Spatial-temporal Land Research Articles

Enhancing Land Use Patterns Understanding with Multi-Sensor, Multi-Temporal Metrics

Spatial-temporal land use patterns in urban environments are essential to understanding city dynamics. To uncover these patterns, many researchers have used the digital fingerprints of people’s interaction with urban infrastructure, such as phone calls, facility check-ins, and geolocated social media activity. Despite multiple studies on the detection of land use patterns in urban environments, the need for more consensus on the appropriate metrics to define which set of patterns best describes the dynamics of a city remains a significant limitation. This evaluation is often subjective and depends on the researcher’s in-depth knowledge of the study area, which makes an extensive comparison of multiple cities difficult.This paper introduces a novel set of metrics to determine the patterns that best represent urban activity, diminishing subjective interpretations. Our methodology, which tests our metrics on land use patterns obtained from a dynamic topic model, is a fresh approach to the field. To apply our methodology, we use a dataset of human urban activities collected over 17 years in cities with more than 1 million inhabitants or country capitals. Our results demonstrate that these metrics are a starting point for understanding, analyzing, and choosing the land use patterns that best describe the dynamics and use given to urban space. The practical implications of our research are significant, as it can guide decision-making processes and contribute to the sustainable development of urban areas. However, it is important to highlight that there is still work to be done to reach a consensus on the optimal metrics to evaluate these patterns.

Air pollution exposure and plasma fatty acid profile in pregnant women: a cohort study.

Air pollution exposure was known to result in body impairments by inducing inflammation and oxidation. But little is known about the associations of air pollutants with plasma fatty acid profile which may play important roles in the impairment of air pollutants based on the related mechanism, especially in pregnant women. This study aimed to explore the relationships of air pollution exposure with plasma fatty acid profile and the potential effect modification by pre-pregnancy body mass index (BMI). Based on a cohort in Wuhan, China, we measured concentrations of plasma fatty acids of 519 pregnant women enrolled from 2013 to 2016 by gas chromatography-mass spectrometry (GC-MS). Levels of exposure to air pollutants (fine particulate matter (PM2.5), inhalable particles (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO)) were estimated by using spatial-temporal land use regression models and calculated in three periods (average concentrations during 1 day, 1 week, and 1 month before the phlebotomizing day in the first trimester). Per interquartile range increment of the levels of air pollution exposure 1 day before phlebotomizing was related to 1.21-2.01% increment of omega-6 polyunsaturated fatty acids (n-6PUFA) and 0.63-1.74% decrement of omega-3 polyunsaturated fatty acids (n-3PUFA). Besides, relationships above were kept robust in the analysis during 1 week and 1 month before phlebotomizing. In women with normal BMI, plasma fatty acid profile was observed to be more sensitive to air pollutants. Our study demonstrated that increment of exposure to air pollutants was associated with higher plasma n-6PUFA known to be pro-inflammatory and lower plasma n-3PUFA known to be anti-inflammatory, which was more sensitive in pregnant women with normal BMI. Our findings suggested that changes in plasma fatty acid profile should cause concerns and may serve as biomarkers in the further studies. Future studies are needed to validate our findings and elucidate the underlying mechanisms.

Changes in Socioeconomic Disparities for Traffic-Related Air Pollution Exposure During Pregnancy Over a 20-Year Period in Texas

