Land Surface Temperature Research Articles

Study on the spatiotemporal pattern evolution of surface urban heat island in shrinking cities: Fushun and Tieling

Under rapid urbanization, the urban heat island (UHI) problem impacts not only large cities, but also poses severe challenges to shrinking cities with rapidly declining population. In China, most shrinking cities are characterized by population loss alongside the expansion of built-up areas due to policy. Urban warming exacerbates the human settlement environment, with UHI intensifying due to urban expansion, while population loss simultaneously alleviates it. This raises a question: will the UHI problem be mitigated in shrinking cities? In this study, we analyze the spatiotemporal pattern evolution of surface urban heat island (SUHI) in Fushun and Tieling from 2000 to 2020 using Landsat series products. We combine landscape pattern indices and SUHI indicators, and perform correlation analyses of the factors influencing SUHI with multiscale geographically weighted regression (MGWR). The findings reveal that in Fushun, mining activities significantly impact SUHI, while in Tieling, extremely Land Surface Temperature (LST) zones are expanding and dispersing. SUHI patterns are notably shaped by subsurface conditions, and spatial configurations play key roles in regulating SUHI. However, population loss has not significantly influenced SUHI, even in shrinking cities. This study offers a new perspective for SUHI research and provides further insights into urban planning strategies.

Field scale partitioning of Landsat land surface temperature into soil and canopy components for evapotranspiration assessment using a two-source energy balance model

Field scale partitioning of Landsat land surface temperature into soil and canopy components for evapotranspiration assessment using a two-source energy balance model

Exploring the LULC dynamics and its relation with land surface temperature variation using split window algorithm: A study of Barasat subdivision, West Bengal, India.

Rapid urban growth in an unplanned way, decreased forest cover and consequential changes in land use in urban and rural areas have become pivotal obstacles for policymakers and urban planners to a great extent. Specifically, in a developing country like India, the repercussions of these changes have generated a substantial alteration in climate in the form of increased land surface temperature (LST), heat islands and associated human health risks. This research was initiated to assess the interrelationship between LST and LULC dynamics using spatial techniques over the Barasat Sub-Division in West Bengal (India). This study encompasses the extraction of LST using satellite images, seasonal variations of LST, the relationship of different land indices (i.e. NDVI, NDBI, NDMI and NDWI) with LST, and hotspot analysis of the landscape to get a comprehensive understanding of LST changes from 1988 to 2023, seasonal patterns, and relationships with LULC dynamics in the study area. The results of this research signify a substantial percentage increase in built-up spaces (51%), a decline in fallow areas (- 73%), relatively higher LST changes (3.41°C) in high-density built-up areas (e.g. Madhyamgram), a positive relationship (e.g. r2 = 0.59) between NDBI and LST, a transition from a cold spot to a hot spot share of 20% and a persisting hot spot of 42% throughout the summer season (1988 to 2023) in the selected landscape. The findings of this study will improve our present knowledge of seasonal fluctuations in land surface temperature (LST) and the variables that influence them. These findings will also draw attention to the growing worries about the fast variations in LST and the climatic and health hazards they pose. It is advised to establish planned urban expansion, efficient land use and land cover (LULC) management laws and encourage green areas that support sustainable development in India's urban surroundings in order to reduce these dangers.

Spatial Analysis of the Impacts Caused by Changes in Land Use on the Estimation of Surface Temperature in the The City of Paracatu (MG)

