Monitoring Earth’s climate variables with satellite laser altimetry

Nature Reviews Earth & Environment, Год журнала: 2024, Номер 5(2), С. 120 - 136

Опубликована: Янв. 30, 2024

Язык: Английский

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Mapping vegetation height and identifying the northern forest limit across Canada using ICESat-2, Landsat time series and topographic data

Remote Sensing of Environment, Год журнала: 2024, Номер 305, С. 114097 - 114097

Опубликована: Март 7, 2024

The northern forest-tundra ecotone is one of the fastest warming regions globe. Models vegetation change generally predict a northward advance boreal forests and corresponding retreat tundra. Previous satellite remote sensing analyses in this region have focused on mapping greenness tree cover derived from optical multi-spectral sensors. Changes structure relating to height biomass are less frequently investigated due limited availability lidar data over space time comparison with platforms. As such, there an opportunity combine products for continuous at high-latitudes, emphasis transition. In study, we used Ice, Cloud land Elevation Satellite (ICESat-2) classify canopy presence/absence, across 120 million hectares Canadian 30 m spatial resolution. Spatially predictors Landsat archive (2012−2021) ASTER (Advanced Spaceborne Thermal Emission Reflection Radiometer) Digital Model were extrapolate 98th percentile ICESat-2 Land Vegetation Height (ATL08) product using Random Forests models developed R (version 4.2.2). accuracy was assessed Land, Ice Sensor (LVIS), large-footprint airborne system. overall presence classification 89%, detected 88% accuracy. showed R2 0.54 RMSE 2.09 m. Finally, these methods map limit 3 forest Canada compared our model outputs MODIS Continuous Fields datasets. This work demonstrates challenges potential horizontal vertical within sparse, high latitude both data.

Язык: Английский

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Correcting forest aboveground biomass biases by incorporating independent canopy height retrieval with conventional machine learning models using GEDI and ICESat-2 data

Ecological Informatics, Год журнала: 2025, Номер unknown, С. 103045 - 103045

Опубликована: Янв. 1, 2025

Язык: Английский

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Improving extraction of forest canopy height through reprocessing ICESat-2 ATLAS and GEDI data in sparsely forested plain regions

GIScience & Remote Sensing, Год журнала: 2024, Номер 61(1)

Опубликована: Авг. 27, 2024

Forest canopy height (FCH) is one of the most important variables for carbon stock estimation. While many studies have focused on extracting FCH from spaceborne LiDAR in regions with spatially continuous and large patch sizes forested lands, limited research has addressed challenges extraction plain sparse fragmented forest distributions. In this study, we proposed innovative processing approaches to extract ICESat-2 photons GEDI footprints Anhui Province, China. Specifically, a sectional photon denoising method data geolocation error correction data. Airborne were used validate extracted products across typical regions. The results demonstrated effectiveness methods improving accuracy. Evaluation indicated that directly ATL08 L2A had Pearson's correlation coefficients (r) 0.6 0.93, respectively. After methods, 2019 exhibited r 0.82 relative root mean square (rRMSE) 31.11% based 3,217 segments, showed 0.96 rRMSE 18.35% 4,862 footprints. Further application these years 2020, 2021, 2022 their promise addressing vegetation coverage

Язык: Английский

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Russian forests show strong potential for young forest growth

Communications Earth & Environment, Год журнала: 2025, Номер 6(1)

Опубликована: Янв. 30, 2025

Abstract Climate warming has improved conditions for boreal forest growth, yet the region’s fate as a carbon sink of aboveground biomass remains uncertain. Forest height is powerful predictor biomass, and access to spatially detailed height-age relationships could improve understanding dynamics in this ecosystem. The capacity land grow trees, defined forestry site index, was estimated by analyzing recent measurements canopy against chronosequence stand age derived from historical satellite record. Forest-height estimates were then subtracted predicted index estimate growth potential across region. Russia, which comprised 73% change domain, had strong departures model expectation 2.4–4.8 ± 3.8 m 75th 90th percentiles. Combining observations revealed large young if allowed recover disturbance.

Язык: Английский

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Global open‐access DEM vertical elevation and along track neighbouring structure evaluations in the Tibetan Plateau using ICESat‐2 ATL03 points

Earth Surface Processes and Landforms, Год журнала: 2025, Номер 50(2)

Опубликована: Фев. 1, 2025

Abstract Global digital elevation models (GDEMs) are critical in the measurement and analysis of Earth's surface, should be evaluated prior to use. However, existing GDEM evaluations mainly use global statistical metrics evaluate vertical (VE) differences with reference data, ignoring relationship between a centre pixel its neighbouring pixels, which is defined as GDEM's structure (NS). Along track ATL03 points allow evaluation along NS (ATNS). This study comprehensively accesses VE ATNS accuracy 1 arc‐second GDEMs, including Copernicus, NASA, AW3D30 ASTER DEM, using for first time ICESat‐2 throughout Tibetan Plateau, where rugged terrains various features make it difficult maintain NS's accuracy. introduces continuous discrete metrics, then evaluates their effectiveness by analysing relationships errors terrain derivatives. Finally, better‐performing metric used across parameters, landforms land covers. The proposed framework achieved DEM from pixel‐by‐pixel local assessment. Evaluation results demonstrate that GDEMs linearly correlated RMSE errors. Overall, ranked Copernicus< < NASA< ASTER. conducted Andes Alps reveal regional variations these rankings. endeavours introduce new large‐scale evaluations, conclusions beneficial selection further applications.

