Spatiotemporal and spectral analysis of sand encroachment dynamics in southern Tunisia

Abstract

Aeolian processes in drylands often transcend into sand encroachment, a common form of land degradation. Highly reflective desert features, hence sandy areas, often cause spectral confusion, and mapping through remote sensing techniques can be challenging. This work aims at designing an efficient classification method that minimises spectral confusion of desert features, hence two types of sandy areas. Moreover, we employ land cover (LC) change detection over the last 30 years. The extraction and spatiotemporal variations of LC and sand encroachment areas in the Dahar-Jeffara Medenine site (southeastern Tunisia) are assessed by employing Landsat imagery (1984 and 2014), a 30 m digital elevation model of Shuttle Radar Topography Mission (SRTMGL 1 arc second), field data and X-ray diffraction analyses of sand samples. Five new spectral indices were designed and employed in a Decision Tree (DT) classifier for the extraction of 11 LC classes, including two different types of sandy areas. The DT map yielded an overall accuracy of around 89%. Change detection results showed substantial change in several landscape components and an increase of sand units by 29% within the Jeffara-Medenine plain over the last three decades. Geomorphological observations and multi-temporal, spectral and mineral analyses indicate a main, possible in-situ source area of sand. ARTICLE HISTORY

Fluvial gravel bar mapping with spectral signal mixture analysis

“…For water, the reflectance values of the automatically selected endmembers are sometimes higher (as in the case of Landsat 8) and sometime lower (Sentinel-2 2015) than the values of the manually selected endmembers. Spectral signature shapes are similar to those reported in the literature both for Landsat (Afrasinei et al, 2018;Wu, 2004) and for Sentinel-2 (Mylona et al, 2018). The spectral signatures of gravel are very similar to those of built-up land from the literature, while forest (vegetation 2) is less bright than reported in similar studies (Xi et al, 2019), possibly due to terrain shadow.…”

Assessing and mapping wind erosion-prone areas in northeastern Algeria using additive linear model, AHP, FAHP approaches, GIS, and medium resolution multisource remotely sensed data

“…Africa (Malaki et al, 2009;Afrasinei et al, 2018 ;Gómez et al, 2018) and other arid, and semiarid regions around the world (Mirmousavi, 2016;Baumgertel et al, 2019), which indicate that wind erosion vulnerability increase from north to south, refereeing to the effect of related factors which encourage wind erosion-human impact, degrades soil structure, denudation of natural vegetation, extent of sandy soils, and harsh climatic conditions.…”

Quantitative Assessment of Desertification in an Arid Oasis Using Remote Sensing Data and Spectral Index Techniques

“…Several remote sensing methods and techniques were developed to map and assess desertification in arid and semiarid areas, which can be classified into: Visual interpretation [7], SMA [29,30], classification algorithms [14,31,32] and spectral indices (NDVI, albedo) [2] and image transformation, such as TCT [33].…”