Mapping forest height using photon-counting LiDAR data and Landsat 8 OLI data: A case study in Virginia and North Carolina, USA

Zhu, Xinhua; Wang, Cheng; Nie, Sheng; Pan, Feifei; Xi, Xiaohuan; Hu, Zhenyue

doi:10.1016/j.ecolind.2020.106287

Cited by 41 publications

(17 citation statements)

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“…Based on previous research conclusions, 38 , 39 there are four parameters included in the key steps of the classification algorithm: 37 , 39 – 41 window size (determined by the maximum noncircular feature size), which is preset to 200 m; Di, which represents the distance between the unclassified photon and the two initial adjacent seed ground photons; At, which is the angle between the line connecting the unclassified photon and its seed ground photon and the ground segment line; and Ds, which represents the distance from the unclassified photon to the ground surface. The photon classification algorithm flowchart is shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Carbon storage estimation of mountain forests based on deep learning and multisource remote sensing data

Liu

Shu

Sun

et al. 2023

J. Appl. Rem. Sens.

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.Remote sensing monitoring of forest carbon storage against the background of climate change is a popular research topic, and multisource remote sensing is significant for improving its accuracy at a regional scale. Improving accuracy at a low cost is a current challenge. In this study, the spaceborne light detection and ranging (lidar) ICESat-2/advanced terrain laser altimeter system (ATLAS), along with an optimized random forest regression (RF) model and 54 sample plots, was used to obtain early estimates of carbon storage at the canopy scale in the footprints of ICESat-2/ATLAS. On this basis, combined with Landsat 8 operational land imager (OLI) data and a deep neural network (DNN) model, a regional-scale remote sensing estimation of forest carbon storage in Shangri-La, a mountainous area in Southwest China, was performed. The coefficient of determination (R2) and root mean square error (RMSE) were used to assess the estimation results. The results showed that (1) the apparent surface reflectance (ASR) and other parameters of the ATLAS spots had a strong correlation with the carbon storage of mountain forests, and the optimized RF model estimated the carbon storage well at the canopy scale. The modeling accuracy was R2 = 0.8890, the verification accuracy was R2 = 0.7750, and the contribution rate of the ASR was 22.76% in the model. (2) Using the carbon storage of 74,873 ATLAS footprints obtained by the RF model as the training sample for deep learning modeling, along with the Landsat 8 OLI vegetation index, a regional-scale forest carbon storage (DNN) estimation model was constructed. The model verification accuracy was R2 = 0.5433 and RMSE = 6.6402 Mg / ha. (3) The estimated population carbon storage based on the DNN model was 4.717 × 107 Mg, with an average value of 49.04 Mg / ha in the study area. The model estimates were valid. In conclusion, the results estimated by a small sample were used as a large sample training dataset for region-scale deep learning, which can be effective in reducing costs.

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Section: Methodsmentioning

confidence: 99%

Carbon storage estimation of mountain forests based on deep learning and multisource remote sensing data

Liu

Shu

Sun

et al. 2023

J. Appl. Rem. Sens.

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“…The number and weight of different canopy heights (N%) in Table 3 show that the vegetation canopy is mainly distributed in [0, 5) and [10,25). Moreover, it was found that as the canopy height increaseed, the RMSE and MAE also increased, and thus were positively correlated, and as the canopy height increases, the R 2 decreases, and is thus negatively correlated.…”

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confidence: 98%

“…The specific formula is classed_pc_ index+ph_index_beg -1. Using this formula, the photon position information in the ATL03 product , and the markers are noise 0, ground 1, canopy 2, and canopy top 3 [25,26]. In this study, we used all photons with a classification marker ground 1 as the forest ground photons (8487 and 2297 strong and weak beam ground photons, respectively).…”

Section: The Zone Number In the Photon Segment Informationmentioning

confidence: 99%

Forest Terrain Inversion Based on Icesat-2/ATLAS with Different Laser Intensities

Xi¹,

Li²,

Shu³

et al. 2022

Pol. J. Environ. Stud.

