My research focuses on the use of satellite remote sensing technology to characterize multi-scale patterns of terrestrial biodiversity. I am interested in understanding how ecosystem structure and function changes for multiple taxonomic groups as a result of anthropogenic forces. These forces include land use change, climate change, establishing protected areas, the development of novel ecosystems, and assisted migration.
Carnegie Airborne Observatory Researcher, Carnegie Institution for Science (April 1, 2009 - March 1, 2016)
I performed research with the Carnegie Airborne Observatory and Carnegie Spectranomics Project to link measurements of leaf chemistry with airborne imaging spectroscopy data. Once we established these links, we used airborne and satellite data to measure the distribution of traits across large geographic scales in order to determine the underlying genetic, geologic, topographic and climatic drivers of plant functional diversity within tropical forests worldwide.
260 Panama St, Stanford, CA, USA
Nature and mental health: An ecosystem service perspective.
2019; 5 (7): eaax0903
A growing body of empirical evidence is revealing the value of nature experience for mental health. With rapid urbanization and declines in human contact with nature globally, crucial decisions must be made about how to preserve and enhance opportunities for nature experience. Here, we first provide points of consensus across the natural, social, and health sciences on the impacts of nature experience on cognitive functioning, emotional well-being, and other dimensions of mental health. We then show how ecosystem service assessments can be expanded to include mental health, and provide a heuristic, conceptual model for doing so.
View details for DOI 10.1126/sciadv.aax0903
View details for PubMedID 31355340
- Reimagining the potential of Earth observations for ecosystem service assessments SCIENCE OF THE TOTAL ENVIRONMENT 2019; 665: 1053–63
- A global test of ecoregions (vol 2, pg 1889, 2018) NATURE ECOLOGY & EVOLUTION 2019; 3 (4): 708
Author Correction: A global test of ecoregions.
Nature ecology & evolution
The original paper was published without unique DOIs for GBIF occurrence downloads. These have now been inserted as references 70-76, and the error has been corrected in the PDF and HTML versions of the article.
View details for PubMedID 30858593
Reimagining the potential of Earth observations for ecosystem service assessments.
The Science of the total environment
2019; 665: 1053–63
The benefits nature provides to people, called ecosystem services, are increasingly recognized and accounted for in assessments of infrastructure development, agricultural management, conservation prioritization, and sustainable sourcing. These assessments are often limited by data, however, a gap with tremendous potential to be filled through Earth observations (EO), which produce a variety of data across spatial and temporal extents and resolutions. Despite widespread recognition of this potential, in practice few ecosystem service studies use EO. Here, we identify challenges and opportunities to using EO in ecosystem service modeling and assessment. Some challenges are technical, related to data awareness, processing, and access. These challenges require systematic investment in model platforms and data management. Other challenges are more conceptual but still systemic; they are byproducts of the structure of existing ecosystem service models and addressing them requires scientific investment in solutions and tools applicable to a wide range of models and approaches. We also highlight new ways in which EO can be leveraged for ecosystem service assessments, identifying promising new areas of research. More widespread use of EO for ecosystem service assessment will only be achieved if all of these types of challenges are addressed. This will require non-traditional funding and partnering opportunities from private and public agencies to promote data exploration, sharing, and archiving. Investing in this integration will be reflected in better and more accurate ecosystem service assessments worldwide.
View details for PubMedID 30893737
A global test of ecoregions.
Nature ecology & evolution
A foundational paradigm in biological and Earth sciences is that our planet is divided into distinct ecoregions and biomes demarking unique assemblages of species. This notion has profoundly influenced scientific research and environmental policy. Given recent advances in technology and data availability, however, we are now poised to ask whether ecoregions meaningfully delimit biological communities. Using over 200 million observations of plants, animals and fungi we show compelling evidence that ecoregions delineate terrestrial biodiversity patterns. We achieve this by testing two competing hypotheses: the sharp-transition hypothesis, positing that ecoregion borders divide differentiated biotic communities; and the gradual-transition hypothesis, proposing instead that species turnover is continuous and largely independent of ecoregion borders. We find strong support for the sharp-transition hypothesis across all taxa, although adherence to ecoregion boundaries varies across taxa. Although plant and vertebrate species are tightly linked to sharp ecoregion boundaries, arthropods and fungi show weaker affiliations to this set of ecoregion borders. Our results highlight the essential value of ecological data for setting conservation priorities and reinforce the importance of protecting habitats across as many ecoregions as possible. Specifically, we conclude that ecoregion-based conservation planning can guide investments that simultaneously protect species-, community- and ecosystem-level biodiversity, key for securing Earth's life support systems into the future.
