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.

In recent decades, climatic pressures have altered the forested landscape of Bavaria. Widespread loss of trees has unevenly impacted the entire state, of which 37% is covered by forests (5% more than the national average). In 2018 and 2019—due in large part to drought and subsequent insect infestations—more tree-covered areas were lost in Bavaria than in any other German state. Moreover, the annual crown condition survey of Bavaria has revealed a decreasing trend in tree vitality since 1998. We conducted a systematic literature review regarding the remote sensing of forests in Bavaria. In total, 146 scientific articles were published between 2008 and 2023. While 88 studies took place in the Bavarian Forest National Park, only five publications covered the whole of Bavaria. Outside of the national park, the remaining 2.5 million hectares of forest in Bavaria are understudied. The most commonly studied topics were related to bark beetle infestations (24 papers); however, few papers focused on the drivers of infestations. The majority of studies utilized airborne data, while publications utilizing spaceborne data focused on multispectral; other data types were under-utilized- particularly thermal, lidar, and hyperspectral. We recommend future studies to both spatially broaden investigations to the state or national scale and to increase temporal data acquisitions together with contemporaneous in situ data. Especially in understudied topics regarding forest response to climate, catastrophic disturbances, regrowth and species composition, phenological timing, and in the sector of forest management. The utilization of remote sensing data in the forestry sector and the uptake of scientific results among stakeholders remains a challenge compared to other heavily forested European countries. An integral part of the Bavarian economy and the tourism sector, forests are also vital for climate regulation via atmospheric carbon reduction and land surface cooling. Therefore, forest monitoring remains centrally important to attaining more resilient and productive forests.

Anthropogenic and climatic pressures can transform contiguous forests into smaller, less connected fragments. Forest biodiversity and ecosystem functioning can furthermore be compromised or enhanced. We present a descriptive analysis of forest fragmentation in Bavaria, the largest federal state in Germany. We calculated 22 metrics of fragmentation using forest polygons, aggregated within administrative units and with respect to both elevation and aspect orientation. Using a forest mask from September 2024, we found 2.384 million hectares of forest across Bavaria, distributed amongst 83,253 forest polygons 0.1 hectare and larger. The smallest patch category (XS, 3594 ha) dominated higher elevations, covering 30–60% of land surface area between 600 and 1400 m.a.s.l. The distribution of the largest patches follows the higher terrain and corresponds well to protected areas. K-Means clustering delineated 3 clusters, which corresponded well with the predominance of patchiness, aggregation, and edginess within districts.

Accurately quantifying global tree biodiversity is critical for enhancing forest ecosystem management and forest biodiversity conservation. With the launch of NASA’s Global Ecosystem Dynamics Investigation (GEDI), we evaluated the efficacy of space-borne lidar metrics in predicting tree species richness globally and explored whether integrating spectral vegetation metrics with space-borne lidar data could improve model performances. Using Forest Global Earth Observatory (ForestGEO) data, we developed three models using the random forest algorithm to predict global tree species richness across climate zones, including a dynamic habitat index (DHI)-only model, a GEDI-only model, and a combined GEDI-DHI model. We also developed four new canopy indices for our model and determined the optimal extent for aggregating GEDI metrics. Applying the optimal pixel size (5600 m), we found that the GEDI-only model predicted tree species richness across climate zones well (R² = 0.55). One of our new GEDI metrics, representing canopy structure complexity, was among the top five most important features. The GEDI-DHI model performed similarly to the GEDI-only model using the ForestGEO dataset (R² = 0.55). Our study provides an efficient and innovative method for using GEDI data to predict global tree species richness. However, the integration of GEDI metrics with DHIs did not significantly improve the model’s performance compared to the GEDI-only model. Considering the substantial variation in tree species richness across different climate zones, we recommend modeling tree species richness for each climate zone rather than using a global model. Additionally, incorporating open-source ground-measured tree species richness data can improve predictions and inform decision-making in forest conservation management.

Anthropogenic and climatic pressures can transform contiguous forests into smaller, less connected fragments. Forest biodiversity and ecosystem functioning can further-more be compromised or enhanced. We present a descriptive analysis of recent forest fragmentation in Bavaria, the largest federal state in Germany. We calculated 22 metrics of fragmentation on forest polygons, aggregated within administrative units and with respect to both elevation and aspect orientation. Using a forest mask from September 2024, we found 2.384 million hectares of forest across Bavaria distributed amongst 83,253 forest polygons 0.1 hectare and larger. XS patches (< 25 ha), outnumber all other size classes (25-160 ha, 160-789 ha, 789-3,594 ha, and 3,594-48,703 ha) by nearly 13 to 1. Edge zones, where microclimatic effects may distinguish an area up to 100m from the forest perimeter, accounted for more than 1.68 million hectares, leaving less than 703,000 remaining hectares as core forest. Although south-facing slopes dominated the state, the highest forest cover (~36%) was found on least abundant east-oriented slopes. Most of the area is located at 400-600 m.a.s.l. with around 30% of this area covered by forests, however, XL forest patches (> 3,594 ha) dominated higher elevations, covering 30-60% of land surface area between 600-1400 m.a.s.l. The distribution of the largest patches follows the higher terrain and corresponds well to protected areas.