Richard Gloaguen

DEM-Based Analysis of Interactions between Tectonics and Landscapes in the Ore Mountains and Eger Rift (East Germany and NW Czech Republic)

Tectonics modify the base-level of rivers and result in the progressive erosion of landscapes. We propose here a new method to classify landscapes according to their erosional stages. This method is based on the combination of two DEM-based geomorphic indices: the hypsometric integral, which highlights elevated surfaces, and surface roughness, which increases with the topographic elevation and the incision by the drainage network. The combination of these two indices allows one to produce a map of erosional discontinuities that can be easily compared with the known structural framework. In addition, this method can be easily implemented (e.g., in MATLAB) and provides a quick way to analyze regional-scale landscapes. We propose here an example of a region where this approach becomes extremely valuable: the Ore Mountains and adjacent regions. The lack of young stratigraphic markers prevents a detailed analysis of recent fault activity. However, discontinuities in mapped geomorphic indices coupled to the analysis of river longitudinal profiles suggest a tight relationship between erosional discontinuities and main tectonic lineaments.

Improving the Estimation of Above Ground Biomass Using Dual Polarimetric PALSAR and ETM+ Data in the Hyrcanian Mountain Forest (Iran)

The objective of this study is to develop models based on both optical and L-band Synthetic Aperture Radar (SAR) data for above ground dry biomass (hereafter AGB) estimation in mountain forests. We chose the site of the Loveh forest, a part of the Hyrcanian forest for which previous attempts to estimate AGB have proven difficult. Uncorrected ETM+ data allow a relatively poor AGB estimation, because topography can hinder AGB estimation in mountain terrain. Therefore, we focused on the use of atmospherically and topographically corrected multispectral Landsat ETM+ and Advanced Land-Observing Satellite/Phased Array L-band Synthetic Aperture Radar (ALOS/PALSAR) to estimate forest AGB. We then evaluated 11 different multiple linear regression models using different combinations of corrected spectral and PolSAR bands and their derived features. The use of corrected ETM+ spectral bands and GLCM textures improves AGB estimation significantly (adjusted R2 = 0.59; RMSE = 31.5 Mg/ha). Adding SAR backscattering coefficients as well as PolSAR features and textures increase substantially the accuracy of AGB estimation (adjusted R2 = 0.76; RMSE = 25.04 Mg/ha). Our results confirm that topographically and atmospherically corrected data are indispensable for the estimation of mountain forest’s physical properties. We also demonstrate that only the joint use of PolSAR and multispectral data allows a good estimation of AGB in those regions.

Water Balance Modeling in a Semi-Arid Environment with Limited in situ Data Using Remote Sensing in Lake Manyara, East African Rift, Tanzania

The purpose of this paper is to estimate the water balance in a semi-arid environment with limited in situ data using a remote sensing approach. We focus on the Lake Manyara catchment, located within the East African Rift of northern Tanzania. We use a distributed conceptual hydrological model driven by remote sensing data to study the spatial and temporal variability of water balance parameters within the catchment. Satellite gravimetry GRACE data is used to verify the trends of the inferred lake level changes. The results show that the lake undergoes high spatial and temporal variations, characteristic of a semi-arid climate with high evaporation and low rainfall. We observe that the Lake Manyara water balance and GRACE equivalent water depth show comparable trends; a decrease after 2002 followed by a sharp increase in 2006-2007. Our modeling confirms the importance of the 2006-2007 Indian Ocean Dipole fluctuation in replenishing the groundwater reservoirs of East Africa. We thus demonstrate that water balance modeling can be performed successfully using remote sensing data even in complex climatic settings.

The Need for Accurate Geometric and Radiometric Corrections of Drone-Borne Hyperspectral Data for Mineral Exploration: MEPHySTo—A Toolbox for Pre-Processing Drone-Borne Hyperspectral Data

Drone-borne hyperspectral imaging is a new and promising technique for fast and precise acquisition, as well as delivery of high-resolution hyperspectral data to a large variety of end-users. Drones can overcome the scale gap between field and air-borne remote sensing, thus providing high-resolution and multi-temporal data. They are easy to use, flexible and deliver data within cm-scale resolution. So far, however, drone-borne imagery has prominently and successfully been almost solely used in precision agriculture and photogrammetry. Drone technology currently mainly relies on structure-from-motion photogrammetry, aerial photography and agricultural monitoring. Recently, a few hyperspectral sensors became available for drones, but complex geometric and radiometric effects complicate their use for geology-related studies. Using two examples, we first show that precise corrections are required for any geological mapping. We then present a processing toolbox for frame-based hyperspectral imaging systems adapted for the complex correction of drone-borne hyperspectral imagery. The toolbox performs sensor- and platform-specific geometric distortion corrections. Furthermore, a topographic correction step is implemented to correct for rough terrain surfaces. We recommend the c-factor-algorithm for geological applications. To our knowledge, we demonstrate for the first time the applicability of the corrected dataset for lithological mapping and mineral exploration.

