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Mapping of Land use and land cover (LULC) changes over time requires automated processes and has been investigated using various machine learning algorithms and, more recently, deep learning models for semantic classification. New applications of these models to different satellite data and areas are regularly published. However, studies on the transfer of these models to other data and study areas are rather scarce. In a previous study [1], we used multi-modal and –temporal Sentinel data for LULC classification using traditional and novel deep learning models. The data covered parts of the Amazon basin and was comprised of a twelve-month time series of radar imagery (Sentinel-1), combined with a singular multi-spectral image (Sentinel-2). All satellite images were captured throughout the year 2018. The label map (Collection 4) of the Amazon produced by the MapBiomas project [2] was used as training and test labels. Besides state-of-the-art models, we developed five variations of a deep learning model—DeepForest—which leverages on the multi-temporal and -modal aspect of the data. The best model variation (DF1c) reached an overall accuracy of 74.4% on the test data.Currently we are investigating the transferability of these models to more recent data of the same region. The new dataset was processed in the same way as in the previous study. It comprises a Sentinel-1 time-series and a single Sentinel-2 images from 2020, with an updated version of the label map of the MapBiomas project (Collection 6). This posed some challenges, as the classification scheme changed and is not fully backwards compatible with the one used to train the DeepForest models. A test dataset was chosen in the state of Mato Grosso, as the satellite scenes cover most classes used in the classification scheme. However, this data exhibits some class imbalance, as two of the eleven classes are dominating the scene. All five DeepForest variations reached accuracies higher than 79% and thus generalize well on the major LULC classes. For comparison and to further improve our models, we currently retrain the models on the new, larger data set (114,376 training image tiles compared to 18,074). Preliminary results will be shown during the session. ReferencesCherif, E.; Hell, M.; Brandmeier, M. DeepForest: Novel Deep Learning Models for Land Use and Land Cover Classification Using Multi-Temporal and -Modal Sentinel Data of the Amazon Basin. Remote Sensing 2022, 14, 5000, doi:10.3390/rs14195000. MapBiomas Brasil; Available online: https://mapbiomas.org/en

Knowledge about tree species distribution is important for forest management and for modeling and protecting biodiversity in forests. Methods based on images are inherently limited to the forest canopy. Airborne lidar data provide information about the trees’ geometric structure, as well as trees beneath the upper canopy layer. In this paper, the potential of two deep learning architectures (PointCNN, 3DmFV-Net) for classification of four different tree classes is evaluated using a lidar dataset acquired at the Bavarian Forest National Park (BFNP) in a leaf-on situation with a maximum point density of about 80 pts/m$$^{2}$$ 2 . Especially in the case of BFNP, dead wood plays a key role in forest biodiversity. Thus, the presented approaches are applied to the combined classification of living and dead trees. A total of 2721 single trees were delineated in advance using a normalized cut segmentation. The trees were manually labeled into four tree classes (coniferous, deciduous, standing dead tree with crown, and snag). Moreover, a multispectral orthophoto provided additional features, namely the Normalized Difference Vegetation Index. PointCNN with 3D points, laser intensity, and multispectral features resulted in a test accuracy of up to 87.0%. This highlights the potential of deep learning on point clouds in forestry. In contrast, 3DmFV-Net achieved a test accuracy of 73.2% for the same dataset using only the 3D coordinates of the laser points. The results show that the data fusion of lidar and multispectral data is invaluable for differentiation of the tree classes. Classification accuracy increases by up to 16.3% points when adding features generated from the multispectral orthophoto.

Zygaena species are sensitive indicators of ecological changes and habitat destruction. Here, field results on the ecology of two highly endangered species, Zygaena brizae and Zygaena cynarae, from optimal habitats near Col d'Araud in Hautes-Alpes, France are reported. Both species have faced severe population decline in the last decades in Southwest France, probably due to habitat deterioration. During May and June 2022, larvae, imagines and egg batches were mapped in carefully characterized habitat patches. In total, 713 imagines, 67 larvae and 20 egg batches of Zygaena cynarae (which was the most common burnet moth in the area in 2022) and 23 imagines, 84 larvae, 2 pupae and 18 egg batches of Zygaena brizae were observed. While Zygaena brizae occurs both on mesophile and on semi-dry slopes (only north-and west-facing), supported by increasing numbers of the larval food plant (Cirsium tuberosum), larval habitats of Zygaena cynarae are characterized by the common occurrence of the larval food plant (Peucedanum cervaria) and by (optimally) 10-30% tree cover. Most colonized patches are south-facing and flat; open habitat patches with many larval food plants are not inhabited. All inhabited patches are extensively grazed by sheep, goats, cattle or horses-the specifics do not seem to matter. Details of grazing schedule, sunshine duration, slope exposure and other habitat parameters are reported and evaluated to support conservation measures of these burnet moth species. Implications for insect conservation: Our results provide robust data about the specific habitat requirements of these two strongly endangered burnet moth species.

