Spatial partitioning of biomass and diversity in a lowland Bolivian forest: linking field and remote sensing measurements
Broadbent, Department of Global Ecology, Carnegie Institution, 260 Panama Street, Stanford, CA 94305 USA, email@example.com
Asner, Department of Global Ecology, Carnegie Institution, 260 Panama Street, Stanford, CA 94305 USA, firstname.lastname@example.org
Peña-Claros, Instituto Boliviano de Investigación Forestal, Bolivia, , Forest Ecology and Forest Management Group, Wageningen University,Wageningen, the Netherlands, email@example.com
Palace, Complex Systems Research Center, Morse Hall, University of New Hampshire, Durham, NH 03824 USA, firstname.lastname@example.org
Soriano, Instituto Boliviano de Investigación Forestal, Bolivia, email@example.com
Large-scale inventories of forest biomass and structure are necessary for both understanding carbon dynamics and conserving biodiversity. High resolution satellite imagery is starting to enable structural analysis of tropical forests over large areas, but we lack an understanding of how tropical forest biomass links to remote sensing. We quantified the spatial distribution of biomass and tree species diversity over four ha in a Bolivian lowland moist tropical forest, and then linked our field measurements to high resolution Quickbird satellite imagery. Emergent and canopy dominant trees, being those directly visible from nadir remote sensors, comprised the highest diversity of tree species, represented 86 % of all tree species found in our study plots, and contained the majority of forest biomass. Emergent trees obscured 1-15 trees with trunk diameters (at 1.3 m, DBH) ≥ 20 cm, thus hiding 30-50% of forest biomass from nadir viewing. Allometric equations were developed to link remotely visible crown features to stand parameters, showing that the maximum tree crown length explains 50-70 % of the individual tree biomass. We then developed correction equations to derive aboveground forest biomass, basal area, and tree density from tree crowns visible to nadir satellites. We applied an automated tree crown delineation procedure to a high-resolution panchromatic Quickbird image of our study area, which showed promise for identification of forest biomass at community scales, but which also highlighted the difficulties of remotely sensing forest structure at the individual tree level. Results from this study are pertinent for assessing current and developing future remote sensing approaches of forest biomass and tree diversity. An improved capability for large scale, fine resolution and cost effective quantification of biomass and diversity via remote sensing is relevant for forest management and biodiversity conservation throughout all tropical forests.