Normalized Difference Salinity Index Research Articles

Introducing a novel approach for assessment of groundwater salinity hazard, vulnerability, and risk in a semiarid region

The application of machine learning approaches to improve groundwater salinity risk mapping is limited despite all potential advantages. Therefore, there is an ongoing need for investigations that present new techniques, like machine learning, validated against conventional methods. These advances are particularly important for arid and semiarid regions, such as the Bakhtegan Basin (southern Iran), where groundwater use may far outweigh recharge, but groundwater management improvements are essential. To address these needs, groundwater salinity hazard, vulnerability, and risk maps were investigated using an integrated application of statistical (i.e., frequency ratio and statistical index models), machine learning (i.e., random forest and classification and regression trees algorithms), and decision-making models (fuzzy analytic hierarchy process (FAHP)). Results showed that the risk of groundwater salinity was high in the central areas of the region as well as at the margins of Bakhtegan Lake and irrigated farming lands. Based on the modeling results in the testing phase, it was found that the frequency ratio and random forest models exhibited better performance, with Nash–Sutcliffe efficiency metrics of 0.73 and 0.70, respectively, compared to the classification and regression trees and statistical index models, which had Nash–Sutcliffe efficiency metrics of 0.65 and 0.63, respectively. The innovative techniques developed in the current work accurately identified variables such as normalized difference salinity index, distance from mines, and land use associated with the highest weight values (0.59, 0.14, and 0.11, respectively) compared to other variables to identify groundwater salinity areas of vulnerability. The application of the fuzzy analytic hierarchy process method in developing vulnerability maps, integrating them with hazard maps, and developing groundwater salinity risk maps was innovative in this study. The innovative approach is transferrable for groundwater salinity susceptibility, vulnerability, and risk assessments globally in the study area and similar settings.

Response mechanism of rodent burrow density to natural environmental factors in desert areas based on multisource data

Timely and accurate prediction and early warning of rodent infestation are highly important for the sustainable development of desert ecosystems. This study combines data from 1376 UAV images of actual rodent burrows with natural environmental factor data, aiming to reveal the mechanism through which these factors affect rodent burrow density in the desert areas. The relationships between rodent burrow density and the 9 plant communities and 34 plant species in this area were analyzed by the K-W test and Spearman rank correlation analysis, respectively. The Boruta algorithm and the boosted regression trees (BRT) model were used to determine the six main factors influencing rodent burrow density from a total of 32 factors and to determine the distribution pattern of the burrows. The results show that (1) the density of rodent burrows in the Junggar Basin desert area gradually decreases from south to north and from west to east. (2) There were significant differences in rodent burrow density across the different plant community types (P = 0.048**). A total of 50.0 % of the species significantly influenced rodent burrow density at at least one level. (3) Among the 17 factors with lag effects, nine had the most significant correlation with rodent burrow density in April. Only two natural environmental factors demonstrated lag effects of less than one month. (4) The soil clay content, elevation, Normalized Difference Salinity Index (NDSI), vegetation cover, precipitation, and soil bulk density were the most important influencing factors on rodent burrow density. Considering the pairwise interactions among factors, the collective impact of these factors on rodent burrow density is at least sevenfold greater than the independent effect of a single factor. This study provides a theoretical basis for obtaining a timely and accurate understanding of the spatial distribution of rodent burrows, mitigating the degradation trend of desert ecosystems, and ensuring healthy and sustainable development.

Investigating Landsat data for continuous monitoring of ground electrical conductivity: implication for propagation at medium frequency in Ondo West