Deep learning has been used successfully in computer vision problems, e.g. image classification, target detection and many more. We use deep learning in conjunction with ArcGIS to implement a model with advanced convolutional neural networks (CNN) for lithological mapping in the Mount Isa region (Australia). The area is ideal for spectral remote sensing as there is only sparse vegetation and besides freely available Sentinel-2 and ASTER data, several geophysical datasets are available from exploration campaigns. By fusing the data and thus covering a wide spectral range as well as capturing geophysical properties of rocks, we aim at improving classification accuracies and support geological mapping. We also evaluate the performance of the sensors on their own compared to a joint use as the Sentinel-2 satellites are relatively new and as of now there exist only few studies for geological applications. We developed an end-to-end deep learning model using Keras and Tensorflow that consists of several convolutional, pooling and deconvolutional layers. Our model was inspired by the family of U-Net architectures, where low-level feature maps (encoders) are concatenated with high-level ones (decoders), which enables precise localization. This type of network architecture was especially designed to effectively solve pixel-wise classification problems, which is appropriate for lithological classification. We spatially resampled and fused the multi-sensor remote sensing data with different bands and geophysical data into image cubes as input for our model. Pre-processing was done in ArcGIS and the final, fine-tuned model was imported into a toolbox to be used on further scenes directly in the GIS environment. The tool classifies each pixel of the multiband imagery into different types of rocks according to a defined probability threshold. Results highlight the power of using Sentinel-2 in conjunction with ASTER data with accuracies of 75% in comparison to only 70% and 73% for ASTER or Sentinel-2 data alone. These results are similar but examining the different classes shows that there are significant improvements for classes such as dolerite or carbonate sediments that are not that widely distributed in the area. Adding geophysical datasets reduced accuracies to 60%, probably due to an order of magnitude difference in spatial resolution. In comparison, Random Forest (RF) and Support Vector Machines (SVMs) that were trained on the same data only achieve accuracies of 46 % and 36 % respectively. Most insecurity is due to labelling errors and labels with mixed lithologies. However, results show that the U-Netmodel is a powerful alternative to other classifiers for medium-resolution multispectral data.

Degradation of large forest areas such as the Brazilian Amazon due to logging and fires can increase the human footprint way beyond deforestation. Monitoring and quantifying such changes on a large scale has been addressed by several research groups (e.g. Souza et al. 2013) by making use of freely available remote sensing data such as the Landsat archive. However, fully automatic large-scale land cover/land use mapping is still one of the great challenges in remote sensing. One problem is the availability of reliable “ground truth” labels for training supervised learning algorithms. For the Amazon area, several landcover maps with 22 classes are available from the MapBiomas project that were derived by semi-automatic classification and verified by extensive fieldwork (Project MapBiomas). These labels cannot be considered real ground-truth as they were derived from Landsat data themselves but can still be used for weakly supervised training of deep-learning models that have a potential to improve predictions on higher resolution data nowadays available. The term weakly supervised learning was originally coined by (Zhou 2017) and refers to the attempt of constructing predictive models from incomplete, inexact and/or inaccurate labels as is often the case in remote sensing. To this end, we investigate advanced deep-learning strategies on Sentinel-1 timeseries and Sentinel-2 optical data to improve large-scale automatic mapping and monitoring of landcover changes in the Amazon area. Sentinel-1 data has the advantage to be resistant to cloud cover that often hinders optical remote sensing in the tropics.We propose new architectures that are adapted to the particularities of remote sensing data (S1 timeseries and multispectral S2 data) and compare the performance to state-of-the-art models. Results using only spectral data were very promising with overall test accuracies of 77.9% for Unet and 74.7% for a DeepLab implementation with ResNet50 backbone and F1 measures of 43.2% and 44.2% respectively. On the other hand, preliminary results for new architectures leveraging the multi-temporal aspect of SAR data have improved the quality of mapping, particularly for agricultural classes. For instance, our new designed network AtrousDeepForestM2 has a similar quantitative performances as DeepLab (F1 of 58.1% vs 62.1%), however it produces better qualitative land cover maps.To make our approach scalable and feasible for others, we integrate the trained models in a geoprocessing tool in ArcGIS that can also be deployed in a cloud environment and offers a variety of post-processing options to the user.Souza, J., Carlos M., et al. (2013). "Ten-Year Landsat Classification of Deforestation and Forest Degradation in the Brazilian Amazon." Remote Sensing 5(11): 5493-5513. Zhou, Z.-H. (2017). "A brief introduction to weakly supervised learning." National Science Review 5(1): 44-53."Project MapBiomas - Collection 4.1 of Brazilian Land Cover & Use Map Series, accessed on January 2020 through the link: https://mapbiomas.org/colecoes-mapbiomas?cama_set_language=en"

