Hannes Feilhauer

Modeling Species Distribution Using Niche-Based Proxies Derived from Composite Bioclimatic Variables and MODIS NDVI

Vegetation mapping based on niche theory has proven useful in understanding the rules governing species assembly at various spatial scales. Remote-sensing derived distribution maps depicting occurrences of target species are frequently based on biophysical and biochemical properties of species. However, environmental conditions, such as climatic variables, also affect spectral signals simultaneously. Further, climatic variables are the major drivers of species distribution at macroscales. Therefore, the objective of this study is to determine if species distribution can be modeled using an indirect link to climate and remote sensing data (MODIS NDVI time series). We used plant occurrence data in the US states of North Carolina and South Carolina and 19 climatic variables to generate floristic and climatic gradients using principal component analysis, then we further modeled the correlations between floristic gradients and NDVI using Partial Least Square regression. We found strong statistical relationship between species distribution and NDVI time series in a region where clear floristic and climatic gradients exist. If this precondition is given, the use of niche-based proxies may be suitable for predictive modeling of species distributions at regional scales. This indirect estimation of vegetation patterns may be a viable alternative to mapping approaches using biochemistry-driven spectral signature of species.

Robust Change Vector Analysis (RCVA) for multi-sensor very high resolution optical satellite data

Abstract The analysis of rapid land cover/land use changes by means of remote sensing is often based on data acquired under varying and occasionally unfavorable conditions. In addition, such analyses frequently use data acquired by different sensor systems. These acquisitions often differ with respect to sun position and sensor viewing geometry which lead to characteristic effects in each image. These differences may have a negative impact on reliable change detection. Here, we propose an approach called Robust Change Vector Analysis (RCVA), aiming to mitigate these effects. RCVA is an improvement of the widely-used Change Vector Analysis (CVA), developed to account for pixel neighborhood effects. We used a RapidEye and Kompsat-2 cross-sensor change detection test to demonstrate the efficiency of RCVA. Our analysis showed that RCVA results in fewer false negatives as well as false positives when compared to CVA under similar test conditions. We conclude that RCVA is a powerful technique which can be utilized to reduce spurious changes in bi-temporal change detection analyses based on high- or very-high spatial resolution imagery.

Discrimination and characterization of management systems in semi-arid rangelands of South Africa using RapidEye time series

In South African grasslands, rangeland management is strongly related to land tenure. Communal farms are reported to exhibit less desirable vegetation conditions for livestock than commercial farms. Time series of high spatial and temporal resolution imagery may be useful for improved evaluation of these rangelands as they provide information at a spatial scale similar to the typical scale of field assessments and may thus overcome the limited spatio-temporal representativeness of field measurements. A time series of 13 RapidEye images over one growing season (2010–2011) was used to explore spectral differences between and within two management systems (commercial vs. communal). Isomap ordination was applied to map continuous spectral dissimilarities of sample plots. Using regression with simultaneous autoregressive models (SAR), dissimilarities were subsequently related to ecological variables of plant and soil, including indicators for grazing effects. The largest differences were found between sample plots of communal and commercial farms. Vegetation attributes were significantly related to dissimilarities in reflectance, both from the growing season and the dormant period. However, these relationships did not suggest vegetation degradation on communal farms. They further suggest that a management-related pattern of grazing disturbance in the summer months led to spectral differences between farms but could have impaired the detailed characterization of spectral dissimilarities related to differences in vegetation composition.

Estimating Vegetation Cover from High-Resolution Satellite Data to Assess Grassland Degradation in the Georgian Caucasus

In the Georgian Caucasus, unregulated grazing has damaged grassland vegetation cover and caused erosion. Methods for monitoring and control of affected territories are urgently needed. Focusing on the high-montane and subalpine grasslands of the upper Aragvi Valley, we sampled grassland for soil, rock, and vegetation cover to test the applicability of a site-specific remote-sensing approach to observing grassland degradation. We used random-forest regression to separately estimate vegetation cover from 2 vegetation indices, the Normalized Difference Vegetation Index (NDVI) and the Modified Soil Adjusted Vegetation Index (MSAVI2), derived from multispectral WorldView-2 data (1.8 m). The good model fit of R2  =  0.79 indicates the great potential of a remote-sensing approach for the observation of grassland cover. We used the modeled relationship to produce a vegetation cover map, which showed large areas of grassland degradation.

