Abstract
Built-up layers derived from medium resolution (MR) satellite information have proven their contribution to dasymetric mapping, but suffer from important limitations when working at the intra-urban level, mainly due to their difficulty in capturing the whole range of variation in terms of built-up densities. In this regard, very-high resolution (VHR) remote sensing is known for its ability to better capture small variations in built-up densities and to derive detailed urban land use, which plead in favor of its use when mapping urban populations. In this paper, we compare the added value of various combinations of VHR data sets, compared to a MR one. A top-down dasymetric mapping strategy is applied to reallocate population counts from administrative units into a regular 100 × 100 m grid, according to different weighting layers. These weighting layers are created from MR and/or VHR input data, using simple built-up proportion or reallocation “weights”, obtained from a set of multiple ancillary data used to train a Random Forest regression model. The results reveal that (1) a built-up mask derived from VHR can improve the accuracy of the reallocation by roughly 13%, compared to MR; (2) using VHR land-use information alone results in lower accuracy than using a MR built-up mask; and (3) there is a clear complementarity between VHR land cover and land use.
Highlights
The less developed regions of the world have reached a symbolic milestone: Half of the population is living in urban areas [1]
When taking advantage of the detailed spatial and thematic information provided by the very-high resolution (VHR)-land cover (LC) layer, the accuracy of the dasymetric reallocation is significantly improved, with the relative total absolute error (RTAE) reduced to 0.308; corresponding to a drop by 16%, relative to the results obtained using only the built-up mask at medium resolution (MR-BU)
It supports the conclusion that VHR-LC and VHR-land use (LU) are complementary, since there is a clear alternation of land-cover and land-use variables in the sixth most important features
Summary
The less developed regions of the world have reached a symbolic milestone: Half of the population is living in urban areas [1]. As a consequence of these rapid transformations, SSA cities are exposed to increasing urban poverty and intra-urban inequalities [2], while a large part of the urban population is extremely vulnerable to health and disaster risks In this context, detailed population data is essential in improving evidence-based decision-making by relevant authorities and organizations [3,4,5], as well as for any application relying on a human population denominator, such as estimating the population at risk, assessing vulnerability, and deriving health or development goals indicators [6,7,8]. This knowledge is often very limited in SSA and population data are regularly outdated and criticized
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