Dr. Bronwyn Price

Soil properties influence plant physiology and growth, playing a fundamental role in shaping species niches in temperate forest ecosystems. Here, we investigated the impact of soil data quality on the performance of species distribution models (SDMs) of 41 woody plant species in Swiss forests. We compared models based on measured soil properties with those based on digitally mapped soil properties on regional (Swiss Forest Soil Maps) and global scales (SoilGrids). We first calibrated topo-climatic SDMs with measured soil data and plant species presences and absences from mature temperate forest stand plots. We developed further models using the same soil predictors, but with values extracted from digital soil maps at the nearest neighbouring plots of the Swiss National Forestry Inventory. The predictive power of SDMs without soil information compared to those with soil information, as well as measured soil information vs digitally mapped, was evaluated with metrics of model performance and variable contribution. On average, models with measured and digitally mapped soil properties performed significantly better than those without soil information. SDMs based on measured and Swiss Forest Soil Maps showed higher performance, especially for species with an ‘extreme’ niche position (e.g., preference for high or low pH), compared to those using SoilGrids. Nevertheless, if no regional soil maps are available, SoilGrids should be tested for their potential to improve SDMs. Moreover, among the tested soil predictors, pH, and clay content of the topsoil layers most improved the predictive power of SDMs for forest woody plants. In conclusion, we demonstrate the value of regional soil maps for predicting the distribution of woody species across strong environmental gradients in temperate forests. The improved accuracy of SDMs and insights into drivers of distribution may support forest managers in strategies supporting e.g. biodiversity conservation, or climate adaptation planning.

European grasslands face strong declines in extent and quality. Many grassland types are priority habitats for national and European conservation strategies. Countrywide, high spatial resolution maps of their distribution are often lacking. Here, we modelled the spatial distribution of 20 permanent grassland habitats at the level of phytosociological alliances across Switzerland at 10x10 m resolution. First, we applied ensemble models to provide distribution maps of the individual habitat types, using training data from various sources. Copernicus Sentinel satellite imagery and variables describing climate, soil and topography were used as predictors. The performance of these models was assessed based on the true skill statistics with a split-sampling of the data. Second, the individual maps were combined into countrywide maps of the most and second most likely habitat type, respectively, using an expert-based weighting approach. The performance of the combined map for the most likely habitat type was assessed via an independent testing dataset and a comparison of the predicted habitat-type proportions with extrapolations from field surveys. Most individual maps had useful to excellent predictive performance (TSS ≥ 0.6). For most grid cells in the combined maps, the most and second most likely habitat types were either ecologically closely related or representing two grassland types along a nutrient gradient. The same was true for omission errors. We found good agreement between the predicted and estimated proportions from field surveys. The area of raised bogs appears to be underestimated, while dry grasslands showed highest agreement. This work highlights the potential of earth observation data at fine spatial and temporal resolution to map habitats at broad scales, thereby providing the foundation for diverse conservation applications. A particular challenge remains in capturing the transition from nutrient-poor to nutrient-rich grasslands, which is highly important for biodiversity conservation.

Lebensraumkarten mit hoher thematischer und räumlicher Auflösung, die weiträumige Gebiete abdecken, sind grundlegende Instrumente für die Planung ökologischer Netzwerke und das Management von Biodiversität. Um eine Lebensraumkarte für die Schweiz zu erstellen, haben wir die besten verfügbaren räumlichen Daten und Verbreitungsmodelle in einer integrierten Methode zusammengeführt. Der Ansatz stützt sich auf die Segmentierung und Klassifizierung von Fernerkundungsdaten mit hoher räumlicher Auflösung, inklusive Orthobilder (1 m Auflösung) und Satellitenbilder von Planet (3 m Auflösung) und Sentinel-1 und -2 (10 m respektive 20 m Auflösung). 84 TypoCH Lebensräume in 32 Gruppen und 9 übergreifenden Klassen wurden schweizweit räumlich explizit mit feiner räumlicher und thematischer Auflösung kartiert. Die erstellte Karte ist im WSL-Datenportal EnviDat und auf map.geo.admin.ch frei zugänglich.

