Spartina alterniflora has become the main invasive plant along the Chinese coast and now threatens the local ecological environment. Accurately monitoring the distribution of S. alterniflora is urgent and essential for developing cost-effective control strategies. In this study, we applied the One-Class Classification (OCC) methods of Maximum entropy (Maxent) and Biased Support Vector Machine (BSVM) based on Landsat time-series imagery to detect the species on the middle coast of Jiangsu in east China. We conducted four experimental setups (i.e., single-scene analysis, time-series analysis, Normalized Difference Vegetation Index (NDVI) time-series analysis and a compressed time-series analysis), using OCC methods to recognize the species. Then, we tested the performance of a compressed time-series model for S. alterniflora detection and evaluated the expansibility of this approach when it was applied to a larger region. Our principal findings are as follows: (1) Maxent and BSVM performed equally well, and Maxent appeared to have a more balanced performance over the summer months; (2) the Maxent model with the Default Parameter Set (Maxent-DPS) showed a slightly higher accuracy and more overfitting than Maxent with the Akaike Information Criterion corrected for small samples sizes (AICc)-selected parameter set model, but a t-test found no significant difference between these two settings; (3) April and December were deemed to be important periods for the detection of S. alterniflora; (4) a compressed time-series analysis model—including only three variables (December NDVI, March green and the third Principal Component in January, PC3)—yielded higher accuracy than single-scene analyses, which indicated that time-series analysis can better detect S. alterniflora than single-scene analyses; and (5) the Maxent model using the reconstructed optimal variables and 70 training samples over a larger region produced encouraging results with an overall accuracy of 90.88% and a Kappa of 0.78. The one-class classification method combined with a phenology-based detection strategy is therefore promising for the application of the long-term detection of S. alterniflora over extended areas.

Spartina alterniflora (S. alterniflora) is one of the worst plant invaders in the coastal wetlands of China. Accurate and repeatable mapping of S. alterniflora invasion is essential to develop cost-effective management strategies for conserving native biodiversity. Traditional remote-sensing-based mapping methods require a lot of fieldwork for sample collection. Moreover, our ability to detect this invasive species is still limited because of poor spectral separability between S. alterniflora and its co-dominant native plants. Therefore, we proposed a novel scheme that uses an ensemble one-class classifier (EOCC) in combination with phenological Normalized Difference Vegetation Index (NDVI) time-series analysis (TSA) to detect S. alterniflora. We evaluated the performance of the EOCC algorithm in two scenarios, i.e., single-scene analysis (SSA) and NDVI-TSA in the core zones of Yancheng National Natural Reserve (YNNR). Meanwhile, a fully supervised classifier support vector machine (SVM) was tested in the two scenarios for comparison. With these scenarios, the crucial phenological stages and the advantage of phenological NDVI-TSA in S. alterniflora recognition were also investigated. Results indicated the EOCC using only positive training data performed similarly well with the SVM trained on complete training data in the YNNR. Moreover, the EOCC algorithm presented a more robust transferability with notably higher classification accuracy than the SVM when being transferred to a second site, without a second training. Furthermore, when combined with the phenological NDVI-TSA, the EOCC algorithm presented more balanced sensitivity–specificity result, showing slightly better transferability than it performed in the best phenological stage (i.e., senescence stage of November). The achieved results (overall accuracy (OA), Kappa, and true skill statistic (TSS) were 92.92%, 0.843, and 0.834 for the YNNR, and OA, Kappa, and TSS were 90.94%, 0.815, and 0.825 for transferability to the non-training site) suggest that our detection scheme has a high potential for the mapping of S. alterniflora across different areas, and the EOCC algorithm can be a viable alternative to traditional supervised classification method for invasive plant detection.