View details for PubMedID 30397301
- Biodiversity monitoring, earth observations and the ecology of scale ECOLOGY LETTERS 2018; 21 (10): 1572–85
Biodiversity monitoring, earth observations and the ecology of scale.
Human activity and land-use change are dramatically altering the sizes, geographical distributions and functioning of biological populations worldwide, with tremendous consequences for human well-being. Yet our ability to measure, monitor and forecast biodiversity change - crucial to addressing it - remains limited. Biodiversity monitoring systems are being developed to improve this capacity by deriving metrics of change from an array of insitu data (e.g. field plots or species occurrence records) and Earth observations (EO; e.g. satellite or airborne imagery). However, there are few ecologically based frameworks for integrating these data into meaningful metrics of biodiversity change. Here, I describe how concepts of pattern and scale in ecology could be used to design such a framework. I review three core topics: the role of scale in measuring and modelling biodiversity patterns with EO, scale-dependent challenges linking insitu and EO data and opportunities to apply concepts of pattern and scale to EO to improve biodiversity mapping. From this analysis emerges an actionable approach for measuring, monitoring and forecasting biodiversity change, highlighting key opportunities to establish EO as the backbone of global-scale, science-driven conservation.
View details for PubMedID 30004184
The CCB-ID approach to tree species mapping with airborne imaging spectroscopy.
2018; 6: e5666
Background: Biogeographers assess how species distributions and abundances affect the structure, function, and composition of ecosystems. Yet we face a major challenge: it is difficult to precisely map species across landscapes. Novel Earth observations could overcome this challenge for vegetation mapping. Airborne imaging spectrometers measure plant functional traits at high resolution, and these measurements can be used to identify tree species. In this paper, I describe a trait-based approach to species identification with imaging spectroscopy, the Center for Conservation Biology species identification (CCB-ID) method, which was developed as part of an ecological data science evaluation competition.Methods: These methods were developed using airborne imaging spectroscopy data from the National Ecological Observatory Network (NEON). CCB-ID classified tree species using trait-based reflectance variation and decision tree-based machine learning models, approximating a morphological trait and dichotomous key method inspired by botanical classification. First, outliers were removed using a spectral variance threshold. The remaining samples were transformed using principal components analysis (PCA) and resampled to reduce common species biases. Gradient boosting and random forest classifiers were trained using the transformed and resampled feature data. Prediction probabilities were calibrated using sigmoid regression, and sample-scale predictions were averaged to the crown scale.Results: CCB-ID received a rank-1 accuracy score of 0.919, and a cross-entropy cost score of 0.447 on the competition test data. Accuracy and specificity scores were high for all species, but precision and recall scores varied for rare species. PCA transformation improved accuracy scores compared to models trained using reflectance data, but outlier removal and data resampling exacerbated class imbalance problems.Discussion: CCB-ID accurately classified tree species using NEON data, reporting the best scores among participants. However, it failed to overcome several species mapping challenges like precisely identifying rare species. Key takeaways include (1) selecting models using metrics beyond accuracy (e.g., recall) could improve rare species predictions, (2) within-genus trait variation may drive spectral separability, precluding efforts to distinguish between functionally convergent species, (3) outlier removal and data resampling can exacerbate class imbalance problems, and should be carefully implemented, (4) PCA transformation greatly improved model results, and (5) targeted feature selection could further improve species classification models. CCB-ID is open source, designed for use with NEON data, and available to support species mapping efforts.
