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Ali Mansourian

Professor

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GEE-based environmental monitoring and phenology correlation investigation using Support Vector Regression

Författare

  • Fatemeh Parto Dezfooli
  • Mohammad Javad Valadan Zoej
  • Ali Mansourian
  • Fahimeh Youssefi
  • Saied Pirasteh

Summary, in English

Environmental changes over time and across different regions profoundly affect agriculture, forestry, water management, public health, and ecosystems. Therefore, monitoring these fluctuations is crucial for informing decision-making and developing strategies for long-term sustainability. While ground-based methods provide valuable insights into environmental dynamics, they are inherently limited in scope and coverage. Consequently, satellite-based techniques have become essential for comprehensive ecological monitoring over extensive spatial and temporal scales. This study investigates spatio-temporal patterns of environmental factors and their correlation with phenology in Ilam Province, Iran, from 2014 to 2021, utilizing remote sensing data and Google Earth Engine (GEE). Landsat 8 satellite data was used to generate time series maps and timelines for land cover, temperature, and soil moisture, using the Soil-Adjusted Vegetation Index (SAVI), Land Surface Temperature (LST) anomaly, and Soil Moisture Index (SMI). Subsequently, the Temporal Soil-Adjusted Vegetation Phenology Index (TSPI) was calculated to track annual vegetation variations and analyze its correlation with the specified parameters using Support Vector Regression (SVR). Our results revealed significant trends in environmental factors, highlighting robust correlations with the TSPI. Soil moisture peaked in late winter and early spring, declining during the summer, with the highest levels recorded in 2018. Vegetation reached its maximum density in mid-spring and its minimum in winter, with a notable greening surge observed in 2019. Temperatures were highest in summer and lowest in winter, showing minimal year-to-year variation. Spatial analysis indicated a consistent increase in land surface temperature from the northeast toward the southwest, corresponding to declines in vegetation and soil moisture levels. Regression analysis specified strong associations between the TSPI and environmental variables, with R-squared values of 0.83 for LST, 0.86 for SAVI, and 0.79 for SMI. These findings emphasize the effectiveness of remote sensing methods, such as time series satellite imagery and streamlined indices, for large-scale ecological analyses using the GEE platform and underscore the potential of TSPI as a proper indicator for future environmental management research.

Avdelning/ar

  • LU profilområde: Naturbaserade framtidslösningar
  • MECW: The Middle East in the Contemporary World
  • BECC: Biodiversity and Ecosystem services in a Changing Climate
  • Centrum för geografiska informationssystem (GIS-centrum)
  • Institutionen för naturgeografi och ekosystemvetenskap

Publiceringsår

2025-01

Språk

Engelska

Publikation/Tidskrift/Serie

Remote Sensing Applications: Society and Environment

Volym

37

Dokumenttyp

Artikel i tidskrift

Förlag

Elsevier

Ämne

  • Physical Geography
  • Earth and Related Environmental Sciences

Nyckelord

  • Environmental monitoring
  • Google Earth Engine (GEE)
  • Phenological index
  • Remote sensing
  • Support Vector Regression (SVR)

Aktiv

Published

ISBN/ISSN/Övrigt

  • ISSN: 2352-9385