GIS-Based Analysis of Wildfire Distribution Across Slopes in the Wadi Safsaf Watershed, Northeastern Algeria

Estimation of land surface temperature and LULC changes impact on groundwater resources in the semi-arid region of Madhya Pradesh, India

Sylhet Sadar suffers from excessive rainfall causing the enormous soil loss in this area. C-factor is one of the most important parameter of the Universal Soil Loss Equation (USLE) and revised version of USLE for estimating soil loss. The current study was conducted for determining the crop management factor (C) using remote sensing (RS) and geographical information system (GIS) tools. Satellite images (Landsat 7 ETM+ of 2000, 2010 and Landsat 8 OLI-TIRS of 2020) of the study area were analyzed using ArcGIS 10.5 and ENVI 5.1 software to generate maps for land use and land cover (LULC), normalized differential vegetative index (NDVI) and crop management factor (C). Results showed that the percentage of each LULC classes from 2000 to 2020 was changed over time. There was rapid growth in crop land, water bodies and settlement areas between 2000 and 2020 while the same period observed lessening in vegetation coverage. The relationship between C-factor and vegetation indices such as NDVI was identified. As NDVI and C-factor values are correlated, both lowest and highest NDVI (-0.506–0.629) and C-factor value (0.185–0.753) was found at the same year (2000). By the next 20 years in 2020, the highest NDVI value reduced to 0.373 and lowest C-factor value increased to 0.314 indicates that vegetation coverage of the study area is decreasing. To ensure proper soil management it is therefore imperative to encourage forest regeneration activities, tree planting exercises, crop rotation, conservation tillage practices and shrub growth schemes.

Wind speed profile has a significant role in environmental transport phenomena whereby Roughness length is a governing factor that determines wind speed profile. Normally, relatively crude estimates of roughness length are used in global climate models based on constant values for each specific land cover category. However, a significant variation may exist within any one of these land cover categories. For this reason, it is necessary to derive detailed roughness length distribution over areas under consideration. Satellite observations can be used to assess the details of roughness length distribution. The aim of the present study was to develop method for roughness length assessment based on remotely sensed Normalized Difference Vegetation Index (NDVI) and landscape images for the entire Iraq area. The NDVI was generated using MODIS data (MOD13Q1) at 250 m resolution acquired on 23th April 2015 for whole Iraq. And the landscape images were classified based on land use and land cover (LU/LC) that relates general classes of LU/LC and values of roughness length coefficient. Taking into consideration a specific range of NDVI values of each landscape category, a correlation analysis was used to determine the aerodynamic roughness between the values of (NDVI) and the aerodynamic roughness based on the European Wind Atlas classification and the Royal Netherlands Meteorological Institute. A quantitative relationship was set up to retrieve the aerodynamic roughness length from MODIS data (MOD13Q1) where R2 = 0.96. Experiments prove that the proposed methodology can provide accurate roughness length estimations for the spatial and temporal analysis of land surface. The findings of this study will enhance sustainability in Iraq and many other regions of the world, thus, supporting the Sustainable Development Goals (SDGs) through the establishment of wind farms especially in countries with coastal areas that can serve as a major source of the national electricity.

Planning, managing, and developing sustainably all depend on mapping and monitoring changes in land use and land cover (LULC) in Phu Tho Province, Vietnam. In this study, remote sensing and geographic information system (GIS) techniques were employed to track LULC changes in the study area from 1992 to 2022. The supervised classification method in ArcGIS 10.8 software was applied to Landsat satellite data (Landsat 5-TM for 1992 and 2010, and Landsat 9-OLI/TIRS for 2022) to detect and classify five main LULC classes: agricultural land, barren land, vegetation, built-up, and waterbodies. The accuracy of the classification was assessed using kappa coefficients, which were found to be 0.882, 0.891, and 0.915 for the years 1992, 2010, and 2022, respectively. During the period 1992-2022, the areas of agricultural land, built-up, and waterbodies increased by 148.84 km2, 304.15 km2, and 41.04 km2, respectively. However, the areas of barren land and vegetation decreased by 289.39 km2 and 204.64 km2, respectively. Furthermore, the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) were utilized to rapidly assess changes in LULC, and their trends were found to be consistent with the supervised classification results. These changes in LULC pose a significant threat to the environment, and the findings of this study can serve as a valuable resource for future land use planning and management in the area.