Habitat maps with high thematic and spatial resolution covering broad extents are fundamental tools for planning ecological networks and managing biodiversity. To produce a habitat map for Switzerland, we brought together the best available spatial data and distribution models in an integrated method. The approach is based on the segmentation and classification of remote sensing data with high spatial resolution. 84 TypoCH habitats in 32 groups and 9 overarching classes were mapped in a spatially explicit manner for Switzerland. The map produced is freely accessible on the WSL data portal EnviDat and map.geo.admin.ch.

Habitat maps at high thematic and spatial resolution and broad extents are fundamental tools for biodiversity conservation, the planning of ecological networks and the management of ecosystem services. To derive a habitat map for Switzerland, we used a composite methodology bringing together the best available spatial data and distribution models. The approach relies on the segmentation and classification of high spatial resolution (1 m) aerial imagery. Land cover data, as well as habitat and species distribution models built on Earth observation data from Sentinel 1 and 2, Landsat, Planetscope and LiDAR, inform the rule-based classification to habitats defined by the hierarchical Swiss Habitat Typology (TypoCH). A total of 84 habitats in 32 groups and 9 overarching classes are mapped in a spatially explicit manner across Switzerland. Validation and plausibility analysis with four independent datasets show that the mapping is broadly plausible, with good accuracy for most habitats, although with lower performance for fine-scale and linear habitats, habitats with restricted geographical distributions and those predominantly characterised by understorey species, especially forest habitats. The resulting map is a vector dataset available for interactive viewing and download from open EnviDat data sharing platform. The methodology is semi-automated to allow for updates over time.

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).

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.

Context: Open data policies and accessible computation platforms allow efficient extraction of information from remote sensing data for landscape research. Landscape ecology is strongly influenced by remote sensing, and the value of fine resolution temporal information for characterising landscapes is under-explored.
Objectives: We highlighted the importance of temporal information extracted from remote sensing data gathered over a period of time for landscape research. A case study approach was used to show how time-series information can benefit the mapping of land cover and landscape elements in a heterogeneous landscape dominated by agricultural land use.
Methods: We constructed four composite images of the study area, each incorporating different levels of temporal information. The images either represent a single date or summarise temporal information into single values as the median of spectral bands or vegetation indices. Random forest and k-means clustering methods were used to classify the images. Results: The overall accuracy of the landscape classifications ranged between 0.3 to 0.8, increasing substantially when including temporal information, for mapping both land cover and small landscape elements. Using temporal information and a RF-based classification it was generally possible to map crop and forest types. The size of landscape elements was overestimated, although the clustering model predicted elements close to their true size and complexity.
Conclusions: The approach highlights the importance of temporal resolution for landscape ecology research. The easy-to-implement methodology offers an opportunity for landscape ecologists to increase the accuracy of landscape mapping and identify ecologically important landscape elements that might otherwise be missed.

In der Schweiz gibt es zahlreiche unterschiedliche Lebensräume. Die WSL hat eine digitale Verbreitungskarte erstellt.

En Suisse, il existe d'innombrables milieux naturels différents, dont l'Institut fédéral de recherches sur la forêt, la neige et le paysage (WSL) a dressé une carte synoptique numérique.

The demand for precise mapping and monitoring of forest resources, such as above ground biomass (AGB), has increased rapidly. National accounting and monitoring of AGB requires regularly updated information based on consistent methods. While remote sensing technologies such as airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) have been shown to deliver the necessary 3D spatial data for AGB mapping, the capacity of repeat acquisition, remotely sensed, vegetation structure data for AGB monitoring has received less attention. Here, we use vegetation height models (VHMs) derived from repeat acquisition DAP data (with ALS terrain correction) to map and monitor woody AGB dynamics across Switzerland over 35 years (1983-2017 inclusive), using a linear least-squares regression approach. We demonstrate a consistent relationship between canopy height derived from DAP and field-based NFI measures of woody AGB across four inventory periods. Over the environmentally heterogeneous area of Switzerland, our models have a comparable predictive performance (R² = 0.54) to previous work predicting AGB based on ALS metrics. Pearson correlation coefficients between measured and predicted changes in woody AGB over time increased with shorter time gaps (< 2 years) between image capture and field-based measurements, ranging between 0.76 and 0.34. A close temporal match between field surveys and remote sensing data acquisition is thus key to reliable mapping and monitoring of AGB dynamics, especially in areas where forest management and natural disturbances trigger relatively fast canopy dynamics. We show that VHMs derived from repeat DAP capture constitute a cost effective and reliable approach to map and monitor changes in woody AGB at a national extent and can provide an important information source for national carbon accounting and monitoring of ecosystem service provisioning.

