Ronald C. Estoque

Effects of landscape composition and pattern on land surface temperature: An urban heat island study in the megacities of Southeast Asia

Due to its adverse impacts on urban ecological environment and the overall livability of cities, the urban heat island (UHI) phenomenon has become a major research focus in various interrelated fields, including urban climatology, urban ecology, urban planning, and urban geography. This study sought to examine the relationship between land surface temperature (LST) and the abundance and spatial pattern of impervious surface and green space in the metropolitan areas of Bangkok (Thailand), Jakarta (Indonesia), and Manila (Philippines). Landsat-8 OLI/TIRS data and various geospatial approaches, including urban-rural gradient, multiresolution grid-based, and spatial metrics-based techniques, were used to facilitate the analysis. We found a significant strong correlation between mean LST and the density of impervious surface (positive) and green space (negative) along the urban-rural gradients of the three cities, depicting a typical UHI profile. The correlation of impervious surface density with mean LST tends to increase in larger grids, whereas the correlation of green space density with mean LST tends to increase in smaller grids, indicating a stronger influence of impervious surface and green space on the variability of LST in larger and smaller areas, respectively. The size, shape complexity, and aggregation of the patches of impervious surface and green space also had significant relationships with mean LST, though aggregation had the most consistent strong correlation. On average, the mean LST of impervious surface is about 3°C higher than that of green space, highlighting the important role of green spaces in mitigating UHI effects, an important urban ecosystem service. We recommend that the density and spatial pattern of urban impervious surfaces and green spaces be considered in landscape and urban planning so that urban areas and cities can have healthier and more comfortable living urban environments.

Measuring Sustainability Based Upon Various Perspectives: A Case Study of a Hill Station in Southeast Asia

A hill station is a town or city situated in mountain regions in the tropics founded during the western colonization in the nineteenth and early twentieth centuries. Hill stations have moderate temperatures, and are known for their relatively good natural environments, which generate valuable ecosystem services that benefit the local population. However, rapid urbanization threatens the sustainability of these areas. This study evaluates the sustainability of the urbanization process of Baguio City, a hill station city in Southeast Asia and the summer capital of the Philippines, by determining the relationship between its velocity of urbanization and velocity of urban sustainability based upon various perspectives. From an equal weight perspective (of the triple bottom line of sustainability components, namely environmental, social, and economic) and a pro-economic perspective, the results revealed that the urbanization of Baguio City has been moving toward a “sustainable urbanization.” However, from the environmental and eco-sustainable human development perspectives, the results indicated that it has been moving toward an “unsustainable urbanization.” The paper discusses the implications of the findings for the planning of sustainable development for Baguio City, including some critical challenges in sustainability assessment and the applicability of the framework used for future sustainability assessments of the other hill stations in Southeast Asia.

Quantifying landscape pattern and ecosystem service value changes in four rapidly urbanizing hill stations of Southeast Asia

ContextHill stations are known for their favorable cool climate and natural environments which generate valuable ecosystem services that benefit the local population, tourists and visitors. However, rapid urbanization threatens the sustainability of these highly valued fragile landscapes.ObjectivesWe aim to characterize and quantify the changes in the landscape patterns and ecosystem service values (ESVs) of Baguio (Philippines), Bogor (Indonesia), Dalat (Vietnam), and Pyin Oo Lwin (Myanmar), and discuss their implications to landscape sustainability.MethodsWe used remote sensing imagery to map land-use/cover (2001 and 2014), and spatial metrics and gradient analysis to characterize the changes in landscape pattern. We employed a benefit transfer method to estimate the changes in ESV and human-to-ESV ratio. A land-change model was used to simulate different scenarios of future built-up expansions (2014–2030).ResultsThe landscapes of Dalat and Pyin Oo Lwin are becoming more fragmented, while those of Baguio and Bogor are getting more aggregated. Dalat had the highest decrease (absolute change) in ESV and H-ESV ratio, while Bogor had the highest percentage decrease (2001–2014).ConclusionsRapid urbanization has been a major factor in the landscape transformation of Baguio, Bogor, Dalat and Pyin Oo Lwin. If the current built-up expansion rate will speed up, the decline in future ESV and H-ESV ratio (2014–2030) will be higher than if the rate will continue or slow down. Unless the concept of landscape sustainability is taken seriously in landscape and urban planning, the respective ‘values’ of these precious hill stations will become less and less.

