Resource information
In the mountainous regions of northern Laos, shifting cultivation, or slash-and-burn agriculture, is widely practiced. However, the crop–fallow rotation cycle is becoming shorter owing to forest conservation policies and population pressure, causing loss of productivity that deleteriously affects farmers’ livelihoods in the region. To investigate regional land use conditions, we have developed a method of identifying the crop–fallow rotation cycle from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper+ (ETM+) data. We assessed the impact of the identified cycle on plant production measured by Normalized Difference Vegetation Index (NDVI). The study site was an area in Luang Prabang Province. Using eight TM and ETM+ images acquired annually from 1995 to 2003, except for 1998, when cloud-free data were not collected, we classified land use in each year as crop or fallow by the presence of vegetation in the late dry season. Conformity with fallow age determined by field investigation was 69.1%. The cultivation frequency from 1995 to 2002 showed that 77,000 ha (17.3% of the study site) had not been used for cropping during the period, but 41,000 ha (9.2%) had been used every year. Of the study site, 129,000 ha (29.1%) was cultivated one or two times, 83,000 ha (18.7%) was three or four times, and 54,000 ha (12.2%) was five or six times. The NDVI of crops in November did not provide sufficient evidence to prove the assumption that a longer fallow period would result in better crop yields. Instead, the regeneration of fallow vegetation was evidenced by the higher NDVI values after longer fallow. More than 8 years would be needed to reach the same NDVI as forest. From the produced maps indicating fallow age and cultivation frequency, we found that areas with high potential for regeneration decreased as cultivation frequency increased. Areas near rivers were intensively used, and fallow length was accordingly short. Low-potential areas were found in the western basin of the Mekong River. This spatial information can be used to detect areas where biomass productivity is at high risk of deteriorating.
