Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> Efforts to preserve biological diversity increasingly require work across spatial scales and disciplinary boundaries. Remote sensing represents an important tool capable not only of characterizin g landsc apes, but also helping to understand biological and socio-economic factors relevant to addressing conservation problems. The challenge is to develop information products that are meaningful, practical and accessible to scientists, managers and a variety of other stakeholders with often contrasting interests, backgrounds, values and technical capacities. This paper describes the combined use of multitemporal , multiscale remote sensing data to understand and support diverse chimpanzee research and conservation needs in Gombe National Park and northwestern Tanzania. At Gombe, Dr. Jane Goodall has carried out chimpanzee behavior research since 1960. For 30 years field assistants have followed chimpanzees, every 15 minutes recording their location, party membership, and feeding behavior. Remote sensing is the only tool that enables the combination of such a unique long-term animal dataset with historical information on habitat change at the local and regional scales. In this paper we describe how historical aerial photos, Landsat MSS, TM, ETM+, ASTER and IKONOS data can aid chimpanzee behavior research and conservation inside and outside the park. The vegetation map clearly shows drastic decreases in forest and woody canopies from 1972 to 2001 adjacent to the park and along the major roads and settlements close to the Tanzanian-Burundi border. This coincides with the political crisis in adjacent Burundi and Democratic Republic of Congo and suggests a possible role of refugees in aggravating the chimpanzee habitat loss in the region. <s> BIB001 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> This paper presents an overview of the role of remote sensing technology in the context of the United Nations Framework Convention on Climate Change (UNFCCC) Kyoto Protocol and is based largely on discussions held at an international workshop in MI, USA, and the report that followed [A. Rosenqvist, M. Imhoff, T. Milne, C. Dobson (Eds.), Remote Sensing and the Kyoto Protocol: A Review of Available and Future Technology for Monitoring Treaty Compliance, Workshop Report, Ann Arbor, MI, USA, 20–22 October 1999, 2000a, 159 pp. Available at http://www.eecs.umich.edu/kyoto]. The implications of significant decisions pertaining to the definition of the key terms forest and afforestation, reforestation and deforestation (ARD) activities taken at the conference of parties (COP 6:2 and COP 7) meetings in Bonn and Marrakesh, respectively in 2001 are also discussed. Past, current and near-future remote sensing instruments with applications appropriate to Kyoto requirements are short listed; research topics that need to be advanced to support use of these are outlined, and future actions recommended. © 2003 Elsevier Ltd. All rights reserved. <s> BIB002 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> Animal-attached remote sensing, or bio-logging, refers to the deployment of autonomous recording tags on free-living animals, so that multiple variables can be monitored at rates of many times per second, thereby generating millions of data points over periods ranging from hours to years. Rapid advances in technology are allowing scientists to use data-recording units to acquire huge, quantitative datasets of behavior from animals moving freely in their natural environment. In other words, scientists can examine wild animals in the field, behaving normally, with the same rigor that is normally used in the laboratory. The flexibility of such recording systems means that bio-logging science operates at the interface of several biological disciplines, looking at a wide array of aquatic, airborne, and terrestrial species, monitoring not only the physical characteristics of the environment, but also the animal's reactions to it. This approach is critically important in an era when global change threatens the survival of species and where habitat loss is leading to widespread extinctions. <s> BIB003 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> Accurate spatial information on the location and extent of mountain pine beetle infestation is critical for the planning of mitigation and treatment activities. Areas of mixed forest and variable terrain present unique challenges for the detection and mapping of mountain pine beetle red-attack damage, as red-attack has a more heterogeneous distribution under these conditions. In this study, mountain pine beetle red-attack damage was detected and mapped using a logistic regression approach with a forward stepwise selection process and a set of calibration data representing samples of red-attack and non-attack from the study area. Variables that were considered for inclusion in the model were the enhanced wetness difference index (EWDI) derived from a time series of Landsat remotely sensed imagery, elevation, slope, and solar radiation (direct, diffuse, and global). The output from the logistic regression was a continuous probability surface, which indicated the likelihood of red-attack damage. Independent validation data were used to assess the accuracy of the resulting models. The final model predicted red-attack damage with an accuracy of 86%. These results indicate that for this particular site, with mixed forest stands and variable terrain, remotely sensed and ancillary spatial data can be combined, through logistic regression, to create a mountain pine beetle red-attack likelihood surface that accurately identifies damaged forest stands. The use of a probabilistic approach reduces dependence upon the definition of change by the application of thresholds (upper and lower bounds of change) at the image processing stage. Rather, a change layer is generated that may be interpreted liberally or conservatively, depending on the information needs of the end user. <s> BIB004 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> Amongst many ongoing initiatives to preserve biodiversity, the Millennium Ecosystem Assessment again shows the importance to slow down the loss of biological diversity. However, there is still a gap in the overview of global patterns of species distributions. This paper reviews how remote sensing has been used to assess terrestrial faunal diversity, with emphasis on proxies and methodologies, while exploring prospective challenges for the conservation and sustainable use of biodiversity. We grouped and discussed papers dealing with the faunal taxa mammals, birds, reptiles, amphibians, and invertebrates into five classes of surrogates of animal diversity: (1) habitat suitability, (2) photosynthetic productivity, (3) multi-temporal patterns, (4) structural properties of habitat, and (5) forage quality. It is concluded that the most promising approach for the assessment, monitoring, prediction, and conservation of faunal diversity appears to be the synergy of remote sensing products and auxiliary data with ecological biodiversity models, and a subsequent validation of the results using traditional observation techniques. <s> BIB005 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> The