| Population explosion process in developing nations has increased in recent years and will continue into the twenty–four century.However,the population explosion process has also brought many ecological and environmental issues,especially the urban heat island effect.Therefore,it is necessary to carry out corresponding monitoring and impact analysis on the population explosion process model and the urban heat island effect.At present,Africa and Asia are rapidly growing continent in the globe,but the regional differences in the population explosion are obvious,the spatial patterns are not the same,and the environmental issues are also different.Therefore,this research takes Sierra Leone,west Africa and China,Asia as the study area,and select two main cities in Sierra Leone(Freetown)and China(Beijing)as an example.Based on US mid-resolution terrestrial satellite data(Landsat 5,7and 8)combined with socioeconomic data,Remote sensing technology was used to estimate the land use land cover(LULC)change and its effects on land surface temperature(LST).The main research contents of the thesis are as follows:1.The study obtained a high classification accuracy in a coastal city(Freetown)and an inland city(Beijing).From change analysis of land use and land cover,it was observed that there is an expansion in the built-up region by 8.68%and a decrease in vegetation by 5.07%in Beijing.Here,the fundamental change is that vegetation zones and bare lands got changed over to the developed area.The results of the comprehensive index refer to the degree of development and utilization in the second stage is greatly reduced compared the first stage,from the comprehensive utilization of land use.The overall increase in the index shows that construction has gradually entered a stable phase,and land-use pressure has gradually declined.It was shown that the value in all periods were higher than zero indicates that the development stage is dominant in all periods except the first period from 1997 to 2005 indicating that this period has no new projects.High values related to the development,new construction and building area.Finally,urbanization responds mainly to the spatiotemporal dynamics of LULC in Beijing.The results of the Freetown analysis show that the LULC dynamic has a negative impact on ecosystem service value;the comprehensive index has increased from 2000 to 2018,reflecting the growth of the study area.The study area’s key dynamics are urbanization and population growth.Therefore,land-use dynamics should be considered for the study area’s long-term sustainability and to protect the environment from degradation.2.Because of severe urban development,the majority of green areas have been transformed into impermeable features,which act to increase the LST in urban areas.During the study period,the average surface temperature in Beijing increased.The rising amount of LST resulted from some anthropogenic activities as expansions in urban and development factories.In general,higher LST values were observed in high residential areas,followed by Low residential and bare soil surfaces.The three LULC type trees,vegetation,and water had a lower LST compared with high-low residential areas.The LST was increased from one year to another due to development of human activities and climatic change of Freetown.The high levels of LST were concentrated in the northern parts of Freetown.Despite having a larger population,a larger built-up area,and a faster pace of growth,Freetown was still cooler than Beijing.The city’s low temperatures are due to its proximity to the sea and a large amount of vegetation that surrounds it.Even though Freetown is close to the sea and has a lot of vegetation,the city has a higher temperature than the rest of the country.The results are critical for developing countries’inland and coastal cities to implement heat mitigation strategies.3.LST maps of Beijing and Freetown were produced using the image-based approach.The research adopts different mathematical models to forecast land surface temperature.Generally,there is a lack of literature using different land cover indices as an independent factor to derive the land surface temperature.Furthermore,Polynomial curve fitting has not yet been used to predict land cover indices to derive the future land surface temperature.This study applied a Polynomial curve fitting model and regression equation to estimate future urban climate designs with anticipated vegetation and non-vegetation indices.Estimating surface temperatures using different factors requires that the relationship between the prediction factors and surface temperature should be high.The land cover indices(water index,urban index and salinity index)and LST values were extracted from every pixel in the study region for each point data type.The indices with the most noteworthy relative with land surface temperature were chosen for use in the linear regression model to anticipate future land surface temperatures.To achieve the goal of the research,the future land covers(water index,urban index and salinity index)of the region were first simulated.The polynomial curve fitting model was used to