Elsevier

Remote Sensing of Environment

Volume 113, Issue 2, 16 February 2009, Pages 398-407
Remote Sensing of Environment

Extracting urban vegetation characteristics using spectral mixture analysis and decision tree classifications

https://doi.org/10.1016/j.rse.2008.10.005Get rights and content

Abstract

Urban vegetation cover is a critical component in urban systems modeling and recent advances in remote sensing technologies can provide detailed estimates of vegetation characteristics. In the present study we classify urban vegetation characteristics, including species and condition, using an approach based on spectral unmixing and statistically developed decision trees. This technique involves modeling the location and separability of vegetation characteristics within the spectral mixing space derived from high spatial resolution Quickbird imagery for the City of Vancouver, Canada. Abundance images, field based land cover observations and shadow estimates derived from a LiDAR (Light Detection and Ranging) surface model are applied to develop decision tree classifications to extract several urban vegetation characteristics. Our results indicate that along the vegetation-dark mixing line, tree and vegetated ground cover classes can be accurately separated (80% and 94% of variance explained respectively) and more detailed vegetation characteristics including manicured and mixed grasses and deciduous and evergreen trees can be extracted as second order hierarchical categories with variance explained ranging between 67% and 100%. Our results also suggest that the leaf-off condition of deciduous trees produce pixels with higher dark fractions resulting from branches and soils dominating the reflectance values. This research has important implications for understanding fine scale biophysical and social processes within urban environments.

Introduction

As our understanding of urban systems has evolved, researchers have become increasingly aware of the importance of detailed land surface characteristics to many processes established in social and physical geographic sciences. These land cover features include both natural and anthropogenic attributes and are characterized as being in a state of constant change due to the pervasive influence of human activity (Ben Dor, 2006). Urban meteorology and hydrology provide examples of disciplines which apply spatial land cover information to explain biophysical processes. Specifically, the impervious surfaces of urban areas represent an essential component of macro-scale models representing established phenomena including urban heat island effects (Oke, 1982). Spatial variation in pervious and impervious surface composition has since been demonstrated to affect surface thermal and moisture conditions; attributes that are key determinants of urban climate (Grimmond et al., 1996, Voogt and Oke, 1997).

Understanding the more complex relationships among land surface characteristics and urban climates requires that meteorologists incorporate a wide range of features beyond the basic division between impervious and pervious surfaces. Detailed land cover characteristics including surface albedo, shade, and vegetation condition inform meteorological studies at local (e.g. 102 to 5 ×10 4 m) and micro- (e.g. 10 2 to 103 m) scales (Sawaya et al., 2003, Mueller and Day, 2005). Vegetation is of particular interest as it presents a versatile resource for effectively managing and moderating a variety of problems associated with urbanization. The spatial distribution and abundance of urban vegetation, for example, is recognized as a key factor influencing numerous biophysical processes of the urban environment, including air and water quality, temperature, moisture, and precipitation regimes (Avissar, 1996, Grimmond et al., 1996, Nowak and Dwyer, 2000). Detailed vegetation characteristics, such as the structure of plant canopies and their physiological condition also exert a strong influence on more complex processes such as urban wind flow and rates of transpiration (Avissar, 1996, Wang et al., 2008). In addition, vegetated areas such as gardens, parks, and forests have been related to positive social outcomes including reductions in crime (Kuo & Sullivan, 2001), health benefits (Coen & Ross, 2006), and advanced childhood development (Taylor et al., 1998). Given the associations between vegetated land cover and the biophysical and social processes of urban systems there exists an ongoing demand for effective urban vegetation mapping and classification techniques.

Mapping detailed land cover attributes within urban environments has been primarily reliant on conventional cadastral information from municipal agencies. However, the high cost and time consuming nature of interpreting this data, as well as difficulties in accessing data, can restrict the capacity for quantitative studies of vegetation impacts on biophysical and social processes in urban areas. In addition, cadastral information is often limited to areas of public access, resulting in large gaps of detailed land cover information across cities. In contrast, remote sensing imagery can provide information that is well suited to extensive mapping of vegetated surfaces and recent developments in high spatial resolution sensors (e.g. < 5 m) such as IKONOS and Quickbird have further enabled detailed analysis of urban areas. Herold et al. (2004) suggest that the visible region of the electromagnetic spectrum provides the most prominent spectral information required for separating urban land cover materials. As a result, high resolution broadband sensors with multiple channels positioned in this region of the spectrum can begin to resolve some of the detailed land cover components necessary for informing current microclimate (Noilhan and Mahfouf, 1996, Voogt and Oke, 1997) and ecological models (Zipperer et al., 1997).

