Dominique Weber

1. While many studies on insect diversity report declines, others show stable, fluctuating or increasing trends. For a thorough understanding of insect trends and their effects on ecosystem functioning, it is important to simultaneously assess insect richness, abundance and biomass, and to report trends for multiple taxa.
2. We analysed insect richness, abundance and biomass data for all insects and for eight insect taxa (Buprestidae, Cerambycidae, Carabidae, other Coleoptera, Aculeata, other Hymenoptera, Heteroptera and Lepidoptera) from 42 sites across Switzerland from 2000 to 2007, representing three major habitat types in Switzerland (agricultural, unmanaged [open and forested] and managed forest habitats). As potential drivers of temporal patterns, we evaluated weather- and land-use-related factors. As predictors, we included temperature and precipitation as well as the vegetation index and the habitat type, respectively.
3. We found a consistent pattern of stable or increasing trends for richness, abundance and biomass of insects in total and the eight taxa over 8 years. Both overall patterns and six out of eight taxa (except for Cerambycidae and Lepidotpera) showed the highest values in agricultural habitats. However, when accounting for elevation, there was no difference in open habitats regardless of whether they were used agriculturally.
4. Habitat types were the most important predictors, followed by weather- and vegetation-related factors. Modelled responses to mean temperature were unimodal, whereas the standard deviation of temperature showed positive and precipitation negative effects. Longer time series are needed to draw robust inferences and to investigate potential negative effects of future warming.

Copernicus ist das europäische Erdbeobachtungsprogramm und liefert wertvolle Informationen für das Land Monitoring, auch für die Schweiz. Das bekannteste Produkt ist CORINE Land Cover, ein über mehrere Jahrzehnte konsistentes Inventar von 44 Bodenbedeckungs- und Bodennutzungsklassen für ganz Europa. Koordiniert durch die Europäische Umweltagentur wird eine Vielzahl weiterer Produkte angeboten. Dieser Artikel gibt einen Überblick über die verfügbaren Produkte und ihre Anwendungsmöglichkeiten. Es werden laufende Entwicklungen, geplante Produkte und neue Möglichkeiten vorgestellt.

Copernicus est le programme européen d'observation terrestre et fournit des informations précieuses pour le monitoring terrestre, également pour la Suisse. Le produit le plus connu est CORINE Land Cover, un inventaire consistant depuis plusieurs décennies de 44 classes de couvertures et d'utilisations du sol pour toute l'Europe. Coordonnée par l'Agence européenne de l'environnement une multitude d'autres produits est offerte. Cet article donne un aperçu des produits disponibles et de leurs possibilités d'application. Des développements en cours, des produits planifiés et des nouvelles possibilités y sont présentés.

Copernico è il programma europeo di osservazione della Terra e fornisce preziose informazioni per il monitoraggio del territorio, anche per la Svizzera. Il prodotto più noto è CORINE Land Cover, un copioso inventario raccolto durante diversi decenni, con 44 classi copertura e utilizzo del suolo in tutt'Europa. L'Agenzia europea dell'ambiente coordina anche l'offerta di tutta una serie di altri prodotti. Quest'articolo fornisce uno spaccato dei prodotti disponibili e delle loro possibilità di applicazione. In aggiunta, si presenta una carrellata sugli sviluppi attuali, i prodotti progettati e le nuove possibilità.

Land-use intensification in grassland ecosystems (i.e. increased mowing frequency, intensified grazing) has a strong negative effect on biodiversity and ecosystem services. However, accurate information on grassland-use intensity is difficult to acquire and restricted to the local or regional level. Recent studies have shown that mowing events can be mapped for large areas using satellite image time series. The transferability of such approaches, especially to mountain areas, has been little explored, however, and the relevance for ecological applications in biodiversity and conservation has hardly been investigated. Here, we used a rule-based algorithm to produce annual maps for 2018–2021 of grassland-management events, that is, mowing and/or grazing, for Switzerland using Sentinel-2 and Landsat 8 satellite data. We assessed the detection of management events based on independent reference data, which we acquired from daily time series of publicly available webcams that are widely distributed across Switzerland. We further examined the relationships between the generated grassland-use intensity measures and plant species richness and ecological indicator values derived from a nationwide field survey. The webcam-based verification for 2020 and 2021 revealed that most detected management events were actual mowing/grazing events (≥78%), but that a substantial number of events were not detected (up to 57%), particularly grazing events at higher elevations. We found lower plant species richness and higher mean ecological indicator values for nutrients and mowing tolerance with more frequent management events and those starting earlier in the year. A large proportion of the variance was explained by our use-intensity measures. Our findings therefore highlight that remotely assessed management events can characterise land-use intensity at fine spatial and temporal resolutions across broad scales and can explain plant biodiversity patterns in grasslands.

