Incorporating Canopy Cover for Airborne-Derived Assessments of Forest Biomass in the Tropical Forests of Cambodia

Minerva Singh; Damian Evans; David A Coomes; Daniel A Friess; Boun Suy Tan; Chan Samean Nin

doi:10.1371/journal.pone.0154307

Incorporating Canopy Cover for Airborne-Derived Assessments of Forest Biomass in the Tropical Forests of Cambodia

PLoS One. 2016 May 13;11(5):e0154307. doi: 10.1371/journal.pone.0154307. eCollection 2016.

Authors

Minerva Singh¹, Damian Evans², David A Coomes¹, Daniel A Friess³, Boun Suy Tan⁴, Chan Samean Nin⁵

Affiliations

¹ Forest Ecology and Conservation Group, David Attenborough Building, Department of Plant Sciences, Downing Street, University of Cambridge, Cambridge, CB2 3EA, United Kingdom.
² École française d'Extrême-Orient, Siem Reap, Cambodia.
³ Department of Geography, National University of Singapore, 1 Arts Link, 117570 Singapore, Singapore.
⁴ APSARA National Authority, Angkor International Research and Documentation Centre, Siem Reap, Cambodia.
⁵ APSARA National Authority, Department of Forestry Management, Cultural Landscape and Environment, Siem Reap, Cambodia.

Abstract

This research examines the role of canopy cover in influencing above ground biomass (AGB) dynamics of an open canopied forest and evaluates the efficacy of individual-based and plot-scale height metrics in predicting AGB variation in the tropical forests of Angkor Thom, Cambodia. The AGB was modeled by including canopy cover from aerial imagery alongside with the two different canopy vertical height metrics derived from LiDAR; the plot average of maximum tree height (Max_CH) of individual trees, and the top of the canopy height (TCH). Two different statistical approaches, log-log ordinary least squares (OLS) and support vector regression (SVR), were used to model AGB variation in the study area. Ten different AGB models were developed using different combinations of airborne predictor variables. It was discovered that the inclusion of canopy cover estimates considerably improved the performance of AGB models for our study area. The most robust model was log-log OLS model comprising of canopy cover only (r = 0.87; RMSE = 42.8 Mg/ha). Other models that approximated field AGB closely included both Max_CH and canopy cover (r = 0.86, RMSE = 44.2 Mg/ha for SVR; and, r = 0.84, RMSE = 47.7 Mg/ha for log-log OLS). Hence, canopy cover should be included when modeling the AGB of open-canopied tropical forests.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Biomass*
Cambodia
Forests*
Geography
Models, Theoretical
Plant Leaves / physiology*
Remote Sensing Technology
Statistics as Topic
Tropical Climate*

Grants and funding

The authors thank PT McElhanney Indonesia for LiDAR acquisition, and acknowledge support and funding from the eight institutions of the Khmer Archaeology LiDAR Consortium: the APSARA Authority, the University of Sydney, the École française d'Extrême Orient, Société Concessionaire d’Aéroport, the Hungarian Southeast Asian Research Institute, Japan-APSARA Safeguarding Angkor, the Archaeology and Development Foundation, and the World Monuments Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.