Air pollution presents clear environmental justice issues. However, few studies have specifically examined traffic-related air pollution (TRAP), a source driven by historically racist infrastructure policies, among pregnant individuals, a population susceptible to air pollution effects. How these disparities have changed over time is also unclear but has important policy implications. To examine changes in TRAP exposure by sociodemographic characteristics among recorded pregnancies over a 20-year period. This population-based birth cohort study used descriptive analysis among pregnant individuals in Texas from 1996 to 2016. All pregnant individuals with valid residential address, socioeconomic, and demographic data were included. Individual-level race and ethnicity, education, and maternal birthplace data were extracted from birth certificates and neighborhood-level household income and historical neighborhood disinvestment (ie, redlining) data were assessed via residential addresses. Data analysis occurred between June 2022 and June 2023. The main outcome, TRAP exposure at residential addresses, was assessed via traffic levels, represented by total and truck-specific vehicle miles traveled (VMT) within 500 m; nitrogen dioxide (no2) concentrations from a spatial-temporal land use regression model (ie, vehicle tailpipe emissions); and National Air Toxic Agency cancer risk index from on-road vehicle emissions. TRAP exposure differences were assessed by sociodemographic indicators over the 1996 to 2016 period. Among 7 043 598 pregnant people (mean [SD] maternal age, 26.8 [6.1] years) in Texas from 1996 to 2016, 48% identified as Hispanic or Latinx, 4% identified as non-Hispanic Asian or Pacific Islander, 12% identified as non-Hispanic Black, and 36% identified as non-Hispanic White. There were differences in TRAP for pregnant people by all sociodemographic variables examined. The absolute level of these disparities decreased from 1996 to 2016, but the relative level of these disparities increased: for example, in 1996, non-Hispanic Black pregnant individuals were exposed to a mean (SD) 15.3 (4.1) ppb of no2 vs 13.5 (4.4) ppb of no2 for non-Hispanic White pregnant individuals, compared with 2016 levels of 6.7 (2.4) ppb no2 for Black pregnant individuals and 5.2 (2.4) ppb of no2 for White pregnant individuals. Large absolute and relative differences in traffic levels were observed for all sociodemographic characteristics, increasing over time. For example, non-Hispanic Black pregnant individuals were exposed to a mean (SD) of 22 836 (32 844) VMT within 500 m of their homes, compared with 12 478 (22 870) VMT within 500 m of the homes of non-Hispanic White pregnant individuals in 2016, a difference of 83%. This birth cohort study found that while levels of air pollution disparities decreased in absolute terms over the 20 years of the study, relative disparities persisted and large differences in traffic levels remained, requiring renewed policy attention.

Utilizing Google Earth Data to Assess Spatial-Temporal Land Use Changes around Jatibarang Reservoir, Semarang City

This study aims to assess the spatial-temporal land use changes around Jatibarang Reservoir in Semarang City, Indonesia City from 2012 to 2022, utilizing Google Earth data. The Jatibarang Reservoir and its surrounding areas have been subject to rapid urbanization and land use transformations in recent years, leading to significant environmental and socio-economic implications. The classification accuracy of seven land use categories was validated, demonstrating a high level of accuracy. The analysis reveals a dominance of agricultural land use, with notable shifts observed in forest cover, settlements, and reservoir areas. Population growth, economic development, and policy interventions are identified as the main drivers behind these land use changes. The spatial analysis using Nearest Neighbor Analysis (NNA) indicates a scattered distribution pattern of land use changes, primarily concentrated in the western part of the reservoir area. These land use changes have significant social, economic, and environmental implications, impacting the sustainability of the reservoir and surrounding areas. The study highlights the need for improved data quality, rigorous validation methods, and spatial modeling approaches in future research. By understanding the dynamics of land use changes and their implications, policymakers and stakeholders can make informed decisions for sustainable land management and environmental conservation in similar contexts.

Modeling landscape hydrological response to spatial–temporal land use/cover changes

Modeling landscape hydrological response to spatial–temporal land use/cover changes

Modelling spatial–temporal expansion of Lilongwe City using Shannon’s entropy model through semi-dynamic environmental mapping and analysis

