Retrospective monitoring of the spatial variability of crystalline iron in soils of the east shore of Urmia Lake, Iran using remotely sensed data and digital maps

Shahbazi, Farzin; McBratney, Alex B.; Malone, Brendan; Oustan, Shahin; Minasny, Budiman

doi:10.1016/j.geoderma.2018.11.024

Cited by 18 publications

(10 citation statements)

References 43 publications

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“…We then calculated the correlation rate using linear regression. Earlier studies reported the linear regression to be an efficient correlation assessment method for soil studies [16,19]. The linear regression is calculated based on equation 10, where 'y' is modeled as beta1 (b1) times x, plus a constant beta0 (b0), plus an error term e. y = b0 + b1 × x + e (10) Table 4.…”

Section: Accuracy Assessment Of Soil Salinity Monitoring Indicesmentioning

confidence: 99%

“…Various environmental stressors (e.g., poor land use and management, pesticides, erosion), often impacted by the effects of climate change, drought, heavy and harmful metals, soil salinity, and flooding, can endanger agricultural lands and food production [9,10]. The impacts of soil salinity, and associated environmental challenges for food production, have been addressed by earlier studies [1][2][3]9,[11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, an efficient decision support system is required to evaluate the regions at risk. The conventional research on soil salinity and degradation assessment has primarily been based on field measurements and laboratory analyzes [16].…”

Section: Introductionmentioning

confidence: 99%

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Impacts of the Urmia Lake Drought on Soil Salinity and Degradation Risk: An Integrated Geoinformatics Analysis and Monitoring Approach

et al. 2022

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Recent improvements in earth observation technologies and Geographical Information System (GIS) based spatial analysis methods require us to examine the efficiency of the different data-driven methods and decision rules for soil salinity monitoring and degradation mapping. The main objective of this study was to analyze the environmental impacts of the Lake Urmia drought on soil salinity and degradation risk in the plains surrounding the hyper-saline lake. We monitored the impacts of the lake drought on soil salinity by applying spatiotemporal indices to time-series satellite images (1990–2020) in Google Earth Engine environment. We also computed the soil salinity ratio to validate the results and determine the most efficient soil salinity monitoring techniques. We then mapped the soil degradation risk based on GIS spatial decision-making methods. Our results indicated that the Urmia Lake drought is leading to the formation of extensive salt lands, which impact the fertility of the farmlands. The land affected by soil salinity has increased from 2.86% in 1990 to 16.68% in 2020. The combined spectral response index, with a performance of 0.95, was the most efficient image processing method to assess soil salinity. The soil degradation risk map showed that 38.45% of the study area has a high or very high risk of degradation, which is a significant threat to food production. This study presents an integrated geoinformation approach for time-series soil salinity monitoring and degradation risk mapping that supports future studies by comparing the efficiency of different methods as state of the art. From a practical perspective, the results also provide key information for decision-makers, authorities, and local stakeholders in their efforts to mitigate the environmental impacts of lake drought and sustain the food production to sustain the 7.3 million residents.

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Section: Accuracy Assessment Of Soil Salinity Monitoring Indicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Impacts of the Urmia Lake Drought on Soil Salinity and Degradation Risk: An Integrated Geoinformatics Analysis and Monitoring Approach

et al. 2022

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“…The KDD is the automated extraction of valid, novel, understandable and useful patterns representing knowledge in databases (Rokach and Maimon 2005;Han et al 2011) and Data mining is the core of the KDD process (Rokach and Maimon 2005). Data mining classifier algorithms have been developed in recent decades and have been broadly used for classification, modeling and rule induction (Peters et al 2007;Taghizadeh-Mehrjardi et al 2015;Rodriguez-Galiano et al 2012Yoo et al 2016;Rahmati et al 2016;Naghibi et al 2017;Sahoo et al 2018;Arabameri et al 2019;Chen et al 2019;Shahbazi et al 2019;Miraki et al 2019;Sherafatpour et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Decision Tree-Based Data Mining and Rule Induction for Identifying High Quality Groundwater Zones to Water Supply Management: a Novel Hybrid Use of Data Mining and GIS

