Assessment of the Impact of Coastal Wetland Saltmarsh Vegetation Types on Aboveground Biomass Inversion DOI Creative Commons
Nan Wu, Chao Zhang, Zhuo Wei

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(24), P. 4762 - 4762

Published: Dec. 20, 2024

Coastal wetlands play an important carbon sequestration role in China’s “carbon peaking” and neutrality” goals. Monitoring aboveground biomass (AGB) is crucial for wetland management. Satellite remote sensing enables efficient retrieval of AGB. However, a variety statistical models can be used inversion, depending on factors such as the vegetation type inversion method. In this study, Landsat 8 Operational Land Imager (OLI) images were preprocessed study area through radiation calibration atmospheric correction modeling. terms model selection, 13 different models, including univariate regression model, multiple machine learning compared their accuracy estimating various types under respective optimal parameters. The findings revealed that: (1) varied across types, with estimates decreasing order Scirpus spp. > Spartina alterniflora Phragmites australis; (2) overall modeling, without distinguishing addressed challenges limited samples availability sampling difficulty. Among them, random forest outperformed others wet dry AGB R2 values 0.806 0.839, respectively. (3) Comparatively, individual modeling better reflect each type, especially spp., whose RMSE increased by 0.248 11.470 g/m2, This evaluates impact coastal saltmarsh estimation, providing insights into dynamics valuable support conservation restoration, potential contributions to global habitat assessment international policies like 30x30 Conservation Agenda.

Language: Английский

Cross-scale observation of riparian vegetation: Testing the potential of satellite-UAV-Field integrated observations for large-scale herbaceous species DOI Creative Commons

Weiwei Jiang,

Chenyu Li, Henglin Xiao

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103016 - 103016

Published: Jan. 1, 2025

Language: Английский

Citations

1
Spatiotemporal analysis of AGB and BGB in China: Responses to climate change under SSP scenarios DOI Creative Commons
Chuanmei Zhu,

Yupu Li,

Jianli Ding

et al.

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

Published: March 1, 2025

Language: Английский

Citations

1
Spatiotemporal Dynamics and Driving Mechanism of Aboveground Biomass Across Three Alpine Grasslands in Central Asia over the Past 20 Years Using Three Algorithms DOI Creative Commons
Xu Wang, Yansong Li, Yanming Gong

et al.

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

Citations

0
Modeling pine forest growing stock volume in subtropical regions of China using airborne Lidar data DOI Creative Commons

Zige Lan,

Xiandie Jiang, Guiying Li

et al.

GIScience & Remote Sensing, Journal Year: 2025, Volume and Issue: 62(1)

Published: March 19, 2025

Language: Английский

Citations

0
Forest aboveground biomass retrieval integrating ICESat-2, Landsat-8, and environmental factors DOI Creative Commons

Shiping Ma,

Jisheng Xia, Chun Wang

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103194 - 103194

Published: May 1, 2025

Language: Английский

Citations

0
Research on Forage–Livestock Balance in the Three-River-Source Region Based on Improved CASA Model DOI Creative Commons

Chenlu Hu,

Yichen Tian,

Kai Yin

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(20), P. 3857 - 3857

Published: Oct. 17, 2024

As an important ecological barrier and a crucial base for animal husbandry in China, the forage–livestock balance Three-River-Source Region (TRSR) directly impacts both degradation recovery of grassland. This study examines TRSR over past 13 years (2010–2022) by calculating theoretical actual livestock carrying capacity, thereby providing scientific basis regional policies. Firstly, Carnegie–Ames–Stanford Approach (CASA) model was improved to fit specific characteristics alpine grassland ecosystem TRSR. enhanced subsequently used calculate net primary productivity (NPP) grassland, from which yield capacity were derived. Secondly, calculated spatialized based on number year-end livestock. Finally, pressure index determined using capacity. The results revealed several key findings: (1) average NPP 145.44 gC/m2, 922.7 kg/hm2, 0.55 SU/hm2 2010 2022. Notably, all three metrics showed increasing trend years, indicates rise vegetation activities. (2) 13-year period 0.46 SU/hm2, showing decreasing whole. spatial distribution displayed pattern higher east lower west. (3) Throughout generally maintained balance, with 0.96 (insufficient). However, is rise, serious overloading observed western part Qumalai County northern Tongde County. Slight also noted Zhiduo, Maduo, Zeku Counties. Tanggulashan Town, Qumalai, Maduo Counties significant increases pressure, while Zaduo eastern regions experienced decreases. In conclusion, this not only provides feasible technical methods assessing managing but contributes significantly sustainable development region’s industry.

