Land Surface Temperature Values Research Articles

CONTRIBUTION INDEX OF LAND COVER AND LAND SURFACE TEMPERATURE CHANGES IN UPPER HILL NAIROBI, KENYA

Abstract. Urban heat island is the difference in thermal temperature between rural and urban areas. The urbanization process alters the material type with impervious surfaces being absorbers of incoming radiation during the day and emitting it at night. The research involved the use of time-series satellite imagery from Sentinel, Landsat, ASTER and MODIS for the period 1986, 1995, 2000, 2005, 2011, 2015 and 2017 over the Upper Hill, Nairobi. Morning, afternoon and night land surface temperatures (LST) were calculated for each of these years and analyzed together with the land cover. The mean albedo was calculated to determine the relationship between each land cover and mean LST. The contribution index was calculated to determine whether a land contributed positively or negatively to the mean LST in Upper Hill. Results indicated that built-up land cover had increased from 1986 to 2017 by 0.86% per annum while forest land cover had decreased by 0.99% per annum. Sparse grassland had higher albedo and LST values of 0.81 and 27.9 °C respectively, whereas water had lower albedo and LST values of 0.09 and 25.1 °C. Water had the lowest mean LST during the day but highest mean LST in the afternoon and night in each of the years due to its high thermal capacity. Bare ground tends to have a higher contribution index compared to other land covers, while forest land cover has a negative contribution index, indicating the impact land cover types have on LST and the urban heat island effect.

The Use of Land Cover Indices for Rapid Surface Urban Heat Island Detection from Multi-Temporal Landsat Imageries

The aims of this study were to determine surface urban heat island (SUHI) effects and to analyze the land use/land cover (LULC) and land surface temperature (LST) changes for 11 time periods from the years 2002 to 2020 using Landsat time series images. Bursa, which is the fourth largest metropolitan city in Turkey, was selected as the study area, and Landsat multi-temporal images of the summer season were used. Firstly, the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified normalized difference water index (MNDWI) and index-based built-up index (IBI) were created using the bands of Landsat images, and LULC classes were determined by applying automatic thresholding. The LST values were calculated using thermal images and SUHI effects were determined. The results show that NDVI, SAVI, MNDWI and IBI indices can be used effectively for the determination of the urban, vegetation and water LULC classes for SUHI studies, with overall classification accuracies between 89.60% and 95.90% for the used images. According to the obtained results, generally the LST values increased for almost all land cover areas between the years 2002 and 2020. The SUHI magnitudes were computed by using two methods, and it was found that there was an important increase in the 18-year time period.

Urbanization and its effects on water resources: Scenario of a tropical river basin in South India

Karamana River Basin (KRB), set in the tropical monsoon climate (i.e., Koppen's Am), hosts the drinking water supply to the capital city of Thiruvanathapuram, one of the highly urbanized cities in the southwestern seaboard of India. Primary focus of the study is a scrutiny of future water security status of KRB, amidst the rising population and subsequent urban sprawl. The study was done through a combination of analysis of remotely sensed data, and collation of data on population growth, surface water distribution and decadal-level groundwater monitoring. An uptrend of the decadal-level population and unscientific constructions across KRB led to the decline of per capita water entitlement and causing conflicts around water service delivery. So this study has an imperative focus on the effects of rapidly growing urban life and its impact on water resources in KRB. This was accomplished by studying the land use, land surface temperature (LST), annual precipitation, and groundwater trend for two decades, followed by land use modeling and quantifying total water deposit. The estimated LST values in KRB, robustly substantiate an upward shift in surface temperature between 2001 (47.55%) to 2020 (64.01%), a testimony of urban sprawl and it may be the major cause to reduce the rate of rainwater infiltration and increase in runoff. The Normalized Difference Vegetation Index (NDVI), used to generate land use map, and LST of the basin have been assessed for the years 2001, 2011, and 2020 to model whether or not land use has been modulated by urbanization. Based on this, future trends of land use changes for 2030 and 2050 have been predicted using CA-Markov model - a model combining Cellular Automata and Markov chain. We also carried out a quantification of annual water deposit, potential evapo-transpiration, infiltration, surface runoff, and storage. Decadal trends of population change, degree of urbanization and consequent rise in domestic water demand and shrinkage of area of open space/soil cover have also been factored in assessing water security in KRB. The results show that, as of today, the city is facing an acute annual shortage of surface water by 321.51 MCM. Furthermore, we propose potential sources for future water security of the state's capital region.

