China's vegetation restoration programs accelerated vegetation greening on the Loess Plateau

Agricultural and Forest Meteorology, Journal Year: 2024, Volume and Issue: 350, P. 109994 - 109994

Published: April 2, 2024

Language: Английский

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Central African biomass carbon losses and gains during 2010–2019

One Earth, Journal Year: 2024, Volume and Issue: 7(3), P. 506 - 519

Published: Feb. 14, 2024

Language: Английский

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Satellite-Observed Increase in Aboveground Carbon over Southwest China during 2013-2021

Journal of Remote Sensing, Journal Year: 2024, Volume and Issue: 4

Published: Jan. 1, 2024

Over the past 4 decades, Southwest China has fast vegetation growth and aboveground biomass carbon (AGC) accumulation, largely attributed to active implementation of ecological projects. However, been threatened by frequent extreme drought events recently, potentially countering expected large AGC increase caused Here, we used L-band optical depth quantify dynamics over during period 2013-2021. Our results showed a net sink 0.064 [0.057, 0.077] Pg C year −1 (the range represents maximum minimum values), suggesting that acted as an study period. Note loss 0.113 [0.101, 0.136] was found 2013-2014, which could mainly be negative influence droughts on changes in China, particularly Yunnan province. For each land use type (i.e., dense forests, persistent nonforests, afforestation, forestry), largest stock 0.032 [0.028, 0.036] owing their widespread cover rate China. density per unit area), afforestation areas 0.808 [0.724, 0.985] Mg ha , reflecting positive effect increase. Moreover, karst exhibited higher increasing than nonkarst areas, ecosystems have high capacity

Language: Английский

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The potential of optical and SAR time-series data for the improvement of aboveground biomass carbon estimation in Southwestern China’s evergreen coniferous forests

GIScience & Remote Sensing, Journal Year: 2024, Volume and Issue: 61(1)

Published: April 26, 2024

Accurate assessments of forest biomass carbon are invaluable for managing resources, evaluating effects on ecological protection, and achieving goals related to climate change sustainable development. Currently, the integration optical synthetic aperture radar (SAR) data has been extensively utilized in estimating aboveground (AGC), while it is limited by using single-phase remote sensing images. Time-series data, which capture interannual dynamic growth seasonal variations photosynthetic phenology forests, can sufficiently describe characteristics. However, there remains a gap research focusing utilizing satellite-based time-series AGC estimation, especially SAR sensors. This study investigated potential AGC. Here, we undertook nine quantitative experiments estimation from Landsat 8 Sentinel-1 tested several regression algorithms (including multiple linear (MLR), random forests (RF), artificial neural network (ANN), extreme gradient boosting (XGBoost)) explore contributions spatiotemporal features estimation. The results suggested that XGBoost algorithm was suitable with explanatory solid power stable performance. temporal representing trends periodic characteristics (such as coefficients continuous wavelet transform) were more valuable than spatial both sensor types, accounting around 40% ~50% variance compared 17% ~25%. combination produced best performance (R2 = 0.814, RMSE 18.789 Mg C/ha, rRMSE 26.235%), when or alone (optical: R2 0.657 35.317%; SAR: 0.672 34.701%). Feature importance analysis also verified vegetation indices, SWIR 1/2 bands, backscatter VV polarization most critical variables Furthermore, incorporating into modeling illustrated be effective reducing saturation within high-biomass forests. demonstrated superiority While applicability this methodology only evergreen coniferous may provide viable approach needed make full use increasingly better free satellite estimate high accuracy, supporting policy making management

Language: Английский

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Spatiotemporal analysis of tropical vegetation ecosystems and their responses to multifaceted droughts in Mainland Southeast Asia using satellite-based time series

GIScience & Remote Sensing, Journal Year: 2024, Volume and Issue: 61(1)

