Kvantificiranje stope erozije vodenog tla korištenjem pristupa RUSLE, GIS i RS za sliv rijeke Al-Qshish, Latakija, Sirija

Soil erosion is one of the most prominent geomorphological hazards threatening environmental sustainability in the coastal region of western Syria. The current war conditions in Syria has led to a lack of field data and measurements related to assessing soil erosion. Mapping the spatial distribution of potential soil erosion is a basic step in implementing soil preservation procedures mainly in the river catchments. The present paper aims to conduct a comprehensive assessment of soil erosion severity using revised universal soil loss equation (RUSLE) and remote sensing (RS) data in geographic information system (GIS) environment across the whole Al-Qshish river basin. Quantitatively, the annual rate of soil erosion in the study basin was 81.1 t ha−1 year−1 with a spatial average reaching 55.2 t ha−1 year−1. Spatially, the soil erosion risk map was produced with classification into five susceptible-zones: very low (41 %), low (40.5%), moderate (8.9%), high (5.4%) and very high (4.2%). The current study presented a reliable assessment of soil loss rates and classification of erosion-susceptible areas within the study basin. These outputs can be relied upon to create measures for maintaining areas with high and very high soil erosion susceptibility under the current war conditions.Erozija tla jedna je od najistaknutijih geomorfoloških opasnosti koja prijeti održivosti okoliša u obalnoj regiji zapadne Sirije. Trenutni ratni uvjeti u Siriji doveli su do nedostatka terenskih podataka i mjerenja vezanih za procjenu erozije tla. Kartiranje prostorne distribucije potencijalne erozije tla osnovni je korak u provedbi postupaka očuvanja tla uglavnom u riječnim slivovima. Ovaj rad ima za cilj provesti sveobuhvatnu procjenu ozbiljnosti erozije tla korištenjem revidirane univerzalne jednadžbe gubitka tla (RUSLE) i podataka daljinske detekcije (RS) u okolišu geografskog informacijskog sustava (GIS) u cijelom slivu rijeke Al-Qshish. Kvantitativno gledano, godišnja stopa erozije tla u istraživanom bazenu iznosila je 81,1 t ha−1 godina−1 s prostornim prosjekom od 55,2 t ha−1 godina−1. Prostorno, izrađena je karta rizika od erozije tla s razvrstavanjem u pet osjetljivih zona: vrlo niska (41 %), niska (40,5 %), umjerena (8,9 %), visoka (5,4 %) i vrlo visoka (4,2 %). Sadašnja studija dala je pouzdanu procjenu stopa gubitka tla i klasifikaciju područja osjetljivih na eroziju unutar istraživanog bazena. Na te se rezultate može osloniti za stvaranje mjera za održavanje područja s visokom i vrlo visokom osjetljivošću tla na eroziju u trenutnim ratnim uvjetima

Monitoring the dynamic changes in vegetation cover using spatio-temporal remote sensing data from 1984 to 2020

Anthropogenic activities and natural climate changes are the central driving forces of global ecosystems and agriculture changes. Climate changes, such as rainfall and temperature changes, have had the greatest impact on different types of plant production around the world. In the present study, we investigated the spatiotemporal variation of major crops (cotton, rice, wheat, and sugarcane) in the District Vehari, Pakistan, from 1984 to 2020 using remote sensing (RS) technology. The crop identification was pre-processed in ArcGIS software based on Landsat images. After pre-processing, supervised classification was used, which explains the maximum likelihood classification (MLC) to identify the vegetation changes. Our results showed that in the study area cultivated areas under wheat and cotton decreased by almost 5.4% and 9.1% from 1984 to 2020, respectively. Vegetated areas have maximum values of NDVI (>0.4), and built-up areas showed fewer NDVI values (0 to 0.2) in the District Vehari. During the Rabi season, the temperature was increased from 19.93 °C to 21.17 °C. The average temperature was calculated at 34.28 °C to 35.54 °C during the Kharif season in the District Vehari. Our results showed that temperature negatively affects sugarcane, rice, and cotton crops during the Rabi season, and precipitation positively affects sugarcane, rice, and cotton crops during the Kharif season in the study area. Accurate and timely assessment of crop estimation and relation to climate change can give very useful information for decision-makers, governments, and planners in formulating policies regarding crop management and improving agriculture yields

