22.5.11.1 Urban Trees, Individual Trees, Street Trees

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Trees. Urban Trees. See also Trees, Individual Trees, Tree Crowns. See also Trees, Forest, Stem Volume, Biomass Measurements.

Zhang, Y.[Yun],
Texture-Integrated Classification of Urban Treed Areas in High-Resolution Color-Infrared Imagery,
PhEngRS(67), No. 12, December 2001, pp. 1359-1366. To effectively extract tree textural features and eliminate noise, use conditional variance detection. It consists of a directional variance detection and a local variance detection.
WWW Link. 0201
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Ouma, Y.O.[Yashon O.], Tateishi, R.,
Urban-trees extraction from Quickbird imagery using multiscale spectex-filtering and non-parametric classification,
PandRS(63), No. 3, May 2008, pp. 333-351.
Elsevier DOI 0711
Quickbird; Urban-trees; Multiscale texture; Multiscale spectex-filtering; Non-parametric classification BibRef

Ardila, J.P.[Juan P.], Tolpekin, V.A.[Valentyn A.], Bijker, W.[Wietske], Stein, A.[Alfred],
Markov-random-field-based super-resolution mapping for identification of urban trees in VHR images,
PandRS(66), No. 6, November 2011, pp. 762-775.
Elsevier DOI 1112
Image classification; Markov random field; Super resolution mapping; Urban trees; Contextual classification BibRef

Ardila, J.P.[Juan P.], Bijker, W.[Wietske], Tolpekin, V.A.[Valentyn A.], Stein, A.[Alfred],
Quantification of crown changes and change uncertainty of trees in an urban environment,
PandRS(74), No. 1, November 2012, pp. 41-55.
Elsevier DOI 1212
Change detection; Fuzzy change; Object change detection; Tree crown detection; Urban trees BibRef

Höfle, B.[Bernhard], Hollaus, M.[Markus], Hagenauer, J.[Julian],
Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data,
PandRS(67), No. 1, January 2012, pp. 134-147.
Elsevier DOI 1202
Laser scanning; LiDAR; Calibration; Vegetation; Object based image analysis; Full-waveform BibRef

Shrestha, R., Wynne, R.,
Estimating Biophysical Parameters of Individual Trees in an Urban Environment Using Small Footprint Discrete-Return Imaging Lidar,
RS(4), No. 2, February 2012, pp. 484-508.
DOI Link 1203
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Zhang, K., Hu, B.,
Individual Urban Tree Species Classification Using Very High Spatial Resolution Airborne Multi-Spectral Imagery Using Longitudinal Profiles,
RS(4), No. 6, June 2012, pp. 1741-1757.
DOI Link 1208
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Agarwal, S., Vailshery, L., Jaganmohan, M., Nagendra, H.,
Mapping Urban Tree Species Using Very High Resolution Satellite Imagery: Comparing Pixel-Based and Object-Based Approaches,
IJGI(2), No. 1, 2013, pp. 220-236.
DOI Link 1303
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Wu, B., Yu, B., Yue, W., Shu, S., Tan, W., Hu, C., Huang, Y., Wu, J., Liu, H.,
A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data,
RS(5), No. 2, February 2013, pp. 584-611.
DOI Link 1303
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Zhou, J.H.[Jian-Hua], Yu, B.[Bailang], Qin, J.[Jun],
Multi-Level Spatial Analysis for Change Detection of Urban Vegetation at Individual Tree Scale,
RS(6), No. 9, 2014, pp. 9086-9103.
DOI Link 1410
BibRef

Zhang, C.Y.[Cai-Yun], Zhou, Y.H.[Yu-Hong], Qiu, F.[Fang],
Individual Tree Segmentation from LiDAR Point Clouds for Urban Forest Inventory,
RS(7), No. 6, 2015, pp. 7892.
DOI Link 1507
BibRef