This study involves analysis of the urban spatial–temporal expansion of Lilongwe City from 1973 to 2020 using Shannon’s entropy model through time-series satellite mapping. Landsat images from 1973 to 2020 in a nearly 10-year interval are used to determine spatial–temporal land use/cover changes. The city is zoned into 1 km concentric rings and four pie sections to determine both directional and spatial urban expansion trends while Shannon’s entropy model is employed to determine the degree of dispersity of the city’s sprawl. A linear regression model incorporating population as an explanatory variable is then applied to predict the spatial expansion trends for Lilongwe City. Results show that the built-up area in Lilongwe City expanded by 465.4% (9.9% per year) from 1973 to 2020, making it the second-largest land use/cover type in the city, after vegetation. Consequently, vegetation cover decreased by − 32.7% (− 0.7% per year) during the same period. High relative entropy indices (> 0.9) obtained from Shannon’s entropy model indicate a dispersed urban development for the city during the entire period of study. The North-West quadrant of the city has the highest proportion of urban expansion while the North-East quadrant has the lowest proportion, in a relative sense. Regression model predictions show that the city will most likely continue to expand by the year 2023 and then increase exponentially by the year 2033, due to high population growth. The results of this study will assist city authorities to control the expansion of the city and anticipate patterns for future urban sprawl.

The Spatio-Temporal Patterns and Driving Forces of Land Use in the Context of Urbanization in China: Evidence from Nanchang City.

Land use change has been one of the common problems in the context of urbanization in China. Social economy and land use interact with each other, and it is especially important for human society to adhere to sustainable development, and to deal with the contradictory relationship between the social-economic needs and land use change. The objectives of this study are: (1) Obtain time-series land-use classification data and its spatial distribution in Nanchang City; (2) Identify the characteristics and driving force of spatial-temporal land use changes in Nanchang City from 2000 to 2020; (3) Discuss the relationship between the urban expansion and social economy in Nanchang City. The results show that the spatial distribution of land use in Nanchang City has changed significantly from 2000 to 2020, and the largest area of land-use type in Nanchang City has been cropland. The cropland has continuously declined, and the urban area has increased significantly. A lot of cropland has been transformed into urban areas, and land use degree in Nanchang City has significantly increased. The spatial pattern of land use has greatly changed, and the city spatial pattern has become more aggregated, while the spatial distribution of cropland, forest and grassland has become more fragmented. Moreover, there has been an obvious correlation between social-economic development and the level of land use, and GDP has been the main driver of land use change. The central urban area of Nanchang city has been the main hotspot of land use change.

Predictive Scenarios of LULC Changes Supporting Public Policies: The Case of Chapecó River Ecological Corridor, Santa Catarina/Brazil

The studies of spatial-temporal land use and land cover (LULC) change patterns, supported by future scenarios and simulation methods based on the assumption of natural socio-economic and territorial driving forces, allow us to go beyond an accurate diagnosis of the dynamics that have occurred so far, providing a picture of possible alternative futures, and are fundamental in assisting with the planning and policy-making in the territory. In this paper, we use LULC maps and explanatory variables aggregated in five dimensions (physical/natural, economic, sociocultural, technological, and demographic) to identify which are the main drinving forces in the evolution process and the simulation of LULC dynamics for 2036, using as a case study the Chapecó River ecological corridor (Chapecó EC) area. The Chapecó EC was created by the state government in 2010 with the goal of combining nature conservation with local and regional development. In this region, in the last two decades, the loss of areas of natural grassland and forest was on average five times higher than the average recorded in the state. Based on scenario-building methods using artificial neural networks, six predictive scenarios were elaborated, based on three socioeconomic scenarios (current conditions, growth, and socioeconomic recession) and two territorial intervention options (actions). This includes an action based on maintaining the current LULC, and another action of a conservationist nature with the recovery of forest and natural grassland areas to the proportions of areas found in 1990. The results indicate that if the current LULC is maintained, forest, pasture and agriculture areas tend to increase, while silviculture and natural grassland areas decrease, driven by economic and physical/natural driving forces. If there is a conservationist action, natural grassland and pasture areas tend to increase and silviculture and agriculture tend to lose area due to economic, technological, and physical/natural driving forces. These trends have revealed that the natural grassland preservation/restoration, the encouragement of conservationist agricultural practices combined with economic strategies, and the technological development of the rural sector seem to form the basis of economic development combined with biodiversity conservation.