Assessment of urban growth in relation to urban sprawl using landscape metrics and Shannon’s entropy model in Jalpaiguri urban agglomeration, West Bengal, India

AbstractThe rapid urban growth and anthropogenic activities have posed a threat to the local environment and ecosystem around the world. This situation has become a hindrance to planners and policy makers for sustainable urban development. Therefore, this study mainly focuses on the assessment of urban growth patterns in relation to urban sprawl in Jalpaiguri urban agglomeration. Multi-temporal Landsat data have been used for land use change detection and urban sprawl quantification. The maximum likelihood classifier technique has been performed to create land use land cover maps for each study year (2001, 2011 and 2021). Urban expansion intensity index has been applied to determine the magnitude of urban expansion. Landscape metrics and Shannon’s entropy have been employed to assess the urban sprawl to a spatial extent. Spatiotemporal land use changes reveal that the non-urban class (vegetation, agriculture, water bodies, and fallow) have been decreasing consistently with an increase in built-up areas over time. Built-up area has increased by almost seven times in the span of the last 20 years (2001–2021). In the first decade, the growth rate of urban areas was 145.42% with a medium speed of expansion and in the next decade, it was 180.83% with a very high speed. Landscape metrics show that the fragmentation of the entire urban landscape into small patches happened from 2001 to 2011 in a higher magnitude indicating the occurrence of sprawling characteristics. But in recent times, the entire landscape is aggregating into large single urban patches which indicate a clumpy situation and would affect the local ecological environment. Shannon’s entropy model also verifies the compact urban sprawl in different directions and distances from the city centre. The understanding of urban growth dynamics and land use changes is essential for addressing the rapid urbanization within this urban region. There is an immediate need for an appropriate strategy for effective utilization of land use and monitoring of uncontrolled and haphazard urban growth. This research study would help the urban planner to take a specific scope of action for future urban growth and development

Effects of Climate Change on Streamflow in the Godavari Basin Simulated Using a Conceptual Model including CMIP6 Dataset

Hydrological reaction to climate change anticipates water cycle alterations. To ensure long-term water availability and accessibility, it is essential to develop sustainable water management strategies and better hydrological models that can simulate peak flow. These efforts will aid in water resource planning, management, and climate change mitigation. This study develops and compares Sacramento, Australian Water Balance Model (AWBM), TANK, and SIMHYD conceptual models to simulate daily streamflow at Rajegaon station of the Pranhita subbasin in the Godavari basin of India. The study uses daily Indian Meteorological Department (IMD) gridded rainfall and temperature datasets. For 1987–2019, 70% of the models were calibrated and 30% validated. Pearson correlation (CC), Nash Sutcliffe efficiency (NSE), Root mean square error (RMSE), and coefficient of determination (CD) between the observed and simulated streamflow to evaluate model efficacy. The best conceptual (Sacramento) model selected to forecast future streamflow for the SSP126, SSP245, SSP370, and SSP585 scenarios for the near (2021–2040), middle (2041–2070), and far future (2071–2100) using EC-Earth3 data was resampled and bias-corrected using distribution mapping. In the far future, the SSP585 scenario had the most significant relative rainfall change (55.02%) and absolute rise in the annual mean temperature (3.29 °C). In the middle and far future, the 95th percentile of monthly streamflow in the wettest July is anticipated to rise 40.09% to 127.06% and 73.90% to 215.13%. SSP370 and SSP585 scenarios predicted the largest streamflow increases in all three time periods. In the near, middle, and far future, the SSP585 scenario projects yearly relative streamflow changes of 72.49%, 93.80%, and 150.76%. Overall, the findings emphasize the importance of considering the potential impacts of future scenarios on water resources to develop effective and sustainable water management practices

Spatial Analysis of Flood Hazard Zoning Map Using Novel Hybrid Machine Learning Technique in Assam, India

