The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLIII-B3-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 631–638, 2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 631–638, 2021

  29 Jun 2021

29 Jun 2021


F. Rumiano1,2, C. Gaucherel3, P. Degenne1,2, E. Miguel4,5, S. Chamaillé-Jammes6,7,8, H. Valls-Fox6,7,9,10, D. Cornélis11,12, M. de Garine-Wichatitsky13,14,15, H. Fritz7,16,17, A. Caron5,13,14,18, and A. Tran1,2,5,13,14 F. Rumiano et al.
  • 1CIRAD, UMR TETIS, F-97490 Sainte-Clotilde, Réunion, France
  • 2TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE, 34090 Montpellier, France
  • 3AMAP, IRD, CIRAD, CNRS, INRAE, Univ Montpellier, 34398 Montpellier, France
  • 4MIVEGEC, Univ. Montpellier, IRD, CNRS, 34090 Montpellier, France
  • 5CREES Centre for Research on the Ecology and Evolution of DiseaSe–Montpellier, 34090 Montpellier, France
  • 6CEFE, Univ Montpellier, CNRS, EPHE, IRD, Univ. Paul Valéry Montpellier 3, 34090 Montpellier, France
  • 7LTSER France, Zone Atelier CNRS “Hwange”, Hwange National Park, Bag 62 Dete, Zimbabwe
  • 8Mammal Research Institute, Department of Zoology & Entomology, University of Pretoria, 0083 Pretoria, South Africa
  • 9CIRAD, UMR SELMET, PPZS, 6189 Dakar, Senegal
  • 10SELMET, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34090 Montpellier, France
  • 11CIRAD, Forêts et Sociétés, F-34398 Montpellier, France
  • 12Forêts et Sociétés, Univ Montpellier, CIRAD, 34090 Montpellier, France
  • 13CIRAD, UMR ASTRE, F-34398 Montpellier, France
  • 14ASTRE, Univ Montpellier, CIRAD, INRAE, 34090 Montpellier, France
  • 15Faculty of Veterinary Medicine, Kasetsart University, 10900 Bangkok, Thailand
  • 16UCBL, UMR CNRS 5558, University of Lyon, 69007 Lyon, France
  • 17World Wildlife Fund, Washington, DC 20037-1193, USA
  • 18Faculdade de Veterinaria, Universidade Eduardo Mondlane, 257 Maputo, Mozambique

Keywords: remote sensing, spatial modelling, mechanistic model, animal movement, surface water, African buffalo, ungulates, savanna

Abstract. In semi-arid savannas, the availability of surface water constrains movements and space-use of wild animals. To accurately model their movements in relation to water selection at a landscape scale, innovative methods have to be developed to i) better discriminate water bodies in space while characterizing their seasonal occurrences and ii) integrate this information in a spatially-explicit model to simulate animal movements according to surface water availability. In this study, we propose to combine satellite remote sensing (SRS) and spatial modelling in the case of the African buffalo (Syncerus caffer caffer) movements at the periphery of Hwange National Park (Zimbabwe).

An existing classification method of satellite Sentinel-2 time-series images has been adapted to produce monthly surface water maps at 10 meters spatial resolution. The resulting water maps have then been integrated into a spatialized mechanistic movement model based on a collective motion of self-propelled individuals to simulate buffalo movements in response to surface water.

The use of spectral indices derived from Sentinel-2 in combination with the short-wave infrared (SWIR) band in a Random Forest (RF) classifier provided robust results with a mean Kappa index, over the time series, of 0.87 (max = 0.98, min = 0.65). The results highlighted strong space and time variabilities of water availability in the study area. The mechanistic movement model showed a positive and significant correlation between observations/simulations movements and space-use of buffalo’s herds (Spearman r = 0.69, p-value < 10 e-114) despite overestimating the presence of buffalo individuals at proximity of the surface water.