Remote Sensing Techniques for Salt Affected Soil Mapping: Application to the Oran Region of Algeria

Abdellatif, Dehni; Mourad, Lounis

doi:10.1016/j.proeng.2012.01.1193

Cited by 157 publications

(52 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multispectral data such as Landsat, Satellite Pour l'Observation de la Terre (SPOT), IKONOS, Quick Bird and the Indian Remote Sensing (IRS) series of satellites, as well as hyperspectrl data such as EO-1 Hyperion and HyMap, proved to be useful in detecting, mapping, and monitoring land cover degradation (Farifteh, 2007;Weng, 2008;Dehni and Lounis, 2012).…”

Section: Introductionmentioning

confidence: 99%

Change Detection of Land Cover and Salt Affected Soils at Siwa Oasis, Egypt

2017

Alexandria Science Exchange Journal: An International Quarterly

View full text Add to dashboard Cite

Constant and accurate change detection of land cover/land use features is highly significant to interpret the correlation between human activities and natural phenomena in order to make better decisions and for investigating the processes and patterns of landscape changes over time and the impact of these changes on sustainable development. Soil salinization is a major problem affecting productivity of irrigated lands. Managing salinity to minimize environmental impact is a prerequisite for sustainable irrigated agriculture. The current study aims at detecting, classifying and mapping the current status for land cover types, salt affected soils and their changes in quantity that have taken place in Siwa Oasis during the period from 1987 to 2017, based on satellite imagery data, GIS facilities, statistical analysis, ground truth and laboratory analysis.An approach of multi-temporal mid resolution satellite images of TM 1987, and OLI 8 2017 combined with visual interpretation, supervised classification and new remote sensing indices namely NDVI to enhance accuracy of land cover classification was used. The study employed normal image classification, and developing model from ECe vs NDSI to produce maps for salt affected soils during the period under study.Results showed that the study area has different land cover / land use units; i.e. Salt marches, Sabkha, Cropland, Grassland, Bare Land, Urban, Lakes, Sand dunes, and Hills. During the study period, a very severe land cover change has taken place as a result of mismanagement of land resources. Change detection at the study area explains the rapid increase of saline lakes and salt marshes, and the consequent hazards to the cultivated lands, roads and archeological sites and urban areas over the last three decades. These changes in land cover led to land degradation and water logging in many parts of the study area. In 1987, very slightly saline soil area was the largest in extent 34.1% of the total area whereas strongly saline, slightly saline and moderately saline soils covered 27.0%, 17.4% and 17.1%, respectively, of the total area. In 2017, strongly saline area increased as compared to 1987 and contributed for 39.1% of the total area, while very slightly, slightly saline and, moderately saline areas represented 18.9% and 13.2% and 14.9% respectively of the total area. Most of the salt affected areas were on shallow water table.The results indicate that long-term irrigation activities would affect agricultural potentiality of the area in the future. RS, GIS, NDVI and NDSI tools are efficient and feasible for detecting land cover/land use and salt affected areas from satellite images.

show abstract

Section: Introductionmentioning

confidence: 99%

Change Detection of Land Cover and Salt Affected Soils at Siwa Oasis, Egypt

2017

Alexandria Science Exchange Journal: An International Quarterly

View full text Add to dashboard Cite

show abstract

“…Multispectral data such as Landsat, Satellite Pour l'Observation de la Terre (SPOT), IKONOS, QuickBird and the Indian Remote Sensing (IRS) series of satellites, as well as hyperspectrl data such as EO-1 Hyperion and HyMap, have been found to be useful in detecting, mapping, and monitoring soil salinity [17][18][19][20][21][22][23]. Generally, remote sensing uses the electromagnetic energy reflected from targets to obtain information about the Earth's surface with different levels of detail.…”

Section: Introductionmentioning

confidence: 99%

Soil Salinity Mapping and Monitoring in Arid and Semi-Arid Regions Using Remote Sensing Technology: A Review