availability of a wide range of satellite measurements of environmental variables at different spatial and temporal resolutions, together with an increasing number of digitized and georeferenced species occurrences, has created the opportunity to model and monitor species geographic distribution and richness at regional to continental scales. In this paper, we examine the application of recently developed global data products from satellite observations in modeling the potential distribution of tree species and diversity in the Amazon basin. We use data from satellite sensors, including MODIS, QSCAT, SRTM, and TRMM, to develop different environmental variables related to vegetation, landscape, and climate. These variables are used in a maximum entropy method (Maxent) to model the geographical distribution of five commercial trees and to classify the patterns of tree alpha-diversity in the Amazon basin. Maxent simulations are analyzed using binomial tests of omission rates and the area under the receiver operating characteristics (ROC) curves to examine the model performance, the accuracy of geographic distributions, and the significance of environmental variables for discriminating suitable habitats. To evaluate the importance of satellite data, we used the Maxent jackknife test to quantify the training gains from data layers and to compare the results with model simulations using climate-only data. For all species and tree alpha-diversity, modeled distributions are in agreement with historical data and field observations. The results compare with climate-derived patterns, but provide better spatial resolution and detailed information on the habitat characteristics. Among satellite data products, QSCAT backscatter, representing canopy moisture and roughness, and MODIS leaf area index (LAI) are the most important variables in almost all cases. Model simulations suggest that climate and remote sensing results are complementary and that the best distribution patterns can be achieved when the two data sets are combined. <s> BIB006 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> Species distribution models (SDMs) are numerical tools that combine observations of species occurrence or abundance with environmental estimates. They are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time. SDMs are now widely used across terrestrial, freshwater, and marine realms. Differences in methods between disciplines reflect both differences in species mobility and in “established use.” Model realism and robustness is influenced by selection of relevant predictors and modeling method, consideration of scale, how the interplay between environmental and geographic factors is handled, and the extent of extrapolation. Current linkages between SDM practice and ecological theory are often weak, hindering progress. Remaining challenges include: improvement of methods for modeling presence-only data and for model selection and evaluation; accounting for biotic interactions; and assessing model uncertainty. <s> BIB007 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> Abstract The current outbreak of mountain pine beetle ( Dendroctonus ponderosae Hopkins) in British Columbia (BC), Canada, has led forest managers to consider thinning as a means of decreasing residual tree susceptibility to attack and subsequent mortality. Previous research indicates that susceptibility to mountain pine beetle is a function of a tree's physiological vigor and the intensity of attack. Trees able to produce ≥ 80 g (g) of wood per m 2 of projected leaf area annually are highly resistant, because they are able to shift resource allocation locally from wood to resin production to isolate blue-stain fungi introduced by attacking beetles. Typically, the leaf area of susceptible stands must be reduced by two-thirds to permit most residual trees to increase their vigor to a safe level. We evaluate whether Landsat Thematic Mapper (TM) imagery (30 × 30 m) provides a means to assess the maximum leaf area index (LAI) of unthinned stands and the extent that thinning reduces LAI. The extent that residual trees in thinned stands may have increased their resistance to attack from mountain pine beetle is predicted from a non-linear relationship between % maximum LAI and mean tree vigor. We investigated the merits of this approach in the vicinity of Parson, British Columbia using four stands of lodgepole pine ( Pinus contorta Dougl.), two of which were heavily thinned (stands were spaced to 4 and 5 m, approximately 70% reduction in stand density). An analysis of archived Landsat TM imagery indicated that prior to thinning in 1993, all four stands had full canopy, which, for mature stands, would translate to mean tree vigor between 40 and 70 g of annual wood production per m 2 of foliage. By 1995, based on estimated changes in LAI derived from a second data of Landsat TM imagery, stand vigor in the unthinned stands had not changed; however, in the thinned stands, a nearly two third reduction in LAI resulted in a predicted increase in vigor to between 100 and 160 g wood m − 2 of leaf area. A subsequent assessment in 2001 indicated that stand vigor remained higher in the thinned stands relative to the control stands. Following an infestation of mountain pine beetle in the study area in 2002, mortality data indicated that the thinned stands experienced no mortality relative to the unthinned stands which experienced 5.5% mortality in the initial years of the attack. In the larger area surrounding the study site, a general relationship was found between predicted stand vigor and mountain pine beetle-induced mortality as estimated from aerial overview survey data ( r 2 = 0.43, p 0.01). <s> BIB008 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> Aim To analyse the effects of simultaneously using spatial and phylogenetic information in removing spatial autocorrelation of residuals within a multiple regression framework of trait analysis. Location Switzerland, Europe. Methods We used an eigenvector filtering approach to analyse the relationship between spatial distribution of a trait (flowering phenology) and environmental covariates in a multiple regression framework. Eigenvector filters were calculated from ordinations of distance matrices. Distance matrices were either based on pure spatial information, pure phylogenetic information or spatially structured phylogenetic information. In the multiple regression, those filters were selected which best reduced Moran’s I coefficient of residual autocorrelation. These were added as covariates to a regression model of environmental variables explaining trait <s> BIB009 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> Abstract European Bison (Bison bonasus) barely escaped extinction in the early 20th century and now only occur in small isolated herds scattered across Central and Eastern Europe. The species’ survival in the wild depends on identifying suitable habitat for establishing bison metapopulations via reintroductions of new herds. We assessed European Bison habitat across the Carpathian Mountains, a stronghold of European Bison and one of the only places where a viable bison metapopulation may be possible. We used maximum entropy models to analyze herd range maps and habitat use data