Assessment of heat mitigation capacity of urban greenspaces with the use of InVEST urban cooling model, verified with day-time land surface temperature data

Accurate quantification of the heat mitigation capacity of urban greenspaces is essential in planning decisions due to increased thermal pressures on existing and new urban environments associated with climate change. However, this often requires data analytical skillsets that may not be available to the planning community. The recently developed InVEST 3.8.7 Urban Cooling model addresses this limitation by using several easily accessible parameters, assigned to a land cover map, to produce a heat mitigation index (HMI) intended to estimate the cooling capacity of vegetation in a spatial context. In this study, we validated the HMI derived for three towns with differing morphologies by comparison to land surface temperature (LST) data using linear regression analysis. We found that the HMI can be used to explain a variable proportion of the variation in LST, with R2 ranging from 0.48 to 0.64 depending on the town, with stronger associations obtained for towns with a higher range of LST values. Higher resemblance to LST data was achieved after resampling of the 2 m resolution model outputs to 30 m resolution, inclusion of water bodies as cooling features, and using cooling distance away from large greenspaces of 100 m. On average, a change in the HMI of 0.1 was associated with 0.76 °C change in LST. We conclude that the model is suitable for assessment of heat mitigation interventions through incorporation of vegetation and water bodies into city plans at scales relevant to masterplans rather than fine-tuning of urban design.

Assessment of land surface temperature and land cover variability during winter: A spatio-temporal analysis of Pabna municipality in Bangladesh

Monitoring the change of land use and land cover (LULC) and land surface temperature (LST) at different spatio-temporal scales is vital for evaluating landscape dynamics and thermal environment. This study investigates the decadal change of LULC and winter LST on Pabna municipality over the period between 1990 and 2020 using Landsat images (TM, ETM+ and OLI). The study further explores LST distribution for different LULC classes and the explanatory power of various land surface indicators for change in LST. A supervised maximum likelihood classification (MLC) technique was used for LULC mapping of the study area. The results showed that built-up areas were increasing rapidly while water bodies, bare lands and vegetation decreased. The built-up area expanded by 358% between 1990 and 2020, with the occupied area rising from 1.44 km2 to 6.60 km2. To obtain reliable LST results, average values of LST obtained from multiple Landsat images for each year were used. The mean LST in the winter season has risen by 0.63 °C over the last 30 years. The variation in LST between separate days of the same year increased significantly, although the change in mean LST was small. Statistical analysis of land surface indicators revealed that NDVI, NDBI and NDBaI have significant explanatory power to describe LST scenarios. The explanatory power of NDBI and NDBaI to explain the rise of LST is increasing over time while the cooling capacity of NDVI is declining. LST had a moderate positive correlation with NDBI and NDBI and a weak negative correlation with NDVI.

Land use/land cover changes and their impact on land surface temperature using remote sensing technique in district Khanewal, Punjab Pakistan

ABSTRACT The aim of this research was to assess the land use/land cover (LULC) changes and its impact on land surface temperature (LST) using remote-sensing (RS) technique in the district Khanewal, Punjab, Pakistan. Data were pre-processed using ERDAS imagine 15 and Arc GIS 10.4 software for layer stacking, mosaicking, and sub-setting of Landsat images. After pre-processing, the supervised classification scheme was applied for the years 1980, 2000, and 2020, which explains the maximum likelihood algorithm to identify LULC changes observed in the study area. “Built-up area” in 1980 occupied 1.75% but in 2020, the build-up area increased (5.27%) compared to 2020. Vegetation area was decreased by 4.12% from 1980 to 2020 in district Khanewal. It was observed that there has been a rapid change in vegetation area to build-up area. The LST values were increased by 0.50°C from 1980 to 2020 due to the increasing build-up area from East to West direction in district Khanewal. Maximum and minimum normalized difference vegetation index (NDVI) values were observed 0.72 and −0.2 for 1980 and 2020. The regression line produced a definitive explanation, showing a strong negative correlation with NDVI and LST. The outcomes of this study indicated that a dramatic transformation took place in district Khanewal regarding a decrease in greenness due to a rapid increase in population density, urban growth, and other infrastructural developments. Thus, these results will be used in regional and urban planning and will be used for managing agriculture in the coming years of rapid environmental changes.

