Identifying saline wetlands in an arid desert climate using Landsat remote sensing imagery. Application on Ouargla Basin, southeastern Algeria

Medjani, Fethi; Aissani, Belkacem; Labar, Sofiane; Djidel, Mohamed; Ducrot, Danielle; Massé, Antoine; Hamilton, Chase

doi:10.1007/s12517-017-2956-6

Cited by 9 publications

(3 citation statements)

References 8 publications

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“…Monitoring changes in the water supplies of chotts is possible by using satellite image classification. This is based on evaluating spectral reflectances of objects that are visible on the surface [1]. Changes in lake contours are used as indicators for monitoring land cover changes and hydrological modeling through the variability of contours in ephemeral stream beds.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

A GRASS GIS Scripting Framework for Monitoring Changes in the Ephemeral Salt Lakes of Chotts Melrhir and Merouane, Algeria

Lemenkova

2023

ASI

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Automated classification of satellite images is a challenging task that enables the use of remote sensing data for environmental modeling of Earth’s landscapes. In this document, we implement a GRASS GIS-based framework for discriminating land cover types to identify changes in the endorheic basins of the ephemeral salt lakes Chott Melrhir and Chott Merouane, Algeria; we employ embedded algorithms for image processing. This study presents a dataset of the nine Landsat 8–9 OLI/TIRS satellite images obtained from the USGS for a 9-year period, from 2014 to 2022. The images were analyzed to detect changes in water levels in ephemeral lakes that experience temporal fluctuations; these lakes are dry most of the time and are fed with water during rainy periods. The unsupervised classification of images was performed using GRASS GIS algorithms through several modules: `i.cluster’ was used to generate image classes; `i.maxlik’ was used for classification using the maximal likelihood discriminant analysis, and auxiliary modules, such as `i.group’, `r.support’, `r.import’, etc., were used. This document includes technical descriptions of the scripts used for image processing with detailed comments on the functionalities of the GRASS GIS modules. The results include the identified variations in the ephemeral salt lakes within the Algerian part of the Sahara over a 9-year period (2014–2022), using a time series of Landsat OLI/TIRS multispectral images that were classified using GRASS GIS. The main strengths of the GRASS GIS framework are the high speed, accuracy, and effectiveness of the programming codes for image processing in environmental monitoring. The presented GitHub repository, which contains scripts used for the satellite image analysis, serves as a reference for the interpretation of remote sensing data for the environmental monitoring of arid and semi-arid areas of Africa.

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Section: Introduction 1backgroundmentioning

confidence: 99%

A GRASS GIS Scripting Framework for Monitoring Changes in the Ephemeral Salt Lakes of Chotts Melrhir and Merouane, Algeria

Lemenkova

2023

ASI

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“…There are currently three main methods for counting wetland areas: remote sensing monitoring, model simulation, and the site monitoring [20]. Remote sensing images have been widely used in extracting wetland areas [21][22][23][24][25]. However, the remote sensing images present the distribution of wetlands under the high-intensity human activities.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Makkeasorn et al [26] applied a machine learning algorithm to monitor the seasonal variation of riparian zone with an overall accuracy of 82.9%. Medjani et al [21] adopted four machine learning methods (support vector machine, maximum likelihood, Neural Networks, and spectral angle mapper) to identify saline-alkali wetlands in arid desert environment, with the accuracy of 91%. However, due to the complexity of wetland formation, global wetland mapping usually has potential biases in reproducing regional wetland distributions.…”

Section: Introductionmentioning

confidence: 99%

The Susceptibility of Wetland Areas in the Yangtze River Basin to Temperature and Vegetation Changes

Ma,

Chen,

Xiao

et al. 2023

Remote Sensing

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Wetlands serve a critical function in water storage and ecological diversity maintenance. However, human activities have resulted in wetland loss in the middle and lower reaches of the Yangtze River Basin (MLYRB), while the wetland distribution in this area shows great discrepancy in previous estimates. It is, therefore, imperative to estimate the distribution of potential wetlands at present and project their variation under future climate change scenarios. In this study, we simulate the wetland distribution in the MLYRB at 15″ resolution using 5 machine learning methods with 19 predicting factors of topographic index, vegetation index, climate data, hydrological data, and soil type data. A 5-fold cross-validation with observed permanent wetlands shows that the reconstructions from Adaptive Boosting tree (AdaBoost) algorithm have the highest accuracy of 97.5%. The potential wetland area in the MLYRB is approximately ~1.25 × 105 km2, accounting for 15.66% of the study region. Direct human activities have led to the loss of nearly half of the potential wetlands. Furthermore, sensitivity experiments with the well-trained models are performed to quantify the response of the total wetland area to each influencing factor. Results indicate vulnerability of wetland areas to increases in leaf area index (LAI), coldest season temperature, warmest season temperature, and solar radiation. By the 2100s, the potential wetland area is expected to decrease by 40.5% and 50.6% under the intermediate and very high emissions scenarios, respectively. The changes in LAI and the coldest season temperature will contribute to 50% and 40% of this loss of potential wetlands, respectively. Wetland loss may further undermine biodiversity, such as waterfowl, and fail to provide functions such as flood protection, and water supply. This work reveals the spatial pattern of potential wetland areas and their sensitivity to climate changes, stressing the need for effective strategies to mitigate wetland loss at specific regions in the MLYRB.

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The Hydrology and Palaeohydrology of the Central Sahara

Knight

2023

World Geomorphological Landscapes

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Identifying saline wetlands in an arid desert climate using Landsat remote sensing imagery. Application on Ouargla Basin, southeastern Algeria

Cited by 9 publications

References 8 publications

A GRASS GIS Scripting Framework for Monitoring Changes in the Ephemeral Salt Lakes of Chotts Melrhir and Merouane, Algeria

A GRASS GIS Scripting Framework for Monitoring Changes in the Ephemeral Salt Lakes of Chotts Melrhir and Merouane, Algeria

The Susceptibility of Wetland Areas in the Yangtze River Basin to Temperature and Vegetation Changes

The Hydrology and Palaeohydrology of the Central Sahara

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