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

Environmental Research Letters, Journal Year: 2022, Volume and Issue: 17(2), P. 024016 - 024016

Published: Jan. 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.

Language: Английский

---

Neural network guided interpolation for mapping canopy height of China's forests by integrating GEDI and ICESat-2 data

Remote Sensing of Environment, Journal Year: 2021, Volume and Issue: 269, P. 112844 - 112844

Published: Dec. 11, 2021

Language: Английский

---

A new SMAP soil moisture and vegetation optical depth product (SMAP-IB): Algorithm, assessment and inter-comparison

Remote Sensing of Environment, Journal Year: 2022, Volume and Issue: 271, P. 112921 - 112921

Published: Feb. 2, 2022

Passive microwave remote sensing at L-band (1.4 GHz) provides an unprecedented opportunity to estimate global surface soil moisture (SM) and vegetation water content (via the optical depth, VOD), which are essential monitor Earth carbon cycles. Currently, only two space-borne radiometer missions operating: Soil Moisture Ocean Salinity (SMOS) Active (SMAP) in orbit since 2009 2015, respectively. This study presents a new mono-angle retrieval algorithm (called SMAP-INRAE-BORDEAUX, hereafter SMAP-IB) of SM VOD (L-VOD) from dual-channel SMAP radiometric observations. The retrievals based on L-MEB (L-band Microwave Emission Biosphere) model is forward SMOS-IC official SMOS algorithms. SMAP-IB product aims providing good performances for both L-VOD while remaining independent auxiliary data: neither modelled data nor indices used as input algorithm. Inter-comparison with other products (i.e., MT-DCA, SMOS-IC, versions DCA SCA-V extracted passive Level 3 product) suggested that performed well L-VOD. In particular, presented higher scores (R = 0.74) capturing temporal trends in-situ observations ISMN (International Network) during April 2015–March 2019, followed by MT-DCA 0.71). While lowest ubRMSD value was obtained version (0.056 m3/m3), best R, (~ 0.058 m3/m3) bias (0.002 when considering (e.g., NDVI). SMAP-IB, were correlated (spatially) aboveground biomass tree height, spatial R values ~0.88 ~ 0.90, All three exhibited smooth non-linear density distribution linear relationship especially high levels, datasets incorporating information algorithms DCA) showed obvious saturation effects. It expected this can facilitate fusion obtain long-term continuous earth observation products.

Language: Английский

---

Mapping high-resolution forest aboveground biomass of China using multisource remote sensing data

GIScience & Remote Sensing, Journal Year: 2023, Volume and Issue: 60(1)

Published: April 26, 2023

Forest aboveground biomass (AGB) estimation is crucial for carbon cycle studies and climate change mitigation actions. However, because of limitations in timely reliable forestry surveys high-resolution remote sensing data, producing a fine resolution spatial continuous forest AGB map China challenging. Here, we combined 4789 ground-truth measurements multisource data such as recently released canopy-height product, optical spectral indexes, topographic climatological soil properties to train random regression model at 30-m resolution. The accuracy the estimated can yield R2 = 0.67 RMSE 70.71 Mg/ha. nationwide estimates show that average total storage were 97.57 ± 23.85 Mg/ha 11.06 Pg C year 2019, respectively. value uncertainty ranges from 0.68 37.80 Mg/ha, was 4.32 1.75 this study correspond reasonably well with derived grassland statistical yearbook provincial level (R2 0.61, 30.15 Mg/ha). In addition, found previous products generally underestimate compared our pixel-level measurements. provides an important alternative source be used baseline management conservation practices.

Language: Английский

---

Mapping forest canopy fuel parameters at European scale using spaceborne LiDAR and satellite data

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 303, P. 114005 - 114005

Published: Jan. 30, 2024

Spatially explicit data on forest canopy fuel parameters provide critical information for wildfire propagation modelling, emission estimations and risk assessment. LiDAR observations enable accurate retrieval of the vertical structure vegetation, which makes them an excellent alternative characterising structures. In most cases, parameterisation has been based Airborne Laser Scanning (ALS) observations, are costly best suited local research. Spaceborne acquisitions overcome limited spatiotemporal coverage airborne systems, as they can cover much wider geographical areas. However, do not continuous data, requiring spatial interpolation methods to obtain wall-to-wall information. We developed a two-step, easily replicable methodology estimate entire European territory, from Global Ecosystem Dynamics Investigation (GEDI) sensor, onboard International Space Station (ISS). First, we simulated GEDI pseudo-waveforms discrete ALS about plots. then used metrics derived mean height (Hm), (CC) base (CBH), national inventory reference. The RH80 metric had strongest correlation with Hm all types (r = 0.96–0.97, Bias −0.16-0.30 m, RMSE 1.53–2.52 rRMSE 13.23–19.75%). A strong was also observed between ALS-CC GEDI-CC 0.94, −0.02, 0.09, 16.26%), whereas weaker correlations were obtained CBH 0.46, 0 0.89 39.80%). second stage generate maps continent Europe at resolution 1 km using GEDI-based estimates within-fuel polygons covered by footprints. available some (mainly Northern latitudes, above 51.6°N). these estimated random regression models multispectral SAR imagery biophysical variables. Errors higher than direct retrievals, but still within range previous results 0.72–0.82, −0.18-0.29 3.63–4.18 m 28.43–30.66% Hm; r 0.82–0.91, 0, 0.07–0.09 10.65–14.42% CC; 0.62–0.75, 0.01–0.02 0.60–0.74 19.16–22.93% CBH). Uncertainty provided grid level, purpose considered individual errors each step in methodology. final outputs, publicly (https://doi.org/10.21950/KTALA8), estimation three modelling crown fire potential demonstrate capacity improve characterisation models.

