David Gwenzi

Plot-level aboveground woody biomass modeling using canopy height and auxiliary remote sensing data in a heterogeneous savanna

Abstract. Remote sensing studies aiming at assessing woody biomass have demonstrated a strong relationship between canopy height and plot-level aboveground biomass, but most of these studies focused on closed canopy forests. To date, a few studies have examined the strength and reliability of this relationship using large footprint lidar in savannas. Furthermore, there have been few studies of appropriate methods for the comparison of models that relate aboveground biomass to canopy height metrics without consideration of variation in species composition (generic models) to models developed for individual species composition or vegetation types. We developed generic models using the classical least-squares regression modeling approach to relate selected canopy height metrics to aboveground woody biomass in a savanna landscape. Hierarchical Bayesian analysis (HBA) was then used to explore the implications of using generic or composition-specific models. Our study used the estimates of aboveground biomass from field data, canopy height estimates from airborne discrete return lidar, and a proxy for canopy cover (the Normalized Difference Vegetation Index) from Landsat 5 Thematic Mapper data, collected from the oak savannas of Tejon Ranch Conservancy in Kern County, California. Models were developed and analyzed using estimates of canopy height and aboveground biomass calculated at the level of 50-m diameter plots, comparable with footprint diameter of existing large footprint spaceborne lidar data. The two generic models that incorporated canopy cover proxies performed better than one model that did not use canopy cover information. From the HBA, we found out that for all models both the intercept and slope had interspecific variability. The valley oak dominated plots consistently had higher slopes and intercepts, whereas the plots dominated by blue oaks had the lowest. However, the intercept and slope values of the composition-specific models did not differ much from the generic (overall) values and their 95% credible intervals (CIs) overlapped the generic mean values. We conclude that the narrow range of the distribution and the overlap of the CIs of the composition-specific and generic parameters suggest that scaling rules do exist for savannas. The distribution of the posterior densities of the differences between composition-level and generic-level parameter values showed high support for the use of generic parameters, suggesting that all three models are applicable across the range of compositions in this study. Therefore, in this case, the choice of method depends more on secondary considerations such as data availability and scale of analyses.

Predictions of Tropical Forest Biomass and Biomass Growth Based on Stand Height or Canopy Area Are Improved by Landsat-Scale Phenology across Puerto Rico and the U.S. Virgin Islands

Remotely-sensed estimates of forest biomass are usually based on various measurements of canopy height, area, volume or texture, as derived from LiDAR, radar or fine spatial resolution imagery. These measurements are then calibrated to estimates of stand biomass that are primarily based on tree stem diameters. Although humid tropical forest seasonality can have low amplitudes compared with temperate regions, seasonal variations in growth-related factors like temperature, humidity, rainfall, wind speed and day length affect both tropical forest deciduousness and tree height-diameter relationships. Consequently, seasonal patterns in spectral measures of canopy greenness derived from satellite imagery should be related to tree height-diameter relationships and hence to estimates of forest biomass or biomass growth that are based on stand height or canopy area. In this study, we tested whether satellite image-based measures of tropical forest phenology, as estimated by indices of seasonal patterns in canopy greenness constructed from Landsat satellite images, can explain the variability in forest deciduousness, forest biomass and net biomass growth after already accounting for stand height or canopy area. Models of forest biomass that added phenology variables to structural variables similar to those that can be estimated by LiDAR or very high-spatial resolution imagery, like canopy height and crown area, explained up to 12% more variation in biomass. Adding phenology to structural variables explained up to 25% more variation in Net Biomass Growth (NBG). In all of the models, phenology contributed more as interaction terms than as single-effect terms. In addition, models run on only fully-forested plots performed better than models that included partially-forested plots. For forest NBG, the models produced better results when only those plots with a positive growth, from Inventory Cycle 1 to Inventory Cycle 2, were analyzed, as compared to models that included all plots

Spatial Modeling of Lidar-Derived Woody Biomass Estimates Collected Along Transects in a Heterogeneous Savanna Landscape

Waveforms from the Ice, Cloud and land Elevation Satellite have successfully estimated footprint-level canopy height and aboveground biomass even in structurally complex savanna ecosystems. However, at the landscape level wall-to-wall maps are preferred since they are more easily integrated with other geospatial data products. We evaluated and compared the utility of inverse distance weighting, cokriging, regression kriging and image segmentation methods to create wall-to-wall maps from footprint-level estimates of biomass across a 13 600-Ha Oak savanna landscape in Santa Clara county, California. The four methods estimated biomass with between 39% (inverse distance weighting) and 66% (image segmentation) of variance explained and RMSE of 42% and 32% of the mean, respectively. When more waveforms were available across or along track to characterize patch biomass with the image segmentation method, 78% of variance in biomass was explained (RMSE = 21% of the mean). Overall, the mean biomass estimated by the four methods did not differ significantly but a visual inspection of the output maps showed marked differences in the ability of each model to mimic the primary variables landscape-level trend. We conclude that transects of lidar data can be used to create wall-to-wall biomass maps in savannas but the methods require a higher sampling intensity and informative auxiliary data to reproduce the variability of the biomass across the landscape. We recommend that future satellite lidar missions increase the sampling intensity across track so that biomass observations are made and characterized at the scale at which they vary.