The implications of urban growth-induced temperature changes on household air conditioning demand were analyzed using Landsat-derived land surface temperature-based degree days. Overall, the results of this study showed that LST, human thermal comfort, and air conditioning energy demand are strongly affected by seasonal and urban growth-induced land cover changes.
PLAGIARISM
PUBLICATIONS AND MANUSCRIPTS
I would like to thank all staff in the Geography Department at UKZN Pietermaritzburg Campus for any support. My sincere gratitude also goes to the staff of the Department of Physics at the University of Zimbabwe for encouraging me to complete the race.
GENERAL INTRODUCTION
- Introduction
- Temperature measurement using remote sensing
- Aim of the study
- Objectives of the thesis
- Thesis outline
- Chapter 1: General introduction
- Chapter 2: Remote sensing applications for monitoring the impacts of urban growth on
- Chapter 3: Improved land use and land cover (LULC) classification for urban growth
- Chapter 4: Spatial variations in extreme heat vulnerability and link to LULC distribution 8
- Chapter 7: Implications of urban surface changes on air conditioning energy demand
- Chapter 8: Remote sensing based future prediction of LULC and land surface
- Chapter 9: Synthesis and conclusion
Therefore, remote sensing represents a practical approach for analyzing surface temperatures at large spatial and temporal scales (Abutaleb et al., 2015). Because the Earth's surface is not a black body and due to differences in spectral properties between land use and land cover, a correction for emissivity is required (Abutaleb et al., 2015).
A REVIEW OF IMPLICATIONS OF URBAN GROWTH ON INDOOR
Abstract
Urban growth and the resulting expansion of impervious surfaces affect a landscape's thermal characteristics by increasing Land Surface Temperatures (LST). This review therefore provides synthesis on the progress of spaceborne remote sensing in monitoring the implications of urban growth on thermal properties.
Introduction
Review articles on urban surface temperature have mostly focused on spatial and temporal variations of LST and retrieval of heat islands using remote sensing (Mohamed et al., 2016; Sattari &. For example, Mohamed et al. 2016) recently reviewed LST methods and obtaining the emission value using satellite data of low and medium spatial resolution.
Implications of urban growth on in-and-outdoor thermal conditions
Remote sensing of impacts of urban growth on in-and-outdoor thermal conditions
However, passive microwave spaceborne sensors have poor spatial resolution (tens of kilometers), hence the wide use of medium-resolution thermal infrared data, which have improved spatial, spectral, radiometric and temporal characteristics (Tomlinson, et al., 2011). . Whereas studies such as Lo et al. 1997) successfully used the airborne Advanced Thermal and Land Application Sensor (ATLAS) over Alabama, USA, their approach may not be viable in resource-limited areas.
Challenges in remote sensing of the impacts of urban growth on in-and-out door thermal
Despite the fact that thermal sensors with high spatial resolution are ideal for characterizing urban surfaces, their utility is limited by cost, especially in developing countries with limited resources. According to Wu, et al. 2014), the biggest limitation of remote sensing is that high spatial resolution sensors have low temporal resolution and vice versa.
Shift towards the use of broadband medium resolution
Specific advantages of Landsat include appropriate spatial resolution, free online access to archive data since 1972 and the introduction of advanced missions such as Landsat 8 with improved data quality (Tao, et al., 2013; Liu & Weng, 2009; Sithole & Odindi, 2015). . However, the recently launched Landsat 8 has two thermal bands that enable the retrieval of temperature using both single-band and split-window techniques (Yang, Lin, et al., 2014; Rasul, et al., 2015).
Analytical algorithms for assessing urban growth and thermal conditions
- Land use and land cover classification for urban growth detection
- Assessment techniques of urban growth induced extreme heat vulnerability
- Monitoring impact of land cover changes on LST using remote sensing
- Remote sensing based prediction of future LST distribution
- Estimation of impact of urbanization on outdoor thermal discomfort
- Impact of urban growth related warming on air-conditioning energy demand
- Local climate zoning and the World Urban Database and Access Portal Tools
Cooling degree days (CDD) provide a measure of space cooling energy, while heating degree days (HDD) provide a measure of household heating energy (Christenson et al., 2006). Degree days have been shown to have a strong positive correlation with household energy consumption in resource countries such as European cities (Balaras et al., 2005).
Future recommendations
Thermal data with high temporal resolution characterize sensors with low spatial resolution, such as geostationary satellites (Sattari & Hashim, 2014). Available literature such as Johnson, et al. 2014) produced heat vulnerability maps at low spatial resolution, for example at district and census block scale.
