An Earth observation based method to assess the influence of seasonal dynamics of canopy interception storage on the urban water balance

Vegetation is often represented in an oversimplified way in hydrological models for urbanised catchments, resulting in a generalised parameterisation of urban green. This is common practice, despite the fact that some studies clearly indicate that both the coverage and influence of urban green is often underestimated. In general vegetated surfaces play an important role as areas of recharge, for the redistribution of precipitation and in the regulation of surface runoff, especially for medium intensity storms. Hence, a more realistic and spatially distributed vegetation parameterisation would be of high value for hydrological modelling in urban catchments. In this paper an Earth observation based methodology is presented as an alternative to quantify the influence of canopy interception storage as well as the influence of seasonal dynamics on the urban water balance. Results indicate that Earth observation based interception storage capacities for the Upper Woluwe catchment (Brussels) are up to 25% higher than values obtained from literature, resulting in an increase of cumulative interception rates with 10% over a two year period. The results seem to vary with the rainfall intensity as well as with seasonal dynamics. In order to prove the general applicability of the proposed approach, these results need further confirmation using multi-year analyses and preferably a validation with ground truthing, which is a challenging future task.In een stedelijke context wordt vegetatie vaak op een overmatig eenvoudige manier voorgesteld in hydrologische modellen. Dit resulteert meestal in een zeer ruwe parameterisering van urbaan groen. Deze benadering is wijd verspreid, ondanks het feit dat studies aantonen dat zowel de dekking, als de invloed van stedelijk groen vaak onderschat wordt. Begroeide oppervlakken spelen een belangrijke rol inzake infiltratie in de bodem en de aanvulling van grondwater, de herverdeling van neerslag en de regulering van oppervlakkige afvoer, in het bijzonder voor middelgrote stormen. Dit maakt dat een meer realistische en ruimtelijk verdeelde parameterisering van urbane vegetatie van grote waarde kan zijn voor de modellering van stedelijke waterbekkens. In deze paper wordt een aardobservatie gebaseerde methode voorgesteld voor de kwantificering van interceptie door het stedelijk landschap (vnl. vegetatieve landschapselementen zoals bomen, struiken, etc.), alsook de seizoenale dynamiek en de invloed op de stedelijke waterbalans. De resultaten tonen dat de aardobservatie gebaseerde interceptie in het bovenstrooms gedeelte van het Woluwe-bekken (Brussel) tot 25% hoger liggen dan de waarden gebaseerd op literatuur. Over een periode van 2 jaar betekent dit een toename met 10% van de cumulatieve interceptie. De interceptiewaarden blijken ook te variëren in functie van de neerslagintensiteit en vertonen een seizoenale dynamiek. Een meerjarige analyse, bij voorkeur met grondmetingen, is noodzakelijk om de bekomen resultaten te bevestigen, wat een uitdaging vormt voor de toekomst

Hierarchical object-based mapping of riverscape units and in-stream mesohabitats using LiDAR and VHR imagery

In this paper, we present a new, semi-automated methodology for mapping hydromorphological indicators of rivers at a regional scale using multisource remote sensing (RS) data. This novel approach is based on the integration of spectral and topographic information within a multilevel, geographic, object-based image analysis (GEOBIA). Different segmentation levels were generated based on the two sources of RS data, namely very-high spatial resolution, near-infrared imagery (VHR) and high-resolution LiDAR topography. At each level, different input object features were tested with Machine Learning classifiers for mapping riverscape units and in-stream mesohabitats. The GEOBIA approach proved to be a powerful tool for analyzing the river system at different levels of detail and for coupling spectral and topographic datasets, allowing for the delineation of the natural fluvial corridor with its primary riverscape units (e.g., water channel, unvegetated sediment bars, riparian densely-vegetated units, etc..) and in-stream mesohabitats with a high level of accuracy, respectively of K=0.91 and K=0.83. This method is flexible and can be adapted to different sources of data, with the potential to be implemented at regional scales in the future. The analyzed dataset, composed of VHR imagery and LiDAR data, is nowadays increasingly available at larger scales, notably through European Member States. At the same time, this methodology provides a tool for monitoring and characterizing the hydromorphological status of river systems continuously along the entire channel network and coherently through time, opening novel and significant perspectives to the river science and management, notably for planning and targeting actions.JRC.H.1-Water Resource

Multiple endmember unmixing of CHRIS/Proba imagery for mapping impervious surfaces in urban and suburban environments

In this paper, the potential of Compact High-Resolution Imaging Spectrometer (CHRIS)/Project for On-Board Autonomy data for impervious surface mapping is tested in a mixed urban/suburban/rural environment including part of the city of Leuven (Belgium) using multiple endmember unmixing. Various unmixing scenarios are compared, using different threshold values for the RMSE criterion applied to select the proper model for unmixing each pixel. Validation based on 25-cm aerial photography shows that the use of threshold values that favor the application of models with a small number of endmembers performs better compared to scenarios that make use of models with more endmembers. Detailed analysis of model selection for pixels with different land-cover composition indicates that the error in fraction estimation is partly related to spectral confusion between impervious surface types and bare soil, leading to the selection of inappropriate models for the unmixing. In spite of the spectral similarity of soil and impervious surface endmembers, average fractional error for impervious surfaces, vegetation, and bare soil is around 15%, which demonstrates the potential of CHRIS data for mapping the major physical components of the urban/suburban environment at the subpixel scale

Large-Scale Urban Impervous Surfaces Estimation Through Incorporating Temporal and Spatial Information into Spectral Mixture Analysis

