The 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.
Landscape naturalness is an important indicator for supporting sustainable development-driven policies and suggesting associated decisions in land management. This study used CORINE Land Cover data to estimate the changes in land cover naturalness in Lithuania since 1995. All the land cover types were ranked according to naturalness level, ranging from purely anthropogenic to natural landscapes. Spatial patterns of the increase or decline in landscape naturalness were investigated at the level of municipalities. Then, publicly available geographic data were mobilised to explain the reasons behind the trends observed. A minor increase in land cover naturalness in the whole area of Lithuania was observed; however, this increase was statistically insignificant. Nevertheless, statistically significant clusters with both increasing and decreasing levels of land cover naturalness were identified when moving to the level of municipalities. The trends in the development of landscape naturalness were associated with the specificity of agricultural and forestry activities in the municipalities. The suitability of lands for agriculture due to soil, terrain, current land use specifics, and related drivers, such as the availability of land reclamation installations and the intensity of land use, were the main drivers for the declining level of land cover naturalness, usually concentrated in northern and central Lithuania. The land cover naturalness did increase in less suitable areas for agriculture, i.e., in the more forested southeastern municipalities. The study emphasised the need for a systematic and spatially explicit monitoring of the land cover patterns and their changes as well as elaborated proposals for land management policies over the next decade, which were mostly in the line with current European Union and national strategies.
Effective management decisions regarding greenhouse gas (GHG) emissions may be hampered by the lack of scientific tools for modeling future land use change. This study addresses methodological principles for land use development scenario modeling assumed for use in processes of GHG accounting and management. Associated land use policy implications in Lithuania are also discussed. Data on land uses, available from the National Forest Inventory (NFI) and collected for GHG accounting from the land use, land use change and forestry (LULUCF) sector in the country, as well as freely available geographic information, were tested as an input for modeling land use development in the country. The modeling was implemented using the TerrSet Land Change Modeler. Calibration of the modeling approach using historical land use data indicated that land use types important for GHG management in the LULUCF sector were predicted with an accuracy above 80% during a five-year period into the future, while the prediction accuracy for forest and built-up land was 96% or more. Based on several land management scenarios tested, it was predicted that the LULUCF sector in Lithuania will accumulate CO2, with the forest land use type contributing most to CO2 absorption. Key measures to improve the GHG balance and carbon stock changes were suggested to be the afforestation of abandoned or unused agricultural land and prevention of the conversion of grassland into producing land.
The trends of forest cover change in Lithuanian municipalities are introduced in the current paper. Two sources of information on the forest cover in 1950s and today (2013) were used in this study: (i) a geographic forest cover database developed using historical orthophotomaps based on aerial photography, which was carried out in the period just after the World War II, and (ii) the information originating from the State Forest Cadaster and referring to the year 2013. These two layers were compared using GIS overlay techniques. The data was made available for the analyses aggregated up to the municipality level. The Global Moran’s I statistic and Anselin Local Moran’s I were used to identify global and local patterns in the distribution of forest cover characteristics in Lithuanian municipalities, respectively. The main finding of this study was that the proportion of the forest cover in 1950 was 26.5%, i. e. notably differing from the official statistics – 19.7%. The proportion of the forest cover increased in all municipalities during the period 1950–2013. The largest increase in forest cover proportion was in the areas less suitable for agriculture. The relatively largest areas of new forests were identified in the south-eastern part of Lithuania, the deforestation was relatively slowest around less forested municipalities, while the afforestation was relatively slowest around the agricultural Pakruojis municipality. Deforestation was most commonly associated with the forest transformation into agricultural land, less often into scrublands or waters.
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