Modeling Future Land Use Development: A Lithuanian Case

Gintautas Mozgeris,Daiva Juknelienė

doi:10.3390/land10040360

Abstract

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.

Highlights

Our methodological approach had to be compatible with that used by Lithuanian authorities to implement their international commitments, including the European Union land use, land use change, and forestry regulation for 2021–2030 [13], that is, we choose a modeling engine that is compatible with the European Forestry Dynamics Model (EFDM), which has been used to calculate the forest reference level for Lithuania and already used to facilitate forest policy building processes
The prediction accuracy of land use types directly related to greenhouse gas (GHG) accounting and emission/absorption management in the LULUCF sector in Lithuania was above 80% over a five-year period into the future
The most challenging was the prediction of land use types on agricultural land, i.e., the separation between producing land and grassland

Summary

Introduction

Climate change is a global issue and needs to be addressed through global cooperation among countries to improve energy efficiency, develop and deploy clean technologies, and increase natural GHG absorption. In this context, the processes in and around land use, land use change, and forestry (LULUCF) are becoming crucially important. In order to actively increase carbon absorption, it is necessary to know and manage the processes involved in the development of land surface layers and land use. Cognitive processes and management decisions will be hampered by a lack of access to scientifically based tools for modeling land use and GHG emissions

Methods

Results

Discussion

Conclusion