Simulating Future Forest Cover Changes in Pakxeng District, Lao People’s Democratic Republic (PDR): Implications for Sustainable Forest Management

Future forest cover changes were simulated under the business-as-usual (BAU), pessimistic and optimistic scenarios using the Markov-cellular automata (MCA) model in Pakxeng district, Lao People’s Democratic Republic (PDR). The Markov chain analysis was used to compute transition probabilities from satellite-derived forest cover maps (1993, 1996, 2000 and 2004), while the “weights of evidence” procedure was used to generate transition potential (suitability) maps. Dynamic adjustments of transition probabilities and transition potential maps were implemented in a cellular automata (CA) model in order to simulate forest cover changes. The validation results revealed that unstocked forest and current forest classes were relatively well simulated, while the non-forest class was slightly underpredicted. The MCA simulations under the BAU and pessimistic scenarios indicated that current forest areas would decrease, whereas unstocked forest areas would increase in the future. In contrast, the MCA model projected that current forest areas would increase under the optimistic scenario if forestry laws are strictly enforced in the study area. The simulation scenarios observed in this study can be possibly used to understand implications of future forest cover changes on sustainable forest management in Pakxeng district

Spatial Simulation Modelling of Future Forest Cover Change Scenarios in Luangprabang Province, Lao PDR

Taking Luangprabang province in Lao Peoples’s Democratic Republic (PDR) as an example, we simulated future forest cover changes under the business-as-usual (BAU), pessimistic and optimistic scenarios based on the Markov-cellular automata (MCA) model. We computed transition probabilities from satellite-derived forest cover maps (1993 and 2000) using the Markov chains, while the “weights of evidence” technique was used to generate transition potential maps. The initial forest cover map (1993), the transition potential maps and the 1993–2000 transition probabilities were used to calibrate the model. Forest cover simulations were then performed from 1993 to 2007 at an annual time-step. The simulated forest cover map for 2007 was compared to the observed (actual) forest cover map for 2007 in order to test the accuracy of the model. Following the successful calibration and validation, future forest cover changes were simulated up to 2014 under different scenarios. The MCA simulations under the BAU and pessimistic scenarios projected that current forest areas would decrease, whereas unstocked forest areas would increase in the future. Conversely, the optimistic scenario projected that current forest areas would increase in the future if strict forestry laws enforcing conservation in protected forest areas are implemented. The three simulation scenarios provide a very good case study for simulating future forest cover changes at the subnational level (Luangprabang province). Thus, the future simulated forest cover changes can possibly be used as a guideline to set reference scenarios as well as undertake REDD/REDD+ preparedness activities within the study area