Desert
Volume:28 Issue: 1, Winter - Spring 2023

Groundwater is a very important natural freshwater resource for drinking and irrigation purposes. In the present study, the aim is to investigate the effect of Land Use/Land Cover (LULC) changes on the quantity and quality of groundwater in Qazvin Plain from 2005 to 2020, through RS-GIS using LULC maps from Landsat 5 TM and 8 OLI images, Groundwater Resource Index (GRI) and Groundwater Quality Index (GQI). For this purpose, the data from groundwater level and quality parameters including K+, Na+, Mg2+, Ca2+, SO42-, Cl-, TDS and EC were employed. The results indicated that in the central and eastern parts of the plain, the area of agricultural land and the number of exploitation wells were more than other parts. The plain was mostly covered with rangeland and agricultural lands. The area of agricultural land had the most changes over the time. GRI results illustrated more droughts in the eastern parts of the plain over time, and GQI results showed that groundwater quality has significantly decreased in the eastern parts in 2020. The non-vegetated lands had increased in the eastern parts of the plain, which can be due to the increase in agricultural lands, in which the excessive use of groundwater resources had reduced its level and thus decreased its quality. Generally, increasing agricultural lands and high density of exploitation wells in these lands had the greatest impact on the quantity and quality of groundwater in the Qazvin plain. So, the use of groundwater resources should be properly managed to prevent the reduction of its quantity and quality.

The Turfgrass industry in saline soil is expanding, making it important to use salinity-tolerant turfgrasses. In this experiment, the effect of salinity stress on some biochemical content in salt-sensitive and salt-tolerant tall fescue was evaluated. The Sanandaj and Daran populations with commercial tall fescue (TF) were evaluated as salt-tolerant tall fescues and the Sanajan population was used as salt-sensitive TF. Five salinity levels of irrigation water (0, 45, 90, 135, and 180 mM NaCl) were applied to turfgrasses to identify the tolerance mechanisms in tolerant tall fescue under salinity stress. Results showed that salinity affected all turfgrasses in proline, chlorophyll, 1-1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging activity, as well as sodium and potassium in their shoots. Sanajan population in 90, 135, and 180 mM salinity had the lowest chlorophyll content among all turfgrasses. Salt stress leads to an increase in the activity of proline compared to the control at the first stage (for evaluating osmotic stress) of measurement. In the second stage (to evaluate ionic stress), at concentrations of 135 and 180 mM NaCl, maximum proline was recorded in Daran and Sanandaj populations, respectively. The interaction effect of salinity and TF was significant for DPPH activity. The Na+/ K+ ratio in the Sanajan population was the highest at all salinity levels. In conclusion, the growth and antioxidant capacity of Festuca arundinaceae populations differ in their response to NaCl treatments. In salt-tolerant TF, proline and antioxidant activity increased with increasing NaCl. These may be a mechanism to protect tolerant TF in salt stress, leading to lower accumulated Na+ in tolerant TF, high K+ uptake, and high chlorophyll content. Based on these results, proline content, DPPH radical scavenging activity, chlorophyll contents, and potassium content could use to distinguish tolerant TF from sensitive TF.

Land Surface Temperature (LST) is one of the most important parameters in land-atmosphere energy exchange that is applicable to many sciences such as climatology, hydrology, agriculture, ecology, etc. One of the most significant limitations of using remote sensing for estimation of LST is the presence of clouds, which remarkably affects the energy reflected from the surface and disrupts the reading ability of the optical and thermal sensors. In the present study, 23 Landsat 8 images in 2015 were used as an annual time series to estimate LST in a part of the pistachio farms of Yazd, Iran. LST in the 23 images was estimated by generalized split-window algorithm. The results showed in the estimated (23 images) LST time series, the minimum, maximum, and mean missing data due to cloud cover were 17%, 28%, and 19%, respectively. SSA algorithm was used to solve the problem of missing data. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) between the original and reconstructed data at the data points in the studied LST time series were 3.4 and 2.5 K, respectively. Moreover, the gap-filling error was estimated by extracting five random images with four iterations of time series and comparing the reconstructed images with the extracted spatio-temporal images. RMSE and MAE were estimated to be 4.4 and 3.6 K in reconstruction of temporal artificial gaps and 3.7 and 2.8 K in the spatial artificial gaps, respectively. Based on the findings, SSA algorithm can be effectively used to fill the problem of missing data due to cloud cover in Landsat 8 LST time series.

