Vegetation structure derived from airborne laser scanning to assess species distribution and habitat suitability: The way forward

Diversity and Distributions, Год журнала: 2022, Номер 29(1), С. 39 - 50

Опубликована: Окт. 30, 2022

Abstract Ecosystem structure, especially vertical vegetation is one of the six essential biodiversity variable classes and an important aspect habitat heterogeneity, affecting species distributions diversity by providing shelter, foraging, nesting sites. Point clouds from airborne laser scanning (ALS) can be used to derive such detailed information on structure. However, public agencies usually only provide digital elevation models, which do not Calculating structure variables ALS point requires extensive data processing remote sensing skills that most ecologists have. extremely valuable for many analyses use distribution. We here propose 10 should easily accessible researchers stakeholders through national portals. In addition, we argue a consistent selection their systematic testing, would allow continuous improvement list keep it up‐to‐date with latest evidence. This initiative particularly needed advance ecological research open datasets but also guide potential users in face increasing availability global products.

Язык: Английский

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

IEEE Transactions on Geoscience and Remote Sensing, Год журнала: 2023, Номер 61, С. 1 - 14

Опубликована: Янв. 1, 2023

Ice, cloud, and land elevation satellite (ICESat-2)/Advanced Topographic Laser Altimeter System (ATLAS) multibeam micropulse photoncounting light detection ranging (LiDAR) can be effectively applied to extract forest canopy height. However, the ICESat-2/ATLAS photon point cloud interfered with signal-to-noise ratio (SNR), fraction vegetation coverage (FVC), terrain slope. The main challenge of this research is high-precision heights. Therefore, article improves height extraction method based on ICESat-2/ATL08 theoretical algorithm. First, an adaptive filter, Threshold Segmentation Spatial Clustering Bimodal Reconstruction (TS-SCABR), proposed, which adapt different SNR scenarios. Then, combined gradient method, discontinuous data are detrended in sections eliminate edge mutation problem data. Based data, iterative filtering algorithm local employed fit ground curve, empirical mode decomposition (EMD)-digital smoothing polynomial (DISPO) remove pseudoground photons identify nonground accurately. Finally, percentile statistics utilized canopy-top from according their difference. results indicate that, under natural conditions, improved has better adaptability than previous Compared original ATL08 ATBD algorithm, accuracy significantly improved, especially low FVC high slope When lower 25%, $R_{2}$ increases by 50.3%, root mean square error (RMSE) reduced 2.175 m, when higher 45°, it 41.7%, RMSE 2.159 m. apparent advantages inverting a mountainous environment lush forests.

Язык: Английский

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Extracting accurate terrain in vegetated areas from ICESat-2 data

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2023, Номер 117, С. 103200 - 103200

Опубликована: Янв. 21, 2023

The uncertainty of ICESat-2 terrain accuracy, especially in vegetated areas, limits its scientific application, and there is barely any comprehensive modeling evaluation for this uncertainty. In study, we propose a quality classification model with measurement extracting accurate from altimetry products, which includes two main parts: 1) training samples are used to construct elevation model; 2) the relationship between vote entropy accuracy analyzed measure predicted results model. Compared airborne LiDAR data multiple areas world, it confirmed that extracted can meet different requirements (95th percentile absolute error: 1 m, 2 3 m) higher than 90% purity (proportion terrain). ∼0.5–0.9 ∼0.9–1.3 m ∼1.1–2.9 respectively, eliminated ∼1.2–3.8 ∼2.0–4.4 ∼3.4–5.8 respectively. also show method extract high-accuracy high vegetation cover (where tree index 80%), has potential applying large-scale even global-scale terrain. Moreover, suitable non-vegetated areas. corresponding (root-mean-square 0.333 0.667 m), their 90%.

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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Correction of global digital elevation models in forested areas using an artificial neural network-based method with the consideration of spatial autocorrelation

International Journal of Digital Earth, Год журнала: 2023, Номер 16(1), С. 1568 - 1588

Опубликована: Апрель 28, 2023

To remove vegetation bias (VB) from the global DEMs (GDEMs), an artificial neural network (ANN)-based method with consideration of elevation spatial autocorrelation is developed in this paper. Three study sites different forest types (evergreen, mixed evergreen-deciduous, and deciduous) are employed to evaluate performance proposed model on three popular 30-m GDEMs, including SRTM1, AW3D30, COPDEM30. Taking LiDAR DTM as ground truth, accuracy GDEMs before after VB correction assessed, well two existing MERIT FABDEM. Results show that all original significantly overestimate types, largest biases 21.5 m for 26.3 27.18 data randomly sampled corrected area training points, reduces mean errors (root square errors) by 98.8%−99.9% (55.1%−75.8%) forests. When have same type GDEM but under local situations, lowers at least 76.9% (44.1%). Furthermore, our consistently outperform cases.

