Forest Canopy Height Extraction Method Based on ICESat-2/ATLAS Data

Huang, X.; Cheng, Feiyan; Wang, J. L.; Duan, P.; Wang, J. S.

doi:10.1109/tgrs.2023.3240848

Cited by 13 publications

(10 citation statements)

References 30 publications

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“…The higher accuracy in this study is attributed to the selection of highprecision training samples and adjustments to specific parametric models. It can be seen that the canopy height extraction algorithm proposed by Huang et al [27] has high research value under complex terrain conditions, but relatively low applicability of the parametric model in different study areas. In addition, among the different dominant tree species, relatively poor fitting effects were shown in Quercus acutissima and Pinus yunnanensis, with R 2 values of 0.545 and 0.526, respectively, while relatively good fitting effects were shown in Pinus densata Mast., Picea asperata Mast., and Abies fabri, with R 2 values of 0.665, 0.623, and 0.616, respectively.…”

Section: Inversion Results Of the Forest Canopy Height Based On The E...mentioning

confidence: 99%

“…According to an algorithm for forest canopy height extraction proposed by Huang et al [27], which is suitable for complex mountainous terrain, the specific steps are as follows: (1) Data preprocessing: to obtain two-dimensional profile point clouds at appropriate scales.…”

Section: Forest Canopy Height Extraction Algorithm Of Icesat-2/atl03mentioning

confidence: 99%

“…However, the data are severely affected by solar background noise, FVC, and terrain slope, and research based on the vegetation height product ATL08 released by the National Snow and Ice Data Center (NSIDC) in the literature [25,26] shows that ATL08 has great uncertainty in accuracy under complex mountain conditions. In response to the aforementioned issues related to ICESat-2/ATLAS, Huang et al [27] have proposed a forest canopy height extraction algorithm that is suitable for complex mountainous areas. The canopy height data obtained under different environmental conditions have higher precision than ICESat-2/ATL08 and other similar ICESat-2 canopy height extraction algorithm estimates.…”

Section: Introductionmentioning

confidence: 99%

“…The only drawback is that the canopy height data are still discretely distributed. Therefore, spatial continuous canopy height inversion under the complex terrain of Shangri-La is conducted by combining the canopy height data at the light spot scale of the literature [27] and Sentinel-2 spectral data and, comparing the applicability of the parameter model in the literature [16] and the non-parametric model (random forest model), more accurate canopy height data in this region are obtained. In addition, as Shangri-La has typical horizontal and vertical zonation, previous studies on ecological zonation need to be more specific, as it dramatically affects the accuracy of estimation models.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Study on Remote Sensing Methods for Forest Height Mapping in Complex Mountainous Environments

et al. 2023

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Forest canopy height is one of the critical parameters for carbon sink estimation. Although spaceborne lidar data can obtain relatively high precision canopy height on discrete light spots, to obtain continuous canopy height, the integration of optical remote sensing image data is required to achieve “from discrete to continuous” extrapolation based on different prediction models (parametric model and non-parametric model). This study focuses on the Shangri-La area and seeks to assess the practical applicability of two predictive models under complex mountainous conditions, using a combination of active and passive remote sensing data from ICESat-2 and Sentinel-2. The research aims to enhance our understanding of the effectiveness of these models in addressing the unique challenges presented by mountainous terrain, including rugged topography, variable vegetation cover, and extreme weather conditions. Through this work, we hope to contribute to the development of improved geospatial prediction algorithms for mountainous regions worldwide. The results show the following: (1) the fitting effect of the selected parametric model (empirical function regression) is poor in the area of Quercus acutissima and Pinus yunnanensis; (2) evaluation of the importance of each explanatory variable in the non-parametric model (random forest regression) shows that topographic and meteorological factors play a dominant role in canopy height inversion; (3) when random forest regression is applied to the inversion of canopy height, there is often a problem of error accumulation, which is of particular concern to the Quercus acutissima and Pinus yunnanensis; (4) the random forest regression with the optimal features has relatively higher precision by comparing the inversion accuracy of canopy height data of the empirical function regression, random forest regression with all features, and random forest regression with the optimal features in the study area, i.e., R2 (coefficient of determination) = 0.865 and RMSE (root mean square error) = 3.184 m. In contrast, the poor estimation results reflected by the empirical function regression, mainly resulting from the lack of consideration of topographic and meteorological factors, are not applicable to the inversion of canopy height under complex topographic conditions.

