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

Spatial information on forest functional composition is needed to inform management and conservation efforts, yet this information is lacking, particularly in tropical regions. Canopy foliar traits underpin the functional biodiversity of forests, and have been shown to be remotely measurable using airborne 350–2510 nm imaging spectrometers. We used newly acquired imaging spectroscopy data constrained with concurrent light detection and ranging (LiDAR) measurements from the Carnegie Airborne Observatory (CAO), and field measurements, to test the performance of the Spectranomics approach for foliar trait retrieval. The method was previously developed in Neotropical forests, and was tested here in the humid tropical forests of Malaysian Borneo. Multiple foliar chemical traits, as well as leaf mass per area (LMA), were estimated with demonstrable precision and accuracy. The results were similar to those observed for Neotropical forests, suggesting a more general use of the Spectranomics approach for mapping canopy traits in tropical forests. Future mapping studies using this approach can advance scientific investigations and applications based on imaging spectroscopy.

[1]  J. Plotkin,et al.  HABITAT PATTERNS IN TROPICAL RAIN FORESTS: A COMPARISON OF 105 PLOTS IN NORTHWEST BORNEO , 2002 .

[2]  B. Goossens,et al.  Plant selection and avoidance by the Bornean elephant (Elephas maximus borneensis) in tropical forest: does plant recovery rate after herbivory influence food choices? , 2014, Journal of Tropical Ecology.

[3]  Roberta E. Martin,et al.  Sources of Canopy Chemical and Spectral Diversity in Lowland Bornean Forest , 2012, Ecosystems.

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

[5]  S. Ollinger,et al.  A generalizable method for remote sensing of canopy nitrogen across a wide range of forest ecosystems , 2008 .

[6]  T. Nakashizuka,et al.  Soil resource availability shapes community trait structure in a species‐rich dipterocarp forest , 2012 .

[7]  S. Ollinger.  Sources of variability in canopy reflectance and the convergent properties of plants. , 2011, The New phytologist.

[8]  Roberta E. Martin,et al.  Large-scale climatic and geophysical controls on the leaf economics spectrum , 2016, Proceedings of the National Academy of Sciences.

[9]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[10]  G. Mercier,et al.  Support vector machines for hyperspectral image classification with spectral-based kernels , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[11]  G. Asner.  Forests and Global Change: A chemical-evolutionary basis for remote sensing of tropical forest diversity , 2014 .

[12]  L. Porter.  Structure and Chemical Properties of the Condensed Tannins , 1992 .

[13]  Roberta E. Martin,et al.  Scale dependence of canopy trait distributions along a tropical forest elevation gradient. , 2017, The New phytologist.

[14]  Philip A. Townsend,et al.  Disentangling the contribution of biological and physical properties of leaves and canopies in imaging spectroscopy data , 2013, Proceedings of the National Academy of Sciences.

[15]  G. A. Blackburn,et al.  Hyperspectral remote sensing of plant pigments. , 2006, Journal of experimental botany.

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

[17]  T. Daufresne,et al.  SCALING OF C:N:P STOICHIOMETRY IN FORESTS WORLDWIDE: IMPLICATIONS OF TERRESTRIAL REDFIELD‐TYPE RATIOS , 2004 .

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

[19]  G. A. Blackburn,et al.  Remote sensing of forest pigments using airborne imaging spectrometer and LIDAR imagery , 2002 .

[20]  K. Kitayama,et al.  Soil phosphorus fractionation and phosphorus-use efficiencies of tropical rainforests along altitudinal gradients of Mount Kinabalu, Borneo , 2000, Oecologia.

[21]  R. Walsh,et al.  The ecoclimatology of Danum, Sabah, in the context of the world's rainforest regions, with particular reference to dry periods and their impact. , 1999, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[22]  G. Toth,et al.  Removal of Dissolved Brown Algal Phlorotannins Using Insoluble Polyvinylpolypyrrolidone (PVPP) , 2001, Journal of Chemical Ecology.

