Deriving and Evaluating City-Wide Vegetation Heights from a TanDEM-X DEM

Vegetation provides important functions and services in urban areas, and vegetation heights divided into vertical and horizontal units can be used as indicators for its assessment. Conversely, detailed area-wide and updated height information is frequently missing for most urban areas. This study sought to assess three vegetation height classes from a globally available TanDEM-X digital elevation model (DEM, 12 × 12 m spatial resolution) for Berlin, Germany. Subsequently, height distribution and its accuracy across biotope classes were derived. For this, a TanDEM-X intermediate DEM, a LiDAR DTM, an UltraCamX vegetation layer, and a biotope map were included. The applied framework comprised techniques of data integration and raster algebra for: Deriving a height model for all of Berlin, masking non-vegetated areas, classifying two canopy height models (CHMs) for bushes/shrubs and trees, deriving vegetation heights for 12 biotope classes and assessing accuracies using validation CHMs. The findings highlighted the possibility of assessing vegetation heights for total vegetation, trees and bushes/shrubs with low and consistent offsets of mean heights (total CHM: −1.56 m; CHM for trees: −2.23 m; CHM bushes/shrubs: 0.60 m). Negative offsets are likely caused by X-band canopy penetrations. Between the biotope classes, large variations of height and area were identified (vegetation height/biotope and area/biotope: ~3.50–~16.00 m; 4.44%–96.53%). The framework and results offer a great asset for citywide and spatially explicit assessment of vegetation heights as an input for urban ecology studies, such as investigating habitat diversity based on the vegetation’s heterogeneity.

[1]  Randolph H. Wynne,et al.  Estimating Biophysical Parameters of Individual Trees in an Urban Environment Using Small Footprint Discrete-Return Imaging Lidar , 2012, Remote. Sens..

[2]  Jianping Wu,et al.  Toward automatic estimation of urban green volume using airborne LiDAR data and high resolution Remote Sensing images , 2013, Frontiers of Earth Science.

[3]  Juliane Mathey,et al.  Urban vegetation structure types as a methodological approach for identifying ecosystem services – Application to the analysis of micro-climatic effects , 2014 .

[4]  A. Pekkarinen,et al.  Estimation of forest stand volumes by Landsat TM imagery and stand-level field-inventory data , 2004 .

[5]  Kevin Curran,et al.  OpenStreetMap , 2012, Int. J. Interact. Commun. Syst. Technol..

[6]  Per Angelstam,et al.  Ecological diversity of birds in relation to the structure of urban green space , 2006 .

[7]  Galina Churkina,et al.  Modeling the carbon cycle of urban systems , 2008 .

[8]  D. Lang,et al.  A review of urban ecosystem services: six key challenges for future research , 2015 .

[9]  Gerhard Krieger,et al.  Relative height error analysis of TanDEM-X elevation data , 2012 .

[10]  Katarina Löfvenhaft,et al.  Biotope patterns and amphibian distribution as assessment tools in urban landscape planning , 2004 .

[11]  Jeffrey P. Prestemon,et al.  The Effect of Trees on Crime in Portland, Oregon , 2012 .

[12]  P. Litkey,et al.  Algorithms and methods of airborne laser-scanning for forest measurements , 2004 .

[13]  Kevin J. Gaston,et al.  Mapping an urban ecosystem service: quantifying above‐ground carbon storage at a city‐wide scale , 2011 .

[14]  F. Escobedo,et al.  Spatial heterogeneity and air pollution removal by an urban forest , 2009 .

[15]  G. Henebry,et al.  Remote sensing of vegetation 3-D structure for biodiversity and habitat: Review and implications for lidar and radar spaceborne missions , 2009 .

[16]  Jonas Lundgren,et al.  Noggrannhetskontroll av laserdata för ny nationell höjdmodell , 2010 .

[17]  Philippe Clergeau,et al.  Birds are also sensitive to landscape composition and configuration within the city centre , 2012 .

[18]  J. Byrne,et al.  A systematic quantitative review of urban tree benefits, costs, and assessment methods across cities in different climatic zones , 2012 .

[19]  E. Minor,et al.  Effects of habitat structure, human disturbance, and habitat connectivity on urban forest bird communities , 2015, Urban Ecosystems.

[20]  Xiaoye Liu,et al.  Airborne LiDAR for DEM generation: some critical issues , 2008 .

[21]  U. Franck,et al.  Zooming into temperature conditions in the city of Leipzig: how do urban built and green structures influence earth surface temperatures in the city? , 2014, The Science of the total environment.

[22]  Desheng Liu,et al.  Extracting Ground-Level DEM From SRTM DEM in Forest Environments Based on Mathematical Morphology , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Gordon M. Heisler,et al.  Effects of individual trees on the solar radiation climate of small buildings , 1986 .

[24]  Tobia Lakes,et al.  TanDEM-X for Large-Area Modeling of Urban Vegetation Height: Evidence from Berlin, Germany , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[25]  György Büttner,et al.  Implementation andachievements of CLC2006 , 2012 .

