Thermal and ecological assessment based on land surface temperature and quantifying multivariate controlling factors in Bogura, Bangladesh

Spatiotemporal characteristics and influential factors of coupling coordination degree of urbanization and ecological environment in the Huaihe River ecological economic belt

The rapid development of urbanization in recent years has led to significant economic and social progress, but it has also resulted in a series of eco-environmental challenges. This study, based on an evaluation model of urbanization and eco-environment for the Huaihe Ecological Economic Belt, conducts a comprehensive analysis of the coupling coordination degree between the two from 2005 to 2020, explores spatial aggregation characteristics, and reveals the reasons for the uneven development of urbanization and the eco-environment. The results are as follows: (1) Between 2005 and 2020, both the development level of urbanization and the coupling coordination degree between urbanization and the eco-environment steadily increased. (2) Global spatial autocorrelation analysis shows that in 2005, urbanization and coupling coordination exhibited a strong positive correlation, which weakened over time, while the eco-environment displayed a random spatial distribution. Local autocorrelation analysis reveals that in 2005, urbanization showed significant spatial clustering in Shandong and Jiangsu provinces, but this clustering became more spatially dispersed over time. (3) In 2005, spatial urbanization was the main factor contributing to the uneven level of urbanization in the Huaihe Ecological Economic Belt. By 2020, population urbanization had become a relatively weak factor in cities such as Taizhou, Yancheng, and Pingdingshan. Regarding the eco-environment, ecological pressure was a prominent issue between 2005 and 2015, but by 2020, ecological pressure had weakened, and the eco-environmental state became a new area of concern. To promote sustainable development in the Huaihe Ecological Economic Belt, it is essential to strengthen the development of central cities such as Bengbu and Huai'an, retain talent, increase the urbanization rate, and implement effective environmental protection policies. These measures will contribute to the region's long-term ecological and urban sustainability.

Modeling the spatially varying effects of biophysical factors on land surface temperature

A growing number of studies have investigated how land surface temperature (LST) is influenced by a variety of driving factors; however, little effort has been made to identify the dominant ones. The suggested method used the Upper Awash Basin (UAB), Ethiopia, as an example to explore the spatial heterogeneity and factors affecting LST, which is critical for selecting effective mitigation strategies to manage the thermal environment. The study employed two models: ordinary least squares (OLS) and geographically weighted regression (GWR). The OLS model was first used to capture the overall relationship between LST and some biophysical factors. The GWR was then utilized to investigate the spatial non-stationary relationships between LST and its influencing biophysical factors. Although the method was tested in UAB, Ethiopia, it can be applied in similar agroecosystems, to identify the dominant factors that influence LST and develop site-specific LST mitigation strategies.•The OLS and GWR models investigated the spatial heterogeneities of the influencing factors and LST.•Biophysical parameters such as enhanced vegetation index (EVI), modified normalized difference water index (MNDWI), normalized difference built-up index (NDBI), normalized difference bareness index (NDBaI), albedo and elevation were used as potential driving environmental factors of LST•The models performance was computed using the adjusted coefficient of determination (adj. R2), Akaike Information Criterion (AICc), and residual sum of squares (RSS).

Spatio-Temporal Heterogeneity of Ecological Quality in a Typical Dryland of Northern China Driven by Climate Change and Human Activities

With the intensification of climate change and anthropogenic impacts, the ecological environment in drylands faces serious challenges, underscoring the necessity for regionally adapted ecological quality evaluation. This study evaluates the suitability of the original Remote Sensing Ecological Index (oRSEI), modified RSEI (mRSEI), and adapted RSEI (aRSEI) in a typical dryland region of northern China. Spatio-temporal changes in ecological quality from 2000 to 2022 were analyzed using Theil-Sen median trend analysis, the Mann-Kendall test, and the Hurst exponent. Multiple regression residual analysis quantified the relative contributions of climate change and human activities to ecological quality changes. Results showed that (1) the aRSEI was the most suitable index for the study area; (2) observed changes exhibited significant spatial heterogeneity, with improvements generally in the inner areas of the Yellow River and declines in the outer areas; and (3) changes in ecological quality were primarily driven by climate change and human activities, with human activities dominating from 2000 to 2011 and the influence of climate change increasing from 2012 to 2022. This study compares the efficacy of RSEIs in evaluating dryland ecological quality, identifies spatio-temporal change patterns, and elucidates driving mechanisms, offering scientific evidence and policy recommendations for targeted conservation and restoration measures to address future changes in dryland regions.

