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One-third of Germany’s land surface area is covered by forest (around 11.4 million hectares), and thus, it characterizes the landscape. The forest is a habitat for a large number of animal and plant species, a source of raw materials, important for climate protection, and a well-being refuge for people, to name just a few of its many functions. During the annual forest condition surveys, the crown condition of German forests is assessed on the basis of field samples at fixed locations, as the crown condition of forest trees is considered an important indicator of their vitality. Since the start of the surveys in 1984, the mean crown defoliation of all tree species has increased, now averaging about 25% for all tree species. Additionally, it shows a strong rise in the rate of dieback. In 2019, the most significant changes were observed. Due to the drastic changes in recent years, efforts are being made to assess the situation of the forest using different remote sensing methods. There are now a number of freely available products provided to the public, and more will follow as a result of numerous projects in the context of earth-observation (EO)-based monitoring and mapping of the forests in Germany. In 2020, the situation regarding the use of remote sensing for the German forest was already investigated in more detail. However, these results no longer reflect the current situation. The changes of the last 3 years are the content of this publication. For this study, 84 citable research publications were thoroughly analyzed and compared with the situation in 2020. As a major result, we found a shift in the research focus towards disturbance monitoring and a tendency to cover larger areas, including national-scale studies. In addition to the review of the scientific literature, we also reviewed current research projects and related products. In congruence to the recent developments in terms of publications in scientific journals, these projects and products reflect the need for comprehensive, timely, large-area, and complementary EO-based information around forests expressed in multiple political programs. With this review, we provide an update of previous work and link it to current research activities. We conclude that there are still gaps between the information needs of forest managers who usually rely on information from field perspectives and the EO-based information products.

This paper presents a novel approach for automated image comparison and robust change detection from noisy imagery, such as synthetic aperture radar (SAR) amplitude images. Instead of comparing pixel values and/or pre-classified features this approach clearly highlights structural changes without any preceding segmentation or classification step. The crucial point is the use of the Curvelet transform in order to express the image as composition of several structures instead of numerous individual pixels. Differentiating these structures and weighting their impact according to the image statistics produces a smooth, but detail-preserved change image. The Curvelet-based approach is validated by the standard technique for SAR change detection, the log-ratio with and without additional gamma maximum-a-posteriori (GMAP) speckle filtering, and by the results of human interpreters. The validation proves that the new technique can easily compete with these automated as well as visual interpretation techniques. Finally, a sequence of TerraSAR-X High Resolution Spotlight images of a factory building construction site near Ludwigshafen (Germany) is processed in order to identify single construction stages by the time of the (dis-)appearance of certain objects. Hence, the complete construction monitoring of the whole building and its surroundings becomes feasible.

Three synthetic aperture radar (SAR) data classification methodologies were used to assess the ability of single-polarization and dual-polarization TerraSAR-X (TSX) data to classify surface water, including open water, ice, and flooded vegetation. Multi-polarization SAR observations contain more information than single-polarization SAR, but the availability of multi-polarization data is much lower, which limits the temporal monitoring capabilities. The study area is a principally natural landscape centered on a seasonally flooding river, in which four TSX dual-co-polarized images were acquired between the months of April and June 2016. Previous studies have shown that single-polarization SAR is useful for analyzing surface water extent and change using grey-level thresholding. The H-Alpha–Wishart decomposition, adapted to dual-polarization data, and the Kennaugh Element Framework were used to classify areas of water and flooded vegetation. Although grey-level thresholding was able to identify areas of water and non-water, the percentage of seasonal change was limited, indicating an increase in water area from 8% to 10%, which is in disagreement with seasonal trends. The dual-polarization methods show a decrease in water over the season and indicate a decrease in flooded vegetation, which agrees with expected seasonal variations. When comparing the two dual-polarization methods, a clear benefit of the Kennaugh Elements Framework is the ability to classify change in the transition zones of ice to open water, open water to marsh, and flooded vegetation to land, using the differential Kennaugh technique. The H-Alpha–Wishart classifier was not able to classify ice, and misclassified fields and ice as water. Although single-polarization SAR was effective in classifying open water, the findings of this study confirm the advantages of dual-polarization observations, with the Kennaugh Element Framework being the best performing classification framework.