Increase In Built-up Area Research Articles

CHANGE IN LAND USE/LAND COVER USING REMOTELY SENSED IMAGES IN KAFANCHAN AREA OF KADUNA STATE, NIGERIA

Vegetation cover in Kafanchan has been on the decrease which has suffered more and undue depletion and degradation. This research assessed the change of land use/land cover for the span of 45 years (1988-2023). This study made use of LANDSAT 2, 4, 8 and 8 images of the year 1988, 1998, 2016 and 2023 accordingly. The images were processed using Arcmap 10.3. For the secondary data collection, the area was demarcated into bare land, water bodies, vegetation and built-up areas. The results shows that there has been an increase of built-up areas from 28.19% of the study area in 1988 to 44.73% in 1998 and 45.98% in 2023. Vegetation land use has also shown a decrease from 49.75% in 1988 to 30.54% in 2023. Water bodies decreased from 12.3% in 1988 to 10.92% in 2023. Vegetated land was the most dominant land cover class in the study area but decreased to 28.84% in 1998, and increase from 28.84% in 1998 to 30.54% in 2023 this is supported by the continuous decline of high-end Normalized Difference vegetation index (NDVI) values from 1988 (0.536), 1998 (0.411), 2016 (0.380) and 2023 (0.280) implying that vegetation cover in Kafanchan is steadily being depleted. The applications of RS and GIS will promote better understanding in the monitoring of land use-land cover interactions to aid planning, management and enterprise.

CLASSIFICATION OF URBAN LAND USE AND LAND COVER WITH K-NEAREST NEIGHBOUR CLASSIFIER IN THE CITY OF CAPE TOWN, SOUTH AFRICA – CAPE FLATS CASE STUDY

Abstract. The rapid growth of cities, owing to rural-urban migration and high birth rate has resulted in encroachment of the land use and land cover (LULC) in the Cape Flats, situated north of Cape Town, Western Cape, South Africa. PlanetScope imagery and K-Nearest Neighbour (KNN) classifier are used to map and monitor the following LULC classes within the Cape Flats; waterbodies, trees, built-up, and vegetation, we further do a time series map between 2016 and 2021.The results showed that LULC changes between 2016 to 2021 are as follows: a decrease of 2.1% in vegetation, 0.4% in water bodies, and 11% in trees. Built-up areas, on the other hand, showed a significant increase of 13.6% over five years. The LULC changes in the Cape Flats were mainly triggered by an increase in built-up areas due to household construction to accommodate the increased population resulting from rural-to-urban migration and high birth rate. Classification accuracy from 2016 and 2021 was as follows; overall accuracy of 98.31% and 0.97 kappa coefficient in 2016, while overall accuracy 96.54% and kappa coefficient 0.95 in 2021. A combination of machine learning and high-resolution imagery showcased that high classification results can be achieved in monitoring subtle LULC changes. We recommended that all relevant stakeholders, including government officials and municipalities, formulate and adopt policies to protect against LULC degradation.

Dynamic assessment and prediction of land use alterations influence on ecosystem service value: A pathway to environmental sustainability

In the face of rapid urbanization, understanding and managing changes in Land Use and Land Cover (LULC) is crucial for the sustainability and resilience of urban ecosystems. Rapid urban growth resulting from changes in LULC, can lead to the fragmentation and degradation of Ecosystem Services (ES), including habitat provision, water regulation, and air purification. This study investigates LULC changes in Dhaka City, Bangladesh, from 2010 to 2021 and forecasts their future impacts on ES in 2030 and 2040. Landsat images were processed using the Google Earth Engine platform, and a cellular automata technique was integrated with an Artificial Neural Network (ANN) for LULC mapping and prediction. Results showed an increase in built-up areas from 31% to 42% between 2010 and 2021, with a projected decrease to 34% and 32% in 2030 and 2040, respectively. Water bodies, vegetation, and agricultural land declined from 2010 to 2021 but are expected to increase in 2030 and 2040. This trend indicates a growing awareness of the need for a sustainable environment and resilience in urban ecosystem development. The value of ES is predicted to drop from $172.66 to $160.23 over thirty years (2010–2040), primarily due to the degradation of water bodies ($7.64) and agricultural land ($6.89). However, the projected increase in water bodies, vegetation, and agricultural land by 2040 suggests a potential for recovery and enhancement of these ES values through sustainable practices. Future land use policies should promote sustainable agriculture and water resource management while fostering resilience against rapid urban growth. This approach will ensure the sustainable development of urban ecosystems. This study informs future land use strategies in Dhaka City, supporting sustainable natural resource management, urban ecosystem quality management decisions, and resilience planning. It underscores the importance of integrating sustainability and resilience into urban development policies for the benefit of both the environment and the city's inhabitants.

