A combined approach for estimating vegetation cover in urban/suburban environments from remotely sensed data

The spatio-temporal distribution of vegetation is an important component of the urban/suburban environment. Therefore, correct estimation of vegetation cover in urban/suburban areas is fundamental in land use studies. In this study, the potential of extracting fractional vegetation cover (FVC) from remotely sensed data and ground measurements is explored. Based on the assumption that pixel has a mosaic structure, sub-pixel models for FVC estimation are first introduced. Then a combined approach of using different sub-pixel models for FVC estimation based on land cover classification is proposed. The experimental result, derived from a case study in Haidian district, Beijing, indicates that the accuracy of FVC estimation using the proposed method can be up to 80.7%. The results suggest that this method may be generally useful for FVC estimation in urban and suburban areas.