Die Intensität der Landnutzung hat einen grossen Einfluss auf die Biodiversität unseres Grünlands. Es ist jedoch aufwändig, sie flächendeckend und detailliert zu erfassen. Wir haben die Nutzungsintensität in der ganzen Schweiz mit Satellitenbildern über mehrere Jahre kartiert. Die frei verfügbaren Karten des Schweizer Grünlands haben ein grosses Potenzial für verschiedene ökologische Anwendungen und die Biodiversitätsforschung.

L'intensité d'exploitation du sol a de grands effets sur la biodiversité de nos surfaces herbagères. Toutefois, recenser cette intensité sur tout le territoire et de manière détaillée nécessite des moyens. Grâce à des images satellite, nous l'avons cartographiée sur plusieurs années et dans toute la Suisse. Les cartes, en libre accès, recèlent un grand potentiel pour différentes applications écologiques et pour la recherche dans le domaine de la biodiversité.

Agricultural landscapes support multiple functions and are of great importance for biodiversity. Heterogeneous agricultural mosaics of cropland and grassland commonly result from variable land use practices and ecosystem service demands. Switzerland's agricultural land use is considerably spatially heterogeneous due to strong variability in conditions, especially topography and climate, thus presenting challenges to automated agricultural mapping. Nationwide knowledge of the location of cropland and grassland is necessary for effective conservation and land use planning. We mapped the distribution of cropland and permanent grassland across Switzerland. We used several indices largely derived from Sentinel-2 satellite imagery captured over multiple growing seasons and parcel-based training data derived from landholder reporting. The mapping was conducted within Google Earth Engine using a random forest classifier. The resulting map has high accuracy in lowlands as well as in mountainous areas. The map will act as a base agricultural land cover dataset for researchers and practitioners working in agricultural areas of Switzerland and interested in land cover and landscape structure. The map as well as the training data and calculation algorithms (using Google Earth Engine) are freely available for download on the EnviDat platform https://doi.org/10.16904/envidat.205 (Pazúr et al., 2021).

Bei grossflächiger Kartierung von Bodenbedeckungen basierend auf Fernerkundungsdaten entstehen oft systematische Fehler. Um solchen entgegenzuwirken, können Active Learning-Verfahren eingesetzt werden, welche eine effiziente Verbesserung der Kartengenauigkeit ermöglichen. Für die Erstellung der fernerkundungsbasierten und schweizweiten Gebüschwaldkarte im Rahmen des Schweizerischen Landesforstinventars wurde ein solches Verfahren angewandt. Anhand dieses Beispiels soll aufgezeigt werden, wie mittels Active Learning der mittlere absolute Fehler einer grossflächigen Karte mit vertretbarem Aufwand von 6.04% auf 2.68% mehr als halbiert werden konnte.

Mit der neusten Generation von Erdbeobachtungssatelliten und modernen Verarbeitungstechniken kann die Erdoberfläche in nahezu Echtzeit analysiert werden. Fast täglich stehen hochaufgelöste Satellitenbilder zur Verfügung und können effizient in der Cloud verarbeitet werden. Damit wird nebst den etablierten Methoden zur Bestimmung der Landbedeckung zunehmend auch die flächendeckende Erfassung der Landnutzung möglich. Dies steht im Einklang mit der aktuellen politischen Agenda und dem steigenden Bedarf an räumlich expliziten und regelmässig aktualisierten Landnutzungsinformationen.

Avec la plus récente génération des satellites d’observation terrestre et les méthodes modernes de traitements la surface terrestre peut être analysée quasiment en temps réel. Presque tous les jours des images satellitaires à haute résolution sont disponibles et peuvent être gérées efficacement dans le cloud. Ainsi, à côté des méthodes établies de reconnaissance de la couverture terrestre, il devient de plus en plus possible de saisir à grande échelle l’utlisation du territoire. Ceci est en conformité avec l’actuelle agenda politique et le besoin grandissant en informations spacialement explicites et régulièrement actualisées sur l’utilisation du sol.

