Evaluating the Spatiotemporal Dynamics and Recovery of Ecological Environmental Quality Post Flood in Lorestan Province Using Remote Sensing

Natural disasters typically cause environmental degradation. A scientific and quantitative assessment of ecological environmental quality and its post-disaster recovery trajectory can provide valuable insights for mitigating future hazards. In this study, the April 2019 flood event in Lorestan Province, Iran, was selected as a case study to investigate the spatiotemporal variations and driving factors of ecological environmental quality prior to and following the flood. Initially, MODIS data were employed to construct the Remote Sensing Ecological Index (RSEI). Subsequently, the spatiotemporal dynamics of ecological environmental quality across Lorestan Province were evaluated from 2015 to 2024, alongside an analysis of spatial autocorrelation relationships. Finally, the Geographical Detector (GeoDetector) model was implemented to identify the key factors influencing post-flood environmental recovery. The results demonstrated that the flood event caused a notable decline in the RSEI from 0.7658 to 0.6081, which subsequently recovered to 0.6514, reflecting the significant impact of the flood phenomenon on the province's environmental quality. Spatially, areas with higher ecological quality were predominantly concentrated in the central and eastern regions of the province, whereas the northwestern and southeastern areas exhibited lower quality. Moran's I index exceeded 0.75, indicating a strong positive spatial autocorrelation and clustering pattern of ecological environmental quality. Based on the GeoDetector analysis, elevation and population density played the most prominent roles in driving the post-flood ecological quality dynamics. Among the interactive factors, the interaction between elevation and slope exhibited the strongest explanatory power regarding the ecological recovery process
Introduction
In recent years, natural disasters such as floods, earthquakes, land subsidence, and landslides have frequently occurred in Iran. Floods, as one of the most severe natural hazards, not only cause extensive human casualties and financial losses but also severely degrade the ecological structure of the environment. Ecosystems serve as the material foundation for human life and development, playing a vital role in climate regulation, environmental purification, pest control, and biodiversity conservation. In April 2019, Lorestan Province experienced a catastrophic flood event that caused severe financial damage to the province's infrastructure, claimed 15 lives, destroyed numerous homes, and left profound psychological impacts on survivors. However, the environmental impact of this flood and its subsequent recovery trajectory required rigorous scientific investigation, which constitutes the primary objective of this study. Despite numerous studies on the Lorestan flood (focusing on landslides, psychological trauma, and social responses), post-disaster ecological recovery monitoring remained unaddressed in this region. Most previous studies focused solely on vegetation cover or land-use changes (Mehrabi, 2021; Moradi et al., 2024), whereas the Remote Sensing Ecological Index (RSEI) provides a more comprehensive assessment of environmental health by integrating multiple core indicators, including greenness, wetness, heat, and dryness. Therefore, this study combines the RSEI and the Geographical Detector (GeoDetector) model to evaluate ecological quality variations and recovery following the flood event. This research aims to support improved environmental management planning and post-disaster sustainable development, thereby contributing to the Sustainable Development Goals (SDGs), particularly in building resilient and sustainable cities and human settlements.
Materials and Methods
This study utilized MODIS satellite data spanning from 2015 to 2024. Two primary datasets were employed: MOD09A1 for surface reflectance across various spectral bands and MOD11A2 for Land Surface Temperature (LST). The key advantages of MODIS data include broad spatial coverage, high temporal resolution (8-day composites), and suitability for time-series analyses, making it ideal for long-term ecological monitoring. The primary software used included the MODIS Reprojection Tool (MRT), ENVI, and ArcGIS. First, the MODIS data for the study area were batch-processed using MRT, which involved image mosaicking, spatial reprojection to a common coordinate system, and format conversion. To address cloud contamination, cloud-affected pixels were identified and masked, and composite clean images were generated to cover the entire study area. Subsequently, using ENVI 5.3, four primary indicators—Normalized Difference Vegetation Index (NDVI) representing greenness, Wetness index from tasseled cap transformation, Land Surface Temperature (LST) representing heat, and Normalized Difference Built-up and Soil Index (NDBSI) representing dryness—were extracted and integrated via Principal Component Analysis (PCA) to construct the composite RSEI.
Results and Discussion
In the first Principal Component (PC1), with the exception of the year 2020, the loadings for NDVI and WET—both positively correlated with favorable ecological conditions—consistently exhibited positive signs. The percentage contribution of eigenvalues for PC1 across the six temporal stages were 71.35%, 63.86%, 67.09%, 62.49%, 52.88%, and 63.29%, respectively, all of which were significantly higher than those of the other components. This indicates that PC1 captured the vast majority of critical information from the four selected indicators. Consequently, the RSEI was constructed based on PC1 to analyze the pre- and post-flood EEQ dynamics. Higher RSEI values denote ecological improvement, whereas lower values indicate EEQ degradation. As presented in Table 2, the mean RSEI value prior to the flood (2015–2019) remained relatively stable above 0.65, reflecting favorable environmental quality across the province. The highest and lowest RSEI values were recorded in 2017 and 2020 at 0.7658 and 0.6081, respectively. This decline of 0.1577 units is directly attributable to the impacts of the flood event, which caused widespread vegetation loss, soil erosion, and surface degradation. Following 2020, RSEI values exhibited a steady increase, indicating substantial environmental recovery and a return toward pre-disaster conditions. Overall, the spatiotemporal variations in EEQ from 2015 to 2024 can be categorized into three distinct phases: the pre-flood period (2015–2019) with favorable ecological conditions; the flood occurrence period (2019–2020) characterized by a marked reduction in mean RSEI due to ecosystem damage; and the post-flood recovery period (2020–2024) marked by a rising mean RSEI and progressive environmental restoration.
Conclusion
The main findings of this study are summarized as follows:
- The temporal RSEI values were recorded at 0.6741, 0.7658, 0.6658, 0.6081, 0.6236, and 0.6514, respectively. The overall trajectory from 2015 to 2024 exhibited a clear "Stability – Decline – Recovery" pattern, which closely corresponds to the EEQ dynamics. These results indicate that while the flood degraded the province's ecological quality, gradual environmental recovery ensued, ultimately restoring overall ecological quality to near pre-crisis levels.
- Area-based statistics comparing pre- and post-flood RSEI revealed that areas experiencing an RSEI increase accounted for only 33% of the total area, whereas areas exhibiting a decrease covered 44% of the study region, indicating that recovery has been uneven across the province.
- Spatial autocorrelation analysis revealed that Moran's I values from 2015 to 2024 were consistently positive, indicating that the spatial distribution of EEQ is significantly clustered rather than randomly distributed. Furthermore, the GeoDetector analysis identified elevation and population density as the dominant drivers of RSEI spatial variation. In terms of factor interactions, the interactive effect of elevation and slope exerted the strongest explanatory power on ecological recovery, consistently explaining over 40% of the variance throughout the study period.