Gradients in urban material composition: A new concept to map cities with spaceborne imaging spectroscopy data

To understand processes in urban environments, such as urban energy fluxes or surface temperature patterns, it is important to map urban surface materials. Airborne imaging spectroscopy data have been successfully used to identify urban surface materials mainly based on unmixing algorithms. Upcoming spaceborne Imaging Spectrometers (IS), such as the Environmental Mapping and Analysis Program (EnMAP), will reduce the time and cost-critical limitations of airborne systems for Earth Observation (EO). However, the spatial resolution of all operated and planned IS in space will not be higher than 20 to 30 m and, thus, the detection of pure Endmember (EM) candidates in urban areas, a requirement for spectral unmixing, is very limited. Gradient analysis could be an alternative method for retrieving urban surface material compositions in pixels from spaceborne IS. The gradient concept is well known in ecology to identify plant species assemblages formed by similar environmental conditions but has never been tested for urban materials. However, urban areas also contain neighbourhoods with similar physical, compositional and structural characteristics. Based on this assumption, this study investigated (1) whether cover fractions of surface materials change gradually in urban areas and (2) whether these gradients can be adequately mapped and interpreted using imaging spectroscopy data (e.g. EnMAP) with 30 m spatial resolution. Similarities of material compositions were analysed on the basis of 153 systematically distributed samples on a detailed surface material map using Detrended Correspondence Analysis (DCA). Determined gradient scores for the first two gradients were regressed against the corresponding mean reflectance of simulated EnMAP spectra using Partial Least Square regression models. Results show strong correlations with R2 = 0.85 and R2 = 0.71 and an RMSE of 0.24 and 0.21 for the first and second axis, respectively. The subsequent mapping of the first gradient reveals patterns that correspond to the transition from predominantly vegetation classes to the dominance of artificial materials. Patterns resulting from the second gradient are associated with surface material compositions that are related to finer structural differences in urban structures. The composite gradient map shows patterns of common surface material compositions that can be related to urban land use classes such as Urban Structure Types (UST). By linking the knowledge of typical material compositions with urban structures, gradient analysis seems to be a powerful tool to map characteristic material compositions in 30 m imaging spectroscopy data of urban areas

Monitoring the spread of invasive plant species in Germany – how many species can we possibly detect by remote sensing and what data do we need?

Combining remote sensing and field data allows for the detection of some invasive alien plant species with an adequate accuracy. Especially the use of satellite data for larger areas or UAS (unmanned aerial system) data for smaller sites may provide alternatives to classical field mapping approaches. A main advantage is that satellite or UAS data is potentially more cost-efficient then the use of for example hyperspectral data, which was frequently applied in research on the detectability of invasive species in the past. This study discusses the possibilities and limitations of remote sensing to contribute to the detection of invasive alien plant species in Germany. Taking into account previous studies on the topic, we estimate the potential for a successful detection of relevant invasive plant species in Germany. Main criteria to determine the potential for detection are the species characteristics (size, detectable traits, habitat) as well as their similarity to other native species. For 19 of the 42 species examined, the use of remote sensing data is most probably successful, mainly for larger species and species with characteristic features such as colorful flowers or leaves. For another 10 species the detection might eventually be feasible. For about 13 species, especially hydrophytes living below the water surface and other species lacking any characteristic features, the detection is currently not possible. We can conclude that remote sensing remote may offer efficient solutions for a small or large scale monitoring of certain invasive plant species or to control the management success and thus support decision-making. In general, more research is needed to develop cost-efficient and user-friendly solutions

Are urban material gradients transferable between areas?