Language: Английский

Citations

2
Estimation of Maize Biomass at Multi-Growing Stage Using Stem and Leaf Separation Strategies with 3D Radiative Transfer Model and CNN Transfer Learning DOI Creative Commons
Dan Zhao, Hao Yang, Guijun Yang

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(16), P. 3000 - 3000

Published: Aug. 15, 2024

The precise estimation of above-ground biomass (AGB) is imperative for the advancement breeding programs. Optical variables, such as vegetation indices (VI), have been extensively employed in monitoring AGB. However, limited robustness inversion models remains a significant impediment to widespread application UAV-based multispectral remote sensing AGB inversion. In this study, novel stem–leaf separation strategy delineated. Convolutional neural network (CNN) and transfer learning (TL) methodologies are integrated estimate leaf (LGB) across multiple growth stages, followed by development an allometric model estimating stem (SGB). To enhance precision LGB inversion, large-scale data image simulation framework over heterogeneous scenes (LESS) model, which three-dimensional (3D) radiative (RTM), was utilized simulate more extensive canopy spectral dataset, characterized broad distribution spectra. CNN pre-trained order gain prior knowledge, knowledge transferred re-trained with subset field-observed samples. Finally, SGB various stages. further validate generalizability, transferability, predictive capability proposed method, field samples from 2022 2023 were target tasks. results demonstrated that 3D RTM + TL method outperformed best estimation, achieving R² 0.73 RMSE 72.5 g/m² 0.84 56.4 dataset. contrast, PROSAIL yielded 0.45 134.55 0.74 61.84 accuracy poor when using only field-measured train without simulated data, values 0.30 0.74. Overall, dataset transferring it new significantly enhanced generalization. Additionally, model’s resulted 0.87 120.87 86.87 exhibiting satisfactory results. Separate both based on strategies promising This can be extended monitor other critical variables.

Language: Английский

Citations

1
Assessment of the Impact of Coastal Wetland Saltmarsh Vegetation Types on Aboveground Biomass Inversion DOI Creative Commons
Nan Wu, Chao Zhang, Zhuo Wei

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(24), P. 4762 - 4762

Published: Dec. 20, 2024

Coastal wetlands play an important carbon sequestration role in China’s “carbon peaking” and neutrality” goals. Monitoring aboveground biomass (AGB) is crucial for wetland management. Satellite remote sensing enables efficient retrieval of AGB. However, a variety statistical models can be used inversion, depending on factors such as the vegetation type inversion method. In this study, Landsat 8 Operational Land Imager (OLI) images were preprocessed study area through radiation calibration atmospheric correction modeling. terms model selection, 13 different models, including univariate regression model, multiple machine learning compared their accuracy estimating various types under respective optimal parameters. The findings revealed that: (1) varied across types, with estimates decreasing order Scirpus spp. > Spartina alterniflora Phragmites australis; (2) overall modeling, without distinguishing addressed challenges limited samples availability sampling difficulty. Among them, random forest outperformed others wet dry AGB R2 values 0.806 0.839, respectively. (3) Comparatively, individual modeling better reflect each type, especially spp., whose RMSE increased by 0.248 11.470 g/m2, This evaluates impact coastal saltmarsh estimation, providing insights into dynamics valuable support conservation restoration, potential contributions to global habitat assessment international policies like 30x30 Conservation Agenda.

Language: Английский

Citations

1