Search

While temperatures are increasing on the global scale, the Arctic regions are especially vulnerable to this changing climate and landscapes underlain by permafrost experience increased thaw and degradation. The enhanced warming of organic-rich frozen ground can have severe consequences on infrastructure and ecosystems and is projected to become a highly relevant driver of greenhouse gas fluxes into the atmosphere. Degrading permafrost landscapes occur extensively in vast areas of the North American Arctic, directly affecting communities and ecosystems. To identify and quantify these widespread degradation phenomena over vast areas, we require highest-resolution Earth observation dataset that we collect during aerial imaging campaigns. We here report on observations and first results from three airborne campaigns in 2018, 2019 and 2021. We performed large-scale monitoring of permafrost-affected areas in northern Canada and Alaska, focusing on sites that experienced disturbances in the past or recently. This included sites with vulnerable settlements, coastal erosion, thaw slumping, lake expansion and drainage, ice-wedge degradation and thaw subsidence, fire scars, pingos, methane seeps, and sites affected by beaver activities. All surveys were flown with the Alfred Wegener Institute's Polar-5 and -6 scientific airplanes at 500-1500 m altitude above terrain. The onboard sensor, the Modular Aerial Camera System (MACS), a very-high-resolution multispectral camera developed by the German Aerospace Center, operated in the visible (RGB) and near-infrared (NIR) domain. From the comprehensive collection of multiple TB of gathered data, super-high-resolution (up to 7 cm/px) RGB+NIR image mosaics and stereophotogrammetric digital surface models were derived. By presenting the data and first analyses, we would like to invite the community to discuss best use for maximized benefit of the data, in order to substantially contribute to our understanding of permafrost thaw dynamics.

Hot and dry summers have led to an increase in forest fires both concerning num-bers and intensity in north-eastern Germany in the last years. In the project FireSense the German Aerospace Center (DLR) has adapted its sensor system MACS (Modular Airborne Camera System) with a set of thermal mid- and long wave infrared (MWIR and LWIR) cam-eras to detect, monitor and quantify high temperature events (HTE) like forest fires. Ground-based, airborne and spaceborne measurements over fire-experiments are synchronized for cross-validation of the systems and to test the developed workflows. In summer 2019 gas flaring tests were conducted in cooperation of DLR and the Federal In-stitute for Material Research and Testing (BAM), parallel several large forest fires in Bran-denburg (Lieberose) and Mecklenburg-Vorpommern (Lübtheen) developed. In coordination with the crisis management group (local authorities, firefighters, armed forces, federal po-lice) to get the permits MACS conducted 3 flights over the fires in altitudes between 6000 (sunny) down to 3500 ft (under clouds), Lübtheen was covered twice, on July 2 and July 4, when the fire was already under control. Synchronously firefighting helicopters operated close to ground, also delivering videos of the fires for visual interpretation. To get both background temperatures for orientation and landscape features and also infor-mation about the fires within one data set, a broad calibration range for the LWIR camera was commanded. Using synchronized position- and orientation data of MACS with given calibration data and a Digital Terrain Model, direct geocoding and the processing of near real-time mosaics was possible using the DLR workflow even without post-processing. The accuracy was sufficient for planning purposes. Geo-tiff maps were delivered shortly after landing within less than three hours. The real-time capabilities of the system could not be used as the flights were conducted on very short notice and the radio link was not installed. The thermal data were delivered as false color heat maps. They show the thermal anomalies very well, clearly discriminating burning area, recently burnt area and unaffected forest. In the RGB data the ground fires are rarely visible as they are covered by and almost did not affect the closely standing crowns. The spread of the fires can be seen in the overlapping re-gions of adjacent flight lines. Data exchange and use of the data proved to be difficult due to limited data rates and IT in-frastructure in the command and situation center in the field, sometimes taking more time than the acquisition and processing. This reduces the practical benefit for the data in the field. For future planned experiments for real-time mapping of forest fires this will be one of the main points to improve the latency of the data transfer to the control center ideally by us-ing a live data link and to optimize the coordination with the control center. Further activi-ties will be coordinated by the Helmholtz Innovation Lab OPTSAL (Optische Technologien für Situationserfassung im Sicherheitsbereich), which was started at DLR in 2020. In OPTSAL hard- and software solutions are developed and activities concerning situational awareness for safety and security are coordinated with industry and authorities.

Der vorliegende Leitfaden ist insbesondere für Praktiker nützlich, die beispielsweise in Forst- oder Naturschutzverwaltungen, in Forstbetrieben oder Ingenieurbüros mit Fernerkundungsdaten arbeiten. Die notwendigen theoretischen Grundlagen für die Ableitung und Nutzung von luftbildbasierten Oberflächenmodellen im Forst- und Umweltbereich sowie deren Folgeprodukte werden angesprochen.

Rapid climate change in the northern high latitudes has a strong impact on permafrost stability, apparent as coastal erosion, subsidence, or lake dynamics with potentially severe consequences for local communities and ecology. In a rapidly warming Arctic, the monitoring of these processes is essential to understand and predict permafrost dynamics over the upcoming decades. These landscape dynamics are highly diverse, localized, but widely distributed and require datasets with very high spatial resolution, which are barely achieved by satellite data alone. Repeat observations over several years allow for unprecedented insights into highly critical landscape dynamics and the potential integration with and validation of more coarse resolution satellite data. AWI’s research aircraft (Polar-5 and Polar-6) were equipped with airborne LiDAR (full-waveform, multi-echo) as well with experimental modular sensors such as the DLR-developed multi-spectral optical Modular Airborne Camera System (MACS) with a spatial resolution of few cm, stereo capabilities and a very broad radiometric range. The incoming data stream of acquired laser return point cloud data as well as hundreds of thousands of high-resolution images for individual campaigns poses new challenges of handling and processing large data volumes. Here we present an overview about past and upcoming flight campaigns in Alaska and northwestern Canada. Furthermore, we will show applications of the acquired datasets, such as assessments of subsidence, coastal erosion or infrastructure development.