Land cover change and utilization of village land forest reserves in Ludewa, Tanzania

Knowledge of land cover change and associated driving factors is important for developing improved management strategies and future monitoring of tropical forests. We present here for the first time the case of two forest reserves (Litwang'ata and Intake) in Ludewa district, Southern Highlands Tanzania. These reserves were recently (2019) declared Village Land Forest Reserves (VLFRs), with their management and ownership devolved to their communities. We used remote sensing and GIS methods to assess spatial-temporal land cover change of the two VLFRs for the period 1996–2016. We also used household interviews (HHIs), focus group discussions (FGDs) and key informants’ interviews (KIIs) to examine the perceived socio-economic drivers of land cover change and the household forest-based incomes derived from the VLFRs. Our findings overall showed that closed woodland decreased by 780.3 ha whereas open woodland, grass/shrub land and agricultural land increased by 700.9 ha, 71.7 ha and 50.9 ha, respectively. Wildfires are perceived as the major drivers of land cover change. To a large extent, local communities utilize VLFRs for their livelihoods, with major resources extracted being firewood, construction poles, fresh water and thatch grass. Of the total forest-based income, logging for timber accounted for about 33% for Intake VLFR users and 36% for villagers who utilize resources from Litwang'ata VLFR. Future monitoring of land cover and livelihood changes is recommended for sustainable forest management.

Changes in socioeconomic and demographic disparities for traffic-related air pollution exposure during pregnancy over a 20-year period in Texas

Background and Aim: Environmental justice issues surround population exposure to ambient air pollution; however, few studies have specifically examined traffic air pollution (a pollutant with localized concentration patterns) and pregnancy exposures (maternal-infant dyads may be particularly susceptible to air pollution effects). How socioeconomic and demographic disparities may change over time is also unclear but could have important policy implications. Methods: We identified ~ 6.9 million pregnant mothers in Texas from 1996 to 2016 using individual-level vital statistics records. We estimated traffic air pollution exposures using full residential addresses and calculated the total vehicle miles travelled (VMT) within 300 m, assigned nitrogen dioxide (NO2) concentrations from a spatial-temporal land use regression model (an indicator of vehicle tailpipe emissions), and linked the National Air Toxic Agency cancer risk index from vehicles emissions to maternal addresses. From census tract data, we examined median household income. We quantified annual absolute and relative mean percent differences in traffic air pollution across socio-demographic indicators of maternal race and ethnicity, educational attainment, and maternal birthplace. Results: We observed marked differences in traffic air pollution exposures by all socio-demographic variables examined. Overall, the strongest disparities were seen among non-White and foreign-born pregnancies. While the absolute level of disparity shrank from 1996 to 2016 (e.g., in 1996 Black individuals were exposed to NO2 levels 1.8 ppb higher than white individuals, while in 2016 NO2 levels were 1.5 ppb higher among Black compared to white individuals), the relative level of disparity continued to increase. Within census tracts (i.e., neighborhoods), we also observed sizeable disparities among socioeconomic and demographic groups. Conclusion: Despite considerable reductions in traffic-related air pollution, the magnitude of disparity has increased when we compared groups with lower socioeconomic positioning to their higher socioeconomic positioned counterparts. Keywords: traffic-related air pollution, pregnancy, environmental justice

Attribution analysis of land degradation in Hainan Island based on geographical detector