The increase in built-up areas, coupled with the suppression of vegetation without the necessary mitigating measures to curb the effects of urbanization, causes changes in the microclimate and directly impacts the health of the local population. In this sense, the present study aims to analyze the influence of land cover type on the variation of Land Surface Temperature (LST) estimation in the city of Paracatu, Minas Gerais (MG), in the years 1990 and 2020.For this, the maps provided by the Annual Mapping of Land Cover and Land Use of Brazil (MapBiomas) and images from the LANDSAT-5 (1990) and LANDSAT-8 (2020) satellites were used. Four images, distributed across seasons, were utilized to estimate LST for these periods, aiming to obtain a representative LST estimate for that year. For the calculation of the LST with bands 6 and 10, respectively of the LANDSAT-5 and 8 satellites, the SCP plugin of the QGIS software was used, and the process of recovery of the LST in the scenes used occurred through conversion of the values of the Digital Number (DN) into radiance at the Top of the Atmosphere (ToA), conversion of radiance into brightness temperature in ToA, correction of atmospheric effects and obtaining LST in Kelvin and, subsequently, in degrees Celsius. After that, the average between them was performed by means of normalization of each of the scenes, obtaining a representative mosaic for the LST of each year,thus enabling the estimation of the spatial behavior of the minimum, mean, and maximum values.. Finally, in order to verify the influence of Changes in Use and Occupation (LULC) on the spatial variation of the LST, the classes found in Vegetation, Exposed Soil, Urban Area and Water Resources were segmented, and it was found that, while the urban area of Paracatu had its territorial extension practically doubled, the vegetation class decreased by almost 25%, a fact that implied in the average increase of the LST by more than 5º C. the minimum and maximum values of these classes varied around 4.5 and 3º C, and it is possible to conclude that, in this interval of 30 years, Paracatu had no urban growth occurring exponentially and without the proper mitigations for the transformation of the microclimate, implying the need for measures that can curb these changes in strategic points of the municipality, as in the Bom Pastor neighborhood, which had its use and occupation totally changed in this temporal hiatus. In addition, in view of the estimates of economic growth in the city, it is believed that these measures should be taken as a matter of urgency, so that the results presented here can support the decisions of the managing public agencies.

The Nexus between Land Use/Cover changes and Land Surface Temperature: Remote sensing based Two-Decadal Analysis

The use of Remote Sensing (RS) data is crucial for promptly detecting and monitoring changes in both short and long term, providing real time information on Land Use/Cover (LULC), Land Surface Temperature (LST), and Enhanced Vegetation Index (EVI), adapting spatio-temporal variations. The primary focus of this study is to assess the effect of LULC changes on LST in Tashk-Bakhtegan and Maharloo (TBM) lakes basin, Iran, within 2001, 2011, and 2021, using MODIS data. Specifically, five main LULC classes involving: water body, rangeland, cropland, urban area, and bareland were identified. Beside accuracy and transition of LULC maps using User Accuracy (UA), Producer Accuracy (PA), and Kappa Coefficient (KC), the analysis included changes in LULC, Enhanced Vegetation Index (EVI), and LST, as well as the relationship among them when vegetation cover was at its peak. Moreover, a one-way analysis of variance (ANOVA) test was performed to group these variables using Duncan's test. The results showed that the accuracy of LULC maps were more than 84% for all the years. Furthermore, the conversion of croplands to rangelands showed the most significant changes, with a total of 1311.38 km2 during 2001–2021. Average EVI remained almost stable across the total area, whereas average LST generally increased by 0.65 °C. Barelands consistently exhibited the highest temperatures in all the years, followed by urban areas. While no significant changes were observed in the EVI averages, significant changes were observed in the LST across all LULC classes in different years. The results also indicated a consistent negative correlation between LST and EVI, stronger in croplands than rangelands, with Spearman's correlation coefficient of −0.714, −0.674, and −0.623 over the total area in 2001, 2011, and 2021, respectively. The findings are crucial for land planners to comprehend the effects of LULC changes on LST to adopt appropriate strategies in the TBM lakes basin.

Prediction of Potential Urban Hot Spots in Urban Environments Using Convolutional Neural Networks and Remote Sensing Techniques

Abstract. With the increase in built-up areas and rising urban populations, Land Surface Temperature (LST) has significantly increased, leading to the proliferation of Urban Hot Spots (UHS) in urban environments. To mitigate UHS proactively, researchers have conducted studies using various models to predict LST. However, current predictions are primarily based on data samples from isolated stations, making them unfeasible for continuous LST prediction on larger scales, such as regional levels. Therefore, this research aims to use Singapore as a case study to predict UHS on a regional scale using machine learning based on essential variables. Specifically, this research proposes training a Convolutional Neural Network (CNN) model using identified independent variables, including elevation, Normalized Difference Built-up Index (NDBI), Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), population, and Land Use and Land Cover (LULC), along with the target variable, UHS. After training, the model achieves high test accuracy and is fed projected data, subsequently producing projected UHS locations. The findings indicate that new UHS primarily appear in southwestern areas, Marina Bay, and the northwest regions of the country. This research predicts that 2.24 percent of the site could be classified as UHS by 2025, compared to the current percentage of 0.95 percent. Based on these projections, the research proposes preventative measures to proactively mitigate UHS. This research fills the gap by constructing a prediction model that can predict UHS locations on a regional scale.

