24.4.13.2 Urban Trees, Individual Trees, Street Trees

Chapter Contents (Back)
Trees. Urban Trees. Street Trees.
See also Trees, Individual Trees.
See also Trees, Forest, Stem Volume, Aboveground 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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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.H.[Hai-Hong], 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
BibRef

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.Y.[Hai-Yan], Zai, D., Wang, C.,
Automated Extraction of 3D Trees from Mobile LiDAR Point Clouds,
CloseRange14(629-632).
DOI Link 1411
BibRef

Le Louarn, M.[Marine], 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
BibRef

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
Award, U.V. Helava, ISPRS. 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
BibRef

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.L.[Wang-Lin],
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
BibRef

Hu, R.H.[Rong-Hai], Bournez, E.[Elena], Cheng, S.Y.[Shi-Yu], Jiang, H.[Hailan], Nerry, F.[Françoise], Landes, T.[Tania], Saudreau, M.[Marc], Kastendeuch, P.[Pierre], Najjar, G.[Georges], Colin, J.[Jérôme], Yan, G.J.[Guang-Jian],
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
BibRef

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
BibRef

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

Zhang, R.[Rong], Chen, J.Q.[Ji-Quan], Park, H.[Hogeun], Zhou, X.[Xuhui], Yang, X.C.[Xu-Chao], Fan, P.L.[Pei-Lei], Shao, C.L.[Chang-Liang], Ouyang, Z.[Zutao],
Spatial Accessibility of Urban Forests in the Pearl River Delta (PRD), China,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Li, X.[Xun], Chen, W.Y.[Wendy Y.], Sanesi, G.[Giovanni], Lafortezza, R.[Raffaele],
Remote Sensing in Urban Forestry: Recent Applications and Future Directions,
RS(11), No. 10, 2019, pp. xx-yy.
DOI Link 1906
BibRef

Brabant, C.[Charlotte], Alvarez-Vanhard, E.[Emilien], Laribi, A.[Achour], Morin, G.[Gwénaël], Nguyen, K.T.[Kim Thanh], Thomas, A.[Alban], Houet, T.[Thomas],
Comparison of Hyperspectral Techniques for Urban Tree Diversity Classification,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link 1906
BibRef

Stubbings, P.[Philip], Peskett, J.[Joe], Rowe, F.[Francisco], Arribas-Bel, D.[Dani],
A Hierarchical Urban Forest Index Using Street-Level Imagery and Deep Learning,
RS(11), No. 12, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Sanesi, G.[Giovanni], Giannico, V.[Vincenzo], Elia, M.[Mario], Lafortezza, R.[Raffaele],
Remote Sensing of Urban Forests,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Kranjcic, N.[Nikola], Medak, D.[Damir], Župan, R.[Robert], Rezo, M.[Milan],
Machine Learning Methods for Classification of the Green Infrastructure in City Areas,
IJGI(8), No. 10, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Gupta, A., Byrne, J., Moloney, D., Watson, S., Yin, H.,
Tree Annotations in LiDAR Data Using Point Densities and Convolutional Neural Networks,
GeoRS(58), No. 2, February 2020, pp. 971-981.
IEEE DOI 2001
Vegetation, Laser radar, Urban areas, Forestry, Training, Feature extraction, Airborne LiDAR, voxelization BibRef

Barbierato, E.[Elena], Bernetti, I.[Iacopo], Capecchi, I.[Irene], Saragosa, C.[Claudio],
Integrating Remote Sensing and Street View Images to Quantify Urban Forest Ecosystem Services,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link 2001
BibRef

Shen, W.J.[Wen-Juan], Mao, X.P.[Xu-Peng], He, J.Y.[Jia-Ying], Dong, J.[Jinwei], Huang, C.Q.[Cheng-Quan], Li, M.S.[Ming-Shi],
Understanding Current and Future Fragmentation Dynamics of Urban Forest Cover in the Nanjing Laoshan Region of Jiangsu, China,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link 2001
BibRef

Laumer, D.[Daniel], Lang, N.[Nico], van Doorn, N.[Natalie], Mac Aodha, O.[Oisin], Perona, P.[Pietro], Wegner, J.D.[Jan Dirk],
Geocoding of trees from street addresses and street-level images,
PandRS(162), 2020, pp. 125-136.
Elsevier DOI 2004
Geocoding, Global optimization, Deep learning, Image interpretation, Object detection, Faster R-CNN, Google Street View BibRef