Ground electrical conductivity (GEC) has many applications, which makes it worthy of continuous research. Apart from the configurations of the radio transmitter and receiver, it is a major determinant of the electric field intensity of radio waves in the Medium Frequency (MF) band. It is usually measured in such a way that both the researcher and the measuring instrument are in direct contact with the ground, and measurements are made at some predetermined constant intervals to ensure good spatial coverage and even spatial distribution. The direct method becomes cumbersome when the field of study is large because the GEC over the licensed coverage area must be known such that electric field intensity can be predicted. At the planning stage, this ensures all parts of the licensed region are reached by useful signals that can suppress interference at all times and seasons. The study is motivated by the possibility of using Landsat to estimate GEC. This study utilizes Landsat images to first estimate the normalized difference salinity index and then, with the aid of map algebra, generate another raster 2 whose pixels' values are the GEC. The study covers a landmass of 967 km in the Ondo West Local Government Area (OWLGA). During analysis, the GEC was divided into five classes. The results reveal that the ranges of very low, low, moderate, high, and very high GEC are 0.124 to 0.437, 0.438 to 0.937, 0.938 to 1.635, 1.636 to 2.524, and 2.525 to 3.600 mS/m, which have mean values of 0.346 ± 0.109, 0.754 ± 0.103, 1.071 ± 0.089, 1.351 ± 2 0.067, and 1.564 ± 0.106 mS/m, and are dispersed over 176.45, 202.24, 214.14, 374.17, and 228.56 km , respectively. The values of GEC, their respective coverage, and spatial distribution recorded in this study are strong enough to sustain the propagation of useful electric field intensity over the entire landmass of Ondo West Local Government if a medium wave is established. The study recommends that the change in GEC with time in this field be studied.

Evaluation on salinity level and Electrical Conductivity of salt-affected areas in ground level through Remote Sensing Techniques

This study aims to develop evaluation methods on salinity level and Electrical Conductivity (EC) of salt-affected areas at ground level through Remote Sensing Techniques. The research methodology was divided into 1) analyzing data obtained from Landsat-8 satellite along with 3 formats of spectral indices, i.e., Salinity Index (SI), Brightness Index (BI), and Normalized Difference Salinity Index (NDSI); 2) conducting field survey to measure Electrical Conductivity (EC) of salt-affected areas in ground level; and 3) conducting linear regression analysis with analyzed data obtained from Landsat-8 satellite and electrical conductivity of salt-affected areas in ground level. The results revealed that SI had a relationship with EC of saline soil at ground level at the highest level with a Confidence Interval of over 70% (R2 = 0.758) and correlation coefficient over 80% (R= 0.870). From such a linear relationship, a map of the distribution of saline soil in the research area could be created. From the total research areas of 73.40 km2, distribution of salt-affected areas in ground level could be divided into 5 levels (calculated to be % per area), i.e., highest level (>16 ds/m), high level (8-16 dS/m), moderate level (4-8 dS/m), low level (2-4 dS/m), and none (area with no effect from saline soil in ground level) (0-2 dS/m) with value of 0.01%, 0.01%, 40%, 59.71%, and 0.01%, respectively.

Analysis of soil salinity monitoring in irrigated areas (in case of Shimbay district, Republic of Karakalpakstan)

Soil salinity increasing is one of the most dangerous factors for food security and ecological conditions in the world and also in Uzbekistan. Currently, salinity affects around 47% of Uzbekistan's irrigated lands. The study area is located in the western part of Uzbekistan and downstream of the Amu Darya River. The satellite images were acquired between the 2015- 2024 years over the 5-year time period. In order to analyse soil salinity was used Normalized Difference Salinity Index (NDSI) by the ArcGIS Pro software and RS data. Especially, slightly saline soils are the biggest occupied land and equal to 30% of the total area of the district, which of 127,735.4 ha. Also, saline soils are the second biggest land class which occupies 28.1% of the total area of the district, of which 119,612.9 ha. Meanwhile, non-saline soils occupied 20.55% of the total area, as well as both strongly saline and extremely saline soils occupied area is 21.34% of the total district of the study area, which is 87456.6 ha with 90827.43 ha.

Remote Sensing Analysis for Vegetation Assessment of a Large-Scale Constructed Wetland Treating Produced Water Polluted with Oil Hydrocarbons

