Monitoring biodiversity in cultural landscapes: development of remote sensing- and GIS-based methods

In this thesis, I explore the relationships between structural and compositional landscape properties, and species diversity, using remotely sensed data on a variety of spatial scales. The thesis shows that increased landscape heterogeneity, measured using environmental and spectral variables that were used both separately and combined, is generally positively related to plant species richness. I further found that plant species richness could be predicted with <20% deviance in species numbers, in approximately 80% of the study area within the province of Scania, using a combination of environmental and spectral descriptors of landscape heterogeneity. Further, I used Landsat satellite data, aided by ancillary data on topography and a spectral proxy of seasonal variation in vegetation phenology, to classify historical (ca 1975) and contemporary (ca 2001) land-cover data within the province of Scania, with +85% accuracy. The produced land use/land cover (LULC) data showed correlations with levels of plant species richness, with the proportion of cropland generally being negatively correlated to levels of plant species richness, and the proportion of LULC classes such as grazed grassland, wetland and deciduous forest being positively correlated to levels of plant species richness. Further, the positive change between the historical and contemporary landscapes in the proportion of deciduous forest, and in the number of unique LULC patches, were positively correlated with contemporary levels of plant species richness. I modeled the importance of non-crop habitat types for plant species richness within the province of Scania, and showed that for the promotion of plant species richness, the most wide-spread non-crop LULC types were most important within the most simplified landscapes, while the amount of non-crop small biotopes were most important in more complex landscapes.In a series of studies on grazed grasslands on the Baltic island of Öland, I showed that dissimilarity in Worldview-2 satellite spectral reflectance was related to plant species dissimilarity within a set of grassland plots, and then used spectral dissimilarity to predict levels of plant species richness in other grassland plots. I used HySpex hyperspectral aerial reflectance data to predict plant species diversity (species richness and Simpson’s diversity), using the full range of wavebands and also using a reduced set of wavebands. Finally, I classified grassland plots into age classes using reflectance data from the HySpex hyperspectral sensor, and achieved better classification results when using a reduced set of wavebands compared to using the full range of wavebands.In summary, the findings of this thesis demonstrate that remote sensing and GIS-based methods can be useful tools in the monitoring of cultural landscapes, because of their combined ability to model landscape properties and relate those measures to species diversity, at a range of spatial scales and within a range of habitats

Classification of grassland successional stages using airborne hyperspectral imagery

Plant communities differ in their species composition, and, thus, also in their functional trait composition, at different stages in the succession from arable fields to grazed grassland. We examine whether aerial hyperspectral (414–2501 nm) remote sensing can be used to discriminate between grazed vegetation belonging to different grassland successional stages. Vascular plant species were recorded in 104.1 m2 plots on the island of Öland (Sweden) and the functional properties of the plant species recorded in the plots were characterized in terms of the ground-cover of grasses, specific leaf area and Ellenberg indicator values. Plots were assigned to three different grassland age-classes, representing 5–15, 16–50 and >50 years of grazing management. Partial least squares discriminant analysis models were used to compare classifications based on aerial hyperspectral data with the age-class classification. The remote sensing data successfully classified the plots into age-classes: the overall classification accuracy was higher for a model based on a pre-selected set of wavebands (85%, Kappa statistic value = 0.77) than one using the full set of wavebands (77%, Kappa statistic value = 0.65). Our results show that nutrient availability and grass cover differences between grassland age-classes are detectable by spectral imaging. These techniques may potentially be used for mapping the spatial distribution of grassland habitats at different successional stages

Airborne Hyperspectral Data Predict Fine-Scale Plant Species Diversity in Grazed Dry Grasslands

Semi-natural grasslands with grazing management are characterized by high fine-scale species richness and have a high conservation value. The fact that fine-scale surveys of grassland plant communities are time-consuming may limit the spatial extent of ground-based diversity surveys. Remote sensing tools have the potential to support field-based sampling and, if remote sensing data are able to identify grassland sites that are likely to support relatively higher or lower levels of species diversity, then field sampling efforts could be directed towards sites that are of potential conservation interest. In the present study, we examined whether aerial hyperspectral (414–2501 nm) remote sensing can be used to predict fine-scale plant species diversity (characterized as species richness and Simpson’s diversity) in dry grazed grasslands. Vascular plant species were recorded within 104 (4 m × 4 m) plots on the island of Öland (Sweden) and each plot was characterized by a 245-waveband hyperspectral data set. We used two different modeling approaches to evaluate the ability of the airborne spectral measurements to predict within-plot species diversity: (1) a spectral response approach, based on reflectance information from (i) all wavebands, and (ii) a subset of wavebands, analyzed with a partial least squares regression model, and (2) a spectral heterogeneity approach, based on the mean distance to the spectral centroid in an ordinary least squares regression model. Species diversity was successfully predicted by the spectral response approach (with an error of ca. 20%) but not by the spectral heterogeneity approach. When using the spectral response approach, iterative selection of important wavebands for the prediction of the diversity measures simplified the model but did not improve its predictive quality (prediction error). Wavebands sensitive to plant pigment content (400–700 nm) and to vegetation structural properties, such as above-ground biomass (700–1300 nm), were identified as being the most important predictors of plant species diversity. We conclude that hyperspectral remote sensing technology is able to identify fine-scale variation in grassland diversity and has a potential use as a tool in surveys of grassland plant diversity

Assessment of fine-scale plant species beta diversity using WorldView-2 satellite spectral dissimilarity

Plant species beta diversity is influenced by spatial heterogeneity in the environment. This heterogeneity can potentially be characterised with the help of remote sensing. We used WorldView-2 satellite data acquired over semi-natural grasslands on The Baltic island of Öland (Sweden) to examine whether dissimilarities in remote sensing response were related to fine-scale, between-plot dissimilarity (beta diversity) in non-woody vascular plant species composition within the grasslands. Fieldwork, including the on-site description of a set of 30 2 m × 2 m plots and a set of 30 4 m × 4 m plots, was performed to record the species dissimilarity between pairs of same-sized plots. Spectral data were extracted by associating each plot with a suite of differently sized pixel windows, and spectral dissimilarity was calculated between pairs of same-sized pixel windows. Relationships between spectral dissimilarity and beta diversity were analysed using univariate regression and partial least squares regression. The study revealed significant positive relationships between spectral dissimilarity and fine-scale (2 m × 2 m and 4 m × 4 m) between-plot species dissimilarity. The correlation between the predicted and the observed species dissimilarity was stronger for the set of large plots (4 m × 4 m) than for the set of small plots (2 m × 2 m), and the association between spectral and species data at both plot scales decreased when pixel windows larger than 3 × 3 pixels were used. We suggest that the significant relationship between spectral dissimilarity and species dissimilarity is a reflection of between-plot environmental heterogeneity caused by differences in grazing intensity (which result in between-plot differences in field-layer height, and amounts of biomass and litter). This heterogeneity is reflected in dissimilarities in both the species composition and the spectral response of the grassland plots. Between-plot dissimilarities in both spectral response and species composition may also be caused by between-plot variations in edaphic conditions. Our results indicate that high spatial resolution satellite data may potentially be able to complement field-based recording in surveys of fine-scale species diversity in semi-natural grasslands