CAPITULO XXIX Registro Minero Nacional
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Figure 4-9 shows, on a pixel by pixel basis, the total number of cloud free images that were available from the time series. Figure 4-10 shows the percentage of cloud free images that were classified as water, while Figure 4-11 shows the percentage of cloud free images that are water related (classified as either water or mixed). The dark blue areas in Figure 4-10 highlight areas of permanent water including major river channels and a significant area in the south west of the country that is associated with pond aquaculture. Areas with regular seasonal inundation such as the large Haor basin in the North West of the country are also clearly visible when the data is viewed at the national scale. Areas in orange i.e. those that are only inundated occasionally may be vulnerable in terms of flood risk (Handisyde et al., 2014). Such areas may represent situations where significant flooding does not normally take place and thus may be treated as largely flood free, but where flooding can occur during more extreme scenarios. It is tentatively suggested that such areas could be of interest under changing climate conditions involving increased precipitation and / or runoff as locations potentially prone to more severe inundation.
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Figure 4-10: Percentage of the available time series for Bangladesh (2000-2014), where pixels classified as water are present.
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Figure 4-11: Percentage of available time series for Bangladesh (2000-2014),where water related pixels classified as belonging to the mixed or water class are present.
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4.10 Concluding remarks
Based on the previous discussion in relation to the construction of classified MODIS time series, the results of the accuracy assessments, and its track record within the literature it is suggested here that the approach described by Sakamoto et al. (2007) and adapted for use in the current study offers a valid means of attempting to define land, mixed and water related pixels using MODIS data within Bangladesh.
The time series presented here in terms of inundation frequency as a percentage of total time series provides valuable insight into areas that contain largely permanent water, are frequently flooded, or infrequently flooded and therefore perhaps areas that are considered largely flood free but may be at risk under future climate regimes. Such information, while valuable in itself, can also make important contributions to broader site suitability models designed to inform development activities such as aquaculture and it is in this context that the current study was originally conceived.
The classification methods investigated based on the use of NDSI thresholds while not outperforming the Sakamoto method when classifying MODIS data performed very well with Landsat ETM+ images for the Bangladesh region and may prove useful in that sense in their own right.
Often classification of water areas using MODIS has aimed to detect relatively large and distinct water bodies or areas of flooding and have made use of a two class system with pixels designated as land or water (e.g. Feng et al., 2012, Li et al., 2011, Khan et al., 2011, Huang et al., 2012). While Bangladesh does contain large uninterrupted areas of water, especially during flood season, much of its area presents a genuinely mixed scenario when attempting classify land cover using moderate resolution data. As such the inclusion of a mixed class as in the case of the current study would seem useful. In the case of the current study regardless of classification method the mixed class achieved low accuracy scores when using both ground control points and higher resolution ETM+ classifications and ground truth data. Visual inspection of MODIS based classifications using the method adapted from Sakamoto et al., (2007) in comparison and similarly classified ETM+ data, as well as true and false colour composites of ETM+ bands, suggests that overall patterns of land cover appear accurate. This and the fact that low accuracy scores are seen for mixed areas when MODIS based classified data is assessed against ETM+ data classified using exactly the same methods suggests that much of the problem lies in the lower resolution of the MODIS data. Due to the complex and intricate nature of
167 land and water coverage in Bangladesh many areas could be seen where ETM+ based classifications would show patterns of land and water pixels within an area that would be covered by a single MODIS pixel. In such a situation the classification of MODIS data as mixed could be viewed as technically correct although it would be viewed as incorrect when accuracy is assessed against the higher resolution data and corresponding ground control points.
With the above in mind it would seem there is potential for further research with regard to mixed pixels over Bangladesh when using MODIS data. Two broad approaches can probably be considered: 1) algorithms that attempt to quantify the level of land and water within a pixel, and 2) data blending methods where data with a high spatial but low temporal resolution (e.g. ETM+) is blended with low spatial resolution, high temporal resolution data (e.g. MODIS).
Guerschman et al. (2011) developed the Open Water Likelihood (OWL) algorithm that has been applied in a number of further studies (Chen et al., 2013, Huang et al., 2014, Karim et al., 2011). The OWL algorithm aims to predict the probability that a MODIS pixel contains water, however it's development and evaluation was based around the proportion of a MODIS pixel occupied by water based on higher resolution Landsat data. It's development and subsequent use has focused on areas within Australia, an environment that is very different to Bangladesh where truly mixed surfaces are likely to be common even at the higher resolutions found in Landsat imagery. Even so the methodology and principles behind it may be worthy of further investigation in the context of Bangladesh.
Methods for blending of MODIS and Landsat data with the spatial resolution of Landsat and temporal resolution of MODIS have been reviewed by Emelyanova et al. (2012). The authors note that the complex algorithms: Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM, Gao et al., 2006), and the Enhanced version of STARFM (ESTARFM, Zhu et al., 2010) were computationally expensive and didn't always outperform simpler methods: the Linear Interpolation Model (LIM) and the Global Empirical Image Fusion Model (GEIFM). In the context of Bangladesh while the implementation of data blending approaches may be worth further consideration, the highly dynamic nature of land cover combined with often very infrequent availability of cloud free Landsat imagery may well limit the feasibility of such methods.
168 A key consideration with regards to the implementation of any of the mixed pixel approaches outlined above would be obtaining ground truth data of sufficient quality and quantity to make such exercises worthwhile.
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