The identification and assessment of plant stress using wetland satellite images is a major task in remote sensing. In this study, one of the largest constructed wetlands (CWs) in the world, located in the Sultanate of Oman, was examined, assessed, and evaluated using remote sensor data from Sentinel-2. This CW system treats produced water generated during oil exploration activities in a desert environment; thus, CW vegetation is subjected to stress induced by oil hydrocarbons and water salinity. This study examined the plant stress and detected changes between the years of 2017 and 2019. Sentinel satellite images were evaluated for vegetation status extraction. The Normalized Difference Vegetation Index (NDVI), Modified Soil-Adjusted Vegetation Index (MSAVI), and Normalized Difference Salinity Index (NDSI) were used to evaluate the vegetation change. The results showed a comprehensive mapping identification of the plant stress and water flow parameter factors including oil in water contamination (OIW), dissolved oxygen (DO), water temperature (WT), and water conductivity (COND). Among the three indices, it was found that the NDVI showed a very good correlation with all parameters in both years with average R2 = 0.78, 0.67, 0.75, and 0.60 for OIW, DO, WT, and COND, respectively. The same trend was found for MSAVI but with R2 = 0.59, 0.48, 0.55, and 0.56 for OIW, DO, WT, and COND, respectively. This shows that the NDVI performed better than the MSAVI in evaluating the water flow parameters. On the other hand, the NDSI showed a strong correlation with one flow parameter, that is, water conductivity, especially at the outlet cells of the CW with R2 = 0.86 and 0.82 for winter time and summer time, respectively. The synchronization and correlation between the water flow parameters and remote sensing vegetation indices in this study lead to a new approach to large-scale landscape wetland monitoring that improves and helps predict any degradation or stress on vegetation growth. Furthermore, the results of this work can help decision makers potentially modify the wetland design and water flow path to improve future expansion phases. The mapping of such a critical and massive industrial CW should consider the use of high spatial resolution sensors where identifications and classifications are further improved. In summary, this research demonstrates that it is feasible to estimate vegetation stress within the constructed wetland using remote sensing techniques across extensive regions when an ample dataset comprising field data, satellite imagery, and supporting information is accessible.

Impact assessment of Cyclone Yaas on the mangrove forest area in the Bhitarkanika National Park (India)

Tropical Cyclone Yaas inflicted substanntial damage on Bhitarkanika National Park (BNP), an eminent wildlife sanctuary housing a vast diversity of flora and fauna, during its occurrence in 2021. The park has experienced a heightened frequency of cyclonic activity in recent years. This study undertakes a comprehensive analysis of the impacts of Tropical Cyclone Yaas on the mangrove forest within BNP, utilizing a broad array of physical, biological, and ecological indices. The assessment method employed in the study encompasses various indicators, such as ecological (Normalized Difference Vegetation Index – NDVI, Enhanced Vegetation Index – EVI, Leaf Area Index – LAI, Normalized Difference Water Index – NDWI, and Normalized Difference Salinity Index – NDSI), biological (Chlorophyll content and Gross Primary Productivity – GPP), and physical (flood monitoring and precipitation) measures. Our findings elucidate the destructive consequences of Cyclone Yaas on the mangrove forest, inflicting significant ecosystem loss attributable to the extreme precipitation and high wind speeds. The biophysical, ecological, and biological indicators reveal profound effects on the local ecosystem, manifested through a decline in vegetation vigor and alterations in soil conditions, notably marked by an increase in salinity.

Optimal timing of satellite data acquisition for estimating and modeling soil salinity in cotton fields of the Mingbulak District, Uzbekistan

Agriculture is frequently hampered by soil salinity, which has a negative impact on crop growth and yield. This study aims to identify the optimal timing of satellite data acquisition to predict soil salinity levels indirectly using satellite images in cotton growth fields as a basis. Data was collected in the Mingbulak district of Uzbekistan, where soil electrical conductivity (EC) was measured in a laboratory using soil samples collected from various fields with similar management practices. In this research, we present a linear regression model that uses satellite data and the Normalized Difference Salinity Index (NDSI) to forecast soil salinity levels indirectly. The results of the linear regression analysis showed a positive correlation between the soil electrical conductivity values and the NDSI values for each month, with August having the highest correlation (R2 = 0.70). The study found that the cotton growth stages and the process of soil salinity formation in the study area were the main factors affecting the correlation between electrical conductivity and NDSI. The model developed in this study has R2 value of 0.70. This suggests a moderate to strong relationship between the two variables, which is promising for the indirect assessment of soil salinity using the NDSI index. The study discovered a positive relationship between soil electrical conductivity and NDSI values, which were highest in pre-flowering and flowering stages of cotton. Our findings show that satellite-based estimation and modeling with NDSI can be used to indirectly assess cotton field soil salinity, especially during the pre-flowering and flowering stages. This study contributes to the development of optimal satellite data acquisition timing, which can improve soil salinity predictions and agricultural productivity.

