ارزیابی اثرات کاهش سطح آب زیرزمینی بر پراکندگی پوشش گیاهی با مدل آنتروپی شانون(منطقه مورد مطالعه: شهرستان رشتخوار)

Understanding vegetation spatial patterns is vital for assessing ecosystem health, especially in water-stressed arid and semi-arid regions. These areas, often characterized by sparse and sensitive vegetation, face significant challenges from climate change and human pressures. Rashtkhar County in Iran exemplifies such an environment, experiencing a severe decline in groundwater levels, which necessitates precise monitoring of its vegetation dynamics. Quantifying the spatial dispersion or heterogeneity of vegetation cover objectively is challenging. While indices like NDVI provide information on vegetation density, they often miss crucial details about spatial structure and fragmentation. Shannon's entropy model, derived from information theory, offers a robust method to measure the complexity or dispersion within a spatial system. Specifically, Shannon's relative entropy (G), a normalized index, allows for standardized comparisons of dispersion across time and space, effectively differentiating between uniform and highly fragmented landscapes. Given the ecological importance of vegetation in Rashtkhar and the intense pressure on its water resources, this study's primary objective was to utilize Shannon's relative entropy model to quantitatively assess the trend of spatial vegetation cover dispersion from 1990 to 2023. A key goal was to analyze the relationship between this dispersion trend, groundwater level fluctuations (approx. 1986-2022), and the region's topography. The findings aim to provide insights for sustainable resource management in similar vulnerable areas, highlighting the need for quantitative tools to understand complex ecosystem dynamics.

This research employed a descriptive-analytical approach using remote sensing and GIS to investigate quantitative trends in vegetation spatial dispersion in Rashtkhar County, a predominantly arid area in Khorasan Razavi Province, Iran (~4,360 km²). The study period covered 1990-2023 for vegetation and water years 1365-66 to 1400-1401 (approx. 1986-2022) for groundwater. Time-series Landsat imagery (TM and OLI sensors, 30m resolution) for 1990, 2000, 2010, 2015, 2020, and 2023 were acquired from USGS. After standard radiometric and atmospheric corrections, NDVI was calculated using ENVI 5.6 to map vegetation cover for each year. Groundwater level data were sourced from the provincial Regional Water Authority. To incorporate topography, the area was classified into five elevation zones using a DEM (Figure 3). Shannon's relative entropy model was then applied to quantify spatial dispersion annually. Relative entropy (G) was calculated as G = H / ln(n), where H = - Σ [pi * ln(pi)], 'pi' is the proportion of vegetation cover in topographic zone 'i', and 'n' is the number of zones (n=5). G ranges from 0 (maximum concentration) to 1 (maximum dispersion). Calculations were performed using Excel and ArcGIS. Finally, the temporal trend of G was compared with groundwater level trends to analyze the interplay between vegetation spatial structure and water resource availability.

The analysis revealed significant and contrasting trends in groundwater levels and vegetation dispersion in Rashtkhar County. Groundwater elevation showed a severe and nearly continuous decline over the ~35-year period, dropping approximately 50 meters from above 1110m to just over 1060m. This drastic reduction highlights critical pressure on groundwater resources and aquifer depletion. Conversely, Shannon's relative entropy (G) for vegetation cover, measuring spatial dispersion, followed a different trajectory. G increased sharply from 0.801 in 1990 to 0.913 in 2000. Despite minor fluctuations, it maintained a high level with a slight upward trend, reaching 0.924 in 2023. This indicates a sustained shift towards greater heterogeneity and spatial dispersion of vegetation cover. While the total vegetation area mapped via NDVI showed an overall increase (from ~8,000 ha to nearly 30,000 ha), its spatial distribution changed, with increased contributions from low and high elevation zones at the expense of mid-elevation areas. The key finding is the stark contrast: severe groundwater depletion occurred concurrently with increased (or stabilization at high levels of) vegetation spatial dispersion. This seemingly paradoxical outcome likely does not signify ecological improvement. Instead, it strongly suggests a complex structural rearrangement of the landscape driven by adaptation to water stress. The increased dispersion might result from factors like shifts towards more drought-resistant but potentially scattered crops (e.g., saffron), fragmentation of agricultural lands due to changing economic viability or land tenure, uneven development, or the patchy survival of native vegetation in micro-refugia. The analysis also confirmed that topography significantly influences how this dispersion manifests, modulating the vegetation system's response to environmental pressures like water scarcity.

This study quantitatively assessed vegetation spatial dispersion in Rashtkhar County using Shannon's relative entropy, relating it to groundwater trends and topography. A major finding was the contrasting long-term trends: a severe, continuous ~50-meter decline in groundwater levels (approx. 1986-2022) alongside a significant increase and stabilization of vegetation spatial dispersion (G rising from 0.801 in 1990 to 0.924 in 2023). This increased heterogeneity, occurring amidst resource depletion, is interpreted not as ecological recovery but as evidence of landscape structural rearrangement. This rearrangement likely reflects adaptive responses to water stress, such as changes in land use, cropping patterns towards more scattered cultivation, land fragmentation, or patchy vegetation survival. Topography was confirmed as a key factor modulating these spatial patterns. The research demonstrates the utility of Shannon's relative entropy as a valuable quantitative tool for capturing complex spatial dynamics and their interactions with environmental drivers like water availability in arid/semi-arid regions. It highlights that relying solely on overall vegetation indices can be misleading, and understanding spatial structure is crucial for assessing ecosystem sustainability. These findings underscore the need to incorporate such spatial metrics into sustainable resource management frameworks, particularly for developing integrated water conservation and land use planning strategies in fragile ecosystems under pressure. Future work could enhance this understanding by incorporating detailed land use data, grazing information, and higher-resolution remote sensing inputs.