Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103011 - 103011
Published: Jan. 1, 2025
Language: Английский
The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 929, P. 172603 - 172603
Published: April 21, 2024
Language: Английский
Citations
18
Forests, Journal Year: 2023, Volume and Issue: 14(3), P. 620 - 620
Published: March 20, 2023
The Yellow River Basin (YRB) is a fundamental ecological barrier in China and one of the regions where environment relatively fragile. Studying spatio-temporal variations vegetation coverage YRB their driving factors through long-time-series dataset great significance to eco-environmental construction sustainable development YRB. In this study, we sought characterize variation its climatic from 2001 2020 by constructing new kernel normalized difference index (kNDVI) based on MOD13 A1 V6 data Google Earth Engine (GEE) platform. Using Theil–Sen median trend analysis, Mann–Kendall test, Hurst exponent, investigated characteristics future trends coverage. were obtained via partial correlation analysis complex associations between kNDVI both temperature precipitation. results reveal following: spatial distribution pattern showed that was high southeast low northwest. Vegetation fluctuated 2020, with main significant increasing growth at rate 0.0995/5a. response strong YRB, stronger precipitation than temperature. Additionally, found be non-climatic factors, which mainly distributed Henan, southern Shaanxi, Shanxi, western Inner Mongolia, Ningxia, eastern Gansu. areas driven northern Shandong, Qinghai, Gansu, northeastern Sichuan. Our findings have implications for ecosystem restoration
Language: Английский
Citations
29
Remote Sensing, Journal Year: 2024, Volume and Issue: 16(7), P. 1280 - 1280
Published: April 5, 2024
Detecting and attributing vegetation variations in the Yellow River Basin (YRB) is vital for adjusting ecological restoration strategies to address possible threats posed by changing environments. On basis of kernel normalized difference index (kNDVI) key climate drivers (precipitation (PRE), temperature (TEM), solar radiation (SR), potential evapotranspiration (PET)) basin during period from 1982 2022, we utilized multivariate statistical approach analyze spatiotemporal patterns dynamics, identified variables, discerned respective impacts change (CC) human activities (HA) on these variations. Our analysis revealed a widespread greening trend across 93.1% YRB, with 83.2% exhibiting significant increases kNDVI (p < 0.05). Conversely, 6.9% vegetated areas displayed browning trend, particularly concentrated alpine urban areas. With Hurst exceeding 0.5 97.5% areas, YRB tends be extensively greened future. Climate variability emerges as pivotal determinant shaping diverse spatial temporal patterns, PRE exerting dominance 41.9% followed TEM (35.4%), SR (13%), PET (9.7%). Spatially, increased significantly enhanced growth arid zones, while controlled non-water-limited such irrigation zones. Vegetation dynamics were driven combination CC HA, relative contributions 55.8% 44.2%, respectively, suggesting that long-term dominant force. Specifically, contributed seen region southeastern part basin, human-induced factors benefited Loess Plateau (LP) inhibiting pastoral These findings provide critical insights inform formulation adaptation conservation thereby enhancing resilience environmental conditions.
Language: Английский
Citations
11
International Journal of Digital Earth, Journal Year: 2024, Volume and Issue: 17(1)
Published: April 8, 2024
Since the initiation of Grain for Green Project (GFGP) in 1999, dramatic change vegetation status on Loess Plateau. Spatially, geographical detector was employed to detect dominant variables influencing spatial arrangement normalized difference index (NDVI). Temporally, lagged or accumulated monthly precipitation, temperature and standardized precipitation evapotranspiration indices (SPEIs) sensitive NDVI were first detected every individual pixel, correlation between meteorological elements with time-lag effects established a random forest model unchanged land cover, followed by attributing impacts climatic alterations human interventions through residual examination across changed cover. The findings indicate that (1) slope, soil dominantly influence NDVI. (2) Precipitation current month cumulative temperatures previous 1–2 months steadily affect growth significantly, optimal accumulation time interval SPEI around 2000 are 8 4 months, respectively. (3) Increases average within woodland meadow Plateau primarily driven climate before 2000, accounting 76.2%, whereas after it activities, 64.16%.
Language: Английский
Citations
11
Atmospheric Research, Journal Year: 2024, Volume and Issue: 304, P. 107405 - 107405
Published: April 7, 2024
Language: Английский
Citations
9
Forests, Journal Year: 2025, Volume and Issue: 16(2), P. 307 - 307
Published: Feb. 10, 2025
In the context of climate change, southern slope Qilian Mountains stands as a pivotal region for China’s ecological security, holding immense significance sustaining sustainable development. This study aims to precisely monitor and predict dynamic changes in vegetation cover within this region, along with their time-lagged effects on thereby providing scientific basis management. By calculating kNDVI from 2001 2020 Google Earth Engine (GEE) platform, integrating Sen’s trend analysis, Hurst exponent, partial correlation we have conducted an in-depth exploration long-term spatiotemporal variations its delayed responses factors. The primary research findings can be summarized follows: exhibits overall positive trend, notable geographical spatial distribution. proportion areas showing improvement is high 84%, while degraded account only 17%. Furthermore, there average lag response 1.6 months precipitation 0.6 temperature region. speed positively correlates coefficient between Notably, more sensitive area Mountains. not fills gap monitoring but also offers support governance green development initiatives Additionally, it showcases innovative application advanced remote sensing technologies statistical analysis methods research, fresh perspectives future management strategies. These hold profound implications promoting conservation area.
Language: Английский
Citations
1
Atmospheric Research, Journal Year: 2025, Volume and Issue: unknown, P. 107989 - 107989
Published: Feb. 1, 2025
Language: Английский
Citations
1
Sustainability, Journal Year: 2025, Volume and Issue: 17(6), P. 2348 - 2348
Published: March 7, 2025
Examining the effects of climate change (CC) and anthropogenic activities (AAs) on vegetation dynamics is essential for ecosystem management. However, time lag accumulation plant growth are often overlooked, resulting in an underestimation CC impacts. Combined with kernel normalized difference index (kNDVI), data during growing season from 2000 to 2023 Three Rivers Source Region (TRSR) trend correlation analyses were employed assess kNDVI dynamics. Furthermore, effect upgraded residual analysis applied explore how climatic human drivers jointly influence vegetation. The results show following: (1) showed a fluctuating but overall increasing trend, indicating improvement growth. Although future likely continue improving, certain areas—such as east western Yangtze River basin, south Yellow parts Lancang basin—will remain at risk deterioration. (2) Overall, both precipitation temperature positively correlated kNDVI, acting dominant factor affecting predominant temporal 0-month 1-month accumulation, while primarily 2–3-month 0–1-month accumulation. main category (PA_TL), which accounted 70.93% TRSR. (3) Together, AA drove dynamics, contributions 35.73% 64.27%, respectively, that played role. incorporating combined enhanced explanatory ability factors
Language: Английский
Citations
1
Remote Sensing, Journal Year: 2023, Volume and Issue: 15(18), P. 4362 - 4362
Published: Sept. 5, 2023
Vegetation is one of the most important indicators climate change, as it can show regional change in environment. health affected by various factors, including drought, which has cumulative and time-lag effects on vegetation response. However, drought different terrestrial China are still unclear. To address this issue, study examined from 2001 to 2020 using Standardized Precipitation Evapotranspiration Index (SPEI) Global SPEI database Normalized Difference (NDVI) MOD13A3. Based Sen-Median trend analysis Mann–Kendall test, significance NDVI were explored. The Pearson correlation coefficient was used analyze between at each scale further vegetation. results following: (1) value increased a rate 0.019/10 years, area accounted for 80.53% mainland China, with spatial low values west high east. (2) average time relevant 7.3 months, effect demonstrated 9–12 months revealed distributions areas. widely distributed 9-month scale, followed 12-month scale. coefficients cropland, woodland grassland peaked 9 months. (3) 6.9 had highest 7-month strongest cropland seen 7 while 6 Woodland lower than scales. research significant their use aiding scientific response disasters making decisions precautions.
Language: Английский
Citations
17
Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 355, P. 120251 - 120251
Published: Feb. 28, 2024
Language: Английский
Citations
8