The number of severe storm events has increased in recent decades due to climate change. These storms are one of the main causes for timber loss in European forests and damaged areas are prone to further degradation by, for example, bark beetle infestations. Usually, manual mapping of damaged areas based on aerial photographs is conducted by forest departments. This is very time-consuming and therefore automatic detection of windthrows based on active and passive remote sensing data is an ongoing research topic. In this study we evaluated state-of-the-art Convolutional Neural Networks (CNNs) in combination with Geographic Information Systems (GIS) for calamity assessment. The study area is in in the northern part of Hesse (Germany) and was covered by twelve Sentinel-2 scenes from 2018. Labels of damaged areas from the Friedericke storm (18 January 2018) were provided by HessenForst. We conducted several experiments based on a custom U-Net setup to derive the optimal architecture and input data as well as to assess the transferability of the model. Results highlight the possibility to detect damaged forest areas using Sentinel-2 data. Using a binary classification, accuracies of more than 92% were achieved with an Intersection over Union (IoU) score of 46.6%. The proposed workflow was integrated into ArcGIS and is suitable for fast detection of damaged areas directly after a storm and for disaster management but is limited by the deca-meter spatial resolution of the Sentinel-2 data.

Forest damage due to storms causes economic loss and requires a fast response to prevent further damage such as bark beetle infestations. By using Convolutional Neural Networks (CNNs) in conjunction with a GIS, we aim at completely streamlining the detection and mapping process for forest agencies. We developed and tested different CNNs for rapid windthrow detection based on PlanetScope satellite data and high-resolution aerial image data. Depending on the meteorological situation after the storm, PlanetScope data might be rapidly available due to its high temporal resolution, while the acquisition of high-resolution airborne data often takes weeks to a month and is, therefore, used in a second step for more detailed mapping. The study area is located in Bavaria, Germany (ca. 165 km2), and labels for damaged areas were provided by the Bavarian State Institute of Forestry (LWF). Modifications of a U-Net architecture were compared to other approaches using transfer learning (e.g., VGG19) to find the most efficient architecture for the task on both datasets while keeping the computational time low. A custom implementation of U-Net proved to be more accurate than transfer learning, especially on medium (3 m) resolution PlanetScope imagery (intersection over union score (IoU) 0.55) where transfer learning completely failed. Results for transfer learning based on VGG19 on high-resolution aerial image data are comparable to results from the custom U-Net architecture (IoU 0.76 vs. 0.73). When using both architectures on a dataset from a different area (located in Hesse, Germany), however, we find that the custom implementations have problems generalizing on aerial image data while VGG19 still detects most damage in these images. For PlanetScope data, VGG19 again fails while U-Net achieves reasonable mappings. Results highlight the potential of Deep Learning algorithms to detect damaged areas with an IoU of 0.73 on airborne data and 0.55 on Planet Dove data. The proposed workflow with complete integration into ArcGIS is well-suited for rapid first assessments after a storm event that allows for better planning of the flight campaign followed by detailed mapping in a second stage.

