TY - JOUR A1 - Cherif, Eya A1 - Hell, Maximilian A1 - Brandmeier, Melanie T1 - DeepForest: novel deep learning models for land use and land cover classification using multi-temporal and -modal sentinel data of the amazon basin JF - Remote Sensing N2 - 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 available remote sensing data. However, automating the process of large-scale land cover mapping remains one of the biggest challenges in the remote sensing community. One issue when using supervised learning is the scarcity of labeled training data. One way to address this problem is to make use of already available maps produced with (semi-) automated classifiers. This is also known as weakly supervised learning. The present study aims to develop novel methods for automated LULC classification in the cloud-prone Amazon basin (Brazil) based on the labels from the MapBiomas project, which include twelve classes. We investigate different fusion techniques for multi-spectral Sentinel-2 data and synthetic aperture radar Sentinel-1 time-series from 2018. The newly designed deep learning architectures—DeepForest-1 and DeepForest-2—utilize spatiotemporal characteristics, as well as multi-scale representations of the data. In several data scenarios, the models are compared to state-of-the-art (SotA) models, such as U-Net and DeepLab. The proposed networks reach an overall accuracy of up to 75.0%, similar to the SotA models. However, the novel approaches outperform the SotA models with respect to underrepresented classes. Forest, savanna and crop were mapped best, with F1 scores up to 85.0% when combining multi-modal data, compared to 81.6% reached by DeepLab. Furthermore, in a qualitative analysis, we highlight that the classifiers sometimes outperform the inaccurate labels. KW - deep learning KW - land use and land cover classification KW - multi-modal and multi-temporal data Y1 - 2022 U6 - https://doi.org/10.3390/rs14195000 SN - 2072-4292 VL - 14 IS - 19 ER - TY - JOUR A1 - Hell, Maximilian A1 - Brandmeier, Melanie A1 - Briechle, Sebastian A1 - Krzystek, Peter T1 - Classification of Tree Species and Standing Dead Trees with Lidar Point Clouds Using Two Deep Neural Networks: PointCNN and 3DmFV-Net JF - PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science N2 - 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/m2. 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. Y1 - 2022 U6 - https://doi.org/10.1007/s41064-022-00200-4 SN - 2512-2819 VL - 90 SP - 103 EP - 121 ER - TY - CHAP A1 - Erbe, Karin A1 - Brandmeier, Melanie A1 - Schmitt, Michael A1 - Donbauer, Andreas A1 - Liebscher, Jan-Andreas A1 - Kolbe, Thomas ED - Kersten, Thomas P. ED - Tilly, Nora T1 - Detektion von Fahrradständern in Luftbildern mittels Deep Learning T2 - 42. Wissenschaftlich-Technische Jahrestagung der DGPF. 5.-6. Oktober 2022 in Dresden Y1 - 2022 U6 - https://doi.org/10.24407/KXP:1795622415 SN - 0942-2870 VL - 30 SP - 27 EP - 39 ER - TY - JOUR A1 - Zimmermann, Robert A1 - Brandmeier, Melanie A1 - Andreani, Louis A1 - Mhopjeni, Kombada A1 - Gloaguen, Richard T1 - Remote Sensing Exploration of Nb-Ta-LREE-Enriched Carbonatite (Epembe/Namibia) JF - Remote Sensing KW - carbonatite KW - decision tree KW - geomorphology KW - HyMap KW - Namibia KW - REE KW - self-organizing maps KW - spectral feature fitting KW - SRTM Y1 - 2016 U6 - https://doi.org/10.3390/rs8080620 SN - 2072-4292 VL - 8 IS - 8 SP - 620 EP - 620 ER - TY - JOUR A1 - Scharvogel, Daniel A1 - Brandmeier, Melanie A1 - Weis, Manuel T1 - A Deep Learning Approach for Calamity Assessment Using Sentinel-2 Data JF - Forests KW - CNNs KW - Deep Learning KW - forest KW - GIS KW - remote sensing KW - windthrow Y1 - 2020 U6 - https://doi.org/10.3390/f11121239 SN - 1999-4907 VL - 11 IS - 12 SP - 1239 EP - 1239 ER - TY - CHAP A1 - Hell, Maximilian A1 - Brandmeier, Melanie A1 - Nüchter, Andreas ED - Kersten, Thomas P. ED - Tilly, Nora T1 - Transferability of Deep Learning Models for Land Use/Land Cover Classification T2 - 43. Wissenschaftlich-Technische Jahrestagung der DGPF in München. 22.