No abstract

The Zagros mountain forests in Iran, constitutes approximately 40 percent of the country's forests expanding in eleven provinces, provides important functions as soil and water preservation. The forestes sustained severe oak decline in places over the last decade, triggered by chain factors such as drought, pathogens and Borer beetles. Determining the extent of the declined regions is the first step to address manage and address the risk posed by such environmental hazards. In this research, we focus on Malekshahi city in Ilam province and use Landsat satellite images in years between 2000 and 2015 for determining spatial pattern of oak decline in this region. Slope of temporal variation of an appropriate vegetation index and a water index, are extracted and analyzed from Landsat imageries. The oak forests are classified in three categories: Healthy forests, lowseverity declined forests, and high-severity declined forests, based on EVI and NDWI. According to the results, approximately 16%, 58% and 26% of the region belongs to healthy regions, low and high level of disease, respectively. Finally, the overall accuracy of the oak decline map, is evaluated based on available ground truth data. About %83 overall accuracy, shows high performance of the proposed method in detecting declined regions against healthy ones. But it has less ability in classifying different levels of decline, since overall acccuracy is about %54 for this purpose.

Quality and quantity of vegetation land cover is considered as one of the important aspects of environment. Detection of trends in natural phenomena such as vegetation, requires long-term studies, more than lifetime of a satellite. On the other hand, combining data from different sensors could lead to formation of false changes. One of the main causes of false changes is different spectral sensitivity functions (SRFs), among sensors under study. In this regard, the impact of these factors should be eliminated or reduced as much as possible by a procedure named relative calibration which is the main goal of this research. There are similarities between Landsat satellites series and SPOT-5 with Sentinel-2 in many aspects, so MSI (the Sentinel-2's sensor) has capacity for data continuity. In this study, by incorporating polynomial equations, Landsat sensors (OLI, ETM +, ETM) and SPOT-5 were calibrated relative to MSI. The combination of radiative transfer models; PROSPECT-4 for leaf and 4SAIL for canopy, were used to simulate 50000 top of canopy synthetic spectral signatures and then soil effect was combined with them using linear spectral mixture model. After all, 150000 signatures were simulated. These spectral signatures were transformed to equivalent reflectance values (Blue, Red, NIR and SWIR) and spectral indices (NDVI, EVI and NDWI). 80% of spectral signatures were selected randomly for solving relative calibration models. Also, for validation purpose, remained simulated (20%) and 38 top of canopy measured spectral signatures were used. According to the results, linear equation can model the difference (caused by SRF) between MSI and others quite well and there is no need for more complicated equations. In general, results of this research show high and acceptable correlation for all reflectance bands and indices. It is more necessary to perform a relative calibration pre-processing step for EVI time series. Amongst reflectance bands, NIR has the highest continuity.