Exploring the nexus between land cover change dynamics and spatial heterogeneity of demographic trajectories in rapidly growing ecosystems of south Asian cities

Rapid urbanization is a global trend that significantly alters Land Use and Land Cover Change (LULCC) patterns. Dhaka, a megacity, and Chittagong, the country’s second-largest city, have experienced substantial shifts in LULCC patterns over the past three decades. This study aims to examine the LULCC in Dhaka and Chittagong districts of Bangladesh from 1991 to 2021, focusing on identifying patterns and quantifying the effects of population growth and urbanization. Changes in LULCC patterns were identified by analyzing Landsat satellite imagery using remote sensing and geospatial techniques. The LULCC classes were classified using the Random Forest model and an inconsistency diagram, demonstrating high precision with an overall accuracy rate exceeding 85 %. The study’s findings reveal significant transformations in LULCC patterns across Dhaka and Chittagong districts from 1991 to 2021. A notable increase in built-up areas was observed in both districts, with Dhaka division witnessing a 129 % increase in built-up areas while Chittagong district saw a 515 % increase. This was due to transforming water bodies and barren land into built-up areas. However, the study also highlighted a promising increase in vegetation cover in both districts, indicating successful ecological restoration efforts. Chittagong and Dhaka saw increments of 597.95 km2 and 252.15 km2 in vegetation cover, respectively. Despite these positive changes, the study identified decreased surface water bodies in both regions. The results show a significant positive correlation between population growth/density and the built-up class, while a significant negative correlation with the barren land was observed in both districts. The findings could potentially guide policymakers in making informed decisions regarding land use, land cover, and urban sprawl management from an ecological restoration perspective. This could contribute to achieving sustainable development goals by promoting resilience ecological restoration practices.

Using integrated GIS and hydrological analysis for sizing culverts of multiple channel crossings at the flooded section of the Daboya-Mankarigu Road (IR10) in Ghana

A culvert is an important structure in Road construction to allow the conveyance of Channels crossing the road. Culverts are sized for a road to accommodate the volume of water crossing the road network to avoid flooding. Ghana Highways has a standard manual for culvert sizing at channel crossing. This manual serves as a guide for the proper sizing of culverts, however, lots of culverts have been found to have failed. Among the reasons for the failure of culverts could be under-sizing, urbanization, climate change, lack of maintenance, etc. The Daboya-Mankarigu Road is situated in the Savanah Region of Ghana in the North Gonja District. The section of the road from Chainage 9 + 075 to 10 + 200 has been experiencing flooding from 2020 to 2021 with a flood depth of 3.315 m in 2020 and 2.00 m in 2021. This study seeks to use integrated GIS and hydrological-based methods to propose new culverts to supplement the existing culverts to control flooding at section (9 + 075 to 10 + 200) of Daboya-Mankarigu Road (IR10). Geographic Information system (GIS) model (SWAT), Hydrological and hydraulic models were used to determine the peak flow at the catchment to Propose new culverts to supplement the existing culverts. Using a design period of 25 years for culverts, the modified rational method was used to determine the Peak flow of the catchment. A 25-year peak flow of 367.155 m3/s was determined and used for hydraulic analysis of the existing culverts. From the study, the existing culvert structures at the section had a hydraulic capacity of 78.732 m3/s which could not accommodate the remaining flow of 288.423 m3/s in the catchment. An observation was made that the changes in the rainfall can cause a change in rainfall intensity. An increase in built-up areas in the catchment can also increase the runoff coefficient which can result in higher peak flow in the catchment. Climate change, change in slope, and Land use in the catchment were also determined to have a huge influence on the adequacy of culverts since the peak flow is dependent on these parameters in the catchment as the years go by. A 4No. 4 m × 4 m box culvert, 3No. 3.5 m × 3.5 m box culvert, and 13 No. 1200 mm pipe culverts with 2 each at different chainages were proposed at suitable locations to supplement the existing culverts using the HDS-5 equations in AutoCAD Civil 3D. A recommendation is made to consider the installation of these new culverts at the flood section to control flooding and avoid overtopping of water on the IR10 road section (Daboya-Mankarigu) in the north Gonja District.

