The Decisive Influence of the Improved Remote Sensing Ecological Index on the Terrestrial Ecosystem in Typical Arid Areas of China DOI Creative Commons

Guo Long,

Chao Xu, Hongqi Wu

et al.

Land, Journal Year: 2024, Volume and Issue: 13(12), P. 2162 - 2162

Published: Dec. 12, 2024

This study aims to assess the spatiotemporal changes in ecological environment quality (EEQ) arid regions, using Xinjiang as a case study, from 2000 2023, with an improved remote sensing index (IRSEI). Due complex ecology of traditional (RSEI) has limitations capturing dynamics. To address this, we propose enhanced IRSEI model that replaces normalization standardization, improving robustness against outliers. Additionally, kernel normalized difference vegetation (kNDVI) and salinity (NDSI) are integrated saline areas more effectively. The methodology includes time series analysis, spatial distribution statistical evaluations method, coefficient variation, Hurst index. Results show accurately reflects dynamics than RSEI. Temporal analysis reveals stable overall EEQ, some improving. Spatially, is generally better north mountainous regions south plains. Statistical suggest positive trend changes, surpassing degraded ones. contributes monitoring, protection, management region ecosystems, emphasizing need for high-resolution data further analysis.

Language: Английский

Normal Difference Vegetation Index Simulation and Driving Analysis of the Tibetan Plateau Based on Deep Learning Algorithms DOI Open Access
Xi Liu, Guoming Du,

Haoting Bi

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(1), P. 137 - 137

Published: Jan. 9, 2024

Global climate warming has profoundly affected terrestrial ecosystems. The Tibetan Plateau (TP) is an ecologically vulnerable region that emerged as ideal place for investigating the mechanisms of vegetation response to change. In this study, we constructed annual synthetic NDVI dataset with 500 m resolution based on MOD13A1 products from 2000 2021, which were extracted by Google Earth Engine (GEE) and processed Kalman filter. Furthermore, considering topographic climatic factors, a thorough analysis was conducted ascertain causes effects NDVI’s spatiotemporal variations TP. main findings are: (1) coverage TP been growing slowly over past 22 years at rate 0.0134/10a, notable heterogeneity due its topography conditions. (2) During study period, generally showed “warming humidification” trend. influence human activities growth exhibited favorable trajectory, acceleration observed since 2011. (3) primary factor influencing in southeastern western regions increasing temperature. Conversely, northeastern central mostly regulated precipitation. (4) Combined principal component analysis, PCA-CNN-LSTM (PCL) model demonstrated significant superiority modeling sequences Plateau. Understanding results paper important sustainable development formulation ecological policies

Language: Английский

Citations

6
An Analysis of the Rice-Cultivation Dynamics in the Lower Utcubamba River Basin Using SAR and Optical Imagery in Google Earth Engine (GEE) DOI Creative Commons
Angel J. Medina-Medina, Rolando Salas López, Jhon Antony Zabaleta Santisteban

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(3), P. 557 - 557

Published: March 8, 2024

One of the world’s major agricultural crops is rice (Oryza sativa), a staple food for more than half global population. In this research, synthetic aperture radar (SAR) and optical images are used to analyze monthly dynamics crop in lower Utcubamba river basin, Peru. addition, study addresses need obtain accurate timely information on areas under cultivation order calculate their production. To achieve this, SAR sensor Sentinel-2 remote sensing were integrated using computer technology, analyzed through mapping geometric calculation surveyed areas. An algorithm was developed Google Earth Engine (GEE) virtual platform classification Sentinel-1 combination both, result which improved ArcGIS Pro software version 3.0.1 spatial filter reduce “salt pepper” effect. A total 168 96 obtained, corrected, classified machine learning algorithms, achieving average accuracy 96.4% 0.951 with respect overall (OA) Kappa Index (KI), respectively, year 2019. For 2020, averages 94.4% OA 0.922 KI. Thus, data offer excellent integration address gaps between them, great importance obtaining robust products, can be applied improving production planning management.

Language: Английский

Citations

4
Remotely-sensed products capture the spatial distribution of regional carbon sequestration potential ability: A case study of Hubei Province, China DOI Creative Commons

Z. M. Li,

Lingya Huang,

Yuanyong Dian

et al.