In the face of the growing challenges brought about by human activities, effective planning and decision-making in biodiversity and ecosystem conservation, restoration, and sustainable development are urgently needed. Ecological models can play a key role in supporting this need and helping to safeguard the natural assets that underpin human wellbeing and support life on land and below water (United Nations Sustainable Development Goals; SDG 15 & 14). The urgency and complexity of safeguarding forest (SDG 15.2) and mountain ecosystems (SDG 15.4), for example, and halting decline in biodiversity (SDG 15.5) in the Anthropocene requires a re-envisioning of how ecological models can best support the comprehensive assessments of biodiversity and its change that are required for successful action.
A key opportunity to advance ecological modeling for both predictive and explanatory purposes arises through a collaboration between ecologists and the Earth observation community, and a close integration of remote sensing and species distribution models. Remote sensing products have the capacity to provide continuous spatiotemporal information about key factors driving the distribution of organisms, therefore improving both the use and accuracy of these models for management and planning.
Here we first survey the literature on remote sensing data products available to ecological modelers interested in improving predictions of species range dynamics under global change. We specifically explore the key biophysical processes underlying the distribution of species in the Anthropocene including climate variability, changes in land cover, and disturbances. We then discuss potential synergies between the ecological modeling and remote sensing communities, and highlight opportunities to close the data and conceptual gaps that currently impede a more effective application of remote sensing for the monitoring and modeling of ecological systems. Specific attention is given to how potential collaborations between the two communities could lead to new opportunities to report on progress towards global agendas - such as the Agenda 2030 for sustainable development of the United Nations or the Post-2020 Global Biodiversity Framework of the Convention for Biological Diversity, and help guide conservation and management strategies towards sustainability.

Spatial planning plays an important role in cropland protection, but its effectiveness is often questioned in the face of ongoing urban and infrastructure growth. Moreover, methods to assess the effectiveness of spatial planning are lacking. In Switzerland, the revision of the national spatial planning act in 2014 was a new starting point for stricter prescriptions on urban development. We assessed whether the new regulations would better protect dedicated prime cropland from conversion to urban areas using land-use suitability models and land-use scenarios. The findings show that with the planning according to the revised planning act, the potential consumption of prime cropland for new urban areas is six times smaller than that occurring through extrapolation of the observed trend in urban development over the past 25 years. However, scenario modeling suggests that, still, more prime cropland will be converted into urban areas than necessary, and that it may be difficult to protect prime cropland to the extent mandated by the Swiss prime cropland protection policy. We have developed an approach to a priori evaluate spatial planning measures. However, the strict implementation of these planning measures will be needed in order to maintain prime cropland to a level required for agricultural self-sufficiency and food security.

Landscapes have been changing at an increasing pace over the past century, with countless consequences for humans and their surrounding environments. Information on past and future land use change and the resulting alteration of landscape service provisioning are valuable inputs for policy making and planning. Land use transitions in Switzerland (2009–2081) were simulated using statistical models informed by past land use changes as well as environmental and socio-economic data (1979–2009). By combining land use types with additional contextual landscape information, eight landscape services, based on both (semi-)natural and artificial landscapes, were quantified and investigated on how they would evolve under projected land use changes. Investigation of land use transitions showed region-dependent trends of urban expansion, loss of agricultural area, and forest regrowth. Landscapes cannot accommodate all services simultaneously, and this study sheds light on some competing landscape services, in particular (i) housing at the expense of agriculture and (ii) vanishing recreation opportunities around cities as city limits, and thus housing and job provisioning, expand. Model projections made it possible to pinpoint potential trade-offs between landscape services in a spatially explicit manner, thereby providing information on service provision losses and supporting planning. While future changes are presented as extrapolations of the patterns quantified in the past, policy changes might cause deviation from the projections presented here. A major challenge is to produce socio-economic and policy scenarios to inform projections that will differ from current landscape management. Given that urban sprawl is affecting many land surfaces globally, the approach used here could be generalized to other countries in similar situations.