Li, D.[Dan], Ke, Y.H.[Ying-Hai], Gong, H.[Huili], Li, X.J.[Xiao-Juan],
Object-Based Urban Tree Species Classification Using Bi-Temporal WorldView-2 and WorldView-3 Images,
RS(7), No. 12, 2015, pp. 15861.
DOI Link 1601
BibRef

Li, L.[Lin], Li, D.[Dalin], Zhu, H.[Haihong], Li, Y.[You],
A dual growing method for the automatic extraction of individual trees from mobile laser scanning data,
PandRS(120), No. 1, 2016, pp. 37-52.
Elsevier DOI 1610
Individual tree BibRef

Guan, H.Y.[Hai-Yan], Cao, S., Yu, Y.T.[Yong-Tao], Li, J.[Jonathan], Liu, N., Chen, P., Li, Y.,
Street-Scene Tree Segmentation from Mobile Laser Scanning Data,
ISPRS16(B3: 221-225).
DOI Link 1610
BibRef

Li, Y.[You], Li, L.[Lin], Li, D.[Dalin], Yang, F.[Fan], Liu, Y.[Yu],
A Density-Based Clustering Method for Urban Scene Mobile Laser Scanning Data Segmentation,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705
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Yu, Y.T.[Yong-Tao], Li, J.[Jonathan], Guan, H.Y.[Hai-Yan], Wang, C.[Cheng], Wen, C.,
Bag of Contextual-Visual Words for Road Scene Object Detection From Mobile Laser Scanning Data,
ITS(17), No. 12, December 2016, pp. 3391-3406.
IEEE DOI 1612
Automobiles BibRef

Yu, Y.T.[Yong-Tao], Li, J.[Jonathan], Guan, H.Y.[Hai-Yan], Wang, C.[Cheng], Cheng, M.[Ming],
A Marked Point Process for Automated Tree Detection from Mobile Laser Scanning Point Cloud Data,
CVRS12(140-145).
IEEE DOI 1302
See also Automated Detection of Three-Dimensional Cars in Mobile Laser Scanning Point Clouds Using DBM-Hough-Forests. See also Traffic Sign Occlusion Detection Using Mobile Laser Scanning Point Clouds. BibRef

Yu, Y.T.[Yong-Tao], Li, J., Guan, H.[Haiyan], Zai, D., Wang, C.,
Automated Extraction of 3D Trees from Mobile LiDAR Point Clouds,
CloseRange14(629-632).
DOI Link 1411
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Louarn, M.L.[Marine Le], Clergeau, P.[Philippe], Briche, E.[Elodie], Deschamps-Cottin, M.[Magali],
'Kill Two Birds with One Stone': Urban Tree Species Classification Using Bi-Temporal Pléiades Images to Study Nesting Preferences of an Invasive Bird,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711
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Branson, S.[Steve], Wegner, J.D.[Jan Dirk], Hall, D.[David], Lang, N.[Nico], Schindler, K.[Konrad], Perona, P.[Pietro],
From Google Maps to a fine-grained catalog of street trees,
PandRS(135), No. Supplement C, 2018, pp. 13-30.
Elsevier DOI 1712
Deep learning, Image interpretation, Urban areas, Street trees, Very high resolution BibRef

Herfort, B.[Benjamin], Höfle, B.[Bernhard], Klonner, C.[Carolin],
3D micro-mapping: Towards assessing the quality of crowdsourcing to support 3D point cloud analysis,
PandRS(137), 2018, pp. 73-83.
Elsevier DOI 1802
LiDAR, Urban trees, Crowdsourcing, Point cloud classification, Quality BibRef

Zhang, Y.L.[Yong-Lin], Dong, R.C.[Ren-Cai],
Impacts of Street-Visible Greenery on Housing Prices: Evidence from a Hedonic Price Model and a Massive Street View Image Dataset in Beijing,
IJGI(7), No. 3, 2018, pp. xx-yy.
DOI Link 1804
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Lin, Y.[Yi], Jiang, M.[Miao],
A New Algorithm for MLS-Based DBH Mensuration and Its Preliminary Validation in an Urban Boreal Forest: Aiming at One Cornerstone of Allometry-Based Forest Biometrics,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806
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Adeline, K.R.M., Briottet, X., Ceamanos, X., Dartigalongue, T., Gastellu-Etchegorry, J.P.,
ICARE-VEG: A 3D physics-based atmospheric correction method for tree shadows in urban areas,
PandRS(142), 2018, pp. 311-327.
Elsevier DOI 1807
Atmospheric correction, Radiative transfer, Hyperspectral, High spatial resolution, Tree shadows, Urban areas BibRef

