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In the mountainous regions of northern Laos, shifting cultivation, or slash-andburn 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 Readers should send their comments on this paper to: BhaskarNath@aol.com within 3 months of publication of this issue. 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.

Basins in many parts of the world are ungauged or poorly gauged, and in some cases existing measurement networks are declining. The purpose of this study was to examine the utility of reanalysis and global precipitation datasets in the river discharge simulation for a data-scarce basin. The White Volta basin of Ghana which is one of international rivers was selected as a study basin. NCEP1, NCEP2, ERA-Interim, and GPCP datasets were compared with corresponding observed precipitation data. Annual variations were not reproduced in NCEP1, NCEP2, and ERA-Interim. However, GPCP data, which is based on satellite and observed data, had good seasonal accuracy and reproduced annual variations well. Moreover, five datasets were used as input data to a hydrologic model with HYMOD, which is a water balance model, and with WTM, which is a river model; thereafter, the hydrologic model was calibrated for each datum set by a global optimization method, and river discharge were simulated. The results were evaluated by the root mean square error, relative error, and water balance error. As a result, the combination of GPCP precipitation and ERA-Interim evaporation data was the best in terms of most evaluations. The relative errors in the calibration and validation periods were 43.1% and 46.6%, respectively. Moreover, the results for the GPCP precipitation and ERA-Interim evaporation were better than those for the combination of observed precipitation and ERA-Interim evaporation. In conclusion, GPCP precipitation data and ERA-Interim evaporation data are very useful in a data-scarce basin water balance analysis.

The objective of the study is to evaluate the use of integration of spectral and textural features derived from IKONOS imagery to identify agricultural land cover types in a mountainous case study area in Pangalengan, West Java, Indonesia. The study includes image preprocessing, development of an image quantization method, calculation of textural measures, development of data sets and an accuracy assessment. Image preprocessing focuses on image registration and topographic normalization. Topographic normalization is conducted to minimize the effect of illumination differences on surface reflectance. In this study, two image quantization methods, i.e. image segmentation and averaging filtered were developed. The image segmentation method classifies the image into several segmentations based on a determination of the total number of pixels per class, while the averaging filtered method classifies the image based on the average of the digital number values within a window size. Four textural measures, inverse difference moment, contrast, entropy and energy, were calculated based on the gray level co-occurrence matrix (GLCM). The results indicate that a combination of spectral and textural aspects significantly improves the classification accuracy compared with classification with pure spectral features only. Image segmentation and averaging filtered methods can reveal more effectively spatial forms of agricultural land cover types than using a 256 gray-level scale. The overall accuracy increased 11.33% when using the integration of spectral and multiple textural features of inverse difference moment (5×5) and energy (9×9).

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