Conclusion
However, there is little literature on future LULC and temperature patterns using remote sensing. It is also necessary to improve temporal analysis of the urban thermal conditions, such as by integrating data from polar orbit with geostationary sensors.
Link with other chapters
Data fusion techniques such as the incorporation of land cover indices have the potential to further improve LULC mapping. The potential of medium-resolution data, such as the Landsat series, to map outdoor thermal disturbance and indoor air conditioning energy demand remains to be tested.
ENHANCED URBAN CLASSIFICATION USING MULTI-SPECTRAL
Abstract
This study sought to assess the potential of the recently launched Landsat 8 sensor thermal band and derived vegetation indices to improve land cover classification in a complex urban landscape using the Support Vector Machine (SVM) classifier. This study compared the individual and combined performance of Landsat 8's reflectance and thermal bands and vegetation indices in urban land use-land cover (LULC) classification.
Introduction
The Normalized Difference Built Index (NDBI) is used to extract built-up areas from remotely sensed data, although it ignores the fact that, in addition to built-up areas, bare areas are also higher in the mid-infrared (MIR) than the near-infrared (NIR). reflect ) band (Hua, et al., 2013). The Normalized Difference Wet Index (NDWI) separates water from other types of surface cover using the principle that water reflects more in the visible spectrum than in the shortwave infrared (Hua, et al., 2013; Stathakis, et al. , 2012).
Materials and methods
- Description of study area
- Field data collection and processing
- Remote sensing data acquisition and pre-processing
- Landsat 8 spectral bands and vegetation indices retrieval
- Image classification
- Accuracy assessment
- Significance of the differences in accuracy between the classification methods
To evaluate the reliability of the results obtained from this study, an accuracy assessment was performed for each land cover class. Producer accuracy is a measure of how correct the classification is, while user accuracy is a measure of the reliability of the map for each class (Namdar, et al., 2014).
Results
- Analysis I: Classification results using the traditional OLI spectral bands
- Analysis II: Classification results using TIRS spectral bands
- Analysis III: Classification results using OLI & TIRS spectral bands
- Analysis IV: Classification results using spectral vegetation indices
- Analysis V: Classification results using TIRS spectral bands and VIs
- Analysis VI: Classification results using OLI spectral bands and VIs
- Analysis VII: Classification results using OLI, TIRS spectral bands and VIs
Comparatively, the results show that the use of Landsat 8-derived vegetation indices produced slightly lower classification results (i.e. overall user and producer accuracy), compared to the use of traditional reflectance bands (see detailed information in Analysis I). The classification results show great improvement in the overall, user and producer accuracies for all the classes covered in this study.
Discussion
Overall, the use of integrated datasets outperformed the use of thermal bands and vegetation indices as stand-alone classification variables. In contrast, the use of the four selected vegetation indices as a stand-alone data set proved to be relatively weak in distinguishing the LULC of complex and heterogeneous urban environments.
Conclusion
Link between Chapter 3 with other chapters
As such, the next chapter (Chapter 4) will relate heat vulnerability to LULC spatial structure derived from classification of Landsat multi-spectral data in Harare.
SPATIAL DISTRIBUTION OF EXTREME HEAT VULNERABILITY AND
Abstract
This study aimed to derive detailed area-specific spatial information on the distribution of heat vulnerability in Harare city, Zimbabwe, valuable for informed urban thermal mitigation, planning and decision-making. Low to moderate heat vulnerability was comparatively observed in the high income northern suburbs with low physical exposure and population density.
Introduction
In previous studies of heat vulnerability, researchers have largely adopted heat exposure factors derived from previous Landsat missions; Landsat 5 and Landsat 7 (Johnson, et al., 2014;. For example, Johnson et al. 2013) have derived NDBI and NDVI from Landsat 7 Earth Thematic Mapper Plus (ETM+) optical information.
Methodology
- Pre-processing of remote sensing datasets
- Processing of vulnerability factors
- Vulnerability mapping
- Derivation of LST from thermal radiances
Each factor of socio-demographic vulnerability was scored on a scale of 0 to 1, with values increasing as vulnerability increased (Buscail et al., 2012). An increase in the proportion of bare and built-up areas increases the area's exposure to high temperatures (Chen et al., 2012).