from radio-collared bison to identify key habitat variables and map European Bison habitat across the entire Carpathian ecoregion (210,000 km2). Forest cover (primarily core and perforated forests) and variables linked to human disturbance best predict bison habitat suitability. Bison show no clear preference for particular forest types but prefer managed grasslands over fallow and abandoned fields. Several large, suitable, but currently unoccupied habitat patches exist, particularly in the eastern Carpathians. This available suitable habitat suggests that European Bison have an opportunity to establish a viable Carpathian metapopulation, especially if recent trends of declining human pressure and reforestation of abandoned farmland continue. Our results also confirm the suitability of a proposed Romanian reintroduction site. Establishing the first European Bison metapopulation would be a milestone in efforts to conserve this species in the wild and demonstrate a significant and hopeful step towards conserving large grazers and their ecological roles in human-dominated landscapes across the globe. <s> BIB010 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> Fifty years after setting foot in Gombe, Jane Goodall calls for urgent action to save our closest living relatives from extinction in the wild. Conservationists and local people must collaborate, she and Lilian Pintea conclude. <s> BIB011 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> This article reviews the use of optical and microwave remote sensing data for soil and terrain mapping with emphasis on applications at regional and coarser scales. Remote sensing is expected to offer possibilities for improving incomplete spatial and thematic coverage of current regional and global soil databases. Traditionally, remotely sensed imagery have been used to support segmentation of the landscape into rather homogeneous soil–landscape units for which soil composition can be established by sampling. Soil properties have also been inferred from optical and microwave data using physically-based and empirical methods. Used as a secondary data source, remotely sensed imagery may support spatial interpolation of sparsely sampled soil property data. Soil properties that have been measured using remote or proximal sensing approaches include mineralogy, texture, soil iron, soil moisture, soil organic carbon, soil salinity and carbonate content. In sparsely vegetated areas, successful use of space borne, airborne, and in situ measurements using optical, passive and active microwave instruments has been reported. On the other hand, in densely vegetated areas, soil data acquisition typically relied on indirect retrievals using soil indicators, such as plant functional groups, productivity changes, and Ellenberg indicator values. Several forms of kriging, classification and regression tree analyses have been used jointly with remotely sensed data to predict soil properties at unvisited locations aiming at obtaining continuous area coverage. We expect that remotely sensed data from existing platforms and planned missions can provide an important data source supporting digital soil mapping. Yet, most studies so far have been performed on a local scale and only few on regional or smaller map scale. Although progress has been made, current methods and techniques still bear potential to further explore the full range of spectral, spatial and temporal properties of existing data sources. For example, space borne spectroscopy has been of limited use in retrieving soil data when compared to laboratory or field spectroscopy. To date, there is no coherent methodology established, where approaches of spatial segmentation, measurements of soil properties and interpolation using remotely sensed data are integrated in a holistic fashion to achieve complete area coverage. Such approaches will enhance the perspectives of using remotely sensed data for digital soil mapping. <s> BIB012 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> Abstract A vegetation index (VI) is presented which is calculated as the inner angles of a triangle. The triangle is spanned between three distinct points (on a spectral curve of imaging spectroscopy data) which are defined for each individual pixel by the wavelength ( x -axis) and the reflection value ( y -axis). The ideal wavelengths of the three points are dependent on the response variable investigated. The case-study within this paper in which this angular VI is applied, aims at the development of an index to automatically detect spruce bark beetle infections in the Bavarian National Park, Germany. In order to determine the optimum wavelengths to separate damaged from non-damaged trees the three-angle-indices (TAIs) for all possible combinations of available wavelengths of HyMap imaging spectroscopy data in the vis–NIR-region (0.455–0.986 μm) were calculated. The resulting 27,417 images served as input predictor variables to a genetic algorithm (GA) which used nearest centroid classifier as fitness functions to detect the most stable predictors and separate the six classes (three damage classes) defined within the study area. The fitness functions integrated in the GA reached classification accuracies of up to 94.8% when using forward selection models of the most stable genes featuring maximum 50 predictors. Based on those results, three from the original 27,417 predictors were extracted to map forest damages over the full image extent based on a support vector machines (SVMs) classification. We conducted the same experiment with 82 vegetation indices described in literature and achieved slightly lower GA overall accuracies of 86.8% using the nearest centroid classifier. The SVM maps produced with the three best VI predictors were comparable to the TAI results. <s> BIB013 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> This work aims at improving the mapping of major mineral variability at regional scale using scale-dependent spatial variability observed in remote sensing data. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and statistical methods were combined with laboratory-based mineral characterization of field samples to create maps of the distributions of clay, mica and carbonate minerals and their abundances. The Material Identification and Characterization Algorithm (MICA) was used to identify the spectrally-dominant minerals in field samples; these results were combined with ASTER data using multinomial logistic regression to map mineral distributions. X-ray diffraction (XRD) was used to quantify mineral composition in field samples. XRD results were combined with ASTER data using multiple linear regression to map mineral abundances. We tested whether smoothing of the ASTER data to match the scale of variability of the target sample would improve model correlations. Smoothing was done with Fixed Rank Kriging (FRK) to represent the medium and long-range spatial variability in the ASTER data. Stronger correlations resulted using the smoothed data compared to results obtained with the original data. Highest model accuracies came from using both medium and long-range scaled ASTER data as input to the statistical models. High correlation coefficients were obtained for the abundances of calcite and