Vegetation index-based biomass model and Land Surface Temperature (LST) from urban green spaces in Bandung City derived from multispectral imageries

Bandung City is the largest metropolitan city in West Java Province, with a population of more than 2.5 million people in 2019. With the vast number of population, the need for land that is inevitable. The need for land in this area continuously increased, but the balanced is not meet with the availability of land. Further, it will encourage the conversion of natural land such as green spaces into a built-up form such as settlements. A disturbed vegetation cover would have an impact on the carbon sequestration capabilities and, on the other hand, might increase the land surface temperature. The goals of this study are to determine the carbon sequestration capabilities of tree biomass in urban green space and to examine the relationship between biomass content and land surface temperature (LST). Some vegetation indices include NDVI, ARVI, and SAVI, derived from multispectral Sentinel 2B are being correlated with allometric-based biomass value to generate biomass estimation models. Spatial analysis is useful to provide information about the value of biomass in the tree canopy and spatial distribution of biomass in the City of Bandung. It results that,NDVI provides a better correlation than SAVI and ARVI. The amount of Biomass in the UGS vegetation in Bandung City is4098.504 ton.Spatial distribution of high-value biomass is located in the north of Bandung, while low-value biomass is spread spatially in the south of Bandung. Large-sized trees with high vegetation density are in areas with high biomass values.While LST values range from 0 to 34.73°C. If there is a correlation between biomass and LST, it produces an inverse correlation of 0.33 with a moderate relationship.

Soil moisture change analysis under watershed management practice using in situ and remote sensing data in a paired watershed.

Soil moisture, vegetation cover, and land surface temperature are vital variables in water-energy balance, eco-hydrological processes, and water resources management, which can be influenced by watershed management activities. This research focused on the spatial and temporal variability of soil moisture, vegetation cover, land surface temperature, and Temperature-Vegetation Dryness Index (TVDI) under a biological watershed management practice in the Taleghan paired watershed, namely, treated (TW) and control watersheds (CW), in Alborz province, Iran. In this research, along with the remote sensing techniques, the soil moisture and vegetation cover data were measured and statistically analyzed in the three aspects of both TW and CW during a growth period from May to October 2017. The results indicated that soil moisture, vegetation cover, and land surface temperature values in the paired watershed were significantly different at the 0.01 level during the study period. The increased vegetation cover in the TW had an inverse effect on the land surface temperature and TVDI, while directly impacted the soil moisture content. The average TVDI in the CW was 0.83, while this index was found to be 0.69 in the TW. Unlike the vegetation cover and soil moisture, the results revealed that the southern aspects had the highest TVDI and land surface temperature compared to the northern and eastern aspects of both watersheds. However, the increased vegetation cover as a biological watershed management activity in the steep terrain and mountainous areas of TW led to an increased soil moisture and a decreased land surface temperature and soil dryness. As a result, decreasing soil dryness in the TW can exert vital controls on the water resources and increasing water availability. In the arid and semiarid countries such as Iran, a proper watershed management activity can effectively increase soil moisture and water availability in the watersheds. In particular, the vegetation cover protection and biological practices can be considered as practical solutions in the rehabilitation of exhausted watersheds in arid and semiarid environments.

Modification effect of urban landscape characteristics on the association between heat and stroke morbidity: A small-scale intra-urban study in Shenzhen, China

