Surface models provide key knowledge of the 3-d structure of forests. Aerial stereo imagery acquired during routine mapping campaigns covering the whole of Switzerland (41,285 km2), offers a ...potential data source to calculate digital surface models (DSMs). We present an automated workflow to generate a nationwide DSM with a resolution of 1 1 m based on photogrammetric image matching. A canopy height model (CHM) is derived in combination with an existing digital terrain model (DTM). ADS40/ADS80 summer images from 2007 to 2012 were used for stereo matching, with ground sample distances (GSD) of 0.25 m in lowlands and 0.5 m in high mountain areas. Two different image matching strategies for DSM calculation were applied: one optimized for single features such as trees and for abrupt changes in elevation such as steep rocks, and another optimized for homogeneous areas such as meadows or glaciers. The country was divided into 165,500 blocks, which were matched independently using an automated workflow. The completeness of successfully matched points was high, 97.9%. To test the accuracy of the derived DSM, two reference data sets were used: (1) topographic survey points (n = 198) and (2) stereo measurements (n = 195,784) within the framework of the Swiss National Forest Inventory (NFI), in order to distinguish various land cover types. An overall median accuracy of 0.04 m with a normalized median absolute deviation (NMAD) of 0.32 m was found using the topographic survey points. The agreement between the stereo measurements and the values of the DSM revealed acceptable NMAD values between 1.76 and 3.94 m for forested areas. A good correlation (Pearson's r = 0.83) was found between terrestrially measured tree height (n = 3109) and the height derived from the CHM. Optimized image matching strategies, an automatic workflow and acceptable computation time mean that the presented approach is suitable for operational usage at the nationwide extent. The CHM will be used to reduce estimation errors of different forest characteristics in the Swiss NFI and has high potential for change detection assessments, since an aerial stereo imagery update is available every six years.
National forest inventories (NFI) are important for the assessment of the state and development of forests. Traditional NFIs often rely on statistical sampling approaches as well as expert assessment ...which may suffer from observer bias and may lack robustness for time series analysis. Over the course of the last decade, close-range remote sensing techniques such as terrestrial and mobile laser scanning became ever more established for the assessment of three-dimensional (3D) forest structure. With the ongoing trend to make the systems smaller, easier to use and more efficient, the pathway is being opened for an operational inclusion of such devices within the framework of an NFI to support the traditional field assessment. Close-range remote sensing could potentially speed up field inventory work as well as increase the area in which certain parameters are assessed. Benchmarks are needed to evaluate the performance of different close-range remote sensing devices and approaches, both in terms of efficiency as well as accuracy. In this study we evaluate the performance of two terrestrial (TLS), one handheld mobile (PLS) and two drone based (UAVLS) laser scanning systems to detect trees and extract the diameter at breast height (DBH) in three plots with a steep gradient in tree and understorey vegetation density. As a novelty, we also tested the acquisition of 3D point-clouds using a low-cost action camera (GoPro) in conjunction with the Structure from Motion (SfM) technique and compared its performance with those of the more costly LiDAR devices. Among the many parameters evaluated in traditional NFIs, the focus of the performance evaluation of this study is set on the automatic tree detection and DBH extraction.
The results showed that TLS delivers the highest tree detection rate (TDR) of up to 94.6% under leaf-off and up to 82% under leaf-on conditions and a relative RMSE (rRMSE) for the DBH extraction between 2.5 and 9%, depending on the undergrowth complexity. The tested PLS system (leaf-on) achieved a TDR of up to 80% with an rRMSE between 3.7 and 5.8%. The tested UAVLS systems showed lowest TDR of less than 77% under leaf-off and less than 37% under leaf-on conditions. The novel GoPro approach achieved a TDR of up to 53% under leaf-on conditions. The reduced TDR can be explained by the reduced area coverage due to the chosen circular acquisition path taken with the GoPro approach. The DBH extraction performance on the other hand is comparable to those of the LiDAR devices with an rRMSE between 2 and 9%.
Further benchmarks are needed in order to fully assess the applicability of these systems in the framework of an NFI. Especially the robustness under varying forest conditions (seasonality) and over a broader range of forest types and canopy structure has to be evaluated.
•Benchmark of close-range remote sensing techniques for forest inventory applications.•Faster approaches to acquire plot level 3D data compared to TLS are needed for NFI.•Dense understorey vegetation reduce tree detection performance for all tested devices.•Low-cost SfM approaches show potential to extract accurate tree information.•PLS and SfM show potential for a fast yet accurate tree detection and DBH retrieval.
