A regional-level spatiotemporal perspective of land use and land cover change impact on forest aboveground biomass in three gorges reservoir region, China DOI Creative Commons
Muhammad Nouman Khan, Yumin Tan, Ahmad Ali Gul

et al.

Geocarto International, Journal Year: 2024, Volume and Issue: 39(1)

Published: Jan. 1, 2024

Language: Английский

Global carbon balance of the forest: satellite-based L-VOD results over the last decade DOI Creative Commons
Jean‐Pierre Wigneron, Philippe Ciais, Xiaojun Li

et al.

Frontiers in Remote Sensing, Journal Year: 2024, Volume and Issue: 5

Published: May 10, 2024

Monitoring forest carbon (C) stocks is essential to better assess their role in the global balance, and model predict long-term trends inter-annual variability atmospheric CO2 concentrations. On a national scale, inventories (NFIs) can provide estimates of stocks, but these are only available certain countries, limited by time lags due periodic revisits, cannot spatially continuous mapping forests. In this context, remote sensing offers many advantages for monitoring above-ground biomass (AGB) on scale with good spatial (50–100 m) temporal (annual) resolutions. Remote has been used several decades monitor vegetation. However, traditional methods AGB using optical or microwave sensors affected saturation effects moderately densely vegetated canopies, limiting performance. Low-frequency passive less effects: occurs at levels around 400 t/ha L-band (frequency 1.4 GHz). Despite its coarse resolution order 25 km × km, method based L-VOD (vegetation depth L-band) index recently established itself as an approach annual variations continental scale. Thus, applied continents biomes: tropics (especially Amazon Congo basins), boreal regions (Siberia, Canada), Europe, China, Australia, etc. no reference study yet published analyze detail terms capabilities, validation results. This paper fills gap presenting physical principles calculation, analyzing performance reviewing main applications tracking balance vegetation over last decade (2010–2019).

Language: Английский

Citations

13
Advanced soil conservation for African drylands: from erosion models to management theories DOI

Suleiman Usman

Pedosphere, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

1
The responses of vegetation water use efficiency to biomass density and CO2 balance in dryland of Central Asia during 21st century DOI Creative Commons
Alphonse Kayiranga, Xi Chen, Xuexi Ma

et al.

Ecological Indicators, Journal Year: 2025, Volume and Issue: 171, P. 113070 - 113070

Published: Feb. 1, 2025

Language: Английский

Citations

0
Construction and analysis of atmospheric visibility and fog-haze datasets in China (2001−2023) based on machine learning models DOI
Haifeng Xu,

Wenhui Luo,

Jinji Ma

et al.

Atmospheric Research, Journal Year: 2025, Volume and Issue: unknown, P. 108160 - 108160

Published: April 1, 2025

Language: Английский

Citations

0
State of the art and for remote sensing monitoring of carbon dynamics in African tropical forests DOI Creative Commons
Thomas Bossy, Philippe Ciais,

Solène Renaudineau

et al.

Frontiers in Remote Sensing, Journal Year: 2025, Volume and Issue: 6

Published: March 17, 2025

African tropical forests play a crucial role in global carbon dynamics, biodiversity conservation, and climate regulation, yet monitoring their structure, diversity, stocks changes remains challenging. Remote sensing techniques, including multi-spectral data, lidar-based canopy height vertical structure detection, radar interferometry, have significantly improved our ability to map forest composition, estimate biomass, detect degradation deforestation features at finer scale. Machine learning approaches further enhance these capabilities by integrating multiple data sources produce maps of attributes track over time. Despite advancements, uncertainties remain due limited ground-truth validation, the structural complexity large spatial heterogeneity forests. Future developments remote should examine how multi-sensor integration high-resolution from instruments such as Planet, Tandem-X, SPOT AI methods can refine storage function maps, large-scale tree biomass improve detection down level. These advancements will be essential for supporting science-based decision-making conservation mitigation.

Language: Английский

Citations

0
Spatiotemporal evolution and risk thresholds of PM2.5 components in China from the human health perspective DOI
Haifeng Xu,

Wenhui Luo,

Cheng Dai

et al.

Environmental Pollution, Journal Year: 2025, Volume and Issue: unknown, P. 126194 - 126194

Published: April 1, 2025

Language: Английский

Citations

0
Dynamics of carbon sequestration in vegetation affected by large-scale surface coal mining and subsequent restoration DOI Creative Commons

Yaling Xu,

Jun Li, Chengye Zhang

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: June 12, 2024

Abstract Surface coal development activities include mining and ecological restoration, which significantly impact regional carbon sinks. Quantifying the dynamic impacts on sequestration in vegetation (VCS) during has been challenging. Here, we provided a novel approach to assess dynamics of VCS affected by large-scale surface subsequent restoration. This effectively overcomes limitations imposed lack finer scale long-time series data through transformation. We found that directly decreased 384.63 Gg CO 2 , while restoration increased 192.51 between 2001 2022. As 2022, deficit at areas still had 1966.7 . The study highlights complete requires compensating not only for loss year destruction but also ongoing accumulation losses throughout lifecycle. findings deepen insights into intricate relationship resource environmental protection.

Language: Английский

Citations

3
Global L-band equivalent AI-based vegetation optical depth dataset DOI Creative Commons
Olya Skulovich, Xiaojun Li, Jean‐Pierre Wigneron

et al.

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

Published: Aug. 28, 2024

The L-band vegetation optical depth data garners significant interest for its ability to effectively monitor vegetation, thanks minimal saturation within this frequency range. However, the existing datasets have limited temporal coverage, constrained by start of respective satellite missions. Global equivalent AI-Based Vegetation Optical Depth or GLAB-VOD is a global long-term consistent microwave dataset created using machine learning expand SMAP-IB VOD coverage from 2015-2020 2002-2020. has an 18-day resolution and 25 km spatial on EASE2 grid covers An auxiliary daily brightness temperature product, called GLAB-TB, developed in parallel ensures consistency product across time periods with different satellites. As result consistency, can be used study regional trends biomass utilized any other applications where necessary. shows excellent correlation globally when compared (up R = 0.92) canopy height (R 0.93), outperforming target dataset, VOD.

Language: Английский

Citations

2
Soil Moisture Content Inversion Model on the Basis of Sentinel Multispectral and Radar Satellite Remote Sensing Data DOI
Fei Guo, Zugui Huang, Xiaolong Su

et al.

Journal of soil science and plant nutrition, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 25, 2024

Language: Английский

Citations

2
A regional-level spatiotemporal perspective of land use and land cover change impact on forest aboveground biomass in three gorges reservoir region, China DOI Creative Commons
Muhammad Nouman Khan, Yumin Tan, Ahmad Ali Gul

et al.

Geocarto International, Journal Year: 2024, Volume and Issue: 39(1)

Published: Jan. 1, 2024

Language: Английский

Citations

1