Rapid and accurate surveys of land are essential for protecting ecosystems. This study aimed to survey the spatial–temporal land degradation patterns and explore its driving mechanism based on a geographical detector in Hainan Island from 2000 to 2020. First, the areas of forest, cultivated land, and construction land were analysed, and their mutation year was determined through the Mann-Kendall abrupt test. Then, change vector analysis was used to create a double-evaluation system by combining the normalized difference vegetation index and net primary productivity. Finally, the geographical detector was used to identify the driving mechanism of different land-use types. The results show that 2013 was the mutation year of the three land-use types. From 2000 to 2013 (Stage 1), the land degradation areas of forest, cultivated land, construction land, grassland, and unutilised land reached 1977.13, 613.25, 165.81, 261.06, and 4.25 km2, respectively. From 2013 to 2020 (Stage 2), the degraded land area accounted for only 0.91 % of the island's total area. From the single factor perspective, the elevation (DEM), least distance to residential area (LDP), and variety of mean annual temperature (VTem) were found to be the dominant factors of land degradation of cultivated land and grassland, forest, construction land in Stage 1. On the other hand, in Stage 2, the main factor of land degradation was the variety of the density of GDP (VGDP) in cultivated land, grassland, and construction land, while it was the variety of mean annual precipitation (VPre) in the forest. Moreover, all pair-factors provided a higher determinant power for degraded land areas than a single factor. From the interaction effect perspective in Stage 1, VPOP ∩ LDR (i.e. the variety of the density of population density and least distance to roads or railways), LDP ∩ LDR, and VTem ∩ LDR were the most significant pair-factors of the interaction effect on land degradation of cultivated land, forest and grassland, and construction land. In Stage 2, the most significant pair-factors of the interaction effect in different land-use types were DEM ∩ VPOP and VPre ∩ POP in degraded cultivated land, VPre ∩ DEM and VPre ∩ LDR in degraded forest land, DEM ∩ VTem in degraded grassland, and VGDP ∩ LDR in degraded construction land. Given the time-lag effects of factors and the attribution analysis of land degradation, we suggest limiting the construction of roads and residential areas, increasing biodiversity, and sheltering forests to protect terrestrial ecosystems in the future.

Spatial–Temporal Land Loss Modeling and Simulation in a Vulnerable Coast: A Case Study in Coastal Louisiana

Coastal areas serve as a vital interface between the land and sea or ocean and host about 40% of the world’s population, providing significant social, economic, and ecological functions. Meanwhile, the sea-level rise caused by climate change, along with coastal erosion and accretion, alters coastal landscapes profoundly, threatening coastal sustainability. For instance, the Mississippi River Delta in Louisiana is one of the most vulnerable coastal areas. It faces severe long-term land loss that has disrupted the regional ecosystem balance during the past few decades. There is an urgent need to understand the land loss mechanism in coastal Louisiana and identify areas prone to land loss in the future. This study modeled the current and predicted the future land loss and identified natural–human variables in the Louisiana Coastal Zone (LCZ) using remote sensing and machine-learning approaches. First, we analyzed the temporal and spatial land loss patterns from 2001 to 2016 in the study area. Second, logistic regression, extreme gradient boosting (XGBoost), and random forest models with 15 human and natural variables were carried out during each five-year and the fifteen-year period to delineate the short- and long-term land loss mechanisms. Finally, we simulated the land-loss probability in 2031 using the optimal model. The results indicate that land loss patterns in different parts change through time at an overall decelerating speed. The oil and gas well density and subsidence rate were the most significant land loss drivers during 2001–2016. The simulation shows that a total area of 180 km2 of land has over a 50% probability of turning to water from 2016 to 2031. This research offers valuable information for decision-makers and local communities to prepare for future land cover changes, reduce potential risks, and efficiently manage the land restoration in coastal Louisiana.

Analysis of the Current and Future Land Use/Land Cover Changes in Peri-Urban Areas of Dar es Salaam City, Tanzania using Remote Sensing and GIS Techniques

A rapid increase in population and urban development has direct impacts on the natural environment of peri-urban areas of big cities, especially in the developing countries. This study examined land use and land cover changes (LULCC) of peri-urban areas of Kinondoni Municipality in Dar es Salaam city for the period between 1999 and 2019 and predicted future changes by 2039. The specific objectives were to; (i) map the spatial-temporal land use and cover changes (ii) analyze settlements (built-up areas) patterns and factors influencing their changes (iii) predict changes that might happen for the next two decades. LULCC for 1999 to 2019 were detected through Maximum Likelihood (ML) supervised classification techniques while Cellular Automata based on Artificial Neural Network (CA-ANN) model was used to predict future changes. Over the past two decades, 33.7% of vegetation has been lost due to increased settlements, as shown by linear development pattern of built-up areas, which has gained about 30% of other land covers. This study predicts that by 2039, built-up areas will increase drastically to occupy 51.6%, while vegetation and water cover will decrease to occupy 40.4% and 1.4%, respectively. Without good planning strategies and land use policies, the control and management of the magnitude and patterns of peri-urban growth and sustainable management of natural and built environments in Tanzania will remain a challenge. This paper seeks to contribute to the body of knowledge required for evidence-based policy formulation and decision making to attain sustainable urbanization through setting suitable management plans for peri-urban areas of Tanzanian cities.
 Keywords: Artificial Neural Network, Land use/cover changes, Maximum likelihood, Peri-urban, Remote sensing