Allbed¹,

Kumar²

2013

ARS

301

159

View full text Add to dashboard Cite

Soil salinity is a serious environmental problem especially in arid and semiarid areas. It either occurs naturally or is human-induced. High levels of soil salinity negatively affect crop growth and productivity leading land degradation ultimately. Thus, it is important to monitor and map soil salinity at an early stage to enact effective soil reclamation program that helps lessen or prevent future increase in soil salinity. Remote sensing has outperformed the traditional method for assessing soil salinity offering more informative and professional rapid assessment techniques for monitoring and mapping soil salinity. Soil salinity can be identified from remote sensing data obtained by different sensors by way of direct indicators that refer to salt features that are visible at the soil surface as well as indirect indicators such as the presence of halophytic plant and assessing the performance level of salt-tolerant crops. The purposes of this paper are to 1) discuss some soil salinity indicators; 2) review the satellite sensors and methods used for remote monitoring, detecting and mapping of soil salinity, particularly in arid and semi-arid regions; 3) review various spectral vegetation and salinity indices that have been developed and proposed for soil salinity detection and mapping, with an emphasis on soil salinity mapping and assessment in arid and semi-arid regions; and 4) highlight the most important issues limiting the use of remote sensing for soil salinity mapping, particularly in arid and semi-arid regions.

show abstract

“…Briefly, it uses a Gaussian model with FWHM (full width at half maximum) to match the desired sensor configuration. We calculated the following: salinity index (SI), normalized differential salinity index (NDSI), vegetation soil salinity index (VSSI) and salinity ratio [50,51]. The calculated indices were correlated to Cl and Na contents.…”

Section: Satellite-based Estimation Of Salinitymentioning

confidence: 99%

“…A higher percentage of variance explained by a lower number of components indicates model stability [47]. Table 5 shows the coefficient of determination between known salinity indices based on the Landsat configuration [50,51] and the Cl/Na contents of the plant samples. As can be seen, when these indices are modified to the Sentinel-2 bands, the correlation ranges from 0.128 to 0.235 for Cl and less than 0.03 for Na.…”

Section: Figurementioning

confidence: 99%

Studying Vegetation Salinity: From the Field View to a Satellite-Based Perspective

2017

View full text Add to dashboard Cite

Salinization of irrigated lands in the semi-arid Jezreel Valley, Northern Israel results in soil-structure deterioration and crop damage. We formulated a generic rule for estimating salinity of different vegetation types by studying the relationship between Cl/Na and different spectral slopes in the visible-near infrared-shortwave infrared (VIS-NIR-SWIR) spectral range using both field measurements and satellite imagery (Sentinel-2). For the field study, the slope-based model was integrated with conventional partial least squares (PLS) analyses. Differences in 14 spectral ranges, indicating changes in salinity levels, were identified across the VIS-NIR-SWIR region (350-2500 nm). Next, two different models were run using PLS regression: (i) using spectral slope data across these ranges; and (ii) using preprocessed spectral reflectance. The best model for predicting Cl content was based on continuum removal reflectance (R 2 = 0.84). Satisfactory correlations were obtained using the slope-based PLS model (R 2 = 0.77 for Cl and R 2 = 0.63 for Na). Thus, salinity contents in fresh plants could be estimated, despite masking of some spectral regions by water absorbance. Finally, we estimated the most sensitive spectral channels for monitoring vegetation salinity from a satellite perspective. We evaluated the recently available Sentinel-2 imagery's ability to distinguish variability in vegetation salinity levels. The best estimate of a Sentinel-2-based vegetation salinity index was generated based on a ratio between calculated slopes: the 490-665 nm and 705-1610 nm. This index was denoted as the Sentinel-2-based vegetation salinity index (SVSI) (band 4 − band 2)/ (band 5 + band 11).

show abstract

Remote Sensing Techniques for Salt Affected Soil Mapping: Application to the Oran Region of Algeria

Cited by 157 publications

References 4 publications

Change Detection of Land Cover and Salt Affected Soils at Siwa Oasis, Egypt

Change Detection of Land Cover and Salt Affected Soils at Siwa Oasis, Egypt

Soil Salinity Mapping and Monitoring in Arid and Semi-Arid Regions Using Remote Sensing Technology: A Review

Studying Vegetation Salinity: From the Field View to a Satellite-Based Perspective

Contact Info

Product

Resources

About