Identifying terrestrial carbon sinks: Classification of successional stages in regenerating tropical forest from Landsat TM data

Journal Article · Fri Mar 01 04:00:00 EST 1996 · Remote Sensing of Environment

DOI:https://doi.org/10.1016/S0034-4257(95)00196-4· OSTI ID:227855

Foody, G M ^[1]; Palubinskas, G ^[2]; Lucas, R M; Curran, P J ^[3]; Honzak, M ^[4]

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1. Univ. of Salford (United Kingdom). Dept. of Geography
2. Inst. of Mathematics and Informatics, Vilnius (Lithuania). Dept. of Data Analysis
3. Univ. of Southampton (United Kingdom). Dept. of Geography
4. Univ. of Wales Swansea (United Kingdom). Dept. of Geography

Remote sensing has generally been used to study the role of tropical forest as a source of atmospheric carbon, primarily through land-use change, such as deforestation, and biomass burning. Regeneration of forest on previously cleared areas, however, is a significant carbon sink. The strength of this carbon sink is dependent on the age and composition of the regenerating forest. The ability to identify regenerating forest classes that may differ in terms of carbon sink strength was investigated with Landsat TM data of a test site near Manaus, Brazil. A number of forest age classes were defined from a time series of Landsat sensor data, and their separability in Landsat TM data was assessed by maximum likelihood classifications. A high level of class separability was observed with a weighted kappa coefficient of 0.8569 obtained for a classification of six forest regeneration classes. Of the classification errors observed most were found to be associated with the youngest forest age class. At the test site, however, two main successional pathways were followed and the differences between areas of forest of the same age but on different pathways was most apparent with the youngest forests. Splitting the regenerating forests by the successional pathway was found to increase classification accuracy, with a weighted kappa coefficient of 0.9315 observed for an 11 class classification. A range of tropical forest classes that vary in strength as a carbon sink could therefore be identified accurately from Landsat TM data.

• OSTI ID: 227855
• Journal Information: Remote Sensing of Environment, Journal Name: Remote Sensing of Environment Journal Issue: 3 Vol. 55; ISSN RSEEA7; ISSN 0034-4257
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
BRAZIL
CARBON CYCLE
CARBON DIOXIDE
CARBON SINKS
CLIMATIC CHANGE
DATA COVARIANCES
ECOLOGICAL CONCENTRATION
FORESTS
PLANT GROWTH
REMOTE SENSING
TERRESTRIAL ECOSYSTEMS