Due to climate change the number of storms and, thus, forest damage has increased over recent years. The state of the art of damage detection is manual digitization based on aerial images and requires a great amount of work and time. There have been numerous attempts to automatize this process in the past such as change detection based on SAR and optical data or the comparison of Digital Surface Models (DSMs) to detect changes in the mean forest height. By using Convolutional Neural Networks (CNNs) in conjunction with GIS we aim at completely streamlining the detection and mapping process.We developed and tested different CNNs for rapid windthrow detection based on Planet data that is rapidly available after a storm event, and on airborne data to increase accuracy after this first assessment. The study area is in Bavaria (ca. 165 square km) and data was provided by the agency for forestry (LWF). A U-Net architecture was compared to other approaches using transfer learning (e.g. VGG32) to find the most performant architecture for the task on both datasets. U-Net was originally developed for medical image segmentation and has proven to be very powerful for other classification tasks.Preliminary results highlight the potential of Deep Learning algorithms to detect damaged areas with accuracies of over 91% on airborne data and 92% on Planet data. The proposed workflow with complete integration into ArcGIS is well-suited for rapid first assessments after a storm event that allows for better planning of the flight campaign, and first management tasks followed by detailed mapping in a second stage.

Urban landscapes are characterized as the fastest changing areas on the planet. However, regularly monitoring of larger areas it is not feasible using UAVs or costly air borne data. In these situations, satellite data with a high temporal resolution and large field of view are more appropriate but suffer from the lower spatial resolution (deca-meters). In the present study we show that by using freely available Sentinel-2 data from the Copernicus program, we can extract anthropogenic features such as roads, railways and building footprints that are partly or completely on a sub-pixel level in this kind of data. Additionally, we propose a new metric for the evaluation of our methods on the sub-pixel objects. This metric measures the performance of the detection of an object while penalizing the false positive classification. Given that our training samples contain one class, we define two thresholds that represent the lower bound of accuracy for the object to be classified and the background. We thus avoid a good score in occasions where we classify correctly our object, but a wide area of the background has been included in our prediction. We investigate the performance of different deep-learning architectures for sub-pixel classification of the different infrastructure elements based on Sentinel-2 multispectral data and the labels derived from the UAV data. Our study area is located in the Rhone valley in Switzerland where very high-resolution UAV data was available from the University of Applied Sciences. Highly accurate labels for the respective classes were digitized in ArcGIS Pro and used as ground-truth for the Sentinel data. We trained different deep learning models based on state-of-the-art architectures for semantic segmentation, such as DeepLab and U-Net. Our approach focuses on the exploitation of the multi spectral information to increase the performance of the RGB channels. For that purpose, we make use of the NIR and SWIR 10m and 20m bands of the Sentinel-2 data. We investigate early and late fusion approaches and the behavior and contribution of each multi spectral band to improve the performance in comparison to only using the RGB channels. In the early fusion approach, we stack nine (RGB, NIR, SWIR) Sentinel-2 bands together, pass them from two convolutions followed by batch normalization and relu layers and then feed the tiles to DeepLab. In the late fusion approach, we create a CNN with two branches with the first branch processing the RGB channels and the second branch the NIR/SWIR bands. We use modified DeepLab layers for the two branches and then concatenate the outputs into a total output of 512 feature maps. We then reduce the dimensionality of the result into the finaloutput equal to the number of classes. The dimension reduction step happens in two convolution layers. We experiment on different settings for all of the mentioned architectures. In the best-case scenario, we achieve 89% overall accuracy. Moreover, we measure 60% building accuracy, streets accuracy 60%, railway accuracy 73%, river accuracy 92% and background accuracy 94%.