mica (R2 = 0.71 and 0.70, respectively). Moderately-high correlation coefficients were found for smectite and kaolinite (R2 = 0.57 and 0.45, respectively). Maps of mineral distributions, obtained by relating ASTER data to MICA analysis of field samples, were found to characterize major soil mineral variability (overall accuracies for mica, smectite and kaolinite were 76%, 89% and 86% respectively). The results of this study suggest that the distributions of minerals and their abundances derived using FRK-smoothed ASTER data more closely match the spatial variability of soil and environmental properties at regional scale. <s> BIB014 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> Abstract. The standard procedure to count birds is a manual one. However a manual bird count is a time consuming and cumbersome process, requiring several people going from nest to nest counting the birds and the clutches. High resolution imagery, generated with a UAS (Unmanned Aircraft System) offer an interesting alternative. Experiences and results of UAS surveys for automatic bird count of the last two years are presented for the bird reserve island Langenwerder. For 2011 1568 birds (± 5%) were detected on the image mosaic, based on multispectral image classification and GIS-based post processing. Based on the experiences of 2011 the results and the accuracy of the automatic bird count 2012 became more efficient. For 2012 1938 birds with an accuracy of approx. ± 3% were counted. Additionally a separation of breeding and non-breeding birds was performed with the assumption, that standing birds cause a visible shade. The final section of the paper is devoted to the analysis of the 3D-point cloud. Thereby the point cloud was used to determine the height of the vegetation and the extend and depth of closed sinks, which are unsuitable for breeding birds. <s> BIB015 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> Abstract The bark beetle (Ips typographus L.) is known for the detrimental impact it can have on Europe’s mature spruce forests with bark beetle outbreaks already having devastated thousands of hectares of spruce forests in Germany. This study analysed the hypothesis that the vitality of spruce vegetation is already susceptible from factors such as climate change or emissions to a certain extent before infestation, so that the role of the subsequent bark beetle infestation is only secondary. Hyperspectral remote-sensing techniques were used to detect changes in biochemical–biophysical vegetation characteristics in the spruce forest of the Bavarian Forest National Park, Germany. For this study, several spectral bands, vegetation indices and specific spectral band combinations of hyperspectral HyMAP remote-sensing data with a 4 m and a 7 m ground resolution were analysed and compared in terms of their classification accuracy, generating an ID3 decision tree. The vitality classes and thus also the attack stages of the spruce vegetation could be estimated with moderate to good accuracy using hyperspectral remote-sensing data. Clear spectral differences between the class with spruce trees that were still green but with reduced vitality (possibly the first stages of green-attack) and the class with healthy spruce trees could be ascertained. The best spectral characteristics, spectral indicators and spectral derivatives related to vitality classes and thus attack stages were typically based on wavebands related to prominent chlorophyll absorption features in the VI within the spectral range of 450–890 nm. Only limited spectral information and derivatives could be found in the short-wave infrared region 1 (SWIR) within the spectral range of 1400–1800 nm, which reflects the water content of the spruce needles. The class of spruce trees that were still green but with reduced vitality (possibly the first stages of green-attack) showed a trend towards detectability and differentiation with spectral indicators and index derivatives. However, the prediction of observed effects with 64% accuracy as observed here is regarded as insufficient in forestry practises. Hyperspectral data with a ground resolution of 4 m were found to contain more information relevant to estimating the vitality class of spruce vegetation compared to hyperspectral data with a ground resolution of 7 m. <s> BIB016 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> © Nathalie Pettorelli 2013. All rights reserved. There has been a recent surge of interest in remote sensing and its use in ecology and conservation. This book focuses explicitly on the Normalized Difference Vegetation Index (NDVI), a simple numerical indicator and powerful tool that can be used to assess spatio-temporal changes in green vegetation. The NDVI opens the possibility of addressing questions on scales inaccessible to ground-based methods alone; it is mostly freely available with global coverage over several decades. This text provides an authoritative overview of the principles and possible applications of the NDVI in ecology, environmental and wildlife management, and conservation. NDVI data can provide valuable information about temporal and spatial changes in vegetation distribution, productivity, and dynamics; allowing monitoring of habitat degradation and fragmentation, or assessment of the ecological effects of climatic disasters such as drought or fire. The NDVI has also provided ecologists with a promising way to couple vegetation with animal distribution, abundance, movement, survival and reproductive parameters. Over the last few decades, numerous studies have highlighted the potential key role of satellite data and the NDVI in macroecology, plant ecology, animal population dynamics, environmental monitoring, habitat selection and habitat use studies, and paleoecology. The chapters are organized around two sections: the first detailing vegetation indices and the NDVI, the principles behind the NDVI, its correlation with climate, the available NDVI datasets, and the possible complications and errors associated with the use of this satellite-based vegetation index. The second section discusses the possible applications of the NDVI in ecology, environmental and wildlife management, and conservation. <s> BIB017 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> Throughout the Amazon region, the age of forests regenerating on previously deforested land is determined, in part, by the periods of active land use prior to abandonment and the frequency of reclearance of regrowth, both of which can be quantified by comparing time-series of Landsat sensor data. Using these time-series of near annual data from 1973–2011 for an area north of Manaus (in Amazonas state), from 1984–2010 for south of Santarem (Para state) and 1984–2011 near Machadinho d’Oeste (Rondonia state), the changes in the area of primary forest, non-forest and secondary forest were documented from which the age of regenerating forests, periods of active land use and the frequency of forest reclearance were derived. At Manaus, and at the end of the time-series, over 50% of regenerating forests were older than 16 years, whilst at Santarem and Machadinho d’Oeste, 57% and 41% of forests respectively were aged 6–15 years, with the remainder being mostly younger forests. These differences were