Coastal ecosystems are considered environments with great potential for carbon storage. Given the difficulties in quantifying biomass, allometric equations and remote sensing have become fundamental tools in the studies of quantification of vegetation biomass and carbon stocks. Thus, the objective of this study is to quantify and estimate the spatial distribution of vegetation biomass and to quantify the carbon stock of the Parnaíba River Delta vegetation. The study was carried out in part of the Parnaíba River Delta Environmental Protection Area and in the Parnaíba River Delta Marine Extractive Reserve, in NE Brazil, in five spots within distinct vegetation types: psammophile pioneer vegetation, dune subevergreen vegetation, mangrove evergreen vegetation, floodplain vegetation and vegetation associated with carnaubals. At 26 collection points, 10 × 20 m plots were marked, in which the diameter at breast height and height of all individuals were measured. The collected data were used in allometric equations for vegetation biomass estimates and these values were converted into carbon stocks. The spatial distribution of aboveground vegetation biomass (AGB) was also estimated by remote sensing, where we extracted and selected spectral variables obtained from Landsat-8 OLI sensor images, on three different dates. Prediction models were calculated by multiple linear regression analysis. It was observed that the mangrove evergreen vegetation obtained higher vegetation biomass and carbon stock than the others. The models obtained through remote sensing that provided the best estimates of AGB were those of November 12th, 2016 (EAM = 6.84; RMSE = 47.89 Mg ha⁻¹; R² = 0.72) and November 28th, 2016 (EAM = 9.63; RMSE = 34.67 Mg ha⁻¹; R² = 0.58).

中文翻译：

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巴西东北部帕纳伊巴河三角洲的植被生物量和碳储量

沿海生态系统被认为具有巨大的碳储存潜力。鉴于生物量的量化困难，异速方程和遥感已成为定量研究植被生物量和碳储量的基本工具。因此，本研究的目的是量化和估算植被生物量的空间分布，并量化帕纳伊巴河三角洲植被的碳储量。这项研究是在帕纳伊巴河三角洲环境保护区的一部分和帕纳伊巴河三角洲海洋提取保护区中进行的在巴西东北部的不同植被类型中有五个点：嗜沙性先驱植被，沙丘亚常绿植被，红树林常绿植被，洪泛区植被和与食人动物相关的植被。在26个采集点，标出10×20 m的地块，其中测量了乳房高度处的直径和所有个体的身高。收集的数据用于植被生物量估算的异速方程中，并将这些值转换为碳储量。还通过遥感估算了地上植被生物量（AGB）的空间分布，我们在三个不同的日期提取并选择了从Landsat-8 OLI传感器图像获得的光谱变量。通过多元线性回归分析计算预测模型。据观察，红树林常绿植被获得的植被生物量和碳储量均高于其他植被。通过遥感获得的可提供AGB最佳估计的模型是2016年11月12日的模型（EAM = 6.84； RMSE = 47.89 Mg ha^-1 ; R ²  = 0.72）和2016年11月28日（EAM = 9.63； RMSE = 34.67 Mg ha ^-1； R ²  = 0.58）。