Origin of 1.8 Ga zircons in Post Eocene mafic dikes in the Roshtkhar area,NE Iran

The Roshtkhar area is located in the Khaf-Kashmar-Bardaskan volcano-plutonic belt to the northeastern Iran along the regional E–W trending Dorouneh Fault, northeastern of the Lut Block. There are several outcrops of subvolcanic rocks occurring mainly as dikes in the area, which intruded into Cenozoic intrusive rocks. We present U–Pb dating of zircons from a diabase dike and syenite rock using LA-ICP-MS that yielded an age of 1778 ± 10 Ma for the dike, indicating this Cenozoic dike has zircon xenocrysts inherited from deeper sources; and 38.0 ± 0.5 Ma, indicating an Late Eocene crystallization age for the syenite. Geochemically, the dikes typical of high-K calc-alkaline to shoshonitic magmas. Petrographic observations and major and trace element variations suggest that diabase melts underwent variable fractionation of clinopyroxene, olivine, and Fe-Ti oxides and minor crustal contamination during the differentiation process. Primitive mantle-normalized multi-element diagrams display enrichment in LILE, such as Rb, Ba, Th, U, and Sr compared to HFSE, as well as negative anomalies of Nb, Ta, P, and Ti, suggesting derivation from subduction-modified mantle. Chondrite-normalized REE plots show moderately LREE enriched patterns (<3.83 La_N/Yb_N <8.27), and no significant Eu anomalies. Geochemical modelling using Sm/Yb versus La/Yb and La/Sm ratios suggests a low-degree of batch melting (~1–3%) of a phlogopite-spinel peridotite source to generate the mafic dikes. The geochemical signatures suggest that the Roshtkhar mafic dikes cannot be related directly to subduction and likely resulted from melting of upper mantle in an extensional setting where the heat flow was provided from deeper levels. These dikes presumably derived the zircon xenocrysts from the assimilation of upper crust of Gondwanian basement. Processes responsible for partial melting of metasomatized lithospheric mantle and post-collision magmatism in NE Iran was triggered by heating due to asthenospheric upwelling in an extensional setting.     

Mapping grassland leaf area index with airborne hyperspectral imagery: A comparison study of statistical approaches and inversion of radiative transfer models

Statistical and physical models have seldom been compared in studying grasslands. In this paper, both modeling approaches are investigated for mapping leaf area index (LAI) in a Mediterranean grassland (Majella National Park, Italy) using HyMap airborne hyperspectral images. We compared inversion of the PROSAIL radiative transfer model with narrow band vegetation indices (NDVI-like and SAVI2-like) and partial least squares regression (PLS). To assess the performance of the investigated models, the normalized RMSE (nRMSE) and R² between in situ measurements of leaf area index and estimated parameter values are reported. The results of the study demonstrate that LAI can be estimated through PROSAIL inversion with accuracies comparable to those of statistical approaches (R² = 0.89, nRMSE = 0.22). The accuracy of the radiative transfer model inversion was further increased by using only a spectral subset of the data (R² = 0.91, nRMSE = 0.18). For the feature selection wavebands not well simulated by PROSAIL were sequentially discarded until all bands fulfilled the imposed accuracy requirements.     

Iranshahr Blueschists as Results of Subduction of the Neotethys Inner Makran Oceanic Crust, SE Iran

<正>The Makran accretionary prism is one of the most extensive subduction accretions on Earth.It is characterized by intense folding,thrust faulting and dislocation of the Cenozoic units consisted of sedimentary,igneous and metamorphic rocks.It is located in the southeast of Iran and southwest of Pakistan.Rock     

Metamorphic history of glaucophane‐paragonite‐zoisite eclogites from the Shanderman area,northern Iran

The Shanderman eclogites and related metamorphosed oceanic rocks mark the site of closure of the Palaeotethys ocean in northern Iran. The protolith of the eclogites was an oceanic tholeiitic basalt with MORB composition. Eclogite occurs within a serpentinite matrix, accompanied by mafic rocks resembling a dismembered ophiolite. The eclogitic mafic rocks record different stages of metamorphism during subduction and exhumation. Minerals formed during the prograde stages are preserved as inclusions in peak metamorphic garnet and omphacite. The rocks experienced blueschist facies metamorphism on their prograde path and were metamorphosed in eclogite facies at the peak of metamorphism. The peak metamorphic mineral paragenesis of the rocks is omphacite, garnet (pyrope‐rich), glaucophane, paragonite, zoisite and rutile. Based on textural relations, post‐peak stages can be divided into amphibolite and greenschist facies. Pressure and temperature estimates for eclogite facies minerals (peak of metamorphism) indicate 15–20 kbar at ~600 °C. The pre‐peak blueschist facies assemblage yields <11 kbar and 400–460 °C. The average pressure and temperature of the post‐peak amphibolite stage was 5–6 kbar, ~470 °C. The Shanderman eclogites were formed by subduction of Palaeotethys oceanic crust to a depth of no more than 75 km. Subduction was followed by collision between the Central Iran and Turan blocks, and then exhumation of the high pressure rocks in northern Iran.     

