XXII ISPRS Congress 2012: Technical Commission VIII

Shortis, M.; Shimoda, H.; Cho, K.

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Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663822514

Title:: Technical Commission VIII

Scope:: 590 Seiten

Year of publication:: 2014

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663822514

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B8)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Shortis, M.; Shimoda, H.; Cho, K.

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VIII/8: Land]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: CLASSIFICATION AND MODELLING OF URBAN MICRO-CLIMATES USING MULTISENSORAL AND MULTITEMPORAL REMOTE SENSING DATA B. Bechtel, T. Langkamp, J. Böhner, C. Daneke, J. Oßenbrügge, S. Schempp

Document type:: Multivolume work

Structure type:: Chapter

-B8, 2012 number 198 33 33 2 6 re) 22 ; 190 SE 484 es per set. e classification Structural types al to become a ndscapes series ach subdivided neters) surface or, fraction of class, surface enic heat flux) ate. Since the hern American d scheme was reference areas high resolution egories. Urban mer city with spires like bell nto the classes - buildings of and Terraced n rows. Blocks orm geometric ises high rise 15) consists of | of greening The industrial industrial or id Port (port) rage facilities t and gardens nd agricultural elevant for the measurement mburg during til the 29" of hn Hamburg r temperature 5 buses were of Driesen & onsiveness to to the fast K311 loggers ors, RFT325 shields. The GPS-loggers by Variotek. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia They were contained in a waterproof OtterBox 3000 case and mounted magnetically on the front roof, where the temperature influence of the bus itself was smallest (tested with surface- temperature-sensors at three positions of the roof). This construction allowed for continuous measuring for five to six days (5 seconds intervals for temperature and 20 meters intervals for position and velocity). Since the air temperature measurements can be contaminated by the roof temperature of the bus at low travelling speeds, data collected at velocities lower than 12 km/h were discarded in the post-processing. Then, the temperature data were linearly interpolated to a frequency of one second and matched with the according time stamps of the GPS data. To reduce the massive dataset, the single measurements were then averaged to one minute intervals and aggregated to an network of virtual stations with approximately 100m spacing (derived from the centers of all measurements within a regular 100 m-grid). Subsequently, the data were transferred to a PostgreSQL / PostGIS database and validated with data of 25 stationary measurement sites from various sources. The comparison of the mobile measurements with near stationary measurements (distance « 130m, +/- 2.5 minutes, n=108) revealed a satisfactory quality of the collected data with a mean difference between stationary and mobile measurements of -0.15 K and a mean absolute error of 0.51 K. The UHI was then calculated as the difference to stationary measurements from the Hamburg Weather Mast operated by the Meteorological Institute of the University of Hamburg. Although this data is likely to contain some urban effects, the offset to the ‘real’ UHI could be neglected for this study. To guarantee a minimum of comparability, ‘stations’ with less than 30 individual measurements were excluded from the subsequent analysis. For the remaining 1260 virtual stations the mean UHI was calculated from all individual measurements. The UHI data are shown in Figure 1. ndustr ;. IN mogcoe - terrace |. urbdens = urbcore : water forest Figure 1. Mean UHI data from the mobile measurement campaign with public transportation buses. Circle size indicates the number of measurements. 3. METHODS 3.1 Feature selection For feature selection the Minimum Redundancy Maximal Relevance approach (MRMR) approach was chosen, which was originally developed in bioinformatics for genome classification (Peng et al, 2005). The algorithm selects features that have both high relevance for classification of the target classes and low redundancy with the prior selected features; the distance between two features is defined by their mutual information. For this study the Mutual Information Quotient (MIQ) criterion was used. pG;. y,) ; (1) Ix. v) > v,)1 (x, y) X y,)log ges where I- mutual information X, y 7 features p(x, y) = joint probabilistic distribution p(x), p(y) = marginal probabilities 3.2 Classifiers Six supervised classifiers (implemented in the Waikato environment for knowledge analysis data mining package; Bouckaert et al., 2009) were used in this study. The Naive Bayes (NB) classifier assumes conditional independency, which reduces the posterior probability of class membership to the product of the estimate of the features’ marginal probabilities. Despite its simplicity it often delivers good results. The Support Vector Machine (SVM) classifier transfers the problem to pairwise classification in a higher dimensional space (Burges, 1998). The Multilayer Perceptron classifier is a feedforward artificial neural network (NN) composed by nodes (neurons) in connected layers. It is trained by a backpropagation algorithm. The Random Forest (RF) classifier utilises a number of tree-structured classifiers as committee to decide with majority and shows excellent classification performance and computing efficiency. The single trees are each generated from a random subset and therefore an ‘out of bag’ error can be estimated without any bias. The number of trees grown and the number of features used for each tree were varied and three configurations were tested. While RF1 worked with 10 trees, RF2 used 30 trees and 20 features, and RF3 50 trees and 30 features (see Bechtel and Daneke, 2012 for more detailed information). 3.3 Empirical models For the evaluation of the UHI data two different empirical models were used. For the Linear Regression (LR) model attributes were selected by the M5 method and collinear attributes were eliminated. The Multilayer Perceptron is again a neuronal networks trained by a backpropagation algorithm, but this time predicts a numerical value instead of class membership probabilities. 4. RESULTS 4.1 Classification of LCZ Table 2 shows the classification results for classifiers and feature sets. All numbers refer to the overall accuracy evaluated

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