Язык: Английский

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Comparison of three global canopy height maps and their applicability to biodiversity modeling: Accuracy issues revealed

Ecosphere, Год журнала: 2024, Номер 15(10)

Опубликована: Окт. 1, 2024

Abstract Global mapping of forest height is an extremely important task for estimating habitat quality and modeling biodiversity. Recently, three global canopy maps have been released, the map (GFCH), high‐resolution model Earth (HRCH), tree (GMTCH). Here, we assessed their accuracy usability biodiversity modeling. We examined by comparing them with reference models derived from airborne laser scanning (ALS). Our results show considerable differences between evaluated maps. The root mean square error ranged 10 18 m GFCH, 9–11 HRCH, 10–17 GMTCH, respectively. GFCH GMTCH consistently underestimated all canopies regardless height, while HRCH tended to overestimate low underestimate tall canopies. Biodiversity using predicted as input data are sufficient simple relationships species occurrence but use leads a decrease in discrimination ability mischaracterization niches where indices (e.g., heterogeneity) concerned. showed that heterogeneity considerably urge temperate areas rich ALS data, activities should concentrate on harmonizing rather than relying modeled products.

Язык: Английский

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Optimizing ground photons for canopy height extraction from ICESat-2 data in mountainous dense forests

Remote Sensing of Environment, Год журнала: 2023, Номер 299, С. 113851 - 113851

Опубликована: Окт. 13, 2023

Язык: Английский

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A high-quality global elevation control point dataset from ICESat-2 altimeter data

International Journal of Digital Earth, Год журнала: 2024, Номер 17(1)

Опубликована: Июнь 13, 2024

The ICESat-2 satellite equipped with a new photon-counting laser altimeter has received much attention as source of accurate elevation observations. However, in this research field, there is lack an open-source high-accuracy control point dataset the specific quality requirements at global scale. To end, using data main source, we constructed and organized useful supplement for field. was generated by methodology based on detection environment evaluation, photon spatial analysis, redundant observation statistics. includes more than 600 million points covers land areas, except Greenland Antarctica. been validated multiple digital models (DEMs) from around world (sourced airborne LiDAR data). results show that points. overall root-mean-square error (RMSE) original elevations about 1.384–4.820 m, but RMSE 0.279–0.642 m. Moreover, obtained study suitable application within high vegetation cover areas.

Язык: Английский

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Improved Mapping of Regional Forest Heights by Combining Denoise and LightGBM Method

Remote Sensing, Год журнала: 2023, Номер 15(23), С. 5436 - 5436

Опубликована: Ноя. 21, 2023

Currently, the integration of satellite-based LiDAR (ICESat-2) and continuous remote sensing imagery has been extensively applied to mapping forest canopy height over large areas. A considerable fraction low-quality photons exists in ICESAT-2/ATL08 products, which restricts performance regional estimation. To solve these problems, a Local Noise Removal-Light Gradient Boosting Machine (LNR-LGB) method was proposed this study, efficiently filtered unreliable ATL08, constructed an extrapolation model by combining multiple data, finally mapped 30 m Hunan Province 2020. verify feasibility method, parameters were also based on ATL08 product attributes (traditional method), accuracy two models compared using 10-fold cross-validation. The conclusions as follows: (1) with traditional model, overall LNR-LGB approximately doubled, R2 increased from 0.46 0.65 RMSE decreased 6.11 3.48 m; (2) ranged 2.53 50.79 average value 18.34 m. will provide new concept for achieving high-accuracy height.

Язык: Английский

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Evaluating the Uncertainties in Forest Canopy Height Measurements Using ICESat-2 Data

Journal of Remote Sensing, Год журнала: 2024, Номер 4

Опубликована: Янв. 1, 2024

Forest ecosystems have been identified as major carbon stocks in terrestrial ecosystems; therefore, their monitoring is critical. Forests cover large areas, making it difficult to monitor and maintain up-to-date information. Advances remote sensing technologies provide opportunities for detailed small-scale global of forest resources. Airborne laser scanning (ALS) data can precise structure measurements, but mainly due its expensive cost limited spatial temporal coverage. Spaceborne lidar (light detection ranging) extensive scales, suitability a replacement ALS measurements remains uncertain. There are still relatively few studies on the performance spaceborne estimate attributes with sufficient accuracy precision. Therefore, this study aimed at assessing ICESat-2 canopy height metrics understanding uncertainties utilities by evaluating agreements ALS-derived Mississippi, United States. We assessed different types, physiographic regions, range cover, diverse disturbance histories using equivalence tests. Results suggest that collected strong beam mode night higher agreement ones. showed great potential estimating heights evergreen forests high cover. This contributes scientific community’s capabilities limitations measure regional scales.

Язык: Английский

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