BackgroundShort-term heat exposure might induce stroke morbidity and mortality, and there were several studies explored the possible vulnerable populations. At present, the research on the modification effect of intra-urban landscape characteristics on the association between heat and stroke morbidity is limited, especially in China. MethodsWe collected data on 22,424 first-ever strokes between 2010 and 2016 in Shenzhen, from June to August of each year. We adopted the case-only study combined with logistic regression models to examine the modification effects of 5 urban landscape characteristics. We studied the characteristics of relevant vulnerable populations through stratification analyses. ResultsHigh values (refer to the median values) of nighttime land surface temperature (LST) and the proportion of impervious surface may aggravate the harmful effects of heat on stroke morbidity, with the OR values (95% CI) of 1.205 (1.053, 1.357) and 1.115 (1.010, 1.220); while, high values of NDVI and the proportion of water bodies may alleviate the harmful effects of heat, with the OR values (95% CI) of 0.772 (0.699, 0.845) and 0.821 (0.741, 0.901). The OR value of daytime LST was 1.004 (0.861, 1.147). Statistically significant modification effects were located in the population without Shenzhen's household registration; as for nighttime LST, statistically significant modification effects were located in females and the elderly. ConclusionsHigh values of nighttime LST and the proportion of impervious surface might aggravate the harmful effects of heat on stroke morbidity, while high green space and water cover might alleviate its effects. Immigrants were the related vulnerable populations. The government should take measures to cope with climate warming and pay attention to the health effects of heat on immigrants.

Estimating and Monitoring the Land Surface Temperature (LST) Using Landsat OLI 8 TIRS

Land Surface Temperature (LST) is average temperature of an element of the exact surface of the Earth calculated from measured radiance which depends on the albedo, the vegetation cover, and the soil moisture. Land Surface Temperature can affect human discomfort, health problem, higher energy bill and further reduce the habitability of urban and sub urban area as Makassar city has been recently undergoing massive urban development. This study tries to monitor and estimate Land Surface Temperature by using Landsat 8 TIRS and the data analyzed by vegetation index, and temperature index in order to derive Land Surface Temperature value. The result shows that the vegetation area declined around 3470 hectares in the last four years while the urban area increased approximately 1509 hectare. In addition, 2015, Makassar, South Sulawesi, Indonesia are experienced the highest temperature at 32 degree Celsius while 2019 shown that the maximum heat reached 29 degree celsius. However, the moderate and high temperature (26 – 29 degree Celsius) in 2019 expand and cover wider area than in 2015 as the area of vegetation declined and built-up area increased significantly

Quantification of the Environmental Impacts of Highway Construction Using Remote Sensing Approach

Highways provide key social and economic functions but generate a wide range of environmental consequences that are poorly quantified and understood. Here, we developed a before–during–after control-impact remote sensing (BDACI-RS) approach to quantify the spatial and temporal changes of environmental impacts during and after the construction of the Wujing Highway in China using three buffer zones (0–100 m, 100–500 m, and 500–1000 m). Results showed that land cover composition experienced large changes in the 0–100 m and 100–500 m buffers while that in the 500–1000 m buffer was relatively stable. Vegetation and moisture conditions, indicated by the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI), respectively, demonstrated obvious degradation–recovery trends in the 0–100 m and 100–500 m buffers, while land surface temperature (LST) experienced a progressive increase. The maximal relative changes as annual means of NDVI, NDMI, and LST were about −40%, −60%, and 12%, respectively, in the 0–100m buffer. Although the mean values of NDVI, NDMI, and LST in the 500–1000 m buffer remained relatively stable during the study period, their spatial variabilities increased significantly after highway construction. An integrated environment quality index (EQI) showed that the environmental impact of the highway manifested the most in its close proximity and faded away with distance. Our results showed that the effect distance of the highway was at least 1000 m, demonstrated from the spatial changes of the indicators (both mean and spatial variability). The approach proposed in this study can be readily applied to other regions to quantify the spatial and temporal changes of disturbances of highway systems and subsequent recovery.

Spatiotemporal changes on land surface temperature, land and water resources of host communities due to artisanal mining.

Assessment of land use/land cover change (LULCC) and its effects on the natural environment are important in policy decision making for sustainable environment. This study investigated and assessed the environmental impacts of artisanal and small-scale mining operations on environmental degradation resulting from such activities in selected host communities (Akoko Edo, Ijero and Ilesa). Vegetation, water, built-up expansion and land surface temperature dynamics were examined and analysed using appropriate indices for the years 1987, 2002 and 2017. A gradual decrease in the normalised difference vegetation index (NDVI) values was observed across Akoko Edo and Ijero Ekiti with a corresponding change in the highest NDVI values while Ilesa witnessed a higher NDVI value in 2017 relative to the previous years. The majority of the normalised difference water index (NDWI) values for 2017 appear to be above 0 in all the host communities. Mean land surface temperature (LST) values are in the order 24.63 °C (1986) < 25.26 °C (2002) < 26.32 °C (2017) for Ijero Ekiti study area while mean LST values are in the order 24.30 °C (1986) < 24.46 °C (2002) < 25.82 °C (2017) in Ilesa study area. Modified Normalized Difference Water Index (MNDWI) seemed a more reliable indicator as the index was able to enhance the water surfaces more clearly as indicated in the case of Akoko Edo study area. Due to its high heat capacity, high land surface temperatures were experienced over urban areas and uncultivated bare land during all the periods of study. The study showed that changes in land covers have affected the normal functioning of the habitats through urbanisation. Adequate natural resource management is therefore necessary to ensure sustainable development through effective policy formulation.

