Multi-temporal Landsat Imagery Research Articles

Patterns of Urban Sprawl and Agricultural Land Loss in Sub-Saharan Africa: The Cases of the Ugandan Cities of Kampala and Mbarara

Sub-Saharan Africa (SSA) is undergoing rapid urbanization, yet research comparing urban expansion and agricultural land loss in peri-urban areas is scarce. This study utilizes multi-temporal Landsat imagery to examine the impact of urban growth on agricultural land and fragile ecosystems in Kampala (a mega city) and Mbarara (a regional urban center) in Uganda. We distinguish between random and systematic land-use and land-cover (LULC) transitions in the landscape. The results reveal substantial urban expansion. Kampala’s urban area surged from 7.14% in 1989 to 55.10% in 2015, while Mbarara increased from 6.37% in 2002 to 30.95% in 2016. Correspondingly, agricultural land decreased, from 48.02% to 16.69% in Kampala, and from 39.92% to 32.08% in Mbarara. Notably, a significant proportion of urban growth in both cities encroached upon agricultural land (66.7% in Kampala and 57.8% in Mbarara). The transition from agricultural to built-up areas accounted for 14.72% to 28.45% of the landscapes. Additionally, unsustainable practices led to the conversion of wetlands and forests to agricultural land, with approximately 13% of wetlands and 23% of Savannah and forests being converted between 2001 and 2015. These findings underscore the necessity of monitoring LULC changes for sustainable urban growth management, emphasizing the importance of preserving agricultural land and ecosystems to ensure present and future food security. This research contributes to the understanding of urbanization’s impact on peri-urban agricultural land and ecosystems in SSA, providing insights that are crucial for informed urban planning and policy formulation aimed at sustainable development in the region.

Change analyses and prediction of land use and land cover changes in Bernam River Basin, Malaysia

Land use and land cover (LULC) change is a dynamic process which is significantly influenced by anthropogenic activities. Analysing historical LULC trends and predicting future dynamics is critical to provide insights for decision-makers and planners aiming for sustainable land management and development. This study focuses on the Bernam River Basin (BRB). It employs an integrated approach that combines the Multi-Layer Perceptron (MLP), the Cellular Automata (CA)-Markov algorithm, remote sensing, and Geographical Information System (GIS) techniques. Using multi-temporal 10m resolution Sentinel-2 Landsat imagery from 2010, 2020, and 2022, the study classified LULC into seven categories: water, forest, wetlands, agriculture, urban, barren, and rangeland areas. Change analysis from 2010 to 2020 was conducted, with 2022 validating predicted LULC transitions. The MLP model, trained on land change driver variables, facilitated the generation of transition potentials for simulating future LULC changes. A spatially explicit CA-Markov model implemented LULC change projections for 2022, 2025, 2050, and 2075, based on the transition potentials. The analysis reveals an annual increase of 0.24% in water, 0.61% in forest, and 2.11% in urban areas, while wetlands (2.69%), agriculture (2.47%), barren (3.51%), and rangeland (4.58%) experienced declines. The CA-Markov approach accurately predicted LULC transitions for 2022, validated through an error matrix with an overall accuracy of 91.56% based on 450 sampling points. Predictions for 2025–2075 indicate rising trends in water (1.76%), wetlands (29.18%), agriculture (60.08%), urban (96.53%), barren (0.59%), and rangeland areas (3.57%). Forests are expected to decrease by 12% (261.52 km2). The study identified agriculture and urban expansion as the primary drivers of LULC changes in the river basin. These findings provide critical information for regional authorities to formulate evidence-based policies and management strategies, ensuring the environmental sustainability of BRB. Furthermore, these predicted LULC patterns can be integrated into complementary models, such as the Soil and Water Assessment Tool, to assess the impacts of LULC changes on water resources.

