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Environmental Degradation in Iraq: Attribution of Climatic Change and Human Influences Through Multi-Factor Analysis

Akram Al-Qaraghuli, Peter North Orcid Logo, Iain Bye, Jacqueline Rosette Orcid Logo, Sietse Los

Remote Sensing, Volume: 18, Issue: 4, Start page: 640

Swansea University Authors: Akram Al-Qaraghuli, Peter North Orcid Logo, Iain Bye, Jacqueline Rosette Orcid Logo, Sietse Los

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DOI (Published version): 10.3390/rs18040640

Abstract

Environmental degradation in Iraq is a critical issue that requires strong monitoring. One indication of land degradation is a decrease in or loss of vegetation cover. This study examines changes in vegetation and productivity in the Thi-Qar region from 2001 to 2022, using the normalized difference...

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Published in: Remote Sensing
ISSN: 2072-4292
Published: MDPI AG 2026
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URI: https://cronfa.swan.ac.uk/Record/cronfa71499
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One indication of land degradation is a decrease in or loss of vegetation cover. This study examines changes in vegetation and productivity in the Thi-Qar region from 2001 to 2022, using the normalized difference vegetation index (NDVI) and net primary production (NPP), and their response to climatic and hydrological factors. To address the gap in assessments that simultaneously quantify the influence of streamflow, rainfall, and temperature across distinct land cover classes in arid and semi-arid regions, we developed a replicable multi-source geospatial framework. We used MODIS data within the Google Earth Engine platform to perform spatiotemporal analysis. We applied models to detect NDVI trends on a pixel-by-pixel basis. This study provides the first integrated, data-driven assessment of vegetation sensitivity to streamflow versus climate in the Thi-Qar Governorate using a harmonized multi-source dataset. This combines the FAO WaPOR NPP dataset with hydrological (streamflow) and climatic (CHIRPS rainfall, MODIS LST) variables within an analytical workflow to extract anthropogenic water management from climatic drivers. The results showed variations in the NDVI and productivity in the southern and southwestern regions, indicating areas of both degradation and improvement. The analysis found that 12% of the study area showed improvement, while 56.5% of the area showed degradation. Additionally, we classified the study area as either vegetation (cropland) or non-vegetation (fallow arable land, bare areas, and sand dunes). A multiple regression model was then applied to these categories to examine the relationships between streamflow, precipitation, land surface temperature (LST), and the NDVI. The multiple regression for the entire region showed that these factors explained 45.1% of NDVI variation, with streamflow being the most significant positive driver (p &lt; 0.001). The result showed that the NDVI in cropland and arable land was strongly positively correlated with both precipitation and streamflow (R = 0.78, R = 0.75). In contrast, bare land and dunes showed weaker relationships (R = 0.26 and 0.51, respectively). 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spelling 2026-03-17T15:00:12.4304290 v2 71499 2026-02-26 Environmental Degradation in Iraq: Attribution of Climatic Change and Human Influences Through Multi-Factor Analysis b05c3a23e1b8c1a60a84924b9d93ad98 Akram Al-Qaraghuli Akram Al-Qaraghuli true false fc45a0cb36c24d6cf35313a8c808652f 0000-0001-9933-6935 Peter North Peter North true false 86392cce1e20157c99cef2546409c208 Iain Bye Iain Bye true false 0307f116e8f87a83cf4080c493fb7590 0000-0002-2589-0244 Jacqueline Rosette Jacqueline Rosette true false 6d529d947d3b37d7597b36956983cf16 Sietse Los Sietse Los true false 2026-02-26 Environmental degradation in Iraq is a critical issue that requires strong monitoring. One indication of land degradation is a decrease in or loss of vegetation cover. This study examines changes in vegetation and productivity in the Thi-Qar region from 2001 to 2022, using the normalized difference vegetation index (NDVI) and net primary production (NPP), and their response to climatic and hydrological factors. To address the gap in assessments that simultaneously quantify the influence of streamflow, rainfall, and temperature across distinct land cover classes in arid and semi-arid regions, we developed a replicable multi-source geospatial framework. We used MODIS data within the Google Earth Engine platform to perform spatiotemporal analysis. We applied models to detect NDVI trends on a pixel-by-pixel basis. This study provides the first integrated, data-driven assessment of vegetation sensitivity to streamflow versus climate in the Thi-Qar Governorate using a harmonized multi-source dataset. This combines the FAO WaPOR NPP dataset with hydrological (streamflow) and climatic (CHIRPS rainfall, MODIS LST) variables within an analytical workflow to extract anthropogenic water management from climatic drivers. The results showed variations in the NDVI and productivity in the southern and southwestern regions, indicating areas of both degradation and improvement. The analysis found that 12% of the study area showed improvement, while 56.5% of the area showed degradation. Additionally, we classified the study area as either vegetation (cropland) or non-vegetation (fallow arable land, bare areas, and sand dunes). A multiple regression model was then applied to these categories to examine the relationships between streamflow, precipitation, land surface temperature (LST), and the NDVI. The multiple regression for the entire region showed that these factors explained 45.1% of NDVI variation, with