Gizachew Belachew, Solberg Svein, Næsset Erik, Gobakken Terje, Bollandsås Ole Martin, Breidenbach Johannes, Zahabu Eliakimu, Mauya Ernest William
Norwegian Institute of Bioeconomy Research, Post Box 115, 1431 Ås, Norway.
Department of Natural Resource Management, Norwegian University of Life Sciences, Post Box 5003, 1432 Ås, Norway.
Carbon Balance Manag. 2016 Jun 24;11(1):13. doi: 10.1186/s13021-016-0055-8. eCollection 2016 Dec.
A functional forest carbon measuring, reporting and verification (MRV) system to support climate change mitigation policies, such as REDD+, requires estimates of forest biomass carbon, as an input to estimate emissions. A combination of field inventory and remote sensing is expected to provide those data. By linking Landsat 8 and forest inventory data, we (1) developed linear mixed effects models for total living biomass (TLB) estimation as a function of spectral variables, (2) developed a 30 m resolution map of the total living carbon (TLC), and (3) estimated the total TLB stock of the study area. Inventory data consisted of tree measurements from 500 plots in 63 clusters in a 15,700 km study area, in miombo woodlands of Tanzania. The Landsat 8 data comprised two climate data record images covering the inventory area.
We found a linear relationship between TLB and Landsat 8 derived spectral variables, and there was no clear evidence of spectral data saturation at higher biomass values. The root-mean-square error of the values predicted by the linear model linking the TLB and the normalized difference vegetation index (NDVI) is equal to 44 t/ha (49 % of the mean value). The estimated TLB for the study area was 140 Mt, with a mean TLB density of 81 t/ha, and a 95 % confidence interval of 74-88 t/ha. We mapped the distribution of TLC of the study area using the TLB model, where TLC was estimated at 47 % of TLB.
The low biomass in the miombo woodlands, and the absence of a spectral data saturation problem suggested that Landsat 8 derived NDVI is suitable auxiliary information for carbon monitoring in the context of REDD+, for low-biomass, open-canopy woodlands.
一个支持诸如减少毁林和森林退化所致排放量（REDD+）等气候变化减缓政策的功能性森林碳测量、报告与核查（MRV）系统，需要对森林生物量碳进行估算，作为估算排放量的输入数据。预计实地清查和遥感相结合可提供这些数据。通过将陆地卫星8号数据与森林清查数据相链接，我们（1）开发了用于估算总活生物量（TLB）的线性混合效应模型，该模型将总活生物量作为光谱变量的函数；（2）绘制了总活碳（TLC）的30米分辨率地图；（3）估算了研究区域的总TLB储量。清查数据包括来自坦桑尼亚米翁博林地一个15700平方公里研究区域内63个集群中500个样地的树木测量数据。陆地卫星8号数据包括覆盖清查区域的两幅气候数据记录图像。
我们发现TLB与陆地卫星8号派生的光谱变量之间存在线性关系，并且没有明确证据表明在较高生物量值时光谱数据会饱和。将TLB与归一化植被指数（NDVI）相链接的线性模型预测值的均方根误差等于44吨/公顷（为平均值的49%）。研究区域的估算TLB为1.4亿吨，平均TLB密度为81吨/公顷，95%置信区间为74 - 88吨/公顷。我们使用TLB模型绘制了研究区域TLC的分布图，其中TLC估算值为TLB的47%。
米翁博林地生物量较低，且不存在光谱数据饱和问题，这表明陆地卫星8号派生的NDVI是REDD+背景下低生物量、开阔冠层林地碳监测的合适辅助信息。
