Soil salinization affects crop production and food security. Mapping spatial distribution and severity of salinity is essential for agricultural management and development. This study was aimed to ...test the effectiveness of machine learning algorithms for soil salinity mapping taking the Mussaib area in Central Mesopotamia as an example. A combined dataset consisting of Landsat 5 Thematic Mapper (TM) and ALOS L‐band radar data acquired at the same time was used for fulfilling the task. Relevant biophysical indicators were derived from the TM images, and the soil component was retrieved by removing the vegetation contribution from the L‐band radar backscattering coefficients. Field‐measured salinity at the three corner plots of triangles were averaged to represent the salinity of these triangular areas. These averaged plots were converted into raster by either direct rasterization or buffering‐based rasterization into different cell size to create the training set (TS). One of the three triangle corners was randomly selected to constitute a validation set (VS). Using this TS, the support vector regression (SVR) and random forest regression (RFR) algorithms were then applied to the combined dataset for salinity prediction. Results revealed that RFR performed better than SVR with higher accuracy (93.4–94.2% vs. 85.2–89.4%) and less normalized root mean square error (NRMSE; 6.10–7.69% vs. 10.29–10.52%) when calibrated with both TS and VS. In comparison, prediction by multivariate linear regression (MLR) achieved in our previous study using the same datasets also showed less NRMSE than SVR. Hence, both RFR and MLR are recommended for soil salinity mapping.
The 2019 pandemic of Severe Acute Respiratory Syndrome-Corona Virus Diseases (COVID-19) has posed a substantial threat to public health and major global economic losses. The Northern Emirates of the ...United Arab Emirates (NEUAE) had imposed intense preventive lockdown measures. On the first of April 2020, a lockdown was implemented. It was assumed, due to lower emissions, that the air quality and Surface Urban Heat Island Intensity (SUHII) had been strengthened significantly. In this research, three parameters for Nitrogen Dioxide (NO2), Aerosol Optical Depth (AOD), and SUHII variables were examined through the NEUAE. we evaluated the percentage of the change in these parameters as revealed by satellite data for 2 cycles in 2019 (March 1st to June 30th) and 2020 (March 1st to June 30th). The core results showed that during lockdown periods, the average of NO2, AOD, and SUHII levels declined by 23.7%, 3.7%, and 19.2%, respectively, compared to the same period in 2019. Validation for results demonstrates a high agreement between the predicted and measured values. The agreement was as high as R2=0.7, R2=0.6, and R2=0.68 for NO2, AOD, and night LST, respectively, indicating significant positive linear correlations. The current study concludes that due to declining automobile and industrial emissions in the NEUAE, the lockdown initiatives substantially lowered NO2, AOD, and SUHII. In addition, the aerosols did not alter significantly since they are often linked to the natural occurrence of dust storms throughout this time of the year. The pandemic is likely to influence several policy decisions to introduce strategies to control air pollution and SUHII. Lockdown experiences may theoretically play a key role in the future as a possible solution for air pollution and SUHII abatement.
Display omitted
•COVID-19 pandemic impact lockdown events in NEUAE greatly reduced the NO2, AOD and SUHII concentrations.•All Emirates in NEUAE showed decline in NO2, AOD and SUHII concentrations during lockdown period.•The average NO2, AOD and SUHII concentrations were decreased by 23.7%, 3.7% and 19.2%, respectively.•Validation for results showed a significant linear correlations between estimated and measured values.
Soil erosion is a problematic issue with detrimental effects on agriculture and water resources, particularly in countries like Pakistan that heavily rely on farming. The condition of major ...reservoirs, such as Tarbela, Mangla, and Warsak, is crucial for ensuring an adequate water supply for agriculture in Pakistan. The Kunhar and Siran rivers flow practically parallel, and the environment surrounding both rivers’ basins is nearly identical. The Kunhar River is one of KP’s dirtiest rivers that carries 0.1 million tons of suspended sediment to the Mangla reservoir. In contrast, the Siran River basin is largely unexplored. Therefore, this study focuses on the Siran River basin in the district of Manshera, Pakistan, aiming to assess annual soil loss and identify erosion-prone regions. Siran River average annual total soil loss million tons/year is 0.154. To achieve this, the researchers integrate Geographical Information System (GIS) and remote sensing (RS) data with the Revised Universal Soil Loss Equation (RUSLE) model. Five key variables, rainfall, land use land cover (LULC), slope, soil types, and crop management, were examined to estimate the soil loss. The findings indicate diverse soil loss causes, and the basin’s northern parts experience significant soil erosion. The study estimated that annual soil loss from the Siran River basin is 0.154 million tons with an average rate of 0.871 tons per hectare per year. RUSLE model combined with GIS/RS is an efficient technique for calculating soil loss and identifying erosion-prone areas. Stakeholders such as policymakers, farmers, and conservationists can utilize this information to target efforts and reduce soil loss in specific areas. Overall, the study’s results have the potential to advance initiatives aimed at safeguarding the Siran River watershed and its vital resources. Protecting soil resources and ensuring adequate water supplies are crucial for sustainable agriculture and economic development in Pakistan.
