An analysis of vegetation indices in relation to tree species diversity using by satellite data in the northern forests of Iran

Vegetation indices have been introduced for analyzing and assessing the status of quantitative and qualitative characteristics of vegetation using satellite images. However, choosing the best indices to be used in forest biodiversity and vegetation is one of the important problems faced by the users. The purpose of this research is to evaluate six vegetation indices in the analysis of tree species diversity in the northern forests of Iran. The present research uses LISS III sensor data from IRS-P6 satellite. Geometric rectification of images was performed using ground control points, and Chavez model was used for atmospheric correction of the data. The six spectral vegetation indices included NDVI, IPVI, Ashburn Vegetation Index (AVI), TVI, TTVI, and RVI. Shannon–Wiener species diversity index was used to analyze diversity, and the value of the index was calculated in each sample plot. Then, the spectral values of each sample plot were extracted from different bands. The best subset regression was used to analyze the relationship between species diversity and the related bands. The results obtained from the regression showed that polynomial equations under scrutiny as independent variables can assess tree and shrub species diversity better than other bands and compounds used (R ²?=?0.47). The obtained results also indicated a higher capacity in the case of the AVI index for estimating tree species diversity in the under study area.     

The application of geochemical pattern recognition to regional prospecting: A case study of the Sanandaj–Sirjan metallogenic zone,Iran

In regional exploration programs, the distribution of elements in known mineral deposits can be used as a guide for the classification of deposits, search for new prospects and modeling ore deposit patterns. The Sanandaj–Sirjan Zone (SSZ) is a major metallogenic zone in Iran, containing lead and zinc, iron, gold, copper deposits. In the central part of the SSZ, lead and zinc mineralization is widespread and hitherto exploration has been based on geological criteria. In this study, we used clustering techniques applied to element distribution for classification lead and zinc deposits in the central part of the SSZ. The hierarchical clustering technique was used to characterize the elemental pattern. Elements associated with lead and zinc deposits were separated into four clusters, encompassing both ore elements and their host rock-forming elements. It is shown that lead and zinc deposits in the central SSZ belong to two genetic groups: a MVT type hosted by limestone and dolomites and a SEDEX type hosted by shale, volcanic rocks and sandstone. The results of elemental clustering were used for pattern recognition by the K-means method and the respective deposits were classified into four distinct categories. K-means clustering also reveals that the elemental associations and spatial distribution of the lead and zinc deposits exhibit zoning in the central part of the SSZ. The ratios of ore-forming elements (Sb, Cd, and Zn) vs. (Pb and Ag) show zoning along an E–W trend, while host rock-forming elements (Mn, Ca, and Mg) vs. (Ba and Sr) show a zoning along a SE–NW trend. Large and medium deposits occur mainly in the center of the studied area, which justify further exploration around occurrences and abandoned mines in this area. The application of a pattern recognition method based on geochemical data from known mineralization in the central SSZ, and the classification derived from it, uncover elemental zoning, identify key elemental associations for further geochemical exploration and the potential to discover possible target areas for large to medium size ore deposits. This methodology can be applied in a similar way to search for new ore deposits in a wide range of known metallogenic zones.     

New age constraints on the cooling and unroofing history of the Trans-Himalayan Ladakh Batholith (Kargil area), N. W. India

Thermotectonic history of the Trans-Himalayan Ladakh Batholith in the Kargil area, N. W. India, is inferred from new age data obtained here in conjunction with previously published ages. Fission-track (FT) ages on apatite fall around 20±2 Ma recording cooling through temperatures of ∼100°C and indicating an unroofing of 4 km of the Ladakh Range since the Early Miocene. Coexisting apatite and zircon FT ages from two samples in Kargil show the rocks to have cooled at an average rate of 5–6°C/Ma in the past 40 Ma. Zircon FT ages together with mica K−Ar cooling ages from the Ladakh Batholith cluster around 40–50 Ma, probably indicating an Eocene phase of uplift and erosion that affected the bulk of the batholith after the continental collision of India with the Ladakh arc at 55 Ma. Components of the granitoids in Upper Eocene-Lower Oligocene sediments of the Indus Molasse in Ladakh supports this idea. Three hornblende K−Ar ages of 90 Ma, 55 Ma, and 35 Ma are also reported; these distinctly different ages probably reflect cooling through 500–550°C of three phases of I-type plutonism in Ladakh also evidenced by other available radiometric data: 102 Ma (mid-Cretaceous), 60 Ma (Palaeocene), and 40 Ma (Late Eocene); the last phase being localised sheet injections. The geodynamic implications of the age data for the India-Asia collision are discussed.     

Prediction of Groundwater Level in Ardebil Plain Using Support Vector Regression and M5 Tree Model

The Ardebil plain, which is located in northwest Iran, has been faced with a recent and severe decline in groundwater level caused by a decrease of precipitation, successive long‐term droughts, and overexploitation of groundwater for irrigating the farmlands. Predictions of groundwater levels can help planners to deal with persistent water deficiencies. In this study, the support vector regression (SVR) and M5 decision tree models were used to predict the groundwater level in Ardebil plain. The monthly groundwater level data from 24 piezometers for a 17‐year period (1997 to 2013) were used for training and test of models. The model inputs included the groundwater levels of previous months, the volume of entering precipitation into every cell, and the discharge of wells. The model output was the groundwater level in the current month. In order to evaluate the performance of models, the correlation coefficient (R) and the root‐mean‐square error criteria were used. The results indicated that both SVR and M5 decision tree models performed well for the prediction of groundwater level in the Ardebil plain. However, the results obtained from the M5 decision tree model are more straightforward, more easily applied, and simpler to interpret than those from the SVR. The highest accuracy was obtained using the SVR model to predict the groundwater level from the Ghareh Hasanloo and Khalifeloo piezometers with R = 0.996 and R = 0.983, respectively.     

Time-dependent seismic hazard analysis for the Greater Tehran and surrounding areas

This study presents a time-dependent approach for seismic hazard in Tehran and surrounding areas. Hazard is evaluated by combining background seismic activity, and larger earthquakes may emanate from fault segments. Using available historical and paleoseismological data or empirical relation, the recurrence time and maximum magnitude of characteristic earthquakes for the major faults have been explored. The Brownian passage time (BPT) distribution has been used to calculate equivalent fictitious seismicity rate for major faults in the region. To include ground motion uncertainty, a logic tree and five ground motion prediction equations have been selected based on their applicability in the region. Finally, hazard maps have been presented.