Waldstrukturen lassen sich mit Methoden der Fernerkundung über grosse Gebiete erfassen. Dabei haben sich Stereoluftbilder als effiziente Grundlage für die Berechnung von digitalen Oberflächenmodellen erwiesen. Zieht man von diesen Oberflächenmodellen das Geländemodell ab und eliminiert künstliche Objekte, erhält man Vegetationshöhenmodelle. Je nach Auflösung der Luftbilder liefern diese in den Vegetationshöhen mehr oder weniger detaillierte Informationen über die Kronenoberflächen. Um Daten zu früheren Vegetationshöhen zu erhalten, wurden im Schweizerischen Landesforstinventar (LFI) auf Basis analoger Schwarz-Weiss-Luftbilder des Bundesamts für Landestopografie aus den Befliegungskampagnen 1979–1985 und 1990–1998 landesweite digitale Oberflächen- und Vegetationshöhenmodelle erstellt. Anschliessend wurden mit den historischen Vegetationshöhenmodellen (Zeitschnitte 1980 und 1990) und dem aktuellen Vegetationshöhenmodell des LFI (Zeitschnitt 2010) die Höhenveränderungen im Schweizer Wald in einer Auflösung von einer Hektare bestimmt. Anders als im Offenland und im Siedlungsgebiet funktionierte die Bildkorrelation im Wald mit den gescannten Schwarz-Weiss-Luftbildern zufriedenstellend. Über eine Million Hektaren Wald konnten in die Analyse einbezogen werden. Sie zeigt, dass die Vegetationshöhe auf fast einem Drittel der Waldfläche seit den 1980er-Jahren kontinuierlich zugenommen hat. Auf einer nahezu gleich grossen Fläche waren die Höhenveränderungen zwischen den drei Zeitschnitten gegenläufig mit Zunahmen in der einen und Abnahmen in der anderen Periode. Die grössten Höhenveränderungen waren im Mittelland zu verzeichnen, die geringsten auf der Alpensüdseite. Kleinräumige Veränderungen und Änderungen unter dem Kronendach können mit diesen Daten allerdings nicht erkannt werden. Trotzdem erlauben die historischen Vegetationshöhenmodelle einen Blick in die Vergangenheit der Schweizer Waldstrukturen.

Forest structure can be captured over large areas using remote sensing methods. Stereo aerial images have proven to be an effective basis for the calculation of digital surface models. If the terrain model is subtracted from these surface models, and artificial objects are eliminated, vegetation height models are obtained. These vegetation height models provide information about the canopy surfaces in forest areas, with a level of detail dependant on the spatial resolution. To obtain data on past forest surfaces, nationwide digital surface and vegetation height models were created in the context of the Swiss National Forest Inventory (NFI) based on analogue black-and-white aerial photographs from the Federal Office of Topography from the 1979–1985 and 1990–1998 aerial campaigns. Subsequently, the historical vegetation height models (time periods 1980 and 1990) and the current vegetation height model of the NFI (time period 2010) were used to determine change in Swiss forest height at a resolution of one hectare. Unlike in open landscapes and settlement areas, the image correlation in forest areas was satisfactory with the scanned black-and-white aerial images. Over one million hectares of forest were included in the analysis. The results show that over almost one third of the forest area vegetation height has continuously increased since the 1980s. Over a nearly equal area, height changes over the two change periods were opposite, with increases in one period and decreases in the other. The largest height changes occurred in the Plateau, the smallest in the Southern Alps. However, smallscale changes and changes under the canopy cannot be detected with these data. Nevertheless, the historical vegetation height models provide a view of past Swiss forest structures.