Prioritization of watersheds for conservation measures is essential for a variety of functions, such as flood control projects in which the determination of top priority areas is an important ...management decision. The purpose of this study is to examine watershed morphological characteristics and identify critical sub-watersheds, which are prone to be damaged, using Remote Sensing/Geographical Information Systems (GIS) and SAW/TOPSIS (Simple Additive Weighting/ Technique for Order of Preference by Similarity to Ideal Solution). Fourteen morphometric parameters were chosen to organize sub-watersheds using SAW/TOPSIS, which examines sub-watersheds (as susceptible zones) from the perspective of classification in four priority levels (namely, low, moderate, high and very high levels). The SAW/TOPSIS approach is a useful strategy to find out potential zones provided that the ultimate goal is to achieve successful management strategies, particularly in particular zones where information accessibility is limited and soil assorted variety is high. Without facing with high cost and exercises in futility, sub-watersheds could be organized through morphometric parameters in executing conservational measures to save soil and the earth at the same time. In short, our results showed that morphometric parameters are highly efficient in identifying erosion-prone areas.
Iraq, a country in the Middle East, has suffered severe drought events in the past two decades due to a significant decrease in annual precipitation. Water storage by building dams can mitigate ...drought impacts and assure water supply. This study was designed to identify suitable sites to build new dams within the Al-Khabur River Basin (KhRB). Both the fuzzy analytic hierarchy process (AHP) and the weighted sum method (WSM) were used and compared to select suitable dam sites. A total of 14 layers were used as input dataset (i.e., lithology, tectonic zones, distance to active faults, distance to lineaments, soil type, land cover, hypsometry, slope gradient, average precipitation, stream width, Curve Number Grid, distance to major roads, distance to towns and cities, and distance to villages). Landsat-8/Operational Land Imager (OLI) and QuickBird optical images were used in the study. Three types of accuracies were tested: overall, suitable pixels by number, and suitable pixels by weight. Based on these criteria, we determined that 11 sites are suitable for locating dams for runoff harvesting. Results were compared to the location of 21 preselected dams proposed by the Ministry of Agricultural and Water Resources (MAWR). Three of these dam sites coincide with those proposed by the MAWR. The overall accuracies of the 11 dams ranged between 76.2% and 91.8%. The two most suitable dam sites are located in the center of the study area, with favorable geology, adequate storage capacity, and in close proximity to the population centers. Of the two selection methods, the AHP method performed better as its overall accuracy is greater than that of the WSM. We argue that when stream discharge data are not available, use of high spatial resolution QuickBird imageries to determine stream width for discharge estimation is acceptable and can be used for preliminary dam site selection. The study offers a valuable and relatively inexpensive tool to decision-makers for eliminating sites having severe limitations (less suitable sites) and focusing on those with the least restriction (more suitable sites) for dam construction.
One of the most dynamic components of the environment is land use land cover (LULC), which have been changing remarkably since after the industrial revolution at various scales. Frequent monitoring ...and quantifying LULC change dynamics provide a better understanding of the function and health of ecosystems. This study aimed at modelling the future changes of LULC for the Erbil governorate in the Kurdistan region of Iraq (KRI) using the synergy Cellular Automata (CA)-Markov model. For this aim, three consecutive-year Landsat imagery (i.e., 1988, 2002, and 2017) were classified using the Maximum Likelihood Classifier. From the classification, three LULC maps with several class categories were generated, and then change-detection analysis was executed. Using the classified (1988–2002) and (2002–2017) LULC maps in the hybrid model, LULC maps for 2017 and 2050 were modelled respectively. The model output (modelled 2017) was validated with the classified 2017 LULC map. The accuracy of agreements between the classified and the modelled maps were K
no
= 0.8339, K
location
= 0.8222, K
standard
= 0.7491, respectively. Future predictions demonstrate between 2017 and 2050, built-up land, agricultural land, plantation, dense vegetation and water body will increase by 173.7% (from 424.1 to 1160.8 km
2
), 79.5% (from 230 to 412.9 km
2
), 70.2% (from 70.2 to 119.5 km
2
), 48.9% (from 367.2 to 546.9 km
2
) and 132.7% (from 10.7 to 24.9 km
2
), respectively. In contrast, sparse vegetation, barren land will decrease by 9.7% (2274.6 to 2052.8 km
2
), 18.4% (from 9463.9-7721 km
2
), respectively. The output of this study is invaluable for environmental scientists, conservation biologists, nature-related NGOs, decision-makers, and urban planners.