Using Local Entropy Mapping as an Approach to Quantify Surface Temperature Changes Induced by Urban Parks in Mexico City

Understanding the mechanisms whereby parks contribute to cooling urban settings is critical to effectively addressing the challenges posed by rising temperatures in densely populated cities and ultimately improving the quality of urban life. This study employs a spatial approach with advanced analytical techniques, including local entropy mapping, to quantify surface temperature changes induced by urban parks across different geographical areas. Using satellite imagery to estimate land surface temperature (LST) during a heat wave in Mexico City, the study provides a practical approach to understanding the complex relationship between urban park size and urban heat island intensity within 300 m. The study’s findings indicate that while parks exert a cooling influence on their immediate vicinity, the extent of this effect varies spatially and depends on factors such as the size and location of the park and the nature of the surrounding terrain. Specifically, the results indicate that this relationship is not randomly distributed across the urban landscape. Instead, there is a clear pattern of spatial clustering within the city. Consequently, this research underlines the complexity of the problem, emphasizing the indispensable role of urban design and planning strategies to harness the full potential of parks as cooling agents within cities.

Assessment of Fine-Scale Urban Heat Health Risk and Its Potential Driving Factors Based on Local Climate Zones in Shenzhen, China

Cities are facing increased heat-related health risks (HHRs) due to the combined effects of global warming and rapid urbanization. However, few studies have focused on HHR assessment based on fine-scale information. Moreover, most studies only analyze spatial HHR patterns and do not explore the potential driving factors. In this study, we estimated the potential HHRs based on the “hazard–exposure–vulnerability” framework by using multisource data, including the modified thermal–humidity index (MTHI), population density, and land cover. Then, the variations in the HHRs among different local climate zones (LCZs) at the fine spatial scale were analyzed in detail. Finally, we compared the different contributions of the LCZs and types of land cover to the HHRs and their three components by using multiple linear regression models. The results indicate that the spatial pattern of the HHRs was different from those of the individual components, and high-hazard regions do not mean high HHRs. There were huge variations in the HHRs among the different LCZs. The built-up LCZs typically had much higher HHRs than the natural ones, with compact LCZs facing the most severe risk. LCZ 6 (open low-rise buildings) had a relatively low HHR and should be paid more attention in future urban planning. Compared to the LCZs, the land covers better explained the variations in the HHR. In contrast, the LCZs better predicted the land surface temperatures. However, both the LCZs and land covers made only slight contributions to the heat exposure and vulnerability. Furthermore, the manmade buildings and impervious surface areas contributed much more to the HHR than the natural land covers. Therefore, the arrangement of the warming LCZs and land cover types is worthy of further investigation from the perspective of HHR mitigation.

Green space-building integration for Urban Heat Island mitigation: Insights from Beijing's fifth ring road district

In this research, we delve into the complex arrangement of urban landscapes, where green spaces and buildings are not merely co-existing but are interwoven into a cohesive fabric that shapes the thermal environment. Our approach transcends the conventional methods of analysis, which typically isolate the roles of greenery or built environments. Instead, we adopt a synergistic perspective that recognizes the collective influence of these landscape constituents on the urban thermal pattern. Key insights are: (1) A linear decrease in average land surface temperature with increasing green space coverage is observed. However, substantial temperature variations (up to 8 °C) within the same coverage interval highlight the significant impact of built-up pattern on thermal conditions; (2) High Building Height and Floor Area Ratio, and low Building Coverage Ratio and Sky View Factor, are linked to cooler temperatures in areas with up to 50 % green space; (3) The study suggests that low-temperature areas can inform the adjustment of built-up patterns in high-temperature areas, offering a strategy for thermal environment optimization within specific green space coverage intervals. This research contributes insights into the integrated planning of green spaces and buildings, with implications for urban development and renewal initiatives aiming to enhance the urban thermal environment.

High resolution (1-km) surface soil moisture generation from SMAP SSM by considering its difference between freezing and thawing periods in the source region of the Yellow River

