Deep learning framework for mapping nitrate pollution in coastal aquifers under land use pressure

In recent years, rapid land use land cover (LULC) changes have continuously taken place in many regions all over the world as a result of human activities. In the present study, the changes in LULC were analyzed by means of multi-temporal remote sensing of Qena-Luxor Governorates in Egypt between 1984 and 2018. In order to map and monitor the land use land cover changes, several remotely sensed data were applied to create multi-maps using (1) the normalized difference vegetation index and (2) supervised classification of Landsat images using field chick and accuracy assessment, including field verification and Google Earth Professional. Therefore, the lands in the study area can be classified as follows: (1) agricultural lands, (2) built-up areas, (3) water bodies, (4) reclaimed lands, and (5) desert lands. The results indicate that agricultural lands grew from an average of 1238.7 km2 (9.8%) in 1984 to 1707.04 km2 (13.40%) in 2018 and urban lands increased from 345.2 km2 (2.7%) in 1984 to 445.28 km2 (3.5%) in 2019. Furthermore, the reclaimed lands increased approximately from 4379.7 km2 in 1984 (i.e., 34.4% of the total study area) to 4521.05 km2 in 2000 (35.507%). However, this class was followed by a marked decline to 4373.51 km2 (34.35%) between 2000 and 2010 and then increased to approximately 4442 km2 (34.89%) between 2010 and 2018. Desert lands (limestone plateau and some lowland desert fringes) decreased from 6635.4 km2 (52.2%) to 6003.5 km2 (47.15%). The results showed that the overall accuracy of the supervised classification of Landsat satellite images ranges from 87 to 92.5% while kappa statistics were from 0.83 to 90.

Establishing the relationship between land use land cover, normalized difference vegetation index and land surface temperature: A case of Lower Son River Basin, India

Post-liberal urban dynamics in India – The case of Gurugram, the ‘Millennium City’

The study attempted to assess land surface temperature (LST) variations in Akure, a millennium development city, and its environs, in Nigeria. The importance of LST as a vital component in global climate change cannot be over emphasized because as the greenhouse gases in the atmosphere increases, the LST also increases. Spatio-temporal assessment of LST variation is therefore becoming imperative to identify the contributing factors and the corresponding magnitude of contributions to the variation using remote sensing and GIS techniques. Landsat TM image of 1991, Landsat ETM+ image of 2002 and Landsat OLI/TIRS of 2015 were used and processed using ArcGIS 10.1, IDRISI and Erdas imagine 9.3 to generate indices such as Normalized Difference Vegetation Index NDVI, Land Use Land Cover (LULC) and Land Surface Temperature (LST). The finding showed that the changes, both spatial and temporal within the land uses influenced the temperature variations in the study area. The built-up, rock outcrops, farmland and vegetation land uses recorded mean temperature change of 4.91±0.7, 3.53±0.3, 3.14±0.2 and 1.87±0.3 respectively, with their respective yearly temperature increase estimated at 0.21°C, 0.15°C, 0.14°C and 0.08°C between 1991 and 2015. The study concludes that the observed increasing variations in LST in all the land uses has been precipitated by rapid land use conversion and modification that may have strong tendency to usher in climate related problems such as flood, human discomfort and other associated environmental hazards. An effective land use policy is therefore suggested to complement Federal Government ‘green policy’ urban environment. Keywords: LST variation, Land Use Land Cover, NDVI, GIS, Remote sensing technique, Nigeria

Hydrological modelling improvements in the assessemnt of water resources of agrarian subbasins in semiarid regions

Land Surface Temperature (LST) affects exchange of energy between earth surface and atmosphere which is important for studying environmental changes. However, research on the relationship between LST, Land Use Land Cover (LULC), and Normalized Difference Vegetation Index (NDVI) with topographic elements in the lower Himalayan region has not been done. Therefore, the present study explored the relationship between LST and NDVI, and LULC types with topographic elements in the lower Himalayan region of Pakistan. The study area was divided into North-South, West-East, North-West to South-East and North-East to South-East directions using ArcMap 3D analysis. The current study used Landsat 8 (OLI/TIRS) data from May 2021 for LULC and LST analysis in the study area. The LST data was obtained from the thermal band of Landsat 8 (TIRS), while the LULC of the study areas was classified using the Maximum Likelihood Classification (MLC) method utilizing Landsat 8 (OLI) data. TIRS collects data for two narrow spectral bands (B10 and B11) with spectral wavelength of 10.6 μm–12.51 μm in the thermal region formerly covered by one wide spectral band (B6) on Landsat 4–7. With 12-bit data products, TIRS data is available in radiometric, geometric, and terrain-corrected file format. The effect of elevation on LST was assessed using LST and elevation data obtained from the USGS website. The LST across LULC types with sunny and shady slopes was analyzed to assess the influence of slope directions. The relationship of LST with elevation and NDVI was examined using correlation analysis. The results indicated that LST decreased from North-South and South-East, while increasing from North-East and South-West directions. The correlation coefficient between LST and elevation was negative, with an R-value of −0.51. The NDVI findings with elevation showed that NDVI increases with an increase in elevation. Zonal analysis of LST for different LULC types showed that built-up and bare soil had the highest mean LST, which was 35.76 °C and 28.08 °C, respectively, followed by agriculture, vegetation, and water bodies. The mean LST difference between sunny and shady slopes was 1.02 °C. The correlation between NDVI and LST was negative for all LULC types except the water body. This study findings can be used to ensure sustainable urban development and minimize urban heat island effects by providing effective guidelines for urban planners, policymakers, and respective authorities in the Lower Himalayan region. The current thermal remote sensing findings can be used to model energy fluxes and surface processes in the study area.