Statistical analysis of earth observing data for physicochemical water quality parameters estimation for Lake Beseka, Northern main Ethiopian rift, Ethiopia

ABSTRACT Water quality deterioration in the Main Ethiopian Rift Lakes is one of the problems affecting the health and socioeconomic development of the area. This study was aimed to develop a method to assess the water quality change of Lake Beseka using satellite image reflectance data and observed data. Multi-temporal Landsat imagery and three variables were used to assess the physicochemical environment: pH, EC, and TDS. Linear regression and correlation were accomplished between observed water quality parameters and surface reflectance of different band operations. The best-fitted linear regression equation for pH was found on SWIR1 while for EC and TDS, the blue/green band ratio was highly fitted. The Pearson coefficients of correlation for pH, EC, and TDS were − 0.79, 0.86, and 0.85, respectively. The RMSE and p-value for validation analysis were 0.25/0.005, 548/0.003, and 367/0.004 for pH, EC, and TDS, respectively. The estimated pH, EC, and TDS were higher in the central portion of the lake grouped under brackish water and lower in the southwestern, southern, and northeastern shores. The estimated EC and TDS for the years 2018 and 2021 were relatively lower than in 2007 and 2023. The spatiotemporal lake water variations were due to anthropogenic and geogenic factors including hot springs and groundwater discharge to the lake, water-level rise and depth variation, and evaporation. These findings are helpful for understanding linear regression equations that developed from surface reflectance and observed data, which are essential to estimating and monitoring water quality change in a cost-effective and time-saving manner.

Employing post classification comparison to detect land use cover change patterns and quantify conversions in Abakaliki LGA Nigeria from 2000 to 2022

Rapid urbanization is restructuring landscapes across sub-Saharan Africa. This study employed post-classification comparison of multi-temporal Landsat imagery to characterize land cover changes in Abakaliki Local Government Area, Ebonyi State, Nigeria between 2000 and 2022, addressing the need for empirical baselines to guide sustainable planning. Four classes were considered and images classified with overall accuracy of 95% for the year 2000 and 97% for the year 2022. Notably, 21,000 hectares of vegetation were lost, while built-up and bare land increased by 7500 and 13,700 hectares respectively. Spatial patterns revealed built-up encroachment from vegetation and bare land; this establishes the first standardized quantification of Abakaliki LGA’s shifting landscape, with results supporting compact development models while conserving ecological services under ongoing transformations. The study makes a significant contribution by establishing an empirical baseline characterizing Nigeria's urbanization trajectory essential for evidence-based stewardship of regional resources and livelihoods in a period of accelerating change.

Application of Multi-Temporal Landsat Imagery and GIS in Analyzing Land Use/Cover Changes in Abakaliki Local Government Area, Ebonyi State, Nigeria From 2000 to 2022

Land use land cover (LULC) change analysis is critical for understanding the effects of human activities on the environment. This study applied object-based image classification to High-Resolution Multi-Temporal Landsat imagery to analyse the LULC patterns in Abakaliki Local Government Area, Ebonyi State, Nigeria between 2000 and 2022. . Classification accuracies were validated using ground-reference data, yielding overall accuracy exceeding 95% for both time periods. Results revealed significant alterations in LULC composition over the 22-year interval. Specifically, vegetation cover declined substantially from 65.1% to 25.54% as bare land and built-up area expanded dramatically, increasing their coverage by over 25% each. These quantified shifts provide clear evidence of intensive urbanization and associated deforestation impacts. The high-fidelity LULC maps produced establish an empirical baseline for ongoing monitoring of environmental changes in the study area. Discriminating four classes with high classification performance (user's/producer's accuracy 87-100%) confirms the robustness of the object-based methodology. Key recommendations stemming from this research include leveraging the spatial datasets to model ecological effects and inform conservation planning through evidence-based strategies. Regular repetition of the mapping process is also advised to continuously track landscape transformations, assess policy interventions and guide development initiatives amid ongoing urban growth pressures across Ebonyi State, Nigeria.
 