Leveraging cloud-based computing and spatial modeling approaches for land surface temperature disparities in response to land cover change: Evidence from Pakistan

Monitoring spatial-temporal land use land cover (LULC) patterns and related processes (e.g., land surface temperature—LST) is essential to sustainable development at local, regional, and national levels. In this context, the present study leverages cloud-computing-based Google Earth Engine and geo-information modelling techniques to provide spatial-temporal insights regarding LULC and LST over the past three decades (1990–2020) in Pakistan—a south Asian country with ∼212 million people. Additionally, using Punjab province (the most populous and developed in Pakistan) as the study area, we empirically evaluate the association between several LULC types (i.e., built-up, forests, agriculture, rangeland, barren, and water) and LST. Our results show that due to the transition from rangeland and agriculture LULC to built-up areas (contributing 38 and 37%, respectively), ∼250% increase is observed in the impervious surface in Punjab during 1990–2020. While the rapid urbanization has resulted in ∼8.5 percent annual increase in built-up area during the study period, the highest percent change (∼10.5%) occurred during the most recent decade (i.e., 2010–2020). This increase in built-up areas has led to LST rise with 1.4 °C increase in maximum annual LST in Punjab. In addition, among the evaluated top-20 cities, the most significant rise in LST is observed by Kasur city followed by Chiniot, Sheikhupura, Sahiwal, and Lahore—areas known for industrial development in Pakistan. While the results on LULC provide important references for rational and optimal utilization of land resource via policy implications, the association between LULC and LST ascertains why it is critical to design sustainable LULC planning and management practices for climate change mitigation and adaptation.

Early life exposure to air pollution and cell-mediated immune responses in preschoolers

BackgroundExposure to air pollution has been linked with altered immune function in adults, but little is known about its effects on early life. This study aimed to investigate the effects of exposure to air pollution during prenatal and postnatal windows on cell-mediated immune function in preschoolers. MethodsPre-school aged children (2.9 ± 0.5 y old, n = 391) were recruited from a mother-child cohort study in Wuhan, China. We used a spatial-temporal land use regression (LUR) model to estimate exposures of particulate matter with aerodynamic diameters ≤2.5 μm (PM2.5) and ≤10 μm (PM10), and nitrogen dioxide (NO2) during the specific trimesters of pregnancy and the first two postnatal years. We measured peripheral blood T lymphocyte subsets and plasma cytokines as indicators of cellular immune function. We used multiple informant models to examine the associations of prenatal and postnatal exposures to air pollution with cell-mediated immune function. ResultsPrenatal exposures to PM2.5, PM10, and NO2 during early pregnancy were negatively associated with %CD3+ and %CD3+CD8+ cells, and during late pregnancy were positively associated with %CD3+ cells. Postnatal exposures to these air pollutants during 1-y or 2-y childhood were positively associated with IL-4, IL-5, IL-6, and TNF-α. We also observed that the associations of prenatal or postnatal air pollution exposures with cellular immune responses varied by child's sex. ConclusionsOur results suggest that exposure to air pollution during different critical windows of early life may differentially alter cellular immune responses, and these effects appear to be sex-specific.

Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

Urban expansion is a dynamic and complex phenomenon, often involving adverse changes in land use and land cover (LULC). This paper uses satellite imagery from Landsat-5 TM, Landsat-8 OLI, Sentinel-2 MSI, and GIS technology to analyse LULC changes in 2000, 2005, 2010, 2015, and 2020. The research was carried out in Opole, the capital of the Opole Agglomeration (south-western Poland). Maps produced from supervised spectral classification of remote sensing data revealed that in 20 years, built-up areas have increased about 40%, mainly at the expense of agricultural land. Detection of changes in the spatial pattern of LULC showed that the highest average rate of increase in built-up areas occurred in the zone 3–6 km (11.7%) and above 6 km (10.4%) from the centre of Opole. The analysis of the increase of built-up land in relation to the decreasing population (SDG 11.3.1) has confirmed the ongoing process of demographic suburbanisation. The paper shows that satellite imagery and GIS can be a valuable tool for local authorities and planners to monitor the scale of urbanisation processes for the purpose of adapting space management procedures to the changing environment.

Land Use and Land Cover Change as an Indicator of Watershed Urban Development in the Kenyan Lake Victoria Basin

The socio-economic and ecological value of Lake Victoria is threatened by significant regional development and urbanization. This study analyzed spatial-temporal land use/land cover changes in the Kenyan Lake Victoria basin from 1978–2018 using Landsat 3, 4-5 and 8 imagery, with a view to identifying the extent and potential impacts of urbanization on the basin. Supervised image classification was undertaken following the Maximum Likelihood algorithm to generate land use/land cover maps at ten-year intervals. Results indicate that the basin is characterized by six main land use/land cover classes namely, agricultural land, water bodies, grasslands and vegetation, bare land, forests and built-up areas. Further, the results indicate that the basin has experienced net increases in built-up areas (+97.56%), forests (+17.30%) and agricultural land (+3.54%) over the last 40 years. During the same period, it experienced net losses in grassland and vegetation (-37.36%), bare land (-9.28%) and water bodies (-2.19%). Generally, the changing landscapes in the basin are characterized by conversion of natural environments to built-up environments and driven by human activities, urban populations and public policy decisions. The study therefore recommends the establishment of a land use system that creates a balance between the ecological realm and sustainable development.

Impact of Urban Land-Cover Changes on the Spatial-Temporal Land Surface Temperature in a Tropical City of Mexico

Climate change has severe consequences on ecosystem processes, as well as on people’s quality of life. It has been suggested that the loss of vegetation cover increases the land surface temperature (LST) due to modifications in biogeochemical patterns, generating a phenomenon known as “urban heat island” (UHI). The aim of this work was to analyze the effects of urban land-cover changes on the spatiotemporal variation of surface temperature in the tropical city of Mérida, Mexico. To find these effects we used both detected land-cover changes as well as variations of the Normalized Difference Vegetation Index (NDVI). Mérida is ranked worldwide as one of the best cities to live due to its quality of life. Data from satellite images of Landsat were analyzed to calculate land use change (LUC), LST, and NDVI. LST increased ca. 4 °C in the dry season and 3 °C in the wet season because of the LUC. In addition, a positive relationship between the LST and the NDVI was observed mainly in the dry season. The results confirm an increase in the LST as a consequence of the loss of vegetation cover, which favors the urban heat island phenomenon.

Presentation of a New Method for the Fusion of Spatial-Temporal Land Surface Temperature Products of ASTER and MODIS Sensors Based on a Two-Dimensional Stationary Wavelet Transform