Soil moisture (SM) is a critical component of the land surface hydrological cycle, significantly impacting various sectors such as hydrology, meteorology, and agriculture. Accurate, high-resolution SM data are essential for effective flood forecasting, water resource management, and understanding the soil freeze-thaw processes in cold regions. This study aims to generate 1 km resolution liquid surface SM (SSM) data with a twice-daily update frequency by downscaling SMAP Level-4 SSM data using random forest (RF) and multiple linear regression (MLR) in the source region of the Yellow River (SRYR), by considering the differences in SM changes between freezing and thawing periods. To obtain the SSM data, 16 downscaling schemes of both RF and MLR were designed for each of the three scenarios. In each downscaling process, both land surface temperature (LST) and normalized difference vegetation index (NDVI) were utilized in MLR and RF models, alongside various combinations of additional variables such as albedo, elevation, leaf area index (LAI), soil texture. Results showed that during the freezing period, RF produced superior SSM estimates when supplemented with NDVI, LST, albedo, elevation, LAI, and soil texture. MLR was more effective during the thawing period when paired with NDVI, LST, elevation, LAI, and soil texture. During the freezing period, the downscaled SMAP SSM exhibited average R, RMSE, ubRMSE of 0.76, 0.029 m3·m-3, and 0.023 m3·m-3, respectively, when compared with in-situ measurements. During the thawing period, the average R, MAE, RMSE, and ubRMSE between the downscaled SMAP SSM and in-situ measurements were 0.52, 0.057 m3·m-3, 0.067 m3·m-3, and 0.054 m3·m-3, respectively, compared to 0.45, 0.070 m3·m-3, 0.083 m3·m-3, and 0.060 m3·m-3 for the original SMAP SSM. Thus, the research significantly enhances both the accuracy and spatial resolution of SMAP SSM estimations, underscoring its vital role in advancing hydrological studies within the SRYR.

Spatiotemporal dynamics of vegetation net primary productivity and its response to climate variability

Despite its significance, net primary productivity is rarely used as an indicator of climate change. The purpose of this study was to assess the spatiotemporal dynamics of vegetation net primary productivity and its response to climate variability in the Welmel watershed, southeastern Ethiopia. The investigation utilized data from the Moderate Resolution Imaging Spectroradiometer, and the Climate Hazards Group Infrared Precipitation with Stations accessed through the Google Earth Engine cloud computing platform. To evaluate the trend and response of net primary productivity to climatic factors, the Mann-Kendall and Theil Sen’s tests, the Pearson correlation coefficient, and stability analysis through the coefficient of variation of net primary productivity were utilized. The analysis of the temporal trend of net primary productivity revealed that the entire watershed, forest, wood, and cultivated land areas exhibited decreasing trends with net primary productivity values of -0.681, -4.361, -1.41, and − 3.25 g C m− 2/year, respectively. On the other hand, shrubs and grazing land displayed increasing trends of 1.15 and 2.04 g C m− 2/year, respectively. The wood and shrubland trends were statistically significant (p < 0.05). In the spatial analysis, an area of 64.6% showed a decreasing trend, whereas 33.78% displayed an increasing trend in net primary productivity values. The areas with stable and unstable variations in net primary productivity accounted for 26.47% and 8.02%, respectively. The net primary productivity of the land cover types and entire watershed were positively correlated with rainfall, however, only forests and woodlands were statistically significantly correlated (p < 0.05). The net primary productivity and land surface temperature were negatively correlated, except for forests and cultivated land, which were positively correlated. The results of this study provide essential information for managing ecosystems, supporting agricultural practices, and enhancing community resilience in a changing climate.

Delineating Urbanicity and Rurality: Impact on Environmental Exposure Assessment.

Environmental exposures and their health impacts can vary substantially between urban and rural areas. However, different methods for classifying these areas could lead to inconsistencies in environmental exposure and health studies, which are often overlooked. We constructed different urban/rural classification systems based on multiple population-based (e.g., total population, population density, and commuting) and built-environment-based (e.g., nighttime light intensity, building density, road density, distance to urban centers, point of interest density, and urban area coverage) indicators and various classification schemes. These classification systems were applied to Virginia and West Virginia, United States. We compared differences in urban/rural spatial patterns, demographic compositions, and exposures of particulate matter (PM2.5), greenspace, and land surface temperature using these urban/rural classification systems to understand their impacts on environmental exposure and health research. Our findings reveal clear differences in spatial patterns and demographic compositions across various systems. We also observed that different systems can lead to changes in the magnitude and direction of urban/rural disparities in environmental exposure assessment. Addressing the complexities in delineating urbanicity and rurality may include careful consideration of classification systems to reflect those aspects of urbanicity and rurality that are relevant to the research question or the use of multiple, complementary systems.

