ABSTRACT

Seagrass provides numerous valuable ecosystem services across a wide range of climatic regions. However, in terms of area and habitat, this resource is in decline globally and there is an urgent need for accurate mapping of extant meadows and biomass to support sustainable seagrass blue carbon conservation and management. This study develops a novel method for a binary mapping of seagrass distribution and estimating seagrass above-ground biomass (AGB) by applying a suite of advanced machine learning (ML) algorithms combined with and without a metaheuristic optimization approach (particle swarm optimization – PSO) to various combinations of multispectral (Sentinel-2) and synthetic aperture radar (Sentinel-1) remote sensing data. Our results reveal that the Sentinel-1 data has potential for the binary mapping of seagrass meadows using an extreme gradient boosting (XGB) model (scores of precision (P) = 0.82, recall (R) = 0.90, and F₁ = 0.86) but is less effective at estimating AGB. The optimal method for estimation of AGB used both Sentinel-1 and Sentinel-2 imagery, the XGB model, and PSO optimization (coefficient of determination (R²) = 0.75, root mean squared error (RMSE) = 0.35, Akaike information criteria (AIC) = 24.80, Bayesian information criteria (BIC) = 44.70). Our findings contribute novel and advanced methods for seagrass detection and improvement of AGB estimation, which are fast and reliable, use open-source data and software and should be easily applicable to intertidal zones across many regions of the world.

摘要

海草为广泛的气候区域提供了大量有价值的生态系统服务。但是，就面积和栖息地而言，这种资源在全球范围内正在减少，迫切需要准确绘制现存的草地和生物量，以支持可持续的海草蓝碳保存和管理。这项研究通过应用一套先进的机器学习（ML）算法，结合或不结合使用元启发式优化方法（粒子群优化– PSO），开发了一种用于海草分布的二进制映射和估计海草地上生物量（AGB）的新方法。多光谱（Sentinel-2）和合成孔径雷达（Sentinel-1）遥感数据的各种组合。P）= 0.82，召回率（R）= 0.90，F ₁  = 0.86），但在估算AGB时效果较差。估计AGB的最佳方法同时使用了Sentinel-1和Sentinel-2图像，XGB模型以及PSO优化（确定系数（R ²）= 0.75，均方根误差（RMSE）= 0.35，Akaike信息标准（ AIC）= 24.80，贝叶斯信息标准（BIC）= 44.70）。我们的发现为海草检测和AGB估计的改进提供了新颖，先进的方法，这些方法快速可靠，使用开源数据和软件，应该很容易应用于世界许多地区的潮间带。