Leveraging machine learning algorithms in dynamic modeling of urban expansion, surface heat islands, and carbon storage for sustainable environmental management in coastal ecosystems

Climate change and rapid urbanization are dramatically altering coastal ecosystems worldwide, with significant implications for land surface temperatures (LST) and carbon stock concentration (CSC). This study investigates the impacts of day and night time LST dynamics on CSC in Cox's Bazar, Bangladesh, from 1996 to 2021, with future projections to 2041. Using Landsat and MODIS imagery, we found that mean daytime LST increased by 3.57 °C over the 25-year period, while nighttime LST showed a slight decrease of 0.05 °C. Concurrently, areas with no carbon storage increased by 355.78%, while high and very high CSC zones declined by 14.15% and 47.78%, respectively. The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model estimated a 28.64 km2 reduction in high CSC areas from 1996 to 2021. Statistical analysis revealed strong negative correlations between LST and vegetation indices (R2 = -0.795 to -0.842, p < 0.001) and positive correlations with built-up indices (R2 = 0.812 to 0.893, p < 0.001). Cross-sectional analysis showed that areas within 2 km of the coastline experienced a lower rate of LST increase (0.03 °C/year) compared to inland areas (0.05 °C/year). A Cellular Automata-Artificial Neural Network model projected that by 2041, 22.51% of the study area may experience LST >32 °C, while areas with LST <24 °C may decrease to 1.68%. These observations underscore the pressing necessity for sustainable strategies in urban planning and conservation in swiftly evolving coastal areas, especially considering the challenges posed by climate change and population growth.

Remote sensing-based seasonal surface urban heat island analysis in the mining and industrial environment

Cooling spaces have an optimistic influence on surface urban heat islands (SUHI). Blue spaces benefit from balancing the changing climate and heat variations. Because of the rapid deforestation and SUHI increase, the climate is gradually changing in Paschim Bardhhaman, West Bengal state, India. Paschim Bardhhaman has two sectors: specifically, Durgapur is the main industrial centre and Asansol has coal mines. This investigation aims to categorize spatiotemporal variations and seasonal differences in cooling spaces and their influence on SUHI, land use and land cover (LULC), and thermal differences using Landsat datasets for the years 1992, 2004, 2012, and 2022 in summer and winter. The coal mining and industrial range decreased from 10,391.92 (1992) to 3591.1 ha (2022), respectively. Open pit mining distresses fresh water by heavy water uses in ore processing, and mining water was applied to excerpt minerals. Among the two sub-divisions, the blue space amount was higher in Asansol because mining actions were higher in Asansol than in Durgapur. The open vegetation volume has reduced from 46,441.03 (1992) to 25,827.55 ha (2022) and dense vegetation has erased from 7368.02 (1992) to 15,608.56 ha (2022). Dense vegetation improved because of heavy precipitation in those regions. Mostly, Raghunathpur, Saraswatiganja, Bhagabanpur, Bistupur, Paschim Gangaram, Garkilla Kherobari, and Gourbazar have dense vegetation. The outcomes similarly demonstrate that the total built-up part has increased by 8412.82 ha in between 30 years. The built-up zone changes near the southeast and western Paschim Bardhhaman district. Those region needs appropriate attention and planning to survive soon.