Predicting the future land use and land cover changes for Saroor Nagar Watershed, Telangana, India, using open-source GIS.

The dynamics of land use and land cover are profoundly affected by the growth, mobility, and demand of people. Thematic maps of land use and land cover (LULC) help planners account for conservation, concurrent uses, and land-use compressions by providing a reference for analysis, resource management, and prediction. The purpose of this research is to identify the transition of land-use changes in the Saroor Nagar Watershed between 2008 and 2014 using the Modules for Land Use Change Evaluation (MOLUSCE) plugin (MLP-ANN) model and to forecast and establish potential land-use changes for the years 2020 and 2026. To predict how these factors affected LULC from 2008 to 2014, MLP-ANN was trained with maps of DEM, slope, distance from the road, and distance to a waterbody. The projected and accurate LULC maps for 2020 have a Kappa value of 0.70 and a correctness percentage of 81.8%, indicating a high degree of accuracy. Changes in LULC are then predicted for the year 2026 using MLP-ANN, which shows a 17.4% increase in built-up area at the expense of vegetation and barren land. The results contribute to the development of sustainable plans for land use and resource management.

Study on the Impact of MODIS-derived NDVI and NDBI on Land Surface Temperature

With the development and expansion of cities, the increase in built-up area and the drop in vegetation coverage have significant influences on the climatic environment of urban areas. This study processed Moderate Resolution Imaging Spectroradiometer (MODIS) data from four months in 2001 to investigate the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), and Land Surface Temperature (LST) in the Chengdu region. This paper explored the correlation between NDVI and LST, as well as the correlation between NDBI and LST. The findings revealed that areas with higher NDBI, indicating a greater extent of urban built-up areas, exhibited higher daytime and nighttime LST, indicating a more pronounced urban heat island effect. Conversely, an increase in vegetation cover was found to lead to a decrease in surface temperature and a certain degree of mitigation of the urban heat island effect. Furthermore, a good linear relationship was observed between NDBI and LST, with a stronger correlation during the daytime compared to the nighttime.

APPLICATION OF A HYBRID CELLULAR AUTOMATON-MARKOV MODEL IN LAND USE CHANGE DETECTION AND PREDICTION IN FLOOD-PRONE AREA, JOHOR, MALAYSIA

Changes in land use can significantly impact natural resource sustainability, socioeconomic activities, and flood risks. Cellular Automata-Markov model (CA-Markov) is utilized in this study to predict land use changes by modeling the spatial dynamics and transitions of land use categories over time in one of the flood-prone area in Segamat district, Johor. Satellite images obtained from Landsat 5 Thematic Mapper and Satellite Pour I’Observation de la Terre (SPOT) 5, 6, and 7 for years 2006, 2011, and 2016 were utilized to assess the magnitude of the land use change via unsupervised and supervised classification. Additionally, ancillary data such as residential, road, water bodies, and slopes were used as input to forecast future land use. The findings revealed that between 2006 to 2026, there was an increase in built-up areas and mixed agriculture up to 26%. The expansion of built-up areas and mixed agricultures involves the removal of forests, further exacerbating flood risks. This fundamental research can provide valuable insights for effective land management and urban planning.

