Predicting aboveground forest biomass with topographic variables in human-impacted tropical dry forest landscapes

Miguel A. Salinas-Melgoza* (Corresponding Author), Margaret Skutsch, Jon C. Lovett

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)
107 Downloads (Pure)

Abstract

Topographic variables such as slope and elevation partially explain spatial variations in aboveground biomass (AGB) within landscapes. Human activities that impact vegetation, such as cattle grazing and shifting cultivation, often follow topographic features and also play a key role in determining AGB patterns, although these effects may be moderated by accessibility. In this study, we evaluated the potential to predict AGB in a rural landscape, using a set of topographical variables in combination with indicators of accessibility. We modeled linear and non-linear relationships between AGB, topographic variables within the territorial boundaries of six rural communities, and distance to roads. Linear models showed that elevation, slope, topographic wetness index, and tangential curvature could explain up to 21% of AGB. Non-linear models found threshold values for the relationship between AGB and diffuse insolation, topographic position index at 19 9 19 pixels scale and differentiated between groups of communities, improving AGB predictions to 33%. We also found a continuous and positive effect on AGB with increased distance from roads, but also a piecewise relationship that improves the understanding of intensity of human activities. These findings could enable AGB baselines to be constructed at landscape level using freely available data from topographic maps. Such baselines may be of use in national programs under the international policy Reducing Emissions from Deforestation and Forest Degradation.

Original languageEnglish
Article numbere02063
JournalEcosphere
Volume9
Issue number1
DOIs
Publication statusPublished - 18 Jan 2018

Keywords

  • Aboveground biomass
  • Landscape approach
  • Reducing emissions from deforestation forest degradation (REDD+)
  • Rural communities
  • Topographic variables

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