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

[24]  S. Wright,et al.  The global spectrum of plant form and function , 2015, Nature.

[25]  Markus Reichstein,et al.  Linking plant and ecosystem functional biogeography , 2014, Proceedings of the National Academy of Sciences.

[26]  G. Asner,et al.  Canopy shadow in IKONOS satellite observations of tropical forests and savannas , 2003 .

[27]  David M. Haaland,et al.  Partial least-squares methods for spectral analyses. 2. Application to simulated and glass spectral data , 1988 .

[28]  regory,et al.  Controls Over Foliar N:P Ratios in Tropical Rain Forests , 2019 .

[29]  Roberta E. Martin,et al.  Functional and biological diversity of foliar spectra in tree canopies throughout the Andes to Amazon region. , 2014, The New phytologist.

[30]  A. K. Skidmore,et al.  Nitrogen prediction in grasses: effect of bandwidth and plant material state on absorption feature selection , 2010 .

[31]  S. Higgins,et al.  TRY – a global database of plant traits , 2011, Global Change Biology.

[32]  E. Ainsworth,et al.  Estimation of total phenolic content and other oxidation substrates in plant tissues using Folin–Ciocalteu reagent , 2007, Nature Protocols.

[33]  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 .

[34]  K. Kitayama.  An altitudinal transect study of the vegetation on Mount Kinabalu, Borneo , 1992, Vegetatio.

[35]  C. Marsh,et al.  Forest Land-Use in Sabah, Malaysia: An Introduction to Danum Valley , 1992 .

[36]  G. Asner.  Biophysical and Biochemical Sources of Variability in Canopy Reflectance , 1998 .

[37]  Susan L Ustin,et al.  Remote sensing of canopy chemistry , 2013, Proceedings of the National Academy of Sciences.

[38]  Gregory P. Asner,et al.  Observing Changing Ecological Diversity in the Anthropocene , 2013 .

[39]  Roberta E. Martin,et al.  Spectranomics: Emerging science and conservation opportunities at the interface of biodiversity and remote sensing , 2016 .

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

[41]  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..

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

[43]  D. Burslem,et al.  Differing Life History Characteristics Support Coexistence of Tree Soil Generalist and Specialist Species in Tropical Rain Forests , 2014 .

[44]  Roberta E. Martin,et al.  Airborne spectranomics: mapping canopy chemical and taxonomic diversity in tropical forests , 2009 .

[45]  Roberta E. Martin,et al.  Spectral and chemical analysis of tropical forests: Scaling from leaf to canopy levels , 2008 .

[46]  D. Roberts,et al.  Spectral and Structural Measures of Northwest Forest Vegetation at Leaf to Landscape Scales , 2004, Ecosystems.

[47]  Sheng Chen,et al.  Sparse modeling using orthogonal forward regression with PRESS statistic and regularization , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

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

[49]  S. Schmidtlein,et al.  Mapping plant strategy types using remote sensing , 2012 .

[50]  G. Asner,et al.  Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation , 2017, Science.

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

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

[53]  K. Kitayama,et al.  Structure, composition and species diversity in an altitude-substrate matrix of rain forest tree communities on Mount Kinabalu, Borneo , 1999, Plant Ecology.

[54]  Gregory Asner,et al.  Spectroscopic Remote Sensing of Non-Structural Carbohydrates in Forest Canopies , 2015, Remote. Sens..

[55]  M. Schaepman,et al.  Review of optical-based remote sensing for plant trait mapping , 2013 .

[56]  P. Ashton.  DIPTEROCARP BIOLOGY AS A WINDOW TO THE UNDERSTANDING OF TROPICAL FOREST STRUCTURE , 1988 .

[57]  P. Curran.  Remote sensing of foliar chemistry , 1989 .

[58]  Roberta E. Martin,et al.  Multi-method ensemble selection of spectral bands related to leaf biochemistry , 2015 .