Potential impact of future land use/cover dynamics on the habitat quality of the Yayo Coffee Forest Biosphere Reserve, southwestern Ethiopia

Human activities, including agricultural expansion, urbanization, and industrial advancement, have led to land use/cover change (LULCC). These changes have had negative consequences, such as the loss of species and the degradation of forest areas. The Yayo Coffee Forest Biosphere Reserve (YCFBR) is undergoing changes due to forest fires and encroachment from coffee plantations, which are predicted to increase in the future. The objective of this study was to simulate the dynamics of LULCC and its impact on habitat quality in the area over the next three decades. The study used classified land cover (LC) maps of 1992, 2022, and 2052 (predicted), as well as factor and constraint maps of the area. In addition, household surveys, key informant interviews (KII), and focus group discussions (FGD) were conducted to understand the factors influencing changes in habitat quality. Habitat quality was simulated under three different future scenarios (Business as Usual; S1), conservation scenario (S2), and development scenario (S3) using CA-Markov, Future Land Use Simulation (FLUS), and Integrated Valuation of Environmental Services Tradeoffs (InVEST) models. The result showed a 28% increase in open forest and a 35% increase in agricultural area under S1, with a minimal increase in built-up area. The medium-level habitat quality increased from 11.3% in 1992 to 27.8% in 2052 S3. The highest average haitat quality value was 0.62 in S2, while the lowest was 0.51 in S3. If S3 is the case, this value is expected to fall below 40%, indicating the lowest possible level of habitat quality in the future. The model also predicts a 7% increase in high forest areas in S2, indicating the possibility of an alternative path to save high forest loss in the area. The main causes of habitat quality deterioration include population growth, agricultural land expansion, a lack of diverse incomes, land tenure issues, and settlement expansion. Based on the findings, S2 appears to be the best future scenario for maintaining habitat quality. This study provides useful information that will help planners and decision-makers effectively prepare future conservation strategies.

How geoecological components of a terroir can be altered by spatial changes of vineyards – A case study from Eger Wine District (Hungary)

Terroir is a concept referring interactions of natural (topography, lithology, climate, soil etc.) and human (economic conditions, traditions, cultivation practices, etc.) factors; therefore, terroir is spatially delimited and subjected to environmental, socio-economic, and temporal changes. The geoecological background of wine districts are considered more stable among them, but, because of its natural diversity and the spatial changes of production sites, changes in abiotic terroir components might occur too. In this study the spatial changes of grape production sites in Eger Wine District (Hungary) across two and a half centuries (1784 to 2018), and their consequences on the composition of the geoecological factors (lithology, topography, soil characteristics) were analyzed. Modernization of cultivation, urbanization and increase of built-up areas around the central settlement resulted in decreased concentration, i.e. increased spatial dispersion to more remote vineyards further from Eger. It also has consequences on the lithological and topographical composition of the production sites. Besides the slightly increasing extent of vineyards (from 5346 ha to 7413 ha) we found a distinct decrease of vineyards at higher elevations and a substantial increase at lower elevations. Distribution according to slope gradient changed also remarkably, with the share of vineyards on <5 % slopes from 38 % to 65 %. These changes resulted in transformations of pedological characteristics according to the comparison of vineyard’s extent with soil map data: vineyards shifted to slightly acidic, more fertile (i.e. deeper soil layer with higher organic carbon content) soils. The share of vineyards with different lithology and parent material also changed: loose, calcareous Tertiary sediments decreased almost to half, and the share of vineyards over acidic volcanics and their weathered regoliths almost doubled. Comparing these two dominant lithological types and soil profiles derived from them, different pedological characters and taxonomic status were found (Phaeozems and Vertisols). However, comparison of these two lithological types based on main topsoil characteristics (pH, SOC, carbonates, depth of fertile soil layer, N, P, K content) according to 25 randomly chosen surficial soil samples at production sites, showed no significant differences.. In the case of this particular wine district, spatial changes of the production sites affected mostly the distribution by elevation, by slope gradient, but did not alter significantly the surface soil character of the terroir.