Multivariate statistical and geospatial analyses based on a compilation of 890 geochemical and ~ 1200 geochronological data for 194 mapped ignimbrites from the Central Andes document the compositional and temporal patterns of large-volume ignimbrites (so-called “ignimbrite flare-ups”) during Neogene times. Rapid advances in computational science during the past decade led to a growing pool of algorithms for multivariate statistics for large datasets with many predictor variables. This study applies cluster analysis (CA) and linear discriminant analysis (LDA) on log-ratio transformed data with the aim of (1) testing a tool for ignimbrite correlation and (2) distinguishing compositional groups that reflect different processes and sources of ignimbrite magmatism during the geodynamic evolution of the Central Andes. CA on major and trace elements allows grouping of ignimbrites according to their geochemical characteristics into rhyolitic and dacitic “end-members” and to differentiate characteristic trace element signatures with respect to Eu anomaly, depletions in middle and heavy rare earth elements (REE) and variable enrichments in light REE. To highlight these distinct compositional signatures, we applied LDA to selected ignimbrites for which comprehensive datasets were available. In comparison to traditional geochemical parameters we found that the advantage of multivariate statistics is their capability of dealing with large datasets and many variables (elements) and to take advantage of this n-dimensional space to detect subtle compositional differences contained in the data. The most important predictors for discriminating ignimbrites are La, Yb, Eu, Al2O3, K2O, P2O5, MgO, FeOt, and TiO2. However, other REE such as Gd, Pr, Tm, Sm, Dy and Er also contribute to the discrimination functions. Significant compositional differences were found between (1) the older (> 13 Ma) large-volume plateau-forming ignimbrites in northernmost Chile and southern Peru and (2) the younger (

With an increasing demand for raw materials, predictive models that support successful mineral exploration targeting are of great importance. We evaluated different machine learning techniques with an emphasis on boosting algorithms and implemented them in an ArcGIS toolbox. Performance was tested on an exploration dataset from the Iberian Pyrite Belt (IPB) with respect to accuracy, performance, stability, and robustness. Boosting algorithms are ensemble methods used in supervised learning for regression and classification. They combine weak classifiers, i.e., classifiers that perform slightly better than random guessing to obtain robust classifiers. Each time a weak learner is added; the learning set is reweighted to give more importance to misclassified samples. Our test area, the IPB, is one of the oldest mining districts in the world and hosts giant volcanic-hosted massive sulfide (VMS) deposits. The spatial density of ore deposits, as well as the size and tonnage, makes the area unique, and due to the high data availability and number of known deposits, well-suited for testing machine learning algorithms. We combined several geophysical datasets, as well as layers derived from geological maps as predictors of the presence or absence of VMS deposits. Boosting algorithms such as BrownBoost and Adaboost were tested and compared to Logistic Regression (LR), Random Forests (RF) and Support Vector machines (SVM) in several experiments. We found performance results relatively similar, especially to BrownBoost, which slightly outperformed LR and SVM with respective accuracies of 0.96 compared to 0.89 and 0.93. Data augmentation by perturbing deposit location led to a 7% improvement in results. Variations in the split ratio of training and test data led to a reduction in the accuracy of the prediction result with relative stability occurring at a critical point at around 26 training samples out of 130 total samples. When lower numbers of training data were introduced accuracy dropped significantly. In comparison with other machine learning methods, Adaboost is user-friendly due to relatively short training and prediction times, the low likelihood of overfitting and the reduced number of hyperparameters for optimization. Boosting algorithms gave high predictive accuracies, making them a potential data-driven alternative for regional scale and/or brownfields mineral exploration.

We use freely available Sentinel-2 data and forest inventory data to evaluate the potential of different machine-learning approaches to classify tree species in two forest regions in Bavaria, Germany. Atmospheric correction was applied to the level 1C data, resulting in true surface reflectance or bottom of atmosphere (BOA) output. We developed a semiautomatic workflow for the classification of deciduous (mainly spruce trees), beech and oak trees by evaluating different classification algorithms (object- and pixel-based) in an architecture optimized for distributed processing. A hierarchical approach was used to evaluate different band combinations and algorithms (Support Vector Machines (SVM) and Random Forest (RF)) for the separation of broad-leaved vs. coniferous trees. The Ebersberger forest was the main project region and the Freisinger forest was used in a transferability study. Accuracy assessment and training of the algorithms was based on inventory data, validation was conducted using an independent dataset. A confusion matrix, with User´s and Producer´s Accuracies, as well as Overall Accuracies, was created for all analyses. In total, we tested 16 different classification setups for coniferous vs. broad-leaved trees, achieving the best performance of 97% for an object-based multitemporal SVM approach using only band 8 from three scenes (May, August and September). For the separation of beech and oak trees we evaluated 54 different setups, the best result achieved an accuracy of 91% for an object-based, SVM, multitemporal approach using bands 8, 2 and 3 of the May scene for segmentation and all principal components of the August scene for classification. The transferability of the model was tested for the Freisinger forest and showed similar results. This project points out that Sentinel-2 had only marginally worse results than comparable commercial high-resolution satellite sensors and is well-suited for forest analysis on a tree-stand level.