Despite the rapid economic and population growth, the risks related to the current dynamics of land use and land cover (LULC) have attracted a lot of attention in Ethiopia. Therefore, a complete investigation of past and future LULC changes is essential for sustainable water resources and land-use planning and management. Since the 1980s, LULC change has been detected in the upper stream of the Awash River basin. The main purpose of this research was to investigate the current dynamics of LULC and use the combined application of the cellular automata and the Markov chain (CA–Markov) model to simulate the year 2038 LULC in the future; key informant interviews, household surveys, focus group discussions, and field observations were used to assess the consequences and drivers of LULC changes in the upstream Awash basin (USAB). This research highlighted the importance of remote sensing (RS) and geographic information system (GIS) techniques for analyzing the LULC changes in the USAB. Multi-temporal cloud-free Landsat images of three sequential data sets for the periods (1984, 2000, and 2019) were employed to classify based on supervised classification and map LULC changes. Satellite imagery enhancement techniques were performed to improve and visualize the image for interpretation. ArcGIS10.4 and IDRISI software was used for LULC classification, data processing, and analyses. Based on Landsat 5 TM-GLS 1984, Landsat 7 ETM-GLS 2000, and Landsat 8 2019 OLI-TIRS, the supervised maximum likelihood image classification method was used to map the LULC dynamics. Landsat images from 1984, 2000, and 2019 were classified to simulate possible LULC in 2019 and 2038. The result reveals that the maximum area is covered by agricultural land and shrubland. It showed, to the areal extent, a substantial increase in agricultural land and urbanization and a decrease in shrubland, forest, grassland, and water. The LULC dynamics showed that those larger change rates were observed from forest and shrubland to agricultural areas. The results of the study show the radical changes in LULC during 1984–2019; the main reasons for this were agricultural expansion and urbanization. From 1984 to 2019, agriculture increased by 62%, urban area increased by 570.5%, and forest decreased by 88.7%. In the same year, the area of shrubland decreased by 68.6%, the area of water decreased by 65.5%, and the area of grassland decreased by 57.7%. In view of the greater increase in agricultural land and urbanization, as well as the decrease in shrubland, it means that the LULC of the region has changed. This research provides valuable information for water resources managers and land-use planners to make changes in the improvement of future LULC policies and development of sub-basin management strategies in the context of sustainable water resources and land-use planning and management.

Rapid urbanization and industrialization cause land use changes, reduce green spaces, and increase the land surface temperature. Green spaces of a city area maintain environmental quality by absorbing air pollutants and reducing land surface temperature. The present study aimed to detect the changes in land use and land cover (LULC), normalized difference vegetation index (NDVI), and land surface temperature (LST) in the Narsingdi district including six Upazilas from 2001 to 2021. The Landsat 7- Enhanced Thematic Mapper Plus (ETM+), the Landsat 8- Operational Land Imager (OLI) imageries and the Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to analyze the LULC, NDVI, and LST by using Google Earth Engine (GEE) and ArcGIS 10.8.2. The images were analyzed into four land use classifications – agricultural land, built-up area, forest vegetation, and water body. Among all the Upazilas, Narsingdi Sadar was the vulnerable area, where agricultural land, forest coverage, and water body decreased significantly by 6.67%, 2.2%, and 4.66%, respectively. The built-up area increased by a considerable amount of 13.53% during the last twenty years. The forest coverage of Narsingdi Sadar Upazila was calculated at only 8.91% in 2021 and decreased from 11.11% in 2001. The NDVI values surprisingly increased in all the Upazilas, but in Narsingdi Sadar, it is comparatively low (0.08) than in other Upazilas. The increasing trend in LST in the Narsingdi Sadar Upazila is alarming, 1.14oC from 2001 to 2021 (0.57oC/decade). The significant changes in LULC, NDVI, and LST made the Narsingdi Sadar a more critical area in the Narsingdi district. The findings of the study will be helpful to policymakers in making appropriate decisions in future city development. J. of Sci. and Tech. Res. 5(1): 93-118, 2023