For several decades, Nigerian cities have been experiencing a decline in their biodiversity resulting from rapid land use land cover (LULC) changes. Anticipating short/long-term consequences, this study hypothesised the effects of LULC variables in Akure, a developing tropical rainforest city in south-west Nigeria. A differentiated trend of urban LULC was determined over a period covering 1999–2019. The study showed the net change for bare land, built-up area, cultivated land, forest cover and grassland over the two decades to be −292.68 km2, +325.79 km2, +88.65 km2, +8.62 km2 and −131.38 km2, respectively. With a projected population increase of about 46.85%, the study identified that the built-up land cover increased from 1.98% to 48.61%. The change detection analysis revealed an upsurge in built area class. The expansion indicated a significant inverse correlation with the bare land class (50.97% to 8.66%) and grassland class (36.33% to 17.94%) over the study period. The study observed that the land consumption rate (in hectares) steadily increased by 0.00505, 0.00362 and 0.0687, in the year 1999, 2009 and 2019, respectively. This rate of increase is higher than studies conducted in more populated cities. The Cellular Automata (CA) Markovian analysis predicted a 37.92% growth of the study area will be the built-up area in the next two decades (2039). The 20-year prediction for Akure built-up area is within range when compared to CA Markov prediction for other cities across the globe. The findings of this study will guide future planning for rational LULC evaluation.

Peri-urban areas are characterised by great heterogeneity and rapid changes of land use. Furthermore, population composition changes as peri-urban areas offer attractive residential alternatives to city centres or more remote locations. The dynamic processes leave peri-urban areas in an in-between situation, neither city nor countryside and home to a range of functions, spanning from agricultural production to residential and recreational areas. The paper investigates the urbanisation of agricultural areas in the Greater Copenhagen region based on quantitative data collected on agricultural properties in nine study areas between 1984 and 2004. The overall conclusion is that agricultural land use has continued largely unaffected by the processes of urbanisation. However, most of the production is concentrated on a few very large full-time farms. In addition, the economic activities have been greatly diversified over the last three decades. The structural components of the areas (land use and landscape elements) thus appear more resilient than the socio-economic system (declining number of full-time farmers and increasing number of owners engaged in other gainful activities). However, at some point this discrepancy will disappear and rapid land use changes may be expected.

Impact of land use/land cover on groundwater resources in tropical unconfined aquifers of south-western India

Peri-urban catchment areas are increasingly susceptible to floods due to rapid land use transformations and unplanned urban expansion. This study comprehensively examines flood vulnerability in the rapidly developing peri-urban areas of North Bhubaneswar, focusing on significant changes in Land Use/Land Cover (LULC) and hydrological dynamics from 2004 to 2024, utilizing Geographic Information System (GIS) tools. The analysis reveals substantial shifts in land use patterns, with the urban footprint expanding by 71.8%, cropland decreasing by 21.7%, and forest areas by 13.6%. These changes have led to increased impervious surfaces, resulting in higher surface runoff and decreased groundwater recharge, thereby exacerbating flood risks in the region. The GRID-based vulnerability analysis classifies 90 villages within the catchment area based on their vulnerability levels, identifying 20 villages as high-risk areas requiring urgent attention, 44 villages as medium vulnerable, and 26 villages as low vulnerable. These classifications are based on factors such as proximity to drainage networks, slope, geomorphology, and LULC characteristics, with areas near drainage channels and low-lying regions being prone to flooding. The analysis integrates multiple factors to provide a comprehensive assessment of flood risk, highlighting the need for sustainable land use planning, conservation of vegetated areas, and the implementation of advanced flood prevention strategies in the peri-urban areas. Extending this research to other fringe regions could offer further valuable insights, guiding flood prevention and sustainable development strategies for areas undergoing significant land use transformations to effectively mitigate future flood risks.