View details for PubMedID 30324011
Scale dependence of canopy trait distributions along a tropical forest elevation gradient
2017; 214 (3): 973–88
Average responses of forest foliar traits to elevation are well understood, but far less is known about trait distributional responses to elevation at multiple ecological scales. This limits our understanding of the ecological scales at which trait variation occurs in response to environmental drivers and change. We analyzed and compared multiple canopy foliar trait distributions using field sampling and airborne imaging spectroscopy along an Andes-to-Amazon elevation gradient. Field-estimated traits were generated from three community-weighting methods, and remotely sensed estimates of traits were made at three scales defined by sampling grain size and ecological extent. Field and remote sensing approaches revealed increases in average leaf mass per unit area (LMA), water, nonstructural carbohydrates (NSCs) and polyphenols with increasing elevation. Foliar nutrients and photosynthetic pigments displayed little to no elevation trend. Sample weighting approaches had little impact on field-estimated trait responses to elevation. Plot representativeness of trait distributions at landscape scales decreased with increasing elevation. Remote sensing indicated elevation-dependent increases in trait variance and distributional skew. Multiscale invariance of LMA, leaf water and NSC mark these traits as candidates for tracking forest responses to changing climate. Trait-based ecological studies can be greatly enhanced with multiscale studies made possible by imaging spectroscopy.
View details for DOI 10.1111/nph.14068
View details for Web of Science ID 000402403900011
View details for PubMedID 27349599
FOREST CONSERVATION Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation
2017; 355 (6323): 385–88
Functional biogeography may bridge a gap between field-based biodiversity information and satellite-based Earth system studies, thereby supporting conservation plans to protect more species and their contributions to ecosystem functioning. We used airborne laser-guided imaging spectroscopy with environmental modeling to derive large-scale, multivariate forest canopy functional trait maps of the Peruvian Andes-to-Amazon biodiversity hotspot. Seven mapped canopy traits revealed functional variation in a geospatial pattern explained by geology, topography, hydrology, and climate. Clustering of canopy traits yielded a map of forest beta functional diversity for land-use analysis. Up to 53% of each mapped, functionally distinct forest presents an opportunity for new conservation action. Mapping functional diversity advances our understanding of the biosphere to conserve more biodiversity in the face of land use and climate change.
View details for DOI 10.1126/science.aaj1987
View details for Web of Science ID 000393172800036
View details for PubMedID 28126815
Large-scale climatic and geophysical controls on the leaf economics spectrum
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2016; 113 (28): E4043-E4051
Leaf economics spectrum (LES) theory suggests a universal trade-off between resource acquisition and storage strategies in plants, expressed in relationships between foliar nitrogen (N) and phosphorus (P), leaf mass per area (LMA), and photosynthesis. However, how environmental conditions mediate LES trait interrelationships, particularly at large biospheric scales, remains unknown because of a lack of spatially explicit data, which ultimately limits our understanding of ecosystem processes, such as primary productivity and biogeochemical cycles. We used airborne imaging spectroscopy and geospatial modeling to generate, to our knowledge, the first biospheric maps of LES traits, here centered on 76 million ha of Andean and Amazonian forest, to assess climatic and geophysical determinants of LES traits and their interrelationships. Elevation and substrate were codominant drivers of leaf trait distributions. Multiple additional climatic and geophysical factors were secondary determinants of plant traits. Anticorrelations between N and LMA followed general LES theory, but topo-edaphic conditions strongly mediated and, at times, eliminated this classic relationship. We found no evidence for simple P-LMA or N-P trade-offs in forest canopies; rather, we mapped a continuum of N-P-LMA interactions that are sensitive to elevation and temperature. Our results reveal nested climatic and geophysical filtering of LES traits and their interrelationships, with important implications for predictions of forest productivity and acclimation to rapid climate change.