 Received: 10 January 2023 / Accepted: 25 February 2024 / Published: 6 March 2024

Urbanisation in Sub-Saharan Cities and the Implications for Urban Agriculture: Evidence-Based Remote Sensing from Niamey, Niger

Urbanisation is the process whereby cities are transformed into large sprawling areas. Urbanisation combined with a continuous increase in population makes food security crucial for sustainable development. Urbanisation poses a threat to agricultural land use within built-up and peri-urban areas. It has resulted in the rapid disappearance and/or total change of agricultural farmland in urban and peri-urban areas. To monitor the changes in agricultural farmland, an understanding of changes in the urban landscape is becoming increasingly important. In this study, multi-temporal Landsat imagery were used to analyse the impact of urbanisation on urban agriculture in the city of Niamey. Changes in the urban landscape were determined using the support vector machine (machine learning) algorithm. Results of this study showed a decrease in land with crops from 3428 ha to 648 ha and an increase in built-up areas from 1352 ha to 11,596 ha between 1975 and 2020. Urbanisation and population growth are the main drivers of urban landscape change in Niamey. There was also a decrease in bare land, rock and vegetation classes, while a small increase in rice and water body classes, comparing the 1975 and 2020 values. This study demonstrates the importance of remote sensing in showing the implications of urbanisation on urban agriculture. These results can assist city planners and resource managers in decision-making and adoption of sustainable mitigation measures which are crucial for urban development.

Analyzing the Landuse Land Cover Change of Sonadia Island From 1990 to 2020

Remote sensing and Geographic Information Systems (GIS) are essential tools in determining the spatiotemporal extent of Landuse, and Land Cover (LULC) changes, as well as a variety of individual concerns, such as annual and seasonal changes in LULC caused by human interferences and interactions between the physical environment, cultural context, and anthropogenic factors. This study focuses on the LULC change of Sonadia Island, and it uses multi-temporal Landsat imagery from 1990 through 2020. The output analysis revealed four sub-features: mudflats, vegetation, open space, and water bodies. Vegetation cover decreased from 1486.71 hectares in 1990 to 986.13 hectares in 2000, and in 2020, the total area covered by vegetation increased significantly, reaching 1186.47 hectares, up from 497.97 hectares in 2010. Open space increased from 317.16 hectares in 1990 to 510.75 hectares in 2000. The net area expanded to 631.98 hectares in 2010 and then lost to 421.29 hectares in 2020. There was a consistent increase in the mudflats section from 1990 to 2010, when the amounts were 491.4 and 1179 hectares, respectively. By 2020, the area extent decreased to 796.41 hectares. From 1990 to 2000, the waterbody declined from 305.46 to 262.17 hectares, then slightly increased to 291.78 hectares in 2010, and then shrank again to 196.56 hectares in 2020. Therefore, this study could help policymakers decide on future landscape planning and evaluate Sonadia Island's current condition for long-term coastal management. J. Asiat. Soc. Bangladesh, Sci. 49(2): 139-148, December 2023

Monitoring Land Surface Temperature in the Karajae Watershed as an influential ecosystem for Pare-Pare City

Temperature is an indicator that is often used to assess environmental conditions in an area because it affects food and air resources, hydrology, and all ecosystems on earth. Monitoring the temperature conditions of an area is also carried out from time to time to see trends that occur. Land surface temperature monitoring using remote sensing which has spatial resolution, temporal resolution, and spectral resolution. Monitoring is only in urban areas without looking at the surrounding conditions. However, temperature is influenced by several factors such as geographic location, position relative to the sun, altitude, and biophysical conditions such as land. This study looks at changes in land surface temperature in the Karajae watershed area as an ecosystem that affects urban areas, Pare-Pare City or vice versa. Analysis of land surface temperature monitoring using remote sensing methods in the form of multi-temporal Landsat Imagery data 2004-2021. The land image displays changes in land surface temperature with a single channel algorithm. In addition, land images are the basis for analyzing land cover changes which are the basis for estimating land surface temperature changes with normalized fractional vegetation cover relationships. The results show that changes in land cover must occur, which are estimated to be twice as large in the future. Settlements have experienced a very large increase in area, while the closure of forest land and dry agricultural land has experienced a reduction in area which will cause disruption to ecosystems and food. The trend of increasing land surface temperature continues to increase every period. The increase in settlements as built-up land, rice fields, dry land agriculture as a form of community activity causes an increase in land surface temperature. In addition, agricultural activities have an influence on increasing land surface temperatures in vegetated land cover. Indirectly, the city of Pare-Pare has an impact on increasing land surface temperatures in the upstream area of the Karajae watershed. This is a concern in the future, if urban activities, transportation, to industrial activities are much faster, and will cause a greater increase in land surface temperature.

