ASPECTOS ECONOMICOS Y SOCIALES DE LA MINERIA CAPITULO XXI

Example output from the land suitability sub-model using MODIS land cover data is provided in Figure 3-15 while the difference between the results when using MODIS and GlobCover as inputs are shown in Figure 3-16. An indication of the differences between constraint areas of the classified MODIS and GlobCover data is given in Figure 3-17 using southern Vietnam as an example. It is worth noting that when viewing the images at full resolution within a GIS environment it is possible to see small areas

98 of suitability in many parts of the world that are not obvious when viewing a global image. The magnified areas in Figures 3-15 and 3-16 showing parts of Ghana and Vietnam help give an indication of the level of spatial detail provided by the model as well as helping to highlight differences between the MODIS and GlobCover based outputs.

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Figure 3-16: Difference between the land suitability sub-model outputs depending on whether MODIS or GlobCover were used to represent land cover. Positive numbers represent a higher score for MODIS, negative numbers for GlobCover.

100 When considered at the continental scale Asia stands out as having large areas of land with high suitability scores. India and Bangladesh are especially notable while China, Thailand and Vietnam also have large areas indicated as highly suitable. Much of the suitability in these areas is a result of cropland being indicated by the land cover data which under the current classification scheme is considered potentially available for conversion to aquaculture ponds. The fact that such areas are often flat and in the case of many Asian countries moderately populated also contribute to their suitability. Rice in particular has been combined with aquaculture in many areas either in the form of deep water areas around paddy fields or as part of a seasonal pattern where fields effectively become fish ponds for part of the year (Ahmed and Garnett, 2011).

While remotely sensed land cover data is invaluable for assessments such as the current one, the considerable differences seen between MODIS and GlobCover along with broadness of classes (e.g. different types of cropland are not specified) highlights the limitations of currently available products. As previously discussed part of the problem lies in the relatively low resolution of the sensors used and resulting mixture of land cover types that may be present in a single pixel. With this and continued improvements in computing capacity in mind the development of higher resolution global land cover products would seem to be potentially beneficial.

The next big step in terms of a global land cover product would seem to be a move to using data of around 30m resolution derived from sensors such as Landsat ETM+. Sexton et al. (2013) demonstrated the rescaling of MODIS data to 30m resolution using Landsat images while Gong et al. (2013) published preliminary results for a Landsat based global land cover product. Figure 3-18 shows an example of the 30m land cover product described by Gong et al. (2013) covering Bangladesh. The edges of the individual Landsat scenes within the area displayed are fairly obvious with sudden shifts from one land cover type to another. It is also notable that much of Bangladesh is classified as bareland or forest in what is an extremely densely populated and cultivated country. Despite these limitations the desire to develop such a product should be encouraged as if it can be improved it could become highly useful under a wide range of applications.

The slope values used in the current assessment were calculated from SRTM data at 3 arcsecond resolution and then aggregated to a 10 arcsecond grid to match the resolution of the GlobCover land cover data. It is possible that maintaining the entire database at 3 arcsecond resolution may have yielded slight improvements in terms of terrain slope information although given the lower resolution

101 of the other data sets any overall benefits would likely be minimal. It is also worth noting that the storage and processing requirements for data sets of such a high resolution would be beyond the capabilities of the current project.

Being a modelled data set the LandScan population density data is also a potential source of inaccuracy. Careful visual inspection in relation to high resolution imagery such as that seen in Google earth suggests that it seems to do a good job of linking higher population densities to areas with obvious urbanisation on the ground. There did appear to be some false positives especially in areas with an overall low population density where the LandScan data would suggest a single more densely populated pixel that did not correspond to any obvious human development. A newer version of LandScan is currently available but at a cost that was prohibitive for the current project. It maybe that an improved algorithm in association with more detailed input data would yield a slightly better, though largely unverifiable, result. This said, the LandScan product used in the current study would seem to offer a significant advantage over the other globally available gridded data sets which are largely derived from a set of variably sized polygons.

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Figure 3-18: A section of the 30m land cover data set for Bangladesh ( Gong et al., 2014).