Spatial structure of above-ground biomass limits accuracy of carbon mapping in rainforest but large scale forest inventories can help to overcome

Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest. The usual mapping methods are based on two hypotheses: a large and long-ranged spatial autocorrelation and a strong environment influence at the regional scale. However, there are no studies of the spatial structure of AGB at the landscapes scale to support these assumptions. We studied spatial variation in AGB at various scales using two large forest inventories conducted in French Guiana. The dataset comprised 2507 plots (0.4 to 0.5 ha) of undisturbed rainforest distributed over the whole region. After checking the uncertainties of estimates obtained from these data, we used half of the dataset to develop explicit predictive models including spatial and environmental effects and tested the accuracy of the resulting maps according to their resolution using the rest of the data. Forest inventories provided accurate AGB estimates at the plot scale, for a mean of 325 Mg.ha-1. They revealed high local variability combined with a weak autocorrelation up to distances of no more than10 km. Environmental variables accounted for a minor part of spatial variation. Accuracy of the best model including spatial effects was 90 Mg.ha-1 at plot scale but coarse graining up to 2-km resolution allowed mapping AGB with accuracy lower than 50 Mg.ha-1. Whatever the resolution, no agreement was found with available pan-tropical reference maps at all resolutions. We concluded that the combined weak autocorrelation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate “wall-to-wall” remote sensing signals provide reliable AGB predictions. Waiting for this, using large forest inventories with low sampling rate (<0.5%) may be an efficient way to increase the global coverage of AGB maps with acceptable accuracy at kilometric resolution.

精准地上生物量（above-ground biomass, AGB）制图，是热带雨林REDD+机制顺利实施的核心挑战之一。常规AGB制图方法基于两项核心假设：一是存在大范围、长程的空间自相关性，二是区域尺度上环境因子具有显著调控作用。然而目前尚无针对景观尺度下AGB空间结构的研究来佐证上述假设。本研究依托法属圭亚那开展的两项大型森林清查数据，探究了不同尺度下AGB的空间变异特征。本数据集涵盖了分布于整个研究区的2507块未受干扰的热带雨林样地，单块样地面积介于0.4至0.5公顷之间。在对基于该数据集得到的估算结果进行不确定性校验后，我们采用半数样地数据构建了纳入空间与环境效应的显式预测模型，并利用剩余样地数据，依据制图分辨率评估了所得AGB分布图的精度。森林清查数据可提供高精度的样地尺度AGB估算值，研究区内样地平均AGB为325 Mg·ha⁻¹。分析结果显示，AGB存在显著的局地变异，且空间自相关性仅在不超过10公里的距离内存在，整体相关性较弱；环境因子仅能解释极小部分的空间变异。纳入空间效应的最优模型在样地尺度下的估算精度误差为90 Mg·ha⁻¹；而当将制图分辨率粗化至2公里时，AGB制图的精度误差可降至50 Mg·ha⁻¹以下。无论采用何种分辨率，本研究所得AGB分布图与现有泛热带参考地图均无一致性。本研究最终得出结论：空间自相关性与环境解释力均较弱的双重局限，制约了热带雨林AGB制图的精度；在可获取全覆盖遥感信号以实现可靠AGB预测之前，亟需在空间分辨率与实际精度之间寻求平衡。在此之前，采用低采样率（<0.5%）的大型森林清查数据，或许是在公里级分辨率下以可接受的精度提升全球AGB制图覆盖范围的高效途径。