Bachelor of Science, University of California Berkeley, Environmental Econ and Policy (2008)
Doctor of Philosophy, Stanford University, EESS-PHD (2017)
Bachelor of Arts, University of California Berkeley, Molecular and Cell Biology (2008)
- Tropical soil nutrient distributions determined by biotic and hillslope processes BIOGEOCHEMISTRY 2016; 127 (2-3): 273-289
- Organismic-Scale Remote Sensing of Canopy Foliar Traits in Lowland Tropical Forests REMOTE SENSING 2016; 8 (2)
- A comparison of plot-based satellite and Earth system model estimates of tropical forest net primary production GLOBAL BIOGEOCHEMICAL CYCLES 2015; 29 (5): 626-644
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
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
Human and environmental controls over aboveground carbon storage in Madagascar.
Carbon balance and management
2012; 7: 2-?
Accurate, high-resolution mapping of aboveground carbon density (ACD, Mg C ha-1) could provide insight into human and environmental controls over ecosystem state and functioning, and could support conservation and climate policy development. However, mapping ACD has proven challenging, particularly in spatially complex regions harboring a mosaic of land use activities, or in remote montane areas that are difficult to access and poorly understood ecologically. Using a combination of field measurements, airborne Light Detection and Ranging (LiDAR) and satellite data, we present the first large-scale, high-resolution estimates of aboveground carbon stocks in Madagascar.We found that elevation and the fraction of photosynthetic vegetation (PV) cover, analyzed throughout forests of widely varying structure and condition, account for 27-67% of the spatial variation in ACD. This finding facilitated spatial extrapolation of LiDAR-based carbon estimates to a total of 2,372,680 ha using satellite data. Remote, humid sub-montane forests harbored the highest carbon densities, while ACD was suppressed in dry spiny forests and in montane humid ecosystems, as well as in most lowland areas with heightened human activity. Independent of human activity, aboveground carbon stocks were subject to strong physiographic controls expressed through variation in tropical forest canopy structure measured using airborne LiDAR.High-resolution mapping of carbon stocks is possible in remote regions, with or without human activity, and thus carbon monitoring can be brought to highly endangered Malagasy forests as a climate-change mitigation and biological conservation strategy.
View details for DOI 10.1186/1750-0680-7-2
View details for PubMedID 22289685
View details for PubMedCentralID PMC3278681
- High-resolution mapping of forest carbon stocks in the Colombian Amazon BIOGEOSCIENCES 2012; 9 (7): 2683-2696