Wegner, J.D., Branson, S., Hall, D., Schindler, K., Perona, P.,
Cataloging Public Objects Using Aerial and Street-Level Images: Urban Trees,
CVPR16(6014-6023)
IEEE DOI 1612
BibRef

Blackman, R.[Raoul], Yuan, F.[Fei],
Detecting Long-Term Urban Forest Cover Change and Impacts of Natural Disasters Using High-Resolution Aerial Images and LiDAR Data,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Maksimainen, M.[Mikko], Vaaja, M.T.[Matti T.], Kurkela, M.[Matti], Virtanen, J.P.[Juho-Pekka], Julin, A.[Arttu], Jaalama, K.[Kaisa], Hyyppä, H.[Hannu],
Nighttime Mobile Laser Scanning and 3D Luminance Measurement: Verifying the Outcome of Roadside Tree Pruning with Mobile Measurement of the Road Environment,
IJGI(9), No. 7, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Xu, J.Z.[Jing-Zhong], Shan, J.[Jie], Wang, G.[Ge],
Hierarchical Modeling of Street Trees Using Mobile Laser Scanning,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Katz, D.S.W.[Daniel S. W.], Batterman, S.A.[Stuart A.], Brines, S.J.[Shannon J.],
Improved Classification of Urban Trees Using a Widespread Multi-Temporal Aerial Image Dataset,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Chi, D.[Dengkai], Degerickx, J.[Jeroen], Yu, K.[Kang], Somers, B.[Ben],
Urban Tree Health Classification Across Tree Species by Combining Airborne Laser Scanning and Imaging Spectroscopy,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Man, Q.X.[Qi-Xia], Dong, P.L.[Pin-Liang], Yang, X.M.[Xin-Ming], Wu, Q.Y.[Quan-Yuan], Han, R.Q.[Rong-Qing],
Automatic Extraction of Grasses and Individual Trees in Urban Areas Based on Airborne Hyperspectral and LiDAR Data,
RS(12), No. 17, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Timilsina, S.[Shirisa], Aryal, J.[Jagannath], Kirkpatrick, J.B.[Jamie B.],
Mapping Urban Tree Cover Changes Using Object-Based Convolution Neural Network (OB-CNN),
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Krucek, M.[Martin], Král, K.[Kamil], Cushman, K.[KC], Missarov, A.[Azim], Kellner, J.R.[James R.],
Supervised Segmentation of Ultra-High-Density Drone Lidar for Large-Area Mapping of Individual Trees,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Wang, Y.J.[Yong-Jun], Jiang, T.P.[Teng-Ping], Liu, J.[Jing], Li, X.R.[Xiao-Rui], Liang, C.[Chong],
Hierarchical Instance Recognition of Individual Roadside Trees in Environmentally Complex Urban Areas from UAV Laser Scanning Point Clouds,
IJGI(9), No. 10, 2020, pp. xx-yy.
DOI Link 2010
BibRef

He, S.B.[Shao-Bai], Du, H.Q.[Hua-Qiang], Zhou, G.[Guomo], Li, X.J.[Xue-Jian], Mao, F.J.[Fang-Jie], Zhu, D.[Di'en], Xu, Y.X.[Yan-Xin], Zhang, M.[Meng], Huang, Z.[Zihao], Liu, H.[Hua], Luo, X.[Xin],
Intelligent Mapping of Urban Forests from High-Resolution Remotely Sensed Imagery Using Object-Based U-Net-DenseNet-Coupled Network,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Wang, Y.T.[Yu-Tang], Wang, J.[Jia], Chang, S.P.[Shu-Ping], Sun, L.[Lu], An, L.[Likun], Chen, Y.H.[Yu-Han], Xu, J.Q.[Jiang-Qi],
Classification of Street Tree Species Using UAV Tilt Photogrammetry,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Safaie, A.H.[Amir Hossein], Rastiveis, H.[Heidar], Shams, A.[Alireza], Sarasua, W.A.[Wayne A.], Li, J.[Jonathan],
Automated Street Tree Inventory Using Mobile LiDAR Point Clouds Based on Hough Transform and Active Contours,
PandRS(174), 2021, pp. 19-34.
Elsevier DOI 2103
Trees inventory, Mobile LiDAR, Point clouds, Hough transform, Active contour, Road safety BibRef

