Soe W. Myint

Using watered landscapes to manipulate urban heat island effects: How much water will it take to cool phoenix?

Problem: The prospect that urban heat island (UHI) effects and climate change may increase urban temperatures is a problem for cities that actively promote urban redevelopment and higher densities. One possible UHI mitigation strategy is to plant more trees and other irrigated vegetation to prevent daytime heat storage and facilitate nighttime cooling, but this requires water resources that are limited in a desert city like Phoenix. Purpose: We investigated the tradeoffs between water use and nighttime cooling inherent in urban form and land use choices. Methods: We used a Local-Scale Urban Meteorological Parameterization Scheme (LUMPS) model to examine the variation in temperature and evaporation in 10 census tracts in Phoenixs urban core. After validating results with estimates of outdoor water use based on tract-level city water records and satellite imagery, we used the model to simulate the temperature and water use consequences of implementing three different scenarios. Results and conclusions: We found that increasing irrigated landscaping lowers nighttime temperatures, but this relationship is not linear; the greatest reductions occur in the least vegetated neighborhoods. A ratio of the change in water use to temperature impact reached a threshold beyond which increased outdoor water use did little to ameliorate UHI effects. Takeaway for practice: There is no one design and landscape plan capable of addressing increasing UHI and climate effects everywhere. Any one strategy will have inconsistent results if applied across all urban landscape features and may lead to an inefficient allocation of scarce water resources. Research Support: This work was supported by the National Science Foundation (NSF) under Grant SES-0345945 (Decision Center for a Desert City) and by the City of Phoenix Water Services Department. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF.

Wavelets for Urban Spatial Feature Discrimination: Comparisons with Fractal, Spatial Autocorrelation, and Spatial Co-Occurrence Approaches

Traditional image processing techniques have proven inadequate for urban mapping using high spatial resolution remotesensing images. This study examined and evaluated wavelet transforms for urban texture analysis and image classification using high spatial resolution ATLAS imagery. For the purpose of comparison and to evaluate the effectiveness of the wavelet approaches, two different fractal approaches (isarithm and triangular prism), spatial autocorrelation (Moran’s I and Geary’s C), and spatial co-occurrence matrix of the selected urban classes were examined using 65 � 65, 33 � 33, and 17 � 17 samples with a pixel size of 2.5 m. Results from this study suggest that a multi-band and multi-level wavelet approach can be used to drastically increase the classification accuracy. The fractal techniques did not provide satisfactory classification accuracy. Spatial autocorrelation and spatial co-occurrence techniques were found to be relatively effective when compared to the fractal approaches. It can be concluded that the wavelet transform approach is the most accurate of all four approaches.

A support vector machine to identify irrigated crop types using time-series Landsat NDVI data

Abstract Site-specific information of crop types is required for many agro-environmental assessments. The study investigated the potential of support vector machines (SVMs) in discriminating various crop types in a complex cropping system in the Phoenix Active Management Area. We applied SVMs to Landsat time-series Normalized Difference Vegetation Index (NDVI) data using training datasets selected by two different approaches: stratified random approach and intelligent selection approach using local knowledge. The SVM models effectively classified nine major crop types with overall accuracies of >86% for both training datasets. Our results showed that the intelligent selection approach was able to reduce the training set size and achieved higher overall classification accuracy than the stratified random approach. The intelligent selection approach is particularly useful when the availability of reference data is limited and unbalanced among different classes. The study demonstrated the potential of utilizing multi-temporal Landsat imagery to systematically monitor crop types and cropping patterns over time in arid and semi-arid regions.

Fractal approaches in texture analysis and classification of remotely sensed data: comparisons with spatial autocorrelation techniques and simple descriptive statistics

There has been growing interest in the application of fractal geometry to observe spatial complexity of natural features at different scales. This study utilized three different fractal approaches--isarithm, triangular prism, and variogram--to characterize texture features of urban land-cover classes in high-resolution image data. For comparison purpose and to better evaluate the efficiency of fractal approaches in image classification, spatial autocorrelation techniques (Morans I and Gearys C ), simple standard deviation, and mean of the selected features were also examined in this study. The discriminant analysis was carried out to discriminate between classes of urban land cover on the basis of texture measures (variables). This study demonstrated that the spatial autocorrelation approach was superior to the fractal approaches. In some cases, simple standard deviation and mean value of the samples gave better accuracy than all or some of the fractal approaches. The results obtained from this analysis suggest that fractal-based textural discrimination methods are applicable but these methods alone may be ineffective in extracting texture features or identifying different land-use and land-cover classes in remotely sensed images.

Identifying Mangrove Species and Their Surrounding Land Use and Land Cover Classes Using an Object-Oriented Approach with a Lacunarity Spatial Measure

Accurate and reliable information on the spatial distribution of mangrove species is needed for a wide variety of applications, including sustainable management of mangrove forests, conservation and reserve planning, ecological and biogeographical studies, and invasive species management. Remotely sensed data have been used for such purposes with mixed results. Our study employed an object-oriented approach with the use of a lacunarity technique to identify different mangrove species and their surrounding land use and land cover classes in a tsunami-affected area of Thailand using Landsat satellite data. Our results showed that the object-oriented approach with lacunarity-transformed bands is more accurate (over-all accuracy 94.2% kappa coefficient = 0.91) than traditional per-pixel classifiers (overall accuracy 62.8% and kappa coefficient = 0.57).