Quasi-planar morphological surfaces may become dissected or degraded with time, but still retain original features related to their geologic-geomorphic origin. To decipher the information hidden in the relief, recognition of such features is required, possibly in an automated manner. In our study, using Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), an existing algorithm has been adapted to recognize quasi-planar features fulfilling specified criteria. The method has been applied to a study area of the Central Andes with Miocene to Quaternary volcanic edifices, tilted ignimbrite surfaces, and basin-filling sediments. The result is a surface segmentation, whereas non-planar features (gullies, tectonic faults, etc.) are sorted out. The main types of geomorphic features that can be distinguished and interpreted are as follows. (1) The west-dipping western margin of the Altiplano is differentiated into segments of the lower sedimentary cover that of increased erosion by tectonic steepening at intermediate levels, and an upper plane with limited erosion. (2) In the central part of the Western Cordillera, the Oxaya ignimbrite block shows a ‘striped’ bulging pattern that results from a smoothly changing surface dip. This pattern is due to continuous folding/warping of the ignimbrite block possibly related to gravitational movements. (3) To the west, large, uniform planes correspond to flat, smooth, tectonically undisturbed surfaces of young sedimentary cover of the Central Basin. (4) The evolution of Taapaca volcanoes with sector collapse events and cone-building phases is shown by several segments with overlapping clastic aprons. (5) To the east, on the western margin of the Altiplano, young intermontane basins filled by Upper Miocene sediments show progressively increasing dip toward basin margins, reflected by a circular pattern of the segmentation planes. We show that the segmentation models provide meaningful images and additional information for geomorphometric analysis that can be interpreted in terms of geological and surface evolution models. Copyright © 2014 John Wiley & Sons, Ltd.

Because formation of ore deposits is linked to volcanic and post-volcanic processes, an understanding of alteration style in volcanic regions has important applications in economic geology. We use ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data and field spectrometry for mineral mapping in selected Miocene to Quaternary volcanic areas in Southern Peru to better characterize and understand the Tertiary volcanic evolution in this region. Our goal is to characterize volcanic regions near Puquio (Ayacucho) by correlating areas of intense alteration and related ignimbrite outflow sheets. In particular, we spectrally and mineralogically map different types and intensities of alteration based on remote sensing and ground-truth data. ASTER ratio images, alteration indices and false color composites were used to select ground-training areas for sample collection and field spectrometry. Alteration samples were characterized geochemically, mineralogically and spectrally. Absorption features correlate with chemical properties (e.g. iron content). Hyperspectral data from field spectrometry allow identification of important alteration minerals such as kaolinite and smectite. Alteration mineral assemblages range from silicic to argillic to “zeolite-type”. Using a support vector machine classification (SVM) algorithm on ASTER data, we mapped the different types and intensities of alteration, along with unaltered ignimbrite and lava flows with an accuracy of 80%. We propose a preliminary model for the interpretation of alteration settings, discuss the potential eruption sites of the ignimbrites in the region and, propose pH and temperature estimates for the respective classes based on the mineral assemblages identified.

On the example of the Epembe carbonatite-hosted Nb-Ta-LREE deposit, we demonstrate the use of hyperspectral reflectance data and geomorphic indicators for improving the accuracy of remote sensing exploration data of structurally-controlled critical raw material deposits. The results further show how exploration can benefit from a combination of expert knowledge and remotely-sensed relief, as well as imaging data. In the first stage, multi-source remote sensing data were used in lithological mapping based on Kohonen Self-Organizing Maps (SOM). We exemplify that morphological indices, such as Topographic Position Index (TPI), and spatial coordinates are crucial parameters to improve the accuracy of carbonate classification as much as 10%. The resulting lithological map shows the spatial distribution of the ridge forming carbonatite dyke, the fenitization zone, syenite plugs and mafic intrusions. In a second step, the internal zones of the carbonatite complex were identified using the Multi-Range Spectral Feature Fitting (MRSFF) algorithm and a specific decision tree. This approach allowed detecting potential enrichment zones characterized by an abundance of fluorapatite and pyroxene, as well as dolomite-carbonatite (beforsite). Cross-validation of the mineral map with field observations and radiometric data confirms the accuracy of the proposed method.