View details for DOI 10.1073/pnas.1604863113
View details for Web of Science ID 000379694100010
View details for PubMedID 27354534
View details for PubMedCentralID PMC4948348
- Tree Species Abundance Predictions in a Tropical Agricultural Landscape with a Supervised Classification Model and Imbalanced Data REMOTE SENSING 2016; 8 (2)
- Progressive forest canopy water loss during the 2012-2015 California drought PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2016; 113 (2): E249-E255
- Mesoscale assessment of changes in tropical tree species richness across a bioclimatic gradient in Panama using airborne imaging spectroscopy REMOTE SENSING OF ENVIRONMENT 2015; 167: 111-120
Operational Tree Species Mapping in a Diverse Tropical Forest with Airborne Imaging Spectroscopy
2015; 10 (7)
Remote identification and mapping of canopy tree species can contribute valuable information towards our understanding of ecosystem biodiversity and function over large spatial scales. However, the extreme challenges posed by highly diverse, closed-canopy tropical forests have prevented automated remote species mapping of non-flowering tree crowns in these ecosystems. We set out to identify individuals of three focal canopy tree species amongst a diverse background of tree and liana species on Barro Colorado Island, Panama, using airborne imaging spectroscopy data. First, we compared two leading single-class classification methods--binary support vector machine (SVM) and biased SVM--for their performance in identifying pixels of a single focal species. From this comparison we determined that biased SVM was more precise and created a multi-species classification model by combining the three biased SVM models. This model was applied to the imagery to identify pixels belonging to the three focal species and the prediction results were then processed to create a map of focal species crown objects. Crown-level cross-validation of the training data indicated that the multi-species classification model had pixel-level producer's accuracies of 94-97% for the three focal species, and field validation of the predicted crown objects indicated that these had user's accuracies of 94-100%. Our results demonstrate the ability of high spatial and spectral resolution remote sensing to accurately detect non-flowering crowns of focal species within a diverse tropical forest. We attribute the success of our model to recent classification and mapping techniques adapted to species detection in diverse closed-canopy forests, which can pave the way for remote species mapping in a wider variety of ecosystems.
View details for DOI 10.1371/journal.pone.0118403
View details for Web of Science ID 000358159700001
View details for PubMedID 26153693
View details for PubMedCentralID PMC4496029
- Landscape biogeochemistry reflected in shifting distributions of chemical traits in the Amazon forest canopy NATURE GEOSCIENCE 2015; 8 (7): 567-U114
Landscape-Scale Controls on Aboveground Forest Carbon Stocks on the Osa Peninsula, Costa Rica
2015; 10 (6)
Tropical forests store large amounts of carbon in tree biomass, although the environmental controls on forest carbon stocks remain poorly resolved. Emerging airborne remote sensing techniques offer a powerful approach to understand how aboveground carbon density (ACD) varies across tropical landscapes. In this study, we evaluate the accuracy of the Carnegie Airborne Observatory (CAO) Light Detection and Ranging (LiDAR) system to detect top-of-canopy tree height (TCH) and ACD across the Osa Peninsula, Costa Rica. LiDAR and field-estimated TCH and ACD were highly correlated across a wide range of forest ages and types. Top-of-canopy height (TCH) reached 67 m, and ACD surpassed 225 Mg C ha-1, indicating both that airborne CAO LiDAR-based estimates of ACD are accurate in tall, high-biomass forests and that the Osa Peninsula harbors some of the most carbon-rich forests in the Neotropics. We also examined the relative influence of lithologic, topoedaphic and climatic factors on regional patterns in ACD, which are known to influence ACD by regulating forest productivity and turnover. Analyses revealed a spatially nested set of factors controlling ACD patterns, with geologic variation explaining up to 16% of the mapped ACD variation at the regional scale, while local variation in topographic slope explained an additional 18%. Lithologic and topoedaphic factors also explained more ACD variation at 30-m than at 100-m spatial resolution, suggesting that environmental filtering depends on the spatial scale of terrain variation. Our result indicate that patterns in ACD are partially controlled by spatial variation in geologic history and geomorphic processes underpinning topographic diversity across landscapes. ACD also exhibited spatial autocorrelation, which may reflect biological processes that influence ACD, such as the assembly of species or phenotypes across the landscape, but additional research is needed to resolve how abiotic and biotic factors contribute to ACD variation across high biomass, high diversity tropical landscapes.
