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Recent Surface Water Extent of Lake Chad from Multispectral Sensors and GRACE

Buma, Willibroad Gabila, Lee, Sang-Il, and Seo, Jae Young, 2018. Recent Surface Water Extent of Lake Chad from Multispectral Sensors and GRACE. Sensors, 18(7):2082, doi:10.3390/s18072082.

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BibTeX

@ARTICLE{2018Senso..18.2082B,
       author = {{Buma}, Willibroad Gabila and {Lee}, Sang-Il and {Seo}, Jae Young},
        title = "{Recent Surface Water Extent of Lake Chad from Multispectral Sensors and GRACE}",
      journal = {Sensors},
     keywords = {sensors, spatial analysis, remote sensing, Lake Chad, Landsat, surface water mapping},
         year = 2018,
        month = jun,
       volume = {18},
       number = {7},
          eid = {2082},
        pages = {2082},
     abstract = "{Consistent observations of lakes and reservoirs that comprise the
        majority of surface freshwater globally are limited, especially
        in Africa where water bodies are exposed to unfavorable climatic
        conditions and human interactions. Publicly available satellite
        imagery has increased the ability to monitor water bodies of
        various sizes without much financial hassle. Landsat 7 and 8
        images were used in this study to estimate area changes around
        Lake Chad. The Automated Water Extraction Index (AWEI),
        Normalized Difference Water Index (NDWI), Modified Normalized
        Difference Water Index (MNDWI) and Normalized Difference
        Vegetation Index (NDVI) were compared for the remote sensing
        retrieval process of surface water. Otsu threshold method was
        used to separate water from non-water features. With an overall
        accuracy of \raisebox{-0.5ex}\textasciitilde96\% and an inter-
        rater agreement (kappa coefficient) of 0.91, the MNDWI was a
        better indicator for mapping recent area changes in Lake Chad
        and was used to estimate the lake's area changes from 2003-2016.
        Extracted monthly areas showed an increasing trend and ranged
        between \raisebox{-0.5ex}\textasciitilde1242 km$^{2}$ and 2231
        km$^{2}$ indicating high variability within the 13-year period,
        2003-2016. In addition, we combined Landsat measurements with
        Total Water Storage Anomaly (TWSA) data from the Gravity
        Recovery and Climate Experiment (GRACE) satellites. This
        combination is well matched with our estimated surface area
        trends. This work not only demonstrates the importance of remote
        sensing in sparsely gauged developing countries, it also
        suggests the use of freely available high-quality imagery data
        to address existing lake crisis.}",
          doi = {10.3390/s18072082},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2018Senso..18.2082B},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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