The Impact of Urban Heat Islands: Assessing Vulnerability in Indonesia

The impacts of global change can be felt by local communities during both short-term events such as intense storms and long-term changes such as rising temperatures and changing rainfall patterns. Natural disasters related to hydrometeorology are likely to increase in severity, while in coastal areas sea-level rises require serious attention. At city scale, with high levels of urbanisation, local rising temperatures can affect the quality of life of communities. Urban heat islands (UHI) reflect the magnitude of the difference in observed ambient air temperature between cities and their surrounding rural regions. This study aims to identify whether the urban heat island phenomena is occurring two cities in Indonesia: Jakarta, a large metropolitan city with a business and industrial background, and Bandar Lampung, a growing city with an agricultural background. The aim is to identify community vulnerability to UHI impacts and community adaptation efforts related to UHI. The results show that UHI is present in both Jakarta and Bandar Lampung. The UHI was clearly evident in morning temperatures in Bandar Lampung, showing that the area surrounding the city had more air moisture due to vegetation land cover, compared to the city area. In Jakarta the UHI effect was clearly visible in the afternoon, and the highest temperature was in high density settlement areas compared to the business and industrial area. Communities in both Bandar Lampung and Jakarta were assessed to have average (moderate) vulnerability levels. Bandar Lampung's moderate vulnerability level is due to low levels of community knowledge of climate change impacts and public facilities, but there were indications of adaptation in the form of natural spontaneous adaptation. Jakarta faces rising temperatures but has low adaptation levels which could be due to low levels of participation in community programmes in general

Data use investigations for applications Explorer Mission A (Heat Capacity Mapping Mission): HCMM's role in studies of the urban heat island, Great Lakes thermal phenomena and radiometric calibration of satellite data

The utility of data from NASA'a heat capacity mapping mission satellite for studies of the urban heat island, thermal phenomena in large lakes and radiometric calibration of satellite sensors was assessed. The data were found to be of significant value in all cases. Using HCMM data, the existence and microstructure of the heat island can be observed and associated with land cover within the urban complex. The formation and development of the thermal bar in the Great Lakes can be observed and quantitatively mapped using HCMM data. In addition, the thermal patterns observed can be associated with water quality variations observed both from other remote sensing platforms and in situ. The imaging radiometer on-board the HCMM satellite is shown to be calibratible to within about 1.1 C of actual surface temperatures. These findings, as well as the analytical procedures used in studying the HCMM data, are included

Modelling the impact of high rise buildings in urban areas on precipitation initiation

The impact of urban areas upon precipitation distribution has been studied for many years. However, the relative importance of the distribution and type of surface morphology and urban heating remains unclear. A simple model of the surface sensible heat flux is used to explore the impact of urban heterogeneity. Sensitivity experiments are carried out to test the validity of the model, and experiments with a schematic urban morphology are used to investigate the impact of different types of building arrays. It is found that high-rise buildings over relatively small areas may have just as much impact as somewhat lower buildings covering a much larger area. The urban area produces considerable spatial variation in surface sensible heat flux. Data from a C-band radar located to the north of Greater Manchester provides evidence that convective cells may be initiated by the sensible heat flux input generated by the high-rise buildings in the city centre when the atmospheric boundary layer is unstable. Copyright © 2007 Royal Meteorological Societ

Monitoring the impact of land cover change on surface urban heat island through google earth engine. Proposal of a global methodology, first applications and problems

All over the world, the rapid urbanization process is challenging the sustainable development of our cities. In 2015, the United Nation highlighted in Goal 11 of the SDGs (Sustainable Development Goals) the importance to "Make cities inclusive, safe, resilient and sustainable". In order to monitor progress regarding SDG 11, there is a need for proper indicators, representing different aspects of city conditions, obviously including the Land Cover (LC) changes and the urban climate with its most distinct feature, the Urban Heat Island (UHI). One of the aspects of UHI is the Surface Urban Heat Island (SUHI), which has been investigated through airborne and satellite remote sensing over many years. The purpose of this work is to show the present potential of Google Earth Engine (GEE) to process the huge and continuously increasing free satellite Earth Observation (EO) Big Data for long-term and wide spatio-temporal monitoring of SUHI and its connection with LC changes. A large-scale spatio-temporal procedure was implemented under GEE, also benefiting from the already established Climate Engine (CE) tool to extract the Land Surface Temperature (LST) from Landsat imagery and the simple indicator Detrended Rate Matrix was introduced to globally represent the net effect of LC changes on SUHI. The implemented procedure was successfully applied to six metropolitan areas in the U.S., and a general increasing of SUHI due to urban growth was clearly highlighted. As a matter of fact, GEE indeed allowed us to process more than 6000 Landsat images acquired over the period 1992-2011, performing a long-term and wide spatio-temporal study on SUHI vs. LC change monitoring. The present feasibility of the proposed procedure and the encouraging obtained results, although preliminary and requiring further investigations (calibration problems related to LST determination from Landsat imagery were evidenced), pave the way for a possible global service on SUHI monitoring, able to supply valuable indications to address an increasingly sustainable urban planning of our cities