With rapid urbanization, impervious surfaces, a major component of urbanized areas, have increased concurrently. As a key indicator of environmental quality and urbanization intensity, an accurate estimation of impervious surfaces becomes essential. Numerous automated estimation approaches have been developed during the past decades. Among them, spectral mixture analysis (SMA) has been recognized as a powerful and widely employed technique. While SMA has proven valuable in impervious surface estimation, effects of temporal and spectral variability have not been successfully addressed. In particular, impervious surface estimation is likely to be sensitive to seasonal changes, majorly due to the shadowing effects of vegetation canopy in summer and the confusion between impervious surfaces and soil in winter. Moreover, endmember variability and multi-collinearity have adversely impacted the accurate estimation of impervious surface distribution with coarse resolution remote sensing imagery. Therefore, the main goal of this research is to incorporate temporal and spatial information, as well as geostatistical approaches, into SMA for improving large-scale urban impervious surface estimation. Specifically, three new approaches have been developed in this dissertation to improve the accuracy of large-scale impervious surface estimation. First, a phenology based temporal mixture analysis was developed to address seasonal sensitivity and spectral confusion issues with the multi-temporal MODIS NDVI data. Second, land use land cover information assisted temporal mixture analysis was proposed to handle the issue of endmember class variability through analyzing the spatial relationship between endmembers and surrounding environmental and socio-economic factors in support of the selection of an appropriate number and types of endmember classes. Third, a geostatistical temporal mixture analysis was developed to address endmember spectral variability by generating per-pixel spatial varied endmember spectra. Analysis results suggest that, first, with the proposed phenology based temporal mixture analysis, a significant phenophase differences between impervious surfaces and soil can be extracted and employed in unmxing analysis, which can facilitate their discrimination and successfully address the issue of seasonal sensitivity and spectral confusion. Second, with the analyzed spatial distribution relationship between endmembers and environmental and socio-economic factors, endmember classes can be identified with clear physical meanings throughout the whole study area, which can effectively improve the unmixing analysis results. Third, the use of the spatially varying per-pixel endmember generated from the geostatistical approach can effectively consider the endmember spectra spatial variability, overcome the endmember within-class variability issue, and improve the accuracy of impervious surface estimates. Major contributions of this research can be summarized as follows. First, instead of Landsat Thematic Mapper (TM) images, MODIS imageries with large geographic coverage and high temporal resolution have been successfully employed in this research, thus making timely and regional estimation of impervious surfaces possible. Second, this research proves that the incorporation of geographic knowledge (e.g. phonological knowledge, spatial interaction, and geostatistics) can effectively improve the spectral mixture analysis model, and therefore improve the estimation accuracy of urban impervious surfaces

Mapping urban surface materials with imaging spectroscopy data on different spatial scales

This work focuses on the development of methods for mapping urban surface materials by means of imaging spectroscopy data with different spatial resolution. General findings from this work represent a sensor- and site-independent framework for the automated extraction of spectrally pure pixels using an urban image spectral library while coping with its potential incompleteness. The extraction of spectrally pure pixels serves as a basic prerequisite for the subsequent use of image analysis methods to obtain detailed urban surface material maps. These material maps enabled the determination of gradual material transitions that were finally related to complex spectral mixtures resulting from 30 m spatial resolution imaging spectroscopy data to analyse typical material compositions within certain administrative units. The findings demonstrate the great potential of using upcoming spaceborne imaging spectroscopy data for a regular area-wide mapping of surface materials in urban areas. Im Fokus dieser Arbeit stand die Entwicklung von Methoden zur Kartierung urbaner Oberflächenmaterialien mittels abbildender Spektroskopiedaten unterschiedlicher räumlicher Auflösung. Das vorgestellte Konzept zur automatisierten sensor- und ortsunabhängigen Extraktion spektral reiner Pixel aus flugzeuggetragenen Fernerkundungsdaten berücksichtigt dabei die mögliche Unvollständigkeit einer urbanen Bildspektralbibliothek. Die Extraktion spektral reiner Pixel dient als Grundvoraussetzung für den späteren Einsatz von Bildanalyseverfahren zur Gewinnung detaillierter Kartierungen urbaner Oberflächenmaterialien. Aus diesen sind Materialgradienten ableitbar, die mit den komplexen Spektralmischungen aus Hyperspektraldaten mit 30 m räumlicher Auflösung in Verbindung gebracht wurden. Die Analyse typischer Materialzusammensetzungen innerhalb städtischer Verwaltungseinheiten zeigt das enorme Potential zukünftiger Hyperspektralsatelliten für die Erfassung des Materialvorkommens von Städten

Evaluating the Impact of Nature-Based Solutions: Appendix of Methods

The Handbook aims to provide decision-makers with a comprehensive NBS impact assessment framework, and a robust set of indicators and methodologies to assess impacts of nature-based solutions across 12 societal challenge areas: Climate Resilience; Water Management; Natural and Climate Hazards; Green Space Management; Biodiversity; Air Quality; Place Regeneration; Knowledge and Social Capacity Building for Sustainable Urban Transformation; Participatory Planning and Governance; Social Justice and Social Cohesion; Health and Well-being; New Economic Opportunities and Green Jobs. Indicators have been developed collaboratively by representatives of 17 individual EU-funded NBS projects and collaborating institutions such as the EEA and JRC, as part of the European Taskforce for NBS Impact Assessment, with the four-fold objective of: serving as a reference for relevant EU policies and activities; orient urban practitioners in developing robust impact evaluation frameworks for nature-based solutions at different scales; expand upon the pioneering work of the EKLIPSE framework by providing a comprehensive set of indicators and methodologies; and build the European evidence base regarding NBS impacts. They reflect the state of the art in current scientific research on impacts of nature-based solutions and valid and standardized methods of assessment, as well as the state of play in urban implementation of evaluation frameworks