Volume XLII-1/W1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W1, 135-141, 2017
https://doi.org/10.5194/isprs-archives-XLII-1-W1-135-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W1, 135-141, 2017
https://doi.org/10.5194/isprs-archives-XLII-1-W1-135-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  31 May 2017

31 May 2017

REMOTE SENSING TECHNIQUES TO ASSESS POST-FIRE EFFECTS AT THE HILLSLOPE AND SUB-BASIN SCALES VIA MULTI-SCALE MODEL

A. Brook1, M. Polinova1, D. Kopel1, D. Malkinson2, L. Wittenberg2, D. Roberts3, and N. Shtober-Zisu4 A. Brook et al.
  • 1Spectroscopy & Remote Sensing Laboratory, Center for Spatial Analysis Research (UHCSISR), Department of Geography and Environmental Studies, University of Haifa, Mount Carmel, 3498838, Israel
  • 2Geomorphology Laboratory, Department of Geography and Environmental Studies, University of Haifa, Israel
  • 3Geography Department, University of California Santa Barbara, Santa Barbara, CA, USA
  • 4Department of Israel Studies, University of Haifa, Israel

Keywords: Multiscale model, log-Gaussian Cox processes, hillslope micro-topography, post-fire environment

Abstract. Post-fire environmental footprint is expected at varying scales in space and in time and demands development of multi-scale monitoring approaches. In this paper, a spatially and temporally explicit multi-scale model that reveals the physical and morphological indicators affecting hillslope susceptibility at varying scales, is explained and demonstrated. The qualitative and quantitative suitability classification procedures are adapted to translate the large-scale space-borne data supplied by satellite systems (Landsat OLS8 and Sentinel 2 and 3) to local scale produced by a regional airborne survey performed by unmanned aerial vehicle (UAV). At the smallest spatial and temporal resolution, a daily airborne imagery collection by UAV is linked to micro-topography model, using statistical and mathematical approaches.