Increasingly available spaceborne sensors provide unprecedented opportunities for large‐scale, timely and continuous tree species diversity (TSD) monitoring. However, given differences in spectral and spatial resolutions, the choice of sensor is not always straightforward. In this work, we investigated the effects of spatial and spectral resolutions for four spaceborne sensors (RapidEye, Landsat‐8, Sentinel‐2 and PlanetScope) on TSD mapping in an area of approximately 4000 km² within the Black Forest, Germany. We employed a random forest (RF) regression model to predict Shannon–Wiener diversity based on seven types of spectral heterogeneity metrics (texture, coefficient of variation, Rao's Q, convex hull volume, spectral angle mapper, convex hull area and spectral species diversity) and a full survey dataset from 135 one‐ha sample plots. We compared the RF model's performance across sensors and spatial resolutions. Our results demonstrated that the Sentinel‐2‐based TSD model achieved the highest accuracy (mean R²: 0.477, mean root‐mean‐square error (RMSE): 0.274). The RapidEye‐based TSD model produced lower accuracy (mean R²: 0.346, mean RMSE: 0.303), but it was better than the PlanetScope‐ and Landsat‐based TSD models. The 10 m (for Sentinel‐2 and RapidEye) and 15 m (for PlanetScope) were the best spatial resolutions for predicting TSD. The NIR band was the most favourable spectral band for predicting TSD. Texture metrics and Rao's Q outperformed the other spectral heterogeneity metrics. Our results highlighted that spaceborne optical imagery (especially Sentinel‐2) can be successfully used for large‐scale TSD mapping but that the choice of sensors can significantly affect the resulting mapping accuracy in temperate montane forests.

Timber‐oriented forest management profoundly alters forest structure and composition, with complex effects on associated biodiversity. However, while species' responses to forest management and resulting structural characteristics have been the subject of numerous studies, direct and indirect effects that cascade through trophic levels are rarely disentangled. As insectivorous bats are particularly sensitive to changes in forest structure, that shape their available flight space, we investigated how forest structure, composition and management also indirectly modify their habitats, for example, by affecting important insect prey groups. We used structural equation models (SEMs) to test bat responses to forest composition, structure (forest heterogeneity, old‐growth attributes) and management intensity, quantifying direct and indirect prey‐mediated effects. For that, three bat guilds—short‐ (SRE), mid‐ (MRE) and long‐range echolocating (LRE) bats—and their prey insects (moths and ground beetles) were analysed from 64 sites in the Black Forest, Germany. We found guild‐specific effects on bats: While the structural heterogeneity of forests directly influenced the activity of bat guilds, the main influence of forest management, composition and structure was mediated through their prey‐groups. SRE activity responded to moths and LRE activity was associated with ground beetles, with positive effects of the insect groups' abundance, but negative effects of the same group's species richness. In addition, the SEM approach revealed a negative top‐down relationship between MRE activities and moths, suggesting predation or avoidance behaviour of moths. While forest management directly or indirectly increased prey insect abundance, it negatively affected the availability of roosting structures for bats. Synthesis and applications. The results highlight the indirect and positive effects of forest management on bats and support the important role of bats in insect regulation within continuous cover forests. Although forest management created small gaps that improved foraging habitats for most bats, it compromised the roosting functionality for bats. The ‘close‐to‐nature forestry’ currently prevalent in Europe mainly promotes continuous‐cover forests in mid‐successional stages. Expanding the forest management portfolio towards open and old‐growth forests would increase roosting opportunities and provide complementary foraging habitats for different bat species, while promoting high biodiversity in managed forest landscapes.

Ecosystems at the interface between terrestrial and aquatic areas are of outstanding ecological and socio-economic importance, yet are under intense pressure due to the concentration of human settlement and agriculture. Despite this, the broader geography of the land-water interface and how it is changing remains poorly understood. Here, we develop an operationalizable definition of the land-water interface and use a fuzzy logic geospatial model to map the land-water interface at high resolution across the European continent. Our results show that the land-water interface covers ~0.87 million km² (~8% of Europe) but is strongly affected by human use: in 2020, about 15% of all built-up areas and more than 8% of all croplands lay within the land-water interface. Analyzing land-cover trends inside the land-water interface since 1992 showed increasing land take: built-up area more than doubled (from 13,752 km² to 29,302 km²), driven primarily by cropland-to-urban conversion. The expansion and densification of built-up areas occurred particularly in lowland river corridors, deltas, and coastal plains. Cropland decline was particularly widespread across Central and Eastern Europe, while cropland expansion occurred in land-water interface regions in northern Europe. Across interface types, the lake land-water interface exhibited the strongest relative increase in built-up areas (>150%) and the sharpest proportional cropland loss compared to riverine and coastal land-water interfaces. Countries inside the European Union had earlier and more intense urbanization trends than non-EU regions, with a slowdown after 2010. More broadly, our results reveal a continental-scale pattern of increasing, and largely irreversible, land take in the land-water interface, likely straining the ecological integrity, buffering capacity, and connectivity of land-water ecosystems. Our land-water interface concept and harmonized maps enable consistent mapping and regional cross-comparisons, support identification of priority areas for conservation and restoration, and provide a baseline to track progress towards flood-risk reduction and restoration targets.