Results
- Variability of selected image based indices during the hot season in Harare
- The vulnerability of the city of Harare to extreme surface temperature
- Spatial correlation between estimated vulnerability and remotely sensed
Furthermore, we performed a spatial correlation between the mapped heat vulnerability and the observed distribution of land surface temperatures. High temperatures (above 35oC) were observed in the southern and western parts, where heat vulnerability was moderate to very high.
Discussion
In western areas of Arizona, high heat vulnerability and high surface temperature were also found to coincide due to physical exposure (Chow, et al., 2012). A study in the same area also found that the northern parts of the city are largely inhabited by high-income strata (Wania, et al., 2014).
Conclusion
This is consistent with Aubrecht and Ozceylan (2013), who found higher rates of heat vulnerability in urbanized areas than in non-urbanized settings in the US. Overall, the heat vulnerability map produced provides a solid basis for guiding policy design and interventions, particularly to increase the capacity of the urban poor to combat extreme heat.
Link between Chapter 4 with other chapters
IMPLICATIONS OF LAND USE AND LAND COVER DISTRIBUTION
- Abstract
- Introduction
- Methodology
- Description of the study area
- Meteorological data collection and processing
- Remote sensing data collection and pre-processing
- Relative humidity retrieval from satellite and field observation
- Retrieval of seasonal thermal discomfort patterns
- Linkage between land cover fraction and thermal discomfort patterns
- Results
- Relationship between air temperature and land surface temperature
- Relationship between relative humidity and air temperature for different sub-
- Performance of the regression models
- Spatial and temporal patterns of thermal discomfort in Harare
- Urban land cover classification and link with observed thermal discomfort
- Distribution of land use/cover types in Harare
- Link between LULC types in seasonal thermal discomfort patterns in Harare
- Discussion
- Conclusion
- Link between Chapter 5 and other chapters
Previously, Landsat TM and ETM have been used in thermal discomfort analysis (Wei-wu et al., 2004). The correlation between air temperature and relative humidity is known to be strongly negative (de-Azevedo et al., 2015).
RESPONSES OF URBAN LAND SURFACE TEMPERATURES TO LONG
- Abstract
- Introduction
- Materials and methods
- Description of the study area
- Pre-processing of remotely sensed data
- Land use and cover classification, accuracy assessment and change detection
- Derivation of thermal characteristics
- Responses of temperature to LULC changes
- Changes in the contribution of land cover to the thermal environment in the city
- Normalized change in average temperature due to land cover changes
- Results and discussion
- Changes in LULC distribution between 1984 and 2015
- Changes in Land surface temperatures between 1984 and 2015
- Changes in distribution of relative temperatures (surface heat island intensities)
- Changes in the contribution of LULC types to thermal characteristics of Harare 112
- Normalized change in average temperature of Harare in response to LULC
- Conclusions
- Link between Chapter 6 and other chapters
Settlements are more spacious in the north, where mainly low and medium density residential areas are found (Wania, et al., 2014). The proposed normalized mean temperature of the study area was calculated using equation 6.6 (Feng, et al., 2014).
ASSESSMENT OF IMPACT OF URBAN LAND SURFACE
Abstract
Landsat and in-situ temperature data were used to determine land use and land cover distribution, as well as to estimate trends in air conditioning energy requirements between 1984 and 2015. Heating degree days (HDD) and cooling degree days (CDD) derived from Landsat thermal data and in situ temperature measurements were used as a measure of indoor heating and cooling energy in the cool and warm season, respectively.
Introduction
Days (CDD) provide a measure of energy for space cooling, while heat degree days (HDD) derive energy for domestic heating (Christenson, et al., 2006). To our knowledge, only a single study (Stathopoulou, et al., 2006) has used satellite data to estimate energy consumption in space cooling using degree days.
Materials and methods
- Description of the study area
Remote sensing data processing
- Acquisition and pre-processing
- In-situ meteorological data
- Energy consumption data
- Urban growth detection between 1984 and 2015
- Link between LULC and seasonal LST changes
- Estimation of impact of urbanization on energy consumption in buildings
- Estimation of mean CDD and HDD using in-situ temperature observations
- Accuracy assessment of degree days’ estimation
𝑪𝑫𝑫 = 𝑵(𝑻𝒂𝒊𝒓− 𝑻𝒃𝒂𝒔𝒆) Equation 7.5 The base temperature was defined as the external temperature above which ambient cooling is required and below which room heating is required (Eto, 1988). The average minimum temperature was used to estimate the average hard drive for the cold season with a base temperature of 18 °C. The mean maximum temperature during the hot season of each year and the same baseline temperature were used to estimate the CDD for the entire period.