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Section: Inversion Results Of the Forest Canopy Height Based On The E...mentioning

confidence: 99%

Section: Forest Canopy Height Extraction Algorithm Of Icesat-2/atl03mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparative Study on Remote Sensing Methods for Forest Height Mapping in Complex Mountainous Environments

et al. 2023

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“…The results showed that the combination of multilevel filtering algorithms can effectively achieve daytime data denoising. Huang [20] proposed an adaptive filter based on spatial clustering and biomedical instruction, which can adapt to different signal-to-noise ratio scenarios. To sum up, the multilevel filtering algorithms can achieve accurate denoising of photon point cloud data.…”

Section: Introductionmentioning

confidence: 99%

Research on Multilevel Filtering Algorithm Used for Denoising Strong and Weak Beams of Daytime Photon Cloud Data with High Background Noise

You,

Li,

Qin

et al. 2023

Remote Sensing

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The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is equipped with the Advanced Topographic Laser Altimeter System (ATLAS), which can achieve high-precision ground detection. However, due to its low pulse energy and high sensitivity, it is also affected by noise when obtaining data. Therefore, data denoising is critical to the subsequent processing and application. In this study, a multilevel filtering algorithm is proposed to denoise the daytime photon cloud data with high background noise. Firstly, the Random Sample Consensus (RANSAC) algorithm is used to roughly denoise the daytime photon cloud data with high background noise, and a signal point cloud buffer is established to remove most of the noise points. Subsequently, the horizontal continuity parameter is calculated based on the photon cloud data following the rough denoising process. Based on this parameter, the shape and size of the search domain of the results of the subsequent fine denoising algorithm are adaptively improved. Finally, three filtering directions (a horizontal direction, an intra-group unified direction, and an adaptive direction for each photon) are proposed, and a hybrid algorithm combining the Ordering Points to Identify the Clustering Structure (OPTICS) density clustering algorithm and the Relative Neighboring Relationship K-nearest neighbors-based noise removal (RNR−KNNB) algorithm is employed to accurately denoise the photon cloud data in the three filtering directions. Furthermore, the RANSAC algorithm based on a sliding overlap window is used to remove outliers for the weak beam fine denoising photon cloud data. The results indicate that, for the strong beams, the denoising accuracy of the multilevel filtering algorithm in the three filtering directions is comparable (Rs ≥ 0.96, F ≥ 0.67), and they are all better than that of the ATL08 algorithm (Rs/Rn/p/F = 0.85/0.67/0.52/0.65). For weak beams, the denoising accuracy of the multilevel filtering algorithm in the horizontal direction and the intra-group unified direction is similar (Rs = 0.92, F = 0.69), and it is superior to the denoising results in the adaptive direction of each photon and the ATL08 algorithm (Rs/Rn/p/F = 0.94/0.84/0.51/0.65, 0.88/0.87/0.55/0.67, respectively). For strong and weak beams, the p-value and F-value of the denoising results of multilevel filtering algorithms in three different filtering directions increase with the increase of SNR value. It is demonstrated that SNR is an important factor affecting the denoising results of algorithms. The multilevel filtering algorithm proposed in the study can effectively achieve precise denoising of daytime photon cloud data with high background noise, and the three different filtering directions have different effects on the denoising results of strong and weak beam photon cloud data. This can provide technical and methodological guidance for subsequent photon cloud data filtering processing.

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Analyzing the Economic and Legal Impacts of Intellectual Property Rights on Plant Breeding Innovations

Gopalakrishna,

Maurya,

Kumar

et al. 2024

Mathematical Modeling in Agriculture

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Forest Canopy Height Extraction Method Based on ICESat-2/ATLAS Data

Cited by 13 publications

References 30 publications

Comparative Study on Remote Sensing Methods for Forest Height Mapping in Complex Mountainous Environments

Comparative Study on Remote Sensing Methods for Forest Height Mapping in Complex Mountainous Environments

Research on Multilevel Filtering Algorithm Used for Denoising Strong and Weak Beams of Daytime Photon Cloud Data with High Background Noise

Analyzing the Economic and Legal Impacts of Intellectual Property Rights on Plant Breeding Innovations

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