Soil Moisture Estimation using Sentinel-1 SAR Data and Land Surface Temperature in Panchmahal District, Gujarat State

This paper presents the potential for soil moisture (SM) retrieval using Sentinel-1 C-band Synthetic Aperture Radar (SAR) data acquired in Interferometric Wide Swath (IW) mode along with Land Surface Temperature (LST) estimated from analysis of LANDSAT-8 digital thermal data. In this study Sentinel-1 data acquired on 27 February 2020 was downloaded from Copernicus website and LANDSAT-8 OLI data acquired on 24 February 2020 from the website https://earthexplorer.usgs.gov/.The soil samples were collected from 70 test fields in different villages of three talukas for estimating soil moisture content using the gravimetric method. The Sentinel-1 SAR microwave data was analysed using open source tools of Sentinel Application Platform (SNAP) software for estimation of backscattering coefficient. Land surface temperature estimated using Landsat-8 thermal data. The Landsat-8, Thermal infrared sensor Band-10 data and operational land imager Band-4 and Band-5 data were used in estimating LST. The Soil Moisture Index (SMI) for all field test sites was computed using the LST values. The regression analysis using σ0VV and σ0VH polarization with soil moisture indicated that σ0VV polarization was more sensitive to soil moisture content as compared to σ0VH polarization. The multiple regression analysis using field measured soil moisture (MS %) as dependent variable, and σ0VV and SMI as independent variable was carried which resulted in the coefficient of determination (R2) of 0.788, 0.777 and 0.778 for Godhra, Goghamba and Kalol talukas, respectively. These linear regression equations were used to compute the predicted soil moisture in three talukas. The maps of spatial distribution of soil moisture in three talukas were generated using the respective regression equations of three talukas.

Impact of Vegetation Density Change on Land Surface Temperature in Kuta Utara Subdistrict, Badung Regency, Bali Province

Land function change can impact on decreasing green open space and land surface temperature (LST), which is one of the important parameters in environmental change. This study aims to identify vegetation density change and its impacts on land surface temperature in Kuta Utara Subdistrict, by comparing two vegetation indices to decide which vegetation index is more suitable if it is associated with LST. The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were used to knowing vegetation density change. This study estimated the brightness temperature of the satellite to find out the land surface temperature. NDVI, EVI, and LST values were obtained through Landsat ETM+ and Landsat OLI in 2010, 2015, and 2020. The values are analyzed using the spatial-temporal analysis method to see the vegetation density change and LST. The results of NDVI and EVI analysis showed that there are changes in vegetation density for each year, especially in the east and west Kuta Utara Subdistrict. The decreased in vegetation density causing an increase in LST from 2010, 2015, and 2020. Based on the analysis results, vegetation density changes based on NDVI and EVI did not have a significant impact on LST in Kuta Utara Subdistrict.

The impact of impervious surface, vegetation, and soil areas on land surface temperatures in a semi-arid region using Landsat satellite images enriched with Ndaisi method data.

