Interferometric Synthetic Aperture RADAR and Radargrammetry towards the Categorization of Building Changes

The purpose of this work is the investigation of SAR techniques relying on multi image acquisition for fully automatic and rapid change detection analysis at building level. In particular, the benefits and limitations of a complementary use of two specific SAR techniques, InSAR and radargrammetry, in an emergency context are examined in term of quickness, globality and accuracy. The analysis is performed using spaceborne SAR data

Partage de tâches adaptatif dans une équipe humain-machine basé sur des modèles quantitatifs de performance et de confiance

RÉSUMÉ Lorsqu’une équipe humain-machine est formée dans le but d’accomplir des tâches de prise de décision, certains facteurs humains peuvent avoir un impact important sur les performances de l’équipe. C’est le cas en particulier de la charge de travail et de la confiance que place un opérateur dans les capacités de la machine. Il peut alors être nécessaire de prendre en compte ces facteurs dans le design de la stratégie de collaboration. Cependant, la confiance et la charge de travail varient dans le temps en fonction des interactions entre l’humain et la machine. Une façon de prendre en compte ces éléments est d’opter pour une stratégie de collaboration adaptative, c’est-à-dire qui varie en fonction de l’état cognitif de l’opérateur. Dans ce mémoire on propose une stratégie de collaboration adaptative sous la forme de suggestions automatiques et dynamiques de partage de tâche. Régulièrement, une proposition de partage de tâche est suggérée à l’opérateur en prenant en compte sa charge de travail ainsi que sa confiance. Cette stratégie est issue de la résolution d’un Processus Décisionnel Markovien Partiellement Observable (POMDP). Pour cela des modèles quantitatifs des performances humaines et de la dynamique de la confiance ont été sélectionnés. Des simulations permettent de montrer le potentiel de la méthode en comparant les performances de la stratégie adaptative proposée à celles d’une stratégie statique plus simple. Les résultats à long terme de l’équipe humain-machine sont en moyenne meilleurs de 24% lorsque la stratégie adaptative est appliquée plutôt que la stratégie statique. L’utilisation de modèles quantitatifs dont certains paramètres doivent être identifiés pose la question de la robustesse de la stratégie aux erreurs de calibration. On montre, toujours en simulation, que malgré certaines erreurs de modèles, la stratégie proposée conserve son avantage.----------ABSTRACT In mixed-initiative systems where human and automation collaborate in order to complete a decision-making task, some human factors can have an impact on the team performance.For instance, the cognitive workload and the trust placed by the operator on the automation capabilities can be determining factors. Hence it could be relevant to take into account these cognitive variables in the design of the collaboration strategy. However, both workload and trust fluctuate with the history of past interactions. One way to include these dynamic variables is to opt for an adaptive collaboration strategy. In this work, we propose an adaptive task allocation suggestion which dynamically allocate task according to the operator’strust level. This adaptive strategy is computed by solving a Partially Observable Markovian Decision Process (POMDP). The POMDP is defined using quantitative models of human performance and trust dynamic. We study this method’s potential by comparing, in simulation, the performance results collected when the adaptive strategy is applied and those when a static strategy is applied. The long term mean team reward is 24% higher with the adaptive strategy than with the static strategy. Moreover we study the impact of model calibration errors on the strategy performance. The proposed method seems to bring benefits even in the presence of errors in the models

Blended-learning educational concept for earth observation at university level

The field of Earth observation has been undergoing a tremendous transformation for several years. From commercial data that used to be processed only by a circle of specialists, we are now in an era where numerous high-quality satellite data can be made available for free and used by diverse user groups in many applications. It is therefore of fundamental importance for new users to understand and use these data in an application-specific way, and teaching concepts need to be adapted accordingly. Specifically for the field of radar remote sensing, several activities already exist that intend to adjust educational offers with needs of the market place and to provide hands-on material for self-paced learning in many fields of application. At university level however, many courses still happen in a traditional classroom way, the lecturer being the principal source of information. We present here a blended-learning approach aiming the integration of high-quality eLearning material in traditional face-to-face courses to enhance the teaching and learning experience. The approach can be resumed in two main goals: 1) the specific integration of eLearning elements on a learning platform for a better preparation and follow-up of the course content by the students; 2) the creation of new eLearning content by the students in a peer-to-peer approach. For the first goal, existing content from Massive Open Online Courses (MOOC) are broken down into learning modules and supplemented with external digital learning content in order to best match the needs of the face-to-face course week by week. This prevents students from being overwhelmed by the enormous volume of online educational resources of the MOOCs and allows a better preparation of students for the current content of the lecture. For the second goal, a further deepening of what has been learned takes place through active co-creation of new digital content. This is based on the principle of the pyramid of learning that the best way to remember something is to explain it yourself. In this way, students who create new content from what they have learned should be able to remember it much longer as if they just listen to it. This blended learning educational model is conducted successfully since two years at university level with bachelor and master students and is being enriched regularly with new material, both from the open educational resources and students contribution

