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

Abstract

a b s t r a c t a r t i c l e i n f oIn 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 (r 2 = 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 (r 2 = 0.77, RMSE = 70.12 Mg/ha). Prediction by LVIS after species stratification of field data reduced errors by 12% (r 2 = 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.

Quantifying Forest Biomass Carbon Stocks From Space

“…Using Hyperion data, le Maire et al [58] was able to estimate canopy leaf biomass. However, the poor relationship between stem biomass and vegetation indices has been indicated by some studies [59,60]. Nevertheless, the use of hyperspectral data in combination with other sensors can improve AGB estimations as shown by Lucas et al [61] and Swatantran et al [60].…”

Using InSAR Coherence to Map Stand Age in a Boreal Forest

“…The lidar-derived canopy height metric RH100 shows considerable within-patch variation (Figure 4(B)). A stronger correlation with age is observed with RH75 (Table 2), a metric generally associated with basal area-weighted height [37].…”

Impacts of Tree Height-DBH Allometry on Lidar-Based Tree Aboveground Biomass Modeling

“…These resources include: various types of sensors (Zolkos et al, 2013), regression approaches (Chen et al, 2010; The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B8, 2016XXIII ISPRS Congress, 12-19 July 2016, Prague, Czech Republic 2015Dalponte et al, 2008;Garcıa-Gutiérreza et al;Gleason and Im, 2012), and whether or not optical remote sensing data is fused with lidar data (Anderson et al, 2008;Koetz et al, 2007;Lefsky et al, 2005;Swatantran et al, 2011;Laurin et al, 2014). However, these sources introducing errors to lidar-based AGB models are more considered as external factors rather than the intrinsic limitations of further improving the performance of lidar in biomass estimation.…”