[26]  Jianping Wu,et al.  A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data , 2013, Remote. Sens..

[27]  Anna-Maria Poznanska.  Bestimmung von Gebäude-und Vegetationshöhen im Berliner Stadtgebiet , 2013 .

[28]  H. Gerhold,et al.  CARBON STORAGE BY URBAN TREE CULTIVARS, IN ROOTS AND ABOVE GROUND , 2003 .

[29]  J. E. Wagner,et al.  Urban forests and pollution mitigation: analyzing ecosystem services and disservices. , 2011, Environmental pollution.

[30]  Michael G. Wing,et al.  Airborne Light Detection and Ranging (LiDAR) for Individual Tree Stem Location, Height, and Biomass Measurements , 2011, Remote. Sens..

[31]  T. Koellner,et al.  The Role of Vegetation in Mitigating Urban Land Surface Temperatures: A Case Study of Munich, Germany during the Warm Season , 2015 .

[32]  D. Lindenmayer,et al.  Large trees are keystone structures in urban parks , 2012 .

[33]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[34]  Jinqiang Cui,et al.  UAV LiDAR for below-canopy forest surveys , 2013 .

[35]  T. Oguchi,et al.  3.6 Digital Terrain Modeling , 2013 .

[36]  Christine Bertram,et al.  The role of urban green space for human well-being , 2015 .

[37]  Stewarta,et al.  URban Biotopes of Aotearoa New Zealand ( URBANZ ) II : Floristics , biodiversity and conservation values of urban residential and public woodlands , Christchurch , 2010 .

[39]  S. Pauleit,et al.  Crown size and growing space requirement of common tree species in urban centres, parks, and forests , 2015 .

[40]  Andreas Schmitt,et al.  THE GLOBAL TANDEM-X DEM: PRODUCTION STATUS AND FIRST VALIDATION RESULTS , 2012 .

[41]  Mathias Schardt,et al.  Forest Assessment Using High Resolution SAR Data in X-Band , 2011, Remote. Sens..

[42]  Fredrik Lindberg,et al.  The influence of vegetation and building morphology on shadow patterns and mean radiant temperatures in urban areas: model development and evaluation , 2011 .

[43]  Lara A. Roman,et al.  Long-term monitoring of Sacramento Shade program trees: Tree survival, growth and energy-saving performance , 2015 .

[44]  Gerhard Krieger,et al.  Coherence evaluation of TanDEM-X interferometric data , 2012 .

[45]  Thomas Esch,et al.  A novel method for building height estmation using TanDEM-X data , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[46]  Y. Imai,et al.  TREE-HEIGHT MEASURING CHARACTERISTICS OF URBAN FORESTS BY LIDAR DATA DIFFERENT IN RESOLUTION , 2004 .

[47]  Heiko Balzter,et al.  Validation of the TanDEM-X Intermediate Digital Elevation Model With Airborne LiDAR and Differential GNSS in Kruger National Park , 2016, IEEE Geoscience and Remote Sensing Letters.

[48]  Jaime Hueso Gonzalez,et al.  TanDEM-X: A satellite formation for high-resolution SAR interferometry , 2007 .

[49]  Patrick Hostert,et al.  Urban vegetation classification: Benefits of multitemporal RapidEye satellite data , 2013 .

[50]  Hans-Peter Kriegel,et al.  LOF: identifying density-based local outliers , 2000, SIGMOD '00.

[51]  Irena Hajnsek,et al.  Tropical-Forest-Parameter Estimation by Means of Pol-InSAR: The INDREX-II Campaign , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[52]  P. Cariñanos,et al.  Estimating the allergenic potential of urban green spaces: A case-study in Granada, Spain , 2014 .

[53]  S. Croci,et al.  Small urban woodlands as biodiversity conservation hot-spot: a multi-taxon approach , 2008, Landscape Ecology.

[54]  Dagmar Haase,et al.  Above-ground carbon storage by urban trees in Leipzig, Germany: Analysis of patterns in a European city , 2012 .

[55]  Erik Næsset,et al.  Deriving forest monitoring variables from X-band InSAR SRTM height , 2010 .

[56]  Jian Kang,et al.  Evaluation of road traffic noise abatement by vegetation treatment in a 1:10 urban scale model. , 2015, The Journal of the Acoustical Society of America.

[57]  Iain H. Woodhouse,et al.  Forest height retrieval from commercial X-band SAR products , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[58]  Gerhard Krieger,et al.  Forest/Non-Forest Classification from TanDEM-X Interferometric Data by means of Multiple Fuzzy Clustering , 2016 .

[59]  Hannes Taubenböck,et al.  Normalization of TanDEM-X DSM Data in Urban Environments With Morphological Filters , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[60]  Tobia Lakes,et al.  Using Airborne LiDAR and QuickBird Data for Modelling Urban Tree Carbon Storage and Its Distribution - A Case Study of Berlin , 2014, Remote. Sens..