Soil salinization is a ubiquitous global problem. The literature supports the integration of remote sensing (RS) techniques and field measurements as effective methods for developing soil salinity ...prediction models. The objectives of this study were to (i) estimate the level of soil salinity in Abu Dhabi using spectral indices and field measurements and (ii) develop a model for detecting and mapping soil salinity variations in the study area using RS data. We integrated Landsat 8 data with the electrical conductivity measurements of soil samples taken from the study area. Statistical analysis of the integrated data showed that the normalized difference vegetation index and bare soil index showed moderate correlations among the examined indices. The relation between these two indices can contribute to the development of successful soil salinity prediction models. Results show that 31% of the soil in the study area is moderately saline and 46% of the soil is highly saline. The results support that geoinformatic techniques using RS data and technologies constitute an effective tool for detecting soil salinity by modeling and mapping the spatial distribution of saline soils. Furthermore, we observed a low correlation between soil salinity and the nighttime land surface temperature.
Drought is a common event in Iraq’s climate, and the country has severely suffered from drought episodes in the last two decades. The Kurdistan Region of Iraq (KRI) is geographically situated in the ...semi-arid zone in Iraq, whose water resources have been limited in the last decades and mostly shared with other neighboring countries. To analyze drought impacts on the vegetation cover and the land surface temperature in the KRI for a span of 20 years from 1998 to 2017, remote sensing (RS) and Geographical Information Systems (GIS) have been adopted in this study. For this study, 120 Landsat satellite images were downloaded and utilized, whereas six images covering the entire study area were used for each year of the study period. The Normalized Difference Vegetation Index (NDVI) and Land Surfaces Temperature Index (LST) were applied to produce multi-temporal classified drought maps. Changes in the area and values of the classified NDVI and LST were calculated and mapped. Mann–Kendall and Sen’s Slope statistical tests were used to assess the variability of drought indices variation in 60 locations in the study area. The results revealed increases in severity and frequency of drought over the study period, particularly in the years 2000 and 2008, which were characterized by an increase in land surface temperatures, a decrease in vegetation area cover, and a lack of precipitation averages. Climate conditions affect the increase/decrease of the vegetated cover area, and geographical variability is also one factor that significantly influences the distribution of vegetation. It can be concluded that the southeast and southwestern parts of the KRI were subjected to the most severe droughts over the past 20 years.
Aerosols are an inextricably linked component of the atmosphere. Nowadays the study of aerosols has attracted the attention of the world community due to the increasing concerns over air pollution ...and climate change. Aerosol optical depth (AOD) is the measure of aerosols distributed within the atmospheric column from the Earth's surface to the top of the atmosphere. This study was conducted to examine the trend in AOD between latitudes 22° and 24.62° N, and longitudes 83.26° and 87.01° E, covering the entire part of the Indian state of Jharkhand. Mann-Kendall (MK) trend test and Sen's slope estimator model were used to examining the trend over 18 years (period: 2000-2017) AOD data obtained from satellite-based sensor namely MODIS. The highest AOD was observed in the north-eastern part, while the lowest was observed in the state's southwestern part. The mean relative percentage change (RPC) analysis showed that the AOD increased from 20 to 60%. Jharkhand State comprises various sub-regions; all the sub-regions, including major cities, have shown a remarkable positive trend. In particular, Dhanbad, Sahibganj, Chaibasa, Jamshedpur, Ranchi, and Hazaribagh demonstrate statistically significant positive trends (99% confidence level). It was observed that the highest positive trend (0.1228) and the lowest negative trend (−0.02587) were in Sahibganj and Gumla districts, respectively. This study revealed a statistically robust significant correlated pattern of AOD with the variability of meteorological factors.
Advanced remote sensing technology has provided spatially distributed variables for estimating land–ocean heat fluxes, allowing for practical applications in drought monitoring, water resources ...management, and climate assessment. This Special Issue includes several research studies using state-of-the-art algorithms for estimating downward longwave radiation, surface net radiation, latent heat flux, columnar atmospheric water vapor, fractional vegetation cover, and grassland aboveground biomass. This Special Issue intends to help scientists involved in global change research and practices better comprehend the strengths and disadvantages of the application of remote sensing for monitoring surface energy, water, and carbon budgets. The studies published in this Special Issue can be applied by natural resource management communities to enhance the characterization and assessment of land–ocean biophysical variables, as well as for more accurately partitioning heat flux into soil and vegetation based on the existing and forthcoming remote sensing data.
PDF