Elevational variation of reproductive traits in five Pardosa (Lycosidae) species

Abstract Differentiations in reproductive traits along climatic gradients can be substantial for a species to spread along a wide spatial range. We compared the reproductive effort allocated to first egg sacs of five species of the genus Pardosa: P. palustris (Linnaeus 1758), P. amentata (Clerck 1757), P. lugubris (Walckenaer 1802), P. hyperborea (Thorell 1872), and P. riparia (C. L. Koch 1833) along three elevation transects in central Norway. We tested whether population differences are consistent among the three transects, respectively along the elevational gradient. We assumed that the harsh environments of alpine areas would lead to adaptations in reproductive traits resulting in larger eggs but smaller clutches at higher elevations. The results show that female size and egg number were positively correlated among all species. However, no clear elevation-related trend was found. Other traits did not change consistently between species and along the elevational gradient. We assume that local microclimatic impacts on spider fitness are a crucial but poorly understood factor. Without further knowledge about adaptation and phenotypic plasticity in ectotherms, modeling of possible future reproduction biology might remain flawed.

Measuring β-diversity by remote sensing: a challenge for biodiversity monitoring

Biodiversity includes multiscalar and multitemporal structures and processes, with different levels of functional organization, from genetic to ecosystemic levels. One of the mostly used methods to infer biodiversity is based on taxonomic approaches and community ecology theories. However, gathering extensive data in the field is difficult due to logistic problems, overall when aiming at modelling biodiversity changes in space and time, which assumes statistically sound sampling schemes. In this view, airborne or satellite remote sensing allow to gather information over wide areas in a reasonable time. Most of the biodiversity maps obtained from remote sensing have been based on the inference of species richness by regression analysis. On the contrary, estimating compositional turnover (beta-diversity) might add crucial information related to relative abundance of different species instead of just richness. Presently, few studies have addressed the measurement of species compositional turnover from space. Extending on previous work, in this manuscript we propose novel techniques to measure beta-diversity from airborne or satellite remote sensing, mainly based on: i) multivariate statistical analysis, ii) the spectral species concept, iii) self-organizing feature maps, iv) multi- dimensional distance matrices, and the v) Raos Q diversity. Each of these measures allow to solve one or several issues related to turnover measurement. This manuscript is the first methodological example encompassing (and enhancing) most of the available methods for estimating beta-diversity from remotely sensed imagery and potentially relate them to species diversity in the field.

Remote Sensing of Vegetation for Nature Conservation

A rapidly changing environment with land use and climate as the most dynamic components causes new challenges for nature conservation and management of protected areas. Dealing with these changes requires a systematic monitoring. To date, such monitoring programs are mostly backed by expert guess or permanent observation plots. Both have their merits but the plot-based approach is certainly more objective. However, even in the case of appropriate sampling, plots provide only punctual information and changes in the area between plots are easily missed. This gap can be closed by remote sensing.

Mapping Plant Functional Groups in Subalpine Grassland of the Greater Caucasus

Plant functional groups—in our case grass, herbs, and legumes—and their spatial distribution can provide information on key ecosystem functions such as species richness, nitrogen fixation, and erosion control. Knowledge about the spatial distribution of plant functional groups provides valuable information for grassland management. This study described and mapped the distribution of grass, herb, and legume coverage of the subalpine grassland in the high-mountain Kazbegi region, Greater Caucasus, Georgia. To test the applicability of new sensors, we compared the predictive power of simulated hyperspectral canopy reflectance, simulated multispectral reflectance, simulated vegetation indices, and topographic variables for modeling plant functional groups. The tested grassland showed characteristic differences in species richness; in grass, herb, and legume coverage; and in connected structural properties such as yield. Grass (Hordeum brevisubulatum) was dominant in biomass-rich hay meadows. Herb-rich grassland featured the highest species richness and evenness, whereas legume-rich grassland was accompanied by a high coverage of open soil and showed dominance of a single species, Astragalus captiosus. The best model fits were achieved with a combination of reflectance, vegetation indices, and topographic variables as predictors. Random forest models for grass, herb, and legume coverage explained 36%, 25%, and 37% of the respective variance, and their root mean square errors varied between 12–15%. Hyperspectral and multispectral reflectance as predictors resulted in similar models. Because multispectral data are more easily available and often have a higher spatial resolution, we suggest using multispectral parameters enhanced by vegetation indices and topographic parameters for modeling grass, herb, and legume coverage. However, overall model fits were merely moderate, and further testing, including stronger gradients and the addition of shortwave infrared wavelengths, is needed.

Mapping plant strategy types and derivatives with imaging spectroscopy

The CSR-strategy model is frequently drawn upon to describe plant functional types as three major strategies. These main strategies reflect the productivity of a stand and the disturbance level at the site. Monitoring of their spatial distribution may provide insights into vital ecosystem processes and functions. In the present study we test whether the spatial distribution of these strategies can be mapped with imaging spectroscopy and whether the maps and derivatives thereof provide meaningful ecological information. The test was implemented in a semi-natural heathland with a statistical approach (Partial Least Squares regression). Our results show that strategy mapping is possible and substantiate the potential of imaging spectroscopy for ecosystem research and management.

Important characteristics of multispectral data for an assessment of floristic variation

Remote sensing offers a large potential for land-cover mapping and monitoring. For many application in nature conservation, however, information beyond the generalization level of regular land-cover maps is desirable.