Michael Förster

Carbon footprints of cities and other human settlements in the UK

A growing body of literature discusses the CO2 emissions of cities. Still, little is known about emission patterns across density gradients from remote rural places to highly urbanized areas, the drivers behind those emission patterns and the global emissions triggered by consumption in human settlements—referred to here as the carbon footprint. In this letter we use a hybrid method for estimating the carbon footprints of cities and other human settlements in the UK explicitly linking global supply chains to local consumption activities and associated lifestyles. This analysis comprises all areas in the UK, whether rural or urban. We compare our consumption-based results with extended territorial CO2 emission estimates and analyse the driving forces that determine the carbon footprint of human settlements in the UK. Our results show that 90% of the human settlements in the UK are net importers of CO2 emissions. Consumption-based CO2 emissions are much more homogeneous than extended territorial emissions. Both the highest and lowest carbon footprints can be found in urban areas, but the carbon footprint is consistently higher relative to extended territorial CO2 emissions in urban as opposed to rural settlement types. The impact of high or low density living remains limited; instead, carbon footprints can be comparatively high or low across density gradients depending on the location-specific socio-demographic, infrastructural and geographic characteristics of the area under consideration. We show that the carbon footprint of cities and other human settlements in the UK is mainly determined by socio-economic rather than geographic and infrastructural drivers at the spatial aggregation of our analysis. It increases with growing income, education and car ownership as well as decreasing household size. Income is not more important than most other socio-economic determinants of the carbon footprint. Possibly, the relationship between lifestyles and infrastructure only impacts carbon footprints significantly at higher spatial granularity.

Testing the red edge channel for improving land-use classifications based on high-resolution multi-spectral satellite data

The incorporation of a red edge channel in multi-spectral satellite sensors has potential for improving land-use classification, as the related electromagnetic spectrum is specifically sensitive to vegetation chlorophyll content. RapidEye is the first high-resolution multi-spectral satellite system that operationally provides a red edge channel. The objective of this study is to test the potential of the RapidEye red edge channel for improving the classification of land use, investigated at a study site west of Berlin. Based on a scene from July 2009, supervised land-use classifications were performed using different sets of spectral feature input, including and excluding red edge information. The algorithms used are support vector machine and maximum likelihood. The results indicate that the incorporation of red edge information can increase classification accuracy. The highest positive effects are observed for vegetation classes in open landscapes, e.g. for bush vegetation.

Comparison of Feature Reduction Algorithms for Classifying Tree Species With Hyperspectral Data on Three Central European Test Sites

Tree species information is a basic variable for forest inventories. Knowledge on tree species is relevant for biomass estimation, habitat quality assessment, and biodiversity characterization. Hyperspectral data have been proven to have a high potential for the mapping of tree species composition. However, open questions remain concerning the robustness of existing classification approaches. Here, a number of classification approaches were compared to classify tree species from airborne hyperspectral data across three forest sites to identify a single approach which continuously delivers high classification performances over all test sites. Examined approaches included three feature selection methods [genetic algorithm (GA), support vector machines (SVM) wrapper, and sparse generalized partial least squares selection (PLS)] each combined with two nonparametric classifiers (SVM and Random Forest). Two further setups included classifications applied to the full hyperspectral dataset and to an image transformed with a minimum noise fraction (MNF) transformation. Results showed that SVM wrapper and the GA slightly outperformed the PLS-based algorithm. In most cases, the best classification runs involving a feature selection algorithm outperformed those incorporating the full hyperspectral dataset. However, the best overall results were obtained when using the first 10-20 components of the MNF-transformed image. Selected bands were frequently located in the visual region close to the green peak, at the chlorophyll absorption feature and the red edge rise as well as in three parts of the short-wave infrared region close to water absorption features. These findings are relevant for improving the robustness of tree species classifications from airborne hyperspectral data incorporating feature reduction techniques.

