Mapping biomass and stress in the Sierra Nevada using lidar and hyperspectral data fusion

Authors

Anu Swatantran, University of MarylandFollow
Ralph Dubayah, University of Maryland
Dar A. Roberts, University of California, Santa BarbaraFollow
Michelle Hofton, University of Maryland
J. Bryan Blair, Goddard Space Flight Center

Date of this Version

2011

Citation

Remote Sensing of Environment 115 (2011) 2917–2930; doi:10.1016/j.rse.2010.08.027

Abstract

In this paper, we explored fusion of structural metrics from the Laser Vegetation Imaging Sensor (LVIS) and spectral characteristics from the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) for biomass estimation in the Sierra Nevada. In addition, we combined the two sensors to map species-specific biomass and stress at landscape scale. Multiple endmember spectral mixture analysis (MESMA) was used to classify vegetation from AVIRIS images and obtain sub-pixel fractions of green vegetation, non-photosynthetic vegetation, soil, and shade. LVIS metrics, AVIRIS spectral indices, and MESMA fractions were compared with field measures of biomass using linear and stepwise regressions at stand (1 ha) level. AVIRIS metrics such as water band indices and shade fractions showed strong correlation with LVIS canopy height (r²=0.69, RMSE=5.2 m) and explained around 60% variability in biomass. LVIS variables were found to be consistently good predictors of total and species specific biomass (r²=0.77, RMSE=70.12 Mg/ha). Prediction by LVIS after species stratification of field data reduced errors by 12% (r²=0.84, RMSE=58.78 Mg/ha) over using LVIS metrics alone. Species-specific biomass maps and associated errors created from fusion were different from those produced without fusion, particularly for hardwoods and pines, although mean biomass differences between the two techniques were not statistically significant. A combined analysis of spatial maps from LVIS and AVIRIS showed increased water and chlorophyll stress in several high biomass stands in the study area. This study provides further evidence that lidar is better suited for biomass estimation, per se, while the best use of hyperspectral data may be to refine biomass predictions through a priori species stratification, while also providing information on canopy state, such as stress. Together, the two sensors have many potential applications in carbon dynamics, ecological and habitat studies.