The impact of climate change and human activities to vegetation carbon sequestration variation in Sichuan and Chongqing

Environmental Research, Journal Year: 2023, Volume and Issue: 238, P. 117138 - 117138

Published: Sept. 15, 2023

Language: Английский

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Quantifying the relative importance of influencing factors on NPP in Hengduan Mountains of the Tibetan Plateau from 2002 to 2021: A Dominance Analysis

Ecological Informatics, Journal Year: 2024, Volume and Issue: 81, P. 102636 - 102636

Published: May 9, 2024

Net primary productivity (NPP) stands as a crucial indicator of ecosystem health and carbon cycling, shaped by complex interplay climate change anthropogenic disturbances. Understanding the relative contributions these factors is essential for informing strategies aimed at achieving neutrality peaking emissions. In this study, we used Carnegie-Ames-Stanford-Approach (CASA) model to assess NPP dynamics in ecologically fragile area middle Hengduan Mountains on southeastern Tibetan Plateau (TPHM). Dominance Analysis (DA) was employed quantify visualize importance disturbances NPP. Our findings revealed significant annual fluctuations NPP, with 61.8% experiencing improvement although overall trend not statistically significant. Needleleaf forests mixed below medium elevation (< 4000 m) suffered negative impacts from human activity intensity, while vegetation medium-high (4000–5000 showed positive effects. The dominated influencing accounted 49.16% study area, followed combined effects (36.86%) climatic (13.98%). Human particularly nature reserves, emerged predominant driver plateau regions, primarily drove decline river valleys few alpine mountains. Moreover, contributed enhancement most regions. This underscores critical role intensity shaping high-elevation regions Plateau, providing valuable insights ecological conservation stock efforts areas.

Language: Английский

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Estimation of NPP in Huangshan District Based on Deep Learning and CASA Model

Forests, Journal Year: 2024, Volume and Issue: 15(8), P. 1467 - 1467

Published: Aug. 21, 2024

Net primary productivity (NPP) is a key indicator of the health forest ecosystems that offers important information about net carbon sequestration capacity these systems. Precise assessment NPP crucial for measuring fixation and assessing general well-being ecosystems. Due to distinct ecological characteristics various types, accurately understanding delineating distribution types studying NPP. Therefore, an accurate forest-type classification necessary prior calculation ensure accuracy reliability research findings. This study introduced deep learning technology constructed HRNet-CASA framework integrates HRNet model CASA achieve estimation in Huangshan District, City, Anhui Province. Firstly, based on VHR remote sensing images, we utilized classify area into six obtained type map area. Then, combined with climate data data, was used estimate area, comparison field proved simulated well. The experimental findings show novel approach precise estimation. Introducing not only enables but also allows different forests. provides more effective tool environmental protection.

Language: Английский

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Gradient diffusion entropy corrected ALNS optimization for vegetation topology interaction networks

Ecological Indicators, Journal Year: 2025, Volume and Issue: 170, P. 113044 - 113044

Published: Jan. 1, 2025

Language: Английский

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Simulation of Vegetation NPP in Typical Arid Regions Based on the CASA Model and Quantification of Its Driving Factors

Land, Journal Year: 2025, Volume and Issue: 14(2), P. 371 - 371

Published: Feb. 11, 2025

To assess the carbon balance of terrestrial ecosystems, it is crucial to consider net primary productivity (NPP) vegetation. Understanding response NPP in Xinjiang’s vegetation climate factors and human activities essential for ecosystem management, Belt Road Initiative, achieving neutrality goals. Based on CASA model, this study uses meteorological data, DEM land cover employing trend analysis partial derivative methods investigate temporal trends spatial distribution Xinjiang from 2000 2020. Additionally, quantifies contributions fluctuations. The key findings are: (1) average annual 101.52 gC/m2, with an upward trend, showing overall growth rate 0.447 gC/m2/yr. Spatially, higher northern than south, mountainous areas compared basins. (2) Over 21 years, contributed 1.054 gC/m2/yr, while 0.239 gC/m2/yr changes. Among factors, temperature, precipitation, sunshine duration 0.003, 0.169, 0.588 respectively, all positive effects NPP. (3) Forests have highest at 443.96 2.69 When forest converted cropland, loss −1.94 even greater conversion grassland, reaching −17.33 gC/m2. (4) changes are driven by both activities. increased 77.25% area, decreased 22.69%. Climate a impact

Language: Английский

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Altitudinal Differences in Decreasing Heat Deficit at the End of the Growing Season of Alpine Grassland on the Qinghai–Tibetan Plateau from 1982 to 2022

Land, Journal Year: 2025, Volume and Issue: 14(4), P. 758 - 758

Published: April 1, 2025

As a measure of the accumulated heat deficit during growing season transition, cooling degree days (CDDs) play crucial role in regulating vegetation phenology and ecosystem dynamics. However, systematic analyses CDD trends their driving mechanisms remain limited, particularly high-altitude regions where climate variability is pronounced. This study investigated spatiotemporal CDDs from 1982 to 2022 alpine grasslands on Qinghai–Tibetan Plateau (TP) quantified contributions key climatic factors. The results indicate that lower values (<350 °C-days) were predominantly found warm, arid regions, whereas higher (>600 concentrated colder, wetter areas. Temporally, area-averaged exhibited significant decline, decreasing 490.9 °C-days 495.8 at rate 3.8 per year. Elevation plays critical shaping patterns, displaying nonlinear relationship: decrease as elevation increases up 4300 m, beyond which they increase, suggesting transition global climate-driven warming elevations local environmental controls elevations, snow–albedo feedback, topographic effects, atmospheric circulation patterns regulate temperature Tmax was identified dominant driver variation, above while radiation showed consistent positive influence across elevations. In contrast, precipitation had limited spatially inconsistent effect. These findings emphasize complex interactions between elevation, temperature, radiation, trends. By providing long-term perspective variations drivers, this enhances our understanding vegetation–climate ecosystems. offer scientific basis for modeling late-season phenological changes, resilience, land-use planning under ongoing change.

