A 30 m global map of elevation with forests and buildings removed

Environmental Research Letters, Год журнала: 2022, Номер 17(2), С. 024016 - 024016

Опубликована: Янв. 20, 2022

Abstract Elevation data are fundamental to many applications, especially in geosciences. The latest global elevation contains forest and building artifacts that limit its usefulness for applications require precise terrain heights, particular flood simulation. Here, we use machine learning remove buildings forests from the Copernicus Digital Model produce, first time, a map of with removed at 1 arc second (∼30 m) grid spacing. We train our correction algorithm on unique set reference 12 countries, covering wide range climate zones urban extents. Hence, this approach has much wider applicability compared previous DEMs trained single country. Our method reduces mean absolute vertical error built-up areas 1.61 1.12 m, 5.15 2.88 m. new is more accurate than existing maps will strengthen models where high quality information required.

Язык: Английский

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Spatially Continuous Mapping of Forest Canopy Height in Canada by Combining GEDI and ICESat-2 with PALSAR and Sentinel

Remote Sensing, Год журнала: 2022, Номер 14(20), С. 5158 - 5158

Опубликована: Окт. 15, 2022

Continuous large-scale mapping of forest canopy height is crucial for estimating and reporting carbon content, analyzing degradation restoration, or to model ecosystem variables such as aboveground biomass. Over the last years, spaceborne Light Detection Ranging (LiDAR) sensor specifically designed acquire structure information, Global Ecosystem Dynamics Investigation (GEDI), has been used extract information over large areas. Yet, GEDI no spatial coverage most forested areas in Canada other high latitude regions. On hand, LiDAR called Ice, Cloud, Land Elevation Satellite-2 (ICESat-2) provides a global but was not specially developed study ecosystems. Nonetheless, both sensors obtain point-based making spatially continuous estimation very challenging. This compared performance LiDAR, ICESat-2, combined with ALOS-2/PALSAR-2 Sentinel-1 -2 data produce maps year 2020. A set-aside dataset airborne (ALS) from national campaign were accuracy assessment. Both overestimated relation ALS data, had better than ICESat-2 mean difference (MD) 0.9 m 2.9 m, root square error (RMSE) 4.2 5.2 respectively. However, have hemi-boreal forests, captures tall heights expected these forests GEDI. PALSAR-2 HV polarization important covariate predict height, showing great potential L-band comparison C-band optical Sentinel-2. The approach proposed here can be operationally annual that lack coverage.

Язык: Английский

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Modeling carbon storage in urban vegetation: Progress, challenges, and opportunities

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2022, Номер 114, С. 103058 - 103058

Опубликована: Окт. 19, 2022

Urban vegetation (UV) and its carbon storage capacity are critical for terrestrial cycling global sustainable development goals (SDGs). With complex spatial distribution, composition ecological functions, UV is essential climate change. Therefore, improving modeling a research hotspot that deserves extensive investigation. However, the uniqueness of lead to great challenges in modeling, including (1) limitations data algorithms due sensitive urban environments; (2) severe scarcity in-city field observation (e.g., EC towers surveys); (3) difficulty parameter inversion canopy height, LAI, etc.); (4) poor transferability when migrating estimation models from natural scenarios. The progress settings reviewed, with detailed discussions on methods major challenges. We then propose strategies overcome existing challenges, implementing novel improved remote sensing (RS) techniques hyper-spectral, LiDAR, satellites, etc.) obtain enhanced structural functional information UV; nodes earth sensor network, especially distribution settings; leveraging "Model-Data Fusion" technology by integrating big reduce uncertainty estimations. This review provides new insights expected help community achieve better understanding towards neutrality.

Язык: Английский

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Aboveground biomass mapping by integrating ICESat-2, SENTINEL-1, SENTINEL-2, ALOS2/PALSAR2, and topographic information in Mediterranean forests

GIScience & Remote Sensing, Год журнала: 2022, Номер 59(1), С. 1509 - 1533

Опубликована: Сен. 20, 2022

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Язык: Английский

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Assessment of terrain elevation estimates from ICESat-2 and GEDI spaceborne LiDAR missions across different land cover and forest types

Science of Remote Sensing, Год журнала: 2022, Номер 6, С. 100067 - 100067

Опубликована: Сен. 5, 2022

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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A systematic evaluation of multi-resolution ICESat-2 ATL08 terrain and canopy heights in boreal forests

Remote Sensing of Environment, Год журнала: 2023, Номер 291, С. 113570 - 113570

Опубликована: Апрель 12, 2023

The launch of NASA's Ice, Cloud, And Elevation Satellite-2 (ICESat-2) in September 2018 provides the scientific community an opportunity to observe high-resolution and three-dimensional surface elevations with global coverage. ICESat-2's Land Vegetation Height (ATL08) data product focuses on along-track terrain canopy heights observations at a 100 m × 11 spatial resolution. This work expands past ATL08 validation studies assess higher resolution (30 m) version ATL08's height product. new dataset enables mapping fusion Landsat data, but has not previously been validated across large geographic extents. In this paper, we examine accuracy multi-resolution ICESat-2 North America boreal forests using Land, Vegetation, Ice Sensor (LVIS), airborne laser ranging system as reference datasets. Overall, strong agreements elevation were found between LVIS both (RMSEterrain = 2.35 m; biasterrain −0.17 RMSEcanopy 4.17 biascanopy 0.08 30 3.19 0.49; 4.75 0.88 resolutions. We measurements constrained by sensor external conditions during time acquisition lower uncertainties observed from samples along high-intensity ground tracks low topography/slope variabilities. Through work, provide insight into use for characterization northern forests. results our study serve benchmark end users select high-quality variety applications.

Язык: Английский

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Effects of environmental conditions on ICESat-2 terrain and canopy heights retrievals in Central European mountains

Remote Sensing of Environment, Год журнала: 2022, Номер 279, С. 113112 - 113112

Опубликована: Июнь 17, 2022

Язык: Английский

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ICESat-2 data classification and estimation of terrain height and canopy height

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2023, Номер 118, С. 103233 - 103233

Опубликована: Фев. 22, 2023

ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) was launched in 2018 with a photon-counting LiDAR (Light Detection Ranging) system, ATLAS (Advanced Topographic Laser Altimeter System). It is collecting massive earth elevation data all over the world, which has shown potential of large-scale forest monitoring. However, energy emitted by system low, received signals are easily affected noise. Accurate classification photons an important step for parameter retrieval. Given limitations existing photon algorithms areas complex terrain, we proposed improved local outlier factor algorithm rotating search area (LOFR). First, transformed to along-track direction, noise preliminarily filtered out using histogram statistical methods. Next, ground extracted LOF (Local Outlier Factor) horizontal ellipse (LOFE) during initial stage filter that far away from ground. During refined stage, core algorithm, terrain slope calculated according classification. The elliptic then rotated align its long axis slope. Finally, LOFR scores remove signal classified into top-of-canopy photons, canopy photons. results show can effectively classify Both estimated height derived good agreement airborne data. mean absolute error (MAE) relative 1.45 m root square (RMSE) 2.82 m. For validation, correlation coefficient (R2), MAE, RMSE at best study scale (80 m) were 0.86, 1.82 m, 2.72 respectively. These demonstrated improve without prior knowledge terrain. Therefore, it could provide robust approach processing.

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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