Title: Characterizing tropical forests with multispectral imagery
Author: Helmer, Eileen; Goodwin, Nicholas R.; Gond, Valery; Souza, Jr., Carlos M.; Asner, Gregory P.;
Source: Land Resources: Monitoring, Modeling and Mapping. Remote Sensing Handbook vol. 2.
Publication Series: Book Chapter
Description: Multispectral satellite imagery, that is, remotely sensed imagery with discrete bands ranging from visible to shortwave infrared (SWIR) wavelengths, is the timeliest and most accessible remotely sensed data for monitoring tropical forests. Given this relevance, we summarize here how multispectral imagery can help characterize tropical forest attributes of widespread interest, particularly attributes that are relevant to GHG emission inventories and other forest C accounting: forest type, age, structure, and disturbance type or intensity; the storage, degradation, and accumulation of C in aboveground live tree biomass (AGLB, in Mg dry weight ha−1); the feedbacks between tropical forest degradation and climate; and cloud screening and gap filling in imagery. In this chapter, the term biomass without further specification is referring to AGLB.
Keywords: greenhouse gases inventories, forest carbon offsets, multispectral satellite imagery, REDD+, mapping
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
XML: View XML
Helmer, E.H.; Goodwin, Nicholas R.; Gond, Valery; Souza, Jr. Carlos M.; Asner, Gregory P. 2015. Characterizing tropical forests with multispectral imagery. Chapter 14. In: Prasad S. Thenkabail, ed. Land Resources: Monitoring, Modeling and Mapping. Remote Sensing Handbook vol. 2. Boca Raton, FL: CRC Press, Taylor & Francis Group. p. 367-396.
Get the latest version of the Adobe Acrobat reader or Acrobat Reader for Windows with Search and Accessibility