Analyzing Vegetation Heterogeneity Trends in an Urban-Agricultural Landscape in Iran Using Continuous Metrics and NDVI

Understanding vegetation heterogeneity dynamics is crucial for assessing ecosystem resilience, biodiversity patterns, and the impacts of environmental changes on landscape functions. While previous studies primarily focused on NDVI pixel trends, shifts in landscape heterogeneity have often been overlooked. To address this gap, our study evaluated the effectiveness of continuous metrics in capturing vegetation dynamics over time, emphasizing their utility in short-term trend analysis. The study area, located in Iran, encompasses a mix of urban and agricultural landscapes dominated by farming-related vegetation. Using 11 Landsat 8 OLI images from 2013 to 2023, we calculated NDVI to analyze vegetation trends and heterogeneity dynamics. We applied three categories of continuous metrics: texture-based metrics (dissimilarity, entropy, and homogeneity), spatial autocorrelation indices (Getis and Moran), and surface metrics (Sa, Sku, and Ssk) to assess vegetation heterogeneity. By generating slope maps through linear regression, we identified significant trends in NDVI and correlated them with the slope maps of the continuous metrics to determine their effectiveness in capturing vegetation dynamics. Our findings revealed that Moran’s Index exhibited the highest positive correlation (0.63) with NDVI trends, followed by Getis (0.49), indicating strong spatial clustering in areas with increasing NDVI. Texture-based metrics, particularly dissimilarity (0.45) and entropy (0.28), also correlated positively with NDVI dynamics, reflecting increased variability and heterogeneity in vegetation composition. In contrast, negative correlations were observed with metrics such as homogeneity (−0.41), Sku (−0.12), and Ssk (−0.24), indicating that increasing NDVI trends were associated with reduced uniformity and surface dominance. Our analysis underscores the complementary roles of these metrics, with spatial autocorrelation metrics excelling in capturing clustering patterns and texture-based metrics highlighting value variability within clusters. By demonstrating the utility of spatial autocorrelation and texture-based metrics in capturing heterogeneity trends, our findings offer valuable tools for land management and conservation planning.