Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1088/1748-9326/aa9f03 |
Estimating mangrove aboveground biomass from airborne LiDAR data: a case study from the Zambezi River delta | |
Fatoyinbo, Temilola1; Feliciano, Emanuelle A.1,2; Lagomasino, David1,3; Lee, Seung Kuk1,3; Trettin, Carl4 | |
2018-02-01 | |
发表期刊 | ENVIRONMENTAL RESEARCH LETTERS |
ISSN | 1748-9326 |
出版年 | 2018 |
卷号 | 13期号:2 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Mangroves are ecologically and economically important forested wetlands with the highest carbon (C) density of all terrestrial ecosystems. Because of their exceptionally large C stocks and importance as a coastal buffer, their protection and restoration has been proposed as an effective mitigation strategy for climate change. The inclusion of mangroves in mitigation strategies requires the quantification of C stocks (both above and belowground) and changes to accurately calculate emissions and sequestration. A growing number of countries are becoming interested in using mitigation initiatives, such as REDD+ (reducing emissions from deforestation and forest degradation), in these unique coastal forests. However, it is not yet clear how methods to measure C traditionally used for other ecosystems can be modified to estimate biomass in mangroves with the precision and accuracy needed for these initiatives. Airborne Lidar (ALS) data has often been proposed as the most accurate way for larger scale assessments but the application of ALS for coastal wetlands is scarce, primarily due to a lack of contemporaneous ALS and field measurements. Here, we evaluated the variability in field and Lidar-based estimates of aboveground biomass (AGB) through the combination of different local and regional allometric models and standardized height metrics that are comparable across spatial resolutions and sensor types, the end result being a simplified approach for accurately estimating mangrove AGB at large scales and determining the uncertainty by combining multiple allometric models. We then quantified wall-to-wall AGB stocks of a tall mangrove forest in the Zambezi Delta, Mozambique. Our results indicate that the Lidar H100 height metric correlates well with AGB estimates, with R-2 between 0.80 and 0.88 and RMSE of 33% or less. When comparing Lidar H100 AGB derived from three allometric models, mean AGB values range from 192Mg ha(-1) up to 252Mg ha(-1). We suggest the best model to predict AGB was based on the East Africa specific allometry and a power-based regression that used Lidar H100 as the height input with an R-2 of 0.85 and an RMSE of 122Mg ha(-1) or 33%. The total AGB of the Lidar inventoried mangrove area (6654 ha) was 1 350 902Mg with a mean AGB of 203Mg ha(-1) +/- 166Mg ha(-1). Because the allometry suggested here was developed using standardized height metrics, it is recommended that the models can generate AGB estimates using other remote sensing instruments that are more readily accessible over other mangrove ecosystems on a large scale, and as part of future carbon monitoring efforts in mangroves. |
英文关键词 | lidar mangrove biomass canopy height Mozambique blue carbon monitoring |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000425340000002 |
WOS关键词 | EVERGLADES-NATIONAL-PARK ; TANDEM-X ; CANOPY HEIGHT ; CARBON STOCKS ; ALLOMETRIC EQUATIONS ; SOUTH FLORIDA ; POL-INSAR ; FORESTS ; VEGETATION ; ICESAT/GLAS |
WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences |
WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/15040 |
专题 | 气候变化 |
作者单位 | 1.NASA Goddard Space Flight Ctr, Biospher Sci Lab, 8800 Greenbelt Rd, Greenbelt, MD 20771 USA; 2.Univ Space Res Assoc, NASA Postdoctoral Program, 7178 Columbia Gateway Dr, Columbia, MD 21046 USA; 3.Univ Maryland, Dept Geog Sci, College Pk, MD 20740 USA; 4.US Forest Serv, USDA, Cordesville, SC 29434 USA |
推荐引用方式 GB/T 7714 | Fatoyinbo, Temilola,Feliciano, Emanuelle A.,Lagomasino, David,et al. Estimating mangrove aboveground biomass from airborne LiDAR data: a case study from the Zambezi River delta[J]. ENVIRONMENTAL RESEARCH LETTERS,2018,13(2). |
APA | Fatoyinbo, Temilola,Feliciano, Emanuelle A.,Lagomasino, David,Lee, Seung Kuk,&Trettin, Carl.(2018).Estimating mangrove aboveground biomass from airborne LiDAR data: a case study from the Zambezi River delta.ENVIRONMENTAL RESEARCH LETTERS,13(2). |
MLA | Fatoyinbo, Temilola,et al."Estimating mangrove aboveground biomass from airborne LiDAR data: a case study from the Zambezi River delta".ENVIRONMENTAL RESEARCH LETTERS 13.2(2018). |
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