Slums Evolution and Sustainable Urban Growth: A Comparative Study of Makoko and Badia-East Areas in Lagos City

This research addresses the evolution of slums in two specific areas of Lagos City, a consequence of rapid urbanization in developing economies. The study aims to analyze the key characteristics of slums’ evolution while analyzing mathematical spatial changes within the Badia-East and Makoko slum areas in two decades, focusing on promoting sustainable urban growth. The integration of Remote Sensing (RS) technology and Geographic Information System (GIS) tools has dramatically facilitated the monitoring of Land Use Land Cover (LULC) changes over two decades. This research used Landsat imagery acquired in 2000, 2010, and 2020 to meet our research aims. The research applied supervised classification and the Normalized Difference Built-up Index (NDBI) for image mapping and mathematical-based analysis. Research created a spatial framework using grid-cell maps, performed change detection, and generated detailed maps to support our analysis. A comparative analysis was also performed in the selected areas with a different history in slum management systems. The findings reveal that both Makoko and Badia-East exhibit slum-like characteristics. Makoko witnessed a significant increase in informal built-up expansion of 27.6% and 7.7% between 2000 and 2010 and between 2010 and 2020, respectively. These changes converted 34.65 hectares of non-built-up land in Makoko into built-up areas. Conversely, Badia-East reported an increase in built-up areas only between 2000 and 2010, which amounted to 8.5%. However, the government’s intervention caused a decrease of 12.7% in built-up areas of Badia-East between 2010 and 2020. The study underscores the urgent need for slum clearance and upgrading initiatives in Makoko, like those implemented in Badia-East between 2013 and 2017. The conclusion drawn from the study of Makoko’s informal built-up areas is that it is causing adverse effects on human life and the environment. The expansion has resulted in an increase in air and water pollution, which is having adverse effects. Therefore, the study recommends upgrading and clearing out Makoko and suggests taking lessons from the successful experience in Badia-East. The study also highlights the importance of prioritizing community needs and voices to ensure efficient resource utilization while safeguarding the well-being of future generations.

CLIMATE VARIABILITY IN RELATION TO LAND USE AND LAND COVER (LULC) CHANGES IN KOTA BHARU, KELANTAN, MALAYSIA

The process of rapid urbanization has significantly altered natural landscapes and contributed to climate variability. Due to urbanization, land surface characteristics are changing, resulting in a changing thermal climate making cities warmer than surrounding rural areas. The study utilized remote sensing and Geographic Information System (GIS) technologies to analyze the connection between land use and land cover (LULC) change and climatic variability in Kota Bharu, Kelantan, Malaysia. The outcome showed that the greatest LULC change resulted from converting vegetation and bare land into built-up areas, with 25.46% and 10.17% respectively. This represents the rapid expansion of urban land caused by population growth. LST increment averaged 3.65°C in the last decade due to this massive increase in built-up areas. A linear regression analysis between LST and LULC indices, NDBI and NDVI shows that they are positively correlated. By understanding these two variables, land use planning could be further improved, hence, reducing the city's vulnerability towards climate variability.

Forecasting land use/cover changes and their influence on groundwater recharge in Chennai, India: Recommendations for sustainable urban development

ABSTRACT Land use/cover (LULC) changes significantly impact various environmental and social factors. In this study, we used a hybrid model that integrates Multi-Layer Perceptron-Markov Chain Analysis to forecast changes in LULC of Chennai city and its surrounding area until the year 2101. We also evaluated the impact of these changes on groundwater recharge potential zones (RPZ) and suggested policy recommendations for better urban planning. Our results indicate a rapid increase in built-up areas (238 km2/decade) and a corresponding decrease in agricultural lands (167 km2/decade), with a continuous reduction in the area occupied by the waterbodies (15 km2/decade). Furthermore, the estimated changes in RPZ demonstrate a decline in high recharge potential areas, emphasizing the need for effective policies to enhance groundwater recharge. The study provides insights into sustainable urban development. We recommended policies for promoting green urban spaces to mitigate LULC impacts on groundwater recharge.