The study of land use/land cover dynamics has been increasingly important in the research of earth surface natural resources. The normalized difference vegetation index (NDVI) is a widely used method for observing land use/land cover change detection. The surface land resources are easily interpreted by computing their NDVI. This study aimed at analyzing Land Use/Land Cover (LULC) changes between 1977 and 2019 in the Rangamati district, Bangladesh using reclassify the NDVI values of the Landsat satellite image and identifying the main drivers to change LULC by household survey. Five different years of Landsat images were used to extract the NDVI values January of 1977, 1989, 2000, 2011 and 2019. The NDVI values are initially computed using the user define method to reclassify the NDVI map to create land use land cover map and change detection. The highest NDVI value was found in 1977 (0.88) which indicates healthy vegetation at that time and thereafter it followed a decreasing trend (0.79 in 1989, 0.74 in 2000, 0.71 in 2011 and 0.53 in 2019) which shows a rapid vegetation cover change in the study area. Analysis of the household survey revealed that population growth, migration from plain land, rapidly urbanization, Kaptai Dam, migration policy of government, high land price, unplanned development, development of tourism industry, firewood collection and poverty have been identified as the major drivers of LULC changes in the study area. Furthermore, analysis of NDVI confirms that the forest vegetation area is being decreased and settlement area and sparseness of vegetation are being increased. The accuracy of the NDVI-based classified images is assessed, using a confusion matrix where overall classification accuracy and Kappa coefficient are computed. The overall classification accuracy was 84% - 90% with corresponding Kappa statistics of 80% - 88% for TM and OLI-TIRS images, respectively. The study serves as a basis of understanding of the LULC changes in the southeastern part of Bangladesh. Bangladesh J. Agri. 2019-2021, 44-46: 127-140

Gelephu, located in the Himalayan region, has undergone significant development activities due to its suitable topography and geographic location. This has led to rapid urbanization in recent years. Assessing land use land cover (LULC) dynamics and Normalized Difference Vegetation Index (NDVI) can provide important information about urbanization trends and changes in vegetation health, respectively. The use of Geographic Information Systems (GIS) and Remote Sensing (RS) techniques based on various satellite products offers a unique opportunity to analyze these changes at a local scale. Exploring Bhutan’s mandate to maintain 60% forest cover and analyzing LULC transitions and vegetation changes using Sentinel-2 satellite imagery at 10 m resolution can provide important insights into potential future impacts. To examine these, we first performed LULC mapping for Gelephu for 2016 and 2023 using a Random Forest (RF) classifier and identified LULC changes. Second, the study assessed the dynamics of vegetation change within the study area by analysing the NDVI for the same period. Furthermore, the study also characterized the resulting LULC change for Gelephu Thromde, a sub-administrative municipal entity, as a result of the notable intensity of the infrastructure development activities. The current study used a framework to collect Sentinel-2 satellite data, which was then used for pre-and post-processing to create LULC and NDVI maps. The classification model achieved high accuracy, with an area under the curve (AUC) of up to 0.89. The corresponding LULC and NDVI statistics were analysed to determine the current status of the LULC and vegetation indices, respectively. The LULC change analysis reveals urban growth of 5.65% and 15.05% for Gelephu and Gelephu Thromde, respectively. The NDVI assessment shows significant deterioration in vegetation health with a 75.11% loss of healthy vegetation in Gelephu between 2016 and 2023. The results serve as a basis for strategy adaption required to examine the environmental protection and sustainable development management, and the policy interventions to minimize and balance the ecosystem, taking into account urban landscape.

Soil plays a critical role in earth’s biosphere by supporting the production of food, fodder and fiber. However, rapid land use changes in recent times in different parts of the world led to increasing concern on soil health. It has been realized that changes in land use systems significantly affect soil properties. Therefore, we studied the impact of land use systems on soil physicochemical properties in the Thar Desert of India. Surface soil samples (0–30 cm) from four land use systems: (1) sand dunes, (2) grazing lands, (3) rainfed croplands and (4) irrigated croplands have been collected and analyzed in laboratory to determine soil pH, electrical conductivity (EC), CaCO3 content, organic carbon content, available P content, available K content and micronutrients (Zn, Fe, Mn, and Cu) content. We observed higher clay, organic carbon and nutrient contents and lower bulk density values in irrigated croplands than in other land use systems. Soil pH and EC were higher in irrigated croplands than in the other land use systems. Principal component analysis of soil physicochemical properties revealed two major soil factors, the clay–carbon factor and salinity factor, which were able to significantly differentiate the land use systems. For irrigated croplands, the clay–carbon factor was found to be higher than the rest of the land uses; however, the salinity factor was the lowest. Higher values of these two factors will lead to a favorable soil physicochemical environment for plant growth or better soil health. These two factors may further be used for assessing the impact of land use systems on soil quality in other regions.

