The role of predictive model data in designing mangrove forest carbon programs
Bibliographic Data
| ID | 15550643 |
|---|---|
| Authors | Jacob J Bukoski (0000-0002-2334-5023, University of California, Berkeley, corresponding author), Angie Elwin (0000-0001-8583-3295, University of Reading), Richard A Mackenzie (0000-0003-3779-787X), Sahadev Sharma (0000-0001-7602-7419, University of Hawaiʻi at Mānoa), J Purbopuspito (0000-0002-4631-596X, Sam Ratulangi University), Benjamin Kopania (University of California, Berkeley), Maybeleen Apwong, Roongreang Poolsiri (0000-0001-7938-4497, Kasetsart University), Matthew D Potts (0000-0001-7442-3944, University of California, Berkeley) |
| Year | 2020 |
| Volume | 15 |
| Issue | 8 |
| Pages | 084019-084019 |
| Publication date | 2020-03-10 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environmental Research Letters (JOURNAL) |
| Journal identifiers | ISSN: 1748-9326 • E-ISSN: 1748-9326 |
| Publisher | IOP Publishing (PUBLISHER • GB) |
| DOI | 10.1088/1748-9326/ab7e4e |
| OpenAlex | W3011360674 |
| Language | EN |
| References cited | 41 |
Estimating baseline carbon stocks is a key step in designing forest carbon programs. While field inventories are resource-demanding, advances in predictive modeling are now providing globally coterminous datasets of carbon stocks at high spatial resolutions that may meet this data need. However, it remains unknown how well baseline carbon stock estimates derived from model data compare against conventional estimation approaches such as field inventories. Furthermore, it is unclear whether site-level management actions can be designed using predictive model data in place of field measurements. We examined these issues for the case of mangroves, which are among the most carbon dense ecosystems globally and are popular candidates for forest carbon programs. We compared baseline carbon stock estimates derived from predictive model outputs against estimates produced using the Intergovernmental Panel on Climate Change’s (IPCC) three-tier methodological guidelines. We found that the predictive model estimates out-performed the IPCC’s Tier 1 estimation approaches but were significantly different from estimates based on field inventories. Our findings help inform the use of predictive model data for designing mangrove forest policy and management actions
Agroforestry · Baseline (sea · Carbon sequestration · Carbon stock · Climate change · Environmental resource management · Field (mathematics · Forest management · Geography · Machine learning · Mangrove · Predictive modelling · Stock (firearms · Coastal wetland ecosystem dynamics · Computer Science · Conservation, Biodiversity, and Resource Management · Environmental Science · Mathematics · Oil Palm Production and Sustainability · Ecology
Mangroves among the most carbon-rich forests in the tropics
Benchmark map of forest carbon stocks in tropical regions across three continents
Measuring mangrove carbon loss and gain in deltas
Can recent pan-tropical biomass maps be used to derive alternative Tier 1 values for reporting Redd+ activities under UNFCCC
A global predictive model of carbon in mangrove soils
A global map of mangrove forest soil carbon at 30 m spatial resolution
Applying the conservativeness principle to Redd to deal with the uncertainties of the estimates
Policy design for forest carbon sequestration
Sensitivity of amounts and distribution of tropical forest carbon credits depending on baseline rules
Beyond Carbon
| Citation velocity | historical |
|---|---|
| Highly cited | No |