Results and discussion
- Urban growth and LULC changes between 1984 and 2015
- LST changes between 1984 and 2015
- Link between LULC and seasonal changes in LST between 1984 and 2015
- Relationship between in-situ and remotely sensed observation of mean Cooling
- Effect of urban heat island on energy demand in Harare
- In-situ observed long-term changes in space cooling and heating requirements
The coverage of warm temperature categories (22-30oC) has increased in the southern and western parts of the city. Throughout the summer, daytime cooling energy demands were greater in CBD and high-density residential areas than in low-medium density residential areas.
Conclusion
Link between Chapter 7 and other chapters
REMOTE SENSING BASED PREDICTION OF URBAN GROWTH AND
Abstract
The objective of the study was to determine the impact of urban growth on Harare's future microclimate by predicting the future distribution of land use and land cover (LULC) and land surface temperature use. Keywords: land surface temperature, Markov cellular automaton, Markov chain analysis, urban growth, urban growth, vegetation indices, Harare.
Introduction
For example, Wania, et al. 2014) used high-resolution SPOT data to map the expansion of built-up areas in Harare between 2004 and 2010 without providing further insight into expected future patterns and impacts. Similarly, using medium-resolution Landsat multi-spectral data, Kamusoko, et al. 2013) constrained the expansion of built-up areas in Harare between 1984 and 2013, but did not focus on the implications for observed and future land surface temperature patterns.
Methods
- Description of the study area
- Radiometric and geometric correction of remote sensing data
- Qualitative LULC mapping and accuracy assessment
- Computation of urban and vegetation indices
- Derivation of land surface temperature
- Variable selection for the prediction of temperature
- Prediction of future LULC and LST using Markov and Cellular Automata analysis
- Prediction of urban growth in Harare using CA Markov analysis
- Prediction of land surface temperature distribution in Harare using land cover
- Statistical significance of the forecast urban growth and land surface temperature
Finally, the actual land surface temperature was obtained after applying emissivity correction to the brightness temperature using equation 8.2 (Weng, et al., 2007). The Urban Index (UI) was selected as described in section 8.2.6 as the best predictive variable for the land surface temperature distribution in the Cellular Automata Markov Chain analysis.
Results
- Observed LULC and transitions from 1986 to 2015
- Observed satellite based temperature transitions from 1984 to 2015
- Variable selection: correlation between urban indices and temperature
- Retrieval of surface temperature from the urban index
- Accuracy of temperature retrievals using the urban index
UI was found to be the best predictor of urban surface temperature compared to other indices due to its higher correlation with land surface temperature. Land surface temperature increased with increasing UI and the relationship between the two was strong (R2 = 0.88) and significant at the 95% significant level (p<0.05).
Future LULC and LST for 2025, 2035 and 2045
- Accuracy assessment of Cellular Automata Markov Chain LULC prediction
- Future LULC distribution in Harare
- Predicted temperature distribution in Harare up to year 2045
Low to medium density residential areas are forecast to decrease slightly from 244.05 to 237.08 km2 over the same period. Forecasts show that land surface temperatures below 32oC may remain common in the northern half, where low- and medium-density residential areas are located.
Discussion
This is consistent with previous studies such as by Adelabu et al (2013) who showed that SVM classifier results in high accuracy maps. For example, Hung et al (2006) and Senanayake, et al., (2013) noted that as urban population increases, urban growth increases, consistent with the urban heat island effect.
Conclusion
Link between Chapter 8 and other chapters
REMOTE SENSING OF THE RESPONSES OF INDOOR AND OUTDOOR
- Introduction
- Chapter 3: Potential of merging thermal data and vegetation indices with multi-spectral
- Chapter 4: Determining extreme heat vulnerability of Harare metropolitan city using
- Chapter 5: Assessment of seasonal and spatial daytime outdoor thermal comfort
- Chapter 6: To link major dynamics in urban near-surface temperatures to long term
- Chapter 7: Understanding the link between built-up density and indoor air-conditioning
- Objectives revisited
- Limitations
- Recommendations and suggestions for future studies
- Conclusion
Chapter 6 therefore further focused on establishing the link between long-term land use changes and land cover distribution and the dynamics of the spatial configuration of land surface temperatures. On the other hand, changes in the spatial structure of land surface temperature were analyzed using the relative radiant temperature.