Landslide poses severe threats to the natural landscape of the Lesser Himalayas and the lives and economy of the communities residing in that mountainous topography. This study aims to investigate whether the landscape change has any impact on landslide occurrences in the Kalsi-Chakrata road corridor by detailed investigation through correlation of the landslide susceptibility zones and the landscape change, and finally to demarcate the hotspot villages where influence of landscape on landslide occurrence may be more in future. The rational of this work is to delineate the areas with higher landslide susceptibility using the ensemble model of GIS-based multi-criteria decision making through fuzzy landslide numerical risk factor model along the Kalsi-Chakrata road corridor of Uttarakhand where no previous detailed investigation was carried out applying any contemporary statistical techniques. The approach includes the correlation of the landslide conditioning factors in the study area with the changes in land use and land cover (LULC) over the past decade to understand whether frequent landslides have any link with the physical and hydro-meteorological or, infrastructure, and socioeconomic activities. It was performed through LULC change detection and landslide susceptibility mapping (LSM), and spatial overlay analysis to establish statistical correlation between the said parameters. The LULC change detection was performed using the object-oriented classification of satellite images acquired in 2010 and 2019. The inventory of the past landslides was formed by visual interpretation of high-resolution satellite images supported by an intensive field survey of each landslide area. To assess the landslide susceptibility zones for 2010 and 2019 scenarios, the geo-environmental or conditioning factors such as slope, rainfall, lithology, normalized differential vegetation index (NDVI), proximity to road and land use and land cover (LULC) were considered, and the fuzzy LNRF technique was applied. The results indicated that the LULC in the study area was primarily transformed from forest cover and sparse vegetation to open areas and arable land, which is increased by 6.7% in a decade. The increase in built-up areas and agricultural land by 2.3% indicates increasing human interference that is continuously transforming the natural landscape. The landslide susceptibility map of 2019 shows that about 25% of the total area falls under high and very high susceptibility classes. The result shows that 80% of the high landslide susceptible class is contained by LULC classes of open areas, scrubland, and sparse vegetation, which point out the profound impact of landscape change that aggravate landslide occurrence in that area. The result acclaims that specific LULC classes, such as open areas, barren-rocky lands, are more prone to landslides in this Lesser Himalayan road corridor, and the LULC-LSM correlation can be instrumental for landslide probability assessment concerning the changing landscape. The fuzzy LNRF model applied has 89.6% prediction accuracy at 95% confidence level which is highly satisfactory. The present study of the connection of LULC change with the landslide probability and identification of the most fragile landscape at the village level has been instrumental in delineation of landslide susceptible areas, and such studies may help the decision-makers adopt appropriate mitigation measures in those villages where the landscape changes have mainly resulted in increased landslide occurrences and formulate strategic plans to promote ecologically sustainable development of the mountainous communities in India's Lesser Himalayas.

Population growth and agricultural expansion cause major changes in land use and land cover (LULC) in Ethiopia. Cultivated lands are mostly expanding without land suitability evaluation. Consequently, crop yields are not increasing as expected. This is particularly the case in the highland catchments draining toward Lake Tana, where severe consequences such as deforestation and the degradation of soil and land can be observed. In this study, the impacts of long-term LULC dynamics on the land suitability potential for selected major crops in three sub-catchments of Lake Tana, Ethiopia (Gilgelabay, Gumara and Ribb), were evaluated. Time series of Landsat images from three periods (1988, 1998, and 2017) were classified. Land suitability was analyzed via a multi criteria approach based on spatial input data such as elevation, soil, and slope maps. The overall accuracy for all LULC classifications was good to very good (89.7% to 91.6%). Five major LULC classes were distinguished: agriculture, forest, shrub/bushland, grassland, and water. In all three catchments, the results revealed that agricultural land was the dominant land cover that expanded at the expense of the other land cover types to 80%-90% in all catchments in 2017. The rate of change in agricultural land in the Gilgelabay catchment (4041.3 ha/yr) was greater than that in the Gumara (1374.5 ha/yr) and Ribb (1362.3 ha/yr) catchments. This is possibly due to the availability of other LULC classes. The natural vegetation of Gilgelabay, Gumara, and Ribb has decreased by 16.0%, 10.5%, and 1.1%, respectively, over the past three decades. However, the present LULC change trends are unsustainable, and any remaining natural vegetation should be maintained. The results from the land suitability analysis revealed that the land suitability for teff, corn, and rice is likely to change with climate change in the future. To ensure sustainable land use management, modifying land use on the basis of land suitability should be preferred over traditional practices to improve crop production. This can be achieved in close collaboration with all stakeholders, including local communities, the government, and NGOs.