-23. März 2023 in München - Publikationen der DGPF Y1 - 2023 U6 - https://doi.org/10.24407/KXP:1841078182 VL - 31 SP - 142 EP - 149 ER - TY - CHAP A1 - Brandmeier, Melanie T1 - The Anatomy of Supervolcanoes T2 - GIS for Science: Applying Mapping and Spatial Analytics Y1 - 2019 UR - https://www.gisforscience.com/chapter3/v1/ PB - Esri ER - TY - JOUR A1 - Deigele, Wolfgang A1 - Brandmeier, Melanie A1 - Straub, Christoph T1 - A Hierarchical Deep-Learning Approach for Rapid Windthrow Detection on PlanetScope and High-Resolution Aerial Image Data JF - Remote Sensing N2 - 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. KW - convolutional neural networks KW - forest damage assessment KW - GIS KW - remote sensing KW - windthrow Y1 - 2020 U6 - https://doi.org/10.3390/rs12132121 SN - 2072-4292 VL - 12 IS - 13 SP - 2121 EP - 2121 ER - TY - CHAP A1 - Brandmeier, Melanie A1 - Wessel, M. ED - Meinel, Gotthard ED - Schumacher, Ulrich ED - Schwarz, Steffen ED - Richter, Benjamin ED - für Ökologische Raumentwicklung, Leibniz-Institut T1 - Workflows für Bilddaten und Big Data Analytics - Das Potenzial von Sentinel-2-Daten zur Baumartenklassifizierung T2 - Flächennutzungsmonitoring. IX: Nachhaltigkeit der Siedlungs- und Verkehrsentwicklung? Y1 - 2017 SN - 978-3-944101-73-6 N1 - Additional Note: Literaturangaben IS - Band 73 SP - 135 EP - 142 PB - Rhombos CY - Berlin ER - TY - JOUR A1 - Hamdi, Zayd Mahmoud A1 - Brandmeier, Melanie A1 - Straub, Christoph T1 - Forest Damage Assessment Using Deep Learning on High Resolution Remote Sensing Data JF - Remote Sensing KW - convolutional neural networks KW - forest damage assessment KW - GIS KW - remote sensing KW - windthrow Y1 - 2019 U6 - https://doi.org/10.3390/rs11171976 SN - 2072-4292 VL - 11 IS - 17 SP - 1976 EP - 1976 ER - TY - JOUR A1 - Wessel, Mathias A1 - Brandmeier, Melanie A1 - Tiede, Dirk T1 - Evaluation of Different Machine Learning Algorithms for Scalable Classification of Tree Types and Tree Species Based on Sentinel-2 Data JF - Remote Sensing KW - forest classification KW - GIS KW - Random Forest KW - remote sensing KW - Support Vector Machines Y1 - 2018 U6 - https://doi.org/10.3390/rs10091419 SN - 2072-4292 VL - 10 IS - 9 SP - 1419 EP - 1419 ER - TY - JOUR A1 - Brandmeier, Melanie A1 - Cabrera Zamora, Irving Gibran A1 - Nykänen, Vesa A1 - Middleton, Maarit T1 - Boosting for Mineral Prospectivity Modeling: A New GIS Toolbox JF - Natural Resources Research Y1 - 2020 U6 - https://doi.org/10.1007/s11053-019-09483-8 SN - 1520-7439 SN - 1573-8981 VL - 29 IS - 1 SP - 71 EP - 88 ER - TY - JOUR A1 - Brandmeier, Melanie A1 - Chen, Y. T1 - Lithological classification using multi-sensor data an convolutional neural networks JF - The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Y1 - 2019 U6 - https://doi.org/10.5194/isprs-archives-XLII-2-W16-55-2019 VL - XLII-2/W16 SP - 55 EP - 59 ER - TY - JOUR A1 - Brandmeier, Melanie A1 - Erasmi, S. A1 - Hansen, C. A1 - Höweling, A. A1 - Nitzsche, K. A1 - Ohlendorf, T. A1 - Mamani, M. A1 - Wörner, G. T1 - Mapping patterns of mineral alteration in volcanic terrains using ASTER data and field spectrometry in Southern Peru JF - Journal of South American Earth Sciences N2 - 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. KW - Alteration mapping KW - ASTER KW - Caldera KW - Peru KW - Remote sensing KW - Support vector machine classification Y1 - 2013 U6 - https://doi.org/10.1016/j.jsames.2013.09.011 SN - 0895-9811 VL - 48 SP - 296 EP - 314 ER - TY - JOUR A1 - Brandmeier, Melanie A1 - Kuhlemann, J. A1 - Krumrei, I. A1 - Kappler, A. A1 - Kubik, P.W. T1 - New challenges for tafoni research. A new approach to understand processes and weathering rates JF - Earth Surface Processes and Landforms N2 - 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. KW - tafoni KW - Corsica KW - cavernous weathering KW - weathering rates KW - salt weathering KW - 10Be dating Y1 - 2011 U6 - https://doi.org/10.1002/esp.2112 VL - 36 IS - 6 SP - 839 EP - 852 ER - TY - JOUR A1 - Brandmeier, Melanie T1 - 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 JF - Geocarto International Y1 - 2010 U6 - https://doi.org/10.1080/10106049.2010.519787 SN - 1010-6049 SN - 1752-0762 VL - 25 IS - 8 SP - 629 EP - 648 ER - TY - JOUR A1 - Freymuth, Heye A1 - Brandmeier, Melanie A1 - Wörner, Gerhard T1 - The origin and crust/mantle mass balance of Central Andean ignimbrite magmatism constrained by oxygen and strontium isotopes and erupted volumes JF - Contributions to Mineralogy and Petrology N2 - 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 (<10 Ma) large-volume dacitic ignimbrites from the