Twenty-two flood-causative factors were nominated based on morphometric, hydrological, soil permeability, terrain distribution, and anthropogenic inferences and further analyzed through the novel hybrid machine learning approach of random forest, support vector machine, gradient boosting, naïve Bayes, and decision tree machine learning (ML) models. A total of 400 flood and nonflood locations acted as target variables of the flood hazard zoning map. All operative factors in this study were tested using variance inflation factor (VIF) values (<5.0) and Boruta feature ranking (<10 ranks) for FHZ maps. The hybrid model along with RF and GBM had sound flood hazard zoning maps for the study area. The area under the receiver operating characteristics (AUROC) curve and statistical model matrices such as accuracy, precision, recall, F1 score, and gain and lift curve were applied to assess model performance. The 70%:30% sample ratio for training and validation of the standalone models concerning the AUROC value showed sound results for all the ML models, such as RF (97%), SVM (91%), GBM (97%), NB (96%), DT (88%), and hybrid (97%). The gain and lift curve also showed the suitability of the hybrid model along with the RF, GBM, and NB models for developing FHZ maps

Spatial Analysis of Flood Hazard Zoning Map Using Novel Hybrid Machine Learning Technique in Assam, India

Twenty-two flood-causative factors were nominated based on morphometric, hydrological, soil permeability, terrain distribution, and anthropogenic inferences and further analyzed through the novel hybrid machine learning approach of random forest, support vector machine, gradient boosting, na&iuml;ve Bayes, and decision tree machine learning (ML) models. A total of 400 flood and nonflood locations acted as target variables of the flood hazard zoning map. All operative factors in this study were tested using variance inflation factor (VIF) values (&lt;5.0) and Boruta feature ranking (&lt;10 ranks) for FHZ maps. The hybrid model along with RF and GBM had sound flood hazard zoning maps for the study area. The area under the receiver operating characteristics (AUROC) curve and statistical model matrices such as accuracy, precision, recall, F1 score, and gain and lift curve were applied to assess model performance. The 70%:30% sample ratio for training and validation of the standalone models concerning the AUROC value showed sound results for all the ML models, such as RF (97%), SVM (91%), GBM (97%), NB (96%), DT (88%), and hybrid (97%). The gain and lift curve also showed the suitability of the hybrid model along with the RF, GBM, and NB models for developing FHZ maps

Pollution Source Identification and Suitability Assessment of Groundwater Quality for Drinking Purposes in Semi-Arid Regions of the Southern Part of India

The quality of groundwater plays an important role in human health, and it majorly influences the agricultural process in the southern part of India. The present study mainly focused on evaluating the quality of groundwater used for domestic purpose in semi-arid regions of the southern part of India. The samples were collected in 36 locations, covering the entire investigation zone. The collected samples were analyzed for various physical and chemical characteristics of groundwater and compared with the world health organization standards. The entropy-weighted water quality index (EWQI) of the groundwater revealed that 16.67% of the samples required primary-level treatment before they could be used for drinking purposes. About 72.23% of the samples were in the good-to-medium category for drinking purposes, as was identified through weighted overlay analysis. The ionic relationship plot was used to identify the source of contamination and it revealed that carbonate weathering and anthropogenic activities are the primary sources of groundwater contamination. The present results show the contaminated zones and offer more helpful solutions to strengthen the water management policy in the study region

An integrated quantitative and qualitative approach for landslide susceptibility mapping in West Sikkim district, Indian Himalaya

AbstractLandslides rank as the third most common natural disaster globally, and the Indian Himalaya Region is no exception, experiencing severe impacts during the rainy season. This study focuses on creating a comparative landslide susceptibility map for the West Sikkim district in India using probabilistic and heuristic approaches. The frequency ratio (FR) and information value (IV) methods are employed for the probabilistic approach, while the analytic hierarchy process (AHP) is used for the heuristic approach. Eleven factors are considered in the analysis. The resulting landslide susceptibility (LS) map demonstrates accuracies of 77% for FR, 74% for IV, and 57% for AHP methods. Preliminary qualitative risk assessment is conducted, incorporating building and population density, as population and buildings are the most vulnerable elements in the society. The LS map with the highest accuracy (from FR) serves as the landslide potential factor, combined with building and population density as the risk damage potential factors for risk zonation. The resulting risk zonation map classifies the study area into high-risk (3%), medium-risk (14%), and low-risk (83%) zones. This study primarily addresses the 3% high-risk area where landslides pose a significant threat to population and infrastructure, aiming to inform policy implementation and mitigation measures