Critical for the interpretation of high spatial resolution remote sensing imagery in urban environments is the development of accurate remote sensing classification techniques. Traditional supervised or unsupervised classifications assign each pixel to a single class and as a result, these classifications can significantly underestimate or overestimate land cover types in urban environments as pixels often contain a mixture of cover types. For example, research by Thomas et al. (2003) compared high resolution urban mapping methods and found that traditional supervised and unsupervised spectral classification methods resulted in map accuracies of around 50%. Urban environments also tend to contain fine scale heterogeneous land covers with narrow linear patterns (Zipperer et al., 1997, Collinge, 1998) that are not always captured within a single image pixel. Due to the inability of traditional classification algorithms to account for mixed pixels, techniques better suited to heterogeneous environments have been developed. Spectral mixture analysis (SMA), in particular, has been used to classify urban vegetation cover (Small, 2001, Small and Lu, 2006). This approach divides pixels into representative fractions of land cover that combine at the instantaneous field of view (IFOV) of the sensor.

In the past decade SMA has developed as the primary method for extracting multiple urban land covers from a single pixel value (Kresller and Steinnocher, 1996, Small, 2001, Rashed et al., 2001). Early urban land cover classification has been theorized according to Ridd's (1995) V–I–S (vegetation–impervious surface–soil) classification scheme. This scheme provides a conceptual model that divides urban environments into three classes: vegetation, impervious surface, and soil. This approach remains problematic in a remote sensing context as it represents features that cannot necessarily be distinguished on the basis of reflectance values alone (Phinn et al., 2002, Powell et al., 2007). As a result, Small (2001) developed a more applicable model that establishes substrate, vegetation, and dark (SVD) features of the urban environment as components for SMA. These pure endmembers represent features at the apexes of the urban mixing space, yet it remains unclear whether more detailed vegetation characteristics including trees and vegetated ground cover can also be quantified in terms of their separability along the mixing line between the dark and vegetation endmembers (Small & Lu, 2006). Although higher order vegetation details including species and condition do not produce distinguishable pure pixels in three endmember mixture models, they represent physically and structurally distinct land cover features whose extraction at high spatial resolutions can inform micro- and local scale urban process models and consequently represents the central focus of the following research.

The objective of this study is to develop a technique to extract vegetation species and condition information using sub-pixel abundance values from high spatial resolution multispectral imagery. We produce fractions of vegetation, high albedo substrate, and dark features by applying spectral mixture analysis to a Quickbird image over the city of Vancouver, Canada. Shadow estimates from a LiDAR (Light Detection and Ranging) hillshade model in addition to field based observations of vegetation condition and species were collected and provided training data for decision tree classifications. These parameters are used in conjunction with the SMA derived fractions of vegetation, high albedo and dark features to quantify the separability of various vegetation elements within the urban environment. Discussion of the results focuses on issues which may impede our procedure and considerations regarding the application of this technique for modeling various fine scale urban biophysical and social processes.

Section snippets

Study area

The City of Vancouver (49° 15′N, 123° 6′W) on the mainland western coast of Canada is located within the larger urban region of metropolitan Vancouver and covers a 114 km2 area. Vegetation including various evergreen needleleaf and deciduous broadleaf tree species, shrubs, and grasses comprises a large portion of the city's surface area and due to the temperate climate of the region much of the vegetation remains green for a majority of the year (Straley, 1992). Areas of manicured grass exist

Results

The principal component analysis on the 4 band Quickbird image showed over 99% of the image variance contained within the first three primary principal components, which is in agreement with earlier research (Small, 2003, Small and Lu, 2006). The resulting distribution of pixel values within the mixing space produced distinctive linear dispersions between the vegetation-dark and dark-high albedo apexes, while a concave dispersion was observed between the vegetation-high albedo apexes indicating

Vegetation separability

Mapping the location and spatial extent of trees, vegetated ground cover, and high level vegetation detail provides a valuable addition to urban land cover mapping using high spatial resolution imagery. Image classification techniques developed to date extract a basic vegetation class which encompasses a broad range of features whose structural and spectral diversity have a variety of impacts on urban processes (Mueller and Day, 2005, Voogt and Oke, 1997). Spectral mixture analysis provides a

Conclusion

Refining our understanding of urban systems through accurate vegetation mapping is critical to the wellbeing of urban residents and the sustainability of our cities. This paper examined the abundance of urban endmembers in a Quickbird image over the City of Vancouver and applied decision tree classifications to quantify and separate various orders of vegetation detail. Results demonstrate successful extraction of trees and vegetated ground cover using our technique. This technique also proved

Acknowledgments

This research was supported by the Environmental Prediction in Canadian Cities (EPiCC) project funded by the Canadian Foundation for Climate and Atmospheric Sciences (CFCAS). The authors would like to thank field assistants Brad Lehrbass, Colin Ferster, Sam Coggins, and Trevor Jones as well as Sue Grimmond, Jinfei Wang, and Timothy Oke for their insight into remote sensing considerations for urban micrometeorological applications. We would also like to thank Christopher Small and two anonymous

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