Modelling and observing urban climate in the Netherlands

Volgens de klimaatscenario’s van het KNMI uit 2006 zal de gemiddelde temperatuur in Nederland in de komende decennia verder stijgen. Hittegolven zullen naar verwachting vaker voorkomen en de intensiteit van met name zomerse buien kan toenemen. In steden zijn de gevolgen van de opwarming extra voelbaar, omdat de temperaturen er door het zogenoemde Urban Heat Island (UHI) effect veel hoger kunnen zijn dan in het omliggende gebied. Zulke periodes met hoge temperaturen gaan veelal gepaard met verslechterde luchtkwaliteit en droogte. Dit alles kan grote gevolgen hebben voor de leefbaarheid en de gezondheid van de bevolking in stedelijke gebieden. Veranderingen in de buienintensiteit beïnvloeden de waterhuishouding van de stad

Thermal infrared remote sensing of surface features for renewable resource applications

The subjects of infrared remote sensing of surface features for renewable resource applications is reviewed with respect to the basic physical concepts involved at the Earth's surface and up through the atmosphere, as well as the historical development of satellite systems which produce such data at increasingly greater spatial resolution. With this general background in hand, the growth of a variety of specific renewable resource applications using the developing thermal infrared technology are discussed, including data from HCMM investigators. Recommendations are made for continued growth in this field of applications

Linking thermal variability and change to urban growth in Harare Metropolitan City using remotely sensed data.

Doctor of Philosophy in Environmental Science. University of KwaZulu-Natal. Pietermaritzburg, 2017.Urban growth, which involves Land Use and Land Cover Changes (LULCC), alters land surface thermal properties. Within the framework of rapid urban growth and global warming, land surface temperature (LST) and its elevation have potential significant socio-economic and environmental implications. Hence the main objectives of this study were to (i) map urban growth, (ii) link urban growth with indoor and outdoor thermal conditions and (iii) estimate implications of thermal trends on household energy consumption as well as predict future urban growth and temperature patterns in Harare Metropolitan, Zimbabwe. To achieve these objectives, broadband multi-spectral Landsat 5, 7 and 8, in-situ LULC observations, air temperature (Ta) and humidity data were integrated. LULC maps were obtained from multi-spectral remote sensing data and derived indices using the Support Vector Machine Algorithm, while LST were derived by applying single channel and split window algorithms. To improve remote sensing based urban growth mapping, a method of combining multi-spectral reflective data with thermal data and vegetation indices was tested. Vegetation indices were also combined with socio-demographic data to map the spatial distribution of heat vulnerability in Harare. Changes in outdoor human thermal discomfort in response to seasonal LULCC were evaluated, using the Discomfort Index (DI) derived parsimoniously from LST retrieved from Landsat 8 data. Responses of LST to long term urban growth were analysed for the period from 1984 to 2015. The implications of urban growth induced temperature changes on household air-conditioning energy demand were analysed using Landsat derived land surface temperature based Degree Days. Finally, the Cellular Automata Markov Chain (CAMC) analysis was used to predict future landscape transformation at 10-year time steps from 2015 to 2045. Results showed high overall accuracy of 89.33% and kappa index above 0.86 obtained, using Landsat 8 bands and indices. Similar results were observed when indices were used as stand-alone dataset (above 80%). Landsat 8 derived bio-physical surface properties and socio-demographic factors, showed that heat vulnerability was high in over 40% in densely built-up areas with low-income when compared to “leafy” suburbs. A strong spatial correlation (α = 0.61) between heat vulnerability and surface temperatures in the hot season was obtained, implying that LST is a good indicator of heat vulnerability in the area. LST based discomfort assessment approach retrieved DI with high accuracy as indicated by mean percentage error of less than 20% for each sub-season. Outdoor thermal discomfort was high in hot dry season (mean DI of 31oC), while the post rainy season was the most comfortable (mean DI of 19.9oC). During the hot season, thermal discomfort was very low in low density residential areas, which are characterised by forests and well maintained parks (DI ≤27oC). Long term changes results showed that high density residential areas increased by 92% between 1984 and 2016 at the expense of cooler green-spaces, which decreased by 75.5%, translating to a 1.98oC mean surface temperature increase. Due to surface alterations from urban growth between 1984 and 2015, LST increased by an average of 2.26oC and 4.10oC in the cool and hot season, respectively. This decreased potential indoor heating energy needed in the cool season by 1 degree day and increased indoor cooling energy during the hot season by 3 degree days. Spatial analysis showed that during the hot season, actual energy consumption was low in high temperature zones. This coincided with areas occupied by low income strata indicating that they do not afford as much energy and air conditioning facilities as expected. Besides quantifying and strongly relating with energy requirement, degree days provided a quantitative measure of heat vulnerability in Harare. Testing vegetation indices for predictive power showed that the Urban Index (UI) was comparatively the best predictor of future urban surface temperature (r = 0.98). The mean absolute percentage error of the UI derived temperature was 5.27% when tested against temperature derived from thermal band in October 2015. Using UI as predictor variable in CAMC analysis, we predicted that the low surface temperature class (18-28oC) will decrease in coverage, while the high temperature category (36-45oC) will increase in proportion covered from 42.5 to 58% of city, indicating further warming as the city continues to grow between 2015 and 2040. Overall, the findings 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. It can be observed that urban greenery and wetlands play a significant role of reducing LST and heat transfer between the surface and lower atmosphere and LST may continue unless effective mitigation strategies, such as effective vegetation cover spatial configuration are adopted. Limitations to the study included inadequate spatial and low temporal resolution of Landsat data, few in-situ observations of temperature and LULC classification which was area specific thus difficult for global comparison. 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

