Mapping oak decline through long-term analysis of time series of satellite images in the forests of Malekshahi, Iran

Abstract

ABSTRACT The Zagros Mountains forests extend across 11 provinces in Iran and constitute approximately 40.0% of the country’s woodlands. These forests have important soil conservation and water regulation functions. Over the last decade, these forests have been declining in oak populations in many places, triggered by factors such as drought, pathogens like the fungus Biscogniauxia mediterranea, and pests such as borer beetles. Mapping the regions that show such a decline is the first step to addressing and managing the risks posed by this environmental calamity. In this research, we focus on the forests surrounding Malekshahi city in the Ilam province of Iran. Using Landsat data from the years 2000 to 2016, we determined the spatial distribution of oak decline in the region. After applying a forest/non-forest classification, appropriate spectral indices including Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were selected. Together with ground truth data, two regression methods (linear regression and support vector regression (SVR)) were used to model the decline score of each pixel based on the slope of variation of selected spectral indices during the observed 17 years. The oak forests were then classified into four categories: healthy forests, low-severity-declined forests, mid-severity declined forests, and high-severity declined forests, based on the respective estimated decline scores. SVR mapped different severities of oak decline with an overall accuracy of 51%, which appears to be due to the dependency of the method on the time of decline during the 17-year timeframe. However, in a binary classification mode – meaning classifying decline score to be either ‘Healthy’ or ‘decline’ – both regression methods were able to detect declined pixels with a producer’s accuracy of 100%.