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.

Multicriteria decision approach for land use land cover change using Markov chain analysis and a cellular automata approach

This study used the postclassification change detection approach to identify land use land cover changes in Norman, Oklahoma, between September 1979 and July 1989 using Landsat multispectral scanner and thematic mapper (TM) images. An integration of Markov chain analysis and a cellular automata approach was employed to predict land use land cover of Norman in 2000 using multicriteria decision-making and fuzzy parameter standardization approaches. Accuracy assessment was carried out using a stratified random sampling technique. The identified random sample points were displayed on Landsat enhanced thematic mapper (ETM) image data acquired on 22 May 2000, with the help of local area knowledge, ground information collection, and existing land use maps of Norman to identify the classes. We also directly compared projected results against the classified output of the same Landsat TM image. This study demonstrates the usefulness of Markov and cellular modeling for urban landscape changes. A checklist of the sources of limitation or uncertainty in the application of this approach is also reported.

Measuring the spatial arrangement of urban vegetation and its impacts on seasonal surface temperatures

Urban forestry is an important component of the urban ecosystem that can effectively ameliorate temperatures by providing shade and through evapotranspiration. While it is well known that vegetation abundance is negatively correlated to land surface temperature, the impacts of the spatial arrangement (e.g. clustered or dispersed) of vegetation cover on the urban thermal environment requires further investigation. In this study, we coupled remote sensing techniques with spatial statistics to quantify the configuration of vegetation cover and its variable influences on seasonal surface temperatures in central Phoenix. The objectives of this study are to: (1) determine spatial arrangement of green vegetation cover using continuous spatial autocorrelation indices combined with high-resolution remotely-sensed data; (2) examine the role of grass and trees, especially their spatial patterns on seasonal and diurnal land surface temperatures by controlling the effects of vegetation abundance; (3) investigate the sensitivity of the vegetation–temperature relationship at varying geographical scales. The spatial pattern of urban vegetation was measured using a local spatial autocorrelation index—the local Moran’s Iv . Results show that clustered or less fragmented patterns of green vegetation lower surface temperature more effectively than dispersed patterns. The relationships between the local Moran’s Iv and surface temperature are evidenced to be strongest during summer daytime and lowest during winter nighttime. Results of multiple regression analyses demonstrate significant impacts of spatial arrangement of vegetation on seasonal surface temperatures. Our analyses of vegetation spatial patterns at varying geographical scales suggest that an area extent of ˜200 m is optimal for examining the vegetation–temperature relationship. We provide a methodological framework to quantify the spatial pattern of urban features and to examine their impacts on the biophysical characteristics of the urban environment. The insights gained from our study results have significant implications for sustainable urban development and resource management.

Enhancing Hydrologic Modelling in the Coupled Weather Research and Forecasting-Urban Modelling System

Urbanization modifies surface energy and water budgets, and has significant impacts on local and regional hydroclimate. In recent decades, a number of urban canopy models have been developed and implemented into the Weather Research and Forecasting (WRF) model to capture urban land-surface processes. Most of these models are inadequate due to the lack of realistic representation of urban hydrological processes. Here, we implement physically-based parametrizations of urban hydrological processes into the single layer urban canopy model in the WRF model. The new single-layer urban canopy model features the integration of, (1) anthropogenic latent heat, (2) urban irrigation, (3) evaporation from paved surfaces, and (4) the urban oasis effect. The new WRF–urban modelling system is evaluated against field measurements for four different cities; results show that the model performance is substantially improved as compared to the current schemes, especially for latent heat flux. In particular, to evaluate the performance of green roofs as an urban heat island mitigation strategy, we integrate in the urban canopy model a multilayer green roof system, enabled by the physical urban hydrological schemes. Simulations show that green roofs are capable of reducing surface temperature and sensible heat flux as well as enhancing building energy efficiency.

Modelling land-cover types using multiple endmember spectral mixture analysis in a desert city

Spectral mixture analysis is probably the most commonly used approach among sub‐pixel analysis techniques. This method models pixel spectra as a linear combination of spectral signatures from two or more ground components. However, spectral mixture analysis does not account for the absence of one of the surface features or spectral variation within pure materials since it utilizes an invariable set of surface features. Multiple endmember spectral mixture analysis (MESMA), which addresses these issues by allowing endmembers to vary on a per pixel basis, was employed in this study to model Landsat ETM+ reflectance in the Phoenix metropolitan area. Image endmember spectra of vegetation, soils, and impervious surfaces were collected with the use of a fine resolution Quickbird image and the pixel purity index. This study employed 204 (3×17×4) total four‐endmember models for the urban subset and 96 (6×6×2×4) total five‐endmember models for the non‐urban subset to identify fractions of soil, impervious surface, vegetation and shade. The Pearson correlation between the fraction outputs from MESMA and reference data from Quickbird 60 cm resolution data for soil, impervious, and vegetation were 0.8030, 0.8632, and 0.8496 respectively. Results from this study suggest that the MESMA approach is effective in mapping urban land covers in desert cities at sub‐pixel level.