Przewozna, P.[Patrycja], Hawrylo, P.[Pawel], Zieba-Kulawik, K.[Karolina], Inglot, A.[Adam], Maczka, K.[Krzysztof], Wezyk, P.[Piotr], Matczak, P.[Piotr],
Use of Bi-Temporal ALS Point Clouds for Tree Removal Detection on Private Property in Racibórz, Poland,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Luo, H.F.[Hai-Feng], Khoshelham, K.[Kourosh], Chen, C.C.[Chong-Cheng], He, H.X.[Han-Xian],
Individual Tree Extraction from Urban Mobile Laser Scanning Point Clouds Using Deep Pointwise Direction Embedding,
PandRS(175), 2021, pp. 326-339.
Elsevier DOI 2105
Mobile laser scanning point clouds, Individual tree extraction, Semantic segmentation, Deep learning
See also Automatic Extraction of Roadside Traffic Facilities From Mobile Laser Scanning Point Clouds Based on Deep Belief Network. BibRef

Lumnitz, S.[Stefanie], Devisscher, T.[Tahia], Mayaud, J.R.[Jerome R.], Radic, V.[Valentina], Coops, N.C.[Nicholas C.], Griess, V.C.[Verena C.],
Mapping trees along urban street networks with deep learning and street-level imagery,
PandRS(175), 2021, pp. 144-157.
Elsevier DOI 2105
Deep learning, Instance segmentation, Monocular depth estimation, Street-level images, Urban forest management BibRef

Wang, Z.[Zhe], Fan, C.[Chao], Xian, M.[Min],
Application and Evaluation of a Deep Learning Architecture to Urban Tree Canopy Mapping,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Kim, A.R.[A Reum], Lim, C.H.[Chi Hong], Lim, B.S.[Bong Soon], Seol, J.W.[Jae-Won], Lee, C.S.[Chang Seok],
Phenological Changes of Mongolian Oak Depending on the Micro-Climate Changes Due to Urbanization,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Abbas, S.[Sawaid], Peng, Q.[Qian], Wong, M.S.[Man Sing], Li, Z.L.[Zhi-Lin], Wang, J.[Jicheng], Ng, K.T.K.[Kathy Tze Kwun], Kwok, C.Y.T.[Coco Yin Tung], Hui, K.K.W.[Karena Ka Wai],
Characterizing and classifying urban tree species using bi-monthly terrestrial hyperspectral images in Hong Kong,
PandRS(177), 2021, pp. 204-216.
Elsevier DOI 2106
Urban tree, Hyperspectral library, Tree species, Seasonality, Deep learning, SPECIM-IQ BibRef

Jiang, F.[Fugen], Chen, C.[Chuanshi], Li, C.J.[Cheng-Jie], Kutia, M.[Mykola], Sun, H.[Hua],
A Novel Spatial Simulation Method for Mapping the Urban Forest Carbon Density in Southern China by the Google Earth Engine,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Martins, J.A.C.[José Augusto Correa], Nogueira, K.[Keiller], Osco, L.P.[Lucas Prado], Gomes, F.D.G.[Felipe David Georges], Furuya, D.E.G.[Danielle Elis Garcia], Gonçalves, W.N.[Wesley Nunes], Sant'Ana, D.A.[Diego André], Ramos, A.P.M.[Ana Paula Marques], Liesenberg, V.[Veraldo], dos Santos, J.A.[Jefersson Alex], de Oliveira, P.T.S.[Paulo Tarso Sanches], Junior, J.M.[José Marcato],
Semantic Segmentation of Tree-Canopy in Urban Environment with Pixel-Wise Deep Learning,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

You, H.K.[Hang-Kai], Li, S.H.[Shi-Hua], Xu, Y.F.[Yi-Fan], He, Z.[Ze], Wang, D.[Di],
Tree Extraction from Airborne Laser Scanning Data in Urban Areas,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Mngadi, M.[Mthembeni], Odindi, J.[John], Mutanga, O.[Onisimo],
The Utility of Sentinel-2 Spectral Data in Quantifying Above-Ground Carbon Stock in an Urban Reforested Landscape,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Velasquez-Camacho, L.[Luisa], Cardil, A.[Adrián], Mohan, M.[Midhun], Etxegarai, M.[Maddi], Anzaldi, G.[Gabriel], de-Miguel, S.[Sergio],
Remotely Sensed Tree Characterization in Urban Areas: A Review,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Hu, T.Y.[Tian-Yu], Wei, D.J.[Deng-Jie], Su, Y.J.[Yan-Jun], Wang, X.D.[Xu-Dong], Zhang, J.[Jing], Sun, X.L.[Xi-Liang], Liu, Y.[Yu], Guo, Q.H.[Qing-Hua],
Quantifying the shape of urban street trees and evaluating its influence on their aesthetic functions based on mobile lidar data,
PandRS(184), 2022, pp. 203-214.
Elsevier DOI 2202
Mobile mapping system, Street tree, Shape, Aesthetical value, Greenness BibRef