Land use patterns and consumption occur widely due to fast industrialization and development in the previous several decades, which might lead to problems such as over-exploitation of land resources, food shortages, and pollution. Monitoring and subsequent modeling of land use land cover (LULC) changes has become critical. A study of the variations in the LULC pattern of the Baghpat District of Uttar Pradesh, India, was attempted. This study assessed spatial patterns and fluctuations in growth in the Baghpat District of Uttar Pradesh (India) from 1991 to 2021. The study also analyzed the land cover changes and their effects on land surface temperature (LST), normalized difference vegetation index (NDVI), and soil indices in the Baghpat district. Decadal land use and land cover (LULC) changes were analyzed using Multitemporal Landsat Imagery and applying the maximum likelihood classifier in ENVI (Image Processing Software). Post-classification spatial measures were used to examine changes in LULC and the spatial distribution of urban growth, as well as to identify changes using ArcMap (GIS Software) across the period. Various AI techniques were used to show the trend variation in NDVI, LST, and SAVI indices using IBM-SPSS, Microsoft Office, OriginLab, and MATLAB for the study area to comprehend the variation in the index within the given period. The findings indicated significant improvements in agriculture between 1991 and 2021 (from 58.94 to 84.79%), but significant declines in vegetation cover (from 29.53 to 1.14%). The yearly percentage growth of the parallel built-up area was 3.77%, 5.59%, 6.71%, and 6.90%, respectively. Approximately 43.85% of the increase in agricultural land between 1991 and 2021 came from the conversion of vegetation covers, which fell by 96.13%. The analysis of LST, NDVI, and SAVI data revealed a substantial negative association for all years, except a slight positive correlation. NDVI and SAVI values were highest in agricultural fields with the lowest LST values, whereas fallow land regions exhibited the reverse pattern. With the help of these findings, urban planners and designers may reduce various socio-economic and environmental consequences.

Surface water quality deterioration is mainly occurring due to anthropogenic activities at an alarming rate in developing countries. Jharkhand has been undergoing exponential urbanisation and mining, causing immense surface water pollution and water stress. The state is heavily dependent on artificial dams for its daily water supply demands. Therefore, an effort is made to monitor and ascertain the surface water quality and the influence of nearby land use pattern on water quality, in the selected five dams, namely, Hatia dam, Kanke dam, Getalsud dam, Galudih barrage, and Chandil dam are done. These dams are built on the Subarnarekha river basin, located in the Jharkhand state on a seasonal basis and associated land use land cover (LULC) changes, changes in vegetation cover using normalised difference vegetation index (NDVI) and water body changes using normalised difference water index (NDWI) that have occurred in a 5-year gap i.e. 2016 and 2021. The secondary data for the year 2016 was obtained from the Jharkhand pollution control board report published by the government of Jharkhand, India. For the year 2021, the samples were collected from sampling sites for pre, post and monsoon seasons. The chemical analysis of collected water samples was done in the laboratory for parameters like pH, dissolved oxygen, biological oxygen demand, total calcium and magnesium, hardness, total dissolved and suspended solids, alkalinity, chlorine etc. and compared with the standard values prescribed by world health organisation (WHO) and Indian standards (IS) 10500:2012. The seasonal water quality status was analysed using the water quality index (WQI) for the pre, post and monsoon seasons of 2016 and 2021. Then, the use of supervised classification method for land use land cover (LULC), normalised difference vegetation index (NDVI) and normalised difference water index (NDWI) was opted to understand the relation between the change in water quality and quantity concerning its land use and land cover, by comparison of results from the year 2016 to 2021. LULC were found using the supervised maximum likelihood classification method in ArcGIS and its accuracy was checked using the kappa accuracy method, which was found to be varying from 87 to 95% for all sites. The results showed that the overall water quality varied from good to poor indicating that it can be used for human activities but may need pre-treatment before drinking. NDWI showed a massive increase in severe drought areas for Hatia, Kanke, Chandil and Galudih barrage, whereas moderate drought regions increased for Hatia, Getalsud, and Kanke. NDVI showed dense and moderate vegetation both decreased massively for all the dam sites indicating an alarming situation and the need to adopt better land management practices.