Urban areas contain a complex mixture of surface materials resulting in mixed pixels that are challenging to handle with conventional mapping approaches. In particular, for spaceborne hyperspectral images (HSIs) with sufficient spectral resolution to differentiate urban surface materials, the spatial resolution of 30 m (e.g. EnMAP HSIs) makes it difficult to find the spectrally pure pixels required for detailed mapping of urban surface materials. Gradient analysis, which is commonly used in ecology to map natural vegetation consisting of a complex mixture of species, is therefore a promising and practical tool for pattern recognition of urban surface material mixtures. However, the gradients are determined in a data-driven manner, so analysis of their spatial transferability is urgently required. We selected two areas—the Ostbahnhof (Ost) area and the Nymphenburg (Nym) area in Munich, Germany—with simulated EnMAP HSIs and material maps, treating the Ost area as the target area and the Nym area as the well-known area. Three gradient analysis approaches were subsequently proposed for pattern recognition in the Ost area for the cases of (i) sufficient samples collected in the Ost area; (ii) some samples in the Ost area; and (iii) no samples in the Ost area. The Ost samples were used to generate an ordination space in case (i), while the Nym samples were used to create the ordination space to support the pattern recognition of the Ost area in cases (ii) and (iii). The Mantel statistical results show that the sample distributions in the two ordination spaces are similar, with high confidence (the Mantel statistics are 0.995 and 0.990, with a significance of 0.001 in 999 free permutations of the Ost and Nym samples). The results of the partial least square regression models and 10-fold cross-validation show a strong relationship (the calculation-validation R2 values on the first gradient among the three approaches are 0.898, 0.892; 0.760, 0.743; and 0.860, 0.836, and those on the second gradient are 0.433, 0.351; 0.698, 0.648; and 0.736, 0.646) between the ordination scores of the samples and their reflectance values. The mapping results of the Ost area from three approaches also show similar patterns (e.g. the distribution of vegetation, artificial materials, water, and ceremony area) and characteristics of urban structures (the intensity of buildings). Therefore, our findings can help assess the transferability of urban material gradients between similar urban areas

Spatially autocorrelated training and validation samples inflate performance assessment of convolutional neural networks

Deep learning and particularly Convolutional Neural Networks (CNN) in concert with remote sensing are becoming standard analytical tools in the geosciences. A series of studies has presented the seemingly outstanding performance of CNN for predictive modelling. However, the predictive performance of such models is commonly estimated using random cross-validation, which does not account for spatial autocorrelation between training and validation data. Independent of the analytical method, such spatial dependence will inevitably inflate the estimated model performance. This problem is ignored in most CNN-related studies and suggests a flaw in their validation procedure. Here, we demonstrate how neglecting spatial autocorrelation during cross-validation leads to an optimistic model performance assessment, using the example of a tree species segmentation problem in multiple, spatially distributed drone image acquisitions. We evaluated CNN-based predictions with test data sampled from 1) randomly sampled hold-outs and 2) spatially blocked hold-outs. Assuming that a block cross-validation provides a realistic model performance, a validation with randomly sampled holdouts overestimated the model performance by up to 28%. Smaller training sample size increased this optimism. Spatial autocorrelation among observations was significantly higher within than between different remote sensing acquisitions. Thus, model performance should be tested with spatial cross-validation strategies and multiple independent remote sensing acquisitions. Otherwise, the estimated performance of any geospatial deep learning method is likely to be overestimated

Sampling Robustness in Gradient Analysis of Urban Material Mixtures

Many studies analyzing spaceborne hyperspectral images (HSIs) have so far struggled to deal with a lack of pure pixels due to complex mixtures of urban surface materials. Recently, an alternative concept of gradients in urban surface material composition has been proposed and successfully applied to map cities with spaceborne HSIs without the requirement for a previous determination of pure pixels. The gradient concept treats all pixels as mixed and aims to describe and quantify gradual transitions in the cover fractions of surface materials. This concept presents a promising approach to tackle urban mapping using spaceborne HSIs. However, since gradients are determined in a data-driven way, their transferability within urban areas needs to be investigated. For this purpose, we analyze the robustness of urban surface material gradients and their dependence across six systematic and three simple random sampling schemes. The results show high similarity between nine sampling schemes in the primary gradient feature space (Pspace) and individual gradient feature spaces (Ispaces). Comparing the Pspace with the Ispaces, the Mantel statistics show the resemblance of samples' distribution in the Pspace, and each Ispace is rather strong with high credibility, as the significance level is P < 0.01. Therefore, it can be concluded that the material gradients defined in the test area are independent of the specific sampling scheme. This study paves the way for subsequent analysis of the stability of urban surface material gradients and the interpretation of material gradients in other urban environments

Leaf Mass per Area of Wetland Vegetation under Water Stress Analyzed with Imaging Spectroscopy