Impact of COVID-19 Lockdown on Vegetation Indices and Heat Island Effect: A Remote Sensing Study of Dhaka City, Bangladesh

It is predicted that the COVID-19 lockdown decreased environmental pollutants and, hence, urban heat island. Using the hypothesis as a guide, the objective of this research is to observe the change in vegetation pattern and heat-island effect zones in Dhaka, Bangladesh, before and after COVID-19 lockdown in relation to different forms of land use and land cover. Landsat-8 images were gathered to determine the vegetation pattern and the heat island zones. The normalized difference vegetation index (NDVI) and the modified soil-adjusted vegetation index (MSAVI12) were derived for analyzing the vegetation pattern. According to the results of the NDVI, after one month of lockdown, the health of the vegetation improved. In the context of the MSAVI12, the highest MSAVI12 coverages in March of 2019, 2020, and 2021 (0.45 to 0.70) were 22.15%, 21.8%, and 20.4%, respectively. In May 2019, 2020, and 2021, dense MSAVI12 values accounted for 23.8%, 25.5%, and 18.4%, respectively. At the beginning of lockdown, the calculated LST for March 2020 was higher than March 2019 and March 2021. However, after more than a month of lockdown, the LST reduced (in May 2020). After the lockdown in May 2020, the highest UHI values ranging from 3.80 to 5.00 covered smaller land-cover regions and reduced from 22.5% to 19.13%. After the end of the lockdown period, however, industries, markets, and transportation resumed, resulting in the expansion of heat island zones. In conclusion, strong negative correlations were observed between the LST and vegetation indices. The methodology of this research has potential for scholarly and practical implications. Secondly, urban policymakers can use the methodology of this paper for the low-cost monitoring of urban heat island zones, and thus take appropriate spatial counter measures

Impact of COVID-19 Lockdown on Vegetation Indices and Heat Island Effect: A Remote Sensing Study of Dhaka City, Bangladesh

It is predicted that the COVID-19 lockdown decreased environmental pollutants and, hence, urban heat island. Using the hypothesis as a guide, the objective of this research is to observe the change in vegetation pattern and heat-island effect zones in Dhaka, Bangladesh, before and after COVID-19 lockdown in relation to different forms of land use and land cover. Landsat-8 images were gathered to determine the vegetation pattern and the heat island zones. The normalized difference vegetation index (NDVI) and the modified soil-adjusted vegetation index (MSAVI12) were derived for analyzing the vegetation pattern. According to the results of the NDVI, after one month of lockdown, the health of the vegetation improved. In the context of the MSAVI12, the highest MSAVI12 coverages in March of 2019, 2020, and 2021 (0.45 to 0.70) were 22.15%, 21.8%, and 20.4%, respectively. In May 2019, 2020, and 2021, dense MSAVI12 values accounted for 23.8%, 25.5%, and 18.4%, respectively. At the beginning of lockdown, the calculated LST for March 2020 was higher than March 2019 and March 2021. However, after more than a month of lockdown, the LST reduced (in May 2020). After the lockdown in May 2020, the highest UHI values ranging from 3.80 to 5.00 covered smaller land-cover regions and reduced from 22.5% to 19.13%. After the end of the lockdown period, however, industries, markets, and transportation resumed, resulting in the expansion of heat island zones. In conclusion, strong negative correlations were observed between the LST and vegetation indices. The methodology of this research has potential for scholarly and practical implications. Secondly, urban policymakers can use the methodology of this paper for the low-cost monitoring of urban heat island zones, and thus take appropriate spatial counter measures