Singh, K.K.[Kunwar K.], Chen, Y.H.[Yin-Hsuen], Smart, L.[Lindsey], Gray, J.[Josh], Meentemeyer, R.K.[Ross K.],
Intra-annual phenology for detecting understory plant invasion in urban forests,
PandRS(142), 2018, pp. 151-161.
Elsevier DOI 1807
Biological invasion, Vegetation indices, Vegetation phenology, Normalized difference vegetation index, , Chinese privet, Random forest BibRef

Vahidi, H.[Hossein], Klinkenberg, B.[Brian], Johnson, B.A.[Brian A.], Moskal, L.M.[L. Monika], Yan, W.[Wanglin],
Mapping the Individual Trees in Urban Orchards by Incorporating Volunteered Geographic Information and Very High Resolution Optical Remotely Sensed Data: A Template Matching-Based Approach,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808
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Hu, R.H.[Rong-Hai], Bournez, E.[Elena], Cheng, S.[Shiyu], Jiang, H.[Hailan], Nerry, F.[Françoise], Landes, T.[Tania], Saudreau, M.[Marc], Kastendeuch, P.[Pierre], Najjar, G.[Georges], Colin, J.[Jérôme], Yan, G.[Guangjian],
Estimating the leaf area of an individual tree in urban areas using terrestrial laser scanner and path length distribution model,
PandRS(144), 2018, pp. 357-368.
Elsevier DOI 1809
Individual tree, Leaf area, Foliage area volume density, Terrestrial laser scanner, Urban areas, Path length distribution BibRef

Wu, J.W.[Jian-Wei], Yao, W.[Wei], Polewski, P.[Przemyslaw],
Mapping Individual Tree Species and Vitality along Urban Road Corridors with LiDAR and Imaging Sensors: Point Density versus View Perspective,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
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Mozgeris, G.[Gintautas], Juodkiene, V.[Vytaute], Jonikavicius, D.[Donatas], Straigyte, L.[Lina], Gadal, S.[Sébastien], Ouerghemmi, W.[Walid],
Ultra-Light Aircraft-Based Hyperspectral and Colour-Infrared Imaging to Identify Deciduous Tree Species in an Urban Environment,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
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Aval, J.[Josselin], Demuynck, J.[Jean], Zenou, E.[Emmanuel], Fabre, S.[Sophie], Sheeren, D.[David], Fauvel, M.[Mathieu], Adeline, K.[Karine], Briottet, X.[Xavier],
Detection of individual trees in urban alignment from airborne data and contextual information: A marked point process approach,
PandRS(146), 2018, pp. 197-210.
Elsevier DOI 1812
Street tree, Urban remote sensing, Airborne data, Geographic information system, Marked point process. BibRef

Gobeawan, L., Lin, E.S., Tandon, A., Yee, A.T.K., Khoo, V.H.S., Teo, S.N., Yi, S., Lim, C.W., Wong, S.T., Wise, D.J., Cheng, P., Liew, S.C., Huang, X., Li, Q.H., Teo, L.S., Fekete, G.S., Poto, M.T.,
Modeling Trees for Virtual Singapore: From Data Acquisition To Citygml Models,
GeoInfo18(55-62).
DOI Link 1901
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Hofierka, J., Gallay, M., Kanuk, J., Šupinský, J., Šašak, J.,
High-resolution Urban Greenery Mapping for Micro-climate Modelling Based On 3D City Models,
GeoInfo17(7-12).
DOI Link 1805
BibRef