Geo-ecological, shoreline dynamic, and flooding impacts of Cyclonic Storm Mocha: A geospatial analysis

This research comprehensively assesses the aftermath of Cyclonic Storm Mocha, focusing on the coastal zones of Rakhine State and the Chittagong Division, spanning Myanmar and Bangladesh. The investigation emphasizes the impacts on coastal ecology, shoreline dynamics, flooding patterns, and meteorological variations. Employed were multiple vegetation indices-Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Modified Vegetation Condition Index (mVCI), Disaster Vegetation Damage Index (DVDI), and Fractional Vegetation Cover (FVC)-to evaluate ecological consequences. The Digital Shoreline Assessment System (DSAS) aided in determining shoreline alterations pre- and post-cyclone. Soil exposure and flood extents were scrutinized using the Bare Soil Index (BSI) and Modified Normalized Difference Water Index (MNDWI), respectively. Additionally, the study encompassed an analysis of microclimatic variables, comparing meteorological data across pre- and post-cyclone periods. Findings indicate significant ecological impacts: an estimated 8985.46 km2 of dense vegetation (NDVI >0.6) was adversely affected. Post-cyclone, there was a discernible reduction in EVI values. The mean mVCI shifted negatively from -0.18 to -0.33, and the mean FVC decreased from 0.39 to 0.33. The DVDI underscored considerable vegetation damage in various areas, underscoring the cyclone's extensive impact. Meteorological analysis revealed a 245 % increase in rainfall (20.22 mm on May 14, 2023 compared to the May average of 5.86 mm), and significant increases in relative humidity (14 %) and wind speed (205 %). Erosion was observed along 74.60 % of the studied shoreline. These insights are pivotal for developing comprehensive strategies aimed at the rehabilitation and conservation of critical coastal ecosystems. They provide vital data for emergency response initiatives and offer resources for entities engaged in enhancing coastal resilience and protecting local community livelihoods.

A spatio-temporal analysis of the magnitude and trend of land use/land cover changes in Gilgel Gibe Catchment, Southwest Ethiopia

Analyzing alterations in land use/land cover is crucial for water Scientists, planners, and decision-makers in watershed management. This examination enables the development of effective solutions to mitigate the adverse impacts resulting from such changes. The focus of this research was analyzing alterations in land use/land cover within the Gilgel Gibe Catchment in 1991 - 2021. LULC data of 1991-2021 were derived from multispectral Landsat images. Data were also gathered using field observations and key informant interview. Data of LULC classes (1991-2021) were generated utilizing supervised classification with maximum likelihood algorithm of ENVI 5.1 and ArcGIS 10.5. Change detection analysis and accuracy assessment were done where accuracy levels all the study periods were > 85 %, and the overall Kappa statistics of the periods were > 0.89. Built-up area and cultivated land of the catchment are increasing with increasing magnitude of change; whereas, while forest cover and grazing land of the catchment are shrinking with declining magnitudes of change, shrubland covers and water body are declining with increasing magnitude of change in the catchment. The net increase in degraded land is a reflection of the increasing degradation of natural resources in the catchment. Swift escalation of population and the subsequent raising demand for farmland and forest and shrub (e.g. fuel-wood and construction) products, decline yield, unemployment and lack of alternative income source, and open access and limited conservation of resources are the principal factors for the dramatic shrinkages of grazing, forest, water body and shrubland resources. Thus, concerned bodies should take rehabilitation measures to restore degraded lands, improve production and yield of farmland by increasing improved farm-inputs and irrigation, and create employment and alternative income sources for the youth, women and the poor so as to ensure sustainable rural livelihoods and to curb the impacts on forest, shrubland and other resources.