southern portion of the Central Andes have distinctly more radiogenic Sr and heavier O isotopes and thus contrast with older dacitic ignimbrites in northernmost Chile and southern Peru. Results of assimilation and fractional crystallization (AFC) models show that the largest chemical changes occur in the lower crust where magmas acquire a base-level geochemical signature that is later modified by middle to upper crustal AFC. Using geospatial analysis, we estimated the volume of these ignimbrite deposits throughout the Central Andes during the Neogene and examined the spatiotemporal pattern of so-called ignimbrite flare-ups. We observe a N–S migration of maximum ages of the onset of large-volume “ignimbrite pulses” through time: Major pulses occurred at 19–24 Ma (e.g. Oxaya, Nazca Group), 13–14 Ma (e.g. Huaylillas and Altos de Pica ignimbrites) and <10 Ma (Altiplano and Puna ignimbrites). Such “flare-ups” represent magmatic production rates of 25 to >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 (<10 Ma, >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. KW - Central Andes KW - Crustal assimilation KW - Ignimbrite volumes KW - Ignimbrites KW - O isotopes Y1 - 2015 U6 - https://doi.org/10.1007/s00410-015-1152-5 SN - 1432-0967 VL - 169 IS - 6 SP - 58 EP - 58 ER - TY - JOUR A1 - Székely, Balázs A1 - Koma, Zsófia A1 - Karátson, Dávid A1 - Dorninger, Peter A1 - Wörner, Gerhard A1 - Brandmeier, Melanie A1 - Nothegger, Clemens T1 - Automated recognition of quasi-planar ignimbrite sheets as paleosurfaces via robust segmentation of digital elevation models: an example from the Central Andes JF - Earth Surface Processes and Landforms N2 - 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. KW - digital elevation model KW - dissected surfaces KW - geomorphometry KW - paleosurfaces KW - robust segmentation Y1 - 2014 U6 - https://doi.org/10.1002/esp.3606 SN - 1096-9837 VL - 39 IS - 10 SP - 1386 EP - 1399 ER - TY - JOUR A1 - Anwander, Julia A1 - Brandmeier, Melanie A1 - Paczkowski, Sebastian A1 - Neubert, Tarek A1 - Paczkowska, Marta T1 - Evaluating Different Deep Learning Approaches for Tree Health Classification Using High-Resolution Multispectral UAV Data in the Black Forest, Harz Region, and Göttinger Forest JF - Remote Sensing N2 - 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 truth data (GTD), nearly 1500 tree polygons with related attribute information on the health status of the trees were used. This data were collected during extensive fieldwork using a mobile application and subsequent individual tree segmentation. Extensive preprocessing included normalization, NDVI calculations, data augmentation to deal with the underrepresented classes, and splitting the data into training, validation, and test sets. We conducted several experiments using a classical machine learning approach (random forests), as well as different convolutional neural networks (CNNs)—ResNet50, ResNet101, VGG16, and Inception-v3—on different datasets and classes to evaluate the potential of these algorithms for tree health classification. Our first experiment was a binary classifier of healthy and damaged trees, which did not consider the degree of damage or tree species. The best results of a 0.99 test accuracy and an F1 score of 0.99 were obtained with ResNet50 on four band composites using the red, green, blue, and infrared bands (RGBI images), while VGG16 had the worst performance, with an F1 score of only 0.78. In a second experiment, we also distinguished between coniferous and deciduous trees. The F1 scores ranged from 0.62 to 0.99, with the highest results obtained using ResNet101 on derived vegetation indices using the red edge band of the camera (NDVIre images). Finally, in a third experiment, we aimed at evaluating the degree of damage: healthy, slightly damaged, and medium or heavily damaged trees. Again, ResNet101 had the best performance, this time on RGBI images with a test accuracy of 0.98 and an average F1 score of 0.97. These results highlight the potential of CNNs to handle high-resolution multispectral UAV data for the early detection of damaged trees when good training data are available. KW - tree health KW - classification KW - deep learning KW - CNNs KW - UAV KW - multispectral Y1 - 2024 U6 - https://doi.org/10.3390/rs16030561 VL - 16 IS - 3 SP - 561 EP - 561 ER -