Distinguishing forest types in restored tropical landscapes with UAV-borne LIDAR-猫眼课题宝

DOI：

10.1016/j.rse.2023.113533

发表时间：

2023-05

期刊：

Remote Sensing of Environment

影响因子：

13.5

通讯作者：

中科院分区：

工程技术1区

文献类型：

作者：

J. Scheeres;Johan de Jong;Benjamin Brede;P. Brancalion;Eben N Broadbent;A. Zambrano;E. Gorgens;C. A. Silva;R. Valbuena;P. Molin;S. Stark;R. Rodrigues;G. Santoro;A. F. Resende;C. T. de Almeida;D. R. D. de Almeida-D.-R.-D.-de-Almeida-29769378

恢复森林景观是减轻气候变化负面影响、保护生物多样性和确保森林未来可持续性的全球优先事项，国际认捐集中在热带森林地区。为了使恢复工作负责任并监测其结果，通过实地调查监测树木覆盖率增加的传统战略已经不足，因为它们是劳动密集型和昂贵的。与此同时，遥感方法还无法区分因采用不同的恢复方法（养护与生产重点）而产生的不同森林类型。搭载激光雷达（LiDAR）的无人机（UAV）可以观测森林的垂直和水平结构变化，具有区分森林类型的潜力。在这项研究中，我们探讨了这种潜力的无人机载激光雷达区分森林类型的景观恢复在巴西东南部使用监督分类方法。研究区域包括150个森林样地，分为两个森林组：保护（残留森林，自然再生和积极恢复种植）和生产（单一栽培，混合和废弃种植）森林。利用无人机机载激光雷达数据，以高分辨率提取基于冠层高度模型（CHM）、体素和点云统计的指标进行分析。使用随机森林分类模型，我们可以成功地对保护林和生产林进行分类（准确率为90%）。整个六种类型的分类不太准确（62%），混淆矩阵显示保护和生产类型之间的鸿沟。林下叶面积指数（LAI）和植被密度的变化在上半部分的冠层是最重要的分类指标。特别是叶面积指数林下表现出最大的变化，并可能有助于提高生态恢复的认识。分类成功的差异强调了区分在管理、更新动态和结构方面非常相似的个别森林类型的困难。在恢复的背景下，我们展示了无人机载激光雷达在地块尺度上识别复杂森林结构的能力，并以中等到高精度识别广泛分布在不同恢复景观中的群体和类型。未来的研究可能会探索无人机载激光雷达与光学传感器的融合，并在分析中包括演替阶段，以进一步表征和区分森林类型及其对景观恢复的贡献。

Forest landscape restoration is a global priority to mitigate negative effects of climate change, conserve biodiversity, and ensure future sustainability of forests, with international pledges concentrated in tropical forest regions. To hold restoration efforts accountable and monitor their outcomes, traditional strategies for monitoring tree cover increase by field surveys are falling short, because they are labor-intensive and costly. Meanwhile remote sensing approaches have not been able to distinguish different forest types that result from utilizing different restoration approaches (conservation versus production focus). Unoccupied Aerial Vehicles (UAV) with light detection and ranging (LiDAR) sensors can observe forests` vertical and horizontal structural variation, which has the potential to distinguish forest types. In this study, we explored this potential of UAV-borne LiDAR to distinguish forest types in landscapes under restoration in southeastern Brazil by using a supervised classification method. The study area encompassed 150 forest plots with six forest types divided in two forest groups: conservation (remnant forests, natural regrowth, and active restoration plantings) and production (monoculture, mixed, and abandoned plantations) forests. UAV-borne LiDAR data was used to extract several Canopy Height Model (CHM), voxel, and point cloud statistic based metrics at a high resolution for analysis. Using a random forest classification model we could successfully classify conservation and production forests (90% accuracy). Classification of the entire set of six types was less accurate (62%) and the confusion matrix showed a divide between conservation and production types. Understory Leaf Area Index (LAI) and the variation in vegetation density in the upper half of the canopy were the most important classification metrics. In particular, LAI understory showed the most variation, and may help advance ecological understanding in restoration. The difference in classification success underlines the difficulty of distinguishing individual forest types that are very similar in management, regeneration dynamics, and structure. In a restoration context, we showed the ability of UAV-borne LiDAR to identify complex forest structures at a plot scale and identify groups and types widely distributed across different restored landscapes with medium to high accuracy. Future research may explore a fusion of UAV-borne LiDAR with optical sensors, and include successional stages in the analyses to further characterize and distinguish forest types and their contributions to landscape restoration.

Distinguishing forest types in restored tropical landscapes with UAV-borne LIDAR

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