Modeling the behavior of Vegetation Indices in the salt dome of Korsia in North-East of Darab, Fars, Iran

Mokarram, Marzieh; Hojjati, Majid; Roshan, Gholamreza; Negahban, Saeed

doi:10.1007/s40808-015-0029-y

Cited by 27 publications

(16 citation statements)

References 28 publications

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“…The inverse association of some indices with salinity was for the reason that most of such indices used near-infrared band. In effect, as salinity increased, the spectral value of the near-infrared band decreased [41]. Moreover, the Normalized Difference Vegetation Index had a significant negative correlation with soluble sodium, calcium, and magnesium at a 95% confidence level.…”

Section: Resultsmentioning

confidence: 90%

Monitoring Soil Salinity Changes, Comparison of Different Maps and Indices Extracted from Landsat Satellite Images (Case Study: Atabieh, Khuzestan)

Golabkesh¹,

Ghanavati²,

Nazarpour³

et al. 2021

Pol. J. Environ. Stud.

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Soil salinity is the dominant process in the degradation of arid and semi-arid soils, which in turn reduces crop yields, increases erosion, and exacerbates desertification. In recent years, soil salinity has affected much of the land in the Atabieh area located in the west of Khuzestan province in Iran. The purpose of this study was thus to evaluate and map soil salinity changes in the region over 15 years using Landsat 7 and 8 satellite images. To that end, the spectra of saline soils in the study area were extracted from the satellite data, and after the initial pre-processing in EVNI software version 5.3, the SI1, SI2, SI3, BI, NDVI, and NDSI indices were prepared. Using the supervised classification method, the salinity map with four different classes was then plotted in Arc GIS version 10.2, and the changes in saline soil area were investigated. Moreover, field surveys, surface soil sampling, soil EC measurement and identification of available minerals were performed by X-ray diffraction (XRD) technique and satellite images. Among the studied indices, the BI index with the highest correlation (0.71) was considered as the best index, and NDVI with the correlation coefficient of 0.35 at the 95% confidence level, was the best index for vegetation cover. Examination of changes in BI index by Landsat 7 images showed that the non-saline land area decreased from 1023.54 ha in 2000 to 143.43 ha in 2010, while the area with medium salinity increased by 14.57%. Besides, the salinity severity in the NDVI index had a growth rate of 72.86%. In turn, XRD studies confirmed the presence of abundant evaporate minerals (Halites, Calcite and Dolomite) corresponding to the values (real numbers) of salinity and mineralogical maps obtained from the Landsat 8 images.

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Section: Resultsmentioning

confidence: 90%

Monitoring Soil Salinity Changes, Comparison of Different Maps and Indices Extracted from Landsat Satellite Images (Case Study: Atabieh, Khuzestan)

Golabkesh¹,

Ghanavati²,

Nazarpour³

et al. 2021

Pol. J. Environ. Stud.

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“…Despite this, these authors have demonstrated that low-cost UAVs may improve forest monitoring after disturbance, even in those habitats and situations where resource limitation is an issue. In general, VARI shows a minimal sensitivity to atmospheric effects [26,61,62].…”

Section: Correlations Of Vari With Vegetation Cover Using Remote Sensing Techniquesmentioning

confidence: 99%

Comparison of Satellite and Drone-Based Images at Two Spatial Scales to Evaluate Vegetation Regeneration after Post-Fire Treatments in a Mediterranean Forest

et al. 2021

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The evaluation of vegetation cover after post-fire treatments of burned lands is important for forest managers to restore soil quality and plant biodiversity in burned ecosystems. Unfortunately, this evaluation may be time consuming and expensive, requiring much fieldwork for surveys. The use of remote sensing, which makes these evaluation activities quicker and easier, have rarely been carried out in the Mediterranean forests, subjected to wildfire and post-fire stabilization techniques. To fill this gap, this study evaluates the feasibility of satellite (using LANDSAT8 images) and drone surveys to evaluate changes in vegetation cover and composition after wildfire and two hillslope stabilization treatments (log erosion barriers, LEBs, and contour-felled log debris, CFDs) in a forest of Central Eastern Spain. Surveys by drone were able to detect the variability of vegetation cover among burned and unburned areas through the Visible Atmospherically Resistant Index (VARI), but gave unrealistic results when the effectiveness of a post-fire treatment must be evaluated. LANDSAT8 images may be instead misleading to evaluate the changes in land cover after wildfire and post-fire treatments, due to the lack of correlation between VARI and vegetation cover. The spatial analysis has shown that: (i) the post-fire restoration strategy of landscape managers that have prioritized steeper slopes for treatments was successful; (ii) vegetation growth, at least in the experimental conditions, played a limited influence on soil surface conditions, since no significant increases in terrain roughness were detected in treated areas.

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“…Equation 1 is used to estimate the fraction of vegetation with a minimal sensitivity to atmospheric effects (Gitelson et al, 2002;Mokarram et al, 2015;Mokarram et al, 2016). The addition of blue-band data in Equation 1 is to minimize atmospheric effects (Schneider et al, 2008).…”

Section: Vegetation Index Formulasmentioning

confidence: 99%

The Use of VARI, GLI, and VIgreen Formulas in Detecting Vegetation In aerial Images

Eng¹,

Ismail²,

Hashim³

et al. 2019

IJTech

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Vegetation monitoring is a task that requires much time and human effort, but by using an unmanned aerial vehicle with a system that can store captured data digitally, the task can be more manageable and efficient. Past research has shown many formulas were developed by researchers to capture vegetation data in varying conditions and equipment. This paper discusses an experiment conducted to test three of those formulas using visible band data images. The formulas are the visible atmospherically resistant index, the green leaf index, and the visible atmospherically resistant indices green. The objective of this paper is to report and discuss our findings from experiments conducted using each formula as well as to compare the accuracy of these formulas.

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Modeling the behavior of Vegetation Indices in the salt dome of Korsia in North-East of Darab, Fars, Iran

Cited by 27 publications

References 28 publications

Monitoring Soil Salinity Changes, Comparison of Different Maps and Indices Extracted from Landsat Satellite Images (Case Study: Atabieh, Khuzestan)

Monitoring Soil Salinity Changes, Comparison of Different Maps and Indices Extracted from Landsat Satellite Images (Case Study: Atabieh, Khuzestan)

Comparison of Satellite and Drone-Based Images at Two Spatial Scales to Evaluate Vegetation Regeneration after Post-Fire Treatments in a Mediterranean Forest

The Use of VARI, GLI, and VIgreen Formulas in Detecting Vegetation In aerial Images

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