Rapid forest carbon assessments of oceanic islands: a case study of the Hawaiian archipelago

Gregory P Asner; Sinan Sousan; David E Knapp; Paul C Selmants; Roberta E Martin; R Flint Hughes; Christian P Giardina

doi:10.1186/s13021-015-0043-4

Rapid forest carbon assessments of oceanic islands: a case study of the Hawaiian archipelago

Carbon Balance Manag. 2016 Jan 8;11(1):1. doi: 10.1186/s13021-015-0043-4. eCollection 2016 Dec.

Authors

Gregory P Asner¹, Sinan Sousan¹, David E Knapp¹, Paul C Selmants², Roberta E Martin¹, R Flint Hughes³, Christian P Giardina³

Affiliations

¹ Department of Global Ecology, Carnegie Institution for Science, 260 Panama St, Stanford, CA 94305 USA.
² Department of Natural Resources and Environmental Management, University of Hawaii at Manoa, 1910 East-West Rd., Honolulu, HI 96822 USA.
³ USDA Forest Service, Pacific Southwest Research Station, Institute of Pacific Islands Forestry, 60 Nowelo Street, Hilo, HI 96720 USA.

Abstract

Background: Spatially explicit forest carbon (C) monitoring aids conservation and climate change mitigation efforts, yet few approaches have been developed specifically for the highly heterogeneous landscapes of oceanic island chains that continue to undergo rapid and extensive forest C change. We developed an approach for rapid mapping of aboveground C density (ACD; units = Mg or metric tons C ha^-1) on islands at a spatial resolution of 30 m (0.09 ha) using a combination of cost-effective airborne LiDAR data and full-coverage satellite data. We used the approach to map forest ACD across the main Hawaiian Islands, comparing C stocks within and among islands, in protected and unprotected areas, and among forests dominated by native and invasive species.

Results: Total forest aboveground C stock of the Hawaiian Islands was 36 Tg, and ACD distributions were extremely heterogeneous both within and across islands. Remotely sensed ACD was validated against U.S. Forest Service FIA plot inventory data (R² = 0.67; RMSE = 30.4 Mg C ha^-1). Geospatial analyses indicated the critical importance of forest type and canopy cover as predictors of mapped ACD patterns. Protection status was a strong determinant of forest C stock and density, but we found complex environmentally mediated responses of forest ACD to alien plant invasion.

Conclusions: A combination of one-time airborne LiDAR data acquisition and satellite monitoring provides effective forest C mapping in the highly heterogeneous landscapes of the Hawaiian Islands. Our statistical approach yielded key insights into the drivers of ACD variation, and also makes possible future assessments of C storage change, derived on a repeat basis from free satellite data, without the need for additional LiDAR data. Changes in C stocks and densities of oceanic islands can thus be continually assessed in the face of rapid environmental changes such as biological invasions, drought, fire and land use. Such forest monitoring information can be used to promote sustainable forest use and conservation on islands in the future.

Keywords: Carbon stocks; Carnegie Airborne Observatory; Forest inventory; Invasive species; LiDAR; Random Forest Machine Learning.