Quantification of crown changes and change uncertainty of trees in an urban environment

Abstract Local authorities require a detailed report of the state of green resources in cities to quantify the benefits of urban trees and determine urban forestry interventions. This study uses bitemporal remote sensing data to monitor changes of urban trees over time. It presents a fuzzy approach to recognize the fuzziness of tree crowns from high resolution images in urban areas. The method identifies tree crown elliptical objects after iterative fitting of a Gaussian function to crown membership images of two dates. Gradual and abrupt changes are obtained, as well as a measure of change uncertainty for the retrieved objects. The method allows a dual treatment of change both as a crisp and as a fuzzy process. This is demonstrated in two residential areas in The Netherlands using a set of Quickbird and aerial images. Results show that the proposed method is informative of the changes at the object level, recognizes the fuzzy character and mixed-pixel effect of tree crowns in images and it provides useful information to end users on change uncertainty.

[1]  Rob J Hyndman,et al.  Detecting trend and seasonal changes in satellite image time series , 2010 .

[2]  C. Woodcock,et al.  The factor of scale in remote sensing , 1987 .

[3]  Knut Conradsen,et al.  Multivariate Alteration Detection (MAD) and MAF Postprocessing in Multispectral, Bitemporal Image Data: New Approaches to Change Detection Studies , 1998 .

[4]  Peter F. Fisher,et al.  Fuzzy sets , 1965 .

[5]  R. Myneni,et al.  Atmospheric effects and spectral vegetation indices , 1994 .

[6]  K. Soudani,et al.  Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands , 2006 .

[7]  Martien Molenaar,et al.  Fuzzy spatial objects and their dynamics , 2000 .

[8]  S. Liang.  Quantitative Remote Sensing of Land Surfaces , 2003 .

[9]  Frank Y. Shih,et al.  Image Processing and Mathematical Morphology: Fundamentals and Applications , 2017 .

[10]  Alfred Stein.  Modern developments in image mining , 2008 .

[11]  D. Lu,et al.  Change detection techniques , 2004 .

[12]  C. Brack.  Updating urban forest inventories: An example of the DISMUT model , 2006 .

[13]  Lorenzo Bruzzone,et al.  Detection of land-cover transitions by combining multidate classifiers , 2004, Pattern Recognit. Lett..

[14]  Max J. Egenhofer,et al.  Reasoning about Gradual Changes of Topological Relationships , 1992, Spatio-Temporal Reasoning.

[15]  Markus Schneider,et al.  VASA: An algebra for vague spatial data in databases , 2010, Inf. Syst..

[16]  Martine De Cock,et al.  Efficient Algorithms for Fuzzy Qualitative Temporal Reasoning , 2009, IEEE Transactions on Fuzzy Systems.

[17]  Thomas Behr,et al.  Topological relationships between complex spatial objects , 2006, TODS.

[18]  G. R. Johnson,et al.  Geospatial methods provide timely and comprehensive urban forest information , 2007 .

[19]  Mathias Schardt,et al.  Representation of an alpine treeline ecotone in SPOT 5 HRG data , 2007 .

[20]  P. Mather,et al.  Classification Methods for Remotely Sensed Data , 2001 .

[21]  Peter Lancaster,et al.  Curve and surface fitting - an introduction , 1986 .

[22]  Alex Hagen,et al.  Fuzzy set approach to assessing similarity of categorical maps , 2003, Int. J. Geogr. Inf. Sci..

[23]  E. Mcpherson,et al.  Quantifying urban forest structure, function, and value: the Chicago Urban Forest Climate Project , 1995, Urban Ecosystems.

[24]  P. Atkinson,et al.  Uncertainty in remote sensing and GIS , 2002 .

[25]  Didier Tanré,et al.  Atmospherically resistant vegetation index (ARVI) for EOS-MODIS , 1992, IEEE Trans. Geosci. Remote. Sens..

[26]  John R. Jensen,et al.  A change detection model based on neighborhood correlation image analysis and decision tree classification , 2005 .

[27]  David Rines,et al.  Measuring urban forestry performance and demographic associations in Massachusetts, USA , 2011 .

[28]  N. Coops,et al.  Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification , 2007 .

[29]  Alfred Stein,et al.  A system of types and operators for handling vague spatial objects , 2007, Int. J. Geogr. Inf. Sci..

[30]  Peng Gong,et al.  Using local transition probability models in Markov random fields for forest change detection , 2008 .

[31]  Alfred Stein,et al.  Markov-random-field-based super-resolution mapping for identification of urban trees in VHR images , 2011 .

[32]  Benjamin A. Stern,et al.  Interactive Data Language , 2000 .

[33]  Christian Heipke,et al.  Automatic extraction and delineation of single trees from remote sensing data , 2007, Machine Vision and Applications.

[34]  Warren B. Cohen,et al.  Trajectory-based change detection for automated characterization of forest disturbance dynamics , 2007 .

[35]  D. Leckie,et al.  The Individual Tree Crown Approach Applied to Ikonos Images of a Coniferous Plantation Area , 2006 .

[36]  Hui Lin,et al.  Remote sensing change detection based on canonical correlation analysis and contextual bayes decision , 2007 .

[37]  Mathias Schardt,et al.  Single tree detection in very high resolution remote sensing data , 2007 .

[38]  Morten Larsen,et al.  Optimizing templates for finding trees in aerial photographs , 1998, Pattern Recognit. Lett..

[39]  Naoto Matsumura,et al.  Seasonal patterns of canopy structure, biochemistry and spectral reflectance in a broad-leaved deciduous Fagus crenata canopy , 2002 .

[40]  김정훈,et al.  u-City 통합운영센터의 제도적 정착방안에 관한 연구; 기능 및 입지를 중심으로 , 2009 .

[41]  Giles M. Foody,et al.  Detection of partial land cover change associated with the migration of inter-class transitional zones , 1999 .

[42]  G. Foody.  Assessing the accuracy of land cover change with imperfect ground reference data , 2010 .