Published: Aug. 5, 2024

Drought ranks among the costliest of all climate-related phenomena and manifests in various forms, posing significant challenges understanding its influence on agriculture natural ecosystems. Mainland Southeast Asia (MSEA), a region tropical vegetation ecosystems, has become increasingly susceptible to drought hazards. In this study, we characterized assessed dynamics their impacts using correlation analysis explainable machine learning methods under different types elevation zones during dry growing seasons from 2000 2022. Specifically, trend space time. Next, vegetation-drought responses consideration meteorological, hydrological, agricultural droughts land cover characteristics. Lastly, used an method quantify drivers multifaceted undisturbed Our findings revealed that nearly 70% MSEA experienced greening despite large areas vegetative damage years. Vietnam witnessed increasing condition most observed years while declining was mainly found Cambodia southern Laos. Vegetation-drought showed had high sensitivity conditions, stronger were rainfed crop, mixed forest, deciduous forest at lower altitude areas. short-term meteorological disturbances accounted for 93% variations vegetation. Among examined indices, 3-month Standardized Precipitation Evapotranspiration (SPEI-3) Temperature Condition Index (TCI) identified as factors having largest influence, together explaining about 55% variations. These deepen our underlying Such insights could provide valuable information assist national local governments developing effective management adaptation programs safeguard production ecosystems amidst climate challenges.

Language: Английский

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Satellite observed aboveground carbon dynamics in Africa during 2003–2021

Remote Sensing of Environment, Journal Year: 2023, Volume and Issue: 301, P. 113927 - 113927

Published: Dec. 1, 2023

Language: Английский

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Quantitative assessment of various proxies for downscaling coarse-resolution VOD products over the contiguous United States

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 130, P. 103910 - 103910

Published: May 16, 2024

Vegetation Optical Depth (VOD), a vegetation parameter that quantifies the extinction effect of microwaves penetrating canopy, plays crucial role in global-scale biomass monitoring and climate change research. However, spatial gridding existing long-term VOD products is relatively coarse (approximately 25 km), with restrictions on their application at regional scale. High-resolution active-microwave proxies optical indices can potentially be used to disaggregate coarse-resolution VOD, but it unclear which proxy optimal. In this paper, Normalized Difference Index (NDVI) (VH, VV, cross-polarization ratio CR) from Sentinel-1 were quantitatively assessed various frequencies (L-/C-/X-VOD) across contiguous United States (U.S.). The results showed VH (R = 0.80) NDVI 0.77) exhibit high correlation L-VOD products. For temporal correlation, had highest overall performances all products, good correlations also achieved CR and, lesser extent, VH. Further comparisons performance between Brightness Temperature (TB) revealed while TB displayed strong proxies, its such low. contrast, both temporally spatially (e.g., VH). These evidences suggested downscaling using combination other could an alternative promising method estimate high-resolution VOD.

Language: Английский

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Mapping global drought-induced forest mortality based on multiple satellite vegetation optical depth data

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 315, P. 114406 - 114406

Published: Sept. 13, 2024

Language: Английский

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Evaluation of optical and microwave-derived vegetation indices for monitoring aboveground biomass over China

Geo-spatial Information Science, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 16

Published: March 21, 2024

The microwave-derived vegetation optical depth (VOD) products were used to monitor aboveground biomass (AGB) at regional global scales, but the ability of VOD AGB in China is uncertain. This study evaluated sensitivity four (e.g. L-VOD, IB-VOD, LPDR-VOD, and Liu-VOD) indices (VI) NDVI, EVI, LAI, tree cover from MODIS) across China. Our results showed product has highest spatial agreement with reference AGBs (indicated by median correlation value 0.85), followed L-VOD (with a 0.80), which performs better than other VIs VODs. Further comparisons between estimated computed using fitted logistic regression that estimations outperformed VODs over most types (except forest), indicated higher 0.86 0.83 lower RMSD 23.9 27.3 Mg/ha, respectively. good performance could be partly due not independent AGBs. can explained its characteristics entire canopy (including woody component), relative VIs. Among six products, Saatchi-WT Saatchi-RF found have best correlations demonstrates microwave VODs, particularly are effective proxies for large-scale monitoring

Language: Английский

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Dominant role of the non‐forest woody vegetation in the post 2015/16 El Niño tropical carbon recovery

Global Change Biology, Journal Year: 2024, Volume and Issue: 30(7)

Published: July 1, 2024

Abstract The extreme dry and hot 2015/16 El Niño episode caused large losses in tropical live aboveground carbon (AGC) stocks. Followed by climatic conditions conducive to high vegetation productivity since 2016, AGC are expected recover from during the episode; however, recovery rate its spatial distribution remain unknown. Here, we used low‐frequency microwave satellite data track changes, showed that stocks returned pre‐El levels end of 2020, resulting an sink Pg C year −1 2014–2020. This was dominated strong increases ( ) non‐forest woody 2016–2020, compensating forest attributed event, loss, degradation. Our findings highlight is increasingly important contributor interannual decadal variability global cycle.

Language: Английский

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