Estimation of secondary forest parameters by integrating image and point cloud-based metrics acquired from unmanned aerial vehicle

Xu, Ziqian; Li, Weizheng; Li, Yuanyuan; Shen, Xin; Ruan, Honghua

doi:10.1117/1.jrs.14.022204

Cited by 13 publications

(7 citation statements)

References 44 publications

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“…Aerial photography provides high-resolution remote sensing images ( Song et al, 2021 ) and is often used to map, manage, and analyze tree distributions ( Xu et al, 2019 ). However, the captured tree crowns consistently exhibit considerable differences in appearance due to varying capture positions between UAV-loaded cameras and the target trees.…”

Section: Discussionmentioning

confidence: 99%

Individual Tree Crown Segmentation and Crown Width Extraction From a Heightmap Derived From Aerial Laser Scanning Data Using a Deep Learning Framework

et al. 2022

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Deriving individual tree crown (ITC) information from light detection and ranging (LiDAR) data is of great significance to forest resource assessment and smart management. After proof-of-concept studies, advanced deep learning methods have been shown to have high efficiency and accuracy in remote sensing data analysis and geoscience problem solving. This study proposes a novel concept for synergetic use of the YOLO-v4 deep learning network based on heightmaps directly generated from airborne LiDAR data for ITC segmentation and a computer graphics algorithm for refinement of the segmentation results involving overlapping tree crowns. This concept overcomes the limitations experienced by existing ITC segmentation methods that use aerial photographs to obtain texture and crown appearance information and commonly encounter interference due to heterogeneous solar illumination intensities or interlacing branches and leaves. Three generative adversarial networks (WGAN, CycleGAN, and SinGAN) were employed to generate synthetic images. These images were coupled with manually labeled training samples to train the network. Three forest plots, namely, a tree nursery, forest landscape and mixed tree plantation, were used to verify the effectiveness of our approach. The results showed that the overall recall of our method for detecting ITCs in the three forest plot types reached 83.6%, with an overall precision of 81.4%. Compared with reference field measurement data, the coefficient of determination R2 was ≥ 79.93% for tree crown width estimation, and the accuracy of our deep learning method was not influenced by the values of key parameters, yielding 3.9% greater accuracy than the traditional watershed method. The results demonstrate an enhancement of tree crown segmentation in the form of a heightmap for different forest plot types using the concept of deep learning, and our method bypasses the visual complications arising from aerial images featuring diverse textures and unordered scanned points with irregular geometrical properties.

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Section: Discussionmentioning

confidence: 99%

Individual Tree Crown Segmentation and Crown Width Extraction From a Heightmap Derived From Aerial Laser Scanning Data Using a Deep Learning Framework

et al. 2022

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“…Three-dimensional (3D) point clouds are generated either directly using active lidar UAS sensors, e.g., as in [60,68], or indirectly from passive optical UAS digital aerial photography (DAP) using the structure from motion approach (SfM) [22,[69][70][71].…”

Section: Three-dimensional Point Cloudsmentioning

confidence: 99%

The Application of UASs in Forest Management and Monitoring: Challenges and Opportunities for Use in the Miombo Woodland

Shamaoma

Chirwa

Ramoelo

et al. 2022

Forests

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The Miombo woodland is the most extensive tropical woodland in south-central Africa. However, field sample plot data on forest cover changes, species distribution and carbon stocks in the Miombo ecoregion are inadequate for effective forest management. Owing to logistical challenges that come with field-based inventory methods, remote sensing plays an important role in supplementing field methods to fill in data gaps. Traditional satellite and manned aircraft remote sensing platforms have their own advantages and limitations. The advent of unmanned aerial systems (UASs) has made it possible to acquire forest data at unprecedented spatial and temporal scales. UASs are adaptable to various forest applications in terms of providing flexibility in data acquisition with different sensors (RGB, multispectral, hyperspectral, thermal and light detection and ranging (lidar)) at a convenient time. To highlight possible applications in the Miombo woodlands, we first provide an overview of the Miombo woodlands and recent progress in remote sensing with small UASs. An overview of some potential forest applications was undertaken to identify key prospects and challenges for UAS applications in the Miombo region, which will provide expertise and guidance upon which future applications in the Miombo woodlands should be based. While much of the potential of using UASs for forest data acquisition in the Miombo woodlands remains to be realized, it is likely that the next few years will see such systems being used to provide data for an ever-increasing range of forest applications.

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“…Studies carried out in tropical forests show that this technology can be used to estimate biomass (Sumatra [97], Ecuador [160]), to detect changes in structure (Myanmar [93]), to monitor forest regeneration (Brazil [53], Costa Rica [161]), to characterize composition (Panama [143], Brazil [144]), to detect invasive plants (Hawaii [162]) or liana infestation (Malaysia [163]) and to locate emerging trees (Sumatra [164]). Other studies should be carried out in tropical moist forests to confirm this potential but also to test the contribution of UAVs to the measurement of forest attributes, currently requiring the installation of inventory plots, such as Lorey's height, root mean square diameter, basal area, volume and number of stems per hectare (China [165], Canada [166]). Moreover, "UAS-mounted sensors offer an extraordinary opportunity to bridge the existing gap between field observations and traditional air-and space-borne remote sensing, by providing high spatial detail over relatively large areas in an entirely new capacity for enhanced temporal retrieval" [167].…”

Section: Remote Sensing Metrics and Indices Used To Detect Tropical Moist Forest Degradationmentioning

confidence: 99%

How Can Remote Sensing Help Monitor Tropical Moist Forest Degradation?—A Systematic Review

2020

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In the context of the climate and biodiversity crisis facing our planet, tropical forests playing a key role in global carbon flux and containing over half of Earth’s species are important to preserve. They are today threatened by deforestation but also by forest degradation, which is more difficult to study. Here, we performed a systematic review of studies on moist tropical forest degradation using remote sensing and fitting indicators of forest resilience to perturbations. Geographical repartition, spatial extent and temporal evolution were analyzed. Indicators of compositional, structural and regeneration criteria were noted as well as remote sensing indices and metrics used. Tropical moist forest degradation is not extensively studied especially in the Congo basin and in southeast Asia. Forest structure (i.e., canopy gaps, fragmentation and biomass) is the most widely and easily measured criteria with remote sensing, while composition and regeneration are more difficult to characterize. Mixing LiDAR/Radar and optical data shows good potential as well as very high-resolution satellite data. The awaited GEDI and BIOMASS satellites data will fill the actual gap to a large extent and provide accurate structural information. LiDAR and unmanned aerial vehicles (UAVs) form a good bridge between field and satellite data. While the performance of the LiDAR is no longer to be demonstrated, particular attention should be brought to the UAV that shows great potential and could be more easily used by local communities and stakeholders.

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Estimation of secondary forest parameters by integrating image and point cloud-based metrics acquired from unmanned aerial vehicle

Cited by 13 publications

References 44 publications

Individual Tree Crown Segmentation and Crown Width Extraction From a Heightmap Derived From Aerial Laser Scanning Data Using a Deep Learning Framework

Individual Tree Crown Segmentation and Crown Width Extraction From a Heightmap Derived From Aerial Laser Scanning Data Using a Deep Learning Framework

The Application of UASs in Forest Management and Monitoring: Challenges and Opportunities for Use in the Miombo Woodland

How Can Remote Sensing Help Monitor Tropical Moist Forest Degradation?—A Systematic Review

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