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. 708projected 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.

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ABSTRACT:Various technical studies for building forest monitoring system for MRV system of REDD+ has been implemented utilizing satellite remote sensing technology and ground survey upon configuring two pilot study areas, at whole Louangphabang (LPB) province (approximately 20,000 km 2 ) and in Bolikhmxai(BLK) province (approximately 4,400 km 2 ) in Lao PDR. Multi-temporal land use/cover data were prepared for making analyses of deforestation and forest degradation caused by various driving factors, and to establish reference scenario for REDD+. In addition to ordinary method of forest carbon stock estimation based on the forest plot surveys, land use/cover maps and IPCC's emission factors (GOFC-GOLD, 2010), improved methods were studied introducing a concept of biomass classing derived from multispectral data and tree height measurement utilizing ALOS/PRIS stereo images, in order to reduce difficulty of field surveys at high altitude and steep mountain forest, especially in natural forest areas. First, multi-temporal land use/cover maps were prepared for two pilot study areas for analyzing deforestation and forest degradation of the subjected area. Then, the biomass level of "Current Forest" area of the land use/cover maps were classified into three classes as high, medium, and low applying spectral analyses of LANDSAT/TM and SPOT images, and based on visual interpretation results of pan-sharpened ALOS/AVNIR2 images in addition to limited number of field surveys as references. Matching accuracies were around 60% at this stage of study (This number improved to 85% at the later stage). Based on the field survey data, the forest carbon stock vs. tree height model was established, and furthermore it was related to the forest biomass classes. ALOS/PRISM images were used to measure heights at about 1,500 forest stands selected at 2 -4 km grid intervals. Accuracy analyses showed that the standard deviation of the tree height measurement errors was approximately 4 -5 m, but the mean value of the measured data were within 1-2 m comparing to the field survey data. Finally, wall-to-wall, above-ground forest carbon stock estimation maps which would be useful for forest management and REDD+ were prepared. As a conclusion, it can be said that 3D measurement, in addition to multi-spectral information, of the forest provides useful information for monitoring forest carbon stock for REDD+ although further refinement of technologies is to be needed. And, the results and experiences obtained from the studies will provide useful data for establishing actual MRVsystem.