attributed to the time since deforestation commenced but also the greater frequencies of reclearance of forests at the latter two sites with short periods of use in the intervening periods. The majority of clearance for agriculture was also found outside of protected areas. The study suggested that a) the history of clearance and land use should be taken into account when protecting deforested land for the purpose of restoring both tree species diversity and biomass through natural regeneration and b) a greater proportion of the forested landscape should be placed under protection, including areas of regrowth. <s> BIB018 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> The activity patterns of most terrestrial animals are regarded as being primarily influenced by light, although other factors, such as sexual cycle and climatic conditions, can modify the underlying patterns. However, most activity studies have been limited to a single study area, which in turn limit the variability of light conditions and other factors. Here we considered a range of variables that might potentially influence the activity of a large carnivore, the Eurasian lynx, in a network of studies conducted with identical methodology in different areas spanning latitudes from 49°7'N in central Europe to 70°00'N in northern Scandinavia. The variables considered both light conditions, ranging from a day with a complete day-night cycle to polar night and polar day, as well as individual traits of the animals. We analysed activity data of 38 individual free-ranging lynx equipped with GPS-collars with acceleration sensors, covering more than 11,000 lynx days. Mixed linear additive models revealed that the lynx activity level was not influenced by the daily daylight duration and the activity pattern was bimodal, even during polar night and polar day. The duration of the active phase of the activity cycle varied with the widening and narrowing of the photoperiod. Activity varied significantly with moonlight. Among adults, males were more active than females, and subadult lynx were more active than adults. In polar regions, the amplitude of the lynx daily activity pattern was low, likely as a result of the polycyclic activity pattern of their main prey, reindeer. At lower latitudes, the basic lynx activity pattern peaked during twilight, corresponding to the crepuscular activity pattern of the main prey, roe deer. Our results indicated that the basic activity of lynx is independent of light conditions, but is modified by both individual traits and the activity pattern of the locally most important prey. <s> BIB019 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> The behavior of individuals determines the strength and outcome of ecological interactions, which drive population, community, and ecosystem organization. Bio-logging, such as telemetry and animal-borne imaging, provides essential individual viewpoints, tracks, and life histories, but requires capture of individuals and is often impractical to scale. Recent developments in automated image-based tracking offers opportunities to remotely quantify and understand individual behavior at scales and resolutions not previously possible, providing an essential supplement to other tracking methodologies in ecology. Automated image-based tracking should continue to advance the field of ecology by enabling better understanding of the linkages between individual and higher-level ecological processes, via high-throughput quantitative analysis of complex ecological patterns and processes across scales, including analysis of environmental drivers. <s> BIB020 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> Terrestrial ecosystem and carbon cycle feedbacks will significantly impact future climate, but their responses are highly uncertain. Models and tipping point analyses suggest the tropics and arctic/boreal zone carbon-climate feedbacks could be disproportionately large. In situ observations in those regions are sparse, resulting in high uncertainties in carbon fluxes and fluxes. Key parameters controlling ecosystem carbon responses, such as plant traits, are also sparsely observed in the tropics, with the most diverse biome on the planet treated as a single type in models. We analyzed the spatial distribution of in situ data for carbon fluxes, stocks and plant traits globally and also evaluated the potential of remote sensing to observe these quantities. New satellite data products go beyond indices of greenness and can address spatial sampling gaps for specific ecosystem properties and parameters. Because environmental conditions and access limit in situ observations in tropical and arctic/boreal environments, use of space-based techniques can reduce sampling bias and uncertainty about tipping point feedbacks to climate. To reliably detect change and develop the understanding of ecosystems needed for prediction, significantly, more data are required in critical regions. This need can best be met with a strategic combination of remote and in situ data, with satellite observations providing the dense sampling in space and time required to characterize the heterogeneity of ecosystem structure and function. <s> BIB021 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> Monitoring Land Surface Phenology (LSP) is important for understanding both the responses and feedbacks of ecosystems to the climate system, and for representing these accurately in terrestrial biosphere models. Moreover, by shedding light on phenological trends at a variety of scales, LSP has the potential to fill the gap between traditional phenological (field) observations and the large-scale view of global models. ::: In this study, we review and evaluate the variability and evolution of satellite-derived Growing Season Length (GSL) globally and over the past three decades. We used the longest continuous record of Normalized Difference Vegetation Index (NDVI) data available to date at global scale to derive LSP metrics consistently over all vegetated land areas and for the period 1982-2012. We tested GSL, Start- and End-Of Season metrics (SOS and EOS, respectively) for linear trends as well as for significant trend shifts over the study period. We evaluated trends using global environmental stratification information in place of commonly used land cover maps to avoid circular findings. ::: Our results confirmed an average lengthening of the growing season globally during 1982-2012 – averaging 0.22-0.34 days/year, but with spatially heterogeneous trends. 