Estimation and validation of biomass of a mountainous agroecosystem by means of sampling, spectral data and QuickBird satellite image

Abstract

Biomass estimation in agroecosystems (AESs) is important to understand their role in carbon exchange for a sustainable environment. We used field spectra and sampled biomass of an AES including cultivated and abandoned croplands to develop a simple biomass estimation model. The digital number (DN) of a QuickBird (QB) satellite image was converted to a reflectance factor using the dark object subtraction method and the spectral reflectance of asphalt. The relationship between the reflectance factor of field-based spectra and the QB image obtained in early July 2007 was insignificant in the blue (R ² = 0.15) and green (R ² = 0.18) bands but was significant (p < 0.05) in the red (R ² = 0.57) and near-infrared (NIR, R ² = 0.45) bands in the AES. Better correlations were obtained between field-based and QB-based vegetation indices (VIs). The best correlations were obtained with the normalized difference vegetation index (NDVI) (R ² = 0.97, p < 0.001) and the ratio vegetation index (RVI) (R ² = 0.99, p < 0.001). Biomass was significantly correlated with both field-based NDVI and RVI (R ² = 0.79 and 0.72, respectively, p < 0.001). Although RVI saturated at higher biomass densities (>600 g m⁻²), NDVI showed a linear relationship. Other field-based VIs showed poorer correlations with biomass. The model was evaluated by incorporating it into high-resolution QB images to obtain the observed biomass. The relationship between field-estimated and QB-observed biomass appeared to be a one-to-one linear relationship (R ² = 0.79). Thus, models using field spectra and sampled biomass can be applied to QB images for remote estimation of biomass in an AES.

Keywords:

• abandoned cropland
• cropland
• remote sensing
• vegetation indices

Acknowledgements

We thank the Remote Sensing Group of the River Basin Research Center of Gifu University, Japan, for their kind cooperation in this research. This research was supported as a part of the Satellite Ecology project by the Center of Excellence (COE) Programme of the Ministry of Education, Culture, Sports, Science and Technology Japan (MEXT) at Gifu University.