Integrating UAV LiDAR and multispectral data to assess forest status and map disturbance severity in a West African forest patch

IF 5.8 2区 环境科学与生态学 Q1 ECOLOGY Ecological Informatics Pub Date : 2024-11-08 DOI:10.1016/j.ecoinf.2024.102876
Chima J. Iheaturu , Samuel Hepner , Jonathan L. Batchelor , Georges A. Agonvonon , Felicia O. Akinyemi , Vladimir R. Wingate , Chinwe Ifejika Speranza
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Abstract

Unmanned aerial vehicle (UAV) technologies have emerged as promising tools to improve forest ecosystem assessments. These technologies offer high-resolution data that can significantly enhance evaluations of forest structure, condition, and disturbance severity. UAV sensors such as LiDAR and multispectral provide complementary information about forest attributes, capturing structural and spectral details, yet their integration for comprehensive forest assessment remains understudied. In this paper, we explored the potential of combining UAV LiDAR and multispectral data to assess the disturbance severity of a West African forest patch (Benin). We developed an integrated disturbance index (IDI) that fuses structural properties from LiDAR data and spectral characteristics from multispectral vegetation indices through principal component analysis (PCA). This allowed us to delineate low (> 0.65), medium (0.35–0.65), and high (< 0.35) forest disturbance levels. We applied the IDI to the 560-ha Ewe-Adakplame relict forest in Benin, West Africa, and achieved 95 % overall accuracy in disturbance detection, outperforming both LiDAR-only (80 %) and multispectral-only (75 %) approaches. The IDI revealed that 23 % of the forest area has experienced low disturbance, while 28 % and 49 % face medium and high disturbance levels, respectively. These findings indicate that more than three-quarters of this relict forest exhibits medium to high levels of disturbance, underscoring the urgent need for tailored conservation strategies to strengthen forest resilience. This method's ability to differentiate disturbance levels can inform resource allocation, prioritize conservation efforts, and guide the development of site-specific management plans. The integration of UAV LiDAR and multispectral data demonstrated here has potential for application across diverse tropical forest patches, providing an effective means to monitor forest health, assess disturbance severity, and support data-driven decision-making in forest conservation and sustainable management.

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整合无人机激光雷达和多光谱数据,评估西非森林片区的森林状况并绘制干扰严重程度图
无人飞行器(UAV)技术已成为改善森林生态系统评估的有效工具。这些技术提供的高分辨率数据可以大大提高对森林结构、状况和干扰严重程度的评估。无人机传感器(如激光雷达和多光谱)可提供有关森林属性的补充信息,捕捉结构和光谱细节,但它们在森林综合评估中的整合仍未得到充分研究。在本文中,我们探索了结合无人机激光雷达和多光谱数据评估西非森林(贝宁)干扰严重程度的潜力。我们开发了一种综合干扰指数(IDI),通过主成分分析(PCA)将激光雷达数据的结构特性和多光谱植被指数的光谱特性融合在一起。这使我们能够划分出低(0.65)、中(0.35-0.65)和高(0.35)森林干扰水平。我们将 IDI 应用于西非贝宁面积为 560 公顷的 Ewe-Adakplame 遗迹森林,其干扰检测的总体准确率达到 95%,优于纯激光雷达方法(80%)和纯多光谱方法(75%)。国际干扰指数显示,23% 的森林面积经历过低度干扰,28% 和 49% 的森林面积分别面临中度和高度干扰。这些研究结果表明,这片孑遗森林有四分之三以上受到中度到高度干扰,因此迫切需要制定有针对性的保护战略,以加强森林的恢复能力。该方法区分干扰程度的能力可为资源分配提供信息,确定保护工作的优先次序,并指导制定特定地点的管理计划。本文所展示的无人机激光雷达与多光谱数据的整合有可能应用于各种热带森林斑块,为监测森林健康、评估干扰严重程度以及支持森林保护和可持续管理中的数据驱动决策提供了一种有效的方法。
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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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