Assessing canopy height measurements from ICESat-2 and GEDI orbiting LiDAR across six different biomes with G-LiHT LiDAR

Yu, Qiuyan; Ryan, Michael G; Ji, Wenjie; Prihodko, Lara; Anchang, Julius Y; Kahiu, Njoki; Nazir, Abid; Dai, Jingyu; Hanan, Niall P

doi:10.1088/2752-664x/ad39f2

Cited by 2 publications

(2 citation statements)

References 71 publications

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“…Queinnec et al [12] compared the accuracy of ICESat-2 ATL08 forest height measurements in different stand ages, demonstrating that the accuracy is significantly reduced in over-mature stands with complex structure and forest height variability. Existing studies showed that the estimation of forest structural parameters based on ICESat-2 data was strongly reliable, but there was still potential for an improvement in accuracy [16][17][18][19][20][21]. Also, the weak power and diurnal laser signal from ATLAS significantly impacted the accuracy of ICESat-2 products [22].…”

Section: Introductionmentioning

confidence: 99%

An Advanced Terrain Vegetation Signal Detection Approach for Forest Structural Parameters Estimation Using ICESat-2 Data

Li,

Shen,

Cao

2024

Remote Sensing

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Accurate forest structural parameters (such as forest height and canopy cover) support forest carbon monitoring, sustainable forest management, and the implementation of silvicultural practices. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), which is a spaceborne Light Detection and Ranging (LiDAR) satellite, offers significant potential for acquiring precise and extensive information on forest structural parameters. However, the ICESat-2 ATL08 product is significantly influenced by the geographical environment and forest characteristics, maintaining considerable potential for enhancing the accuracy of forest height estimation. Meanwhile, it does not focus on providing canopy cover data. To acquire accurate forest structural parameters, the Terrain Signal Neural Network (TSNN) framework was proposed, integrating Computer Vision (CV), Ordering Points to Identify the Clustering Structure (OPTICS), and deep learning. It encompassed an advanced approach for detecting terrain vegetation signals and constructing deep learning models for estimating forest structural parameters using ICESat-2 ATL03 raw data. First, the ATL03 footprints were visualized as Profile Raster Images of Footprints (PRIF), implementing image binarization through adaptive thresholding and median filtering denoising to detect the terrain. Second, the rough denoising buffers were created based on the terrain, combining with the OPTICS clustering and Gaussian denoising algorithms to recognize the terrain vegetation signal footprints. Finally, deep learning models (convolutional neural network (CNN), ResNet50, and EfficientNetB3) were constructed, training standardized PRIF to estimate forest structural parameters (including forest height and canopy cover). The results indicated that the TSNN achieved high accuracy in terrain detection (coefficient of determination (R2) = 0.97) and terrain vegetation signal recognition (F-score = 0.72). The EfficientNetB3 model achieved the highest accuracy in forest height estimation (R2 = 0.88, relative Root Mean Squared Error (rRMSE) = 13.5%), while the CNN model achieved the highest accuracy in canopy cover estimation (R2 = 0.80, rRMSE = 18.5%). Our results have significantly enhanced the accuracy of acquiring ICESat-2 forest structural parameters, while also proposing an original approach combining CV and deep learning for utilizing spaceborne LiDAR data.

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Section: Introductionmentioning

confidence: 99%

An Advanced Terrain Vegetation Signal Detection Approach for Forest Structural Parameters Estimation Using ICESat-2 Data

Li,

Shen,

Cao

2024

Remote Sensing

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“…Namely, there is in the first place the basic fact that electromagnetic interactions constituting remote sensing data could not even in theory explain all the variability of forest attributes. And even if this were the case, they would still be susceptible to imperfections in remote sensing input data, whether from lidar sources (Roy et al, 2021;Schleich et al, 2023;Tang et al, 2023;Yu et al, 2024) or imaging sources (Teillet et al, 1982;Joshi et al, 2017;Mutanga et al, 2023), as well as to various modeling choices and parameterizations. Therefore it makes sense that all of these factors combined induce models to fail to spatio-temporally reproduce a substantial part of the variability of the forest attributes.…”

Section: Introductionmentioning

confidence: 99%

Remote sensing-based high-resolution mapping of the forest canopy height: some models are useful, but might they be even more if combined?

Besic,

Picard,

Vega

et al. 2024

Preprint

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Abstract. The development of high-resolution mapping models for forest attributes based on remote sensing data combined with machine or deep learning techniques, has become a prominent topic in the field of forest observation and monitoring. This has resulted in an extensive availability of multiple sources of information, which can either lead to a potential confusion, or to the possibility to learn both about models and about forest attributes through the joint interpretation of multiple models. This article seeks to endorse the latter, by relying on the Bayesian model averaging (BMA) approach, which can be used to diagnose and interpret differences among predictions of different models. The predictions in our case are the forest canopy height estimations for the metropolitan France coming from five different models (Lang et al., 2023; Liu et al., 2023; Morin et al., 2022; Potapov et al., 2021; Schwartz et al., 2024). An independent reference dataset, containing four different definitions of the forest height (dominant, mean, maximum and Lorey’s), comes from the French National Forest Inventory (NFI) providing some 5 500 plots used in the study, distributed across the entire area of interest. In this contribution, we line up the evoked models with respect to their probabilities to be the ones generating measurements/estimations at the NFI plots. Stratifying the probabilities based on French sylvo-ecological regions reveals spatial variation in the respective model probabilities across the area of interest. Furthermore, we observe significant variability in these probabilities depending on the forest height definition used. This leads us to infer that the different models inadvertently exhibit dominant predictions for different types of canopy height. We also present the respective inter-model and intra-model variance estimations, allowing us to come to understand where the employed models have comparable weights but contrasted predictions. We show that the mountainous terrain has an important impact on the models spread. Moreover, we observe that the forest stand vertical structure, the dominant tree species and the type of forest ownership systematically appear to be statistically significant factors influencing the models divergence. Finally, we demonstrate that the derived mixture models exhibit higher R2 scores and lower RMSE values compared to individual models, although they may not necessarily exhibit lower biases.

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Assessing canopy height measurements from ICESat-2 and GEDI orbiting LiDAR across six different biomes with G-LiHT LiDAR

Cited by 2 publications

References 71 publications

An Advanced Terrain Vegetation Signal Detection Approach for Forest Structural Parameters Estimation Using ICESat-2 Data

An Advanced Terrain Vegetation Signal Detection Approach for Forest Structural Parameters Estimation Using ICESat-2 Data

Remote sensing-based high-resolution mapping of the forest canopy height: some models are useful, but might they be even more if combined?

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