Interactions of urbanisation, climate variability, and infectious disease dynamics: insights from the Coimbatore district of Tamil Nadu.

Climate change and shifts in land use/land cover (LULC) are critical factors affecting the environmental, societal, and health landscapes, notably influencing the spread of infectious diseases. This study delves into the intricate relationships between climate change, LULC alterations, and the prevalence of vector-borne and waterborne diseases in Coimbatore district, Tamil Nadu, India, between 1985 and 2015. The research utilised Landsat-4, Landsat-5, and Landsat-8 data to generate LULC maps, applying the maximum likelihood algorithm to highlight significant transitions over the years. This study revealed that built-up areas have increased by 67%, primarily at the expense of agricultural land, which was reduced by 51%. Temperature and rainfall data were obtained from APHRODITE Water Resources, and with a statistical analysis of the time series data revealed an annual average temperature increase of 1.8°C and a minor but statistically significant rainfall increase during the study period. Disease data was obtained from multiple national health programmes, revealing an increasing trend in dengue and diarrhoeal diseases over the study period. In particular, dengue cases surged, correlating strongly with the increase in built-up areas and temperature. This research is instrumental for policy decisions in public health, urban planning, and climate change mitigation. Amidst limited research on the interconnections among infectious diseases, climate change, and LULC changes in India, our study serves as a significant precursor for future management strategies in Coimbatore and analogous regions.

Impact of Land Surface Temperature (LST) and Ground Air Temperature (Tair) on Land Use and Land Cover (LULC): An Investigative Study

The Land Surface Temperature (LST) is the fundamental concept of measuring the temperature of the ground or soil using remote sensing technologies. LST is an essential environmental parameter that has several uses in meteorology, climatology, agriculture, urban planning, and environmental monitoring. Remote sensing instruments, such as thermal infrared sensors aboard satellites, provide the means to capture LST data on a global scale. LST and ground air temperature ( ) are two related but distinct measures that provide valuable insights into different aspects: LST refers to the temperature of the Earth's surface, and it provides information about the temperature of the soil or crop canopy, which can impact plant growth and development, on the other hand measures the temperature of the surrounding air, which influences the rate of evaporation, transpiration, and photosynthesis in plants. In this study, a linear regression equation was developed to correlate the LST and using air temperature data of 19th March 2017, and 14th March 2021 from the two meteorological stations namely Veganpur and Main Maize Research Station (MMRS) of the Godhra region. The Landsat 8 thermal and optical bands were used to estimate LST, whereas Sentinel 2 optical data was used for the Land Use and Land Cover (LULC) purpose. To correlate LST with , eight ground truth (GT) points were collected from both stations, namely the minimum and maximum air temperatures in 2017 and 2021. The satellite derived LST data and the maximum ground air temperature ( ) were found to strongly concur, with an R2 value of 0.97. It was noted that a temperature difference of 2.9°C was found between the maximum ground air temperature and the Land Surface Temperature (LST). Study showed that from 2017 to 2021, the area used for agriculture, forestry, water bodies, and barren land decreased from 75.62 to 71.51%, 15.17 to 14.30%, 1.60 to 1.38%, and 0.03 to 0.02%, respectively, however the area used for build-up increased from 7.59 to 12.79%. During a five-year period, the study identified an enhancement in Land Surface Temperature (LST) due to an increase in built-up area. A temperature change was also seen throughout the course of the five-year period as a result of variations in LULC. Overall, it came to light that, under specific conditions and speculation, LST estimated using thermal data from Landsat 8 can be closely associated with ground air maximum temperature.

Land use dynamics and its influences on groundwater depth levels in South region of National Capital Territory (NCT) of Delhi, India.

