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.

中文翻译：

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未来土地利用发展的建模：一个立陶宛案例

缺乏用于模拟未来土地利用变化的科学工具，可能会阻碍有关温室气体（GHG）排放的有效管理决策。这项研究阐述了假设用于温室气体核算和管理过程中的土地利用发展情景模型的方法学原理。还讨论了在立陶宛的相关土地使用政策。从国家森林清单（NFI）获得并从该国土地利用，土地利用变化和林业（LULUCF）部门收集的用于温室气体核算的土地利用数据，以及可免费获得的地理信息，都作为输入进行了测试。用于模拟该国的土地利用发展。该建模是使用TerrSet土地变更建模器实现的。使用历史土地利用数据对建模方法进行的校准表明，在未来五年内，对土地利用，土地利用变化和林业领域中对温室气体管理至关重要的土地利用类型的预测准确性将达到80％以上，而对森林和人工林的预测准确性土地面积达到或超过96％。根据测试的几种土地管理方案，预计立陶宛的土地利用，土地利用变化和林业部门将积累二氧化碳。_{如图2所示}，林地使用类型对CO ₂吸收起最大作用。建议改善温室气体平衡和碳储量变化的关键措施是对废弃或未使用的农用土地进行绿化，并防止草原转化为生产用地。