simulate land cover for the years 2017 and 2020 for Beijing and Freetown respectively.Based on four statistical indices the regression model was tested using an independent Landsat thermal infrared information,and the model nearly resembled the temperature patterns.The stimulation results propose that the development will be accompanied by surface temperature increases,especially in Beijing and Freetown.The temperature prevailing in the west of the metropolitan area may increase in the city somewhere in the range from 2017 to 2020.Additionally,the results of the LST prediction show that the model is perfect.Our discoveries can be represented as a helpful device for policymakers and community awareness by giving a scientific basis for sustainable urban planning and management.To improve the study results in this section,four methods are applied for predicting temperature changes namely,(DENFIS(ANFIS),(WNN)and(MARS).Here,it should be mentioned that the WNN,DENFIS,and MARS models have not been used so far to predict the LST changes from satellite data.Furthermore,the ANFIS model is limited to the prediction of LST changes.The LST for 1995,2004,2010 and 2015 and the geodetic coordinates are used for the selection points.Firstly,the input variables should be evaluated and assessed.According to the previous studies,the NDVI,NDWI,NDBI,UI,and topographic changes are the main factors that affect the LST changes.Thus,the NDVI,NDWI,NDBI,and UI of the study area for 2010 are calculated and used to model the LST.The important factor(IF)is calculated from 0 to 100 for each variable,it can be seen that the impact of topographic changes is high for Beijing area.In addition,the influence of previous temperature records is greater than 70%with regard to the prediction of LST.It means that the previous LST records can be used to estimate the future LST.The data for 2010 is deployed as the target for the training stage,and the LST for 2015 is utilized as the target for the testing stage.The evaluation of the prediction models shows that the ANFIS and DENFIS are the optimum models for detecting the LST changes in the Beijing area.Also,the WNN model showed the weakest performance in predicting the LST changes.Moreover,the ANFIS model is applied to detect the LST for the years 2025 and 2035.The prediction values show that the LST will increase rapidly,and the slope of change is 0.33~OC/year.In addition,it is seen that the prediction values for the LST of water,forest,and farm areas do not show any significant changes compared to the urban areas.What’s more,the results provide a scientific basis for decision-makers in land use management and provide a reference for researchers.4.The dissertation results show that the investigation study areas lack land resources.The loss of water bodies from 2000 to 2018 reflects the decline in water resources.The total value of ecosystem surface value(ESV)declined from 2000 to 2026,affecting climate regulation,food production,water supply,and waste treatments.Analyses of land use dynamic showed an increase in the comprehensive index from 2000 to 2018,indicating great urbanization,causing direct loss of biodiversity of the ecosystem and ESV reduction.Additionally,transition analysis reveals that;high amounts of bare land and vegetation were converted to the urban areas,causing an adverse impact on local ecosystem.Freetown City,Sierra Leone,is a semiarid and arid area with a series of droughts.A vegetation Index and LST from Landsat were used as indicators for drought monitoring.Moreover,Correlation analyses between Soil moisture index and LULC indices are applied to monitor drought patterns in this area.The approach integrates the land surface reflectance and thermal properties and the NDVI changes to identify the extent and pattern of the past drought years.Also,Normalized differences water index(NDWI),differences water index(DWI),and salinity index(S2)were selected as best predictors for drought.According to these investigation results,a large amount of land has been affected by agricultural facilities due to increasing drought effects and uncontrolled use of water in Freetown.This investigation provides a simple and efficient way of addressing the issues of drought in these research areas.Changes in LULC were attributed to anthropogenic activities,causing severe impacts on the selected study area’s sustainable development.Therefore,land-use dynamics should be taken into consideration for sustainable development of the study area.The findings of this study can assist,support and improve sustainable development policies.The importance of this study will be vital for preserving the constancy and sustainable development around the globe.Furthermore,the results provide a scientific basis for decision-makers in land use management and provide a reference for researchers.Recommendations for future studies included data merging to improve spatial and temporal representation of remote sensing data,resource mobilization to increase urban weather station density and image classification into local climate zones which are of easy global interpretation and comparison. |
PDF Full Text Request