The present study envisions the influences of land use dynamics on the spatial trend of groundwater depth levels over a period of two decades in the south region of NCT Delhi, India. The findings have inferred that among five major LULC categories, built-up has shown a sprawling trend (+2.17 km2/year) from the north, northeastern, and central portions to the confined patches observed in the south and southwest fringes of South Delhi from 2001 to 2021. Likewise, vegetation class has also witnessed significant increment (+1.91 km2/year) to the peripheral boundary, i.e., southern, southwest/eastern fringes, and central portions under recent initiatives of city forests, plantation drives, and urban green spaces programs. On the contrary, a negative rate of change has been observed in fallow land (-2.78 km2/year), agriculture (-1.22 km2/year), and water bodies (-0.07 km2/year). LULC transition matrix has also showed prominent conversion of fallow land into vegetation and built-up class, and change of vegetation into built-up, and fallow land category. Subsequently, corresponding LULC maps have been superimposed with long-term trends of groundwater depth levels (as spatial contours). For entire South Delhi region, depth to water levels ranged from 2.02 to 66.45 meters below ground level (mbgl) where shallower depths followed a negative trend and remain persistent throughout the time period in north and northeastern fringes. Higher fluctuations in groundwater depletion with positive trends directly get influenced with allied land use transitions such as a steady increase in built-up area and steeper depth levels (> 40 mbgl) as observed in central, southwestern, and southern parts. Moreover, buffer peripheries in the proximity of groundwater monitoring stations viz., Hauz Khas, Pushp Vihar, Jamali, Gadaipur, and Bhatti Kalan have observed deeper groundwater levels allied to built-up expansion. Thus, groundwater depletion trends can be ascribed to the incessant conversion of recharging areas into impervious zones along with uneven distribution of groundwater usage and supply. Conversely, expanding vegetative land has also shown improved groundwater depth levels. Therefore, land use influences must be managed in the long run for ensuring sustainable management of groundwater resources.

Changes in the Patterns of Population Distribution and Built-Up Areas of the Rural–Urban Fringe in Post-Socialist Context—A Central European Case Study

The rapid and significant expansion of urban areas is observed worldwide; however, considerable differences are detected within the characteristics of the process. The rural–urban fringe is changing most dynamically from the aspect of land use and this tends to be relevant in the case of post-socialist cities in Central Europe even with a stagnating or decreasing population. Debrecen (Hungary) and its hinterland adequately represent the migration trends of Hungarian cities and the great administrative area provided wide intra-urban suburbanization processes. The current study put the emphasis on the analysis of the spatial pattern of built-up areas and the distribution of residents. In order to discover the processes of the post-socialist transition period, detailed point layers were created to illustrate every built-up parcel in the rural–urban fringe of Debrecen (for the years 1980, 2000, and 2020). The most important characteristics were discovered with the help of GIS methods—Kernel-density, grid pattern analysis of the object density, and analysis of land cover/land use changes using Corine Land Cover Change (CLCC) databases. The dynamic and extended expansion of built-up areas was seen until 2000, in which the outskirts (including hobby gardens) densified spectacularly. The urban sprawl has been less intensive since the millennium and the increase in built-up areas has become more concentrated. As a consequence of the transition period, extended territories—primarily the least dense parts of the rural–urban fringe—are faced with the disappearance of buildings due to agricultural cultivation reasons.

The extent, perceived causes and impacts of land use and land cover change in Tyhume Valley, South Africa