Schmohl, S.[Stefan], Vallejo, A.N.[Alejandra Narváez], Soergel, U.[Uwe],
Individual Tree Detection in Urban ALS Point Clouds with 3D Convolutional Networks,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
BibRef

Yue, N.[Ning], Zhang, Z.X.[Zhen-Xin], Jiang, S.[Shan], Chen, S.[Siyun],
Deep Feature Migration for Real-Time Mapping of Urban Street Shading Coverage Index Based on Street-Level Panorama Images,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Qin, L.[Longjun], Mao, P.[Peng], Xu, Z.B.[Zhen-Bang], He, Y.[Yang], Yan, C.H.[Chun-Hua], Hayat, M.[Muhammad], Qiu, G.Y.[Guo-Yu],
Accurate Measurement and Assessment of Typhoon-Related Damage to Roadside Trees and Urban Forests Using the Unmanned Aerial Vehicle,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Guo, Q.[Qian], Zhang, J.[Jian], Guo, S.J.[Shi-Jie], Ye, Z.X.[Zhang-Xi], Deng, H.[Hui], Hou, X.L.[Xiao-Long], Zhang, H.[Houxi],
Urban Tree Classification Based on Object-Oriented Approach and Random Forest Algorithm Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Li, X.C.[Xiang-Cai], Tian, J.Y.[Jin-Yan], Li, X.J.[Xiao-Juan], Wang, L.[Le], Gong, H.[Huili], Shi, C.[Chen], Nie, S.[Sheng], Zhu, L.[Lin], Chen, B.B.[Bei-Bei], Pan, Y.[Yun], He, J.[Jijun], Ni, R.G.[Rong-Guang], Diao, C.Y.[Chun-Yuan],
Developing a sub-meter phenological spectral feature for mapping poplars and willows in urban environment,
PandRS(193), 2022, pp. 77-89.
Elsevier DOI 2210
Urban, Tree species classification, Phenology, Sub-meter, Multi-scale, Deep learning BibRef

Yan, J.[Jin], Chen, Y.Y.[Yuan-Yuan], Zheng, J.Z.[Jia-Zhu], Guo, L.[Lin], Zheng, S.Q.[Si-Qi], Zhang, R.C.[Rong-Chun],
Multi-Source Time Series Remote Sensing Feature Selection and Urban Forest Extraction Based on Improved Artificial Bee Colony,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Hua, Z.[Zhouyang], Xu, S.[Sheng], Liu, Y.G.[Yin-Gan],
Individual Tree Segmentation from Side-View LiDAR Point Clouds of Street Trees Using Shadow-Cut,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Li, Z.Y.[Zhi-Yuan], Wang, J.[Jian], Zhang, Z.Y.[Zhen-Yu], Jin, F.X.[Feng-Xiang], Yang, J.T.[Jun-Tao], Sun, W.X.[Wen-Xiao], Cao, Y.[Yi],
A Method Based on Improved iForest for Trunk Extraction and Denoising of Individual Street Trees,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Liu, Y.[Yang], Zhang, H.Q.[Huai-Qing], Cui, Z.[Zeyu], Lei, K.[Kexin], Zuo, Y.Q.[Yuan-Qing], Wang, J.[Jiansen], Hu, X.T.[Xing-Tao], Qiu, H.Q.[Han-Qing],
Very High Resolution Images and Superpixel-Enhanced Deep Neural Forest Promote Urban Tree Canopy Detection,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Shi, S.[Shuo], Tang, X.T.[Xing-Tao], Chen, B.[Bowen], Chen, B.[Biwu], Xu, Q.[Qian], Bi, S.[Sifu], Gong, W.[Wei],
Point Cloud Data Processing Optimization in Spectral and Spatial Dimensions Based on Multispectral Lidar for Urban Single-Wood Extraction,
IJGI(12), No. 3, 2023, pp. xx-yy.
DOI Link 2303
LiDAR processing to deal with large height variations with the tree. BibRef