Quantitative assessment of Land use/land cover changes in a developing region using machine learning algorithms: A case study in the Kurdistan Region, Iraq

The identification of land use/land cover (LULC) changes is important for monitoring, evaluating, and preserving natural resources. In the Kurdistan region, the utilization of remotely sensed data to assess the effectiveness of machine learning algorithms (MLAs) for LULC classification and change detection analysis has been limited. This study monitors and analyzes LULC changes in the study area from 1991 to 2021 using a quantitative approach with multi-temporal Landsat imagery. Five MLAs were applied: Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), and Extreme Gradient Boosting (XGBoost). The results showed that the RF algorithm produced the most accurate maps of the three-decade study period, accompanied by a high kappa coefficient (0.93–0.97) compared with the SVM (0.91–0.95), ANN (0.91–0.96), KNN (0.92–0.96), and XGBoost (0.92–0.95) algorithms. Consequently, the RF classifier was implemented to categorize all obtainable satellite images. Socioeconomic changes throughout these transition periods were revealed by the change detection results. Rangeland and barren land areas decreased by 11.33 % (−402.03 km2) and 6.68 % (−236.8 km2), respectively. The transmission increases of 13.54 % (480.18 km2), 3.43 % (151.74 km2), and 0.71 % (25.22 km2) occurred in agricultural land, forest, and built-up areas, respectively. The outcomes of this study contribute significantly to LULC monitoring in developing regions, guiding stakeholders to identify vulnerable areas for better land use planning and sustainable environmental protection.

Analysis of Land Cover Changes in the Brown Canyon Mining Area Associated with Restrictions on Community Activities

The high land use as area mining has resulted in changes in land cover and impacts the surrounding environment. In 2020, the Semarang City government implemented a Restrictions on Community Activities (RCA) policy to prevent the spread of the COVID-19 virus, indirectly limiting mining activities in the area of the Brown Canyon mine. This study aims to analyze the land cover change in the Brown Canyon mine area linked to the RCA policy. Assessment of land cover change is carried out by land cover classification on multitemporal Landsat imagery using Random Forest (RF) method. Based on the classification results, it was found that there was a change in mining land cover in Brown Canyon which increased by 3.713 Ha when the RCA policy was implemented from 46.305 Ha to 50.018 Ha. Likewise, after the implementation of RCA, there was an increase of 3.510 Ha from 50.018 Ha to 53.528 Ha. The accuracy of land cover classification before RCA was 90.12%. During RCA was 88.18%, and after RCA was 89.94%. Then, if related to the results of the interview data processing, the change in mining land cover that occurred was influenced by mining activities in the study area during the RCA period. So, it can also be said that RCA does not significantly influence mining activities in the Brown Canyon mining area. Several suggestions can be used for further research, such as using high-resolution satellite imagery and having mining area boundary data for each site.

LULC Change Effects on Environmental Quality and Ecosystem Services Using EO Data in Two Rural River Basins in Thrace, Greece

Rural abandonment and associated rapid urbanization, agricultural intensification, and climate change have been key factors transforming terrestrial landscapes, with significant impacts on the environmental quality and the ecosystem services (ES) provided to human welfare. In this study, two understudied rural river basins located in Thrace, North Greece, were selected to assess changes in landscape pattern, composition, and eco-environmental quality and ecosystem services values (ESV). Cloud-based remote sensing (RS) analyses of multitemporal Landsat imagery in the Google Earth Engine (GEE) platform were applied for multitemporal land use/land cover changes (LULCC) quantification between 1984–2021, along with landscape pattern, eco-environmental quality, and ES assessment. Although ecosystem changes observed were not extensive over this period, eco-environmental quality appeared to be affected due to fragmentation. Preserving the ecosystem’s naturalness can enhance cultural ES to avoid further values loss originating from provisioning ES exploitation. This study highlights the strong connection between landscape configuration and eco-environmental quality, emphasizing the strong impact that anthropogenic activities have on the environment. The monitoring of the effects of LULCC on ecosystem health and the economic value of ES is crucial for the introduction of spatial planning and restoration policies.