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There are various problems concerning the acquisition of forest digital terrain using satellite-based LiDAR (LiDAR). In this paper, a method for extracting the classified photon point cloud is summarised based on the photon point cloud acquired by ICESat-2/ATLAS strong and weak beams for forest digital terrain inversion. In this study, ATLAS was used as the data source to group canopy heights, and airborne LiDAR G-LiHT was used as the validation data to verify the effect of different canopy heights on the inversion of the understorey terrain. The results show that, when the canopy heights were not grouped, the inversion accuracy of both strong and weak ATLAS beams was higher, with an R 2 greater than 0.99, an RMSE of less than 0.7 m, and an MAE of less than 0.4 m. Overall, the strong beams were superior. Moreover, when the canopy heights were grouped, the R 2 was greater than 0.99 and the accuracy was best at the canopy height of 5-10 m. The R 2 decreased when the height exceeded 35 m. The inversion accuracy of both the strong and weak ATLAS beams was higher than 0.99. The accuracy of both strong and weak beam inversions was extremely high, with the strong beam being superior overall. However, both strong and weak beams can provide a scientific basis for the inversion of understory DTMs. Canopy height affected the inversion accuracy, which was inversely proportional to the canopy height, with an increase in canopy height causing a decrease in the accuracy. The inversion of the understorey topography was better than that of the dwarf tree and shrub layers.

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“…The forest ecosystem has the largest distribution area and richest biodiversity on land, and it plays an important role in climate regulation as well as water and soil conservation [1]. Forest canopy height is an important indicator of the forest's ecological structure, which is used to evaluate forest productivity and estimate forest above-ground biomass and biodiversity [2,3].…”

Section: Introductionmentioning

confidence: 99%

A Rapid and Easy Way for National Forest Heights Retrieval in China Using ICESat-2/ATL08 in 2019

Gao

Zhu

2023

Forests

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Continuous and extensive monitoring of forest height is essential for estimating forest above-ground biomass and predicting the ability of forests to absorb CO2. In particular, forest height at the national scale is an important indicator reflecting the national forestry economic construction, environmental governance, and ecological balance. However, the lack of inventory data restricts large-scale monitoring of forest height to some extent. Conducting manual surveys of forest height for large-scale areas would be labor-intensive and time-consuming. The successful launch of the new generation of spaceborne light detection and ranging (LiDAR) (The Ice, Cloud, and Land Elevation Satellite-2/the Advanced Topographic Laser Altimeter System, ICESat-2/ATLAS) has brought new opportunities for national-scale forestry resource surveys. This paper explores a method to survey national forest canopy height from the new generation of ICESat-2/ATLAS data. In view of the sparse sampling and little overlap between repeated spaceborne LiDAR data, a strategy for assessing the overall change of canopy height for large scales is provided. Some spatially continuous ancillary data were used to assist ICESat-2/ATLAS data to generate a wall-to-wall (spatially continuous) forest canopy height map in China by using the machine learning approach and then quantifying the analysis of forest canopy height in various provinces. The results show that there is a good correlation between the model forest height and the verification data, with a root mean squared error (RMSE) of 3.30 m and a coefficient of determination (R2) of 0.87. This indicates that the method for retrieving national forest canopy height is reliable. There are some limitations in areas with lower vegetation coverage or complex topography which need additional filtering or terrain correction to achieve higher accuracy in measuring forest canopy height. Our analysis suggests that ICESat-2/ATLAS data can achieve the retrieval of national forest height at an overall level, and it would be feasible to use ICESAT-2/ATLAS products to estimate forest canopy height change for large-scale areas.

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Mapping forest height using photon-counting LiDAR data and Landsat 8 OLI data: A case study in Virginia and North Carolina, USA

Cited by 41 publications

References 50 publications

Carbon storage estimation of mountain forests based on deep learning and multisource remote sensing data

Carbon storage estimation of mountain forests based on deep learning and multisource remote sensing data

Forest Terrain Inversion Based on Icesat-2/ATLAS with Different Laser Intensities

A Rapid and Easy Way for National Forest Heights Retrieval in China Using ICESat-2/ATL08 in 2019

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