Assessment of soil salinity by geo-spatial technology in Ballia district of Uttar Pradesh

Soils vary in their characteristics from one place to another place due to interactions of soil forming and processes. Assessment of spatial and temporal variation of characteristics is needed for any mean or use it's crucial to take into account the diverse spatial differences in soil properties, which has long in this context. Geographic information system (GIS) technology has enormous potential in the field of soil's assessment for upgrading soil statistic systems for an accurate and cost-effective means of understanding changes in the landscape. The future agricultural monitoring of the nutritional quality of the soil in diverse regions and villages is crucial. In this paper assessment of spatial variation of soil salinity using GIS techniques around the Ballia District of Uttar Pradesh is presented from the year 1994 to 2019. Normalized Difference Salinity Index (NDSI) was used. It showed increasing trend in salinity from 1994 to 2019 for each block. Using Landsat (TM/OLI) imageries with a 30 m spatial resolution, the spatial distribution of salinity from 1994 to 2019 was determined and low negative NDSI value was observed from - 0.143 to -0.117 in the southeast part of Ballia which can be an important explanation for soil degradation due to increasing soil salinity.

Satellite-Based Estimation of Soil Moisture Content in Croplands: A Case Study in Golestan Province, North of Iran

Soil moisture content (SMC) plays a critical role in soil science via its influences on agriculture, water resources management, and climate conditions. There is broad interest in finding relationships between groundwater recharge, soil characteristics, and plant properties for the quantification of SMC. The objective of this study was to assess the potential of optical satellite imagery for estimating the SMC over cropland areas. For this purpose, we collected 394 soil samples as targets in Gonbad-e Kavus in the Golestan province in the north of Iran, where a variety of crop types are cultivated. As input data, we first computed several spectral indices from Sentinel 2 (S2) and Landsat 8 (L8) images, such as the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Salinity Index (NDSI), and then analyzed their relationships with surveyed SMC using four machine learning regression algorithms: random forests (RFs), XGBoost, extra tree decision (EDT), and support vector machine (SVM). Results revealed a high and rather similar correlation between the spectral indices and measured SMC values for both S2 and L8 data. The EDT regression algorithm yielded the highest accuracy, with an R2 = 0.82, MAE = 3.74, and RMSE = 1.08 for S2 and R2 = 0.88, RMSE = 2.42, and MAE = 1.08 for L8 images. Results also revealed that MNDWI, NDWI, and NDSI responded most sensitively to SMC estimation.

Assessment of Ecological Disturbance on Indian Sundarbans with Special Reference to Amphan Cyclone by Using Geospatial Technology

The mangrove ecosystem of Sundarbans region plays a significant ecological and socio-economic role in both India and Bangladesh. An increase in coastal human settlements makes the area more vulnerable to natural disasters such as tsunamis and cyclones. Mangroves, as an exclusive group of plants, provide many ecological services such as reducing the impacts of cyclonic storms and gales and minimizing the sea level rise due to global warming. Sundarbans were facing immense destruction due to super cyclones during the past years. The present study investigated the ecological disturbance caused by the cyclone Amphan in Indian Sundarbans using remote sensing multispectral imagery. The pre and post-cyclonic Landsat data were analyzed to quantify the damaged area in Sundarbans due to the effect of the cyclone, Amphan. Normalized Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were used to estimate the vegetative changes or losses that occurred due to the cyclone. Normalized Difference Water Index (NDWI), Normalized Difference Bareness Index (NDBaI) and Normalized Difference Salinity Index (NDSI)) were the other indices used to study the effects of cyclone. The study found that from pre to post-cyclonic period, mangroves reduced from 1,851.88 km2 to 1,643.74 km2 and about 50.18 km2 of vegetation becomes sparse. High salinity values were observed during the post-cyclonic period which indicated that the super cyclone resulted in saltwater intrusion. From pre to post cyclone, NDBaI results showed an increase in barren land from 908.13 km2 to 1,365.57 km2.

Remote sensing and statistical analyses for exploration and prediction of soil salinity in a vulnerable area to seawater intrusion

