seyed mohammad tavakkoli sabour

Taftan is a semi-active volcano located in southeast Iran with many craters. The main objective of this study is to investigate whether subsidence or uplift in Taftan Peak. 58 Sentinel 1-A images acquired from January 2015 to December 2020 in the ascending orbit mode, and 102 Sentinel 1-A Sentinel 1-B images acquired from October 2014 to June 2020 in the descending orbit mode were preprocessed for this purpose. The interferograms with the permanent scatterer interferometry (PSI) method were created using SARPROZ and StaMPS softwares, in which atmospheric corrections were made automatically, and then the surface displacement of Taftan volcano estimated.The Line of Sight (LOS) displacement corresponding to the uplift was 0.5 mm to 1 mm yr-1 for the ascending orbit and 1 mm yr-1for the descending orbit. Because no GPS station was close to Taftan volcano, the GPS measurements of one station located in the study area (Saravan station) was used to check the accuracy of PSI method. The GPS station of SARAVAN has situated inside the town ,and it is appropriate to use and analyze PSI technique in this station. As a result, the researchers found that the PSI method is in accordance with the GPS data.

This research presents a method for extracting the spectral characteristics of oil springs (oil seepages) and their exploration by applying artificial neural networks and maximum likelihood algorithms on Landsat 8 OLI images. The study area covers three provinces: Khuzestan, Fars, kohkiluyeh, and Boyar Ahmad. Vegetation, water, soil spectral indices, and tasseled cap transformation outputs along surface reflectance images generated the feature space required to explore oil springs. Samples were manually collected from all the features of oil springs to identify and explore oil springs, including different land uses and the existing oil springs already recorded by the Ministry of Petroleum. The separability of oil spring samples was examined with the help of spectral values and signs of sample features. Then, using the statistical data of a part of the samples, the feedforward neural network training with 8 hidden layers was carried out and evaluated. Also, the model parameters for the maximum likelihood algorithm were estimated using the extracted samples. Finally, the trained neural network and maximum likelihood algorithm were applied to the spectral characteristics of the entire study area to extract the probable locations of the oil springs. The results based on the test data showed that the neural network with a kappa coefficient of 92.07% and an overall accuracy of 99.53% separated the oil springs from other land uses. However, the maximum likelihood algorithm showed Poor performance in separating the oil springs from other land uses. The kappa coefficient and its overall accuracy were equal to 22.93% and 73.35%, respectively. By examining the classified image obtained from the neural network, 15 new points were extracted as promising locations for oil springs. These points were verified and confirmed by Google Earth images.

Vegetation is considered to be one of the most important elements in all major ecosystems on the Earth. Thus, a proper understanding of vegetation and its growth trends and other environmental factors has always been of particular importance for environmental research. Estimating vegetation phenology parameters (VPPs) requires continuous NDVI data collection over a specific period of time. However, soil moisture, cloud cover, and particulate matter may affect the energy reflected from the vegetation cover and result in noisy images or erroneous data. Vegetation phenology parameters cannot be extracted from raw data due to the presence of random errors. These errors do not follow the phenological process and thus, overestimate or underestimate NDVI and fail to produce accurate results. Smoothing functions and especially the TIMESAT model are used to resolve this issue and eliminate errors in the NDVI time series. There is still no general consensus on which function acts more efficient and accurate in the TIMESAT model especially regarding the highlands. Naturally, each method yields different results in different regions, and thus it is necessary to compare and evaluate different functions used in the TIMESAT model and determine their accuracy in producing a continuous time series. The present study aimed to evaluate the performance of various functions such as asymmetric Gaussian (AG), double-logistic (DL), and Savitzky–Golay (SG) used to extract VPPs especially in mountainous regions.