View details for DOI 10.1371/journal.pone.0126748
View details for Web of Science ID 000355979500016
View details for PubMedID 26061884
View details for PubMedCentralID PMC4465637
Regional-Scale Drivers of Forest Structure and Function in Northwestern Amazonia
2015; 10 (3)
Field studies in Amazonia have found a relationship at continental scales between soil fertility and broad trends in forest structure and function. Little is known at regional scales, however, about how discrete patterns in forest structure or functional attributes map onto underlying edaphic or geological patterns. We collected airborne LiDAR (Light Detection and Ranging) data and VSWIR (Visible to Shortwave Infrared) imaging spectroscopy measurements over 600 km2 of northwestern Amazonian lowland forests. We also established 83 inventories of plant species composition and soil properties, distributed between two widespread geological formations. Using these data, we mapped forest structure and canopy reflectance, and compared them to patterns in plant species composition, soils, and underlying geology. We found that variations in soils and species composition explained up to 70% of variation in canopy height, and corresponded to profound changes in forest vertical profiles. We further found that soils and plant species composition explained more than 90% of the variation in canopy reflectance as measured by imaging spectroscopy, indicating edaphic and compositional control of canopy chemical properties. We last found that soils explained between 30% and 70% of the variation in gap frequency in these forests, depending on the height threshold used to define gaps. Our findings indicate that a relatively small number of edaphic and compositional variables, corresponding to underlying geology, may be responsible for variations in canopy structure and chemistry over large expanses of Amazonian forest.
View details for DOI 10.1371/journal.pone.0119887
View details for Web of Science ID 000352084200125
View details for PubMedID 25793602
View details for PubMedCentralID PMC4368595
- Quantifying forest canopy traits: Imaging spectroscopy versus field survey REMOTE SENSING OF ENVIRONMENT 2015; 158: 15-27
Amazonian landscapes and the bias in field studies of forest structure and biomass
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2014; 111 (48): E5224-E5232
Tropical forests convert more atmospheric carbon into biomass each year than any terrestrial ecosystem on Earth, underscoring the importance of accurate tropical forest structure and biomass maps for the understanding and management of the global carbon cycle. Ecologists have long used field inventory plots as the main tool for understanding forest structure and biomass at landscape-to-regional scales, under the implicit assumption that these plots accurately represent their surrounding landscape. However, no study has used continuous, high-spatial-resolution data to test whether field plots meet this assumption in tropical forests. Using airborne LiDAR (light detection and ranging) acquired over three regions in Peru, we assessed how representative a typical set of field plots are relative to their surrounding host landscapes. We uncovered substantial mean biases (9-98%) in forest canopy structure (height, gaps, and layers) and aboveground biomass in both lowland Amazonian and montane Andean landscapes. Moreover, simulations reveal that an impractical number of 1-ha field plots (from 10 to more than 100 per landscape) are needed to develop accurate estimates of aboveground biomass at landscape scales. These biases should temper the use of plots for extrapolations of forest dynamics to larger scales, and they demonstrate the need for a fundamental shift to high-resolution active remote sensing techniques as a primary sampling tool in tropical forest biomass studies. The potential decrease in the bias and uncertainty of remotely sensed estimates of forest structure and biomass is a vital step toward successful tropical forest conservation and climate-change mitigation policy.
View details for DOI 10.1073/pnas.1412999111
View details for Web of Science ID 000345920800012
View details for PubMedID 25422434
View details for PubMedCentralID PMC4260573
Targeted carbon conservation at national scales with high-resolution monitoring
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2014; 111 (47): E5016-E5022
Terrestrial carbon conservation can provide critical environmental, social, and climate benefits. Yet, the geographically complex mosaic of threats to, and opportunities for, conserving carbon in landscapes remain largely unresolved at national scales. Using a new high-resolution carbon mapping approach applied to Perú, a megadiverse country undergoing rapid land use change, we found that at least 0.8 Pg of aboveground carbon stocks are at imminent risk of emission from land use activities. Map-based information on the natural controls over carbon density, as well as current ecosystem threats and protections, revealed three biogeographically explicit strategies that fully offset forthcoming land-use emissions. High-resolution carbon mapping affords targeted interventions to reduce greenhouse gas emissions in rapidly developing tropical nations.