Impervious surfaces are a significant issue of both urbanization and environmental assessment. However, it is a problem to classify impervious surface (IS) and soil areas as separate classes in land cover classification. The objectives of this study are to obtain impervious surface, vegetation, and soil areas clearly of an urban complex with a semi-arid climate and to better determine the relationships of IS, vegetation, and soil areas with land surface temperatures (LSTs). For this purpose, IS, vegetation, and soil areas in a semi-arid city of Turkey-Kayseri city were identified by using Normalized Difference Anthropogenic Impervious Surface Index (NDAISI) data and support vector machine (SVM) method together in the classification of different areas. Landsat 5, 7, and 8 satellite images of 1987, 2000, and 2013 were used, respectively, in this study. Afterward, the effects of these areas on LSTs were analyzed. Regression analysis was used to determine the relationships between land cover areas and surface temperatures. To better demonstrate these relationships, besides common pixel-based and classical regional-based approaches, a new density-based regional analysis approach was proposed. This study is an innovative one in terms of detecting IS and indicating relationships between land cover areas and surface temperatures in semi-arid regions. Another innovation of the study is related to the results produced. The results showed that decreasing LST values were observed with increasing IS and vegetation cover values and increasing LST values were observed with increasing soil areas. The present findings may provide significant contributions to the literature and will facilitate the development of urban planning strategies in semi-arid regions.

Substitution of satellite-based land surface temperature defective data using GSP method

Land surface temperature (LST) as an important environmental variable provides valuable information for earth environmental system modelling. Currently, LST is obtained through satellite thermal sensors at various spatial and temporal resolutions. Although spatially continuous satellite-based LST measurements are intended to overcome the shortcomings of sparse ground-based LST measurements, LST images often contain anomalous values due to the existence of clouds or sensor malfunctioning. The problem becomes more serious where the users deal with high spatial resolution characterized by low temporal resolution. This study examines the capability of a newly developed graph signal processing (GSP) method using two-dimensional single-date thermal data. For this purpose, four Landsat/TIRS datasets are analyzed. The data of five elliptical regions on thermal images are eliminated and then reconstructed through the GSP method and using the LST values of the enclosing rectangles containing the ellipsoids. The results indicate that the temperature variation determined by the GSP method generally conforms to the original image LST values. According to a correlation test conducted on the original image LST and those obtained through the GSP method, the values vary from 58% to 95%, which is an above-the-average rate (RMSE from 0.69 to 2.27). The statistical analysis of the original image LST in both the elliptical regions and the enclosing rectangles containing the ellipsoids indicates that an increase in the variance of LST data causes an increased error in the calculation of temperature by the GSP method, and vice versa. The results of the analysis of variance (ANOVA) and Duncan test indicated that an increase in the number of the non-zero spectral bins would result in increased RMSE values for all the dates and the regions. Moreover, the model errors were significant at the 0.05 level across all the image date and five elliptical study regions. Based on the results, the use of this method is recommended for the reconstruction of LST missing values, where dissimilarity of atmospheric conditions limits the use of other methods that depend on the time series data of various dates and a great deal of data calculation.

Artificial Neural Network based thermal anomalies associated with earthquakes in Pakistan from MODIS LST

There are several reports about thermal anomalies associated with the impending earthquakes (EQs); among which the underlying mechanism is linked with the main shock with long, intermediate and short-term precursors. In this paper, we analyzed thermal anomalies from Moderate Resolution Imaging Spectroradiometer (MODIS) based Land Surface Temperature (LST) of three different magnitudes and shallow depth EQs in Pakistan. Our focus is to investigate the thermal anomalies by the statistical approach and Artificial Neural Network (ANN) in spatial and temporal LST values within 10 days before and after the main shock as short-term precursors. After implementing statistical and the ANN approach, LST revealed that thermal anomalies occurred within 1–10 days before the main shock of Mw > 6.0 EQs. However, a low intensity LST anomaly is also recorded within 20–25 days before the main shock of Mw 5.2 EQ. We study that LST anomalies are magnitude and depth dependent and it is more likely to occur before EQ of Mw > 6.0 and shallow depth within 10 days before/after the main shock day. The results depict that Mw 5.2 EQ anomaly is not clearly associated to the main shock, as it locates outside the window of 1–10 days before/after the main shock. Similarly, two out of three events caused post-seismic thermal anomalies of less magnitude as compared to the pre-seismic thermal anomalies. The pre-seismic LST anomalies occur with high intensity before shallow depth and large magnitude EQs than post seismic LST anomalies. The LST anomalies occurred before all the EQs suggesting it to be a reliable precursor of short to intermediate interval associated with an impending EQ.