20 Years SAR Interferometry for Monitoring Ground Deformation over the former Potash-Mine “Glückauf” in Thuringia

Geophysical processes and anthropogenic activities cause the deformation of the Earth's surface, both mechanisms interacting sometimes simultaneously. While the occurrence of those processes in rural areas may not always directly a ect the population, the determination of surface deformation in inhabited areas is of high relevance to prevent risks. Traditional surveying techniques provide exact but usually spatially and temporally limited deformation information, making a regular monitoring of whole urban areas di cult. Since about 20 years, RADAR remote sensing, especially SAR interferometry, provide dense and accurate ground motion information, completing hereby the traditional monitoring techniques. This present study investigates ground surface dynamics in a town close to a former potash-mine situated in the northern part of Thuringia, Germany, by means of multi-temporal SAR interferometry. Using the method of Persistent Scatterer Interferometry, 20 years of RADAR data from multiple sensors are evaluated and compared to in-situ data. It shows that ground subsidences decreased since the closing and back lling of the mine, which is in accordance with surveying activities on this site

Sentinel-1 backscatter time series for characterization of evapotranspiration dynamics over temperate coniferous forests

Forests’ ecosystems are an essential part of the global carbon cycle with vast carbon storage potential. These systems are currently under external pressures showing increasing change due to climate change. A better understanding of the biophysical properties of forests is, therefore, of paramount importance for research and monitoring purposes. While there are many biophysical properties, the focus of this study is on the in-depth analysis of the connection between the C-band Copernicus Sentinel-1 SAR backscatter and evapotranspiration (ET) estimates based on in situ meteorological data and the FAO-based Penman–Monteith equation as well as the well-established global terrestrial ET product from the Terra and Aqua MODIS sensors. The analysis was performed in the Free State of Thuringia, central Germany, over coniferous forests within an area of 2452 km2, considering a 5-year time series (June 2016–July 2021) of 6- to 12-day Sentinel-1 backscatter acquisitions/observations, daily in situ meteorological measurements of four weather stations as well as an 8-day composite of ET products of the MODIS sensors. Correlation analyses of the three datasets were implemented independently for each of the microwave sensor’s acquisition parameters, ascending and descending overpass direction and co- or cross-polarization, investigating different time series seasonality filters. The Sentinel-1 backscatter and both ET time series datasets show a similar multiannual seasonally fluctuating behavior with increasing values in the spring, peaks in the summer, decreases in the autumn and troughs in the winter months. The backscatter difference between summer and winter reaches over 1.5 dB, while the evapotranspiration difference reaches 8 mm/day for the in situ measurements and 300 kg/m2/8-day for the MODIS product. The best correlation between the Sentinel-1 backscatter and both ET products is achieved in the ascending overpass direction, with datasets acquired in the late afternoon, and reaches an R2-value of over 0.8. The correlation for the descending overpass direction reaches values of up to 0.6. These results suggest that the SAR backscatter signal of coniferous forests is sensitive to the biophysical property evapotranspiration under some scenarios

Relationship between Lidar-Derived Canopy Densities and the Scattering Phase Center of High-Resolution TanDEM-X Data

Abstract: The estimation of forestry parameters is essential to understanding the three-dimensional structure of forests. In this respect, the potential of X-band synthetic aperture radar (SAR) has been recognized for years. Many studies have been conducted on deriving tree heights with SAR data, but few have paid attention to the effects of the canopy structure. Canopy density plays an important role since it provides information about the vertical distribution of dominant scatterers in the forest. In this study, the position of the scattering phase center (SPC) of interferometric X-band SAR data is investigated with regard to the densest vegetation layer in a deciduous and coniferous forest in Germany by applying a canopy density index from high-resolution airborne laser scanning data. Two different methods defining the densest layer are introduced and compared with the position of the TanDEM-X SPC. The results indicate that the position of the SPC often coincides with the densest layer, with mean differences ranging from −1.6 m to +0.7 m in the deciduous forest and +1.9 m in the coniferous forest. Regarding relative tree heights, the SAR signal on average penetrates up to 15% (3.4 m) of the average tree height in the coniferous forest. In the deciduous forest, the difference increases to 18% (6.2 m) during summer and 24% (8.2 m) during winter. These findings highlight the importance of considering not only tree height but also canopy density when delineating SAR-based forest heights. The vertical structure of the canopy influences the position of the SPC, and incorporating canopy density can improve the accuracy of SAR-derived forest height estimations