Язык: Английский

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Evaluation and Comparison of ICESat-2 and GEDI Data for Terrain and Canopy Height Retrievals in Short-Stature Vegetation

Remote Sensing, Год журнала: 2023, Номер 15(20), С. 4969 - 4969

Опубликована: Окт. 15, 2023

Two space-borne light detection and ranging (LiDAR) missions, Global Ecosystem Dynamics Investigation (GEDI) Ice, Cloud, land Elevation Satellite-2 (ICESat-2), have demonstrated high capabilities in extracting terrain canopy heights forest environments. However, there been limited studies evaluating their performance for height retrievals short-stature vegetation. This study utilizes airborne LiDAR data to validate compare the accuracies of vegetation using latest versions ICESat-2 (Version 5) GEDI 2). Furthermore, this also analyzes influence various factors, such as type, slope, height, cover, on retrievals. The results indicate that (bias = −0.05 m, RMSE 0.67 m) outperforms 0.39 1.40 extraction, with similar observed from both missions. Additionally, findings reveal significant differences retrieval between under different acquisition scenarios. Error analysis demonstrate slope plays a pivotal role influencing accuracy extraction particularly data, where decreases significantly increasing slope. has most substantial impact estimation heights. Overall, these confirm strong potential areas, provide valuable insights future applications vegetation-dominated ecosystems.

Язык: Английский

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Error-Reduced Digital Elevation Model of the Qinghai-Tibet Plateau using ICESat-2 and Fusion Model

Scientific Data, Год журнала: 2024, Номер 11(1)

Опубликована: Июнь 5, 2024

Abstract The Qinghai-Tibet Plateau (QTP) holds significance for investigating Earth’s surface processes. However, due to rugged terrain, forest canopy, and snow accumulation, open-access Digital Elevation Models (DEMs) exhibit considerable noise, resulting in low accuracy pronounced data inconsistency. Furthermore, the glacier regions within QTP undergo substantial changes, necessitating updates. This study employs a fusion of DEMs high-accuracy photons from Ice, Cloud, land Satellite-2 (ICESat-2). Additionally, cover canopy heights are considered, an ensemble learning model is presented harness complementary information multi-sensor elevation observations. innovative approach results creation HQTP30, most accurate representation 2021 terrain. Comparative analysis with high-resolution imagery, UAV-derived DEMs, control points, ICESat-2 highlights advantages HQTP30. Notably, non-glacier regions, HQTP30 achieved Mean Absolute Error (MAE) 0.71 m, while it reduced MAE by 4.35 m compared state-of-the-art Copernicus DEM (COPDEM), demonstrating its versatile applicability.

Язык: Английский

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LightGBM hybrid model based DEM correction for forested areas

PLoS ONE, Год журнала: 2024, Номер 19(10), С. e0309025 - e0309025

Опубликована: Окт. 7, 2024

The accuracy of digital elevation models (DEMs) in forested areas plays a crucial role canopy height monitoring and ecological sensitivity analysis. Despite extensive research on DEMs recent years, significant errors still exist due to factors such as occlusion, terrain complexity, limited penetration, posing challenges for subsequent analyses based DEMs. Therefore, CNN-LightGBM hybrid model is proposed this paper, with four different types forests (tropical rainforest, coniferous forest, mixed broad-leaved forest) selected study sites validate the performance correcting COP30DEM forest area In choice was made use Densenet architecture CNN LightGBM primary model. This LightGBM’s leaf-growth strategy histogram linking methods, which are effective reducing data’s memory footprint utilising more data without sacrificing speed. uses values from ICESat-2 ground truth, covering several parameters including COP30DEM, height, coverage, slope, roughness relief amplitude. To superiority correction compared other models, test model, CNN-SVR SVR conducted within same sample space. prevent issues overfitting or underfitting during training, although common meta-heuristic optimisation algorithms can alleviate these problems certain extent, they have some shortcomings. overcome shortcomings, paper cites an improved SSA search algorithm that incorporates ingestion FA increase diversity solutions global capability, Firefly Algorithm-based Sparrow Search Optimization Algorithm (FA-SSA algorithm) introduced. By comparing multiple validating airborne LiDAR reference dataset, results show R 2 (R-Square) improves by than 0.05 performs better experiments. FA-SSA-CNN-LightGBM has highest accuracy, RMSE 1.09 meters, reduction 30% when models. Compared (such FABDEM GEDI), its 50%, significantly commonly used areas, indicating feasibility method importance advancing topographic mapping.

Язык: Английский

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Accuracy assessment of topography and forest canopy height in complex terrain conditions of Southern China using ICESat-2 and GEDI data

Frontiers in Plant Science, Год журнала: 2025, Номер 16

Опубликована: Март 20, 2025

ICESat-2 and GEDI offer unique capabilities for terrain canopy height retrievals; however, their performance measurement precision are significantly affected by conditions. Furthermore, differences in data scales complicate direct comparisons of capabilities. This study evaluates the accuracy retrievals from LiDAR complex environments. Jinghong City Pu’er Southwest China were selected as areas, with high-precision airborne serving a reference. Ground elevation retrieval accuracies compared before after scale unification to 30 m × under varying slope Results indicate that shows significant advantage retrieval, RMSE values 4.75 4.21 unification, respectively. In comparison, achieved 4.94 4.96 m. Both systems maintain high flat regions, but declines increasing slope. For outperforms ICESat-2. Before an R² 0.73 5.15 m, 0.67 5.32 contrast, showed lower performance, 0.65 7.42 0.53 8.29 unification. maintains higher across all levels. Post-scale both show ground being superior. achieves better accuracy. These findings highlight synergistic strengths ICESat-2’s photon-counting GEDI’s full-waveform techniques, demonstrating advancements satellite laser altimetry retrieval.

Язык: Английский

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