Soils along the Egyptian coast are vulnerable to environmental degradation and soil salinity problems. The main objective of this study is to identify and rapidly predict salt affected soils using remote sensing data and multivariate statistical analysis. To achieve this objective, the Operational Land Imager 8 (OLI) of Landsat imagery acquired in March 2022 was processed through the Maximum Likelihood classifier to assess Landscape features and to produce Normalized difference salinity index (NDSI) and normalized difference vegetation index (NDVI). Water and soil samples were collected from 13 field sites as ground truth data and to investigate representative physical and chemical properties. Linear regression model was used to predict soil salinity while soil parameters were mapped using Inverse Distance Weight (IDW) in ArcGIS 10.5. In order to explore the soil salinity content using VNIR-SWIR spectra, this work investigated the potential of Partial least squares regression (PLS regression) and SVM (Support vector machine). For simulating salinity in the investigated area, a total number of 65 different sites were identified considering that almost 75 % (50 sites) were used to develop the model and 25 % (15 sites) for validation of the established model. The results indicated that EC levels of water samples are not suitable for irrigation (> 3 mS/cm). Majority of the collected soil samples represent saline-alkaline soils. NDSI ranged from −0.83 to 0.57 with mean of −0.25. Based on the variance of components, 90 % of data were obtained from the first three PCA. The PLS model's R2 score of 0.763 and extremely low p value indicates how well it predicts soil salinity. SVM model R2, on the other hand, is 0.719. Further, Ca++ and Mg++ are the main significant parameters selected in the predicted model. This shows that remote sensing data and multivariate analysis are very important tools to map spatial variation and predict soil salinity. The developed model for salinity considered both the spectral retrieved parameters and lab analyses of cations giving higher accuracy.

Analysis of soil salinity in irrigated agricultural land using remote sensing data: case study of Chinoz district in Uzbekistan

Soil salinity is a serious agricultural concern in Uzbekistan, causing plant growth to be hampered and crop productivity to be diminished. This issue is especially prevalent in semi-desert and desert regions, compounding problems such as soil erosion, land degradation, subsidence, corrosion, and poor groundwater quality. On the other hand, Geographic Information Systems (GIS) and Remote Sensing (RS) technologies provide more efficient, cost-effective, and timely tools and procedures for mapping soil salinity. Different indices and methods can be used to detect and quantify soil salinity levels using the spectral information acquired by the Landsat-8 OLI sensor. Among these are the Normalized Difference Salinity Index (NDSI) and the Normolazed Difference Vegetation Index (NDVI). GIS software integrates satellite imagery with auxiliary data such as soil type and topography, allowing for a thorough assessment of soil salinity distribution over the research area. Compared to traditional methods, integrating remote sensing data with GIS analysis provides a more efficient and cost-effective approach to soil salinity assessment. The findings of this study will help us understand the distribution of soil salinity in the study area and provide insights for decision-making processes connected to sustainable land management.

Sentinel-2 Satellite Imagery Application to Monitor Soil Salinity and Calcium Carbonate Contents in Agricultural Fields

The estuary tides affect groundwater dynamics; these areas are susceptible to waterlogging and salinity issues. A study was conducted on two fields with a total area of 60 hectares under a center pivot irrigation system that works with solar energy and belong to a commercial farm located in Northern Sudan. To monitor soil salinity and calcium carbonate in the area and stop future degradation of soil resources, easy, non-intrusive, and practical procedures are required. The objective of this study was to use remote sensing-determined Sentinel-2 satellite imagery using various soil indices to develop prediction models for the estimation of soil electrical conductivity (EC) and soil calcium carbonate (CaCO3). Geo-referenced soil samples were collected from 72 locations and analyzed in the laboratory for soil EC and CaCO3. The electrical conductivity of the soil saturation paste extract was represented by average values in soil dataset samples from two fields collected from the topsoil layer (0 to 15 cm) characteristic of the local salinity gradient. The various soil indices, used in this study, were calculated from the Sentinel-2 satellite imagery. The prediction was determined using the root mean square error (RMSE) and cross validation was done using coefficient of determination. The results of regression analysis showed linear relationships with significant correlation between the EC analyzed in laboratory and the salinity index-2 “SI2” (Model-1: R2 = 0.59, p = 0.00019 and root mean square error (RMSE = 1.32%) and the bare soil index “BSI” (Model-2: R2 = 0.63, p = 0.00012 and RMSE = 6.42%). Model-1 demonstrated the best model for predicting soil EC, and validation R2 and RMSE values of 0.48% and 1.32%, respectively. The regression analysis results for soil CaCO3 determination showed linear relationships with data obtained in laboratory and the bare soil index “BSI” (Model- 3: R2 = 0. 45, p = 0.00021 and RMSE = 1.29%) and the bare soil index “BSI” & Normalized difference salinity index “NDSI” (Model-4: R2 = 0.53, p = 0.00015 and RMSE = 1.55%). The validation confirmed the Model-3 results for prediction of soil CaCO3 with R2 and RMSE values of 0.478% and 1.29%, respectively. Future soil monitoring programs might consider the use of remote sensing data for assessing soil salinity and CaCO3 using soil indices results generated from satellite image (i.e., Sentinel-2).

