Remotely-Sensed Ecosystem Health Assessment (RSEHA) model for assessing the changes of ecosystem health of Lake Urmia Basin. (3rd April 2022)

• Record Type: Journal Article
• Authors: Abbaszadeh Tehrani, Nadia; Mohd Shafri, Helmi Zulhaidi; Salehi, Sara; Chanussot, Jocelyn; Janalipour, Milad
• Is Part Of: International journal of image and data fusion. Volume 13:Number 2(2022)
• Page Start: 180
• Page End: 205
• Publication Date: 2022-04-03
• Subjects: RSEHA model -- VORS -- ecosystem health -- remote sensing -- Lake Urmia Basin; Image processing -- Periodicals; Multisensor data fusion -- Periodicals; Multisensor data fusion; Periodicals; 621.36705
• DOI: 10.1080/19479832.2021.1924880
• Languages: English
• ISSNs: 1947-9832

Abstract

ABSTRACT: The widespread, severe negative impacts of human activities on Earth's ecosystems over the past few decades have highlighted the importance of continuous and up-to-date monitoring of ecosystems health. On the other hand, it has been proven that the use of remote sensing technology in environmental studies can lead to accurate and reliable results with spending less cost and time. This research attempts to use remote sensing indicators and the framework of Vigour, Organization, Resilience, and Services (VORS) to assess ecosystem health by introducing Remotely Sensed Ecosystem Health Assessment (RSEHA) Model. By applying 10 spatiotemporal indices, ecosystem health has been assessed in Lake Urmia Basin (LUB) during the years 2001–2014. The results showed that the health status of LUB in its different parts varied from 'very strong' to 'very poor'. The health status around LUB has changed from 'poor' to 'very poor', while it has improved, especially in cultivated lands. The health of the lake has been sacrificed in favour of the development of agricultural areas in the basin. Based on validation results, the RSEHA model can determine the ecosystem conditions at pixel level at any time at reasonable cost and accuracy.