Pixel-based and object-based classifications using high- and medium-spatial-resolution imageries in the urban and suburban landscapes

With the increasing availability of high-spatial-resolution remote sensing imageries and with the observed limitations of pixel-based techniques, the development and testing of geographic object-based image analysis (GEOBIA) techniques for image classification have become one of the main research areas in geospatial science. This paper examines and compares the classification performance of a pixel-based method and an object-based method as applied to high- (QuickBird satellite image) and medium- (Landsat TM image) spatial-resolution imageries in the context of urban and suburban landscapes. For the pixel-based classification, the maximum-likelihood supervised classification approach was employed. And for the object-based classification, the pixel-based classified maps were integrated with a set of image segments produced using various calibrations. The results show evidence that the object-based method can produce classifications that are more accurate for both high- and medium-spatial- resolution imageries in the context of urban and suburban landscapes.

An Urban Heat Island Study of the Colombo Metropolitan Area, Sri Lanka, Based on Landsat Data (1997–2017)

One of the major impacts associated with unplanned rapid urban growth is the decrease of urban vegetation, which is often replaced with impervious surfaces such as buildings, parking lots, roads, and pavements. Consequently, as the percentage of impervious surfaces continues to increase at the expense of vegetation cover, surface urban heat island (SUHI) forms and becomes more intense. The Colombo Metropolitan Area (CMA), Sri Lanka, is one of the rapidly urbanizing metropolitan regions in South Asia. In this study, we examined the spatiotemporal variations of land surface temperature (LST) in the CMA in the context of the SUHI phenomenon using Landsat data. More specifically, we examined the relationship of LST with the normalized difference vegetation index (NDVI) and the normalized difference built-up index (NDBI) at three time points (1997, 2007 and 2017). In addition, we also identified environmentally critical areas based on LST and NDVI. We found significant correlations of LST with NDVI (negative) and NDBI (positive) (p < 0.001) across all three time points. Most of the environmentally critical areas are located in the central business district (CBD), near the harbor, across the coastal belt, and along the main transportation network. We recommend that those identified environmentally critical areas be considered in the future urban planning and landscape development of the city. Green spaces can help improve the environmental sustainability of the CMA.

Spatiotemporal Analysis of Urban Growth Using GIS and Remote Sensing: A Case Study of the Colombo Metropolitan Area, Sri Lanka

Understanding urban growth spatiotemporally is important for landscape and urban development planning. In this study, we examined the spatiotemporal pattern of urban growth of the Colombo Metropolitan Area (CMA)—Sri Lanka’s only metropolitan area—from 1992 to 2014 using remote sensing data and GIS techniques. First, we classified three land-use/cover maps of the CMA (i.e., for 1992, 2001, and 2014) using Landsat data. Second, we examined the temporal pattern of urban land changes (ULCs; i.e., land changes from non-built-up to built-up) across two time intervals (1992–2001 and 2001–2014). Third, we examined the spatial pattern of ULCs along the gradients of various driver variables (e.g., distance to roads) and by using spatial metrics. Finally, we predicted the future urban growth of the CMA (2014–2050). Our results revealed that the CMA’s built-up land has increased by 24,711 ha (221%) over the past 22 years (11,165 ha in 1992 to 35,876 ha in 2014), at a rate of 1123 ha per year. The analysis revealed that ULC was more intense or faster during the 2000s (1268 ha per year) than in the 1990s (914 ha per year), coinciding with the trends of population and economic growth. The results also revealed that most of the ULCs in both time intervals occurred in close proximity to roads and schools, while also showing some indications of landscape fragmentation and infill urban development patterns. The ULC modeling revealed that by 2030 and 2050, the CMA’s built-up land will increase to 42,500 ha and 56,000 ha, respectively. Most of these projected gains of built-up land will be along the transport corridors and in proximity to the growth nodes. These findings are important in the context of landscape and urban development planning for the CMA. Overall, this study provides valuable information on the landscape transformation of the CMA, also highlighting some important challenges facing its future sustainable urban development.