Understanding changes in land use and land cover (LULC) is crucial for effective land management, environmental planning, and decision-making. It helps identify areas of environmental concern, assess the impacts of human activities on ecosystems, and develop strategies for conservation efforts and sustainable land use. In this study, remote sensing and geographic information systems (GIS) were used to monitor LULC changes in Binh Duong province, Vietnam from 1988 to 2023. The supervised classification method in ArcGIS 10.8 software was applied to Landsat satellite data (Landsat 5-TM for 1988 and 2004, and Landsat 9-OLI/TIRS for 2023) to detect and classify five main LULC types: arable land, barren land, built-up areas, forests and waterbodies. The classification accuracy was evaluated using kappa coefficients, which were 0.877, 0.894 and 0.908 for 1988, 2004, and 2023, respectively. During the period of 1988–2023, the forest, barren land, and waterbodies class areas decreased by 560.55 km2, 200.04 km2, and 19.68 km2, respectively. Meanwhile, the arable land and built-up areas classes increased by 343.80 km2 and 436.47 km2, respectively. Furthermore, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) were used to quickly assess changes in LULC, and their trends were found to be consistent with the supervised classification results. These changes in LULC pose significant threats to the environment and the findings of this study can serve as valuable resources for future land management and planning in the region.

Introduction: Land use and Land cover (LULC) are now major worldwide issues. The need for land is growing due to urbanisation and industrialisation, thus to meet this need, forest and vegetation land are transformed to open land that is either utilised for colonisation of urban areas or industrial usage. Patents are done on the calculation of LST. Method: The study aims to provide a detailed analysis of land and temperature change with variation in Normalized difference vegetation index (NDVI) and normalized difference build-up index (NDBI) for the study area using a geospatial technique. The LULC classification is performed based on four classes which are Bare land, Built-up, Vegetation, and Waterbodies from the year 2000 to 2020. The classified data is further used to extract the Land Surface Temperature (LST) data from the thermal band to generate LST maps. The NDVI and NDBI maps are also generated using the land sat imageries. From the above-mentionedanalysis, it is found that Nagpur city temperature has risen by 3.67 °C in two decades. Whereas, LULC results show that bare land and vegetation decreased by 11.88% and 14.93% respectively, while an increase is seen for built-up and water bodies by 25.62% and 0.19% respectively. Result: Regression analysis between temperature and NDVI, NDBI shows that temperature and NDVI have a negation relation and NDBI has a positive relation with temperature (Pearson’s r: between -0.89 to -0.81and between 0.90 to 0.81respectively) for both the years. The increased temperature is a result of urbanization in the study area. The study reveals that for assessment of LULC and LST with the incorporation of GIS and Remote sensing can be effective and swift. Conclusion: This study recommends that policymakers develop policies that should minimize the transition of different classes and check the outcome of industries and the temperature of the surroundings.

The application of remote sensing (RS) and Geographic Information System (GIS) have become an effective tool, which is labor, cost and time effective to assess and widely used in detecting, monitoring environmental change on the earth surface. Moreover, it can be used to study a pattern of land use and land cover (LULC) in the past, present and future. For this study, Landsat imageries were used as a data to deal with the assessment of land use and land cover changes (LULCC) before and after 3 flooded periods in 1999, 2006 and 2013. The images were used to create a false color composite and classified by supervised classification process, it can be identified as 9 LULC types, which consist of paddy field, field crop, para rubber, orchard, aquaculture, forest, mangrove, urban and built-up area, including water body. According to the result of this study, LULC types which mainly cover in the watershed are forest and orchard. After that, LULC were reclassified into 4 main classes for change detection, comprising of agricultural, forest, urban and built-up, and water bodies. The results showed LULC transformation mainly occur among agricultural land into the urban built-up area in the period 1999, 2006, and 2013. As a result, this study found that the socioeconomics factor plays an important role in LULC in Chanthaburi watershed. The results of this study can be used as data for making decision and planning LULC management, also in disaster response planning and flood risk management.

Anthropogenic land alterations in Maun village have transformed natural vegetation into urban infrastructure, including pavements, and residential and commercial areas, leading to elevated Land Surface Temperature (LST). This urban expansion resulted from economic growth driven by population increase and tourism-related development. This study aims to evaluate the relationship between Land Use and Land Cover Changes (LULCCs) and LST. Utilising Landsat 5-TM and Landsat-8 data from 1990, 2000, and 2020, we employed a random forest algorithm for supervised classification, generating Land Use and Land Cover (LULC) maps. The mono-window algorithm was used to extract LST data from Landsat 5 and 8 images, alongside Normalised Difference Vegetation Index (NDVI) maps. s regression analysis assessed the LST-NDVI correlation. Results indicate that urban LULCCs significantly contribute to rising LST. Minimum and maximum LST values for 1990, 2000, and 2020 were 18.6°C, 22.8°C, 22.6°C, and 26.7°C, 34.5°C, and 42.1°C, respectively. NDVI values ranged from −0.2 to 0.56 in 1990, −0.17 to 0.58 in 2000, and 0.07 to 0.46 in 2020. Roads, pavements, barren land, and built-up areas displayed the highest LST (44.6°C), while water bodies and healthy vegetation exhibited the lowest (16.1°C). Additionally, NDVI exhibited a negative correlation with LST. Our findings emphasise the role of human activities in exacerbating LST. They highlight the need for regulated urban growth patterns to ensure sustainable development. Moreover, quantifying spatiotemporal variations in LULC, LST, and NDVI holds importance for conserving land resources and enhancing land use planning policies. Policymakers and city planners can utilise this research to mitigate heat stress effects and promote sustainable urban environments by evaluating distribution maps of LULC, NDVI, and LST.