Assessing trends and seasonal changes in elephant poaching risk at the small area level using spatio-temporal Bayesian modeling

Knowledge about changes in wildlife poaching risk at fine spatial scale can provide essential background intelligence for law enforcement and crime prevention. We assessed interannual trends and seasonal changes in elephant poaching risk for Kenya’s Greater Tsavo ecosystem for 2002 to 2012 using spatio-temporal Bayesian modeling. Poaching data were obtained from the Kenya Wildlife Service’s database on elephant mortality. The novelty of our paper is (1) combining space and time when defining poaching risk for elephant; (2) the inclusion of environmental risk factors to improve the accuracy of the spatio-temporal Bayesian model; and (3) the separate analysis of dry and wet seasons to understand season-dependent poaching patterns. Although Tsavo’s overall poaching level increased over time, the risk of poaching differed significantly across space. Three of the 34 spatial units had a consistently high poaching risk regardless of whether models included environmental risk factors. Adding risk factors enhanced the model’s predictive power. We found that highest poaching risk areas differed between the wet and dry season. The findings improve our understanding of elephant poaching and highlight high risk areas within Tsavo where action to reduce elephant poaching is required.     

A multi‐label cellular automata model for land change simulation

The use of cellular automata (CA) has for some time been considered among the most appropriate approaches for modeling land‐use changes. Each cell in a traditional CA model has a state that evolves according to transition rules, taking into consideration its own and its neighbors’ states and characteristics. Here, we present a multi‐label CA model in which a cell may simultaneously have more than one state. The model uses a multi‐label learning method—a multi‐label support vector machine, Rank‐SVM—to define the transition rules. The model was used with a multi‐label land‐use dataset for Luxembourg, built from vector‐based land‐use data using a method presented here. The proposed multi‐label CA model showed promising performance in terms of its ability to capture and model the details and complexities of changes in land‐use patterns. Applied to historical land use data, the proposed model estimated the land use change with an accuracy of 87.2% exact matching and 98.84% when including cells with a misclassification of a single label, which is comparably better than a classical multi‐class model that achieved 83.6%. The multi‐label cellular automata outperformed a model combining CA and artificial neural networks. All model goodness‐of‐fit comparisons were quantified using various performance metrics for predictive models.     

LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements

The study shows that leaf area index (LAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) can be mapped in a heterogeneous Mediterranean grassland from canopy spectral reflectance measurements. Canopy spectral measurements were made in the field using a GER 3700 spectroradiometer, along with concomitant in situ measurements of LAI and LCC. We tested the utility of univariate techniques involving narrow band vegetation indices and the red edge inflection point, as well as multivariate calibration techniques, including stepwise multiple linear regression and partial least squares regression. Among the various investigated models, CCC was estimated with the highest accuracy (, ). All methods failed to estimate LCC (), while LAI was estimated with intermediate accuracy ( values ranged from 0.49 to 0.69). Compared with narrow band indices and red edge inflection point, stepwise multiple linear regression generally improved the estimation of LAI. The estimations were further improved when partial least squares regression was used. When a subset of wavelengths was analyzed, it was found that partial least squares regression had reduced the error in the retrieved parameters. The results of the study highlight the significance of multivariate techniques, such as partial least squares regression, rather than univariate methods such as vegetation indices in estimating heterogeneous grass canopy characteristics.     

A policy making framework for resilient port infrastructure systems

This research is an effort to develop a Risk Management-based Decision Analysis (RMDA) framework based on the common fundamental elements that define the nature of resilience in Port Infrastructure Systems (PIS). While developing a systematic process for making strategic and investment decisions, RMDA guides the decision-makers to identify, analyze, and prioritize risks involved in PIS operations; to define ways for risk mitigation, plan for contingencies, and devise mechanisms for continuously monitoring and controlling risk factors and threats to the system; and to value the adopted resilience investment plans and strategies. Our suggested RMDA framework is a policy making tool that utilizes a Decision Tree Analysis (DTA) methodology for assessing the cost-effectiveness of the devised strategies.     

