ESTIMATING URBAN LEAF AREA USING FIELD MEASUREMENTS AND SATELLITE REMOTE SENSING DATA

Abstract. Accurate estimation of urban leaf area is important in understanding the urban forest's role in heat island mitigation, pollution removal, and carbon sequestration. Remotely sensed satellite data provide an alternative method to inexpensively and nondestructively estimate this important urban biophysical variable. Ceptometer measurements of leaf area index (LAI) at 143 urban sites in Terre Haute, Indiana, U.S., were modeled as a function of reflected radiance flux sensed by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Multiple regression models of LAI were compared to estimates produced by feed-forward back-propagation artificial neural networks. The most accurate estimation was produced by the neural network utilizing the ASTER green band and the ratio of the ASTER red and near-infrared bands. In this case, the simple correlation between the observed and predicted LAI values was moderately high (R = 0.71). The standard error of the LAI estimate was 1.35. In every case, the predictive accuracy of the neural network models exceeded the multiple regression models. Examination of the parameters in the successful models indicates that the estimation of urban LAI in Terre Haute is physically predicated on the relative proportions of leaf chlorophyll, leaf spongy mesophyll, and indurate matter (e.g., concrete, asphalt, soil) constituting the individual picture elements of the satellite image.

Key Words. Leaf area; remote sensing; ceptometer; leaf area index.

The social value of the urban forest to local urban populations has long been recognized. In contrast, the impact of the urban forest on global and local environments is not clearly understood, and the impact of urban trees on carbon sequestration, mitigation of urban heat, and removal of pollution remain topics of contemporary scientific study. Land cover conversion in urban areas is typically faster than in wildland areas, thus there is a need for rapid measurement methods of urban biophysical variables that are repeatable and economically efficient.

Leaf area index (LAI) has been identified as one of the core biophysical variables for landscape monitoring at all scales (Pierce and Running 1988; Lymburner et al. 2000). LAI has three definitions in the literature but is usually standardized to represent the green area (m^sup 2^) of flat horizontal leaves per unit of ground area (m^sup 2^) (Chen and Black 1992; Chen et al. 1997; Barclay 1998). Many scenarios of season and landscape allow LAl measurement by earth resource satellites, and LAI is a derivative data product of many remote sensing initiatives. However, few studies have examined methods of combining satellite LAI estimates with those made using ceptometers in the urban forest to estimate LAI over large urban areas.

This research extends the work of Peper and McPherson (2003) that compared the accuracy of various nondestructive field measurement devices to accurately measure urban tree LAI. In the context of that previous work, algorithmically manipulated satellite data used in this study become an additional nondestructive method of measuring urban LAI.

The objective of this research is to develop transfer equations that can be used to convert satellite LAI measurements to their gap-fraction equivalents. Our hypothesis proposes that satellite and ground LAI measurements are related and that statistical and neural network approaches can be used to interconvert between the two methods of measurement.

URBAN REMOTE SENSING

Instruments aboard remote sensing satellites measure the electromagnetic energy emitted or reflected from Earth or its atmosphere, allowing terrestrial objects to be distinguished and characterized. For example, when illuminated by the noonday sun, grass on an irrigated golf course is not only visibly green but also reflects intercepted infrared solar energy in proportion to the amount of its spongy mesophyll. Grass receiving insufficient moisture to maintain mesophyll turgidity may appear equally as green as adjacent well-watered grass but would decrease significantly in infrared reflectance. If spatially extensive, this stress would be detectable from spaceborne instrumentation and would allow researchers to accurately map the affected area. Using similar logic, land cover types are mapped, and vegetation biophysical variables are measured from spaceborne instruments.

Historically, remote sensing in urban areas has been constrained by the spatial complexity of urban scenes. The problem is related to the spatial resolution of the satellite sensor. A single image resolution element (pixel) may be measuring the spectral response of a land cover mixture rather than a single land cover type. For example, a suburban pixel may represent a mixture of grass, asphalt, concrete, and roof shingles. This kind of spectral mixing makes urban remote sensing less amenable to statistical methods that assume normal distributions and no measurement error. Newer spaceborne instruments, having finer spatial resolutions, reduce the constraint and provide better data for urban remote sensing (Jensen et al. 2003). The improvement in resolution is fortunate, because governments (e.g., state, county, city) and private companies annually invest hundreds of millions of dollars acquiring remotely sensed data that detail the urban landscape more effectively than through traditional "windshield surveys" (Jensen 2000).