Jeihouni

Toomanian

Mansourian

2019

Water Resour Manage

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Groundwater is an important source to supply drinking water demands in both arid and semiarid regions. Nevertheless, locating high quality drinking water is a major challenge in such areas. Against this background, this study proceeds to utilize and compare five decision treebased data mining algorithms including Ordinary Decision Tree (ODT), Random Forest (RF), Random Tree (RT), Chi-square Automatic Interaction Detector (CHAID), and Iterative Dichotomiser 3 (ID3) for rule induction in order to identify high quality groundwater zones for drinking purposes. The proposed methodology works by initially extracting key relevant variables affecting water quality (electrical conductivity, pH, hardness and chloride) out of a total of eight existing parameters, and using them as inputs for the rule induction process. The algorithms were evaluated with reference to both continuous and discrete datasets. The findings were speculative of the superiority, performance-wise, of rule induction using the continuous dataset as opposed to the discrete dataset. Based on validation results, in continuous dataset, RF and ODT showed higher and RT showed acceptable performance. The groundwater quality maps were generated by combining the effective parameters distribution maps using inducted rules from RF, ODT, and RT, in GIS environment. A quick glance at the generated maps reveals a drop in the quality of groundwater from south to north as well as from east to west in the study area. The RF showed the highest performance (accuracy of 97.10%) among its counterparts; and so the generated map based on rules inducted from RF is more reliable. The RF and ODT methods are more suitable in the case of continuous dataset and can be applied for rule induction to determine water quality with higher accuracy compared to other tested algorithms.

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“…The results showed the potential of DSM and remote sensing-derived products to produce detailed FI maps for tropical regions. The bare soil composite was an important predictor that revealed spatial changes in topsoil colour, a morphological feature highly related to soil mineralogy and iron content Shahbazi et al, 2019b).…”

Section: Final Fi Content Map and Relationship With Soilsmentioning

confidence: 99%

Geotechnologies applied in digital soil mapping

Mendes¹

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Geotechnologies applied in digital soil mappingThe civilisation lives in a world of maps and soil maps are vital at regional and farm levels to achieve best management agricultural practices. Soil is the substrate for plant growth and vital to the fulfilment of the food demand. However, the cartographic scale of those soil maps, which for the best management agricultural practice (BMAP) have to be the most detailed as possible and they are scarce. The Digital Soil Mapping (DSM) became the easiest and feasible approach to achieve such demand. Despite previous studies have tried to better characterise soil depths, there is space for improvements on its dynamics and mapping. Looking at this goal, Remote Sensing (RS) technologies have proven to be a great power on this task. Nevertheless, some aspects of that approach still need to be tested using another hybrid, stochastic, and deterministic models for the predictions of magnetic susceptibility (MS) and soil attributes at surface and subsurface. Therefore, chapter 1 presents the evaluation of nine machine learning algorithms (MLAs) to predict the free iron content at the soil surface (e.g. 0 -20 cm) using the DSM framework. Based on the best performance of those nine MLAs, we selected five MLAs. Chapter 2 shows the use of those five MLAs with usual and new environmental variables (e.g. DEM, drainage network, and soil spectroscopy) to predict the MS and soil attributes up to 100 cm depth. Attempts on quantifying soil mineral consist of having an observation measured using traditional laboratory soil analysis. However, developments in interpreting and analysing the visible and near-infrared (VNIR) diffuse reflectance have allowed quantifying some soil minerals. In chapter 3, it implements a novel framework using VNIR spectroscopy to quantify the main soil minerals and evaluates the application of digital soil mapping framework to spatialise those soil minerals. Last but not least, the chapter 4 presents the novelty of using all predict soil components as predictors of the soil mapping units in the region of Piracicaba-SP at farm scale (1:20,000), generating the first detailed digital soil map of the region. Additionally in this chapter, it was created the digital yield environmental map for sugarcane production. Thus, this thesis presents a new integrative framework to achieve detail soil maps for the BMAP and serves as a guide for future soil surveys across the world.

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Retrospective monitoring of the spatial variability of crystalline iron in soils of the east shore of Urmia Lake, Iran using remotely sensed data and digital maps

Cited by 18 publications

References 43 publications

Impacts of the Urmia Lake Drought on Soil Salinity and Degradation Risk: An Integrated Geoinformatics Analysis and Monitoring Approach

Impacts of the Urmia Lake Drought on Soil Salinity and Degradation Risk: An Integrated Geoinformatics Analysis and Monitoring Approach

Decision Tree-Based Data Mining and Rule Induction for Identifying High Quality Groundwater Zones to Water Supply Management: a Novel Hybrid Use of Data Mining and GIS

Geotechnologies applied in digital soil mapping

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