Identification of the Driving factors impacts of Land Surface Albedo over Iran: An analysis with the MODIS data

Albedo is a key parameter in climatic research and depends on environmental and climatic factors. Modeling these factors greatly contributes to understanding environmental variations. To this end, the data of Land Surface Albedo, Land Surface Temperature (LST), Vegetation, Snow, Elevation, Slope, and Aspect of the MODIS sensor from 1/1/2001 to 30/12/2021 with a 1000-m spatial resolution were used. After pre-processing, monthly, seasonal, and annual albedo modeling was performed using multiple linear regression (MLR) in the highlands of Iran. The results of monthly modeling revealed the salient direct role of snow on the albedo of Iran's highlands in all months, except for July, August, and September. In these months, due to the lack of snow coverage and the fruiting of agricultural lands and gardens, the inverse role of vegetation on albedo variations is determining. Seasonal examinations also showed that snow plays a significant role on the albedo of Iran's highlands in winter, spring, and fall; however, vegetation has a determining role in the summer. The annual results indicated that snow, vegetation, elevation, slope, LST, and aspect, respectively, are the factors affecting albedo in the highlands of Iran. Furthermore, the role of snow, LST, and aspect is positive, while the role of vegetation, elevation, and slope is negative on albedo.

Analysis of the Biennial Productivity of Arabica Coffee with Google Earth Engine in the Northeast Region of São Paulo, Brazil

Numerous challenges are associated with the classification of satellite images of coffee plantations. The spectral similarity with other types of land use, variations in altitude, topography, production system (shaded and sun), and the change in spectral signature throughout the phenological cycle are examples that affect the process. This research investigates the influence of biennial Arabica coffee productivity on the accuracy of Landsat-8 image classification. The Google Earth Engine (GEE) platform and the Random Forest algorithm were used to process the annual and biennial mosaics of the Média Mogiana Region, São Paulo (Brazil), from 2017 to 2023. The parameters evaluated were the general hits of the seven classes of land use and coffee errors of commission and omission. It was found that the seasonality of the plant and its development phases were fundamental in the quality of coffee classification. The use of biennial mosaics, with Landsat-8 images, Brightness, Greenness, Wetness, SRTM data (elevation, aspect, slope), and LST data (Land Surface Temperature) also contributed to improving the process, generating a classification accuracy of 88.8% and reducing coffee omission errors to 22%.

Cooling benefits from urban planting depend on local background canopy cover: A continental cross-city comparison

Numerous studies have shown that the cooling efficiency (CE) of urban trees varies by cities with different climate backgrounds, and recent research further indicated that there may be large within-city variations in CE. However, how such within-city variations differ across cities, and their relations to the local percent of urban tree canopy (Ptree) remain poorly understood. This study aims to fill this gap based on a comparison study across 118 cities with different biomes and climates in the continental USA. We used the tree canopy layer of the National Land Cover Dataset (NLCD) 2011 to measure urban tree canopy (UTC), and calculated land surface temperature (LST) based on Landsat thermal bands. We found: 1) CE had larger within-city and cross-city spatial variations in cities located in arid and semi-arid biomes. 2) CE was related to Ptree in nonlinear ways in >90 % of the study cities. In most cities (approximately 70 %), CE had an L-shaped relationship with Ptree, showing that CE first declined quickly with the increase of Ptree, but then gradually dropped in a slower way or became relatively stable after Ptree reached a certain threshold. 3) While there was no significant difference in the types of CE-Ptree relationship among biomes and climates, the threshold of Ptree in CE-Ptree nonlinear ways was smaller in arid cities. The results of this threshold linking cooling benefits and current UTC can serve as a useful tool to prioritize locations for urban planting to maximize cooling benefits.

An integrated approach for analysing land surface temperature, heatwave and air pollution for sustainable planning in Delhi and its environs, India

An integrated approach for analysing land surface temperature, heatwave and air pollution for sustainable planning in Delhi and its environs, India

Forecasting malaria dynamics based on causal relations between control interventions, climatic factors, and disease incidence in western Kenya.

