مدل‌سازی حساسیت اکولوژیکی به بیابان‌زایی با استفاده از مؤلفه‌های Tasseled Cap و منطق فازی در استان واسط عراق

Desertification is a creeping and multifactorial environmental phenomenon that severely affects arid and semi-arid regions, leading to ecological degradation, loss of natural resources, 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 utilizes 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 central research question is: How can Tasseled Cap components and fuzzy logic be used to quantitatively and spatially model ecological sensitivity to desertification in Wasit Province over a multi-year period?Desertification is a major environmental threat in arid and semi-arid regions, causing ecological degradation, resource loss, and socio-economic instability. Wasit Province in Iraq is particularly vulnerable due to its dry climate, geographic location, and increasing human pressures. 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). After applying the Tasseled Cap transformation, the three components were normalized using triangular fuzzy membership functions and integrated via fuzzy overlay to generate ecological sensitivity maps. Sensitivity was classified into five levels: very stable, stable, semi-stable, sensitive, and highly sensitive. In 2010, 12.7% of the province was categorized as stable to semi-stable, while 65.7% was highly sensitive. By 2022, stable areas declined to 11.2%, and highly sensitive zones expanded to 73.2%, indicating intensified desertification. Spatial maps revealed that stable zones were concentrated along the Tigris River, emphasizing the importance of hydrological features in ecological resilience. Peripheral areas showed warmer colors, reflecting critical 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, integrating MODIS data, Tasseled Cap components, and fuzzy logic offers a robust framework for monitoring desertification, identifying critical zones, and supporting informed ecological management in vulnerable landscapes like Wasit Province.In 2010, approximately 12.7% of the 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 (orange and red), reflecting critical conditions. Fuzzy change detection analysis showed that ecological changes were predominantly gradual, uniform, and widespread rather than abrupt or localized. This study demonstrates that combining 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 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.In 2010, approximately 12.7% of the 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 (orange and red), reflecting critical conditions. Fuzzy change detection analysis showed that ecological changes were predominantly gradual, uniform, and widespread rather than abrupt or localized. 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

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 showing 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.