Dry grassland sites are amongst the most species-rich habitats of central Europe and it is necessary to design effective management schemes for monitoring of their biomass production. This study explored the potential of hyperspectral remote sensing for mapping aboveground biomass in grassland habitats along a dry-mesic gradient, independent of a specific type or phenological period. Statistical models were developed between biomass samples and spectral reflectance collected with a field spectroradiometer, and it was further investigated to what degree the calibrated biomass models could be scaled to Hyperion data. Furthermore, biomass prediction was used as a surrogate for productivity for grassland habitats and the relationship between biomass and plant species richness was explored. Grassland samples were collected at four time steps during the growing season to capture normally occurring variation due to canopy growth stage and management factors. The relationships were investigated between biomass and (1) existing broad- and narrowband vegetation indices, (2) narrowband normalized difference vegetation index (NDVI) type indices, and (3) multiple linear regression (MLR) with individual spectral bands. Best models were obtained from the MLR and narrowband NDVI-type indices. Spectral regions related to plant water content were identified as the best estimators of biomass. Models calibrated with narrowband NDVI indices were best for up-scaling the field-developed models to the Hyperion scene. Furthermore, promising results were obtained from linking biomass estimations from the Hyperion scene with plant species richness of grassland habitats. Overall, it is concluded that ratio-based NDVI-type indices are less prone to scaling errors and thus offer higher potential for mapping grassland biomass using hyperspectral data from space-borne sensors.

Increasing urbanization degrades quantity, quality, and the functionality of spatial cohesion of natural areas essential to biodiversity and ecosystem functioning worldwide. The uncontrolled pace of building activity and the erosion of blue (i.e., aquatic) and green (i.e., terrestrial) landscape elements threaten existing habitat ranges and movability of wildlife. Local scale measures, such as nature-inspired engineered Blue-Green Infrastructure (BGI) are emerging mitigation solutions. Originally planned to promote sustainable stormwater management, adaptation to climate change and improved human livability in cities, such instruments offer interesting synergies for biodiversity in support of existing ecological infrastructure. BGI are especially appealing for globally declining amphibians, a rich and diverse vertebrate assemblage sensitive to urbanization. We integrated biological and highly resolved urban-rural land-cover data, ensemble models of habitat suitability, and connectivity models based on circuit theory to improve multi-scale and multi-species protection of core habitats and ecological corridors in the Swiss lowlands. Considering a broad spectrum of amphibian biodiversity, we identified distributions of amphibian biodiversity hotspots and four landscape elements essential to amphibian movability at the regional scale, namely i) forest edges, ii) wet-forest habitats, iii) soils with variable moisture and iv) riparian zones. Our work shows that cities can make a substantial contribution (e.g., up to 15% of urban space in the study area) to wider landscape habitat connectivity. We highlight the importance of planning BGI locally in strategic locations across urban and peri-urban areas to promote the permeability and availability of 'stepping stone' habitats in densely populated landscapes, essential to the maintenance of regional habitat connectivity and thereby enhancing biodiversity and ecosystem functioning.

Secondary forest succession on abandoned agricultural land has played a significant role in land cover changes in Europe over the past several decades. However, it is difficult to quantify over large areas. In this paper, we present a conceptual framework for mapping forest succession patterns using vegetation structure information derived from LiDAR data supported by national topographic vector data. This work was performed in the Szczawnica commune in the Polish Carpathians. Using object-based image analysis segments of no vegetation, and sparse/dense low/medium/high vegetation were distinguished and subsequently compared to the national topographic dataset to delineate agricultural land that is covered by vegetation, which indicates secondary succession on abandoned fields. The results showed that 18.7% of the arable land and 40.4% of grasslands, that is 31.0% of the agricultural land in the Szczawnica commune, may currently be experiencing secondary forest succession. The overall accuracy of the approach was assessed using georeferenced terrestrial photographs and was found to be 95.0%. The results of this study indicate that the proposed methodology can potentially be applied in large-scale mapping of secondary forest succession patterns on abandoned land in mountain areas.

Mapping and monitoring agricultural land-use intensity (LUI) changes are essential for understanding their effects on biodiversity. Current land-use models provide a rather coarse spatial resolution, while in-situ measurements of LUI cover only a limited extent and are time-consuming and expensive. The purpose of this study is to evaluate the feasibility of using habitat type, topo-climatic, economic output, and remote-sensing data to map LUI at a high spatial resolution. To accomplish this, we first rated the habitat types across the agricultural landscape in terms of the amount and frequency of fertiliser input, pesticide input, ploughing, grazing, mowing, harvesting, and biomass output. We consolidated these ratings into one LUI index per habitat type that we then related to topo-climatic, economic output, and remote-sensing predictors. The results showed that the LUI index was strongly related to plant indicator values for mowing tolerance and soil nutrient content and to aerial nitrogen deposition, and thus, is an adequate index. Topo-climatic, and, to a smaller extent, economic output and remote-sensing predictors, proved suitable for mapping LUI. Large- to medium-scale patterns are explained by topo-climatic predictors, while economic output predictors explain medium-scale patterns and remote-sensing predictors explain local-scale patterns. With the fine-scale LUI map produced from this study, it is now possible to estimate within unvarying land-use classes, the effect on agrobiodiversity of an increase in LUI on fertile and accessible lands and of a decrease of LUI by the abandonment of marginal agricultural lands, and thus, provide a valuable base for understanding the effects of LUI on biodiversity. Due to the worldwide availability of remote-sensing and climate data, our methodology can be easily applied to other countries where habitat-type data are available. Given their low explanatory power, economic output variables may be omitted if not available.

Information on canopy chemical concentrations is of great importance for the study of nutrient cycling, productivity and for input to ecosystem process models. In particular, foliar carbon to nitrogen ratio (C:N) drives terrestrial biogeochemical processes such as decomposition and mineralization, and thus strongly influences soil organic matter concentrations and turnover rates. This study evaluated the effects of using spatial estimates of foliar C:N derived from hyperspectral remote sensing for simulating NPP by means of the ecosystem process model Biome-BGC. The main objectives of this study were to calibrate spatial statistical models for the prediction of foliar C:N for grassland habitats at the regional scale, using airborne HyMap hyperspectral data, to use the foliar C:N predictions as input to the ecosystem process model Biome-BGC and derive NPP estimates and finally to compare these results to NPP estimates derived using C:N value reported in literature and derived from field measurements. Results from this research indicate that NPP estimates using the HyMap predicted C:N differed significantly from those when C:N values from "global" or "regional" measurements were used. Extending the current research to broader spatial scales can help to initialise, validate and adjust better ecological process models.