Airborne spectranomics: mapping canopy chemical and taxonomic diversity in tropical forests

Tree canopies play an enormous role in the maintenance of tropical forest diversity and ecosystem function, and are therefore central to conservation, management, and resource policy development in tropical regions. However, high-resolution mapping of tropical forest canopies is very difficult, because traditional field, airborne, and satellite measurements cannot resolve the number of canopy species, or particular species of interest, over the large regional scales commensurate with conservation goals and strategies. Newer technologies, such as imaging spectroscopy and light detection and ranging (lidar), are just now reaching performance levels that will allow monitoring of tropical forest diversity from the air, but the methods for applying these technologies are not yet ready. Here, we present concepts that combine chemical and spectral remote sensing perspectives to facilitate canopy diversity mapping. Using examples from our ongoing work in the Hawaiian Islands, we demonstrate how a new “airborne sp...

[1]  Roberta E. Martin,et al.  Leaf Chemical and Optical Properties of Metrosideros polymorpha Across Environmental Gradients in Hawaii , 2009 .

[2]  Roberta E. Martin,et al.  Remote sensing of native and invasive species in Hawaiian forests , 2008 .

[3]  Gregory Asner,et al.  Hyperspectral Remote Sensing of Canopy Chemistry, Physiology, and Biodiversity in Tropical Rainforests , 2008 .

[4]  Roberta E. Martin,et al.  Hyperspectral Remote Sensing of Canopy Biodiversity in Hawaiian Lowland Rainforests , 2007, Ecosystems.

[5]  Stephen P. Hubbell,et al.  Soil nutrients influence spatial distributions of tropical tree species , 2007, Proceedings of the National Academy of Sciences.

[6]  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 .

[7]  Gregory P Asner,et al.  Controls over foliar N:P ratios in tropical rain forests. , 2007, Ecology.

[8]  Roberta E. Martin,et al.  Genetic variation in leaf pigment, optical and photosynthetic function among diverse phenotypes of Metrosideros polymorpha grown in a common garden , 2007, Oecologia.

[9]  John A. Gamon,et al.  Diverse Optical and Photosynthetic Properties in a Neotropical Dry Forest during the Dry Season: Implications for Remote Estimation of Photosynthesis 1 , 2005 .

[10]  S. Ollinger,et al.  Net Primary Production and Canopy Nitrogen in a Temperate Forest Landscape: An Analysis Using Imaging Spectroscopy, Modeling and Field Data , 2005, Ecosystems.

[11]  Christopher A. Lepczyk,et al.  RESORPTION EFFICIENCY DECREASES WITH INCREASING GREEN LEAF NUTRIENTS IN A GLOBAL DATA SET , 2005 .

[12]  D. Roberts,et al.  Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales , 2005 .

[13]  P. Vitousek,et al.  Remote analysis of biological invasion and biogeochemical change. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[14]  Peter M. Vitousek,et al.  Nutrient Cycling and Limitation: Hawai'i as a Model System , 2004 .

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

[16]  D. Clark,et al.  Tropical forests and global warming: slowing it down or speeding it up? , 2004 .

[17]  Marie-Louise Smith,et al.  Analysis of hyperspectral data for estimation of temperate forest canopy nitrogen concentration: comparison between an airborne (AVIRIS) and a spaceborne (Hyperion) sensor , 2003, IEEE Trans. Geosci. Remote. Sens..

[18]  Chein-I. Chang.  Hyperspectral Imaging: Techniques for Spectral Detection and Classification , 2003 .

[19]  Steven E. Franklin,et al.  Remote Sensing of Forest Environments , 2003, Springer US.

[20]  D. Sims,et al.  Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages , 2002 .

[21]  Jane R. Foster,et al.  Comparison of EO-1 Hyperion to AVIRIS for mapping forest composition in the Appalachian Mountains, USA , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[22]  Pablo J. Zarco-Tejada,et al.  Chlorophyll Fluorescence Effects on Vegetation Apparent Reflectance: I. Leaf-Level Measurements and Model Simulation , 2000 .

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

[24]  M. Cochrane.  Using vegetation reflectance variability for species level classification of hyperspectral data , 2000 .

[25]  Leighton,et al.  Impact of El Nino and logging on canopy tree recruitment in borneo , 1999, Science.

[26]  J. Muller,et al.  New directions in earth observing: Scientific applications of multiangle remote sensing , 1999 .

[27]  William L. Wagner,et al.  Manual of the Flowering Plants of Hawai'i , 1999 .

[28]  S. Sandmeier,et al.  Physical Mechanisms in Hyperspectral BRDF Data of Grass and Watercress , 1998 .

[29]  Margaret E. Gardner,et al.  Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models , 1998 .

[30]  Warren B. Cohen,et al.  Empirical methods to compensate for a view-angle-dependent brightness gradient in AVIRIS imagery☆ , 1997 .

[31]  S. Ustin,et al.  Estimating leaf biochemistry using the PROSPECT leaf optical properties model , 1996 .

[32]  F. Baret,et al.  Leaf optical properties with explicit description of its biochemical composition: Direct and inverse problems , 1996 .

[33]  S. Jacquemoud,et al.  Investigation of leaf biochemistry by statistics , 1994, Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium.

[34]  Frédéric Baret,et al.  Use of spectral analogy to evaluate canopy reflectance sensitivity to leaf optical properties , 1994 .

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

[36]  A F Goetz,et al.  Imaging Spectrometry for Earth Remote Sensing , 1985, Science.