Solar-Induced Chlorophyll Fluorescence-Based GPP Estimation and Analysis of Influencing Factors for Xinjiang Vegetation DOI Open Access
Cong Xue, Mei Zan, Yanlian Zhou

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(12), P. 2100 - 2100

Published: Nov. 27, 2024

With climate change and the intensification of human activity, drought event frequency has increased, affecting Gross Primary Production (GPP) terrestrial ecosystems. Accurate estimation GPP in-depth exploration its response mechanisms to are essential for understanding ecosystem stability developing strategies adaptation. Combining remote sensing technology machine learning is currently mainstream method estimating in ecosystems, which can eliminate uncertainty model parameters errors input data. This study employed extreme gradient boosting, random forest (RF), light use efficiency models. Additionally, we integrated solar-induced chlorophyll fluorescence (SIF), near-infrared reflectance vegetation, leaf area index (LAI) construct various The standardised precipitation evapotranspiration (SPEI) was utilised at timescales analyse relationship between SPEI during dry years. Moreover, potential pathways coefficients environmental factors that influence were explored using structural equation modelling. Our key findings include following: (1) combining SIF RF algorithms exhibits higher accuracy applicability vegetation arid zone Xinjiang, with an overall (MODIS R2) 0.775; (2) Xinjiang had different characteristics drought, optimal timescale respond 9 months, a mean correlation coefficient 0.244 grass land SPEI09, indicating high sensitivity; (3) modelling, found temperature affect both directly indirectly through LAI. provides reliable tool methodology conclusions important references similar environments. In addition, this bridges research gap timescales, mechanism natural on scientific basis early warning management. Further validation longer time series required confirm robustness model.

Language: Английский

Predictive modeling of regional carbon storage dynamics in response to land use/land cover changes: An InVEST-based analysis DOI Creative Commons
Zeeshan Zafar, Muhammad Zubair, Yuanyuan Zha

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: 82, P. 102701 - 102701

Published: June 21, 2024

Assessment of carbon stock (CS) in various land use/land cover (LULC) types is essential for environmental policies focused on reducing CO2 emissions and mitigating climate change. This study utilized the CA-Markov model to simulate future LULC scenarios InVEST evaluate CS changes Pakistan from 2001 2030. The employed two decades yearly composite data MODIS, achieving high accuracy with a kappa value 0.856. results indicate that an increase 38.1 × 103 km2 cultivated could lead increment 13.5 Tg Pakistan's total CS. In comparison, forest area can be reason raising above-ground (AGC) by 16.8 Tg. These findings enhance understanding long-term Pakistan. provides valuable insights governments refine use strategies, adjust emission reduction policies, design better regulations based study's findings. Key recommendations include promoting vertical urban development preserve sequestration areas, implementing strict agricultural zoning laws, expanding afforestation initiatives like Billion Tree Tsunami Green Pakistan, establishing national monitoring program. Integrating sources will create comprehensive database inform policy decisions management practices, contributing global change mitigation efforts.

Language: Английский

Citations

29
A framework for dynamic assessment of soil erosion and detection of driving factors in alpine grassland ecosystems using the RUSLE-InVEST (SDR) model and Geodetector: A case study of the source region of the Yellow River DOI Creative Commons

Hucheng Li,

Jianjun Chen, Ming Ling

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: unknown, P. 102928 - 102928

Published: Nov. 1, 2024

Language: Английский

Citations

6
Spatial-temporal dynamics of meteorological and agricultural drought in Northwest China: Propagation, drivers and prediction DOI
Yining Ma, Jiawei Ren,

Kang Shaozhong

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 650, P. 132492 - 132492

Published: Dec. 12, 2024

Language: Английский

Citations

6
Comprehensive Drought Risk Assessment and its driving mechanism for the water source area of the Western Route of South-to-North Water Diversion Project in China DOI
Wenyu Li,

Jun Xie,

Juan Cao

et al.

International Journal of Disaster Risk Reduction, Journal Year: 2025, Volume and Issue: unknown, P. 105423 - 105423

Published: March 1, 2025

Language: Английский

Citations

0
Enhancing Ready-to-Implementation subseasonal crop growth predictions in central Southwestern Asia: A machine learning-climate dynamical hybrid strategy DOI Creative Commons
Tao Zhu, Mengqian Lu, Jing Yang

et al.

Agricultural and Forest Meteorology, Journal Year: 2025, Volume and Issue: 370, P. 110582 - 110582

Published: May 2, 2025

Language: Английский

Citations

0
Spatio-temporal evaluation of MODIS temperature vegetation dryness index in the Middle East DOI Creative Commons
Younes Khosravi, Saeid Homayouni, Taha B. M. J. Ouarda

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: 84, P. 102894 - 102894

Published: Nov. 13, 2024

Language: Английский

Citations

3
Research on Soil Erosion Based on Remote Sensing Technology: A Review DOI Creative Commons
Jiaqi Wang, Jiuchun Yang, Zhi Li

et al.