Remote sensing for mapping natural habitats and their conservation status – New opportunities and challenges

Safeguarding the diversity of natural and semi-natural habitats in Europe is one of the aims set out by the Habitats Directive (Council Directive 92/43/EEC on the conservation of natural habitats and of wild fauna and flora) and one of the targets of the European 2020 Biodiversity Strategy, and is to be accomplished by maintaining a favourable conservation status. To reach this aim a high-level understanding of the distribution and conditions of these habitats is needed. Remote sensing can considerably contribute to habitat mapping and their observation over time. Several European projects and a large number of scientific studies have addressed the issue of mapping and monitoring natural habitats via remote sensing and the deriving of indicators on their conservation status. The multitude of utilized remote sensing sensors and applied methods used in these studies, however, impede a common understanding of what is achievable with current state-of-the-art technologies. The aim of this paper is to provide a synthesis on what is currently feasible in terms of detection and monitoring of natural and semi-natural habitats with remote sensing. To focus this endeavour, we concentrate on those studies aimed at direct mapping of individual habitat types or discriminating between different types of habitats occurring in relatively large, spatially contiguous units. By this we uncover the potential of remote sensing to better understand the distribution of habitats and the assessment of their conservation status in Europe.

Grassland habitat mapping by intra-annual time series analysis – Comparison of RapidEye and TerraSAR-X satellite data

Abstract Remote sensing concepts are needed to monitor open landscape habitats for environmental change and biodiversity loss. However, existing operational approaches are limited to the monitoring of European dry heaths only. They need to be extended to further habitats. Thus far, reported studies lack the exploitation of intra-annual time series of high spatial resolution data to take advantage of the vegetations’ phenological differences. In this study, we investigated the usefulness of such data to classify grassland habitats in a nature reserve area in northeastern Germany. Intra-annual time series of 21 observations were used, acquired by a multi-spectral (RapidEye) and a synthetic aperture radar (TerraSAR-X) satellite system, to differentiate seven grassland classes using a Support Vector Machine classifier. The classification accuracy was evaluated and compared with respect to the sensor type – multi-spectral or radar – and the number of acquisitions needed. Our results showed that very dense time series allowed for very high accuracy classifications (>90%) of small scale vegetation types. The classification for TerraSAR-X obtained similar accuracy as compared to RapidEye although distinctly more acquisitions were needed. This study introduces a new approach to enable the monitoring of small-scale grassland habitats and gives an estimate of the amount of data required for operational surveys.

Correction on “Towards Detecting Swath Events in TerraSAR-X Time Series to Establish NATURA 2000 Grassland Habitat Swath Management as Monitoring Parameter”

Spatial monitoring tools are necessary to respond to the threat of global biodiversity loss. At the European scale, remote sensing tools for NATURA 2000 habitat monitoring have been requested by the European Commission to fulfill the obligations of the EU Habitats Directive. This paper introduces a method by which swath events in semi-natural grasslands can be detected from multi-temporal TerraSAR-X data. The investigated study sites represent rare and endangered habitats (NATURA 2000 codes 6410, 6510), located in the Doberitzer Heide nature conservation area west of Berlin. We analyzed a time series of 11 stripmap images (HH-polarization) covering the vegetation period affected by swath (June to September 2010) at a constant 11-day acquisition rate. A swath detection rule was established to extract the swath events for the NATURA 2000 habitats as well as for six contrasting pasture sites not affected by swath. All swath events observed in the field were correctly allocated. The results indicate the potential to allocate semi-natural grassland swath events to 11-day-periods using TerraSAR-X time series. Since the conservation of semi-natural grassland habitats requires compliance with specific swath management rules, the detection of swath events may thus provide new parameters for the monitoring of NATURA 2000 grassland habitats.