Leaf to canopy upscaling approach affects the estimation of canopy traits

In remote sensing applications, leaf traits are often upscaled to canopy level using sunlit leaf samples collected from the upper canopy. The implicit assumption is that the top of canopy foliage material dominates canopy reflectance and the variability in leaf traits across the canopy is very small. However, the effect of different approaches of upscaling leaf traits to canopy level on model performance and estimation accuracy remains poorly understood. This is especially important in short or sparse canopies where foliage material from the lower canopy potentially contributes to the canopy reflectance. The principal aim of this study is to examine the effect of different approaches when upscaling leaf traits to canopy level on model performance and estimation accuracy using spectral measurements (in-situ canopy hyperspectral and simulated Sentinel-2 data) in short woody vegetation. To achieve this, we measured foliar nitrogen (N), leaf mass per area (LMA), foliar chlorophyll and carbon together with leaf area index (LAI) at three vertical canopy layers (lower, middle and upper) along the plant stem in a controlled laboratory environment. We then upscaled the leaf traits to canopy level by multiplying leaf traits by LAI based on different combinations of the three canopy layers. Concurrently, in-situ canopy reflectance was measured using an ASD FieldSpec-3 Pro FR spectrometer, and the canopy traits were related to in-situ spectral measurements using partial least square regression (PLSR). The PLSR models were cross-validated based on repeated k-fold, and the normalized root mean square errors (nRMSE_cv) obtained from each upscaling approach were compared using one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test. Results of the study showed that leaf-to-canopy upscaling approaches that consider the contribution of leaf traits from the exposed upper canopy layer together with the shaded middle canopy layer yield significantly (p < 0.05) lower error (nRMSE_cv < 0.2 for canopy N, LMA and carbon) as well as high explained variance (R² > 0.71) for both in-situ hyperspectral and simulated Sentinel-2 data. The widely-used upscaling approach that considers only leaf traits from the upper illuminated canopy layer yielded a relatively high error (nRMSE_cv>0.2) and lower explained variance (R² < 0.71) for canopy N, LMA and carbon. In contrast, canopy chlorophyll upscaled based on leaf samples collected from the upper canopy and total canopy LAI exhibited a more accurate relationship with spectral measurements compared with other upscaling approaches. Results of this study demonstrate that leaf to canopy upscaling approaches have a profound effect on canopy traits estimation for both in-situ hyperspectral measurements and simulated Sentinel-2 data in short woody vegetation. These findings have implications for field sampling protocols of leaf traits measurement as well as upscaling leaf traits to canopy level especially in short and less foliated vegetation where leaves from the lower canopy contribute to the canopy reflectance.     

Iranshahr blueschist: subduction of the inner Makran oceanic crust

The Makran accretionary prism in SE Iran and SW Pakistan is one of the most extensive subduction accretions on Earth. It is characterized by intense folding, thrust faulting and dislocation of the Cenozoic units that consist of sedimentary, igneous and metamorphic rocks. Rock units forming the northern Makran ophiolites are amalgamated as a mélange. Metamorphic rocks, including greenschist, amphibolite and blueschist, resulted from metamorphism of mafic rocks and serpentinites. In spite of the geodynamic significance of blueschist in this area, it has been rarely studied. Peak metamorphic phases of the northern Makran mafic blueschist in the Iranshahr area are glaucophane, phengite, quartz±omphacite+epidote. Post peak minerals are chlorite, albite and calcic amphibole. Blueschist facies metasedimentary rocks contain garnet, phengite, albite and epidote in the matrix and as inclusions in glaucophane. The calculated P–T pseudosection for a representative metabasic glaucophane schist yields peak pressure and temperature of 11.5–15 kbar at 400–510 °C. These rocks experienced retrograde metamorphism from blueschist to greenschist facies (350–450 °C and 7–8 kbar) during exhumation. A back arc basin was formed due to northward subduction of Neotethys under Eurasia (Lut block). Exhumation of the high‐pressure metamorphic rocks in northern Makran occurred contemporarily with subduction. Several reverse faults played an important role in exhumation of the ophiolitic and HP‐LT rocks. The presence of serpentinite shows the possible role of a serpentinite diapir for exhumation of the blueschist. A tectonic model is proposed here for metamorphism and exhumation of oceanic crust and accretionary sedimentary rocks of the Makran area. Vast accretion of subducted materials caused southward migration of the shore.