Spatiotemporal Monitoring of Land Reclamation in the Southeastern Desert, Upper Egypt Using Satellite Data and GIS

The objective of this study was to monitor the reclamation development and assess the LULC changes in a reclaimed area in Upper Egypt. GIS and remote sensing-based multi-temporal Landsat imageries (i.e., Landsat-5 and Landsat-8; 30m) were utilized for mapping and analyzing the spatiotemporal dynamics between 2005 to 2020. Both supervised-based maximum likelihood classifier (MLC) and normalized difference vegetation index (NDVI)-based thresholds were implemented. The results of both methods were cross-compared and showed that the agriculture activities started in 2004 with small and sparse agriculture patches. The bare land occupied more than 65.1% of the total area between 2005-2008. Overall, using the MLC and NDVI-based classification, the authors observed an increase of approximately 455.6% (17,027.7 ha) and 477.2% (16,973.5 ha) over 15 years (2005-2020), respectively. The results could be very useful for assessing the success of the Egyptian strategies to sustain the agricultural land areas and food production through horizontal expansion and investment in the desert areas.

Land Use and Land Cover Change in the Vaal Dam Catchment, South Africa: A Study Based on Remote Sensing and Time Series Analysis

Understanding long-term land use/land cover (LULC) change patterns is vital to implementing policies for effective environmental management practices and sustainable land use. This study assessed patterns of change in LULC in the Vaal Dam Catchment area, one of the most critically important areas in South Africa, since it contributes a vast portion of water to the Vaal Dam Reservoir. The reservoir has been used to supply water to about 13 million inhabitants in Gauteng province and its surrounding areas. Multi-temporal Landsat imagery series were used to map LULC changes between 1986 and 2021. The LULC classification was performed by applying the random forest (RF) algorithm to the Landsat data. The change-detection analysis showed grassland being the dominant land cover type (ranging from 52% to 57% of the study area) during the entire period. The second most dominant land cover type was agricultural land, which included cleared fields, while cultivated land covered around 41% of the study area. Other land use types covering small portions of the study area included settlements, mining activities, water bodies and woody vegetation. Time series analysis showed patterns of increasing and decreasing changes for all land cover types, except in the settlement class, which showed continuous increase owing to population growth. From the study results, the settlement class increased considerably for 1986–1993, 1993–2000, 2000–2007, 2007–2014 and 2014–2021 by 712.64 ha (0.02%), 10245.94 ha (0.26%), 3736.62 ha (0.1%), 1872.09 ha (0.05%) and 3801.06 ha (0.1%), respectively. This study highlights the importance of using remote sensing techniques in detecting LULC changes in this vitally important catchment.

IDENTIFIKASI DAMPAK AKSES JALAN TOL TERHADAP PENGGUNAAN LAHAN PERMUKIMAN DI KECAMATAN TEMBALANG

Construction of toll roads can be a generator in the development of Semarang City. It can be seen from the indications of an increase in population, and an increase in built-up land. A significant increase in the number of residents was in Tembalang District, namely 76,856 people. The expansion of built-up land is starting to be seen clearly in the city of Semarang, one of which is Bulusan Village and Tembalang Village which are located in Tembalang District. The development that took place in the Kelurahan was in the form of settlements (clean and slums) and trade and services and began to move rapidly since there was access to the Tembalang toll gate in the Tembalang Sub-District. In addition, there is a main driving factor for this increase, namely the difference between the Undip campus which is in the Tembalang Village. This study aims to identify how far the impact caused by the existence of toll gates in Tembalang Village from the spatial aspect in the form of residential land use (clean and slum). In achieving this goal, a spatial analysis of changes in land use was carried out from 1998 to 2018. Spatial urban development can be identified using land use data extracted from multitemporal Landsat imagery (Landsat 5 TM 1998, Landsat 7 ETM+ years, Landsat 7 ETM+ years 2003, Landsat 7 ETM+ in 2008, Landsat 7 ETM+ in 2013, Landsat 8 OLI in 2018). The results of the classification of land use show that the overall development of the city of Semarang leads to the east and south, the sub-districts with the number of built-up land growth are Tembalang, Banyumanik, Ngaliyan, Genuk, and Pedurungan. Then it was lowered again until the sub-district unit in Tembalang District saw an increase in built-up land in Tembalang and Bulusan Districts, both of which are the main gates using toll road access from the Tembalang Toll Gate.