Guo, J.H.[Jian-Hua], Xu, Q.S.[Qing-Song], Zeng, Y.[Yue], Liu, Z.H.[Zhi-Heng], Zhu, X.X.[Xiao Xiang],
Nationwide urban tree canopy mapping and coverage assessment in Brazil from high-resolution remote sensing images using deep learning,
PandRS(198), 2023, pp. 1-15.
Elsevier DOI 2304
Urban tree canopy, Brazil, Remote sensing, Semi-supervised learning, Urban ecosystem services BibRef

Hyyppä, E.[Eric], Manninen, P.[Petri], Maanpää, J.[Jyri], Taher, J.[Josef], Litkey, P.[Paula], Hyyti, H.[Heikki], Kukko, A.[Antero], Kaartinen, H.[Harri], Ahokas, E.[Eero], Yu, X.W.[Xiao-Wei], Muhojoki, J.[Jesse], Lehtomäki, M.[Matti], Virtanen, J.P.[Juho-Pekka], Hyyppä, J.[Juha],
Can the Perception Data of Autonomous Vehicles Be Used to Replace Mobile Mapping Surveys: A Case Study Surveying Roadside City Trees,
RS(15), No. 7, 2023, pp. 1790.
DOI Link 2304
BibRef

Yankovich, E.P.[Elena Petrovna], Yankovich, K.S.[Ksenia Stanislavovna], Baranovskiy, N.V.[Nikolay Viktorovich],
Dynamics of Forest Vegetation in an Urban Agglomeration Based on Landsat Remote Sensing Data for the Period 1990-2022: A Case Study,
RS(15), No. 7, 2023, pp. 1935.
DOI Link 2304
BibRef

Wang, P.C.[Peng-Cheng], Tang, Y.[Yong], Liao, Z.[Zefan], Yan, Y.[Yao], Dai, L.[Lei], Liu, S.[Shan], Jiang, T.[Tengping],
Road-Side Individual Tree Segmentation from Urban MLS Point Clouds Using Metric Learning,
RS(15), No. 8, 2023, pp. 1992.
DOI Link 2305
BibRef

Jiang, T.P.[Teng-Ping], Wang, Y.J.[Yong-Jun], Liu, S.[Shan], Zhang, Q.[Qinyu], Zhao, L.[Lin], Sun, J.[Jian],
Instance recognition of street trees from urban point clouds using a three-stage neural network,
PandRS(199), 2023, pp. 305-334.
Elsevier DOI 2305
Urban point cloud, Semi-supervised semantic segmentation, Individual tree segmentation, Tree modeling, Deep learning BibRef

Wang, M.[Meilian], Wong, M.S.[Man Sing],
Exploring Influences of Leaves on Urban Species Identification Using Handheld Laser Scanning Point Cloud: A Case Study in Hong Kong,
RS(15), No. 11, 2023, pp. 2826.
DOI Link 2306
BibRef

Wang, X.[Xuan], Xiang, H.Y.[Han-Yu], Niu, W.Y.[Wen-Yuan], Mao, Z.[Zhu], Huang, X.F.[Xian-Feng], Zhang, F.[Fan],
Oblique photogrammetry supporting procedural tree modeling in urban areas,
PandRS(200), 2023, pp. 120-137.
Elsevier DOI 2306
Oblique photogrammetry, Procedural modeling, Inverse procedural modeling, L-system, Parametric model, Metropolis-Hastings BibRef

Qin, H.M.[Hai-Ming], Wang, W.M.[Wei-Min], Yao, Y.[Yang], Qian, Y.G.[Yu-Guo], Xiong, X.Y.[Xiang-Yun], Zhou, W.Q.[Wei-Qi],
First Experience with Zhuhai-1 Hyperspectral Data for Urban Dominant Tree Species Classification in Shenzhen, China,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307
BibRef

Wu, H.[Hui], Zhuang, M.H.[Ming-Hao], Chen, Y.C.[Yuan-Chi], Meng, C.[Chen], Wu, C.Y.[Cai-Yan], Ouyang, L.[Linke], Liu, Y.H.[Yu-Han], Shu, Y.[Yi], Tao, Y.Z.[Yu-Zhong], Qiu, T.[Tong], Li, J.X.[Jun-Xiang],
Urban Treetop Detection and Tree-Height Estimation from Unmanned-Aerial-Vehicle Images,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link 2308
BibRef

An, S.M.[Seung Man],
A Study on Urban-Scale Building, Tree Canopy Footprint Identification and Sky View Factor Analysis with Airborne LiDAR Remote Sensing Data,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link 2308
BibRef