Agriculture, Journal Year: 2024, Volume and Issue: 15(1), P. 18 - 18

Published: Dec. 25, 2024

Monitoring and assessing soil erosion is essential for reducing land degradation ensuring food security. It provides critical scientific insights developing effective policies implementing targeted preventive measures. The emergence of remote sensing technology has significantly bolstered research, empowering researchers to comprehensively accurately understand address erosion-related challenges. Consequently, become pivotal in research methodologies. In recent years, significant progress been made on erosion. This study aims encapsulate the current status advancements applications research. catalogs commonly used data sources introduces innovative methodologies detecting soil-erosion-related information utilizing technology. Furthermore, it delves into analysis acquisition methods factors influencing examines crucial role prevalent simulation prediction models. Additionally, this identifies existing challenges outlines prospects developmental directions emphasizing its potential contribute sustainable management practices environmental conservation efforts.

Language: Английский

Citations

3
Response of solar-induced chlorophyll fluorescence-based spatial and temporal evolution of vegetation in Xinjiang to multiscale drought DOI Creative Commons
Cong Xue,

Mei Zan,

Yanlian Zhou

et al.

Frontiers in Plant Science, Journal Year: 2024, Volume and Issue: 15

Published: Aug. 9, 2024

Climate change and human activities have increased droughts, especially overgrazing deforestation, which seriously threaten the balance of terrestrial ecosystems. The ecological carrying capacity vegetation cover in arid zone Xinjiang, China, are generally low, necessitating research on response to drought such regions. In this study, we analyzed spatial temporal characteristics Xinjiang from 2001 2020 revealed mechanism SIF multi-timescale different types using standardized precipitation evapotranspiration index (SPEI), solar-induced chlorophyll fluorescence (SIF), normalized difference (NDVI), enhanced (EVI) data. We employed trend analysis, anomaly (SAI), Pearson correlation, prediction techniques. Our investigation focused correlations between GOSIF (a new product based Global Orbital Carbon Observatory-2), NDVI, EVI with SPEI12 for over past two decades. Additionally, examined sensitivities various scales SPEI a typical year predicted future trends Xinjiang. results that distribution GOSIF, were consistent, mean at 0.197, 0.156, 0.128, respectively. exhibited strongest correlation SPEI, reflecting impact stress photosynthesis. Therefore, proves advantageous monitoring purposes. Most showed robust 9-month scale during year, grassland being particularly sensitive drought. predictions indicate decreasing coupled an increasing This study found compared traditional greenness index, has obvious advantages Furthermore, it makes up lack multiple timescales zone. These provide strong theoretical support investigating monitoring, assessment, is vital comprehending mechanisms carbon water cycles

Language: Английский

Citations

2
Identification of gully erosion activity and its influencing factors: A case study of the Sunshui River Basin DOI Creative Commons
Fengjie Fan,

Xingli Gu,

Jun Luo

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(11), P. e0309672 - e0309672

Published: Nov. 21, 2024

Gully erosion is one of the most severe forms land degradation and poses a serious threat to regional food security, biodiversity, human survival. However, there are few methods for quantitative evaluation gully activity, relationships between activity influencing factors require further in-depth study. This study takes Sunshui River Basin, as case Based on field investigation, unmanned aerial vehicle (UAV) photography remote sensing images, 71 typical gullies were identified. The vegetation coverage (VC), slope main-branch ratio (MBGR) used indicators, was calculated using fuzzy mathematics membership degree then evaluated quantitatively. different active also analyzed. results showed that (1) comprehensive method can be identify activity. Different levels defined based index. indices stable ranged from 0–0.25, those semiactive 0.25–0.75, 0.75–1. (2) 0.054 0.999, with an average value 0.656. There 31 gullies, gullies. A total 87.32% in area early or middle stage development. intense, which consistent reality soil erosion. (3) affected by multiple factors. It significantly positively correlated topographic relief (TR) (r = 0.64, P <0.01) surface curvature (SC) 0.51, <0.01), while it negatively use type (LUT) -0.5, <0.01). Surface roughness (SR) 0.2, activity; but not significantly. no significant correlation aspect (As) this helpful quantitatively determining level understanding development process mechanism controlling providing reference research related regions geomorphologic information.

Language: Английский

Citations

2
A novel model for mapping soil organic matter: Integrating temporal and spatial characteristics DOI Creative Commons
Xinle Zhang, Guowei Zhang, Sheng‐Qi Zhang

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: unknown, P. 102923 - 102923

Published: Nov. 1, 2024

Language: Английский

Citations

1