Language: Английский

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Spatiotemporal Dynamics of Vegetation Net Primary Productivity and Its Drivers in China's Three Eco‐Zones and Four Shelterbelts Region

Land Degradation and Development, Journal Year: 2025, Volume and Issue: unknown

Published: May 2, 2025

ABSTRACT Understanding the spatiotemporal dynamics of vegetation carbon stocks in ecologically functional areas and identifying their driving factors remain crucial for informing ecosystem protection restoration efforts. The three eco‐zones four shelterbelts (TEFS) region encompasses critical ecological barriers zones China. Utilizing MODIS NDVI data, alongside climatic topographic variables, we developed regionally optimized models to estimate net primary productivity (NPP) across TEFS from 2000 2023. Subsequently, variability NPP its associated drivers were explored using trend analysis, correlation, residual analysis. results revealed a significant increase approximately 90% between 2023, with an average annual rate 3.46 gC m −2 yr −1 . most rapid increases occurred along Yellow River basin. changes driven by combined effects climate change (CC) human activities (HA) over 24‐year period. While CC contributed positively 67.9% total area, HA had positive impact 75.4% region. Notably, dominated as driver western regions, whereas exerted stronger influence many eastern areas. Enhanced efforts desertification control coastal wetland ecosystems are recommended improve sequestration potential.

Language: Английский

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Vegetation–Lake–Sand Landscape of Northeast China Sandy Land between 1980 and 2022: Pattern, Evolution, and Driving Forces

Sustainability, Journal Year: 2024, Volume and Issue: 16(8), P. 3382 - 3382

Published: April 18, 2024

Northeast China’s sandy region is an arid and semi-arid zone highly susceptible to climate change. Investigating the long-term changes in China land (Northeast land, DBSL) landscape can provide important basis for ecological restoration of this region. This study analyzed remote sensing data DBSL from 1980 2022 explored spatial pattern, evolution, driving mechanisms. In 2022, vegetation was mainly distributed northwest, center, southwest, covering a total area 30,508.82 km2. Areas with high medium cover showed strong aggregation characteristics were whereas those low coverage dispersed widely central Lakes northwest regions, 2736.43 last 42 years, decreased by 24.48%. size, first increased then decreased, overall decreases 35.35%, 19.16%, 6.88%, respectively. The various degrees degradation. Shrinking dry lakes concentrated hinterland. lake changed significantly 1990 2010, decrease 27.41%. contrast, 25.65%, indicating degree desertification. However, 2005 desertification decelerated. most factors evolution socio-economic factors. increase human disturbance will have certain impact on short term. national policy returning farmland fields grasslands affect term, sand excessive animal husbandry be reduced. provides scientific sustainable development China.

Language: Английский

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Analysis of Dynamic Changes in Vegetation Net Primary Productivity and Its Driving Factors in the Two Regions North and South of the Hu Huanyong Line in China

Land, Journal Year: 2024, Volume and Issue: 13(6), P. 722 - 722

Published: May 22, 2024

Human activities and global environmental changes have transformed terrestrial ecosystems, notably increasing vegetation greenness in China. However, this greening is less effective across the Hu Huanyong Line (Hu Line). This study analyzes dynamic driving factors of nine net primary productivities (NPPs) regions divided by using remote sensing data, trend analysis, Geodetector model. Findings reveal that from 2001 to 2022, 38.22% regional NPP China increased, especially Loess Plateau, Sichuan Basin, Northeast Plains, while 2.39% decreased, primarily southeastern region southern Tibet. Grasslands contributed 39.71% north Line, cultivated 50.58% south. The explanatory power on side generally greater than south side. Natural drive changes, with human having impact. Combined factors, particularly climate elevation, significantly enhance (q, 0–1). joint effects elevation precipitation grassland dynamics (q = 0.602) are notable. GDP’s influence broadleaf forests 0.404) significant. respond strongly land use population density, a combined effect q 0.535. Shrubs, alpine vegetation, meadows show minimal response individual < 0.2). These findings offer insights for devising ecological protection measures tailored local conditions.

Language: Английский

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Monitoring the Net Primary Productivity of Togo's Ecosystems in Relation to Changes in Precipitation and Temperature

Published: Aug. 26, 2024

Climate variability influences plant growth. With the variation in atmospheric CO2 concentration leading to global warming, it is urgent monitor performance of ecosystems for optimal carbon sequestration. Net primary productivity (NPP) perfect measurement tool as measures net flux between atmosphere and green plants factors that affect it. This study applied remote sensing techniques, specifically one radiation use efficiency models; CASA model (Carnegie-Ames-Stanford approach) assess spatio-temporal dy-namics NPP Togo from 1987 2022 climatic parameters influence The annual average over 36 years 4565.31 Kg C ha⁻¹. Variability observed with extremes 2017 (6312.26 ha⁻¹) 1996 (3394.29 ha⁻¹). Natural formations identified high-production areas saw their increase 2000 2022. interaction climate change land changes negatively Total Production (PT) 2022, while individually, these positively (58.28% 188.63%). correlation result positive higher light (LUE) (r² = 0.75). Actual evapotranspiration also shows a 0.43). A but weak precipitation, potential 0.20; 0.10 respectively). Temperatures have almost no 0.5). Climatic whole under LUE banner more. helps understand ecosystem context To-go&#039;s commitments reduce greenhouse gas emissions combat change.

Language: Английский

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