13-19% of global land areas displayed significant GSL change, and over 30% of trends occurred in the boreal/alpine biome of the Northern Hemisphere, which showed diverging GSL evolution over the past 3 decades. Within this biome, the “Cold and Mesic” environmental zone appeared as an LSP change hotspot. We also examined the relative contribution of SOS and EOS to the overall changes, finding that EOS trends were generally stronger and more prevalent than SOS trends. These findings constitute a step towards the identification of large-scale phenological drivers of vegetated land surfaces, necessary for improving phenological representation in terrestrial biosphere models. <s> BIB022 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> Global agricultural production will likely need to increase in the future due to population growth, changing diets, and the rising importance of bioenergy. Intensifying already existing cropland is often considered more sustainable than converting more natural areas. Unfortunately, our understanding of cropping patterns and intensity is weak, especially at broad geographic scales. We characterized and mapped cropping systems in Europe, a region containing diverse cropping systems, using four indicators: (a) cropping frequency (number of cropped years), (b) multi-cropping (number of harvests per year), (c) fallow cycles, and (d) crop duration ratio (actual time under crops) based on the MODIS Normalized Difference Vegetation Index (NDVI) time series from 2000 to 2012. Second, we used these cropping indicators and self-organizing maps to identify typical cropping systems. The resulting six clusters correspond well with other indicators of agricultural intensity (e.g., nitrogen input, yields) and reveal substantial differences in cropping intensity across Europe. Cropping intensity was highest in Germany, Poland, and the eastern European Black Earth regions, characterized by high cropping frequency, multi-cropping and a high crop duration ratio. Contrarily, we found lowest cropping intensity in eastern Europe outside the Black Earth region, characterized by longer fallow cycles. Our approach highlights how satellite image time series can help to characterize spatial patterns in cropping intensity—information that is rarely surveyed on the ground and commonly not included in agricultural statistics: our clustering approach also shows a way forward to reduce complexity when measuring multiple indicators. The four cropping indicators we used could become part of continental-scale agricultural monitoring in order to identify target regions for sustainable intensification, where trade-offs between intensification and the environmental should be explored. <s> BIB023 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> Abstract Field surveys are often a primary source of data for aboveground biomass (AGB) and forest area estimates — two fundamental parameters in forest resource assessments and for measurement, reporting, and verification (MRV) under the United Nations Collaborative Program on Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD +). However, plot-based estimates of such parameters are often not sufficiently precise for their intended purposes, and especially so in developing and tropical countries in which implementation of extensive sample surveys can be cost-prohibitive or infeasible due to inaccessibility. Remotely sensed data can improve the precision of estimates and thereby reduce the need for field samples. To guide investment decision in MRV systems, comparative analyses of the contribution of different types of remotely sensed data to improve precision of estimates are required. The aim of the current study was to quantify the contribution of data from (1) airborne laser scanning (ALS), (2) interferometric synthetic aperture radar (InSAR) derived from TanDEM-X, (3) RapidEye optical imagery, and global forest map products derived from (4) Landsat and (5) ALOS PALSAR L-band radar imagery to improve precision of AGB and forest area estimates beyond the precision that could be obtained by a pure field-based survey in miombo woodlands of Tanzania. Miombo woodlands is one among the most wide-spread vegetation types in eastern, central, and southern Africa, occupying about 9% of the entire African land area. A 365.6 km 2 region in Liwale district in Tanzania served as area of interest for this study. Eighty-eight ground plots distributed on 11 clusters of eight plots each according to a probability-based single-stage cluster sampling design served as field data for regression model calibration used for mapping and estimation of AGB and forest area. Model-assisted estimators were used in the estimation. The relative efficiency (RE) of the ALS-assisted estimates of mean AGB per hectare (variance of the field-based estimate relative to the variance of the ALS-assisted estimate) was 3.6. Relative efficiency translates directly to the factor by which the sample size used for the ALS-assisted estimate would have to be multiplied to arrive at the same precision for a pure field-based estimate. RE values for InSAR and RapidEye were 2.8 and 3.3, while the global Landsat and PALSAR map products contributed only marginally to improve precision (RE = 1.3–1.4). For forest area estimation, ALS-assisted estimates showed an RE of 3.7–4.6, while InSAR, RapidEye, and global Landsat and PALSAR maps resulted in RE values of 1.0–1.3, 2.0–2.1, 1.4–1.8, and 1.7, respectively. <s> BIB024 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> Abstract South East Asia has the highest rate of lowland forest loss of any tropical region, with logging and deforestation for conversion to plantation agriculture being flagged as the most urgent threats. Detecting and mapping logging impacts on forest structure is a primary conservation concern, as these impacts feed through to changes in biodiversity and ecosystem functions. Here, we test whether high-spatial resolution satellite remote sensing can be used to map the responses of aboveground live tree biomass (AGB), canopy leaf area index (LAI) and fractional vegetation cover (FCover) to selective logging and deforestation in Malaysian Borneo. We measured these attributes in permanent vegetation plots in rainforest and oil palm plantations across the degradation landscape of the Stability of Altered Forest Ecosystems project. We found significant mathematical relationships between field-measured structure and satellite-derived spectral and texture information, explaining up to 62% of variation in biophysical structure across forest and oil palm plots. These relationships held at different aggregation levels from plots to forest disturbance types and oil palms allowing us to map aboveground biomass and canopy structure across the degradation landscape. The maps reveal considerable spatial variation in the impacts of previous logging, a pattern that was less clear when considering field data alone. Up-scaled maps revealed a pronounced decline in aboveground live tree biomass with increasing disturbance, impacts which are also clearly visible in the field data even a decade after logging. Field data demonstrate a rapid recovery in forest canopy structure with the canopy recovering to pre-disturbance levels a decade after logging. Yet, up-scaled maps show that both LAI and FCover are still reduced in logged compared to primary forest stands and markedly lower in oil palm stands. While uncertainties remain, these maps can now be utilised to identify conservation win–wins, especially when combining them with ongoing biodiversity surveys and measurements of carbon sequestration, hydrological cycles and microclimate. <s> BIB025 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> Aim ::: ::: Species distribution modelling typically relies completely or partially on climatic variables as predictors, overlooking the fact that these are themselves predictions with associated uncertainties. This is particularly critical when such predictors are interpolated between sparse