Exacerbated by climate change, Europe has experienced series of hot and dry summer since the beginning of the 21st century. The importance of land conditions became an international concern with a dedicated sustainable development goal (SDG), the SDG 15. It calls for developing and finding innovative solutions to follow and evaluate impacts of changing land conditions induced by various driving forces. In Switzerland, drought risk will significantly increase in the coming decades with severe consequences on agriculture, energy production and vegetation. In this paper, we used a 35-year satellite-derived annual and seasonal times-series of normalized difference water index (NDWI) to follow vegetation water content evolution at different spatial and temporal scales across Switzerland and related them to temperature and precipitation to investigate possible responses of changing climatic conditions. Results indicate that there is a small and slow drying tendency at the country scale with a NDWI mean decreasing slope of −0.22%/year for the 23% significant pixels across Switzerland. This tendency is mostly visible below 2000 m above sea level (m.a.s.l.) and in all biogeographical regions. The Southern Alps regions appear to be more responsive to changing drying conditions with a significant and slight negative NDWI trend (−0.39%/year) over the last 35 years. Moreover, NDWI values are mostly a function of temperature at elevations below the tree line rather than precipitation. Findings suggest that multi-annual and seasonal NDWI can be a valuable indicator to monitor vegetation water content at different scales, but other components such as land cover type and evapotranspiration should be considered to better characterize NDWI variability. Satellite Earth Observations data can provide valuable complementary observations for national statistics on the ecological state of vegetation to support SDG 15 to monitor land affected by drying conditions.

The continual increase of shrub forest in the Swiss Alps over the past few decades impacts biodiversity, forest succession and the protective function of forests. Therefore, up-to-date and area-wide information on its distribution is of great interest. To detect the shrub forest areas for the whole of Switzerland (41,285 km²), we developed an approach that uses Random Forest (RF), bias correction techniques and data from multiple remote sensing sources. Manual aerial orthoimage interpretation of shrub forest areas was conducted in a non-probabilistic way to derive initial training data. The multi-sensor and open access predictor data included digital terrain and vegetation height models obtained from Airborne Laser Scanning (ALS) and stereo-imagery, as well as Synthetic Aperture Radar (SAR) backscatter from Sentinel-1 and multispectral imagery from Sentinel-2. To mitigate the expected bias due to the training data sampling strategy, two techniques using RF probability estimates were tested to improve mapping accuracy. 1) an iterative and semi-automated active learning technique was used to generate further training data and 2) threshold-moving related object growing was applied. Both techniques facilitated the production of a shrub forest map for the whole of Switzerland at a spatial resolution of 10 m. An accuracy assessment was performed using independent data covering 7640 regularly distributed National Forest Inventory (NFI) plots. We observed the influence of the bias correction techniques and found higher accuracies after each performed iteration. The Mean Absolute Error (MAE) for the predicted shrub forest proportion was reduced from 6.04% to 2.68% while achieving a Mean Bias Error (MBE) of close to 0. The present study underscores the potential of combining multi-sensor data with bias correction techniques to provide cost-effective and accurate countrywide detection of shrub forest. Moreover, the map complements currently available NFI plot sample point data.

Die Sentinel-2-Satellitenmission ermöglicht seit 2015 die zeitnahe und kontinuierliche Überwachung von Wäldern in einem hohen Detaillierungsgrad. Um dieses Potenzial für die Praxis nutzbar zu machen, wurde im Rahmen eines Forschungsprojekts an der BFH-HAFL der Einsatz dieser Daten in drei konkreten Use-Cases erarbeitet. In Zusammenarbeit mit der Praxis wurden einfache und intuitiv bedienbare Kartenviewer entwickelt, um die technischen Hürden in der Anwendung auf ein Minimum zu reduzieren.