Sentinel-2 Satellite Imagery-Based Assessment of Soil Salinity in Irrigated Rice Fields in Portugal

Salinization is a major soil degradation threat in irrigated lands worldwide. In Portugal, it affects several pockets of irrigated agricultural areas, but the spatial distribution and intensity of soil salinity are not well known. Unlike conventional approaches to appraise soil salinity, remote sensing multispectral data have great potential for detecting, monitoring, and investigating soil salinity problems in agricultural areas. This study explores the assessment of soil salinity in irrigated rice cultivation fields using two types of multispectral-based indices calculated from Sentinel-2 satellite imagery: (i) vegetation indices (Normalized Difference Vegetation Index, Green Normalized Difference Vegetation Index, Generalized Difference Vegetation Index and Soil Adjusted Vegetation Index), to monitor the indirect effect of salinity on rice growth; and (ii) salinity indicators, namely those based on visible and near-infrared bands (Normalized Difference Salinity Index) and on shortwave infrared bands (Salinity Index ASTER). The data are for the Lower Mondego Valley (Central Portugal) and the period 2017–2018. Results revealed that salinity indices can be used for mapping soil salinity and constitute a valuable soil salinity assessment tool in rice cultivation areas affected by salinity issues. As there is less reported inventorying of spatial extent of such degradation in irrigated agricultural areas of Portugal, this innovative approach allowed by remote sensing technology can add to understanding the spatial extent of such areas and undertaking more such studies spatially and temporally.

Soil Salinity Prediction Using Remotely Piloted Aircraft Systems under Semi-Arid Environments Irrigated with Salty Non-Conventional Water Resources

The effects of climate change on food security have been unfavorable, particularly in the area of Murcia where there is a water shortage. To satisfy crop needs, farmers combine several irrigation water sources, such as brackish groundwater, desalinated water, reclaimed water, and desalinated water. Good agricultural and irrigation practices are essential for preventing soil salinization and production losses, and remote sensing might be used to evaluate these practices. This research, performed in an experimental field under greenhouse conditions and in an open-air commercial lettuce field irrigated with non-conventional water sources, determined that the Salinity Index (SI) applied to bare soil is a useful spectral index, providing an R2 range of 0.40 to 0.83. The other metric used to the bare soil, the Normalized Difference Salinity Index (NDSI), exhibited poor correlations, with R2 values as high as 0.49. Moreover, the thermal camera did not operate well within the greenhouse, but it performed in the commercial plot, where the canopy temperature was linearly correlated, with an R2 value of 0.50. The second analyzed vegetative metric, the Normalized Difference Plants Index (NDVI), was exclusively applied to the vegetation and showed minimal relationships with the soil salinity. In the visual evaluation of the maps, the temperature patterns of the canopy were strikingly comparable to the electrical conductivity of the soil, which was not the case for the other analyzed indices. The use of non-conventional moderately saline irrigation water sources negatively impacts the lettuce development by decreasing the fresh head weight and increasing the sodium and chloride leaf concentrations.

Semantic segmentation of soil salinity using in-situ EC measurements and deep learning based U-NET architecture

Soil salinity may occur naturally through pedogenetic processes or may result from abiotic factors resulting from human activities such as irrigation with poor quality water, lack of drainage and land development. The infertility of agricultural lands due to soil salinity brings many economic and social problems on a local and global scale. In this study, the salinity problem in the Harran Plain, one of the largest agricultural areas in Turkey, was investigated using deep learning-based U-NET algorithm. Different combinations of Normalized Difference Salinity Index (NDSI), Salinity Index I (SI), Salinity Index II (SII), and Normalized Difference Vegetation Index (NDVI) indices integrated into the RapidEye multispectral image to increase the segmentation accuracy. The most successful result (93.78% overall accuracy) was achieved when the algorithm was trained with 300 iterations and only the SII index was added to the original image. The same images were also segmented by the SVM method, and the U-NET deep learning architecture was able to detect soil salinity about 20–32% more accurately than SVM for three different 5-band test images. This study demonstrates that the delineation and mapping of soil salinity can be done automatically thanks to a deep learning (DL) network with greater accuracy, less time and effort, and without requiring continuous in-situ Electrical Conductivity (EC) measurements and repetitive supervised learning for each new satellite image.

