Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation

Airborne spectroscopy for forest traits The development of conservation priorities in the tropics is often hampered by the sparseness of ground data on biological diversity and the relative crudeness of larger-scale remote sensing data. Asner et al. developed airborne instrumentation to make large-scale maps of forest functional diversity across 72 million hectares of the Peruvian Andes and Amazon basin (see the Perspective by Kapos). They generated a suite of forest canopy functional trait maps from laser-guided imaging spectroscopy and used them to define distinct forest functional classes. These were then compared with government deforestation and land allocation data, which enabled an analysis of conservation threats and opportunities across the region. Science, this issue p. 385; see also p. 347 Large-scale mapping of tropical forest trait diversity offers an approach for conservation. 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.

[1]  Alice Boit,et al.  Resilience of Amazon forests emerges from plant trait diversity , 2016 .

[2]  Roberta E. Martin,et al.  Convergent elevation trends in canopy chemical traits of tropical forests , 2016, Global change biology.

[3]  Gregory Asner,et al.  Organismic-Scale Remote Sensing of Canopy Foliar Traits in Lowland Tropical Forests , 2016, Remote. Sens..

[4]  Roberta E. Martin,et al.  Landscape biogeochemistry reflected in shifting distributions of chemical traits in the Amazon forest canopy , 2015 .

[5]  Roberta E. Martin,et al.  Regional-Scale Drivers of Forest Structure and Function in Northwestern Amazonia , 2015, PloS one.

[6]  Roberta E. Martin,et al.  Quantifying forest canopy traits: Imaging spectroscopy versus field survey , 2015 .

[7]  D. Tilman,et al.  Biodiversity and Ecosystem Functioning , 2014 .

[8]  Roberta E. Martin,et al.  Targeted carbon conservation at national scales with high-resolution monitoring , 2014, Proceedings of the National Academy of Sciences.

[9]  Malika Charrad,et al.  NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set , 2014 .

[10]  Jens Kattge,et al.  The emergence and promise of functional biogeography , 2014, Proceedings of the National Academy of Sciences.

[11]  Elise S Gornish,et al.  Foliar functional traits that predict plant biomass response to warming , 2014 .

[12]  Roberta E. Martin,et al.  Amazonian functional diversity from forest canopy chemical assembly , 2014, Proceedings of the National Academy of Sciences.

[13]  Roberta E. Martin,et al.  A Tale of Two “Forests”: Random Forest Machine Learning Aids Tropical Forest Carbon Mapping , 2014, PloS one.

[14]  G. Asner,et al.  Elevated rates of gold mining in the Amazon revealed through high-resolution monitoring , 2013, Proceedings of the National Academy of Sciences.

[15]  R. DeFries,et al.  Annual multi-resolution detection of land cover conversion to oil palm in the Peruvian Amazon , 2013 .

[16]  Anelena L. de Carvalho,et al.  Bamboo-Dominated Forests of the Southwest Amazon: Detection, Spatial Extent, Life Cycle Length and Flowering Waves , 2013, PloS one.

[17]  Gregory Asner,et al.  Mapping Savanna Tree Species at Ecosystem Scales Using Support Vector Machine Classification and BRDF Correction on Airborne Hyperspectral and LiDAR Data , 2012, Remote. Sens..

[18]  Roberta E. Martin,et al.  Carnegie Airborne Observatory-2: Increasing science data dimensionality via high-fidelity multi-sensor fusion , 2012 .

[19]  Roberta E. Martin,et al.  Spectroscopy of canopy chemicals in humid tropical forests , 2011 .

[20]  Kalle Ruokolainen,et al.  Geological control of floristic composition in Amazonian forests , 2011, Journal of biogeography.

[21]  W. Salas,et al.  Benchmark map of forest carbon stocks in tropical regions across three continents , 2011, Proceedings of the National Academy of Sciences.

[22]  Y. Malhi,et al.  Upslope migration of Andean trees , 2011 .

[23]  Luiz E. O. C. Aragão,et al.  Net primary productivity allocation and cycling of carbon along a tropical forest elevational transect in the Peruvian Andes , 2010 .

[24]  T. Stadler,et al.  Amazonia Through Time: Andean Uplift, Climate Change, Landscape Evolution, and Biodiversity , 2010, Science.

[25]  Roberta E. Martin,et al.  Brightness-normalized Partial Least Squares Regression for hyperspectral data , 2010 .

[26]  J. Lamarque,et al.  Global Biodiversity: Indicators of Recent Declines , 2010, Science.

[27]  Yadvinder Malhi,et al.  Basin-wide variations in foliar properties of Amazonian forest: phylogeny, soils and climate. , 2009 .

[28]  R. Kokaly,et al.  Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies , 2009 .

[29]  J. Darrozes,et al.  Geomorphic evidence for recent uplift of the Fitzcarrald Arch (Peru): a response to the Nazca Ridge subduction , 2009 .

[30]  David E. Knapp,et al.  Automated mapping of tropical deforestation and forest degradation: CLASlite , 2009 .

[31]  Gregory P Asner,et al.  The biogeochemical heterogeneity of tropical forests. , 2008, Trends in ecology & evolution.

[32]  Anne-Laure Boulesteix,et al.  Partial least squares: a versatile tool for the analysis of high-dimensional genomic data , 2006, Briefings Bioinform..

[33]  Roberta E. Martin,et al.  Carnegie Airborne Observatory: in-flight fusion of hyperspectral imaging and waveform light detection and ranging for three-dimensional studies of ecosystems , 2007 .

[34]  O. Phillips,et al.  Continental-scale patterns of canopy tree composition and function across Amazonia , 2006, Nature.

[35]  F. Chapin,et al.  EFFECTS OF BIODIVERSITY ON ECOSYSTEM FUNCTIONING: A CONSENSUS OF CURRENT KNOWLEDGE , 2005 .

[36]  D. Roberts,et al.  Using Imaging Spectroscopy to Study Ecosystem Processes and Properties , 2004 .

[37]  Sean C. Thomas,et al.  The worldwide leaf economics spectrum , 2004, Nature.

[38]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[39]  Kalle Ruokolainen,et al.  Dispersal, Environment, and Floristic Variation of Western Amazonian Forests , 2003, Science.

[40]  Harald Martens,et al.  Reliable and relevant modelling of real world data: a personal account of the development of PLS Regression , 2001 .

[41]  R. B. Jackson,et al.  Global biodiversity scenarios for the year 2100. , 2000, Science.

[42]  R. Mittermeier,et al.  Biodiversity hotspots for conservation priorities , 2000, Nature.

[43]  M. Loreau,et al.  Biodiversity and ecosystem functioning: a mechanistic model. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[44]  Ranga B. Myneni,et al.  Estimation of global leaf area index and absorbed par using radiative transfer models , 1997, IEEE Trans. Geosci. Remote. Sens..

[45]  J. A. Barone,et al.  HERBIVORY AND PLANT DEFENSES IN TROPICAL FORESTS , 1996 .

[46]  S. Running,et al.  Satellite monitoring of global land cover changes and their impact on climate , 1995 .

[47]  Hanna Tuomisto,et al.  Dissecting Amazonian Biodiversity , 1995, Science.

[48]  C. Staver Why farmers rotate fields in maize-cassava-plantain bush fallow agriculture in the wet Peruvian Amazon , 1989 .

[49]  E. V. Thomas,et al.  Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information , 1988 .

[50]  A. Humboldt,et al.  Aspects of Nature, in Different Lands and Different Climates , 1849 .