Monitoring Vegetation Patterns at the Syrdarya River Basin from 2000 to 2015 using MODIS Data

The dependence of vegetation condition dynamics as expressed by Normalized Difference Vegetation Index (NDVI) from hydro-climatic factors (Multiyear precipitation, land surface temperature) in the Syrdarya River Basin (SRB) was analyzed for the period of 16 years from 2000 to 2015. The analysis demonstrated a different correlation between NDVI and hydrometric parameters. According to experimental analyses, the average NDVI values reached a maximum in April and minimum in October, while the annual average values of land surface temperature were observed maximum in June and minimum in October. Correlation between precipitation and NDVI was positive and extraordinarily strong in Spring while the correlation between Land Surface Temperature (LST) and NDVI was found negative and strong. Correlation between LST and NDVI changed from positive in spring to negative in summer due to an increase in seasonal temperature and found a decrease of vegetation cover throughout the Syrdarya river basin. Desert vegetation area in plain part of SRB decreased while NDVI of cropland area in Syrdarya and Shu river basins remained the same or increased. Hydro-climatic factors negatively affected a decrease in vegetation cover, which leads to desertification processes.

Possible Atmosphere and Ionospheric Anomalies of the 2019 Pakistan Earthquake Using Statistical and Machine Learning Procedures on MODIS LST, GPS TEC, and GIM TEC

Identifying atmospheric and ionospheric anomalies based on remote sensing satellites has contributed highly to develop the hypothesis of lithosphere-atmosphere-ionosphere coupling over the earthquake (EQ) epicenter during the seismic preparation period. This article has investigated the variations of potential EQ precursor in daytime and nighttime land surface temperature (LST) before and after the 2019 Pakistan EQ from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. The nighttime LST values of MODIS exhibit temporal anomalies during nighttime period within a time window of five days before and after the main shock day. Furthermore, the LST values predicted by artificial neural network (ANN) validate the significant enhancement in nighttime time series of MODIS. The nighttime LST anomalies obtained from the observation and ANN prediction are more than 20% and 7% of normal distribution beyond the confidence bounds, respectively, within five days after the main shock. Likewise, the ionospheric anomaly from daily total electron content (TEC) values at Sukkur Global Positioning System (GPS) station confirms the EQ associated ionospheric perturbations on the day after the main shock. The Global Ionospheric Maps (GIMs) also show the TEC anomalies during 1000–1400 LT on September 25, 2019.

Geospatial Techniques, a Superlative Method to Assess Urban Heat Island Intensity: The Case of Abuja Municipal, Nigeria

Most cities around the world, including Abuja Municipal are being faced with an undesirable increased in air temperature. This is indicated by an increase in non-porous, non-evaporating, highly thermal conductive surfaces such as concrete and asphalt, which has replaced the vegetation biomass resulting to the formation of urban heat island. There is an increasing need for studies on the changing trend of UHI intensity in cities. This research employed geospatial techniques to determine the urban heat island intensity in Abuja Municipal. Temperature characteristics over twenty selected rural and urban locations in Abuja, FCT were derived from the satellite image of 1986, 2001 and 2016 using the “Extract Multi Values to Point” tool in ArcGIS 10.4. These transects pass over various landscapes with different environmental settings, with the aim of understanding the factors shaping the city’s thermal landscape. The intervals of +15 years were deliberately chosen to ensure uniformity between the datasets. The results of this analysis indicate that UHII has been increasing, from 1986-2016, giving credence to the results of the spatial and temporal analysis of the land surface temperature, indicating the development phases had hit full stride. The different periods under study (1986, 2001 and 2016) were also tested using the student “t” test to determine the significant difference in the land surface temperature values to acknowledge the presence of a substantial urban heat island within the study area. The result reveals the calculated “t” values of 2.50, 3.34, 5.57 of 1986, 2001 and 2016 respectively, are higher than the critical value of “t” at 0.05 being 1.73, thus, revealing the temperature differences between the urban and rural stations to be highly significant, indicating the presence of a strong urban heat island. Also, a slide difference in the temperature was observed with the Rubuchi and Karmajiji rural areas having higher temperature readings than their counterparts in the urban areas, Asokoro and Garki, with readings of −0.4°C and −1.3°C. Since effectiveness of a surface in reducing daytime urban air temperatures depends strongly on the amount of heating avoided, the study recommends preserving and replicating greenery, light coloured facades as measures to reduce the effects of urban heat island.