View details for DOI 10.1073/pnas.1419550111
View details for Web of Science ID 000345662700001
View details for PubMedID 25385593
View details for PubMedCentralID PMC4250114
- Linking imaging spectroscopy and LiDAR with floristic composition and forest structure in Panama REMOTE SENSING OF ENVIRONMENT 2014; 154: 358-367
Functional and biological diversity of foliar spectra in tree canopies throughout the Andes to Amazon region
2014; 204 (1): 127-139
Spectral properties of foliage express fundamental chemical interactions of canopies with solar radiation. However, the degree to which leaf spectra track chemical traits across environmental gradients in tropical forests is unknown. We analyzed leaf reflectance and transmittance spectra in 2567 tropical canopy trees comprising 1449 species in 17 forests along a 3400-m elevation and soil fertility gradient from the Amazonian lowlands to the Andean treeline. We developed quantitative links between 21 leaf traits and 400-2500-nm spectra, and developed classifications of tree taxa based on spectral traits. Our results reveal enormous inter-specific variation in spectral and chemical traits among canopy trees of the western Amazon. Chemical traits mediating primary production were tightly linked to elevational changes in foliar spectral signatures. By contrast, defense compounds and rock-derived nutrients tracked foliar spectral variation with changing soil fertility in the lowlands. Despite the effects of abiotic filtering on mean foliar spectral properties of tree communities, the spectra were dominated by phylogeny within any given community, and spectroscopy accurately classified 85-93% of Amazonian tree species. Our findings quantify how tropical tree canopies interact with sunlight, and indicate how to measure the functional and biological diversity of forests with spectroscopy.
View details for DOI 10.1111/nph.12895
View details for Web of Science ID 000341193500015
View details for PubMedID 24942328
Amazonian functional diversity from forest canopy chemical assembly
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2014; 111 (15): 5604-5609
Patterns of tropical forest functional diversity express processes of ecological assembly at multiple geographic scales and aid in predicting ecological responses to environmental change. Tree canopy chemistry underpins forest functional diversity, but the interactive role of phylogeny and environment in determining the chemical traits of tropical trees is poorly known. Collecting and analyzing foliage in 2,420 canopy tree species across 19 forests in the western Amazon, we discovered (i) systematic, community-scale shifts in average canopy chemical traits along gradients of elevation and soil fertility; (ii) strong phylogenetic partitioning of structural and defense chemicals within communities independent of variation in environmental conditions; and (iii) strong environmental control on foliar phosphorus and calcium, the two rock-derived elements limiting CO2 uptake in tropical forests. These findings indicate that the chemical diversity of western Amazonian forests occurs in a regionally nested mosaic driven by long-term chemical trait adjustment of communities to large-scale environmental filters, particularly soils and climate, and is supported by phylogenetic divergence of traits essential to foliar survival under varying environmental conditions. Geographically nested patterns of forest canopy chemical traits will play a role in determining the response and functional rearrangement of western Amazonian ecosystems to changing land use and climate.
View details for DOI 10.1073/pnas.1401181111
View details for Web of Science ID 000334288600046
View details for PubMedID 24591585
View details for PubMedCentralID PMC3992634
A Tale of Two "Forests": Random Forest Machine Learning Aids Tropical Forest Carbon Mapping
2014; 9 (1)
Accurate and spatially-explicit maps of tropical forest carbon stocks are needed to implement carbon offset mechanisms such as REDD+ (Reduced Deforestation and Degradation Plus). The Random Forest machine learning algorithm may aid carbon mapping applications using remotely-sensed data. However, Random Forest has never been compared to traditional and potentially more reliable techniques such as regionally stratified sampling and upscaling, and it has rarely been employed with spatial data. Here, we evaluated the performance of Random Forest in upscaling airborne LiDAR (Light Detection and Ranging)-based carbon estimates compared to the stratification approach over a 16-million hectare focal area of the Western Amazon. We considered two runs of Random Forest, both with and without spatial contextual modeling by including--in the latter case--x, and y position directly in the model. In each case, we set aside 8 million hectares (i.e., half of the focal area) for validation; this rigorous test of Random Forest went above and beyond the internal validation normally compiled by the algorithm (i.e., called "out-of-bag"), which proved insufficient for this spatial application. In this heterogeneous region of Northern Peru, the model with spatial context was the best preforming run of Random Forest, and explained 59% of LiDAR-based carbon estimates within the validation area, compared to 37% for stratification or 43% by Random Forest without spatial context. With the 60% improvement in explained variation, RMSE against validation LiDAR samples improved from 33 to 26 Mg C ha(-1) when using Random Forest with spatial context. Our results suggest that spatial context should be considered when using Random Forest, and that doing so may result in substantially improved carbon stock modeling for purposes of climate change mitigation.