An increasing number of studies have reported on forest declines and vegetation shifts triggered by drought. In the Swiss Rhone valley (Valais), one of the driest inner‐Alpine regions, the species ...composition in low elevation forests is changing: The sub‐boreal Scots pine (Pinus sylvestris L.) dominating the dry forests is showing high mortality rates. Concurrently the sub‐Mediterranean pubescent oak (Quercus pubescens Willd.) has locally increased in abundance. However, it remains unclear whether this local change in species composition is part of a larger‐scale vegetation shift. To study variability in mortality and regeneration in these dry forests we analysed data from the Swiss national forest inventory (NFI) on a regular grid between 1983 and 2003, and combined it with annual mortality data from a monitoring site. Pine mortality was found to be highest at low elevation (below 1000 m a.s.l.). Annual variation in pine mortality was correlated with a drought index computed for the summer months prior to observed tree death. A generalized linear mixed‐effects model indicated for the NFI data increased pine mortality on dryer sites with high stand competition, particularly for small‐diameter trees. Pine regeneration was low in comparison to its occurrence in the overstorey, whereas oak regeneration was comparably abundant. Although both species regenerated well at dry sites, pine regeneration was favoured at cooler sites at higher altitude and oak regeneration was more frequent at warmer sites, indicating a higher adaptation potential of oaks under future warming. Our results thus suggest that an extended shift in species composition is actually occurring in the pine forests in the Valais. The main driving factors are found to be climatic variability, particularly drought, and variability in stand structure and topography. Thus, pine forests at low elevations are developing into oak forests with unknown consequences for these ecosystems and their goods and services.
Significance The second plague pandemic in medieval Europe started with the Black Death epidemic of 1347–1353 and killed millions of people over a time span of four centuries. It is commonly thought ...that after its initial introduction from Asia, the disease persisted in Europe in rodent reservoirs until it eventually disappeared. Here, we show that climate-driven outbreaks of Yersinia pestis in Asian rodent plague reservoirs are significantly associated with new waves of plague arriving into Europe through its maritime trade network with Asia. This association strongly suggests that the bacterium was continuously reimported into Europe during the second plague pandemic, and offers an alternative explanation to putative European rodent reservoirs for how the disease could have persisted in Europe for so long.
The Black Death, originating in Asia, arrived in the Mediterranean harbors of Europe in 1347 CE, via the land and sea trade routes of the ancient Silk Road system. This epidemic marked the start of the second plague pandemic, which lasted in Europe until the early 19th century. This pandemic is generally understood as the consequence of a singular introduction of Yersinia pestis , after which the disease established itself in European rodents over four centuries. To locate these putative plague reservoirs, we studied the climate fluctuations that preceded regional plague epidemics, based on a dataset of 7,711 georeferenced historical plague outbreaks and 15 annually resolved tree-ring records from Europe and Asia. We provide evidence for repeated climate-driven reintroductions of the bacterium into European harbors from reservoirs in Asia, with a delay of 15 ± 1 y. Our analysis finds no support for the existence of permanent plague reservoirs in medieval Europe.
Digital surface models (DSMs) are widely used in forest science to model the forest canopy. Stereo pairs of very high resolution satellite and digital aerial images are relatively new and their ...absolute accuracy for DSM generation is largely unknown. For an assessment of these input data two DSMs based on a WorldView-2 stereo pair and a ADS80 DSM were generated with photogrammetric instruments. Rational polynomial coefficients (RPCs) are defining the orientation of the WorldView-2 satellite images, which can be enhanced with ground control points (GCPs). Thus two WorldView-2 DSMs were distinguished: a WorldView-2 RPCs-only DSM and a WorldView-2 GCP-enhanced RPCs DSM. The accuracy of the three DSMs was estimated with GPS measurements, manual stereo-measurements, and airborne laser scanning data (ALS). With GCP-enhanced RPCs the WorldView-2 image orientation could be optimised to a root mean square error (RMSE) of 0.56 m in planimetry and 0.32 m in height. This improvement in orientation allowed for a vertical median error of -0.24 m for the WorldView-2 GCP-enhanced RPCs DSM in flat terrain. Overall, the DSM based on ADS80 images showed the highest accuracy of the three models with a median error of 0.08 m over bare ground. As the accuracy of a DSM varies with land cover three classes were distinguished: herb and grass, forests, and artificial areas. The study suggested the ADS80 DSM to best model actual surface height in all three land cover classes, with median errors <1.1 m. The WorldView-2 GCP-enhanced RPCs model achieved good accuracy, too, with median errors of -0.43 m for the herb and grass vegetation and -0.26 m for artificial areas. Forested areas emerged as the most difficult land cover type for height modelling; still, with median errors of -1.85 m for the WorldView-2 GCP-enhanced RPCs model and -1.12 m for the ADS80 model, the input data sets evaluated here are quite promising for forest canopy modelling.