Plant and community traits of wetland vegetation show a high intra-specific plasticity, originating from the high variability of environmental conditions. Remote sensing approaches promise to be able to retrieve some of these traits and their plasticity from the spectral reflectance signal of the canopy. In the present study, we evaluate a remote-sensing based approach for an analysis of spatial patterns of leaf mass per area (LMA), a key trait for ecosystem functioning and good negative correlate of potential growth rate. The test was conducted in Las Tablas de Daimiel, a National Park in Central Spain. This wetland was affected by a long-term drought, which introduced pronounced trait plasticity as part of the adaptation mechanisms of the vegetation to reduced water availability as well as a decrease in photosynthetic activity. Imaging spectroscopy (HyMap) data of the wetland were acquired in 2009 at peak drought intensity. At the same time, a field campaign was conducted. We applied an inversion of the PROSAIL model on these data to map the LMA distribution across the wetland. PROSAIL is a radiative transfer model that simulates the physical principles of light absorption and scattering in a vegetation canopy. The inversion enables the retrieval of trait information from the spectral signal. Furthermore, we assessed trends in photosynthetic activity and changing species composition across the wetland by analyzing time series of the normalized difference vegetation index (NDVI) as determined from various multispectral sensors. The mapped LMA values were analyzed within and between stands of different species and communities along a gradient of changing photosynthetic activity and species composition. LMA values retrieved for stands of species with high photosynthetic activity at peak drought intensity closely met values reported in trait data bases. The observed intra-specific LMA variability is in line with the expected plasticity of this trait along a moisture gradient that is reflected in a change in photosynthetic activity and species composition. We thus conclude that remote sensing approaches provide sufficient detail to trace the LMA-response of wetland vegetation to long-term drought stress

Let your maps be fuzzy!—Class probabilities and floristic gradients as alternatives to crisp mapping for remote sensing of vegetation

Mapping vegetation as hard classes based on remote sensing data is a frequently applied approach, even though this crisp, categorical representation is not in line with nature\u27s fuzziness. Gradual transitions in plant species composition in ecotones and faint compositional differences across different patches are thus poorly described in the resulting maps. Several concepts promise to provide better vegetation maps. These include (1) fuzzy classification (a.k.a. soft classification) that takes the probability of an image pixel\u27s class membership into account and (2) gradient mapping based on ordination, which describes plant species composition as a floristic continuum and avoids a categorical description of vegetation patterns. A systematic and comprehensive comparison of these approaches is missing to date. This paper hence gives an overview of the state of the art in fuzzy classification and gradient mapping and compares the approaches in a case study. The advantages and disadvantages of the approaches are discussed and their performance is compared to hard classification (a.k.a. crisp or boolean classification). Gradient mapping best conserves the information in the original data and does not require an a priori categorization. Fuzzy classification comes close in terms of information loss and likewise preserves the continuous nature of vegetation, however, still relying on a priori classification. The need for a priori classification may be a disadvantage or, in other cases, an advantage because it allows using categorical input data instead of the detailed vegetation records required for ordination. Both approaches support spatially explicit accuracy analyses, which further improves the usefulness of the output maps. Gradient mapping and fuzzy classification offer various advantages over hard classification, can always be transformed into a crisp map and are generally applicable to various data structures. We thus recommend the use of these approaches over hard classification for applications in ecological research

Ensemble Identification of Spectral Bands Related to Soil Organic Carbon Levels over an Agricultural Field in Southern Ontario, Canada

The recent use of hyperspectral remote sensing imagery has introduced new opportunities for soil organic carbon (SOC) assessment and monitoring. These data enable monitoring of a wide variety of soil properties but pose important methodological challenges. Highly correlated hyperspectral spectral bands can affect the prediction and accuracy as well as the interpretability of the retrieval model. Therefore, the spectral dimension needs to be reduced through a selection of specific spectral bands or regions that are most helpful to describing SOC. This study evaluates the efficiency of visible near-infrared (VNIR) and shortwave near-infrared (SWIR) hyperspectral data to identify the most informative hyperspectral bands responding to SOC content in agricultural soils. Soil samples (111) were collected over an agricultural field in southern Ontario, Canada and analyzed against two hyperspectral datasets: An airborne Nano-Hyperspec imaging sensor with 270 bands (400–1000 nm) and a laboratory hyperspectral dataset (ASD FieldSpec 3) along the 1000–2500 nm range (NIR-SWIR). In parallel, a multimethod modeling approach consisting of random forest, support vector machine, and partial least squares regression models was used to conduct band selections and to assess the validity of the selected bands. The multimethod model resulted in a selection of optimal band or regions over the VNIR and SWIR sensitive to SOC and potentially for mapping. The bands that achieved the highest respective importance values were 711–715, 727, 986–998, and 433–435 nm regions (VNIR); and 2365–2373, 2481–2500, and 2198–2206 nm (NIR-SWIR). Some of these bands are in agreement with the absorption features of SOC reported in the literature, whereas others have not been reported before. Ultimately, the selection of optimal band and regions is of importance for quantification of agricultural SOC and would provide a new framework for creating optimized SOC-specific sensors

Measuring beta-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) Rao's 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