Classifying Complex Mountainous Forests with L-Band SAR and Landsat Data Integration: A Comparison among Different Machine Learning Methods in the Hyrcanian Forest

Forest environment classification in mountain regions based on single-sensor remote sensing approaches is hindered by forest complexity and topographic effects. Temperate broadleaf forests in western Asia such as the Hyrcanian forest in northern Iran have already suffered from intense anthropogenic activities. In those regions, forests mainly extend in rough terrain and comprise different stand structures, which are difficult to discriminate. This paper explores the joint analysis of Landsat7/ETM+, L-band SAR and their derived parameters and the effect of terrain corrections to overcome the challenges of discriminating forest stand age classes in mountain regions. We also verified the performances of three machine learning methods which have recently shown promising results using multisource data; support vector machines (SVM), neural networks (NN), random forest (RF) and one traditional classifier (i.e., maximum likelihood classification (MLC)) as a benchmark. The non-topographically corrected ETM+ data failed to differentiate among different forest stand age classes (average classification accuracy (OA) = 65%). This confirms the need to reduce relief effects prior data classification in mountain regions. SAR backscattering alone cannot properly differentiate among different forest stand age classes (OA = 62%). However, textures and PolSAR features are very efficient for the separation of forest classes (OA = 82%). The highest classification accuracy was achieved by the joint usage of SAR and ETM+ (OA = 86%). However, this shows a slight improvement compared to the ETM+ classification (OA = 84%). The machine learning classifiers proved t o be more robust and accurate compared to MLC. SVM and RF statistically produced better classification results than NN in the exploitation of the considered multi-source data.

TecLines: A MATLAB-Based Toolbox for Tectonic Lineament Analysis from Satellite Images and DEMs, Part 1: Line Segment Detection and Extraction

Geological structures, such as faults and fractures, appear as image discontinuities or lineaments in remote sensing data. Geologic lineament mapping is a very important issue in geo-engineering, especially for construction site selection, seismic, and risk assessment, mineral exploration and hydrogeological research. Classical methods of lineaments extraction are based on semi-automated (or visual) interpretation of optical data and digital elevation models. We developed a freely available Matlab based toolbox TecLines (Tectonic Lineament Analysis) for locating and quantifying lineament patterns using satellite data and digital elevation models. TecLines consists of a set of functions including frequency filtering, spatial filtering, tensor voting, Hough transformation, and polynomial fitting. Due to differences in the mathematical background of the edge detection and edge linking procedure as well as the breadth of the methods, we introduce the approach in two-parts. In this first study, we present the steps that lead to edge detection. We introduce the data pre-processing using selected filters in spatial and frequency domains. We then describe the application of the tensor-voting framework to improve position and length accuracies of the detected lineaments. We demonstrate the robustness of the approach in a complex area in the northeast of Afghanistan using a panchromatic QUICKBIRD-2 image with 1-meter resolution. Finally, we compare the results of TecLines with manual lineament extraction, and other lineament extraction algorithms, as well as a published fault map of the study area.

River Courses Affected by Landslides and Implications for Hazard Assessment: A High Resolution Remote Sensing Case Study in NE Iraq–W Iran

The objective of this study is to understand the effect of landslides on the drainage network within the area of interest. We thus test the potential of rivers to record the intensity of landslides that affected their courses. The study area is located within the Zagros orogenic belt along the border between Iraq and Iran. We identified 280 landslides through nine QuickBird scenes using visual photo-interpretation. The total landslide area of 40.05 km2 and their distribution follows a NW–SE trend due to the tectonic control of main thrust faults. We observe a strong control of the landslides on the river course. We quantify the relationship between riverbed displacement and mass wasting occurrences using landslide sizes versus river offset and hypsometric integrals. Many valleys and river channels are curved around the toe of landslides, thus producing an offset of the stream which increases with the landslide area. The river offsets were quantified using two geomorphic indices: the river with respect to the basin midline (Fb); and the offset from the main river direction (Fd). Hypsometry and stream offset seem to be correlated. In addition; the analysis of selected river courses may give some information on the sizes of the past landslide events and therefore contribute to the hazard assessment.

