Karin Hall

Investigating the use of Landsat thematic mapper data for estimation of forest leaf area index in southern Sweden

The study aims at investigating the use of Landsat thematic mapper (TM) for mapping leaf area index (LAI) in coniferous and deciduous forests in southern Sweden. LAI has been estimated in the field with optical measurements, allometric equations, and litter-trap data, and empirical relationships between LAI estimates and satellite-measured reflectances have been analysed. Several common vegetation indices and multiple regressions where estimated LAI is predicted as a function of various spectral bands are tested. The results indicate significant relationships between Landsat TM reflectances and parameters related to LAI, and the relationships are improved when separating coniferous and deciduous stands. The best relationships occur between Landsat TM data and the product of effective LAI as estimated with the LAI-2000 instrument and a needle clumping factor (LG), which explains about 80% of the variation in coniferous stands and about 50% of the variation in deciduous stands. The best single bands in coniferous stands are the middle-infrared bands (TM5 and TM7), and the best vegetation index is the moisture stress index (TM5/TM4). The best single band in deciduous stands is TM4, and the best vegetation index is the simple ratio (SR).

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 Oland (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.

Modelling as a tool for analysing the temperature-dependent future of the Colorado potato beetle in Europe

A warmer climate may increase the risk of attacks by insect pests on agricultural crops, and questions on how to adapt management practice have created a need for impact models. Phenological models driven by climate data can be used for assessing the potential distribution and voltinism of different insect species, but the quality of the simulations is influenced by a range of uncertainties. In this study, we model the temperature-dependent activity and development of the Colorado potato beetle, and analyse the influence of uncertainty associated with parameterization of temperature and day length response. We found that the developmental threshold has a major impact on the simulated number of generations per year. Little is known about local adaptations and individual variations, but the use of an upper and a lower developmental threshold gave an indication on the potential variation. The day length conditions triggering diapause are known only for a few populations. We used gridded observed temperature data to estimate local adaptations, hypothesizing that cold autumns can leave a footprint in the population genetics by low survival of individuals not reaching the adult stage before winter. Our study indicated that the potential selection pressure caused by climate conditions varies between European regions. Provided that there is enough genetic variation, a local adaption at the northern distribution limit would reduce the number of unsuccessful initiations and thereby increase the potential for spreading to areas currently not infested. The simulations of the impact model were highly sensitive to biases in climate model data, i.e. systematic deviations in comparison with observed weather, highlightening the need of improved performance of regional climate models. Even a moderate temperature increase could change the voltinism of Leptinotarsa decemlineata in Europe, but knowledge on agricultural practice and strategies for countermeasures is needed to evaluate changes in risk of attacks.

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 Oland (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 Oland (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.

NOAA/A AVHRR data for crop productivity estimation in Sweden

Abstract: Studies reported in this paper indicate the Presence of a relation between NOAA/AVHRR data and crop biomass in Sweden. Conclusions so far, however, are that NOAA/AVHRR at the present time can not be successfully applied for operational crop biomass estimation in Sweden. Investigations are now in progress aiming at the practical use of this remotely sensed data for the determination of biomass.

Landscape history confounds the ability of the NDVI to detect fine‐scale variation in grassland communities

The NDVI is a remotely sensed vegetation index that is frequently used in ecological studies. There is, however, a lack of studies that evaluate the ability of the NDVI to detect fine-scale variation in grassland plant community composition and species richness. Ellenberg indicators characterize the environmental preferences of plant species—and community-mean Ellenberg values have been used to explore the environmental drivers of community assembly. We used variation partitioning to test the ability of satellite-based NDVI to explain community-mean Ellenberg nutrient (mN) and moisture (mF) indices, and the richness of habitat-specialist species in dry grasslands of different ages. The grasslands represent a gradient of decreasing soil nutrient status. If community composition is determined by the responses of individual species to the underlying environmental conditions and if, at the same time, community composition determines the optical characteristics of the vegetation canopy, then positive relationships between the NDVI and mN and mF are expected. Many grassland specialists are intolerant of nutrient-rich soils. If specialist richness is negatively related to soil-nutrient levels, then a negative association between the NDVI and specialist richness is expected. However, because grassland community composition is not only influenced by abiotic variables but also by other spatial and temporal drivers, we included spatial variables and grassland age in the statistical analyses. The NDVI explained the majority of the variation in mF, and also contributed to a substantial proportion of the variation in mN. However, variation in specialist richness and the lowest values of mN were explained by grassland age and spatial variables—but were poorly explained by the NDVI. Synthesis and applications. The NDVI showed a good ability to detect variation in plant community composition, and should provide a valuable tool for assessing fine-scale environmental variation in grasslands or for monitoring changes in grassland habitat properties. However, because the concentration of grassland specialists not only depends on environmental variables but also on the age and spatial context of the grasslands, the NDVI is unlikely to allow the identification of grasslands with high numbers of specialist species. (Less)