in Lithuania during the last five decades has been an increase in the area of forest and built-up land and decrease in the area of producing land, meadow/pasture, wetlands, and other land uses. Nevertheless, the development trends for the proportions of producing land and meadow/pasture changed trajectories several times, and the breakpoints were linked with important dates in Lithuanian history and associated with the reorganization of land management and land-use relations. Global Moran’s I statistic and Anselin Local Moran’s I were used to check for global and local patterns in the distribution of land use in Lithuanian municipalities. The proportions of producing land and pasture/meadow remained spatially autocorrelated during the whole period analysed. Local spatial clusters and outliers were identified for all land-use types used in GHG inventories in the LULUCF sector at all the time points analysed. Ordinary least squares (OLS) regression was used to explain the land-use change trends during several historical periods due to differing land management policies, utilizing data from freely available databases as the regressors. The percentage of variance explained by the models ranged from 37 to 65, depending on the land-use type and the period in question.
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