There is limited knowledge on how people living in communal areas perceive land use and land cover (LULC) change and the impacts it has on sustainable access to essential ecosystem goods and services. This study used seven wet season Landsat images covering 1989 to 2019 and the Extreme Gradient Boosting algorithm to map LULC in Tyhume Valley, South Africa. Analyses of trends in LULC and long-term changes in rainfall over the same period were based on the Mann Kendall (MK) statistical technique. Perceptions on the causes and impacts of the observed trends were solicited from 102 respondents and summarized through frequency analysis. Major trends that emerged from image-based analysis include the expansion of Vachellia karroo by 25% (τ = 0.98; p = 0.004), decrease in pastureland by 18% (τ = –0.90, p = 0.007), decrease in cropland by 9.6% (τ = –0.90, p = 0.007), decrease in surface water by 1.1% (-0.90, p = 0.007), and increase in built-up area by 2.5% (τ = 1.00, p = 0.003). Perceived causes of these trends include the eradication of land access control systems, poor management of surface water, lack of farmer support programs, and 14 years of negative rainfall anomalies. The impacts of these changes include long-distance trekking of animals to pastures and watering points, increased livestock malnutrition and mortality, decline of medicinal and culturally significant trees, increased purchasing of stock feed, increased unemployment, and consumption of unhealthy food. The study concludes by highlighting the need to accommodate local perceptions in the formulation of policies and practices for sustainable use of ecosystem services.

Simulation-Based Optimization of the Urban Thermal Environment through Local Climate Zones Reorganization in Changsha City, China with the FLUS Model

Urbanization leads to changes in surface landscapes, such as the increase in built-up areas and the decrease in natural elements, resulting in local changes in land surface temperature, which often create unusually hot weather and affect livability, especially for mid- and low-latitude cities. Therefore, optimizing urban landscapes and adjusting the thermal environment is especially important to improve comfort and to achieve sustainable urban development. Existing studies on optimizing landscapes have considered mainly horizontal land uses/land covers but ignored their elevation. This study considered local climate zones as basic units to describe three-dimensional landscapes; we measured the relationship between local climate zones and land surface temperature, based on which the research further used a genetic algorithm and future land-use simulation models to optimize the spatial layouts of local climate zones in Changsha, China, considering multiple objectives including adjusting land surface temperature without affecting population carrying capacity, economic development, watershed protection, and forest and grass protection. According to the optimization results, the area of open low-rise buildings increased by 5.98% after optimization, and dense trees decreased by 7.64%; open low-rise buildings were suggested to be newly built in the city center and sparsely buildings should be developed in the surrounding administrative district far away from the city center. The optimization results contributed to a −5.2 °C reduction of average land surface temperature, which could significantly improve the thermal environment under the premise of ensuring the population and economic development levels and thus serves as a novel solution for improving urban landscapes to implement sustainable city development.

Future prediction of scenario based land use land cover (LU&LC) using DynaCLUE model for a river basin

Human activities that cause changes to the surface of the Earth lead to alterations in Land Use and Land Cover (LU&LC) which have an impact on biodiversity, ecosystem functioning, and the well-being of humans. In order to comprehend and manage the effects of human activities on the environment, prediction of scenario-based LU&LC in future periods are crucial. Scenario-based predictions of LU&LC provide valuable insights for decision-makers in the sustainable governance of land and water resources. In the present study, the Dynamic Conversion of Land Use and its Effects (DynaCLUE) modelling platform was used to predict future LU&LC for Munneru river basin, India. Using six different user defined scenarios LU&LC change patterns were analyzed in 2030, 2050 and 2080 so as to understand the pressure on the natural resources and to plan sustainable Land Use Planning by preserving the important land use classes. The connection between LU&LC classes and input driving factors was quantified using Binary Logistic Regression (BLR) analysis. The β-coefficient was estimated using LU&LC type as a dependent variable and driving factors as independent variables. The demands of each LU&LC type, spatial policies and constraints, characteristics of each location and land use conversions are used as inputs for prediction of future LU&LC maps. Major conversions in LU&LC observed in this basin from last two decades are the rapid increase in built-up area due to urbanization in the outskirts of cities and towns. The major LU&LC changes projected for the period of 2019–2080 are expansion of built-up area ranging from 42.5% to 88.5%, and a reduction in barren land ranging from 57.3% to 74.5% across all six scenarios in the entire basin. The projected LU&LC maps under different scenarios provide valuable insights that could aid local communities, government agencies, and stakeholders in systematically allocating resources at the local level and in preparing the policies for long-term benefits.