Environmental impact assessment of transportation and land alteration using Earth observational datasets: Comparative study between cities in Asia and Europe

Developments in the transportation field are emerging because of the growing worldwide demand and upgrading requirements. This study measured the transportation development, shortage distance, and decadal land transformation of Kuala Lumpur and Madrid using various remote sensing and GIS approaches. The kernel density estimation (KDE) tool was applied for road and railway density analysis, and hotspot information increased the knowledge about assessable areas. Landsat datasets were used (1991-2021) for land transformation and related analyses. The built-up land increased by 1327.27 and 404.09 km2 in Kuala Lumpur and Madrid, respectively. In the last thirty years, the temperature increased 6.45 °C in Kuala Lumpur and 4.15 °C in Madrid owing to urban expansion and road construction. Chamberi, Retiro, Moratalaz, Salama, Wangsa Maju, Titiwangsa, Bukit Bintang, and Seputeh have very high road densities. KDE measurements showed that the road densities in Kuala Lumpur (4498.34) and Madrid (9099.15) were high in the central parts of the city, and the railway densities were 348.872 and 2197.87, respectively. The observed P values were 0.99 and 0.96 for traffic signals and 0.98 and 0.99 for bus stops, respectively. The information provided by this study can support local planners, administrators, scientists, and researchers in understanding the global transportation issues that require implementation strategies for ensuring sustainable livelihoods.

Assessment of urban thermal field variance index and thermal comfort level of Addis Ababa metropolitan city, Ethiopia

Land use land cover (LULC) conversion around urban areas is the root cause for the increasing trend of land surface temperature (LST) in many cities. The increase in LST is driven by the replacement of vegetation cover and other LULC by impervious surface. This study is aimed to assess the extent of urban thermal field variance index (UTFVI) and thermal comfort level of Addis Ababa city using geospatial techniques and linear regression model. Landsat image of 1990 TM, 2000 of ETM+ and 2020 of OLI/TIRS are used to analyze LST and Urban Heat Islands (UHI) for assessing UTFVI and urban thermal comfort level. The results showed that the UHI over Addis Ababa city is substantial increased over the past decades. The results reveled that LST has increased by 7.9 °C due to decline of vegetation cover and expansion of built-up area. Results show that about 225 km² (42.7%) is excellent comfort for urban resident while about 241.4 km² (45.8%) is categorized as worst ecological evaluation index, which results discomfort to the city dwellers. The key findings of from this study are crucial for informing city administrators and urban planners to reduce urban heat islands by investing on urban green areas and open spaces.

Impact of Urbanization on Urban Heat Island Intensity in Major Districts of Bangladesh Using Remote Sensing and Geo-Spatial Tools

Urbanization is closely associated with land use land cover (LULC) changes that correspond to land surface temperature (LST) variation and urban heat island (UHI) intensity. Major districts of Bangladesh have a large population base and commonly lack the resources to manage fast urbanization effects, so any rise in urban temperature influences the population both directly and indirectly. However, little is known about the impact of rapid urbanization on UHI intensity variations during the winter dry period in the major districts of Bangladesh. To this end, we aim to quantify spatiotemporal associations of UHI intensity during the winter period between 2000 and 2019 using remote-sensing and geo-spatial tools. Landsat-8 and Landsat-5 imageries of these major districts during the dry winter period from 2000 to 2020 were used for this purpose, with overall precision varying from 81% to 93%. The results of LULC classification and LST estimation showed the existence of multiple UHIs in all major districts, which showed upward trends, except for the Rajshahi and Rangpur districts. A substantial increase in urban expansion was observed in Barisal > 32%, Mymensingh > 18%, Dhaka > 17%, Chattogram > 14%, and Rangpur > 13%, while a significant decrease in built-up areas was noticed in Sylhet < −1.45% and Rajshahi < −3.72%. We found that large districts have greater UHIs than small districts. High UHI intensities were observed in Mymensingh > 10 °C, Chattogram > 9 °C, and Barisal > 8 °C compared to other districts due to dense population and unplanned urbanization. We identified higher LST (hotspots) zones in all districts to be increased with the urban expansion and bare land. The suburbanized strategy should prioritize the restraint of the high intensity of UHIs. A heterogeneous increase in UHI intensity over all seven districts was found, which might have potential implications for regional climate change. Our study findings will enable policymakers to reduce UHI and the climate change effect in the concerned districts.