Potential of remote sensing to predict species invasions A modelling perspective

Understanding the causes and effects of species invasions is a priority in ecology and conservation biology. One of the crucial steps in evaluating the impact of invasive species is to map changes in their actual and potential distribution and relative abundance across a wide region over an appropriate time span. While direct and indirect remote sensing approaches have long been used to assess the invasion of plant species, the distribution of invasive animals is mainly based on indirect methods that rely on environmental proxies of conditions suitable for colonization by a particular species. The aim of this article is to review recent efforts in the predictive modelling of the spread of both plant and animal invasive species using remote sensing, and to stimulate debate on the potential use of remote sensing in biological invasion monitoring and forecasting. Specifically, the challenges and drawbacks of remote sensing techniques are discussed in relation to: i) developing species distribution models, and ii) studying life cycle changes and phenological variations. Finally, the paper addresses the open challenges and pitfalls of remote sensing for biological invasion studies including sensor characteristics, upscaling and downscaling in species distribution models, and uncertainty of results.

Evaluating an Intra-Annual Time Series for Grassland Classification—How Many Acquisitions and What Seasonal Origin Are Optimal?

The amount of images used in multitemporal classification studies has greatly increased along with enhanced temporal sensor capacities. Handling large intra-annual time series leads to the question of how the selection of image acquisition dates could be optimized. In this study, an empirical approach for evaluating the relative classification power of single acquisition dates is introduced for the differentiation of seminatural grassland vegetation. The main question is how many acquisitions from which phenological origins are preferable to achieve a certain classification accuracy target. The tested time series contains 24 single RapidEye scenes from 2009 to 2011. The vegetation index composites of these images were iteratively classified into different combinations of acquisition dates using the support vector machine (SVM) algorithm. The subsequent results were tested for significant accuracy improvements over single acquisition dates. These acquisition dates are subsumed under phenological seasons to evaluate adequate temporal acquisition windows. The results show that a three-scene composite reaches more than 0.8 overall accuracy (OAA). The best tradeoff amount between number of acquisition dates and classification accuracy is achieved using a seven-scene NDVI composite. The most important season for the differentiation of seminatural grassland is early summer (ESu). Full spring (FuS), late summer (LSu), and midsummer (MSu) can also be identified as influential temporal windows for data acquisition.

Managing protected areas under climate change: challenges and priorities.

The implementation of adaptation actions in local conservation management is a new and complex task with multiple facets, influenced by factors differing from site to site. A transdisciplinary perspective is therefore required to identify and implement effective solutions. To address this, the International Conference on Managing Protected Areas under Climate Change brought together international scientists, conservation managers, and decision-makers to discuss current experiences with local adaptation of conservation management. This paper summarizes the main issues for implementing adaptation that emerged from the conference. These include a series of conclusions and recommendations on monitoring, sensitivity assessment, current and future management practices, and legal and policy aspects. A range of spatial and temporal scales must be considered in the implementation of climate-adapted management. The adaptation process must be area-specific and consider the ecosystem and the social and economic conditions within and beyond protected area boundaries. However, a strategic overview is also needed: management at each site should be informed by conservation priorities and likely impacts of climate change at regional or even wider scales. Acting across these levels will be a long and continuous process, requiring coordination with actors outside the “traditional” conservation sector. To achieve this, a range of research, communication, and policy/legal actions is required. We identify a series of important actions that need to be taken at different scales to enable managers of protected sites to adapt successfully to a changing climate.

Spectral Unmixing of Forest Crown Components at Close Range, Airborne and Simulated Sentinel-2 and EnMAP Spectral Imaging Scale

Forest biochemical and biophysical variables and their spatial and temporal distribution are essential inputs to process-orientated ecosystem models. To provide this information, imaging spectroscopy appears to be a promising tool. In this context, the present study investigates the potential of spectral unmixing to derive sub-pixel crown component fractions in a temperate deciduous forest ecosystem. However, the high proportion of foliage in this complex vegetation structure leads to the problem of saturation effects, when applying broadband vegetation indices. This study illustrates that multiple endmember spectral mixture analysis (MESMA) can contribute to overcoming this challenge. Reference