Increasing anthropological and economic activity has transformed human–environment connections. Using remote sensing and geographic information system (GIS), severe issues connected to rapid growth, such as supplemental infrastructure, informal residents, demolition of ecological construction, shortage of natural resources, and environmental contamination, have been studied for a fast-growing metropolis like Delhi. Industrialization accelerates urbanization, resulting in land transformations, which are one of the major uses of natural resources. In a fast-growing city like Delhi, the development has been rapid, and it is important to investigate the factors behind these shifts. For the present study, remote sensing and GIS models are used for land use and land cover (LULC) change detection. The applied model in the study uses the satellite datasets of two different satellites, i.e., Landsat 5 (TM) of 2010 and Landsat 8 (OLI) of 2021, from November with little or no cloud cover, which could block the LULC features. The study compares the temporal LULC map and analyzes the change and increase in urbanization for two periods, 2010 and 2021. In the study, it is revealed that urbanization causes constant shifts in vegetation, built-up area ratio, and land-use patterns. To confirm the same, indices like the normalized difference vegetation index and the normalized difference building index are also studied. Inequitable land use is a major contributor to environmental degradation. The spatial datasets from two time periods used in the study and the database results are helpful in extensive LULC investigations, land use planning, spatial growth, and urbanization patterns in the NCT of Delhi. Further, the change detection model used in the study was supported by the standard accuracy assessment of the Kappa coefficient. Overall accuracy in 2010 was 89.52% with a Kappa statistic of 0.863 and 89.92% with a Kappa statistic of 0.868 in 2021. Such studies using remote sensing and GIS are extremely helpful in understanding and monitoring urban sprawl patterns.

Land use and land cover (LULC) change analysis is a critical instrument for studying urban growth across the world. Our objectives were to produce historical LULC maps during the 1988–2016 period for spatial and temporal analysis, forecast future LULC until 2040 by using the Markov model, and identify the impact of LULC on urbanization. Two scenes of Landsat-5 TM for 1988 and 2001 and one scene of Landsat-8 OLI for 2016 were processed and used. The Normalized Difference Vegetation Index (NDVI) model with precise class value ranges was applied to produce land cover maps with six classes of water, built-up, barren land, shrub and grassland, sparse vegetation, and dense vegetation. LULC maps for the years of 1988 and 2001 were used to develop an LULC transformation matrix. It was used to drive an LULC transformation probability matrix using a Markov model for future forecasting of LULC in 2014, 2027, and 2040. The accuracy of 2016 LULC classes was estimated by comparing it against Markov modeled classes. It was found that the areas for: (i) water decreased from 1.43% to 0.51%; (ii) built-up increased from 9.58% to 20.80%; (iii) barren land decreased from 29.50% to 13.40%; (iv) shrub and grass land decreased from 30.57% to 21.10%; (v) sparse vegetation increased from 18% to 20.10%; and (vi) dense vegetation increased from 10.57% to 24.10%. The variations in LULC classes could be noticed by 2040 as compared to 1988. This LULC variation revealed that the water could decrease to 5.32 km2 from 25.37 km2; the built-up could increase to 625.16 km2 from 168.29 km2; the barren land could decrease to 137.53 km2 from 514.13 km2; the shrub and grassland could decrease to 297.68 km2 from 539.46 km2; the sparse vegetation could decrease to 297.68 km2 from 539.46 km2; and the dense vegetation could increase to 409.65 km2 from 191.51 km2. The LULC classification accuracy was 90.27% and 95.11% for 1988 and 2001, respectively. The co-efficient of determination (R2) was found to be 0.90 for 2016 LULC classes obtained from Landsat-8 and derived from a Markov model. For District Lahore, the LULC changes could be related to increasing population and intense migration trends, which had progressive impact on infrastructure development, industrial and economic growth, and detrimental effects on water resources.

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES