Analysis of Spatiotemporal Variation Characteristics and Influencing Factors of Grassland Vegetation Coverage in the Qinghai–Tibet Plateau from 2000 to 2023 Based on MODIS Data DOI Creative Commons

Shi Xing-he,

Dong Yang,

Shijian Zhou

et al.

Land, Journal Year: 2024, Volume and Issue: 13(12), P. 2127 - 2127

Published: Dec. 7, 2024

Changes in grassland fractional vegetation coverage (FVC) are important indicators of global climate change. Due to the unique characteristics Tibetan Plateau ecosystem, variations crucial its ecological stability. This study utilizes Google Earth Engine (GEE) platform retrieve long-term MODIS data and analyzes spatiotemporal distribution FVC across Qinghai–Tibet (QTP) over 24 years (2000–2023). The growth index (GI) is used evaluate annual at pixel level. GI an indicator for measuring status, which can effectively measure changes each year relative base year. trends monitored using Sen-Mann-Kendall slope estimation, coefficient variation, Hurst exponent. Geographic detectors partial correlation analysis then applied explore contribution rates key driving factors FVC. results show: (1) From 2000 2023, exhibited overall upward trend, with rate 0.0881%. on QTP follows a pattern higher values east lower west; (2) Over past years, 54.05% total area has shown significant increase, 23.88% remained stable, only small portion decrease. trend expected continue minimal variability, covering 82.36% area. suggests balanced state growth; (3) precipitation (Pre) soil moisture (SM) main single affecting grasslands (q = 0.59 0.46). In interaction detection, addition highest between Pre other factors, SM also showed impact grassland; hydrothermal grassland. It shows that stronger than temperature. enhanced our understanding change quantitatively described relationship great significance maintaining sustainable development ecosystems.

Language: Английский

Assessment of ecological asset quality and its drivers in Agro-pastoral Ecotone of China DOI Creative Commons

Wenmin Liu,

Zhiyuan Cheng,

Jie Li

et al.

Ecological Indicators, Journal Year: 2025, Volume and Issue: 170, P. 113072 - 113072

Published: Jan. 1, 2025

Language: Английский

Citations

0
Coupled Effects of Water Depth, Vegetation, and Soil Properties on Soil Organic Carbon Components in the Huixian Wetland of the Li River Basin DOI Creative Commons

Yongkang Wang,

Junfeng Dai,

Fan Jiang

et al.

Land, Journal Year: 2025, Volume and Issue: 14(3), P. 584 - 584

Published: March 10, 2025

Wetland ecosystems are essential to the global carbon cycle, and they contribute significantly storage regulation. While existing studies have explored individual effects of water depth, vegetation, soil properties on organic (SOC) components, a comprehensive study interactions between these factors is still lacking, particularly regarding their collective impact composition SOC in wetland soils. This paper focused Huixian Li River Basin. The variations its fractions, namely dissolved carbon, microbial biomass light fraction mineral-associated under different depths vegetation conditions were examined. Additionally, (pH bulk density, total phosphorus (TP), nitrogen (TN), ammonium (NH4-N), nitrate (NO3-N)) changes components quantified. Specific depth–vegetation combinations favor accumulation, with Cladium chinense at depth 20 cm Phragmites communis 40 exhibiting higher content. positively correlated plant biomass, TP, TN, NH4-N. coupling had significant effect contributing 74.4% variation fractions. Among them, explained 7.8%, 7.3%, 6.4% changes, respectively, 25.6% changes. three influenced components. Optimal level management strategic planting can enhance capacity increase research offers valuable insights for effectively managing sinks reserves.

Language: Английский

Citations

0
Analysis of Grassland Vegetation Coverage Changes and Driving Factors in China–Mongolia–Russia Economic Corridor from 2000 to 2023 Based on RF and BFAST Algorithm DOI Creative Commons

Chi Qiu,

Chao Zhang,

Jiani Ma

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(8), P. 1334 - 1334

Published: April 8, 2025

Changes in grassland vegetation coverage (GVC) and their causes the China–Mongolia–Russia Economic Corridor (CMREC) region have been a hot button issue regarding ecological environment sustainable development. In this paper, multi-source remote sensing (RS) data were used to obtain GVC from 2000 2023 based on random forest (RF) regression inversion. The nonlinear characteristics such as number of mutations, magnitude time mutations detected analyzed using BFAST model. Driving factors climatic introduced quantitatively explain driving mechanism changes. results showed that: (1) RF model is optimal for inversion region. R2 training set reached 0.94, RMSE test was 12.86%, correlation coefficient between predicted actual values 0.76, CVRMSE 18.07%. (2) During period 2000–2023, ranged 0 5, there at least 1 mutation 58.83% study area. years with largest proportion 2010, followed by 2016, accounting 14.57% 11.60% all respectively. month highest percentage October, June, 31.73% 22.19% (3) sustained stable positive effect shown precipitation before after maximum mutation. Wind speed negative areas more severe desertification, Inner Mongolia, China parts Mongolia. On other hand, reduced wind mutations. Therefore, guarantee security CMREC, governments should formulate new countermeasures prevent desertification according laws nature strengthen international cooperation.

Language: Английский

Citations

0
High-Altitude CO2 Flux in Cropland and Grassland of Eastern Qilian Mountains, China: Variation and Driving Factors DOI
Weitao Zeng, Hui Hu,

Yuan Deng

et al.

Water Air & Soil Pollution, Journal Year: 2025, Volume and Issue: 236(6)

Published: April 15, 2025

Language: Английский

Citations

0
Estimation of rice yield using multi-source remote sensing data combined with crop growth model and deep learning algorithm DOI Creative Commons
Jian Lü, Jian Li,

Hongkun Fu

et al.

Agricultural and Forest Meteorology, Journal Year: 2025, Volume and Issue: 370, P. 110600 - 110600

Published: May 4, 2025

Language: Английский

Citations

0
Combination of plant species and water depth enhance soil quality in near-natural restoration of reclaimed wetland DOI
Tao Yang, Jin Jiang,

Fengxue Shi

et al.

Ecological Engineering, Journal Year: 2024, Volume and Issue: 208, P. 107376 - 107376

Published: Aug. 28, 2024

Language: Английский

Citations

3
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
Estimation, Spatiotemporal Dynamics, and Driving Factors of Grassland Biomass Carbon Storage Based on Machine Learning Methods: A Case Study of the Hulunbuir Grassland DOI Creative Commons

Qiuying Zhi,

Xiaosheng Hu,

Ping Wang

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(19), P. 3709 - 3709

Published: Oct. 5, 2024

Precisely estimating the grassland biomass carbon storage is vital for evaluating sequestration potential and monitoring management of resources. With increasing intensity climate change (CC) human activities (HA), it necessary to explore spatiotemporal variations in its response CC HA. In this study, we focused on Hulunbuir Grassland, utilizing sample plots data, MODIS environmental factors (terrain, soil, climate), location factor, texture characteristics assess performance four machine learning algorithms: random forest, support vector machine, gradient boosting decision tree, extreme aboveground (AGB). Based optimal model combined with root-shoot ratio distribution content coefficients, driving from 2001–2022 were analyzed. The results showed that (1) forest achieved highest prediction accuracy AGB, making appropriate AGB estimation Grassland. (2) spectral indices key variables especially enhanced vegetation index difference index. (3) 22-year average total (TB) study area was 1037.10 gC/m2, which 48.73 gC/m2 belowground 988.37 showing a spatial feature gradual increase west east. (4) From 2001–2022, TB an insignificant growth trend (p > 0.05). 72.34 ± 18.07 gC. (5) Climate main pattern density, while effects HA contributors interannual density.

Language: Английский

Citations

1
Driving Factors of Above-Ground Biomass in Grasslands on the Northern Slope of the Tianshan Mountains, China DOI

Chenglong Zhang,

Gangyong Li,

Geping Luo

et al.

Published: Jan. 1, 2024

Language: Английский

Citations

0
Estimating Biomass Carbon Stocks of Inner Mongolia Grasslands Using Multi-Source Data DOI Creative Commons
Yong Liu, Shaobo Sun,

Xiaolei Yang

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 17(1), P. 29 - 29

Published: Dec. 26, 2024

Accurate estimates of biomass C stocks grasslands are crucial for grassland management and climate change mitigation efforts. Here, we estimated the mean in Inner Mongolia Autonomous Region (IMAR), China, 2020 at a 10 m spatial resolution by combining multi-source data, including remote sensing, climate, topography, soil properties, field surveys. We used random forest model to estimate aboveground (AGB) grasslands, achieving an R2 value 0.83. established relationship between belowground (BGB) AGB using power function based on which allows us BGB from our estimate. across IMAR be 100.7 g m−2, with total 1.4 × 108 t. The is much higher than AGB, values 526.0 m−2 7.4 t, respectively. Consequently, stock show that store significantly more their (332.6 Tg C) compared (63.7 C). Random analyses suggested remotely sensed vegetation indices moisture most important predictors estimating IMAR. highlight role grasslands.

Language: Английский

Citations

0