A geospatial approach for detecting and characterizing non-stationarity of land-change patterns and its potential effect on modeling accuracy

The non-stationarity of land-change patterns can potentially affect the accuracy of a spatially explicit land-change projection. Thus, methods for understanding this phenomenon are urgently needed. This paper presents a geospatial approach for detecting and characterizing the non-stationarity of land-change patterns and examining its potential effect on land-change modeling accuracy. It proposes two types of non-stationarity of land-change patterns, viz., non-stationarity+ and non-stationarity–. The former is characterized by an increase in the rate of land change, for example, non-built to built, across the calibration and simulation intervals along the gradient of an explanatory variable, for example, slope, while the latter is characterized by a decrease.

Prioritizing Areas for Rehabilitation by Monitoring Change in Barangay-Based Vegetation Cover

Analysis of spatial and temporal changes of vegetation cover using remote sensing (RS) technology, in conjunction with Geographic Information Systems (GIS), is becoming increasingly important in environmental conservation. The objective of this study was to use RS data and GIS techniques to assess the vegetation cover in 1989 and 2009, in the barangays (smallest administrative units) of the city of San Fernando, La Union, the Philippines, for planning vegetation rehabilitation. Landsat images were used to prepare both the 1989 and 2009 land cover maps, which were then used to detect changes in the vegetation cover for the barangays. In addition to conventional accuracy assessment parameters such as; proportion correct, and standard Kappa index of agreement, two other parameters; quantity, and allocation disagreements were used to assess the accuracy of the land cover classification. Results revealed that there were gains and losses of vegetation cover in most of the barangays, but overall vegetation cover increased by 11% (around 625 ha) based on the original extent of 1989. Those barangays that showed substantial net losses in vegetation cover need to be prioritised for rehabilitation planning. As exemplified in this study, the collection, processing and analysis of relevant RS and GIS information, can facilitate priority-setting in the planning of environmental rehabilitation and conservation by the local government at both city and barangay levels.

Analytic Hierarchy Process in Geospatial Analysis

Owing to the increasing demand for land, forests, waterways, and other depleting resources brought about by increasing population growth, there is an urgent need for a scheme that can promote their sustainable utilization. The best use of these resources must be selected so that they remain available to the generations to come. Natural disasters like earthquakes, landslides, and floods have been major concerns in many countries. This makes it necessary for planners to design sound risk-management contingencies to prepare for such disasters. Most of the time, however, decision makers have different and conflicting priorities, concerns, knowledge, and expertise in dealing with these problems. This reality complicates the decision-making process on how a particular resource should be utilized, and on how an analysis of the susceptibility of a particular area to a certain disaster risk hazard should be carried out. In recognition of the complexity, magnitude, and importance of these problems, a decision-making technique that is responsive, transparent, and acceptable to the decision makers and other stakeholders is needed. Multi-criteria decision making (MCDM), or multi-criteria decision analysis (MCDA), is a decision-making technique that helps decision makers who are confronted with conflicting priorities to come up with an acceptable decision using a transparent decision-making process. It has been one of the fastest growing problem-resolving approaches in the past decades (Triantaphyllou 2000).

Geographically Weighted Regression in Geospatial Analysis

Geographically weighted regression (GWR) is a local spatial statistical technique for exploring spatial non-stationarity. The assumption in GWR is that observations nearby have a greater influence on parameter estimates than observations at a greater distance. This is very close to Tobler’s first law of geography—everything is related to everything else, but near things are more related than distant things (Tobler 1970). GWR was developed on the basis of the traditional regression framework which incorporates local spatial relationships into the framework in an intuitive and explicit manner (Brunsdon et al. 1996; Fotheringham and Brunsdon 1999; Fotheringham et al. 2002).