Environmental Investigation of Bahar Al-Najaf Region Using Sentinel-2 Images

This study aims to shed light on the indicators of environmental change and environmental impact assessment during the past five years in a representative area of the western part of Iraq (BAHAR-ALNAJAF). This is to understand the leading causes that led to the environmental changes from (2016 to 2020) due to the change in land cover in the study area. The paper refers to an environmental study for the study area using satellite data within the software environment (ArcGIS) and the application of remote sensing from two aspects: Ecological indices retrieval and the monitoring environment for land cover. Remote sensing and GIS software have been utilized to categorize (Sentinel-2) imagery into seven land use and land cover (LULC) classes: cropland land, orchards land, wetland, sandy area land, mixed barren land, built-up land, and water bodies. Supervised classification and Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), and Normalized Difference Salinity Index (NDSI) were approved and utilized respectively to retrieve its class boundary. From a practical point of view, it was found that there is a rise in water levels in the Bahar Al-Najaf; this rising has led to the flooding of many built-up and vegetated lands. As a result, flooded land areas increased in 2020 to about 50% more than in 2016. Consequently, the built-up growth regions in the study area were very slow to change during the study period (2016-2020). The vegetation cover for 2020 is 56% higher than in 2016 because of the abundance of water and agricultural policy of this year.

Mapping mosquito larval densities and assessing area vulnerable to diseases transmission in Nile valley of Giza, Egypt

Diversity and abundance of mosquito larvae have significant impact on the dynamics of disease transmission. In the present study, remote sensing (RS) technology integrated with field survey and spatial analyses were utilized to map mosquito breeding habitats and assess areas vulnerable to disease transmission in the Nile Valley of Giza governorate. An entomological survey was carried out to 20 accessible breeding sites at the study area during the period 6th–9th December 2019. Mosquito larvae were collected and transferred to the laboratory for density calculation and species identifications. A calibrated Landsat OLI image was processed to produce Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Normalized Difference Moisture Index (NDMI), Normalized Difference Water Index (NDWI), Normalized Difference Salinity Index (NDSI), and Land Surface Temperature (LST). Results revealed that Culex pipiens was the most dominant species in the study region with a total percentage of 95.04%. High density of mosquito larvae was characterized by NDVI 0.12 to 0.39, NDBI − 0.20 to 0.05, NDMI − 0.05 to 0.20, NDWI − 0.34 to − 0.10, NDSI − 0.39 to − 0.12, and LST 19.30 to 24.82 ℃ which represents the optimal conditions for mosquito breeding. It could be concluded that Giza region, particularly Agouza, Kerdasa, Dokki, Bulaq Dakror and Omrania, is highly affected by Cx. pipiens, with a high potentiality of filariasis disease transmission.

Wetland change prediction of Ogun-River Basin, Nigeria: Application of cellular automata Markov and remote sensing techniques

Wetland changes are very serious ecosystem problems, which stem from unrestrained human and environmental actions. Despite significant attention in environmental management research, the means to tackle wetland changes are still gaining momentum in science and research within developing countries. With the increasing availability of remote sensing data and flexible solutions, finding the right solution to wetland changes becomes a key research and policy agenda. Herein, we assessed the wetland change prediction of Ogun River (OR) basin, Nigeria, during 1999, 2009, and 2019 by analyzing land cover change (LCC) images using cellular automata-Markov (CA-Markov) chain and remote sensing (RS) techniques. The acquired shuttle radar topographic mapper and Landsat remotely sensed data were applied to create thematic-maps of the normalized difference vegetation index (NDVI), normalized difference salinity index (NDSI), and digital elevation model (DEM) of the study area. The CA-Markov required the supervised classification of the land cover maps of 1999, 2009, and 2019. The results of the elevation, NDVI, and NDSI revealed the qualitative differences in the study area. The LCC analysis indicated that farmlands and built-up areas increased by 54.56 and 33.21 %, respectively. However, waterbodies, wetlands, and vegetation decreased by 0.42, 3.53, and 8.28 % respectively. These findings agree with the CA-Markov with an accuracy exceeding 70 %, and, thus predict the wetland changes for the year 2030. The major attributions to wetland cover variations in the study area are built-up encroachment with extensive, spontaneous, and uncontrolled agricultural activities. This study, through its findings, provides relevant guidelines and information on wetland changes required by stakeholders for environmental policy, planning, and sustainability.