Information about the state of vegetation is very important for environmental planning, land preparation and achieving sustainable development. In this study normalized differential vegetation index (NDVI) values were calculated based on Landsat 8 satellite images in order to show temporal and spatial changes in the vegetation cover of agricultural lands in Sistan plain over ten years (2011 to 2020) using the Google Earth Engine platform. Additionally, the NDVI index were classified using decision tree algorithm in order to analyze vegetation changes using thematic change workflow method. By comparing classified images with reference samples which collected from ground sampling, validation was carried out. Then, in order to assess accuracy of vegetation maps, the error matrix was prepared, the overall accuracy and kappa indices were determined. The values of overall accuracy and kappa indices indicated optimal accuracy and it can be stated that there is moderate agreement between ground samples and the classified images (i.e., kappa index is 0.48 to 0.7). The central areas of Sistan plain have a decline in vegetation, whereas areas in northern and eastern have an increase. The cover vegetation on lands of Sistan plain decreased in 19260.4 ha while increased over 25633.2 ha throughout ten years. Examination of NDVI index shows instability of production in this area due to aforementioned factors.

In recent years, the dust phenomenon has become one of the most important environmental challenges throughout the world, and one of its negative effects is on the agriculture sector. The aim of this research is first to determine dust emission sources in Alborz Province and then to estimate the willingness to pay (WTP) for reducing the effects of dust on the agriculture of dust emission sources and their surrounding areas. The Index of Land Susceptibility to Wind Erosion (ILSWE) was used to determine dust emission sources. ILSWE consisted of the combination of 5 effective factors in wind erosion, namely climate erosivity, soil erodibility, soil crust, vegetation cover, and surface roughness. In the next step, according to the produced map of dust emission sources, the affected rural districts were identified, and then using the contingent valuation method (CVM), individuals’ WTP for preventing and reducing the negative impacts of the dust phenomenon on the agriculture was calculated by 400 questionnaires. According to the results, the classification map of ILSWE indicated that while classifying the areas in terms of their sensitivity to wind erosion 7.8% of the study area was placed in the very high sensitivity class. This class was considered the center of dust production, which was located chiefly in the southern parts of Alborz Province. Using the CVM method, the expected value rate and the WTP were calculated as 1654231 Rials (approximately $ 5.5). According to the population of the affected area, the total value of protecting the agricultural products against dust phenomenon is 27433766904 Rials ($91445.89) annually.

Mulching is a method of controlling wind erosion in arid and desert areas. In this study, the effects of oil mulch on some properties of soil were investigated, and the optimum amount of mulch to wind erosion control in Dehloran, Iran, was determined. For these purposes, after a mulching practice, soil samples were taken from the three treatments of mulched, control and the afforestation area at two depths of 0-10 and 10-50 cm through monthly field surveys during a year to measure soil parameters, including temperature, soil moisture, pH and EC. Then, the threshold velocity of wind erosion was determined using a wind tunnel and the optimum amount of mulch for erosion control was calculated. The results of soil characteristics analysis showed that soil temperature was significantly affected by the depth and season of sampling and their interactions, in contrast, soil moisture was only affected by the season. Also, soil pH was affected by all independent variables, while EC was only affected by the treatments. Na and SAR were not significantly affected by treatment, depth, and their interactions, while OM was significantly affected by treatment and the interaction between depth and treatment. Finally, the wind tunnel results showed that the erosion threshold velocity in the control area, at the height of 30 cm, was 4.84 m/s. Results also showed that mulching practice can control wind erosion under the maximum wind speed of the region and 7 tons/ha was recommended for future mulching practices in the region and similar areas.