streamflow being the most significant positive driver (p < 0.001). The result showed that the NDVI in cropland and arable land was strongly positively correlated with both precipitation and streamflow (R = 0.78, R = 0.75). In contrast, bare land and dunes showed weaker relationships (R = 0.26 and 0.51, respectively). Of these factors, streamflow had the most significant influence in explaining vegetation change (partial correlation p = 0.53), indicating the importance of human management in addition to climate. Journal Article Remote Sensing 18 4 640 MDPI AG 2072-4292 degradation; NDVI; climate factors; Google Earth Engine (GEE); streamflow; Mann–Kendall 19 2 2026 2026-02-19 10.3390/rs18040640 COLLEGE NANME COLLEGE CODE Swansea University Other NERC National Centre for Earth Observation (NE/Y006216/1) 2026-03-17T15:00:12.4304290 2026-02-26T10:19:35.3933832 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Geography Akram Al-Qaraghuli 1 Peter North 0000-0001-9933-6935 2 Iain Bye 3 Jacqueline Rosette 0000-0002-2589-0244 4 Sietse Los 5 71499__36429__177937f8ecc7439fa775189df1bd83ff.pdf 71499.VoR.pdf 2026-03-17T14:58:12.2829033 Output 6722978 application/pdf Version of Record true © 2026 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. true eng https://creativecommons.org/licenses/by/4.0/
title Environmental Degradation in Iraq: Attribution of Climatic Change and Human Influences Through Multi-Factor Analysis
spellingShingle Environmental Degradation in Iraq: Attribution of Climatic Change and Human Influences Through Multi-Factor Analysis
Akram Al-Qaraghuli
Peter North
Iain Bye
Jacqueline Rosette
Sietse Los
title_short Environmental Degradation in Iraq: Attribution of Climatic Change and Human Influences Through Multi-Factor Analysis
title_full Environmental Degradation in Iraq: Attribution of Climatic Change and Human Influences Through Multi-Factor Analysis
title_fullStr Environmental Degradation in Iraq: Attribution of Climatic Change and Human Influences Through Multi-Factor Analysis
title_full_unstemmed Environmental Degradation in Iraq: Attribution of Climatic Change and Human Influences Through Multi-Factor Analysis
title_sort Environmental Degradation in Iraq: Attribution of Climatic Change and Human Influences Through Multi-Factor Analysis
author_id_str_mv b05c3a23e1b8c1a60a84924b9d93ad98
fc45a0cb36c24d6cf35313a8c808652f
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6d529d947d3b37d7597b36956983cf16
author_id_fullname_str_mv b05c3a23e1b8c1a60a84924b9d93ad98_***_Akram Al-Qaraghuli
fc45a0cb36c24d6cf35313a8c808652f_***_Peter North
86392cce1e20157c99cef2546409c208_***_Iain Bye
0307f116e8f87a83cf4080c493fb7590_***_Jacqueline Rosette
6d529d947d3b37d7597b36956983cf16_***_Sietse Los
author Akram Al-Qaraghuli
Peter North
Iain Bye
Jacqueline Rosette
Sietse Los
author2 Akram Al-Qaraghuli
Peter North
Iain Bye
Jacqueline Rosette
Sietse Los
format Journal article
container_title Remote Sensing
container_volume 18
container_issue 4
container_start_page 640
publishDate 2026
institution Swansea University
issn 2072-4292
doi_str_mv 10.3390/rs18040640
publisher MDPI AG
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Geography{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Geography
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description Environmental degradation in Iraq is a critical issue that requires strong monitoring. One indication of land degradation is a decrease in or loss of vegetation cover. This study examines changes in vegetation and productivity in the Thi-Qar region from 2001 to 2022, using the normalized difference vegetation index (NDVI) and net primary production (NPP), and their response to climatic and hydrological factors. To address the gap in assessments that simultaneously quantify the influence of streamflow, rainfall, and temperature across distinct land cover classes in arid and semi-arid regions, we developed a replicable multi-source geospatial framework. We used MODIS data within the Google Earth Engine platform to perform spatiotemporal analysis. We applied models to detect NDVI trends on a pixel-by-pixel basis. This study provides the first integrated, data-driven assessment of vegetation sensitivity to streamflow versus climate in the Thi-Qar Governorate using a harmonized multi-source dataset. This combines the FAO WaPOR NPP dataset with hydrological (streamflow) and climatic (CHIRPS rainfall, MODIS LST) variables within an analytical workflow to extract anthropogenic water management from climatic drivers. The results showed variations in the NDVI and productivity in the southern and southwestern regions, indicating areas of both degradation and improvement. The analysis found that 12% of the study area showed improvement, while 56.5% of the area showed degradation. Additionally, we classified the study area as either vegetation (cropland) or non-vegetation (fallow arable land, bare areas, and sand dunes). A multiple regression model was then applied to these categories to examine the relationships between streamflow, precipitation, land surface temperature (LST), and the NDVI. The multiple regression for the entire region showed that these factors explained 45.1% of NDVI variation, with streamflow being the most significant positive driver (p < 0.001). The result showed that the NDVI in cropland and arable land was strongly positively correlated with both precipitation and streamflow (R = 0.78, R = 0.75). In contrast, bare land and dunes showed weaker relationships (R = 0.26 and 0.51, respectively). Of these factors, streamflow had the most significant influence in explaining vegetation change (partial correlation p = 0.53), indicating the importance of human management in addition to climate.
published_date 2026-02-19T05:34:53Z
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