TIMESAT model is a time-series analysis model based on remote sensing (RS) vegetation indices. It includes three functions: Savitzky–Golay, asymmetric Gaussian, and double-logistic, which are used to smooth collected data and identify outliers. Savitzky–Golay is an adaptive-degree polynomial filter (ADPF). The other two functions fit the information using nonlinear functions. These functions use unmodified NDVI data as input to produce modified and smoothed NDVI output. Four wheat farms in cold and warm regions of Khorramabad were used in the present study to investigate plant phenological behaviors and extract VPPs. The northern and eastern parts of Khorramabad have a cold climate, while the southern and western parts have a warm climate. One-year time series (2020) data of MODIS sensor was used in the present study. Using the infrared and near-infrared spectral reflectance values, NDVI was calculated in the Google Earth Engine environment. Errors of the NDVI time series were first corrected and a phenology curve was produced for wheat in both warm and cold farms. Asymmetric Gaussian, double-logistic, and Savitzky–Golay filter functions were also used to adapt the NDVI data. Following the reconstruction of growth curves in the time series of vegetation indices and smoothing the curve, various VPPs such as start of the season (SOS), end of the season (EOS), middle of the season (MOS), length of the growing season (LOS), base limit and value, maximum NDVI, vegetation growth season range, large seasonal integral, and small seasonal integral were extracted.

 Results and Discussion:

The model indicated that on average, beginning of the wheat growing season (SOS) in the warm regions of Khoramabad coincided with the 31.5th day of the year in the Gregorian calendar, whereas it happened on the 90th day of the year in the cold regions, thus indicating a 1.5-2 month difference between the beginning of the wheat growing season in cold and warm regions. The wheat growing season ended (EOS) on the 163rd day of year in the warm regions and on the 193rd day in the cold regions. In addition, in order to analyze the effect of climate on VPPs such as SOS and EOS, a comparison was made between the parameters obtained from farms in warm and cold regions. On average, the peak of vegetation growth has occurred in late March (Mar. 28, 2020) in farms of warm regions while cold regions experienced the peak of growth on May 20, 2020. In other words, warm regions have experienced peak growth approximately two months earlier than cold regions. Finally, the models were assessed and obtained values were compared with ground-based data collected in field surveys. Validation results showed that with an average RMSE of 2, Savitzky–Golay smoothing model reconstruct data more accurately as compared to asymmetric Gaussian, and double logistic function with an RMSE of 4 and 11, respectively. In other words, Savitzky–Golay estimates SOS and EOS with a higher accuracy and lower errors.

Findings indicate that Savitzky–Golay filter outperformed asymmetric Gaussian and double logistic functions in extracting VPPs in mountainous areas. Accordingly, it is suggested to use Savitzky–Golay in future studies aiming to investigate the phenological behavior of different vegetation covers in other Iranian highlands. The study has also showed that different climatic conditions within the study area affect plant phenological behaviors, which can lead to differences in SOS, peak of growing season, and EOS in different cold and warm regions of the province. Growing season of plants in cold regions of the province has occurred with an approximately two-month delay compared to the warm regions of the province.

The availability of suitable maps of terrain features is very important because these complications and the process governing them are the sources of many environmental hazards and resources. The purpose of this research, compare the optic and radar data for terrain feature extraction. A panchromatic band of Landsat 8 (OLI) as an optical image and two C-band Sentinel-1 satellite radar images with a resolution of 22×20 m per pixel were used. Two radar images (radar-1 and 2) in two different acquisition directions with different look angels were visually interpreted. In order to reduce the geometric and radiometric effects of the topography, the radar images were corrected using 3-second SRTM data (modified radar). In this study, four feature types were extracted by visual interpretation, which is: valleys, blades, alluvial fans, and debris fans and the results were compared with the World Imagery layer. The accuracy, completeness, quality, kappa, and z-test were calculated for every interpretation. The results showed that the highest accuracies in recognition of valleys and blades using Landsat image were 83.90% and 87.88%, respectively, and the highest accuracies of alluvial fan and the debris fan of the modified radar image were 82.76% and 83.72% respectively. The highest kappa coefficient related to the modified radar data was calculated at 54.72% (Landsat 49.74%) and the highest z-text related to Landsat-radar 1 was calculated 0.9871 (Modified Radar-Landsat 0.6443%).