Imaging Spectroscopy of Urban Environments

Linden, Sebastian van der; Okujeni, Akpona; Canters, Frank; Degerickx, Jeroen; Heiden, Uta; Hostert, Patrick; Priem, Frederik; Somers, Ben; Thiel, Franziska

doi:10.1007/s10712-018-9486-y

Cited by 27 publications

(12 citation statements)

References 95 publications

(140 reference statements)

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“…Khatami et al, 2016;Wulder et al, 2018) and vegetation characterization (e.g. Verrelst et al, 2015). As the PALM model requires high spatial resolution for performing building-resolving simulations, the free and open Sentinel-2 satellite data are of interest as well as the data of commercial satellite constellations like Rapid Eye or World View.…”

Section: Data Sourcesmentioning

confidence: 99%

Geospatial input data for the PALM model system 6.0: model requirements, data sources and processing

et al. 2020

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Abstract. The PALM model system 6.0 is designed to simulate micro- and mesoscale flow dynamics in realistic urban environments. The simulation results can be very valuable for various urban applications, for example to develop and improve mitigation strategies related to heat stress or air pollution. For the accurate modelling of urban environments, realistic boundary conditions need to be considered for the atmosphere, the local environment and the soil. The local environment with its geospatial components is described in the static driver of the model and follows a standardized format. The main input parameters describe surface type, buildings and vegetation. Depending on the desired simulation scenario and the available data, the local environment can be described at different levels of detail. To compile a complete static driver describing a whole city, various data sources are used, including remote sensing, municipal data collections and open data such as OpenStreetMap. This article shows how input data sets for three German cities were derived. Based on these data sets, the static driver for PALM can be generated. As the collection and preparation of input data sets is tedious, prospective research aims at the development of a semi-automated processing chain to support users in formatting their geospatial data.

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Section: Data Sourcesmentioning

confidence: 99%

Geospatial input data for the PALM model system 6.0: model requirements, data sources and processing

et al. 2020

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“…This is specifically challenging when mapping infrastructure and the LC composition of smaller settlements and in rural areas. Even though increased spatial resolution can partly counter this problem, van der Linden et al (2018) found that at 9 m spatial resolution, 80% of the pixels still contained two or more surface types. Moreover, mapping the type and characteristics of settlements also requires knowledge about less abundant LC components, including different vegetation types ( Wentz et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Mapping urban-rural gradients of settlements and vegetation at national scale using Sentinel-2 spectral-temporal metrics and regression-based unmixing with synthetic training data

Schug

Frantz

Okujeni

et al. 2020

Remote Sensing of Environment

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The increasing impact of humans on land and ongoing global population growth requires an improved understanding of land cover (LC) and land use (LU) processes related to settlements. The heterogeneity of built-up areas and infrastructures as well as the importance of not only mapping, but also characterizing anthropogenic structures suggests using a sub-pixel mapping approach for analysing related LC from space. We implement a regression-based unmixing approach for mapping built-up surfaces and infrastructure , woody vegetation and non-woody vegetation for all of Germany and Austria at 10 m resolution to demonstrate the potential of sub-pixel mapping. We map LC fractions for one point in time, using all available Sentinel-2 data from 2017 and 2018 (<70% cloud cover). We combine the concept of synthetically mixed training data with statistical aggregations from spectral-temporal metrics (STM) derived from Sentinel-2 reflectance time series. We specifically examine how STM can be used for creating synthetically mixed training data. STM are known to facilitate large area mapping by being largely independent of image acquisition dates and inherently incorporate phenological information. Vegetation is an important part of settlements and time series information supports its mapping. Synthetically mixed training data facilitates a streamlined training by using pure reference spectra to generate artificial mixtures as input to regression modelling of LC fractions in mixed pixels. We here show how combining both offers great potential for wall-to-wall LC fraction mapping. We further investigate the positive effect of STM on map results by comparing the performance of different subsets of STM combinations. Our results indicate that many STM combinations containing spectral variability and vegetation indices provide suitable input to creating synthetic training data for regression-based fraction mapping. Results for built-up surfaces and infrastructure (MAE 0.13/RMSE 0.18 at 20 m resolution), woody vegetation (0.18, 0.22) and non-woody vegetation (0.14, 0.19) are highly consistent across Germany and Austria. Only a few surface types were not accurately predicted in our nation-wide mapping. Further research is required to optimize mapping of temporally invariant bare soil and rock surfaces that show spectral similarity to built-up surfaces and infrastructure. The proposed methodology combines benefits of both regression-based modelling with synthetically mixed training data and STM, and thus facilitates mapping of LC fractions on a national scale and at high resolution. Such information will allow to better characterize settlements and identifying processes such as densification that are best represented by continuous LC mapping.