Informationen über die Ausdehnung und die Entwicklung von Gebüschwäldern sind für verschiedene forstliche und ökologische Fragestellungen von Bedeutung, jedoch sind aktuelle und flächendeckende Datensätze kaum verfügbar. Im Rahmen dieser Arbeit wurde die Kartierung des Gebüschwaldes mit Dominanz von Alpenerle, Legföhre und Hasel für den Kanton Graubünden auf Basis von landesweit verfügbaren Fernerkundungsdaten untersucht. Es wurden die Satellitendaten von Sentinel-1 und Sentinel-2 sowie ein Vegetationshöhenmodell und ein Geländemodell verwendet. Für die überwachte Klassifikation mit Random Forest, einem auf Entscheidungsbäumen basierenden Verfahren des maschinellen Lernens, wurden vom Kanton bereitgestellte und durch Luftbildinterpretation ergänzte Trainingsflächen verwendet. Die unabhängige Validierung der Ergebnisse wurde mit Daten des Landesforstinventars (LFI) durchgeführt. Alpenerlen- und Legföhrenwälder liessen sich mit einer hohen Genauigkeit von 92.1% bzw. 86.7% klassifizieren, für Haselwälder betrug die interne Modellgüte hingegen nur 66.7%. Die resultierende Flächenausdehnung des Gebüschwaldes war vergleichbar mit den Angaben des LFI. Beim direkten Vergleich mit den LFI-Luftbildinterpretationspunkten wurden grössere Abweichungen festgestellt. Der Hauptgrund dafür ist der unterschiedliche räumliche Detaillierungsgrad. LFI-Flächen mit einem hohen Strauchanteil wurden jedoch zuverlässig als Gebüschwald klassifiziert. Die hier vorgestellte Methode unterstreicht das Potenzial von schweizweit verfügbaren Fernerkundungsdaten für eine weitgehend objektive, kosteneffiziente und grossflächige Kartierung von Gebüschwäldern in einer für die Praxis genügenden Genauigkeit.

Sentinel-2 satellite data have been freely available since the end of 2015. The high spatial resolution, the global coverage, the frequent revisit times and the important spectral bands for vegetation analyses open up new perspectives for forest applications. In this study, the potential of Sentinel-2 was investigated through application examples in the forest enterprise of Burgergemeinde Bern on the Swiss Plateau. For this purpose the determination of the deciduous or coniferous proportion, the recording of interventions, as well as the early detection of natural disturbances were tested. Reference data from the forest enterprise (forest inventory, map of harvesting measures, delimitation of a wildfire) were used as training data and for the validation of the results. By using Sentinel- 2 data from three different dates, 56% of the variation of the deciduous or coniferous proportion could be explained by a regression model. This model was much better than those that used data from one or two time steps only. The distinction of pure deciduous and coniferous stands achieved an accuracy of 90%. The detection of interventions determined by the differences in the spectral information of Sentinel- 2 data from two acquisition dates was highly compliant with the information from the forest enterprise. This was also confirmed by the detection of a forest fire area, which highlights the potential of Sentinel-2 for the early and large-scale detection of natural disturbances.

Dry grasslands are species rich and ecologically valuable habitats that have experienced a massive decline in Switzerland during the last century due to agricultural intensification and land abandonment. Appropriate management is a key factor in maintaining habitat quality of the remaining most valuable sites and should thus be an essential part of monitoring studies. However, information on management is often missing and fine-scale patterns are difficult to assess, especially over large areas and for past decades.
The aim of this study was to predict habitat quality of protected dry grasslands in Switzerland. Using a nation-wide in-situ vegetation data set with plot-based species lists, we derived six habitat quality indicators (management tolerance, light availability, nutrient content, moisture content and species richness). We then tested how well satellite-based phenology metrics, in combination with environmental and climate data, can predict these dry grassland habitat quality indicators. We expected that the seasonal pattern of vegetation activity, based on the Normalized Difference Vegetation Index (NDVI), would represent local productivity and management patterns, two crucial indicators of dry grassland habitat quality. Linear regression analysis was conducted to assess the relative importance and ecological relationship of different NDVI metrics and other environmental and climate predictors for habitat quality. Variance partitioning was applied to assess model contributions of the three variable groups which represent different data sources for productivity and management.
Accuracies for the habitat quality prediction models ranged between 34% and 57% and significant correlations with multiple NDVI metrics were found. Including NDVI phenology improved all models by 7–12%. Single contributions of NDVI phenology were highest for management tolerance and nutrient content. However, we found high variation of contributions between management types. NDVI metrics were highly informative for the habitat qualities of abandoned sites, but grazing and mowing reduced or even cancelled their predictive power. Moreover, our results demonstrate the limitation of single-date NDVI values in predicting habitat quality of dry grasslands, in particular pastures and meadows. For monitoring applications of dry grasslands, we propose using a combination of NDVI metrics, as our results showed that they greatly improve prediction results of essential habitat qualities. The Landsat legacy dataset facilitates the assessment of habitat changes during past decades and can be complemented in the future with higher resolution data, such as Sentinel-2, to increase the temporal and spatial resolution so analyses are more appropriate for the typically limited size of dry grassland habitat sites in Switzerland.