Landslides often result in the formation of dammed lakes along rivers. This study aims to explore the correlation between landslides occurring in the Jajrud region and the subsequent creation of dammed lake. To achieve this objective, a combination of remote sensing techniques, geomorphometry, DEM imagery, GPS, ArcGIS software, MATLAB, Global Mapper, and GMT wereutilized. Radar interferometry and SPL methods were employed to analyze the influential factors contributing to landslides. The SBAS method was utilized to determine the amount of displacement while the SPL method involved analyzing basin morphometry and geomorphology through the Tec DEM model. Additionally, morphometric analysis was conducted to assess and correlate terrace sequences. Finally, based on the findings, the extent of the dammed lake was reconstructed The interferometry results revealed an approximate uplift of 40 mm in the landslide area over a span of three years, leading to long-term rupture and landslides TheSPL analysis demonstrated the active presence of morphotectonic changes in the basin, with faults causing amplitude fragmentation. Furthermore, upstream drifting flows, the valley became obstructed, forming a substantial lake along the Jajrud River.

Change detection of lakes is important to monitor ecosystem health and wind erosion process in arid environments. The main purpose of this research is to evaluate unsupervised classification based on vegetation indices to monitor Land cover changes (LCCs). The Hamoun Biosphere Reserve is located in the east of Iran and is considered one of the most important wetlands in the center of the Iran Plateau. To detect land cover changes, using Landsat images from the 1990s, 2000s, 2010s and 2020s ground control points (GCP) and spectral profiles, four major land cover classes were obtained (sparse vegetation, dense vegetation, bare land, and water bodies). To create AOIs, the pure pixels were selected using obtained spectral profiles of the main land types by GCPs in 2020. The separability of representative AOIs by classes was examined by Jeffries–Matsushita distances and scattering ellipse parameters. A maximum likelihood classifier (MLC) was applied to Landsat images in 2020 with an overall accuracy of 93% and a Kappa statistic of 0.90. Subsequently, based on Soil Adjusted Vegetation Index (SAVI) maps, as additional input data, unsupervised classification was used to classify the same images in 2020.  The observed accuracy and kappa statistic of the used classification technique was up to 0.91 and 0.89 respectively. The finding indicated that in 2000, the area of arid land increased (90% of all areas) and became a major land use type, whereas water bodies (74% of all areas in the 1990s) reached zero in this year. Yearly water body changes revealed a severe dryness condition in this wetland. After 2000, in most cases in subsequent years, the water body completely dried up and in the seasonally flooded years, it did not exceed 10% of the total wetland’s area. On the other hand, before 2000, on average, 60% of the wetland’s area was dominated by the water class. Our study showed that in the time series without GCP for monitoring past changes, an unsupervised SAVI-based technique could provide acceptable accuracy in this region.

Satellite-based precipitation missions can be used to estimate precipitation distribution, especially in areas where there are no rain gauging stations. Nevertheless, these products are still less used because of the lack of accuracy evaluation. This study evaluates the monthly rainfall values of five satellite precipitation products, including ERA5, GPM, CHIRPS, TRMM 3B43, and PERSIANN-CDR, at eight rain gauge networks over the Utah, United States using Google Earth Engine platform (GEE). For this purpose, different validating indices such as R2, RMSE, and MAE were used to evaluate the accuracy of mentioned products from 2009 to 2019. The results showed that CHIRPS outperformed other rainfall products in this region with an R2 value of 0.63. ERA5 ranked second with an R2 of 0.6, and GPM, TRMM, and PERSIANN-CDR were in the subsequent ranks with R2 values of 0.53, 0.52, and 0.32, respectively. The results also indicated that spatial resolution is directly related to the accuracy of the results. CHIRPS rainfall product had the highest spatial resolution (0.05°) among all studied products, which led to the most reliable results. On the other hand, the lowest spatial resolutions belonged to TRMM and PERSIANN-CDR (0.25°), which resulted in the weakest results. The results also revealed that the ERA5 precipitation product was more influenced by elevation, longitude, and rainfall factors than other products.