View details for DOI 10.1371/journal.pone.0085993
View details for Web of Science ID 000330510000028
View details for PubMedID 24489686
View details for PubMedCentralID PMC3904849
- Landscape-scale changes in forest structure and functional traits along an Andes-to-Amazon elevation gradient BIOGEOSCIENCES 2014; 11 (3): 843-856
Forest Canopy Gap Distributions in the Southern Peruvian Amazon
2013; 8 (4)
Canopy gaps express the time-integrated effects of tree failure and mortality as well as regrowth and succession in tropical forests. Quantifying the size and spatial distribution of canopy gaps is requisite to modeling forest functional processes ranging from carbon fluxes to species interactions and biological diversity. Using high-resolution airborne Light Detection and Ranging (LiDAR), we mapped and analyzed 5,877,937 static canopy gaps throughout 125,581 ha of lowland Amazonian forest in Peru. Our LiDAR sampling covered a wide range of forest physiognomies across contrasting geologic and topographic conditions, and on depositional floodplain and erosional terra firme substrates. We used the scaling exponent of the Zeta distribution (λ) as a metric to quantify and compare the negative relationship between canopy gap frequency and size across sites. Despite variable canopy height and forest type, values of λ were highly conservative (λ mean = 1.83, s = 0.09), and little variation was observed regionally among geologic substrates and forest types, or at the landscape level comparing depositional-floodplain and erosional terra firme landscapes. λ-values less than 2.0 indicate that these forests are subjected to large gaps that reset carbon stocks when they occur. Consistency of λ-values strongly suggests similarity in the mechanisms of canopy failure across a diverse array of lowland forests in southwestern Amazonia.
View details for DOI 10.1371/journal.pone.0060875
View details for Web of Science ID 000317563300009
View details for PubMedID 23613748
View details for PubMedCentralID PMC3626694
High-fidelity national carbon mapping for resource management and REDD+.
Carbon balance and management
2013; 8 (1): 7-?
High fidelity carbon mapping has the potential to greatly advance national resource management and to encourage international action toward climate change mitigation. However, carbon inventories based on field plots alone cannot capture the heterogeneity of carbon stocks, and thus remote sensing-assisted approaches are critically important to carbon mapping at regional to global scales. We advanced a high-resolution, national-scale carbon mapping approach applied to the Republic of Panama - one of the first UN REDD + partner countries.Integrating measurements of vegetation structure collected by airborne Light Detection and Ranging (LiDAR) with field inventory plots, we report LiDAR-estimated aboveground carbon stock errors of ~10% on any 1-ha land parcel across a wide range of ecological conditions. Critically, this shows that LiDAR provides a highly reliable replacement for inventory plots in areas lacking field data, both in humid tropical forests and among drier tropical vegetation types. We then scale up a systematically aligned LiDAR sampling of Panama using satellite data on topography, rainfall, and vegetation cover to model carbon stocks at 1-ha resolution with estimated average pixel-level uncertainty of 20.5 Mg C ha-1 nationwide.The national carbon map revealed strong abiotic and human controls over Panamanian carbon stocks, and the new level of detail with estimated uncertainties for every individual hectare in the country sets Panama at the forefront in high-resolution ecosystem management. With this repeatable approach, carbon resource decision-making can be made on a geospatially explicit basis, enhancing human welfare and environmental protection.
View details for DOI 10.1186/1750-0680-8-7
View details for PubMedID 23866822
View details for PubMedCentralID PMC3717137
- Carnegie Airborne Observatory-2: Increasing science data dimensionality via high-fidelity multi-sensor fusion REMOTE SENSING OF ENVIRONMENT 2012; 124: 454-465
- Spectroscopy of canopy chemicals in humid tropical forests REMOTE SENSING OF ENVIRONMENT 2011; 115 (12): 3587-3598