Javed, A.[Aisha], Kim, T.[Taeheon], Lee, C.[Changhui], Oh, J.[Jaehong], Han, Y.[Youkyung],
Deep Learning-Based Detection of Urban Forest Cover Change along with Overall Urban Changes Using Very-High-Resolution Satellite Images,
RS(15), No. 17, 2023, pp. 4285.
DOI Link 2310
BibRef

Fan, X.H.[Xiang-Hua], Chen, Z.W.[Zhi-Wei], Liu, P.L.[Pei-Lin], Pan, W.B.[Wen-Bo],
Simultaneous Vehicle Localization and Roadside Tree Inventory Using Integrated LiDAR-Inertial-GNSS System,
RS(15), No. 20, 2023, pp. 5057.
DOI Link 2310
BibRef

Gong, H.Y.[Hao-Yu], Sun, Q.[Qian], Fang, C.[Chenrong], Sun, L.[Le], Su, R.[Ran],
TreeDetector: Using Deep Learning for the Localization and Reconstruction of Urban Trees from High-Resolution Remote Sensing Images,
RS(16), No. 3, 2024, pp. 524.
DOI Link 2402
BibRef

Yang, Y.N.[Yi-Ning], Shen, X.[Xin], Cao, L.[Lin],
Estimation of the Living Vegetation Volume (LVV) for Individual Urban Street Trees Based on Vehicle-Mounted LiDAR Data,
RS(16), No. 10, 2024, pp. 1662.
DOI Link 2405
BibRef

Balestra, M.[Mattia], Choudhury, M.A.M.[MD Abdul Mueed], Pierdicca, R.[Roberto], Chiappini, S.[Stefano], Marcheggiani, E.[Ernesto],
UAV-Spherical Data Fusion Approach to Estimate Individual Tree Carbon Stock for Urban Green Planning and Management,
RS(16), No. 12, 2024, pp. 2110.
DOI Link 2406
BibRef

Zandler, H.[Harald], Samimi, C.[Cyrus],
Cooling Potential of Urban Tree Species during Extreme Heat and Drought: A Thermal Remote Sensing Assessment,
RS(16), No. 12, 2024, pp. 2059.
DOI Link 2406
BibRef

Kyaw, T.Y.[Thu Ya], Alonzo, M.[Michael], Baker, M.E.[Matthew E.], Eisenman, S.W.[Sasha W.], Caplan, J.S.[Joshua S.],
Predicting Urban Trees' Functional Trait Responses to Heat Using Reflectance Spectroscopy,
RS(16), No. 13, 2024, pp. 2291.
DOI Link 2407
BibRef

Wang, H.[Hexiang], Gong, F.Y.[Fang-Ying],
Quantifying City- and Street-Scale Urban Tree Phenology from Landsat-8, Sentinel-2, and PlanetScope Images: A Case Study in Downtown Beijing,
RS(16), No. 13, 2024, pp. 2351.
DOI Link 2407
BibRef

Stuart, W.[William], Azad Hossain, A.K.M., Hunt, N.[Nyssa], Mix, C.[Charles], Qin, H.[Hong],
Spatiotemporal Analysis of Urban Forest in Chattanooga, Tennessee from 1984 to 2021 Using Landsat Satellite Imagery,
RS(16), No. 13, 2024, pp. 2419.
DOI Link 2407
BibRef

Zhan, W.F.[Wen-Feng], Wang, C.L.[Chun-Li], Wang, S.[Shasha], Li, L.[Long], Ji, Y.Y.[Ying-Ying], Du, H.L.[Hui-Lin], Huang, F.[Fan], Jiang, S.[Sida], Liu, Z.[Zihan], Fu, H.Y.[Hu-Yan],
Fraction-dependent variations in cooling efficiency of urban trees across global cities,
PandRS(216), 2024, pp. 229-239.
Elsevier DOI 2408
Cooling efficiency, Cooling potential, Urban trees, Tree cover percentage, Population heat exposure BibRef

Oliveira, A.A.A.M.[Artur André A. M.], Wang, Z.Y.[Zhang-Yang], Hirata, R.[Roberto],
Locating Urban Trees near Electric Wires using Google Street View Photos: A New Dataset and A Semi-Supervised Learning Approach in the Wild,
UG22(4285-4293)
IEEE DOI 2210
Training, Protocols, Wires, Neural networks, Vegetation mapping, Vegetation, Cost function BibRef

Hirt, P.R., Hoegner, L., Stilla, U.,
A Concept for the Segmentation of Individual Urban Trees From Dense Mls Point Clouds,
ISPRS21(B2-2021: 171-178).
DOI Link 2201
BibRef