Methods and workflows for creating area-wide products using remote sensing methods have been developed for various reasons. For the sample-based estimates in the Swiss National Forest Inventory (NFI), area-wide data sets are used for the two-phase estimations, which have substantially lower estimation errors than one-phase inventories. Using area-wide data sets, it is possible to skip dense manual interpretation of the stereo-images and thus save resources. Further, many functions of the forest, such as biodiversity and protection against natural hazards, can be described and quantified better with area-wide spatial data than with field plot data.

 

The Polish Carpathians, like many mountain areas in Europe, are currently facing dynamic land use changes that will shape their future landscapes. As there are many different possible scenarios of potential change, we compared three different land use scenarios up until the year 2060 and assessed their impact on the potential habitat connectivity of two large carnivores—wolf (Canis lupus) and lynx (Lynx lynx). We first analysed the main directions of change within and outside the pan-European wildlife corridor located in the western part of the Polish Carpathians. Then we calculated and compared least-cost paths among randomly selected points for each land use scenario separately. Our results showed that the main direction of change—forest cover increase—may positively influence habitat connectivity for both wolf and lynx. However, due to the future spread of settlements, this positive impact might be locally limited. Therefore, to realise the potential conservation opportunities resulting from on-going land use changes, adequate orientation of spatial planning towards habitat connectivity is crucial.

Grassland use intensity is a topic of growing interest worldwide, as grasslands are integral in supporting biodiversity, food production, and regulating of the global carbon cycle. Data available for characterizing grasslands management are largely descriptive and collected from laborious field campaigns or questionnaires. The recent launch of the Sentinel-2 earth monitoring constellation provides new possibilities for high temporal and spatial resolution remote sensing data covering large areas. This study aims to evaluate the potential of a time series of Sentinel-2 data for mapping of mowing frequency in the region of Canton Aargau, Switzerland. We tested two cloud masking processes and three spatial mapping units (pixels, parcel polygons and shrunken parcel polygons), and investigated how missing data influence the ability to accurately detect and map grassland management activity. We found that more than 40% of the study area was mown before 15 June, while the remaining part was either mown later, or was not mown at all. The highest accuracy for detection of mowing events was achieved using additional clouds masking and size reduction of parcels, which allowed correct detection of 77% of mowing events. Additionally, we found that using only standard cloud masking leads to significant overestimation of mowing events, and that the detection based on sparse time series does not fully correspond to key events in the grass growth season.

Flooding is a major environmental hazard in Poland with risks that are likely to increase in the future. Land use and land cover (LULC) have a strong influencing on flood risk. In the Polish Carpathians, the two main projected land use change processes are forest expansion and urbanization. These processes have a contradictory impact on flood risk, which makes the future impact of LULC changes on flooding in the Carpathians hard to estimate. In this paper, we investigate the impact of the projected LULC changes on future flood risk in the Polish Carpathians for the test area of Ropa river basin. We used three models of spatially explicit future LULC scenarios for the year 2060. We conduct hydrological simulations for the current state and for the three projected land use scenarios (trend extrapolation, 'liberalization' and 'self-sufficiency'). In addition, we calculated the amount of flood-related monetary losses, based on the current flood plain area and both actual and projected land use maps under each of the three scenarios. The results show that in the Ropa river, depending on scenario, either peak discharge decreases due to the forest expansion or the peak discharge remains constant – the impact of LULC changes on the hydrology of such mountainous basins is relatively low. However, the peak discharges are very diverse across sub-catchments within the modeling area. Despite the overall decrease of peak discharge, there are areas of flow increase and there is a substantial projected increase in flood-related monetary losses within the already flood-prone areas, related to the projected degree of urbanization.

