Remote Sensing and Spatial Modelling of the Urban Environment

Lakes, Tobia; Hostert, Patrick; Kleinschmit, Birgit; Lauf, Steffen; Tigges, Jan

doi:10.1007/978-3-642-17731-6_8

Cited by 4 publications

(2 citation statements)

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“…Such potential errors will be likely reduced in the final global DEM that consists of more than one coverage and will be available in the upcoming years [33]. Further on, for urban-like vegetation, the validity of results had to be restricted since the close proximity between vegetation and neighboring objects likely caused mixed pixels with different heights [56]. This effect can be counteracted by the use of a high-resolution DEM with a spatial resolution of 6 m for selected areas, which will be available in the upcoming years upon request [33].…”

Section: Discussionmentioning

confidence: 99%

TanDEM-X for Large-Area Modeling of Urban Vegetation Height: Evidence from Berlin, Germany

Schreyer

Geis

Lakes

2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Large-area urban ecology studies often miss information on vertical parameters of vegetation, even though they represent important constituting properties of complex urban ecosystems. The new globally available digital elevation model (DEM) of the spaceborne TanDEM-X mission has an unprecedented spatial resolution (12 × 12 m) that allows us to derive such relevant information. So far, suitable approaches using a TanDEM-X DEM for the derivation of a normalized canopy model (nCM) are largely absent. Therefore, this paper aims to obtain digital terrain models (DTMs) for the subsequent computation of two nCMs for urban-like vegetation (e.g., street trees) and forest-like vegetation (e.g., parks), respectively, in Berlin, Germany, using a TanDEM-X DEM and a vegetation mask derived from UltraCam-X data. Initial comparisons between morphological DTM-filter confirm the superior performance of a novel disaggregated progressive morphological filter (DPMF). For improved assessment of a DTM for urban-like vegetation, a modified DPMF and image enhancement methods were applied. For forest-like vegetation, an interpolation and a weighted DPMF approach were compared. Finally, all DTMs were used for nCM calculation. The nCM for urban-like vegetation revealed a mean height of 4.17 m compared to 9.61 m of a validation nCM. For forest-like vegetation, the mean height for the nCM of the weighted filtering approach (9.16 m) produced the best results (validation nCM: 13.55 m). It is concluded that an nCM from TanDEM-X can capture vegetation heights in their appropriate dimension, which can be beneficial for automated height-related vegetation analysis such as comparisons of vegetation carbon storage between several cities. Index Terms-Digital elevation model (DEM), digital terrain model (DTM), morphological filters (MFs), normalized digital surface model (nDSM), Tandem-X, trees, UltraCamX, urban vegetation. I. INTRODUCTION U RBAN vegetation is known to positively regulate urban ecological processes through air purification, noise reduction, urban cooling, and runoff mitigation, as well as to provide aesthetic and environmental benefits that are commonly characterized as urban ecosystem services [1]. In recent Manuscript

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Section: Discussionmentioning

confidence: 99%

TanDEM-X for Large-Area Modeling of Urban Vegetation Height: Evidence from Berlin, Germany

Schreyer

Geis

Lakes

2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The development and contributions of high resolution remote sensing have been successfully applied to provide input for improved modeling of urban ecosystem services concerning area-wide details, which are consistent and up-to-date. Generally, this can help to increase the comparability of different sites [ 38 , 39 ]. Increasing spatial, spectral and temporal resolutions have successfully shown an increase in precision, for example, to monitor the changes at the individual tree level or to identify the diversity of urban tree species [ 40 , 41 ].…”

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confidence: 99%

High resolution remote sensing for reducing uncertainties in urban forest carbon offset life cycle assessments

Tigges

Lakes

2017

Carbon Balance Manage

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BackgroundUrban forests reduce greenhouse gas emissions by storing and sequestering considerable amounts of carbon. However, few studies have considered the local scale of urban forests to effectively evaluate their potential long-term carbon offset. The lack of precise, consistent and up-to-date forest details is challenging for long-term prognoses. Therefore, this review aims to identify uncertainties in urban forest carbon offset assessment and discuss the extent to which such uncertainties can be reduced by recent progress in high resolution remote sensing. We do this by performing an extensive literature review and a case study combining remote sensing and life cycle assessment of urban forest carbon offset in Berlin, Germany.Main textRecent progress in high resolution remote sensing and methods is adequate for delivering more precise details on the urban tree canopy, individual tree metrics, species, and age structures compared to conventional land use/cover class approaches. These area-wide consistent details can update life cycle inventories for more precise future prognoses. Additional improvements in classification accuracy can be achieved by a higher number of features derived from remote sensing data of increasing resolution, but first studies on this subject indicated that a smart selection of features already provides sufficient data that avoids redundancies and enables more efficient data processing. Our case study from Berlin could use remotely sensed individual tree species as consistent inventory of a life cycle assessment. However, a lack of growth, mortality and planting data forced us to make assumptions, therefore creating uncertainty in the long-term prognoses. Regarding temporal changes and reliable long-term estimates, more attention is required to detect changes of gradual growth, pruning and abrupt changes in tree planting and mortality. As such, precise long-term urban ecological monitoring using high resolution remote sensing should be intensified, especially due to increasing climate change effects. This is important for calibrating and validating recent prognoses of urban forest carbon offset, which have so far scarcely addressed longer timeframes. Additionally, higher resolution remote sensing of urban forest carbon estimates can improve upscaling approaches, which should be extended to reach a more precise global estimate for the first time.ConclusionsUrban forest carbon offset can be made more relevant by making more standardized assessments available for science and professional practitioners, and the increasing availability of high resolution remote sensing data and the progress in data processing allows for precisely that.

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Development of an environmental justice index to determine socio-economic disparities of noise pollution and green space in residential areas in Berlin

Lakes

Brückner

Krämer

2013

Journal of Environmental Planning and Management

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Remote Sensing and Spatial Modelling of the Urban Environment

Cited by 4 publications

References 67 publications

TanDEM-X for Large-Area Modeling of Urban Vegetation Height: Evidence from Berlin, Germany

TanDEM-X for Large-Area Modeling of Urban Vegetation Height: Evidence from Berlin, Germany

High resolution remote sensing for reducing uncertainties in urban forest carbon offset life cycle assessments

Development of an environmental justice index to determine socio-economic disparities of noise pollution and green space in residential areas in Berlin

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