Ecological Indicators, Journal Year: 2025, Volume and Issue: 170, P. 113018 - 113018

Published: Jan. 1, 2025

Language: Английский

Citations

0
Investigation of climate-related influences of dams on the Artvin province of Türkiye using remote sensing data DOI Creative Commons
Çiğdem Şerifoğlu Yılmaz

Environmental Earth Sciences, Journal Year: 2025, Volume and Issue: 84(6)

Published: March 1, 2025

Language: Английский

Citations

0
Climate Change as a Double-Edged Sword: Exploring the Potential of Environmental Recovery to Foster Stability in Darfur, Sudan DOI Open Access
Abdalrahman Ahmed, Brian Rotich, Kornél Czimber

et al.

Climate, Journal Year: 2025, Volume and Issue: 13(3), P. 63 - 63

Published: March 18, 2025

The Darfur conflict, which emerged in the early 21st century, represents a multifaceted crisis driven by socio-political and environmental factors, with resource scarcity, exacerbated climate change, playing pivotal role intensifying tensions between agricultural pastoral communities. While change is typically associated adverse outcomes, an analysis of data spanning four decades (1980–2023) reveals contrasting trend increased precipitation, enhanced vegetation, decreased drought frequency recent years. This research explores potential these positive changes to mitigate resource-based conflicts foster political stability as improved conditions are posited create foundation for conflict resolution sustainable peacebuilding. present study integrates trends Enhanced Vegetation Index (EVI) Standardized Precipitation Evapotranspiration (SPEI) examine shifts. EVI data, derived from Moderate Resolution Imaging Spectroradiometer (MODIS) at 250 m resolution, was used assess large-scale vegetation patterns arid semi-arid landscapes. Autoregressive Integrated Moving Average (ARIMA) model employed forecast future precipitation scenarios up year 2034, enhancing understanding long-term climatic trends. Data processing utilized advanced tools, including Google Earth Engine (GEE), ArcGIS Pro (version 3.4), R software 4.3.2). findings reveal significant (33.19%) improvement natural cover 2000 2023, degraded unchanged areas accounting 1.95% 64.86%, respectively. finding aligns marked increase annual reduction intensity over period. Historical SPEI showed persistent events 1980 2012, followed notable decline severity 2013 2024. projections suggest stable trend, potentially supporting further recovery region. These improvements preliminarily linked climate-change-induced increases reductions severity. study’s contribute nuanced interplay dynamics Darfur, offering actionable insights policy interventions aimed fostering peace resilience

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
Evaluating coastal agroecological dynamics using Landsat-derived vegetation and environmental indices embedded in Decision Support System and Monitoring Tools: insights from Guyana towards achieving SDGs DOI Creative Commons

E.G. Hamer,

Temitope D. Timothy Oyedotun,

Gordon Ansel Nedd

et al.

Discover Sustainability, Journal Year: 2025, Volume and Issue: 6(1)

Published: May 8, 2025

Language: Английский

Citations

0
Numerical modeling of effects of vegetation restoration on runoff and sediment yield on the Loess Plateau, China DOI
Ga Zhang, Chenge An, Chenfeng Wang

et al.

CATENA, Journal Year: 2024, Volume and Issue: 247, P. 108501 - 108501

Published: Oct. 31, 2024

Language: Английский

Citations

3
Response of Vegetation Coverage to Climate Drivers in the Min-Jiang River Basin along the Eastern Margin of the Tibetan Plat-Eau, 2000–2022 DOI Open Access
Shuyuan Liu,

Yicheng Gu,

Huan Wang

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(7), P. 1093 - 1093

Published: June 24, 2024

Ecological zonation research is typically conducted in the eastern margin of Tibetan Plateau. In order to enhance structure and function regional ecosystems monitor their quality, it crucial investigate shifts coverage vegetation factors that contribute these shifts. The goal this study assess spatial temporal variations covering partitioning its drivers Minjiang River Basin on edge Plateau between 2000 2022. Mann-Kendall test, Hurst index, Theil-Sen median trend analysis, other techniques were used look at features geographical changes as well potential development trends. climatic influences leading differentiation NDVI (Normalized Difference Vegetation Index) quantified through partial complex correlation analyses with temperature precipitation. results showed (1) watershed performed a stable upward trend, indicating growth was generally good; (2) analysis coefficient variation reached 0.092, which highlighted stability change region; (3) future low, there certain degree ecological risk; (4) main driver non-climate factor, distributed most parts watershed; (5) climate shows localized influence, especially concentrated southwest, downstream part upstream areas watershed.

Language: Английский

Citations

2
Land cover and drought risk assessment in Türkiye’s mountain regions using neutrosophic decision support system DOI
Ayhan Ateşoğlu, Ertuğrul Ayyıldız,

Irem Karakaya

et al.

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(11)

Published: Oct. 12, 2024

Language: Английский

Citations

1