Für die pixelscharfe Erfassung und Korrektur von Wasserflächen in photogrammetrisch erzeugten hochauflösenden digitalen Oberflächenmodellen wurde ein automatisiertes objektbasiertes Verfahren entwickelt. Zur Verkürzung der Laufzeit wurde diesem die automatische Berechnung einer Wassererwartungsmaske vorangestellt, welches Regionen, die potentiell Wasser beinhalten, selektiert und somit das Datenvolumen bis >60 % reduziert. Der objektbasierte Ansatz nutzt Informationen aus dem Bildmatching (Zuverlässigkeit, Höhe), spektrale Indizes und Textur- und Formparameter, es extrahiert die Wasserflächen mit einer Genauigkeit von >90%. Durch die Vielzahl von Eingangsdaten ist das Ruleset sehr robust und sensorunabhängig. Um die Laufzeit weiter zu verringern (>50%), werden vereinfachte Segmentierungs- und Stapelverarbeitungsalgorithmen auch auf niedrigeren Bildpyramiden eingesetzt.

The paper gives a short overview about the existing data base and extraction methods for single tree detection. Forest remote sensing has a long tradition and a variety of methods for single tree extraction have already been developed. Most studies and methods focus either on the analysis of satellite images or airborne laser data and on the extraction of coniferous trees. The automatic detection of deciduous trees is still a great challenge. This paper describes different methods of single tree extraction with focus on the automatic extraction of deciduous trees from aerial imagery. Single trees can be extracted by using aerial true-ortho images and photogrammetrically produced digital surface models as input data and a combination of remote sensing methods and GIS analyses with completeness and correctness over 80 percent. The presented method enables the extraction of tree tops as well as tree-crowns for deciduous trees. For the automatically extracted singles trees important attributes like exact position or average crown diameter are calculated and added to the tree objects. The extracted trees can be used for the modeling of trees in virtual environments or for forest area inventories.

Die Forstfernerkundung hat in Deutschland eine lange Tradition. In den letzten 30 Jahren wurden zudem verschiedenste Verfahren der automatischen Extraktion von Einzelbäumen aus Fernerkundungsdaten entwickelt und veröffentlicht. Dabei bestand jedoch eine Fokussierung auf Satellitenbilder und Laserdaten. Zudem haben viele Ansätze ihre Tauglichkeit vor allem im Nadelwald unter Beweis gestellt. Die automatische Einzelbaumextraktion von Laubbäumen ist dagegen bis heute eine Herausforderung. Daher stand im Mittelpunkt des entwickelten Verfahrens nicht nur die Extraktion von Nadelbäumen aus Luftbildern und photogrammetrisch erzeugten Oberflächen, sondern auch eine genaue Einzelbaumextraktion von im geschlossenen Bestand stehenden Laubbäumen. Wie das vorgestellte Verfahren zeigt, lassen sich unter der Verwendung von TrueOrtho-Luftbildern und photogrammetrisch erzeugten Oberflächen Einzelbäume mit einer sehr hohen Genauigkeit ableiten. Dies gilt dabei nicht nur für Nadelbäume sondern auch für Laubbäume.

High resolution digital elevation models (DEM) are utilized in automatic extraction of single-trees in mixed forests. In this paper the digital surface models (DSM) provided from aerial images with resolutions of 8 cm and 20 cm and airborne laser scanning ALS data are investigated. The results showed a relative good representation of the tree crowns even in image-based DSM. However, increasing the resolution of imagery does not inevitably lead to better quality in DSM of forests. To evaluate the characteristics of deciduous and coniferous trees on such models, two features are introduced. The Inversed Quasi-Flatness (IQF) and the Averaged Height Variation (AHV) of the DSM are implemented on some automatically selected samples at the highest levels of elevation on nDSM to demonstrate the geometric and morphologic characteristics of the different DSMs.

With the availability of large digital frame cameras like the UltraCamX (UCX) additional benefits through a combination of high-resolution multispectral aerial images with highly accurate digital surface models emerge. This ongoing study examines the level of detail of urban information that can be extracted. High resolution and the unprecedented geometric accuracy of the multispectral and the 2.5D object information enable the derivation of detailed and characteristic object features. The method of object-based classification is not only used to extract meaningful objects, even more important is a detailed assessment of semantic relationships. Our study shows the explicit advantage of high geometric resolution to increase the stability of classification and the number of classes in a representative area of Berlin.

This paper considers a processing chain for automatically creating high resolution digital surface models and true ortho-images from aerial and satellite image data. It has been developed at the Institute of Robotics and Mechatronics of the German Aerospace Center (DLR-RM). The processing chain is based on Semi-Global Matching (SGM) that uses a radiometric robust matching cost and an optimization that is based on a global smoothness constraint. SGM is especially suitable for creating models of urban scenes, where sharp depth discontinuities and small details need to be precisely reconstructed. However, the technique also produces very good results in scenes with forest and mountains. In this paper we give an overview of the processing chain and evaluate its results on test data sets from different aerial cameras. It is concluded that SGM permits the creation of high quality surface models that are more accurate and provide much more detail than a surface model from an aerial laser scanner. We also discuss the conditions under which good surface models can be produced by SGM. For very good results, an overlap of 80 % or more along track and 70 % across track should be provided.