An assessment of the spatio-temporal dynamics of Landsat-derived aerosol concentration in relation with land cover and road networks in the Lagos megacity.

The interrelationships between air quality, land cover change, and road networks in the Lagos megacity have not been explored. Globally, there are knowledge gaps in understanding these dynamics, especially using remote sensing data. This study used multi-temporal and multi-spectral Landsat imageries at four epochs (2002, 2013, 2015, and 2020) to evaluate the aerosol optical thickness (AOT) levels in relation to land cover and road networks in the Lagos megacity. A look-up table (LUT) was generated using Py6S, a python-based 6S module, to simulate the AOT using land surface reflectance and top of atmosphere reflectance. A comparative assessment of the method against in situ measurements of particulate matter (PM) at different locations shows a strong positive correlation between the imagery-derived AOT values and the PMs. The AOT concentration across the land cover and road networks showed an increasing trend from 2002 to 2020, which could be explained by urbanization in the megacity. The higher concentration of AOT along the major roads is attributed to the high air pollutants released from vehicles, including home/office generators and industries along the road corridors. The continuous rise in pollutant values requires urgent intervention and mitigation efforts. Remote sensing-based AOT monitoring is a possible solution.

Glacial lake outburst flood risk assessment using remote sensing and hydrodynamic modeling: a case study of Satluj basin, Western Himalayas, India.

Glacier-associated hazards are becoming a common and serious challenge to the high mountainous regions of the world. Glacial lake outburst floods (GLOFs) are one of the most serious unanticipated glacier hazards, with the potential to release a huge amount of water and debris in a short span of time, resulting in the loss of lives, property, and severe damage to downstream valleys. The present study used multi-temporal Landsat and Google earth imageries to analyze the spatio-temporal dynamism of the selected glacial lake (moraine-dammed) in the Satluj basin of Western Himalayas. Furthermore, GLOF susceptibility of the lake was assessed using a multi-criteria decision-based method. The results show that the lake area has increased from 0.11 to 0.26 km2 over the past 28years from 1990 to 2018. The susceptibility index value for the lake was calculated as 0.75, which indicates that the lake is highly susceptible to the GLOF. The depth and volume of the lake were estimated to be 16m and 57 × 105 m3, respectively, using an empirical formula. HEC-RAS, HECGeo-RAS, and Arc-GIS software were utilized in this study to perform unsteady flow analysis and to determine the GLOF impact on the downstream area. The worst-caseGLOF scenario(breach width of 75m) was revealed during an overtopping failure of the moraine dam, resulted in a peak discharge of 4060 m3/s and releasing a total watervolume of 57 × 105 m3. The breach hydrograph has been routed to calculate the spatial and temporal distribution of peak flood, inundation depth, velocity, water surface elevation, and flood peak arrival time along the river channel. The analysis further reveals that the routed flood waves reach the nearest settlement, i.e., Rajpur town, situated at a distance of 102km in the downstream valley of the lake at 6h after the beginning of the lake breach event with a peak discharge/flood of 1757 m3/s and maximum flow velocity of 1.5m/s. With the ongoing climate warming and glacier retreat, moraine-dammed lakes are becoming more hazardous and thus increasing the total threat. Therefore, it is mandatory to monitor and assess such lakes at regular intervals of time to lessen the disastrous impacts of GLOFs on the livelihood and infrastructure in the downstream valleys. The findings of this study will aid in the creation of risk management plans, preparatory tactics, and risk reduction techniques for GLOF hazards in the region.

Assessment of Spatio-Temporal Empirical Forecasting Performance of Future Shoreline Positions

Coasts and coastlines in many parts of the world are highly dynamic in nature, where large changes in the shoreline position can occur due to natural and anthropogenic influences. The prediction of future shoreline positions is of great importance in the better planning and management of coastal areas. With an aim to assess the different methods of prediction, this study investigates the performance of future shoreline position predictions by quantifying how prediction performance varies depending on the time depths of input historical shoreline data and the time horizons of predicted shorelines. Multi-temporal Landsat imagery, from 1988 to 2021, was used to quantify the rates of shoreline movement for different time period. Predictions using the simple extrapolation of the end point rate (EPR), linear regression rate (LRR), weighted linear regression rate (WLR), and the Kalman filter method were used to predict future shoreline positions. Root mean square error (RMSE) was used to assess prediction accuracies. For time depth, our results revealed that the higher the number of shorelines used in calculating and predicting shoreline change rates the better predictive performance was yielded. For the time horizon, prediction accuracies were substantially higher for the immediate future years (138 m/year) compared to the more distant future (152 m/year). Our results also demonstrated that the forecast performance varied temporally and spatially by time period and region. Though the study area is located in coastal Bangladesh, this study has the potential for forecasting applications to other deltas and vulnerable shorelines globally.