Cavernous tafoni-type weathering is a common and conspicuous global feature, creating artistic sculptures, which may be relevant for geochemical budgets. Weathering processes and rates are still a matter of discussion. Field evidence in the type locality Corsica revealed no trend of size variability from the coast to subalpine elevations and the aspect of tafoni seems to be governed primarily by the directions of local fault systems and cleavage, and only subordinately by wind directions or the aspect of insulation. REM analysis of fresh tafone chips confirmed mechanical weathering by the crystallization of salts, as conchoidal fracturing of quartz is observed. The salts are only subordinately provided by sea spray, as calcium and sodium sulfates rather than halite dominate even close to the coast. Characteristic element ratios compare well with aerosols from mixed African and European air masses. Sulfates are largely derived from Sahara dust, indicated by their sulfur isotopic composition. Salt crystals form by capillary rise within the rock and subsequent crystallization in micro-cracks and at grain boundaries inside rain-protected overhangs. Siderophile bacteria identified by raster electron microscopy (REM) analysis of tafone debris contribute to accelerated weathering of biotite and tiny sulfide ore minerals. By applying 10Be-exposure dating, weathering rates of large mature tafone structures were found to be about an order of magnitude higher than those on the exposed top of the affected granite blocks. Copyright © 2010 John Wiley & Sons, Ltd.

A combined approach to detect hydrothermal alteration zones and their mineral distribution is proposed for a relatively remote area around the Carhuarazo volcanic complex in southern Peru encompassing 2222 km2. In this region, tertiary volcanic structures associated with hydrothermal alteration are well known to host epithermal ore deposits. We make an attempt to detect and to quantify alteration minerals based on spectral analysis using ASTER reflectance data product provided by LP-DAAC. Besides commonly used ratio images, mineral indices (MI) and relative band depth images (RBD), we also extracted endmember spectra using Pixel-Purity-Processing preceded by minimum noise fraction transformation. These spectra are thought to represent the spectrally purest pixel of the image and show the typical absorption features of the main constituents. Based on this assumption, we used different spectral analysis methods in order to extract the most important alteration minerals for such an environment. These mineral...

Volcanism during the Neogene in the Central Volcanic Zone (CVZ) of the Andes produced (1) stratovolcanoes, (2) rhyodacitic to rhyolitic ignimbrites which reach volumes of generally less than 300 km3 and (3) large-volume monotonous dacitic ignimbrites of up to several thousand cubic kilometres. We present models for the origin of these magma types using O and Sr isotopes to constrain crust/mantle proportions for the large-volume ignimbrites and explore the relationship to the evolution of the Andean crust. Oxygen isotope ratios were measured on phenocrysts in order to avoid the effects of secondary alteration. Our results show a complete overlap in the Sr–O isotope compositions of lavas from stratovolcanoes and low-volume rhyolitic ignimbrites as well as older (>9 Ma) large-volume dacitic ignimbrites. This suggests that the mass balance of crustal and mantle components are largely similar. By contrast, younger ( 70 km3 Ma−1 km−1 (assuming plutonic/volcanic ratios of 1:5) which are additional to, but within the order of, the arc background magmatic flux. Comparing our results to average shortening rates observed in the Andes, we observe a “lag-time” with large-volume eruptions occurring after accelerated shortening. A similar delay exists between the ignimbrite pulses and the subduction of the Juan Fernandez ridge. This is consistent with the idea that large-volume ignimbrite eruptions occurred in the wake of the N–S passage of the ridge after slab steepening has allowed hot asthenospheric mantle to ascend into and cause the melting of the mantle wedge. In our model, the older large-volume dacitic ignimbrites in the northern part of the CVZ have lower (15–37 %) crustal contributions because they were produced at times when the Central Andean crust was thinner and colder, and large-scale melting in the middle crust could not be achieved. Younger ignimbrite flare-ups further south ( 22°S) formed with a significantly higher crustal contribution (22–68 %) because at that time the Andean crust was thicker and hotter and, therefore primed for more extensive crustal melting. The rhyolitic lower-volume ignimbrites are more equally distributed in the CVZ in time and space and are produced by mechanisms similar to those operating below large stratovolcanoes, but at times of higher melt fluxes from the mantle wedge.