Annual Energy Outlook 2008 With Projections to 2030

This article summarized the spatiotemporal pattern of land use/land cover (LU/LC) and urban heat island (UHI) dynamics in the Metropolitan city of Tehran between 1988 and 2018. The study showed dynamics of each LU/LC class and their role in influencing the UHI. The impervious surface area expanded by 286.04 (48.27% of total land) and vegetated land was depleted by 42.06 km2 (7.10% of total land) during the period of 1988–2018. The mean land surface temperature (LST) has enlarged by approximately 2–3 °C at the city center and 5–7 °C at the periphery between 1988 and 2018 based on the urban–rural gradient analysis. The lower mean LST was experienced by vegetation land (VL) and water body (WB) by approximately 4–5 °C and 5–7 °C, respectively, and the higher mean LST by open land (OL) by 7–11 °C than other LU/LC classes at all time-points during the time period, 1988–2018. The magnitude of mean LST was calculated based on the main LU/LC categories, where impervious land (IL) recorded the higher temperature difference compared to vegetation land (VL) and water bodies (WB). However, open land (OL) recorded the highest mean LST differences with all the other LU/LC categories. In addition to that, there was an overall negative correlation between LST and the normal difference vegetation index (NDVI). By contrast, there was an overall positive correlation between LST and the normal difference built-up index (NDBI). This article, executed through three decadal change analyses from 1988 to 2018 at 10-year intervals, has made a significant contribution to delineating the long records of change dynamics and could have a great influence on policy making to foster environmental sustainability.

Spatiotemporal analysis of land use/land cover and its effects on surface urban heat island using Landsat data: a case study of Metropolitan city Tehran (1988-2018).

This study investigated monthly variations of surface urban heat island intensity (SUHII) and the applicability of the local climate zones (LCZ) scheme for land surface temperature (LST) differentiation within three spatial contexts, including urban, rural and their combination, in Shenyang, China, a city with a monsoon-influenced humid continental climate. The monthly SUHII and LST of Shenyang were obtained through 12 LST images, with one in each month (within the period between 2018 and 2020), retrieved from the Thermal InfraRed Sensor (TIRS) 10 in Landsat 8 based on a split window algorithm. Non-parametric analysis of Kruskal-Wallis H test and a multiple pairwise comparison were adopted to investigate the monthly LST differentiations with LCZs. Overall, the SUHII and the applicability of the LCZ scheme exhibited spatiotemporal variations. July and August were the two months when Shenyang underwent strong heat island effects. Shenyang underwent a longer period of cool than heat island effects, occurring from November to May. June and October were the transition months of cool–heat and heat–cool island phenomena, respectively. The SUHII analysis was dependent on the definition of urban and rural boundaries, where a smaller rural buffering zone resulted in a weaker SUHI or surface urban cool island (SUCI) phenomenon and a larger urban area corresponded to a weaker SUHI or SUCI phenomenon as well. The LST of LCZs did not follow a fixed order, where in July and August, the LCZ-10 (Heavy industry) had the highest mean LST, followed by LCZ-2 (Compact midrise) and then LCZ-7 (Lightweight low-rise). In comparison, LCZ-7, LCZ-8 (Large low-rise) and LCZ-9 (Sparsely built) had the highest LST from October to May. The LST of LCZs varied with urban and rural contexts, where LCZ-7, LCZ-8 and LCZ -10 were the three built LCZs that had the highest LST within urban context, while LCZ-2, LCZ-3 (Compact low-rise), LCZ-8, LCZ-9 and LCZ-10 were the five built LCZs that had the highest LST within rural context. The suitability of the LCZ scheme for temperature differentiation varied with the month, where from July to October, the LCZ scheme had the strongest capability and in May, it had the weakest capability. Urban context also made a difference to the suitability, where compared with the whole study area (the combination of urban and rural areas), the suitability of built LCZs in either urban or rural contexts weakened. Moreover, the built LCZs had a higher level of suitability in an urban context compared with a rural context, while the land-cover LCZs within rural had a higher level of suitability.

Spatial Variability and Temporal Heterogeneity of Surface Urban Heat Island Patterns and the Suitability of Local Climate Zones for Land Surface Temperature Characterization

Cellular automata (CA) models are in growing use for land-use change simulation and future scenario prediction. It is necessary to conduct model assessment that reports the quality of simulation re...

A review of assessment methods for cellular automata models of land-use change and urban growth

Contribution of urban functional zones to the spatial distribution of urban thermal environment

Simulating future land use/cover changes is of great importance for urban planners and decision-makers, especially in metropolitan areas, to maintain a sustainable environment. This study examines the changes in land use/cover in the Tokyo metropolitan area (TMA) from 2007 to 2017 as a first step in using supervised classification. Second, based on the map results, we predicted the expected patterns of change in 2027 and 2037 by employing a hybrid model composed of cellular automata and the Markov model. The next step was to decide the model inputs consisting of the modeling variables affecting the distribution of land use/cover in the study area, for instance distance to central business district (CBD) and distance to railways, in addition to the classified maps of 2007 and 2017. Finally, we considered three scenarios for simulating land use/cover changes: spontaneous, sub-region development, and green space improvement. Simulation results show varied patterns of change according to the different scenarios. The sub-region development scenario is the most promising because it balances between urban areas, resources, and green spaces. This study provides significant insight for planners about change trends in the TMA and future challenges that might be encountered to maintain a sustainable region.