Geospatial measurement of urban sprawl using multi-temporal datasets from 1991 to 2021: case studies of four Indian medium-sized cities

In recent decades, medium-sized Indian cities have experienced accelerated urban growth due to the saturation of large cities. Such rapid urban growth combined with inadequate urban planning has triggered urban sprawl in medium-sized Indian cities. In this context, the present study focuses on the geospatial measurement of urban sprawl in four rapidly expanding Indian medium-sized cities located in diverse physiographic regions, such as Lucknow urban agglomeration (UA), Bhubaneswar UA, Raipur UA, and Dehradun UA. Multi-temporal Landsat imageries from 1991 to 2021 were downloaded for land cover classification through the maximum likelihood classification tool in ArcGIS 10.3. Thereafter, spatiotemporal land cover change detection was performed based on the classified land cover maps. The presence of urban sprawl was detected using the relative entropy index while the urban expansion index quantified the urban sprawl typologies such as edge expansion, leapfrog development, and ribbon development. The results exhibited a rapid rise in built-up land cover from 1991 to 2021. The prevalence of urban sprawl was detected in all four cities as per the relative entropy index. Edge expansion typology of urban sprawl was dominant compared to leapfrog development and ribbon development. Such urban growth phenomenon creates a hindrance in promoting sustainable urban development in medium-sized Indian cities. The results obtained from this paper would assist urban planners and policymakers in developing strategies to encourage planned urban growth. This paper exhibits the potential of geoinformatics to monitor and analyze urban sprawl.

Assessment of wetland dynamics in terms of status and temporal changes using RS &GIS in Aligarh District, UP, India

Wetland is an effective ecosystem but its resources are influenced in varying degrees by global economic development and expansion of potential survival of human life. The accurate mapping of wetland types and monitoring their dynamic changes provide the scientific foundation for wetland conservation and restoration. Aligarh district had many wetlands but due to legal and illegal encroachment, they are shrinking. No wetland mapping has been done before to compare the past and present situation of wetlands. Therefore under this study, an attempt has been made to understand the status of wetlands in Aligarh district in terms of their number and areal extent and also assess the temporal changes during 2002-2017 using the multi-temporal Landsat imageries. Supervised Classification with the Maximum Likelihood algorithm was used to classify the wetlands. There was a 1826.67 ha area covered through wetlands that is 0.5% of its total geographic area. The maximum number of wetlands was point wetlands (having less than 1 ha area. In the last 15 years, the area of wetlands decreased by 57%. The overall accuracy of the supervised image produced was 87.50% with a kappa value of 0.75.

Assessment of wetland dynamics in terms of status and temporal changes using RS &GIS in Aligarh District, UP, India

Wetland is an effective ecosystem but its resources are influenced in varying degrees by global economic development and expansion of potential survival of human life. The accurate mapping of wetland types and monitoring their dynamic changes provide the scientific foundation for wetland conservation and restoration. Aligarh district had many wetlands but due to legal and illegal encroachment, they are shrinking. No wetland mapping has been done before to compare the past and present situation of wetlands. Therefore under this study, an attempt has been made to understand the status of wetlands in Aligarh district in terms of their number and areal extent and also assess the temporal changes during 2002-2017 using the multi-temporal Landsat imageries. Supervised Classification with the Maximum Likelihood algorithm was used to classify the wetlands. There was a 1826.67 ha area covered through wetlands that is 0.5% of its total geographic area. The maximum number of wetlands was point wetlands (having less than 1 ha area. In the last 15 years, the area of wetlands decreased by 57%. The overall accuracy of the supervised image produced was 87.50% with a kappa value of 0.75.