Replacement of conventional energy sources with renewables such as solar panels and wind turbines requires adequate land. Impact assessments should be conducted to identify sites exhibiting least conflict with current and future land-uses and corresponding ecosystem services. We assessed the electricity potential and geographical distribution of wind turbines and solar panels for current land-use and under three Swiss land-change scenarios. The future scenario A2 with limited construction regulations, a liberalized market and more building surfaces increases the electricity potential of solar panels by 69% from 16.6 TWh (potential under current land-use and regulations) to a future 28.2 TWh. An increase of approximately 26% electricity potential from solar panels is expected for scenario B2 (regionalized economy) and the trend scenario. Wind-electricity potential could increase by 61% from 93 to 150 TWh under A2, and 29% under a B2 or trend scenario. The electricity potential for solar panels remains largely unaffected by conflicts with ecosystem services, but electricity production from wind could be reduced by as much as 98% due to conflicts with ecosystem services. Depending on the scenario used, low-conflict sites for solar panels and wind turbines could contribute between 85% (trend and B2 scenario) and >100% (A2 scenario) to the Swiss energy target of generating 25 TWh from new renewable energy sources by 2050. This includes expected technological developments. Positive impacts of sustainable energy production on regional economies are moderate and will not lead to strong changes in regional-economic development.

For more than 100 years, forest cover in Europe has increased substantially due to afforestation and natural forest expansion. The latter, resulting typically from farmland abandonment and subsequent secondary forest succession, has played a major role in marginal mountain areas and possesses various highly important environmental and economic consequences. In the Polish Carpathians, farmland abandonment is a spatially dispersed, locally specific process because of small farm and land parcel sizes, and its extent in the entire region is not well known. This study aims therefore to map current farmland abandonment in the Polish Carpathians and to identify its spatial determinants. To map farmland abandonment we detected secondary forest succession on abandoned fields using Airborne Laser Scanning (ALS) point clouds and topographic data in 230 sample areas (tiles) distributed throughout the Carpathian communes. To reveal the spatial determinants of current farmland abandonment, we investigated variables describing the accessibility and environmental, land cover, and socioeconomic conditions of communes, using best subset regression modelling. The results showed that 13.9% of agricultural land was abandoned and underwent secondary forest succession in recent decades. Topography (mainly slope gradients) and employment outside of agriculture were the two most important spatial determinants of farmland abandonment. Consequently, a substantial forest expansion may be observed in the coming decades throughout the Polish Carpathians if no counteractions are taken.

Mountain forest areas are key for providing a wide range of ecosystem services and are hot spots for land use change processes, in particular, increase in forest cover at the expense of mountain pastures and meadows. Mountain forest systems in eastern and western Europe have likely similar future socio-economic situations but significantly different socio-economic history. Using a scenario-based land use modelling approach (Dyna-CLUE framework) we model three scenarios (trend, liberalisation and self-sufficiency) of future land use in the Polish Carpathians and the Swiss Alps, focussing on forest cover change. We find that forest cover increase can be expected to continue in European mountainous regions under all likely scenarios, limited only by relatively strict policy interventions. Biophysical factors, rather than socio-economic ones, are key for defining the suitability for, and therefore likely locations of future forest cover, but land use legacy plays a very important role in the spatial patterns of future forest cover, particularly in eastern Europe.

Trees outside forest (TOF) can perform a variety of social, economic and ecological functions including carbon sequestration. However, detailed quantification of tree biomass is usually limited to forest areas. Taking advantage of structural information available from stereo aerial imagery and airborne laser scanning (ALS), this research models tree biomass using national forest inventory data and linear least-square regression and applies the model both inside and outside of forest to create a nationwide model for tree biomass (above ground and below ground). Validation of the tree biomass model against TOF data within settlement areas shows relatively low model performance (R² of 0.44) but still a considerable improvement on current biomass estimates used for greenhouse gas inventory and carbon accounting. We demonstrate an efficient and easily implementable approach to modelling tree biomass across a large heterogeneous nationwide area. The model offers significant opportunity for improved estimates on land use combination categories (CC) where tree biomass has either not been included or only roughly estimated until now. The ALS biomass model also offers the advantage of providing greater spatial resolution and greater within CC spatial variability compared to the current nationwide estimates.