Spatiotemporal analysis of AGB and BGB in China: Responses to climate change under SSP scenarios

Geoscience Frontiers, Journal Year: 2025, Volume and Issue: unknown, P. 102038 - 102038

Published: March 1, 2025

Language: Английский

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Spatiotemporal Dynamics and Driving Mechanism of Aboveground Biomass Across Three Alpine Grasslands in Central Asia over the Past 20 Years Using Three Algorithms

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(3), P. 538 - 538

Published: Feb. 5, 2025

Aboveground biomass (AGB) is a sensitive indicator of grassland resource quality and ecological degradation. However, accurately estimating AGB at large scales to reveal long-term trends remains challenging. Here, single-factor parametric models, multi-factor non-parametric models (Random Forest) were developed for three types (alpine meadow, alpine grassland, swampy meadow) in the Bayanbuluk Grassland using MODIS satellite data environmental factors, including climate topography. A 10-fold cross-validation method was employed assess accuracy stability these an remote sensing inversion model established estimate from 2005 2024. Moreover, BEAST mutation test, Theil–Sen median trend analysis, Mann–Kendall test used analyse temporal AGB, identify years points, explore changes across entire study period (2005–2024) 5-year intervals, considering influence climatic factors. The results indicated that machine learning (RF) outperformed both with specific improvements R2 RMSE all types. For instance, RF achieved 0.802 grasslands, outperforming 0.531. overall spatial distribution exhibited heterogeneity, gradual increase northwest southeast over period. Interannual fluctuated significantly, increasing trend. Notably, 2015 2019, 78% area showed nonsignificant AGB. Specifically, 46.7% meadow 23% 8.3% non-significant increases. Further, temperature found be dominant driver stronger effect on meadows grasslands than meadows. This likely due relatively constant moisture levels meadows, where precipitation plays more prominent role. provides comprehensive assessment trends, analyses, which will inform future management.

Language: Английский

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Progress and Limitations in Forest Carbon Stock Estimation Using Remote Sensing Technologies: A Comprehensive Review

Forests, Journal Year: 2025, Volume and Issue: 16(3), P. 449 - 449

Published: March 2, 2025

Forests play a key role in carbon sequestration and oxygen production. They significantly contribute to peaking neutrality goals. Accurate estimation of forest stocks is essential for precise understanding the capacity ecosystems. Remote sensing technology, with its wide observational coverage, strong timeliness, low cost, stock research. However, challenges data acquisition processing include variability, signal saturation dense forests, environmental limitations. These factors hinder accurate estimation. This review summarizes current state research on from two aspects, namely remote methods, highlighting both advantages limitations various sources models. It also explores technological innovations cutting-edge field, focusing deep learning techniques, optical vegetation thickness impact forest–climate interactions Finally, discusses including issues related quality, model adaptability, stand complexity, uncertainties process. Based these challenges, paper looks ahead future trends, proposing potential breakthroughs pathways. The aim this study provide theoretical support methodological guidance researchers fields.

Language: Английский

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Assessing Above-Ground Biomass Dynamics and Carbon Sequestration Potential Using Machine Learning and Spaceborne LiDAR in Hilly Conifer Forests of Mansehra District, Pakistan

Forests, Journal Year: 2025, Volume and Issue: 16(2), P. 330 - 330

Published: Feb. 13, 2025

Consistent and accurate data on forest biomass carbon dynamics are essential for optimizing sequestration, advancing sustainable management, developing natural climate solutions in various ecosystems. This study quantifies the designated forests based GEDI LiDAR datasets with a unique compartment-level monitoring of unexplored hilly areas Mansehra. The integration multisource explanatory variables, employing machine learning models, adds further innovation to reliable above ground (AGB) estimation. Integrating Landsat-9 vegetation indices ancillary improved estimation, random algorithm yielding best performance (R2 = 0.86, RMSE 28.03 Mg/ha, MAE 19.54 Mg/ha). Validation field point-to-point basis estimated mean above-ground 224.61 closely aligning measurement 208.13 Mg/ha 0.71). overall AGB model 189.42 moist temperate area. A critical deficit sequestration potential was analysed, 2022, at 19.94 thousand tons, 0.83 tons nullify CO2 emissions (20.77 tons). proposes estimation reliability offers insights into potential, suggesting policy shift decision-making change mitigation policies.

Language: Английский

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A multimodal and meta-learning approach for improved estimation of 3D vegetation structure from satellite imagery

Applied Geomatics, Journal Year: 2025, Volume and Issue: unknown

Published: March 7, 2025

Language: Английский

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Forest aboveground biomass estimation using deep learning data fusion of ALS, multispectral, and topographic data

International Journal of Remote Sensing, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 39

Published: April 22, 2025

Language: Английский

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Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 312, P. 114310 - 114310

Published: July 28, 2024

There is a pressing need for well-informed management to reduce wildfire hazard and restore fire's beneficial ecological role in the Mediterranean- temperate-climate forests of California, USA. These efforts rely upon accessibility high spatial temporal resolution data on biomass canopy fuel parameters such as base height (CBH), mean height, bulk density (CBD), cover, leaf area index (LAI). Remote sensing using unoccupied aerial system Structure-from-Motion (UAS-SfM) presents promising technology this application due its accessibility, relatively low cost, possibility cadence. However, date, method has not been studied complex mosaic forest types found across California. In study we examined capacity structural multispectral information obtained from UAS-SfM, conjunction with machine learning methods, model aboveground an area-based approach multiple sites representing diversity Based correlations field measurements, separated into vertical (biomass, CBH, height) horizontal (LAI, CBD, cover) groups. UAS-SfM random models performed well modelling structure fuels (R2 0.69–0.75). exhibited strong performance comparison ALS, when transferred novel site. Vertical predictors were prominent these models, did improve addition spectral predictors. mainly used raster-based indices (primarily NDVI) had 0.49–0.59). addition, underperformed ALS poor applied When region widespread coverage, both groups successfully produced contiguous maps that could be fire behavior or decision making monitoring. findings indicate without sensors, suited mapping vertical-structure diverse landscapes supporting wide range types. contrast, identification variables suggests potential multi- hyperspectral sensors high-resolution satellite imagery meeting needs.

Language: Английский

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Utilising RGB drone imagery and vegetation indices for accurate above-ground biomass estimation: a case study of the cradle nature reserve, Gauteng Province, South Africa

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

Published: Jan. 1, 2024

Language: Английский

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Forest Biomass Estimation Using Deep Learning Data Fusion of Lidar, Multispectral, and Topographic Data Remote Sensing of Environment

Published: Jan. 1, 2024

Language: Английский

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