Malaria remains one of the deadliest diseases worldwide, especially among young children in sub-Saharan Africa. Predictive models are necessary for effective planning and resource allocation; however, statistical models suffer from association pitfalls. In this study, we used empirical dynamic modelling (EDM) to investigate causal links between climatic factors and intervention coverage with malaria for short-term forecasting. Based on data spanning the period from 2008 to 2022, we used convergent cross-mapping (CCM) to identify suitable lags for climatic drivers and investigate their effects, interaction strength, and suitability ranges on malaria incidence. Monthly malaria cases were collected at St. Elizabeth Lwak Mission Hospital. Intervention coverage and population movement data were obtained from household surveys in Asembo, western Kenya. Daytime land surface temperature (LSTD), rainfall, relative humidity (RH), wind speed, solar radiation, crop cover, and surface water coverage were extracted from remote sensing sources. Short-term forecasting of malaria incidence was performed using state-space reconstruction. We observed causal links between climatic drivers, bed net use, and malaria incidence. LSTD lagged over the previous month; rainfall and RH lagged over the previous two months; and wind speed in the current month had the highest predictive skills. Increases in LSTD, wind speed, and bed net use negatively affected incidence, while increases in rainfall and humidity had positive effects. Interaction strengths were more pronounced at temperature, rainfall, RH, wind speed, and bed net coverage ranges of 30-35°C, 30-120 mm, 67-80%, 0.5-0.7 m/s, and above 90%, respectively. Temperature and rainfall exceeding 35°C and 180 mm, respectively, had a greater negative effect. We also observed good short-term predictive performance using the multivariable forecasting model (Pearson correlation coefficient = 0.85, root mean square error = 0.15). Our findings demonstrate the utility of CCM in establishing causal linkages between malaria incidence and both climatic and non-climatic drivers. By identifying these causal links and suitability ranges, we provide valuable information for modelling the impact of future climate scenarios.

Assessment of Wind Energy Potential and Optimal Site Selection for Wind Energy Plant Installations in Igdir/Turkey

Wind energy is an eco-friendly, renewable, domestic, and infinite resource. These factors render the construction of wind turbines appealing to nations, prompting numerous governments to implement incentives to augment their installed capacity of wind turbines. Alongside augmenting the installed capacity of wind turbines, identifying suitable locations for their installation is crucial for optimizing turbine performance. This study aims to evaluate potential sites for wind power plant installation via a GIS, a mapping technique. The Analytic Hierarchy Process (AHP) was employed to assess the locations, including both quantitative and qualitative aspects that significantly impact the wind farm suitability map. Utilizing the GIS methodology, all datasets were examined through height and raster transformations of land surface temperature, plant density index, air pressure, humidity, wind speed, air temperature, land cover, solar radiation, aspect, slope, and topographical characteristics, resulting in the creation of a wind farm map. The correlation between the five-year meteorological data and environmental parameters (wind direction, daily wind speed, daily maximum and minimum air temperatures, daily relative humidity, daily average air temperature, solar radiation duration, daily cloud cover, air humidity, and air pressure) influencing the wind power plant in Iğdır province, including Iğdır Airport, Karakoyunlu, Aralık, and Tuzluca districts, was analyzed. If wind energy towers are installed at 1 km intervals across an area of roughly 858,180 hectares in Igdir province, an estimated 858,180 GWh of wind energy can be generated. The GIS-derived wind power plant map indicates that the installation sites for wind power plants are located in regions susceptible to wind erosion.

Natural forest regeneration is projected to reduce local temperatures

Forest regeneration is a crucial strategy for mitigating and adapting to global warming. Yet its precise impact on local climate remains uncertain, a factor that complicates decision-making when it comes to prioritizing investments. Here, we developed global maps illustrating how natural forest regeneration influences key local climate drivers—land surface temperature (LST), albedo, and evapotranspiration—using models fitted at a 1-km spatial resolution with a random forest classifier. We found that natural forest regeneration can alter annual mean LST by 0.01 °C, −0.59 °C, −0.50 °C, and −2.03 °C in Boreal, Mediterranean, Temperate, and Tropical regions, respectively. These variations underscore the region-specific effects of forest regeneration. Importantly, natural forest regeneration reduces LST across 64% of 1 billion hectares and 75% of 148 million hectares of potentially restorable land under different scenarios. These findings improve understanding of how forest regeneration can help regulate local climate, supporting climate adaptation efforts.

Assessing Ecological Environment in Dar es Salaam City: A Settlement Surface Ecological Index (SSEI) Analysis

Urbanization dramatically alters landscapes and ecological conditions. This study examines the environmental conditions within Dar es Salaam, Tanzania, using the Settlement Surface Ecological Index (SSEI). The SSEI combines biophysical parameters from satellite imagery to assess ecological health, revealing disparities across diverse urban typologies. This study’s methodology demonstrates advancements in using the Calculate Composite Index tool for streamlined index creation and greater flexibility in urban analysis. Statistical analysis reveals crucial relationships between ecological conditions and variables like tree cover, impervious surfaces, vegetation moisture, and Land Surface Temperature (LST). Multivariate clustering highlights distinct areas of high and Low SSEI, emphasizing disparities in urban ecological health. This study demonstrates how composite indices like the SSEI can inform targeted urban planning, promote green infrastructure, and mitigate the negative impacts of urbanization, ensuring both equitable and sustainable development in the face of climate change.