Estimating Net Primary Production of Turfgrass in an Urban-Suburban Landscape with QuickBird Imagery

Abstract

Vegetation is a basic component of urban-suburban environments with significant area coverage. As a major vegetation type in US cities, urban turfgrass provides a range of important ecological services. This study examined the biological carbon fixation of turfgrass in a typical residential neighborhood by linking ground-based measurements, high resolution satellite remote sensing, and ecological modeling. The spatial distribution of turfgrass and its vegetative conditions were mapped with QuickBird satellite imagery. The significant amount of shadows existing in the imagery were detected and removed by taking advantage of the high radiometric resolution of the data. A remote sensing-driven production efficiency model was developed and parameterized with field biophysical measurements to estimate annual net primary production of turfgrass. The results indicated that turfgrass accounted for 38% of land cover in the study area. Turfgrass assimilated 0-1,301 g·C·m ). However, lawn grass contributed more to the total net primary production than golf course grass due to its larger area coverage, although with higher spatial variability.

Optical and Thermal Remote Sensing of Turfgrass Quality, Water Stress, and Water Use under Different Soil and Irrigation Treatments

“…Figure 2, for example, compares the spectral signature of turfgrass species in treatment I, measured at two different distances from the sprinklers and averaged for all measurement dates. The spectral signature at the closest distance was similar to that presented in [5]. At this location, 38% of the incident radiation in NIR waveband was reflected on average, an indication of large number of hydrated cell walls and air cavities in the mesophyll tissue of turf blades.…”

“…Hence, improving landscape irrigation management is necessary to foster sustainable management of urban water resources in the western USA [4]. Turfgrass has been the focus of irrigation studies during the past few decades, as it is one of the most dominant components of urban landscapes [5]. Carrow [6] categorized the methods for identifying the appropriate timing of turfgrass irrigation into three groups: (i) based on soil moisture depletion; (ii) based on climatic data and reference evapotranspiration; and, (iii) using canopy characteristics such as temperature and visible signs of stress.…”

Evaluating the Effects of Shadow Detection on QuickBird Image Classification and Spectroradiometric Restoration

“…In particular, SOI was detected more accurately than SOG, probably due to the less varied spectral characteristics of SOI compared with those of SOG [5]. We found that the spectral reflectance of SOG changed with the objects that cast shadows, especially in the NIR band, while that of SOI remained largely unchanged.…”

“…The four multispectral bands of QuickBird data were simulated with the appropriate CROPSCAN bands as weighted averages [5]. The band widths of the spectroradiometer vary from 6.8 nm to 12 nm in the visible and from 11 nm to 13 nm in the near infrared.…”

“…Reduction or loss of spectroradiometric information could potentially lead to misclassification, inaccurate derivation of biophysical parameters, and erroneous interpretation [3][4][5]. Thus, shadows pose an enormous challenge for applying high resolution satellite imagery to urban land use and land cover classification, change detection, and environmental analysis.…”