station data, such as in the tropics. The goal of this study is to provide a new set of satellite-based climatic predictor data and to evaluate its potential to improve modelled species–climate associations and transferability to novel geographical regions. ::: ::: ::: ::: Location ::: ::: Rain forests areas of Central Africa, the Western Ghats of India and South America. ::: ::: ::: ::: Methods ::: ::: We compared models calibrated on the widely used WorldClim station-interpolated climatic data with models where either temperature or precipitation data from WorldClim were replaced by data from CRU, MODIS, TRMM and CHIRPS. Each predictor set was used to model 451 plant species distributions. To test for chance associations, we devised a null model with which to compare the accuracy metric obtained for every species. ::: ::: ::: ::: Results ::: ::: Fewer than half of the studied rain forest species distributions matched the climatic pattern better than did random distributions. The inclusion of MODIS temperature and CHIRPS precipitation estimates derived from remote sensing each allowed for a better than random fit for respectively 40% and 22% more species than models calibrated on WorldClim. Furthermore, their inclusion was positively related to a better transferability of models to novel regions. ::: ::: ::: ::: Main conclusions ::: ::: We provide a newly assembled dataset of ecologically meaningful variables derived from MODIS and CHIRPS for download, and provide a basis for choosing among the plethora of available climate datasets. We emphasize the need to consider the method used in the production of climate data when working on a region with sparse meteorological station data. In this context, remote sensing data should be the preferred choice, particularly when model transferability to novel climates or inferences on causality are invoked. <s> BIB026 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> Anthropogenic drivers of global change include rising atmospheric concentrations of carbon dioxide and other greenhouse gasses and resulting changes in the climate, as well as nitrogen deposition, biotic invasions, altered disturbance regimes, and land-use change. Predicting the effects of global change on terrestrial plant communities is crucial because of the ecosystem services vegetation provides, from climate regulation to forest products. In this paper, we present a framework for detecting vegetation changes and attributing them to global change drivers that incorporates multiple lines of evidence from spatially extensive monitoring networks, distributed experiments, remotely sensed data, and historical records. Based on a literature review, we summarize observed changes and then describe modeling tools that can forecast the impacts of multiple drivers on plant communities in an era of rapid change. Observed responses to changes in temperature, water, nutrients, land use, and disturbance show strong sensitivity of ecosystem productivity and plant population dynamics to water balance and long-lasting effects of disturbance on plant community dynamics. Persistent effects of land-use change and human-altered fire regimes on vegetation can overshadow or interact with climate change impacts. Models forecasting plant community responses to global change incorporate shifting ecological niches, population dynamics, species interactions, spatially explicit disturbance, ecosystem processes, and plant functional responses. Monitoring, experiments, and models evaluating multiple change drivers are needed to detect and predict vegetation changes in response to 21st century global change. <s> BIB027 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> Understanding future tree species migration is challenging due to the unprecedented rate of climate change combined with the presence of human barriers that may limit or impede species movement. Projected changes in climatic conditions outpace migration rates, and more realistic rates of range expansion are needed to make sound environmental policies. In this paper, we develop a modeling approach that takes into account both the geographic changes in the area suitable for the growth and reproduction of tree species, as well as limits imposed geographically on their potential migration using remotely-sensed land cover information. To do so, we combined a physiologically-based decision tree model with a remotely-sensed-derived diffusion-dispersal model to identify the most likely direction of future migration for 15 native tree species in the Pacific Northwest Region of North America, as well as the degree that landscape fragmentation might limit movement. Although projected changes in climate through to 2080 are likely to create favorable environments for range expansion of the 15 tree species by 65% on average, by limiting the potential movement by previously published migration rates and landscape fragmentation, range expansion will likely be 50%–90% of the potential. The hybrid modeling approach using distribution modeling and remotely-sensed data fills a gap between naive and more complex approaches to take into account major impediments on the potential migration of native tree species. <s> BIB028 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> Abstract Impacts of human civilization on ecosystems threaten global biodiversity. In a changing environment, traditional in situ approaches to biodiversity monitoring have made significant steps forward to quantify and evaluate BD at many scales but still, these methods are limited to comparatively small areas. Earth observation (EO) techniques may provide a solution to overcome this shortcoming by measuring entities of interest at different spatial and temporal scales. This paper provides a comprehensive overview of the role of EO to detect, describe, explain, predict and assess biodiversity. Here, we focus on three main aspects related to biodiversity − taxonomic diversity, functional diversity and structural diversity, which integrate different levels of organization − molecular, genetic, individual, species, populations, communities, biomes, ecosystems and landscapes. In particular, we discuss the recording of taxonomic elements of biodiversity through the identification of animal and plant species. We highlight the importance of the spectral traits (ST) and spectral trait variations (STV) concept for EO-based biodiversity research. Furthermore we provide examples of spectral traits/spectral trait variations used in EO applications for quantifying taxonomic diversity, functional diversity and structural diversity. We discuss the use of EO to monitor biodiversity and habitat quality using different remote-sensing techniques. Finally, we suggest specifically important steps for a better integration of EO in biodiversity research. EO methods represent an affordable, repeatable and comparable method for measuring, describing, explaining and modelling taxonomic, functional and structural diversity. Upcoming sensor developments will provide opportunities to quantify spectral traits, currently not detectable with EO, and will surely help to describe biodiversity in more detail. Therefore, new concepts are needed to tightly integrate EO sensor networks with the identification of biodiversity. This will mean taking completely new directions in the future to link complex, large data, different approaches and models. <s> BIB029 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Modelling Stress and Disturbances in Species Distributions and Animal Behaviour in FES <s> Species distribution models (SDMs), which statistically relate species occurrence to climatic variables, are widely used to identify areas suitable for species growth under future climates and to plan for assisted migration. When SDMs are projected across times or spaces, it is assumed that species climatic requirements remain constant. However, empirical evidence supporting this assumption is rare, and SDM predictions could be biased. Historical human-aided movements of tree species can shed light on the reliability of SDM predictions in planning for assisted migration. We used Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) a North American conifer introduced into Europe during the mid-19th century, as a case-study to test niche conservatism. We combined transcontinental datasets of Douglas-fir occurrence and climatic predictors to compare the realized niches between native and introduced ranges. We calibrated a SDM in the native range and compared areas predicted to be climatically suitable with observed presences. The realized niches in the native and introduced ranges showed very limited overlap. The SDM calibrated in North America had very high predictive power in the native range, but failed to predict climatic suitability in Europe where Douglas-fir grows in climates that have no analogue in the native range. We review the ecological mechanisms and silvicultural practices that can trigger such shifts in realized niches. Retrospective analysis of tree species introduction revealed that the assumption of niche conservatism is erroneous. As a result, distributions predicted by SDM are importantly biased. There is a high risk that assisted migration programs may be misdirected and target inadequate species or introduction zones. ::: ::: This article is protected by copyright. All rights reserved. <s> BIB030
The quantification of species-environment relationships based on species distribution models (SDMs) is an important methodical approach BIB006  to monitor and understand the stress or resource limitations on forest species. RS data on different platforms, i.e., camera traps, drones, airand spaceborne data, are able to map and quantify biotic and abiotic land surface traits. In terms of SDM modelling, RS can also be used to record a number of abiotic environmental variables such as topographic- BIB012 soil- BIB014  and climate conditions BIB002  , their status, alterations and, therefore, also their impacts on animal species in FES. SDM based on RS approaches will "shape the next generation" of SDMs  . With RS it is not just the status quo or the long-term monitoring and changes in FES that can be examined, but also significant short-term changes to the taxonomic, structural and functional traits of FES from the regional to the global scale, which is not possible with in-situ recording of animal behaviour. The SDM approach is very different from the detection and quantification of the forest habitat quantification approach. The objective of SDM is "to predict the probabilities of species occurrence as the single target variable " BIB007 . The opening of LandSat RS portals as well as an increasingly more common data policy of freely available RS data and data products such as the Copernicus missions (Sentinel 1-5) or the hyperspectral (EnMAP) satellite missions makes extensive spatio-temporal data available to SDM. Determining the growth period BIB022 deriving global NDVI trends BIB017 , recording shifts in biochemical traits   BIB021 BIB022 , recording changes in land-use BIB018 , land-use intensity BIB023 or long-term deforestation dynamics as well as quantifying structural forest characteristics like fragmentation, spatial distribution, structure, heterogeneity or homogeneity configuration of forest patterns are all factors that provide us with crucial information BIB024  BIB025  . Deblauwe et al., BIB026 used remotely-sensed temperatures and precipitation data derived from RS-data to improve species distribution modelling in tropical forest areas. Franklin et al., BIB027 analysed global change and terrestrial plant community dynamics using remote-sensing based monitoring of vegetation changes. Furthermore, Boiffin et al., BIB030 used SDM to determine the migration rate and the species occurrence of Douglas-fir and climate predictors in North America. Furthermore, Coops et al., BIB028 used RS land cover and RS data to estimate tree species migration. The modelling of animal behaviour is a widespread method for modelling animal distributions (e.g., birds, deer, lynx, bears, foxes, wolves, moose or bison), BIB005 BIB015  BIB010 BIB019 . High-resolution RS data were also used, for example, in an unmanned aerial vehicle (UAV) and RS data for monitoring and discrimination of forest and tree species in the context of assessing the suitability of chimpanzee habitats in Tanzania as a basis for making critical decisions about species conservation BIB001 BIB011 . An extensive overview of recording terrestrial animals using remote sensing is provided by BIB029 BIB005 BIB020 BIB003 . A crucial application of RS is to record changing biochemical forest traits such as chlorophyll and water content or a reduction in the photosynthetic activity in trees. High resolution temporal as well as spectral RS data such as Sentinel-2, WorldView-2 Data or hyperspectral RS sensors such as APEX, AVIRIS, HySpex, AISA and the upcoming spaceborne hyperspectral sensor EnMAP are used to record disturbances in the vitality of forests resulting from climate change, resource limitations and disturbances or damaging insects such as bark beetles or pine beetles, BIB016 BIB008  BIB004 BIB013 as well as fungal infestations (such as the recent infestation of ash trees). In this context, the investigations on early detection of Bark Beetle infestations of spruce trees are particularly noteworthy. In this case, non-infested trees are separated from infested ones that are still not suffering from "green-attack" from already highly infested or dead trees due to their spectral reflectance using RS data. Immitzer & Atzberger  , for example, used WorldView-2 data for the early detection of Bark Beetle infestation in Norway Spruce (Picea abies, L.). The classes that were investigated, namely: "healthy", "infested, but no visible change in colour" and "dead" were separated with a total accuracy of around 75%. Lausch et al., BIB016 on the other hand used hyperspectral HyMAP remote-sensing data with a spatial resolution of 4 m. In spite of the high spectral resolution of the RS data with a spatial resolution of 4 m, only a total accuracy of 64% was obtained. This example shows that the high spatial resolution (1.8 m) of the multispectral WorldView-2 data can lead to better results than the high resolution hyperspectral RS data, if the multi-spectral RS data contain spectral channel information that are decisive for classification. Similar conclusions are drawn by Lausch et al., BIB009 when comparing spatial vs. spectral resolution from RS data.