Evaluating Differences between Ground-Based and Satellite-Derived Measurements of Urban Heat: The Role of Land Cover Classes in Portland, Oregon and Washington, D.C.

The distinction between satellite-based land surface temperature (LST) and air temperature has become an increasingly important part of managing urban heat islands. While the preponderance of urban heat research relies on LST, the emergence of a growing infrastructure of publicly available consumer oriented, ground-based sensor networks has offered an alternative for characterizing microscale differences in temperatures. Recent evidence suggests large differences between LST and air temperatures, yet discerning the reason for these differences between satellite-derived measurements of urban heat islands (UHI) and ground-based measurements of air temperature remains largely unresolved. In this study, we draw on an unusually robust and spatially exhaustive dataset of air temperature in two distinct bioclimates—Portland, Oregon, USA andWashington, D.C., USA—to evaluate the role of land cover on temperature. Our findings suggest that LST in highly built environments is consistently higher than recorded air temperatures, at times varying upwards of 15-degree Celsius, while forested areas contain between 2.5 and 3.5-degree Celsius lower temperatures than LST would otherwise indicate. Furthermore, our analyses points to the effects of land use and land cover features and other geophysical processes may have in determining differences in heat measurements across the two locations. The strength of the present analyses also highlights the importance of hyperlocal scales of data used in conjunction with coarser grain satellite derived data to inform urban heat assessments. Our results suggest a consistent pattern in both study areas, which can further our capacity to develop predictive models of air temperature using freely available descriptions of LST

Towards an operational model for estimating day and night instantaneous near-surface air temperature for urban heat island studies: outline and assessment

Near-surface air temperature (NSAT) is key for assessing urban heat islands, human health, and well-being. However, a widely recognized and cost- and time-effective replicable approach for estimating hourly NSAT is still urgent. In this study, we outline and validate an easy-to-replicate, yet effective, operational model, for automating the estimation of high-resolution day and night instantaneous NSAT. The model is tested on a heat wave event and for a large geographical area. The model combines remotely sensed land surface temperature and digital elevation model, with air temperature from local fixed weather station networks. Achieved NSAT has daily and hourly frequency consistent with MODIS revisiting time. A geographically weighted regression method is employed, with exponential weighting found to be highly accurate for our purpose. A robust assessment of different methods, at different time slots, both day- and night-time, and during a heatwave event, is provided based on a cross-validation protocol. Four-time periods are modelled and tested, for two consecutive days, i.e. 31st of July 2020 at 10:40 and 21:50, and 1st of August 2020 at 02:00 and 13:10 local time. High R2 was found for all time slots, ranging from 0.82 to 0.88, with a bias close to 0, RMSE ranging from 1.45 °C to 1.77 °C, and MAE from 1.15 °C to 1.36 °C. Normalized RMSE and MAE are roughly 0.05 to 0.08. Overall, if compared to other recognized regression models, higher effectiveness is allowed also in terms of spatial autocorrelation of residuals, as well as in terms of model sensitivity