Document Type : Full length article
Authors
Department of Remote Sensing and Geographic Information Systems, Faculity of Planning and Science of Environmet, University of Tabriz, Tabriz, Iran
10.22059/jphgr.2026.400590.1007900
Abstract
ABSTRACT
Reduction in vegetation cover (greenness parameter) and soil moisture (wetness parameter) accompanied by increased reflectance (Brightness parameter) constitute the main signs of ecosystem damage and desertification. Wasit Province in Iraq is particularly vulnerable due to its dry climate, geographic location, and increasing human pressures. Wasit Province is located in the southeast of Iraq and on the border with Iran. This study assesses ecological sensitivity to desertification using Tasseled Cap components—derived from MODIS satellite imagery for 2010 and 2022. MOD09A1 surface reflectance data (500m resolution) were selected for the green season (March–June). Tasseled Cap components were normalized via triangular fuzzy functions and overlaid to create ecological sensitivity maps. Sensitivity was classified into five levels: very stable, stable, semi-stable, sensitive, and highly sensitive. The results exhibit that in 2010, 12.7% of the province was categorized as stable to semi-stable, while 65.7% was highly sensitive By 2022, stable areas fell to 11.2% and high-sensitivity zones grew to 73.2%, signaling worsening desertification. Stable zones clustered along the Tigris River - highlighting water's role in resilience - while peripheral areas showed critical (warm-colored) conditions. Fuzzy change detection between the two years indicated gradual, widespread ecological shifts rather than abrupt changes, likely driven by climate variability, land-use changes, and sustained anthropogenic pressure. The fuzzy overlay method effectively captured transitional zones and spatial heterogeneity, outperforming traditional classification techniques. Overall, MODIS data, Tasseled Cap indices, and fuzzy logic together form a robust framework for desertification monitoring, hotspot identification, and ecological management in vulnerable regions like Wasit Province.
Extended Abstract
Introduction
Desertification is a creeping and multifactorial environmental phenomenon that severely affects arid and semi-arid regions, leading to ecological degradation, natural resource depletion, and socio-economic instability. Wasit Province in southeastern Iraq is particularly vulnerable due to its dry climate, fragile ecosystems, and increasing anthropogenic pressures such as unsustainable land use, groundwater extraction, and vegetation loss. The urgency of addressing desertification in Wasit stems from its accelerating pace and spatial extent, which threaten agricultural productivity, water security, and ecological resilience. Despite the availability of remote sensing technologies, many existing models for desertification assessment remain either qualitative, temporally limited, or spatially coarse, lacking the precision needed for pixel-level ecological sensitivity mapping. This study aims to develop an integrated analytical framework for modeling ecological sensitivity to desertification using satellite-derived indicators and fuzzy logic. Specifically, it utilized Tasseled Cap components—Brightness, Greenness, and Wetness—extracted from MODIS imagery, and applies fuzzy normalization and overlay techniques to generate spatially explicit sensitivity maps for the years 2010 and 2022. The primary research question is: How can Tasseled Cap components and fuzzy logic be employed to develop a quantitative and spatially explicit model of ecological sensitivity to desertification in Wasit Province over a multi-year period?
The selection of the greening season (March to June) was intended to more accurately capture vegetation dynamics and minimize seasonal noise. Key innovative aspects of this study include the simultaneous use of three Tasseled Cap ecological components via fuzzy aggregation; the utilization of Google Earth Engine for scalable, reproducible cloud-based data processing; the generation of pixel-level ecological sensitivity maps suitable for temporal comparison; and the development of a generalizable framework applicable to similar arid regions across the Middle East, North Africa, and Central Asia. This approach can serve as a model for monitoring desertification in other vulnerable areas.
