International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-3/W11
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W11, 101–108, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W11-101-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W11, 101–108, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W11-101-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  14 Feb 2020

14 Feb 2020

IMPROVING THE LAND SURFACE GENERAL DROUGHT INDEX MODEL

A. H. Ngandam Mfondoum1,2, P. G. Gbetkom3, R. Cooper4, S. Hakdaoui5, and M. B. Mansour Badamassi6 A. H. Ngandam Mfondoum et al.
  • 1StatsN'Maps, Private Consulting Firm, Dallas, Texas 75287, USA
  • 2Department of Geography, University of Yaoundé I, Yaoundé, Cameroon
  • 3Department of Geography, University of Aix-Marseille, Marseille, France
  • 4Erik Jonsson School of Engineering and Computer Science, University of Texas in Dallas, Dallas, Texas 75080, USA
  • 5Earth Observation Department, Geo-Biodiversity and Natural Patrimony Laboratory, Geophysics, Natural Patrimony and Green Chemistry Research Center, Scientific Institute, Mohamed V University, Rabat, Morocco
  • 6Laboratory of Botanic, Mycology and Environment, Mohammed V University, Rabat, Morocco

Keywords: Drought, Remote Sensing, Spectral index, Improved Land Surface General Drought Index, Vegetation Moisture Index, Normalized Difference Soil Drought Index, Landsat, Semi-arid regions

Abstract. Drought affects all human activities and ecosystems. Nearly 40 percent of the world’s population inhabit Drylands, and they depend on agriculture for their food, security and livelihoods. Among the remote sensing indices developed, the Land Surface General Drought Index (LSGDI) was recently proposed. This paper proposes an improved model of LSGDI to face the issue of drought in semi-arid and arid regions. The experiment was conducted for the Maga’s floodplain, in North-Cameroon. The method uses satellite images of Landsat in 1987, 2003 and 2018, for January and March or April, corresponding to the middle and the end of the dry season. A Vegetation Moisture Index (VMI) and a Normalized Difference Soil Drought Index (NDSoDI) are both developed. On an orthogonal plan, their projections give a drought line that expresses the improved LSGDI (LSGDI2) as the root sum square of the NDSoDI and the VMI. The LSGDI2 results are ranged in [0.09 – 0.14] interval, which is used to define the threshold and ease the qualifiers for drought classes. The visual patterns easily match the sandy areas of the original Landsat images with the highest values, while the vegetation and water areas match the lowest values. Compared with the LSGDI and Second Modified Perpendicular drought Index (MPDI1), the new index reflectance values are higher. Finally, although LSGDI2 curve’s evolution follows the NDSoDI one at 94%, the new spectral index values depends on the both components, helping to map highest values of drought and moisture in Maga’s floodplain, for a sustainable rice culture expansion.