پایش دمای سطح زمین (LST)، که یکی از پارامترهای مهم زیست‌محیطی محسوب می‌شود، تا کنون با استفاده از سنجنده‌های سنجش از دوری دارای توان تفکیک زمانی بالا، همچون سنجندة مادیس (توان تفکیک زمانی روزانه و توان تفکیک مکانی یک کیلومتر)، به‌طور گسترده‌ای صورت گرفته است. یکی از مهم‌ترین مشکلات این سنجنده‌ها پایین‌بودن توان تفکیک مکانی آنهاست که کارآیی‌شان را، در مواردی همچون شناخت آتش در مناطق جنگلی و مطالعة جزایر گرمایی شهری، محدود کرده است. در مقابل، سنجنده‌هایی با توان تفکیک مکانی بالا، همچون سنجندة ASTER (توان تفکیک مکانی 90 متر و توان تفکیک زمانی شانزده روز در محصول دمای سطح زمین)، توان تفکیک زمانی پایینی دارند که این منجر به ضعف آنها در پایش تغییرات سریع می‌شود. درواقع، به‌دلیل محدودیت‌های فنی، تا کنون سنجنده‌ای وجود نداشته است که، در دو بعد مکانی و زمانی، توان تفکیک بالا داشته باشد. برای حل این مشکل، روش‌های کم‌هزینه و کارآمد ادغام مکانی‌ـ زمانی مطرح شده‌اند. از مهم‌ترین روش‌های مطرح در ادغام مکانی‌ـ زمانی، روش‌های ESTARFM و STDFA شمرده می‌شوند. در این تحقیق، به‌منظور تلفیق داده‌های سنجنده‌های مادیس و ASTER از دمای سطح زمین در بخشی از شهر تهران، روشی جدید (SWT-STDFA) برمبنای روش STDFA مطرح و موجک دوبعدی ایستا تبدیل شد. نتایج حاصل از تلفیق نیز با نتایج دو روش ESTARFM و STDFA مقایسه شدند. همچنین، در ادامه، با توجه به وجود نقشة طبقه‌بندی تهیه‌شده براساس شاخص گیاهی تفاضلی نرمال‌شده در الگوریتم‌های STDFA و SWT-STDFA، درمورد تأثیر استفاده از شاخص‌های گیاهی تفاضلی نرمال‌شدة سبز (GNDVI) و شاخص گیاهی تعدیل‌شدة چندطیفی خاک (SAVI) در دقت تصویر مجازی خروجی بحث شده است. نتایج تحقیق نشان‌دهندة دقت بالای روش پیشنهادی با ریشة میانگین مربع خطاهای 3.03 کلوین، انحراف معیار 2.21 کلوین، میانگین قدرمطلق خطاهای 1.72 کلوین و ضریب همبستگی 0.92 بین تصویر دمای سطح واقعی منطقه و تصویر مجازی پیش‌بینی‌شده در مقایسه با دو روش دیگر است. افزایش شاخص‌های گیاهی GNDVI و SAVI در طبقه‌بندی دو روش STDFA و SWT-STDFA نیز تأثیری چندانی در دقت تصویر تلفیقی مجازی خروجی نداشته است.

Spatial-temporal analysis of changes in land-cover and land surface temperature (LST) within Universiti Putra Malaysia campus area

The urban forest is one of the typical vegetation-covered space in an urban area. The existence and development of the urban forest commonly found within the university campus site. The ecological and environmental characteristics in a university campus site usually resemble and similar to the urban area. The physical environment of the campus area is gradually changing due to development and improvement purposes, and it is impacting the land-cover on the surface. Changing the physical element of the surface will affect the absorption of the sun radiation, and further, it is changing the dynamic of the micro-climatic condition or temperature within the surrounding area. The primary focus of the research is to observe the impact of land-cover change on the land surface temperature (LST) within the campus area. Universiti Putra Malaysia is one of the largest campuses located in the urban area of Serdang district, state of Selangor. The dynamic changes in this site observed through multi-temporal Landsat 8 OLI-TIRS level 1 imageries. Datasets from 2013 to 2015 were collected to employ spatial-temporal analysis on land-cover changes, as well as the LST algorithm to monitor the surface temperature. The result shows that there was an increasing surface temperature during 2013-2015, the maximum LST in September 2015 was found at 32.17 °C, it is higher than the maximum LST in September 2013 (28.84 °C). The result shows that there was an increasing surface temperature during 2013-2015, which corresponds with the inclining built-up area. The research concluded that changes in land-cover would affect the spatial-temporal land surface temperature at University Putra Malaysia.