Spatio-temporal dynamic of soil quality in the central Iranian desert modeled with machine learning and digital soil assessment techniques

Fathizad, Hassan; Ardakani, Mohammad Ali Hakimzadeh; Heung, Brandon; Sodaiezadeh, Hamid; Rahmani, Asghar; Fathabadi, Abolhasan; Scholten, Thomas; Taghizadeh‐Mehrjardi, Ruhollah

doi:10.1016/j.ecolind.2020.106736

Cited by 52 publications

(25 citation statements)

References 122 publications

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“…The region is at an average altitude of 1216 meters above sea level. According to the 15-year data of Yazd Synoptic Station, the mean precipitation, average temperature and relative humidity in this period are 67.7 millimeters, 19.9 and 27%, respectively (Fathizad et al, 2020). Yazd covers an area of 6336 square kilometers.…”

Section: Study Areamentioning

confidence: 99%

Assessment of environmental pollution of heavy metals deposited on the leaves of trees at Yazd bus terminals

Esfandiari

Ardakani

2022

Environ Sci Pollut Res

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There is a lack of information about urban transport activity in adding heavy elements to the environment. This research assesses concentration some of heavy elements in the deposited atmospheric dust in Yazd bus terminals. Dust samples were collected from the green space in the bus terminals of urban transportation. Following the digestion by nitric acid, determination of the total metal concentration of cadmium(Cd), cobalt(Co), copper(Cu), nickel(Ni), lead(Pb), zinc(Zn), chromium(Cr), and manganese(Mn) in the dust were performed via Atomic Absorption Spectrometry. The map representing the spatial distribution of metals was plotted and their source was identified using Pearson correlation coefficients, Principal Component Analysis(PCA), and Cluster Analysis(CA). The findings indicated that the mean concentration of heavy metals in the deposited dust on the leaves of the trees was in the following order; Cd

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Section: Study Areamentioning

confidence: 99%

Assessment of environmental pollution of heavy metals deposited on the leaves of trees at Yazd bus terminals

Esfandiari

Ardakani

2022

Environ Sci Pollut Res

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“…Therefore, in this study, road accessibility and centralized connectivity were used to represent the construction level of the cultivated land. Road accessibility refers to the distance from the cultivated land to the nearest road [52]. Centralized connectivity refers to whether the cultivated land is connected and to which degree of connectivity.…”

Section: Indicator Selectionmentioning

confidence: 99%

Spatiotemporal Patterns of Cultivated Land Quality Integrated with Multi-Source Remote Sensing: A Case Study of Guangzhou, China

et al. 2022

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Scientifically revealing the spatiotemporal patterns of cultivated land quality (CLQ) is crucial for increasing food production and achieving United Nations Sustainable Development Goal (SDG) 2: Zero Hunger. Although studies on the evaluation of CLQ have been conducted, an effective evaluation system that is suitable for the macro-regional scale has not yet been developed. In this study, we first defined the CLQ from four aspects: soil fertility, natural conditions, construction level, and cultivated land productivity. Then, eight indicators were selected by integrating multi-source remote sensing data to create a new CLQ evaluation system. We assessed the spatiotemporal patterns of CLQ in Guangzhou, China, from 2010 to 2018. In addition, we identified the main factors affecting the improvement of CLQ. The results showed that the CLQ continuously improved in Guangzhou from 2010 to 2018. The area of high-quality cultivated land increased by 13.7%, which was mainly distributed in the traditional agricultural areas in the northern and eastern regions of Guangzhou. The areas of medium- and low-quality cultivated land decreased by 8.1% and 5.6%, respectively, which were scattered throughout the whole study area. The soil fertility and high productivity capacity were the main obstacle factors that affected the improvement of CLQ. Simultaneously, the obstacle degree of stable productivity capacity gradually increased during the study period. Therefore, the targeted improvement measures could be put forward by applying biofertilizers, strengthening crop management and constructing well-facilitated farmland. The new CLQ evaluation system we proposed is particularly practical at the macro-regional scale, and the results provided targeted guidance for decision makers to improve CLQ and promote food security.

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“…Although CLQ is comprehensive, complex, and difficult to quantify, it is a mainstream method to construct an evaluation system based on observable indicators using geographic Remote Sens. 2021, 13, 5095 2 of 17 information systems (GIS) technology [6][7][8]. However, it is difficult to achieve rapid evaluation and dynamic update of CLQ for large regions due to the heavy workload and protracted cycle of field investigation and laboratory detection [9].…”

Section: Introductionmentioning

confidence: 99%

“…High resolution satellite data was used in CLQ analysis at small and medium scales. For example, Wu et al [12], Fathizad et al [13], and Binte Mostafiz et al [14] adopted different methods to realize CLQ evaluation based on Landsat series data (30 m) at a small scale. Moreover, Fang et al [15], Xia et al [16], and Zolekar et al [17] realized land evaluation analysis based on SPOT (10 m), GF-1 (8 m), and IRS (5.8 m) data at a small scale.…”

Section: Introductionmentioning

confidence: 99%

Upscaling Remote Sensing Inversion Model of Wheat Field Cultivated Land Quality in the Huang-Huai-Hai Agricultural Region, China

Chang

Wang

et al. 2021

Remote Sensing

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It is an objective demand for sustainable agricultural development to realize fast and accurate cultivated land quality assessment. In this paper, Tengzhou city (county-scale hilly area: scale A), Shanghe county (county-scale plain area: scale B), and Huang-Huai-Hai region (including large-scale hilly and plain area: scale C and D) were taken as research areas. Through the conversion of evaluation systems, the inversion models at the county-scale were constructed. Then, the image scale conversion was carried out based on the numerical regression method, and the upscaling inversion was realized. The results showed that: (1) the conversion models of evaluation systems (CMES) are Y = 1.021x − 4.989 (CMESA−B), Y = 0.801x + 16.925 (CMESA−C), and Y = 0.959x + 3.458 (CMESC−D); (2) the booting stage is the best inversion phase; (3) the back propagation neural network model based on the combination index group (CI-BPNN) is the best inversion model, with the R2 are 0.723 (modeling set) and 0.722 (verification set). CI-BPNN and CI-BPNN-CMESA−B models are suitable for the hilly and plain areas at the county-scale, and the level area ratio difference is less than 4.87%. Furthermore, (4) the reflectance conversion model of short-wave infrared 2 is cubic, and the rest are quadratic. CI-BPNN-CMESA−C and CI-BPNN-CMESA−C-CMESC−D models realized upscaling inversion in the hilly and plain areas, with the maximum level area ratio difference being 1.60%. Additionally, (5) the wheat field quality has improved steadily since 2001 in the Huang-Huai-Hai region. This study proposes an upscaling inversion method of wheat field quality, which provides a scientific basis for cultivated land management and agricultural production in large areas.

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Spatio-temporal dynamic of soil quality in the central Iranian desert modeled with machine learning and digital soil assessment techniques

Cited by 52 publications

References 122 publications

Assessment of environmental pollution of heavy metals deposited on the leaves of trees at Yazd bus terminals

Assessment of environmental pollution of heavy metals deposited on the leaves of trees at Yazd bus terminals

Spatiotemporal Patterns of Cultivated Land Quality Integrated with Multi-Source Remote Sensing: A Case Study of Guangzhou, China

Upscaling Remote Sensing Inversion Model of Wheat Field Cultivated Land Quality in the Huang-Huai-Hai Agricultural Region, China

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