Melanie Brandmeier

• Prof. Dr. rer. nat.
• Professor at Technical University of Applied Sciences Würzburg Germany

Land use and land cover (LULC) mapping is a powerful tool for monitoring large areas. For the Amazon rainforest, automated mapping is of critical importance, as land cover is changing rapidly due to forest degradation and deforestation. Several research groups have addressed this challenge by conducting local surveys and producing maps using freely...

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 p...

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 w...

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 B...

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 s...

Single-tree segmentation on multispectral UAV images shows significant potential for effective forest management such as automating forest inventories or detecting damage and diseases when using an additional classifier. We propose an automated workflow for segmentation on high-resolution data and provide our trained models in a Toolbox for ArcGIS...

Most studies in the field of land use and land cover (LULC) classification in remote sensing rely on supervised classification, which requires a substantial amount of accurate label data. However, reliable data are often not immediately available, and are obtained through time-consuming manual labor. One potential solution to this problem is the us...

In the context of climate change, vineyard monitoring to better understand spatiotemporal patterns of grapevine development is of utter importance for precision viticulture. We present a time series analysis of hyperspectral in situ and multispectral UAV data for different irrigation systems in Lower Franconia and correlate results with sensor data...

We present an evaluation of different deep learning and machine learning approaches for tree health classification in the Black Forest, the Harz Mountains, and the Göttinger Forest on a unique, highly accurate tree-level dataset. The multispectral UAV data were collected from eight forest plots with diverse tree species, mostly conifers. As ground...

Deep learning models in remote sensing are often trained once for benchmarking their results and not further applied to new domains or newer data. In this study, we test five previously developed DeepForest model variations on new data for land use and land cover classification. The models were pre-trained for this task on a multi-modal and-tempora...

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 Southwe...

Zusammenfassung: Dieser Beitrag erörtert anhand der Detektion und Klassifikation von unüberdachten Fahrradständern aus Luftbildern und Oberflächenmodellen, inwiefern Deep Learning eingesetzt werden kann, um Stadtmöbel automatisch zu erfassen und für 3D-Stadtmodelle nutzbar zu machen. Dafür wird am Beispiel der Fahrradständer der Landeshauptstadt Mü...

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...

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-mete...

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 o...

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 se...

Storms can cause significant damage to forest areas, affecting biodiversity and infrastructure and leading to economic loss. Thus, rapid detection and mapping of windthrows are crucially important for forest management. Recent advances in computer vision have led to highly-accurate image classification algorithms such as Convolutional Neural Networ...

Deep learning has been used successfully for 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 (CNNs) for lithological mapping in the Mount Isa region (Australia). The area is ideal for geological remot...

GIS-based multivariate statistical and geospatial analysis of a compilation of 890 geochemical and ca. 1,200 geochronological data for 194 mapped ignimbrites from Central Andes documents the compositional and temporal pattern of large volume ignimbrites (so-called " ignimbrite flare-ups ") during Neogene times. Rapid advances in computational scien...

Poster

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 exp...

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 du...

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 u...

Remote sensing data can provide valuable information about ore deposits and their alteration zones at surface level. High spectral and spatial resolution of the data is essential for detailed mapping of mineral abundances and related structures. Carbonatites are well known for hosting economic enrichments in REE, Ta, Nb and P (Jones et al. 2013). T...

Temporal and compositional patterns of Neogene ignimbrites in the Central Andes were analysed using GIS and geostatistical modelling based on 203 digitized ignimbrite sheets for which geochronological, geochemical, and Sr-­‐Nd-­‐Pb-­‐isotopic data on pumices as well as Sr-­‐O isotopes on minerals from selected samples were compiled and compared to...

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) digit...

This doctoral thesis comprises the following articles that are either published or in preparation, as indicated below: Brandmeier, M., 2010. Remote sensing of Carhuarazo volcanic complex using ASTER imagery in Southern Peru to detect alteration zones and volcanic structures – a combined approach of image processing in ENVI and ArcGIS/ArcScene. Geo...

The Western slope of the Central Andes between 22° and 17°S is characterized by large, quasi-planar landforms with tilted ignimbrite surfaces and overlying younger sedimentary deposits (e.g. Nazca, Oxaya, Huaylillas ignimbrites). These surfaces were only modified by tectonic uplift and tilting of the Western Cordillera preserving minor now fossiliz...

We use ASTER data and field spectrometry (ASD hyper-spectral data) 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. Tertiary volcanism in the Central Andes is punctuated by so-called ignimbrite flare-ups which form extensive pl...

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 as...

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 mak...