Using Sentinel-1 and Sentinel-2 Time Series for Slangbos Encroachment Mapping in the Free State Province, South Africa

Increasing woody cover and overgrazing in semi arid ecosystems are known to be major factors driving land degradation. During the last decades woody cover encroachment has increased over large areas in southern Africa inducing environmental, land cover as well as land use changes. The goal of this study is to synergistically combine SAR (Sentinel 1) and optical (Sentinel 2) earth observation information to monitor the slangbos encroachment on arable land in the Free State province, South Africa, between 2015 and 2020. Both, optical and radar satellite data are sensitive to different land surface and vegetation properties caused by sensor specific scattering or reflection mechanisms they rely on. This study focuses on mapping the slangbos aka bankrupt bush (Seriphium plumosum) encroachment in a selected test region in the Free State province of South Africa. Though being indigenous to South Africa, the slangbos has been documented to be the main encroacher on the grassvelds (South African grassland biomes) and thrive in poorly maintained cultivated lands. The shrub reaches a height and diameter of up to 0.6 m and the root system reaches a depth of up to 1.8 m. Slangbos has small light green leaves unpalatable to grazers due to their high oil content and is better adapted to long dry periods compared to grass communities. We used the random forest approach to predict slangbos encroachment for each individual crop year between 2015 and 2020. Training data were based on expert knowledge and field information from the Department of Agriculture, Forestry and Fisheries (DAFF). Several input variables have been tested according to their model performance, e.g. backscatter, backscatter ratio, interferometric coherence as well as optical indices (e.g. NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), EVI (Enhanced Vegetation Index), etc.). We found that the Sentine 1 VH backscatter (vertical horizontal/cross polarization) and the Sentinel 2 SAVI time series information have the highest importance for the random forest classifier among all input parameters. The estimation of the model accuracy was accomplished via spatial cross validation and resulted in an overall accuracy of above 80 % for each time step, with the slangbos class being close to or above 90 %. Currently we are developing a prototype application to be tested in cooperation with local stakeholders to bring this approach to the farmers level. Once field work in southern Africa is possible again, further ground truthing and interaction with farmers will be carried out

Assessment of terrain elevation estimates from ICESat-2 and GEDI spaceborne LiDAR missions across different land cover and forest types

Accurate measurements of terrain elevation are crucial for many ecological applications. In this study, we sought to assess new global three-dimensional Earth observation data acquired by the spaceborne Light Detection and Ranging (LiDAR) missions Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI). For this, we examined the “ATLAS/ICESat-2 L3A Land and Vegetation Height”, version 5 (20 × 14 m and 100 × 14 m segments) and the “GEDI Level 2A Footprint Elevation and Height Metrics”, version 2 (25 m circle). We conducted our analysis across four land cover classes (bare soil, herbaceous, forest, savanna), and six forest types (temperate broad-leaved, temperate needle-leaved, temperate mixed, tropical upland, tropical floodplain, and tropical secondary forest). For assessment of terrain elevation estimates from spaceborne LiDAR data we used high resolution airborne data. Our results indicate that both LiDAR missions provide accurate terrain elevation estimates across different land cover classes and forest types with mean error less than 1 m, except in tropical forests. However, using a GEDI algorithm with a lower signal end threshold (e.g., algorithm 5) can improve the accuracy of terrain elevation estimates for tropical upland forests. Specific environmental parameters (terrain slope, canopy height and canopy cover) and sensor parameters (GEDI degrade flags, terrain estimation algorithm; ICESat-2 number of terrain photons, terrain uncertainty) can be applied to improve the accuracy of ICESat-2 and GEDI-based terrain estimates. Although the goodness-of-fit statistics from the two spaceborne LiDARs are not directly comparable since they possess different footprint sizes (100 × 14 m segment or 20 × 14 m segment vs. 25 m circle), we observed similar trends on the impact of terrain slope, canopy cover and canopy height for both sensors. Terrain slope strongly impacts the accuracy of both ICESat-2 and GEDI terrain elevation estimates for both forested and non-forested areas. In the case of GEDI the impact of slope is, however, partly caused by horizontal geolocation error. Moreover, dense canopies (i.e., canopy cover higher than 90%) affect the accuracy of spaceborne LiDAR terrain estimates, while canopy height does not, when considering samples over flat terrains. Our analysis of the accuracy and precision of current versions of spaceborne LiDAR products for different vegetation types and environmental conditions provides insights on parameter selection and estimated uncertainty to inform users of these key global datasets