Organismic-Scale Remote Sensing of Canopy Foliar Traits in Lowland Tropical Forests

Abstract

Airborne high fidelity imaging spectroscopy (HiFIS) holds great promise for bridging the gap between field studies of functional diversity, which are spatially limited, and satellite detection of ecosystem properties, which lacks resolution to understand within landscape dynamics. We use Carnegie Airborne Observatory HiFIS data combined with field collected foliar trait data to develop quantitative prediction models of foliar traits at the tree-crown level across over 1000 ha of humid tropical forest. We predicted foliar leaf mass per area (LMA) as well as foliar concentrations of nitrogen, phosphorus, calcium, magnesium and potassium for canopy emergent trees (R 2 : 0.45-0.67, relative RMSE: 11%-14%). Correlations between remotely sensed model coefficients for these foliar traits are similar to those found in laboratory studies, suggesting that the detection of these mineral nutrients is possible through their biochemical stoichiometry. Maps derived from HiFIS provide quantitative foliar trait information across a tropical forest landscape at fine spatial resolution, and along environmental gradients. Multi-nutrient maps implemented at the fine organismic scale will subsequently provide new insight to the functional biogeography and biological diversity of tropical forest ecosystems.

An Approach for Foliar Trait Retrieval from Airborne Imaging Spectroscopy of Tropical Forests

“…Over half of the traits exhibited retrieval errors of less than 35% relative RMSE, and most were in agreement with the previous studies using this method at the leaf or crown levels [15,17]. Although validation statistics showed slightly lower performances than did the calibration statistics, they followed the same overall pattern, indicating model consistency in foliar trait retrieval.…”

“…Model development was similar to that presented in Chadwick and Asner [17], which generated crown-level models using a random selection of individual prescreened reflectance spectra from each crown. The advantage of this method is that it utilizes suitable spectra from any flightline that intersects with the crowns sampled for foliar traits in the field.…”

On the challenges of using field spectroscopy to measure the impact of soil type on leaf traits

“…Spectroscopy can provide predictions of a range of foliar traits at the leaf and canopy scales within diverse tropical ecosystems (Asner et al, 2011a;Doughty et al, 2011) and temperate forests (Wessman et al, 1988;Serbin et al, 2014). However, some traits do not have absorption features within the visible and shortwave infrared spectral range of spectrometers conventionally used for vegetation analyses, but can be estimated indirectly through their covariance with traits that do have absorption features in the visible-to-shortwave-infrared region ("constellation effects" sensu Dana Chadwick and Asner, 2016). These traits include elemental concentrations and isotope ratios (e.g.…”

Preface: Remote Sensing of Biodiversity

“…Zhao et al [23] address the optimal detection of biochemical indicators for species mapping, and two papers show the potential of mapping foliar traits related to ecosystem functionality. Chadwick and Asner [17] used airborne imaging spectroscopy to map leaf mass area (LMA) and the foliar concentrations of nitrogen, phosphorus, calcium, magnesium and potassium for dominant trees in the Peruvian wet tropics, and McManus et al [15] address the relationships between foliar reflectance spectra and the phylogenetic composition of a tropical forest on Barro Colorado Island, Panama. The paper by Coops et al [24] take into account forest fragmentation and land use with distribution modeling to predict forest species migration in the Pacific Northwest of North America under climate change, while Zhang et al [25] identify a MODIS based Dynamic Habitat Index Analysis using the Photosynthetically Active Radiation (fPAR) product for China that characterizes terrestrial biodiversity, while Barboas et al [26] used imaging spectroscopy data to identify the subcanopy invasive species Psidium cattleianum in Hawaiian forests.…”

“…Graves et al [16] address developing operational models for species classification using imaging spectroscopy and addressed the accuracy problems of imbalanced training data. Chadwick and Asner [17] collected Carnegie Airborne Observatory data at high spatial and spectral resolution and map leaf mass area (LMA) and several key mineral nutrients including foliar nitrogen, phosphorous, magnesium, potassium, and calcium. Revermann et al [18] used land surface phenology metrics from MODIS and Shuttle Radar Topographic Mission (SRTM) data to map alpha diversity across the Okavango Basin, one of the largest inland deltas in the world, originating in the Angolan Central Plateau and terminating in the Okavango Delta of Botswana.…”