Next Article in Journal
Scene Semantic Understanding Based on the Spatial Context Relations of Multiple Objects
Previous Article in Journal
A Modified Multi-Source Parallel Model for Estimating Urban Surface Evapotranspiration Based on ASTER Thermal Infrared Data
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessFeature PaperArticle
Remote Sens. 2017, 9(10), 1031; doi:10.3390/rs9101031

Desertification Susceptibility Mapping Using Logistic Regression Analysis in the Djelfa Area, Algeria

1
Faculty of Earth Science, Geography and Land Planning, University of Sciences and Technology Houari Boumediene (USTHB), BP 32 El-Alia Bab Ezzouar, 16111 Algiers, Algeria
2
Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technologies, Division “Exploration Technology”, Chemnitzerstrasse 40, 09599 Freiberg, Germany
*
Author to whom correspondence should be addressed.
Received: 5 July 2017 / Revised: 28 September 2017 / Accepted: 4 October 2017 / Published: 9 October 2017
View Full-Text   |   Download PDF [5154 KB, uploaded 12 October 2017]   |  

Abstract

The main goal of this work was to identify the areas that are most susceptible to desertification in a part of the Algerian steppe, and to quantitatively assess the key factors that contribute to this desertification. In total, 139 desertified zones were mapped using field surveys and photo-interpretation. We selected 16 spectral and geomorphic predictive factors, which a priori play a significant role in desertification. They were mainly derived from Landsat 8 imagery and Shuttle Radar Topographic Mission digital elevation model (SRTM DEM). Some factors, such as the topographic position index (TPI) and curvature, were used for the first time in this kind of study. For this purpose, we adapted the logistic regression algorithm for desertification susceptibility mapping, which has been widely used for landslide susceptibility mapping. The logistic model was evaluated using the area under the receiver operating characteristic (ROC) curve. The model accuracy was 87.8%. We estimated the model uncertainties using a bootstrap method. Our analysis suggests that the predictive model is robust and stable. Our results indicate that land cover factors, including normalized difference vegetation index (NDVI) and rangeland classes, play a major role in determining desertification occurrence, while geomorphological factors have a limited impact. The predictive map shows that 44.57% of the area is classified as highly to very highly susceptible to desertification. The developed approach can be used to assess desertification in areas with similar characteristics and to guide possible actions to combat desertification. View Full-Text
Keywords: desertification; logistic regression; steppe; Djelfa desertification; logistic regression; steppe; Djelfa
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Djeddaoui, F.; Chadli, M.; Gloaguen, R. Desertification Susceptibility Mapping Using Logistic Regression Analysis in the Djelfa Area, Algeria. Remote Sens. 2017, 9, 1031.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top