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“…The use of imaging spectroscopy (IS) compared to multispectral imagery has opened the way to providing access to more physical surface properties for better monitoring of land cover mapping, vegetation, and soil monitoring in both urban and natural ecosystems [1][2][3]. However, their derivation requires accurate surface reflectance retrieval.…”

Section: Introductionmentioning

confidence: 99%

Impact of Tree Crown Transmittance on Surface Reflectance Retrieval in the Shade for High Spatial Resolution Imaging Spectroscopy: A Simulation Analysis Based on Tree Modeling Scenarios

et al. 2021

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With the advancement of high spatial resolution imaging spectroscopy, an accurate surface reflectance retrieval is needed to derive relevant physical variables for land cover mapping, soil, and vegetation monitoring. One challenge is to deal with tree shadows using atmospheric correction models if the tree crown transmittance Tc is not properly taken into account. This requires knowledge of the complex radiation mechanisms that occur in tree crowns, which can be provided by coupling the physical modeling of canopy radiative transfer codes (here DART) and the 3D representations of trees. First in this study, a sensitivity analysis carried out on DART simulations with an empirical 3D tree model led to a statistical regression predicting Tc from the tree leaf area index (LAI) and the solar zenith angle with good performances (RMSE ≤ 4.3% and R2 ≥ 0.91 for LAI ≤ 4 m2.m-2). Secondly, more realistic 3D voxel-grid tree models derived from terrestrial LiDAR measurements over two trees were considered. The comparison of DART-simulated Tc from these models with the previous predicted Tc over 0.4–2.5 µm showed three main sources of inaccuracy quoted in order of importance: 1) the global tree geometry shape (mean bias up to 21.5%), 2) the transmittance fraction associated to multiple scattering, Tscat (maximum bias up to 13%), and 3) the degree of realism of the tree representation (mean bias up to 7.5%). Results showed that neglecting Tc leads to very inaccurate reflectance retrieval (mean bias > 0.04), particularly if the background reflectance is high, and in the near and shortwave infrared – NIR and SWIR – due to Tscat. The transmittance fraction associated to the non-intercepted transmitted light, Tdir, can reach up to 95% in the SWIR, and Tscat up to 20% in the NIR. Their spatial contributions computed in the tree shadow have a maximum dispersion of 27% and 8% respectively. Investigating how to approximate Tdir and Tscat spectral and spatial variability along with the most appropriate tree 3D modeling is crucial to improve reflectance retrieval in tree shadows when using atmospheric correction models.

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Imaging Spectroscopy of Urban Environments

Cited by 27 publications

References 95 publications

Geospatial input data for the PALM model system 6.0: model requirements, data sources and processing

Geospatial input data for the PALM model system 6.0: model requirements, data sources and processing

Mapping urban-rural gradients of settlements and vegetation at national scale using Sentinel-2 spectral-temporal metrics and regression-based unmixing with synthetic training data

Impact of Tree Crown Transmittance on Surface Reflectance Retrieval in the Shade for High Spatial Resolution Imaging Spectroscopy: A Simulation Analysis Based on Tree Modeling Scenarios

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