Spatiotemporal Simulation of Future Land Use/Cover Change Scenarios in the Tokyo Metropolitan Area

Rapid urbanization is one of the most concerning issues in the 21st century because of its significant impacts on various fields, including agriculture, forestry, ecology, and climate. The urban heat island (UHI) phenomenon, highly related to the rapid urbanization, has attracted considerable attention from both academic scholars and governmental policymakers because of its direct influence on citizens’ daily life. Land surface temperature (LST) is a widely used indicator to assess the intensity of UHI significantly affected by the local land use/cover (LULC). In this study, we used the Landsat time-series data to derive the LULC composition and LST distribution maps of Nanjing in 2000, 2014, and 2018. A correlation analysis was carried out to check the relationship between LST and the density of each class of LULC. We found out that cropland and forest in Nanjing are helping to cool the city with different degrees of cooling effects depending on the location and LULC composition. Then, a Cellar Automata (CA)-Markov model was applied to predict the LULC conditions of Nanjing in 2030 and 2050. Based on the simulated LULC maps and the relationship between LST and LULC, we delineated high- and moderate-LST related risk areas in the city of Nanjing. Our findings are valuable for the local government to reorganize the future development zones in a way to control the urban climate environment and to keep a healthy social life within the city.

Spatiotemporal Analysis of Land Use/Cover Patterns and Their Relationship with Land Surface Temperature in Nanjing, China

An urban heat island (UHI) is a serious phenomenon associated with built environments and presents threats to human health. It is projected that UHI intensity will rise to record levels in the following decades due to rapid urban expansion, as two-thirds of the world population is expected to live in urban areas by 2050. Nevertheless, the last two decades have seen a considerable increase in the number of studies on surface UHI (SUHI)—a form of UHI quantified based on land surface temperature (LST) derived from satellite imagery—and its relationship with the land use/cover (LULC) changes. This surge has been facilitated by the availability of freely accessible five-decade archived remotely sensed data, the use of state-of-art analysis methods, and advancements in computing capabilities. The authors of this systematic review aimed to summarize, compare, and critically analyze multiple case studies—carried out from 2001 to 2020—in terms of various aspects: study area characteristics, data sources, methods for LULC classification and SUHI quantification, mechanisms of interaction coupled with linking techniques between SUHI intensity with LULC spatial and temporal changes, and proposed alleviation actions. The review could support decision-makers and pave the way for scholars to conduct future research, especially in vulnerable cities that have not been well studied.

Understanding the Links between LULC Changes and SUHI in Cities: Insights from Two-Decadal Studies (2001–2020)

Finding accurate methods for estimating and mapping land prices at the macro-scale based on publicly accessible and low-cost spatial data is an essential step in producing a meaningful reference for regional planners. This asset would assist them in making economically justified decisions in favor of key investors for development projects and post-disaster recovery efforts. Since 2005, the Ministry of Land, Infrastructure, and Transport of Japan has made land price data open to the public in the form of observations at dispersed locations. Although this data is useful, it does not provide complete information at every site for all market participants. Therefore, estimating and mapping land prices based on sound statistical theories is required. This paper presents a comparative study of spatial prediction of land prices in 2015 in Fukushima prefecture based on geostatistical methods and machine learning algorithms. Land use, elevation, and socioeconomic factors, including population density and distance to railway stations, were used for modeling. Results show the superiority of the random forest algorithm. Overall, land prices are distributed unevenly across the prefecture with the most expensive land located in the western region characterized by flat topography and the availability of well-connected and highly dense economic hotspots.

A comparative study of land price estimation and mapping using regression kriging and machine learning algorithms across Fukushima prefecture, Japan

Fukushima prefectural government adopted a vision to become100% renewable energy self-sufficient by 2040. Wind energy stands firm as one of the important renewable energy sources in the prefecture as it has a huge onshore potential that has not been exploited yet. The purpose of this study was to identify and evaluate the suitable locations for the sitting of onshore wind facilities in Fukushima prefecture based on a suggested framework that combines Geographical Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) approach namely Analytic Hierarchy Process (AHP). The framework consisted of three key steps: First, we excluded all areas where wind farms cannot be installed due to law or landscape constraints. Second, we identified nine criteria influencing the suitability of areas for wind energy. These criteria were classified into three categories (environmental, social, and economic) and their significance weights in site assessment process were calculated by applying AHP approach based on opinions of local wind energy experts and stakeholders. Consequently, we evaluated areas according to their suitability. The third and the last step was to eliminate all the excluded areas from the evaluated ones, which we mapped and classified into ten suitability classes ranging from low to high. The results revealed the availability of 11% (1,561 km2) of areas for wind energy mostly located in the prefecture eastern side, 92% of which were labelled as moderately suitable. Interestingly, we found out that “Soso” where the famous 2011’s Fukushima Daiichi power plant is located, is the sub region containing the largest share of suitable areas, which suggests an available alternative to the unsafe and unpopular nuclear power. The produced map coupled with detailed statistics provides a comprehensive reference and essential insights not only for private wind farm developers but also for regional planners and researchers in quest of achieving the aforementioned vision.

Ahmed Derdouri

Papers

Spatiotemporal Simulation of Future Land Use/Cover Change Scenarios in the Tokyo Metropolitan Area

Spatiotemporal Analysis of Land Use/Cover Patterns and Their Relationship with Land Surface Temperature in Nanjing, China

Understanding the Links between LULC Changes and SUHI in Cities: Insights from Two-Decadal Studies (2001–2020)

A comparative study of land price estimation and mapping using regression kriging and machine learning algorithms across Fukushima prefecture, Japan

Onshore Wind Farm Suitability Analysis Using GIS-based Analytic Hierarchy Process: A Case Study of Fukushima Prefecture, Japan