Language: Английский

---

DeltaDTM: A global coastal digital terrain model

Scientific Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: March 6, 2024

Abstract Coastal elevation data are essential for a wide variety of applications, such as coastal management, flood modelling, and adaptation planning. Low-lying areas (found below 10 m +Mean Sea Level (MSL)) at risk future extreme water levels, subsidence changing weather patterns. However, current freely available datasets not sufficiently accurate to model these risks. We present DeltaDTM, global Digital Terrain Model (DTM) in the public domain, with horizontal spatial resolution 1 arcsecond (∼30 m) vertical mean absolute error (MAE) 0.45 overall. DeltaDTM corrects CopernicusDEM spaceborne lidar from ICESat-2 GEDI missions. Specifically, we correct bias CopernicusDEM, apply filters remove non-terrain cells, fill gaps using interpolation. Notably, our classification approach produces more results than regression methods recently used by others DEMs, that achieve an overall MAE 0.72 best. conclude will be valuable resource impact modelling other applications.

Language: Английский

---

Soil moisture controls over carbon sequestration and greenhouse gas emissions: a review

npj Climate and Atmospheric Science, Journal Year: 2025, Volume and Issue: 8(1)

Published: Jan. 14, 2025

This literature review synthesizes the role of soil moisture in regulating carbon sequestration and greenhouse gas emissions (CS-GHG). Soil directly affects photosynthesis, respiration, microbial activity, organic matter dynamics, with optimal levels enhancing storage while extremes, such as drought flooding, disrupt these processes. A quantitative analysis is provided on effects CS-GHG across various ecosystems climatic conditions, highlighting a "Peak Decline" pattern for CO₂ at 40% water-filled pore space (WFPS), CH₄ N₂O peak higher (60–80% around 80% WFPS, respectively). The also examines ecosystem models, discussing how dynamics are incorporated to simulate nutrient cycling. Sustainable management practices, including conservation agriculture, agroforestry, optimized water management, prove effective mitigating GHG by maintaining ideal levels. further emphasizes importance advancing multiscale observations feedback modeling through high-resolution remote sensing ground-based data integration, well hybrid frameworks. interactive model-experiment framework emerges promising approach linking experimental model refinement, enabling continuous improvement predictions. From policy perspective, shifting focus from short-term agricultural productivity long-term crucial. Achieving this shift will require financial incentives, robust monitoring systems, collaboration among stakeholders ensure sustainable practices effectively contribute climate mitigation goals.

Language: Английский

---

Calibration of GEDI footprint aboveground biomass models in Mediterranean forests with NFI plots: A comparison of approaches

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 375, P. 124313 - 124313

Published: Jan. 31, 2025

Observations from the NASA Global Ecosystem Dynamics Investigation (GEDI) provide global information on forest structure and biomass. Footprint-level predictions of aboveground biomass density (AGBD) in GEDI mission are based training data sourced sparsely distributed field plots coincident with airborne laser scanning surveys. National Forest Inventories (NFI) rarely used to calibrate footprint models because their sampling positional accuracy prevent accurate colocation or ALS. This omission can limit harmonization jurisdictional estimates NFI's GEDI; however, there methods available improve NFI footprints. Focusing Mediterranean forests Spain, we compared different approaches collocation data: (i) simulated waveforms ALS; (ii) nearest-neighbor on-orbit waveforms; (iii) imputed plot locations using a novel geostatistical method. These potential solutions local performance address systematic deviations between estimates. We assess advantages limitations these locally quantify impact geolocation errors reference data. The new each method were predict level AGBD, which then gridded for province North-West Spain. It was found that imputation approach is not sensitive common geolocation, but it outperform ALS-based simulation some cases, highlighting benefit multiple footprints proximate improving predictions. research provides users benchmark techniques locally-calibrate models.

Language: Английский

---

Assessing the agreement of ICESat-2 terrain and canopy height with airborne lidar over US ecozones

Remote Sensing of Environment, Journal Year: 2021, Volume and Issue: 266, P. 112711 - 112711

Published: Sept. 24, 2021

Language: Английский

---

Fusing GEDI with earth observation data for large area aboveground biomass mapping

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2022, Volume and Issue: 115, P. 103108 - 103108

Published: Nov. 17, 2022

An accurate and spatially explicit estimation of biomass is required for sustainable forest management, prevention biodiversity loss, carbon accounting climate change mitigation. This study offers a methodology to generate wall-to-wall aboveground density (AGBD) maps that exclusively relies on open access earth observation (EO) data. Specifically, spaceborne Global Ecosystem Dynamics Investigation (GEDI) LiDAR data were fused with Sentinel-1 synthetic-aperture radar, Sentinel-2 multispectral, elevation, land cover produce Australia the United States 2020. The gradient boosting machine learning framework was applied predict AGBD its uncertainty at resolutions 100 m 200 m. performance models based (1) imagery (2) combination elevation compared. most model identified using Bayesian hyperparameter optimization 5-fold cross-validation. analysis resulted in estimated coefficient determination (R2) 0.61 – 0.71, root-mean-square error (RMSE) 59 86 Mg/ha, relative (RMSE%) 45 80%. accuracy improved addition data: R2 0.66 0.74, RMSE 55 81 RMSE% 41 77%. It found cover-derived predictors important estimating annual AGBD. proposed method also reduced saturation effect, which common high areas when predicting satellite imagery. Prediction produced this could serve as baseline current AGB stocks forested lands equal 9.8 Pg 37.1 States, respectively. Overall, research highlights methodological opportunities combining EO yield more globally applicable through fusion.

Language: Английский

---