Advertisement

Canopy Texture Analysis for Large-Scale Assessments of Tropical Forest Stand Structure and Biomass

  • Pierre PlotonEmail author
  • Raphaël PélissierEmail author
  • N. Barbier
  • Christophe Proisy
  • B. R. Ramesh
  • P. Couteron
Chapter

Abstract

The structural organization of a forest canopy is an important descriptor that may provide spatial information for vegetation mapping and management planning, such as attributes of plant species distributions, intensity of disturbances, aboveground biomass or carbon stock. A variety of airborne and satellite images characterize forest stands from above the canopy, providing the advantage of a rapid exploration of extensive and sometimes inaccessible zones. Unfortunately, this approach has limited applicability in wet tropical regions, because most optical and radar signals that deliver medium to high spatial resolution data will saturate at intermediate levels of biomass ranges (ca. 150–200 t.ha−1) or leaf area index values (Gibbs et al. 2007). As a consequence, while forest vs. non-forest classifications are nowadays routinely performed from such data, variations in stand structure and biomass within forests of fairly closed canopy remain almost undetectable with classical techniques, and the forest treetops seen from above appear as a homogeneously undulating green carpet. However, rainforest structure varies substantially from place to place either naturally (as the soil, composition or forest dynamics vary) or from anthropogenic degradations. Detecting, characterizing and mapping these variations over vast areas are critical to emerging policies, such as the REDD+ agenda (Maniatis and Mollicone 2010), whereby participating countries will monitor their carbon stock variation. Although promising technology such as LiDAR (light detection and ranging) has great potential, they remain very expensive to systematically assess large expanses of tropical forests (but see Asner et al. 2010). We propose as a cost-effective alternative “canopy grain texture analysis” from very-high-resolution air- or space-borne images, which proved efficient for retrieving and mapping stand structure parameters including aboveground biomass over vast poorly documented areas of tropical forest.

Keywords

Canopy texture Fourier spectra High-resolution images Forest structure Above-ground biomass 

References

  1. Asner GP, Powell GVN, Mascaro J, Knapp DE, Clark JK, Jacobson J, Kennedy-Bowdoin T, Balaji A, Paez-Acosta G, Victoria E (2010) High-resolution forest carbon stocks and emissions in the Amazon. Proc Natl Acad Sci 107:16738PubMedCrossRefGoogle Scholar
  2. Barbier N, Couteron P, Proisy C, Malhi Y, Gastellu Etchegorry J-P (2010) The variation of apparent crown size and canopy heterogeneity across lowland Amazonian forests. Glob Ecol Biogeogr 19:72–84CrossRefGoogle Scholar
  3. Barbier N, Proisy C, Vega C, Sabatier D, Couteron P (2011) Bidirectional texture function of high resolution optical images of tropical forest: an approach using LiDAR hillshade simulations. Remote Sens Environ 115:167–179CrossRefGoogle Scholar
  4. Barbier N, Couteron P, Gastelly-Etchegorry JP, Proisy C (2012) Linking canopy images to forest structural parameters: potential of a modeling framework. Ann For Sci 69(2):305–311Google Scholar
  5. Couteron P (2002) Quantifying change in patterned semi-arid vegetation by Fourier analysis of digitized aerial photographs. Int J Remote Sens 23(17):3407–3425CrossRefGoogle Scholar
  6. Couteron P, Pélissier R, Nicolini E, Paget D (2005) Predicting tropical forest stand structure parameters from Fourier transform of very high-resolution remotely sensed canopy images. J Appl Ecol 42:1121–1128CrossRefGoogle Scholar
  7. Enquist BJ, West GB, Brown JH (2009) Extensions and evaluations of a general quantitative theory of forest structure and dynamics. Proc Natl Acad Sci 106:7046PubMedCrossRefGoogle Scholar
  8. Gibbs HK, Brown S, Niles JO, Foley JA (2007) Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environ Res Lett 2:1–13Google Scholar
  9. Maniatis D, Mollicone D (2010) Options for sampling and stratification for national forest inventories to implement REDD+ under the UNFCCC. Carbon Balance Manag 5:1–14CrossRefGoogle Scholar
  10. Ploton P (2010) Analyzing canopy heterogeneity of the tropical forests by texture analysis of very-high resolution images – a case study in the Western Ghats of India. Pondy Pap Ecol 10:1–71Google Scholar
  11. Ploton P, Pélissier R, Proisy C, Flavenot T, Barbier N, Rai SN, Couteron P (2012) Assessing aboveground tropical forest biomass using Google Earth canopy images. Ecol Appl 22(3):993–1003Google Scholar
  12. Proisy C, Couteron P, Fromard F (2007) Predicting and mapping mangrove biomass from canopy grain analysis using Fourier-based textural ordination of IKONOS images. Remote Sens Environ 109:379–392CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Pierre Ploton
    • 1
    • 2
    Email author
  • Raphaël Pélissier
    • 1
    • 3
    Email author
  • N. Barbier
    • 3
  • Christophe Proisy
    • 3
  • B. R. Ramesh
    • 1
  • P. Couteron
    • 3
  1. 1.Ecology DepartmentFrench Institute of Pondicherry, UMIFRE 21 MAEE-CNRSPondicherryIndia
  2. 2.IRD, UMR AMAP, University of Yaounde IYaoundeCameroon
  3. 3.IRD, UMR AMAPMontpellierFrance

Personalised recommendations