Estimation of mangrove heights and aboveground biomass using UAV-LiDAR, Sentinel-1 and ZY-3 stereo images

IF 5.8 2区 环境科学与生态学 Q1 ECOLOGY
Bolin Fu , Yingying Wei , Linhang Jiang , Hang Yao , Xiaomin Li , Yanli Yang , Mingming Jia , Weiwei Sun
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引用次数: 0

Abstract

Mangroves are crucial blue carbon ecosystems that are essential for promoting sustainable global development. Tree height is a key indicator of mangrove health; however, accurately estimating mangrove height in complex coastal environments is challenging. In this study, we constructed mangrove height inversion models using multiple types of remote sensing data and machine learning algorithms (partial least squares regression (PLSR), random forest (RF), and mixture density network (MDN)). We evaluated the performance of UAV-LiDAR point clouds, ZY-3 stereo images, and Sentinel-1 polarimetric and interferometric data in mangrove height inversion, and explored the accuracy differences among the dominant species. We also estimated the aboveground biomass of different dominant mangrove species to better understand their ecological functions and health conditions. The results showed the following: (1) The canopy height model and height variables of the LiDAR point clouds, DVI and near-infrared bands of the ZY-3 stereo images, and polarimetric decomposition parameters of the Sentinel-1 SAR images were more sensitive to mangrove heights. (2) The LiDAR point clouds and Sentinel-1 SAR images achieved the highest inversion accuracy when using the RF algorithm, with R2 values of 0.875 and 0.685, respectively. The ZY-3 stereo images based on MDN obtained the optimal inversion results (R2 = 0.719), with an improvement ranging from 0.143 to 0.198 when compared to the PLSR and RF algorithms. (3) Avicennia marina was associated with the highest estimation accuracy (R2 = 0.897) compared to the other dominant mangrove species. Aegiceras corniculatum and Avicennia marina were associated with the highest inversion accuracy within the height range of 2–3 m (R2 = 0.925, R2 = 0.814, respectively), whereas Kandelia candel yielded the optimal inversion results at the height range of 1–2 m (R2 = 0.652). (4) The aboveground biomass of Aegiceras cornicatum and Kandelia candel ranged from 20.176 to 103.164 Mg/ha and 132.019 to 719.226 Mg/ha, respectively, and the aboveground biomass of Avicennia marina was mainly distributed within the range of 169.916 to 803.204 Mg/ha. Our study provides a reference for monitoring the heights and health of mangroves, as well as their protection and development.
利用UAV-LiDAR、Sentinel-1和ZY-3立体影像估算红树林高度和地上生物量
红树林是至关重要的蓝碳生态系统,对促进全球可持续发展至关重要。树高是红树林健康状况的关键指标;然而,在复杂的沿海环境中准确估计红树林高度是具有挑战性的。在这项研究中,我们利用多种类型的遥感数据和机器学习算法(偏最小二乘回归(PLSR)、随机森林(RF)和混合密度网络(MDN))构建了红树林高度反演模型。利用无人机- lidar点云、ZY-3立体影像和Sentinel-1偏振和干涉数据对红树林高度进行反演,并探讨了优势树种间反演精度的差异。为了更好地了解红树林的生态功能和健康状况,我们还对不同优势树种的地上生物量进行了估算。结果表明:(1)LiDAR点云的冠层高度模型和高度变量、ZY-3立体影像的DVI和近红外波段以及Sentinel-1 SAR影像的极化分解参数对红树林高度更为敏感。(2)采用RF算法时,LiDAR点云和Sentinel-1 SAR影像的反演精度最高,R2值分别为0.875和0.685。基于MDN的ZY-3立体图像反演结果最优(R2 = 0.719),较PLSR和RF算法提高0.143 ~ 0.198。(3)与其他红树林优势种相比,滨红木的估算精度最高(R2 = 0.897)。在2 ~ 3 m高度范围内,青竹和海棠花的反演精度最高(R2 = 0.925, R2 = 0.814),而在1 ~ 2 m高度范围内,candelia canddel的反演精度最佳(R2 = 0.652)。(4)山楂地上生物量在20.176 ~ 103.164 Mg/ha和132.019 ~ 719.226 Mg/ha之间,山楂地上生物量主要分布在169.916 ~ 803.204 Mg/ha之间。本研究为红树林的高度和健康监测以及红树林的保护和发展提供了参考。
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来源期刊
Ecological Informatics
Ecological Informatics 环境科学-生态学
CiteScore
8.30
自引率
11.80%
发文量
346
审稿时长
46 days
期刊介绍: The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.
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