Application of Remote-sensing Data and Decision-Tree Analysis to Mapping Salt-Affected Soils over Large Areas

Elnaggar, A.; Noller, Jay S.

doi:10.3390/rs2010151

Cited by 124 publications

(85 citation statements)

References 21 publications

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“…DT provides a robust and flexible approach when examining the effects of each input feature to determine each split in the final tree and select the most important input variables that achieve the best classification results. Studies indicate that decision tree classification is superior over traditional image classifiers [21,[46][47][48][49].…”

Section: Decision Treementioning

confidence: 99%

Monitoring Soil Salinization in Keriya River Basin, Northwestern China Using Passive Reflective and Active Microwave Remote Sensing Data

et al. 2015

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Soil salinization is one of the most widespread soil degradation processes on Earth, especially in arid and semi-arid areas. The salinized soil in arid to semi-arid Xinjiang Uyghur Autonomous Region in China accounts for 31% of the area of cultivated land, and thus it is pivotal for the sustainable agricultural development of the area to identify reliable and cost-effective methodologies to monitor the spatial and temporal variations in soil salinity. This objective was accomplished over the study area (Keriya River Basin, northwestern China) by adopting technologies that heavily rely on, and integrate information contained in, a readily available suite of remote sensing datasets. The following procedures were conducted: (1) a selective principle component analysis (S-PCA) fusion image was generated using Phased Array Type L-band SAR (PALSAR) backscattering coefficient (σ°) OPEN ACCESSRemote Sens. 2015, 7 8804 and Landsat Enhanced Thematic Mapper Plus (ETM+) multispectral image of Keriya River Basin; and (2) a support vector machines (SVM) classification method was employed to classify land cover types with a focus on mapping salinized soils; (3) a cross-validation method was adopted to identify the optimum classification parameters, and obtain an optimal SVM classification model; (4) Radarsat-2 (C band) and PALSAR polarimetric images were used to analyze polarimetric backscattering behaviors in relation to the variation in soil salinization; (5) a decision tree (DT) scheme for multi-source optical and polarimetric SAR data integration was proposed to improve the estimation and monitoring accuracies of soil salinization; and (6) detailed field observations and ground truthing were used for validation of the adopted methodology, and quantity and allocation disagreement measures were applied to assess classification outcome. Results showed that the fusion of passive reflective and active microwave remote sensing data provided an effective tool in detecting soil salinization. Overall accuracy of the adopted SVM classifier with optimal parameters for fused image of ETM+ and PALSAR data was 91.25% with a Kappa coefficient of 0.89, which was further improved by the DT data integration and classification method yielding an accuracy of 93.01% with a Kappa coefficient of 0.92 and lower disagreement of quantity and allocation.

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Section: Decision Treementioning

confidence: 99%

Monitoring Soil Salinization in Keriya River Basin, Northwestern China Using Passive Reflective and Active Microwave Remote Sensing Data

et al. 2015

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“…These include Landsat Thematic Mapper (TM), Landsat Multispectral Scanner System (MSS), Landsat Enhanced Thematic Mapper Plus (ETM+), SPOT, Advanced Spaceborne Thermal Emission and Reflection Radiometer (Terra-ASTER), Linear imaging self-scanning sensor (LISS-III) and IKONOS [23,49,50]. For example, in the United States of America (USA), Elnaggar and Noller [51] used Landsat TM imagery integrated with decision-tree analysis (DTA) to map soil salinity in central Malheur County. They found that there was a significant relationship between EC values and reflectance in Landsat bands 1, 2, 3 and 4 as well as the Brightness (BI) and Wetness (WI) indices.…”

Section: Multispectral Satellite Sensors For Mapping and Monitoring Smentioning

confidence: 99%

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

Allbed¹,

Kumar²

2013

ARS

315

159

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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.

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“…These include Landsat Thematic Mapper (TM), Landsat Multispectral Scanner System (MSS), Landsat Enhanced Thematic Mapper Plus (ETM+), SPOT, and Advanced Spaceborne Thermal Emission and Reflection Radiometer (Terra-ASTER) (Dwivedi, 2001;Verma et al, 1994). Elnaggar and Noller (2009) index. Maximum likelihood supervised classification was used to classify the image into nonsaline soils (EC < 4 dS/m) and saline soils, with accuracy of 97% and 60% respectively, whereas DTA predicted five classes of soil salinity with an overall accuracy of approximately 99%.…”

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

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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.

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Application of Remote-sensing Data and Decision-Tree Analysis to Mapping Salt-Affected Soils over Large Areas

Cited by 124 publications

References 21 publications

Monitoring Soil Salinization in Keriya River Basin, Northwestern China Using Passive Reflective and Active Microwave Remote Sensing Data

Monitoring Soil Salinization in Keriya River Basin, Northwestern China Using Passive Reflective and Active Microwave Remote Sensing Data

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

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

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