Increased population growth and urbanization, has caused increase in demand for land around the city of Cochabamba, which has led to encroachment into the National Park and the construction of illegal settlements. This has created a number of problems, some of which are the direct consequences of the new settlements in the 'Tunari' foothills. These settlements constitute a threat to the environment and people because of their location in flood risk and aquifer recharge areas. In response to this problem the city authorities are considering relocating the boundary between the National Park and the city. This study has focused on the design and evaluation of alternative locations for a sustainable boundary between the north of 'Cochabamba City' and the southern boundary of the 'Tunari National Park'. In this process, a rational and systematic approach of collaborative-decision-making, supported by geographical information systems (GIS) and multicriteria evaluation (MCE) has been employed and evaluated.

No abstract

No abstract

No abstract

This paper compares a region-based and a pixel-based disaggregation method used to improve obtaining actual evapotranspiration (aET) data from MODIS images. Using these methods and the relationship between different vegetation indices form Landsat-5 and aET from MODIS, a 1 km resolution aET image was disaggregated to 250 and 30 m resolutions in two steps. Disaggregated aET images were compared with aET data obtained from a Landsat-5 TM image. A sensitivity analysis using synthetic data showed the impacts of land-cover homogeneity and registration error of the input images at the three scale levels. Accuracy assessment illustrated that the region-based disaggregation method using the Normalized Difference Vegetation Index (NDVI) has a good agreement with the Landsat-5 aET, having a mean absolute error equal to 0.93 mm. This method can be powerful for improving irrigation management, as it allows to increase the spatial resolution of aET derived from remote sensing images. The study concluded that a region-based method with NDVI data performs best to disaggregate MODIS aET data.