Improving Lithological Mapping by SVM Classification of Spectral and Morphological Features: The Discovery of a New Chromite Body in the Mawat Ophiolite Complex (Kurdistan, NE Iraq)

The mineral ore potential of many mountainous regions of the world, like the Kurdistan region of Iraq, remains unexplored. For logistical and sometimes political reasons, these areas are difficult to map using traditional methods. We highlight the improvement in remote sensing geological mapping that arises from the integration of geomorphic features in classifications. The Mawat Ophiolite Complex (MOC) is located in the NE of Iraq and is known for its mineral deposits. The aims of this study are: (I) to refine the existing lithological map of the MOC; (II) to identify the best discriminatory datasets for lithological classification, including geomorphic features and textures; and (III) to identify potential locations with high concentrations of chromite. We performed a Support Vector Machine (SVM) classification method to allow the joint use of geomorphic features, textures and multispectral data of the Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER) satellite. The updated map allowed the identification of a new mafic body and a substantial improvement of the geometry of the known lithological units. The use of geomorphic features allowed for the increase of the overall accuracy from 73% to 79.3%. In addition, we detected chromite occurrences within the ophiolite by applying Spectral Angle Mapping (SAM) technique. We identified two new locations having high concentrations of chromite and verified one of these promising areas in the field. This new body covers ~0.3 km2 and has coarsely crystalline chromite within dunite host rock. The chromium (Cr2O3) concentration is ~8.46%. The SAM and SVM methods applied on ASTER satellite data show that these can be used as a powerful tool to explore ore deposits and to further improve lithological mapping in mountainous semi-arid regions.

Growth and collapse of the Tibetan Plateau

Despite agreement on first-order features and mechanisms, critical aspects of the origin and evolution of the Tibetan Plateau, such as the exact timing and nature of collision, the initiation of plateau uplift, and the evolution of its height and width, are disputed, untested or unknown. This book gathers papers dealing with the growth and collapse of the Tibetan Plateau. The timing, the underlying mechanisms, their interactions and the induced surface shaping, contributing to the Tibetan Plateau evolution are tightly linked via coupled and feedback processes. We present interdisciplinary contributions allowing insight into the complex interactions between lithospheric dynamics, topography building, erosion, hydrological processes and atmospheric coupling. The book is structured in four parts: early processes in the plateau formation; recent growth of the Tibetan Plateau; mechanisms of plateau growth; and plateau uplift, surface processes and the monsoon.

Automatic Extraction and Size Distribution of Landslides in Kurdistan Region, NE Iraq

This study aims to assess the localization and size distribution of landslides using automatic remote sensing techniques in (semi-) arid, non-vegetated, mountainous environments. The study area is located in the Kurdistan region (NE Iraq), within the Zagros orogenic belt, which is characterized by the High Folded Zone (HFZ), the Imbricated Zone and the Zagros Suture Zone (ZSZ). The available reference inventory includes 3,190 landslides mapped from sixty QuickBird scenes using manual delineation. The landslide types involve rock falls, translational slides and slumps, which occurred in different lithological units. Two hundred and ninety of these landslides lie within the ZSZ, representing a cumulated surface of 32 km2. The HFZ implicates 2,900 landslides with an overall coverage of about 26 km2. We first analyzed cumulative landslide number-size distributions using the inventory map. We then proposed a very simple and robust algorithm for automatic landslide extraction using specific band ratios selected upon the spectral signatures of bare surfaces as well as posteriori slope and the normalized difference vegetation index (NDVI) thresholds. The index is based on the contrast between landslides and their background, whereas the landslides have high reflections in the green and red bands. We applied the slope threshold map to remove low slope areas, which have high reflectance in red and green bands. The algorithm was able to detect ~96% of the recent landslides known from the reference inventory on a test site. The cumulative landslide number-size distribution of automatically extracted landslide is very similar to the one based on visual mapping. The automatic extraction is therefore adapted for the quantitative analysis of landslides and thus can contribute to the assessment of hazards in similar regions.