Methodology
This study utilized MODIS surface reflectance data (MOD09A1, 500m resolution) for the green season (March to June) in 2010 and 2022. Tasseled Cap transformation was applied to derive three ecological indicators: Brightness (soil exposure), Greenness (vegetation density), and Wetness (surface moisture). Each component was normalized using triangular fuzzy membership functions and integrated through fuzzy overlay to produce ecological sensitivity maps. The final outputs were classified into five sensitivity levels: very stable, stable, semi-stable, sensitive, and highly sensitive. Validation was performed using the membership function assigned to the input values. The final output for each year consisted of three GeoTIFF files corresponding to the Tasseled Cap components:
Brightness_YYYY.tif, Greenness_YYYY.tif, and Wetness_YYYY.tif
These files were saved in a Google Drive folder named "Wasit_TasseledCap." The outputs have a spatial resolution of 500 meters and were clipped to the boundaries of Wasit Province.
Results and discussion
In 2010, approximately 12.7% of the Wasit province was classified as stable to semi-stable, while 65.7% fell into the highly sensitive category. By 2022, stable areas declined to 11.2%, and highly sensitive zones expanded to 73.2%, indicating intensified desertification and ecological fragility. Spatial maps revealed that stable zones were concentrated along the Tigris River, highlighting the critical role of hydrological features in maintaining ecological resilience. Peripheral regions exhibited warmer colors - ranging from orange to red - reflecting critical conditions. Fuzzy change detection analysis showed that ecological changes were predominantly gradual, uniform, and widespread rather than abrupt or localized.
The integration of Tasseled Cap components with fuzzy logic enabled a more nuanced representation of transitional zones and spatial heterogeneity compared to conventional crisp classification methods. The Tigris River emerged as a key stabilizing factor, with surrounding areas demonstrating less ecological degradation. The expansion of highly sensitive zones over the decade suggests escalating land degradation driven by climatic variability, land-use changes, and sustained human pressure. The Tigris River, flowing through the center of the province, serves as a key hydrological element and plays a significant role in the pattern of changes. More pronounced ecological changes are observed along the riverbanks—particularly in downstream areas—likely resulting from water level fluctuations, land-use changes, or anthropogenic pressures. This spatial pattern indicates that ecological changes in Wasit Province have been heterogeneous in terms of both intensity and spatial distribution; central areas adjacent to the Tigris River have been more susceptible to severe changes, whereas peripheral areas distant from water sources have experienced less alteration. It demonstrates consistency. The range of phase changes extends from approximately 0.51 to 0.65, indicating that a significant portion of the study area experiences changes of moderate to high intensity. The absence of negative values in the generated and analyzed histogram reveals that the change function is defined in a normalized manner using positive values; consequently, higher values signify greater change intensity, without specifying the direction of the change (positive or negative). The normal distribution of phase changes indicates that ecological processes in the region have occurred gradually and extensively, rather than locally or abruptly. This pattern may stem from uniform climatic factors, gradual changes in land use, or homogeneous management practices across the region. Given these results, it is recommended that supplementary spatial analyses be conducted on the change map to characterize the spatial patterns of change and identify areas experiencing more intense changes or those that are particularly sensitive.
Conclusion
This study demonstrates that the combination of MODIS data, Tasseled Cap indicators, and fuzzy logic provides a robust framework for monitoring desertification and assessing ecological sensitivity. The approach offers precise spatial insights, supports temporal comparisons, and facilitates the identification of critical zones for targeted intervention. Future research studies should consider integrating additional datasets such as soil properties, precipitation, and land use to enhance model accuracy and applicability. The framework is scalable and transferable to other vulnerable regions across the Middle East, North Africa, and Central Asia, contributing to land degradation neutrality and sustainable development goals.
Funding
There is no funding support.
Authors’ Contribution
Authors contributed equally to the conceptualization and writing of the article. All of the authors approved the content of the manuscript and agreed on all aspects of the work declaration of competing interest none.
Conflict of Interest
Authors declared no conflict of interest.
Acknowledgments
We are grateful to all the scientific consultants of this paper.
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