AIM: To investigate the performance and relative importance of abiotic and biotic predictors of species richness of three taxa in forest‐dominated landscapes across an environmentally heterogeneous ...mountain region. LOCATION: Switzerland (central Europe). METHODS: We used a broad set of nationally available environmental predictors grouped into (1) climate, (2) topography and soil and (3) 3‐D vegetation structure derived from airborne Light Detection and Ranging (LiDAR) data to spatially predict the forest species richness of vascular plants, butterflies and breeding birds. We used presence data of 212 plant, 157 butterfly and 92 bird species from multiple transect samples in > 220 1 km² squares at elevations between 261 and 2123 m a.s.l. across 41,248 km². We applied an ensemble modelling approach consisting of five modelling techniques and evaluated their predictive performance using the cross‐validated percentage of explained variance of each predictor group separately and the combinations thereof. We investigated the relative importance and response of each predictor and partitioned the variation into independent and shared components per variable group. RESULTS: Climate performed best in predicting forest species richness across taxa. Vegetation structure particularly improved the predictions of butterfly and bird species richness, while soil pH was an important predictor for forest plant species richness. Climate appeared to be mainly indirectly related to butterfly species richness, via correlations with habitat type and structure. The strength and direction of the relationships between the predictors and species richness were taxon‐specific with low cross‐taxon congruence. MAIN CONCLUSIONS: The growing availability of LiDAR data offers powerful new tools for describing vegetation structure and associated animal habitat quality across large areas. This will further our understanding of niche‐driven assembly processes in forest landscapes. Although climate was the dominant factor controlling species richness across taxa from different trophic levels, the taxon‐specific distributional pattern and response to environmental conditions emphasize the difficulty of accounting for a range of taxa in prioritising biodiversity conservation measures.
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.
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.
Although wall-to-wall, accurate, and up-to-date forest composition maps at the stand level are a fundamental input for many applications, ranging from global environmental issues to local forest ...management planning, countrywide mapping approaches on the tree type level remain rare. This paper presents and validates an innovative remote sensing based approach for a countrywide mapping of broadleaved and coniferous trees in Switzerland with a spatial resolution of 3 m. The classification approach incorporates a random forest classifier, explanatory variables from multispectral aerial imagery and a Digital Terrain Model (DTM) from Airborne Laser Scanning (ALS) data, digitized training polygons and independent validation data from the National Forest Inventory (NFI). The methodological workflow was optimized for an area of 41,285 km2 that is characterized by temperate forests within a complex topography. Whereas high model overall accuracies (0.99) and kappa (0.98) were achieved, the comparison of the tree type map with independent NFI data revealed significant deviations that are related to underestimations of broadleaved trees (median of −3.17%). Constraints of the tree type mapping approach are mostly related to the acquisition date and time of the imagery and the topographic (negative) effects on the prediction. A comparison with the most recent High Resolution Layers (HRL) forest 2012 from the European Environmental Agency revealed that the tree type map is superior regarding spatial resolution, level of detail and accuracy. The high-quality map achieved with the approach presented here is of great value for optimizing forest management and planning activities and is also an important information source for applications outside the forestry sector.
Since the opening of Earth Observation (EO) archives (USGS/NASA Landsat and EC/ESA Sentinels), large collections of EO data are freely available, offering scientists new possibilities to better ...understand and quantify environmental changes. Fully exploiting these satellite EO data will require new approaches for their acquisition, management, distribution, and analysis. Given rapid environmental changes and the emergence of big data, innovative solutions are needed to support policy frameworks and related actions toward sustainable development. Here we present the Swiss Data Cube (SDC), unleashing the information power of Big Earth Data for monitoring the environment, providing Analysis Ready Data over the geographic extent of Switzerland since 1984, which is updated on a daily basis. Based on a cloud-computing platform allowing to access, visualize and analyse optical (Sentinel-2; Landsat 5, 7, 8) and radar (Sentinel-1) imagery, the SDC minimizes the time and knowledge required for environmental analyses, by offering consistent calibrated and spatially co-registered satellite observations. SDC derived analysis ready data supports generation of environmental information, allowing to inform a variety of environmental policies with unprecedented timeliness and quality.
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