Classification of Airborne LiDAR Point Cloud Data Based on Multiscale Adaptive Features

Shujuan, 杨书娟 Yang; Keshu, 张珂殊 Zhang; Yongshe, 邵永社 Shao

doi:10.3788/aos201939.0228001

Cited by 2 publications

(1 citation statement)

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“…In comparison to traditional algorithms like linear regression, machine learning approaches such as LSTM, U-Net, and transformer approach exhibit superior capability and reliability in enhancing the accuracy of forest biomass estimation and conducting in-depth analysis of multisource data [25][26][27]. Machine learning's applications in remote sensing within the field of forestry primarily concentrate on tasks such as land-use/land-cover classification, vegetation succession prediction, tree species identification, forest pest and fire damage detection, and the estimation of parameters like forest leaf area index, stock volume, and biomass [28][29][30][31]. Regarding the remote sensing estimation of forest biomass using machine learning, it can be categorized into image-based and object-oriented methods [32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Combining Multisource Data and Machine Learning Approaches for Multiscale Estimation of Forest Biomass

Hong,

Xu,

et al. 2023

Forests

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Forest biomass is an important indicator of forest ecosystem productivity, and it plays vital roles in the global carbon cycling, global climate change mitigating, and ecosystem researches. Multiscale, rapid, and accurate extraction of forest biomass information is always a research topic. In this study, comprehensive investigation of a larch (Larix olgensis) plantation was performed using remote sensing and field-based monitoring methods, in combination with LiDAR-based multisource data and machine learning methods. On this basis, a universal, multiscale (single tree, stand, management unit, and region), and unit-high-precision continuous monitoring method was proposed for forest biomass components. The results revealed the following. (1) Airborne LiDAR point cloud variables exhibited significant correlation with the aboveground components (except leaves) and the whole-plant biomass (Radj2 > 0.91), suitable for extraction or estimation of forest parameters such as biomass and stock volume. (2) In terms of biomass monitoring at forest stand and management unit scale, a random forest model performed well in fitting accuracy and generalization ability, whereas a multiple linear regression model produced clearer explanation regarding the biomass of each forest component. (3) Using seasonal phenological characteristics in the study area, larch distribution information was extracted effectively. The overall accuracy reached 90.0%, and the kappa coefficient reached 0.88. (4) A regional-scale forest biomass component estimation model was constructed using a long short-term memory model, which effectively reduced the probability of biomass underestimation while ensuring good estimation accuracy, with R2 exceeding 0.6 for the biomass of the aboveground and whole-plant components. This research provides theoretical support for rapid and accurate acquisition of large-scale forest biomass information.

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Section: Introductionmentioning

confidence: 99%

Combining Multisource Data and Machine Learning Approaches for Multiscale Estimation of Forest Biomass

Hong,

Xu,

et al. 2023

Forests

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A two-stage ALS point cloud segmentation framework for urban areas

Han

Huang

2020

IOP Conf. Ser.: Mater. Sci. Eng.

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In this paper, a novel methodology for semantic classification of airborne laser scanning point clouds is introduced in this paper. A two-stage framework integrating point-based classification and cluster-based rules is proposed with a general end to end processing scheme, which is from scattered airborne laser scanning (ALS) data to points with semantic labels. In the first stage, energy function consists of a point-wise-based soft labelings term obtained by random forest classifier and a spatially smooth labelling term. Then, a constrained mean-shift-based clustering algorithm combined with sematic rules is proposed to refine the hard labelings of class-wise point clouds, which can be as a post-processing stage. To verify the effectiveness of our method, the classification results derived for an ALS benchmark dataset are shown in Section 3. Base on the semantic classification results, the two-stage framework can be demonstrated with high accuracy and high plausibility in comparison to some classic approaches.

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Classification of Airborne LiDAR Point Cloud Data Based on Multiscale Adaptive Features

Cited by 2 publications

References 0 publications

Combining Multisource Data and Machine Learning Approaches for Multiscale Estimation of Forest Biomass

Combining Multisource Data and Machine Learning Approaches for Multiscale Estimation of Forest Biomass

A two-stage ALS point cloud segmentation framework for urban areas

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