3D Point Cloud Data in Conveying Information for Local Green Factor Assessment
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Hannu Hyyppä | Juho-Pekka Virtanen | Matti Vaaja | Arttu Julin | Heikki Kauhanen | Kaisa Jaalama | Aino Keitaanniemi | Toni Rantanen | Matias Ingman | Marika Ahlavuo | H. Hyyppä | M. Vaaja | Juho-Pekka Virtanen | Kaisa Jaalama | A. Julin | Marika Ahlavuo | H. Kauhanen | M. Ingman | T. Rantanen | Aino Keitaanniemi
[1] K. Gaston,et al. Ecological connectivity in the three-dimensional urban green volume using waveform airborne lidar , 2017, Scientific Reports.
[2] Gherardo Chirici,et al. Modeling Mediterranean forest structure using airborne laser scanning data , 2017, Int. J. Appl. Earth Obs. Geoinformation.
[3] J. Niemelä,et al. Impacts of residential infilling on private gardens in the Helsinki Metropolitan Area , 2017 .
[4] J. Hyyppä,et al. Automatic detection of harvested trees and determination of forest growth using airborne laser scanning , 2004 .
[5] V. Gundersen,et al. Urban nature in a time of crisis: recreational use of green space increases during the COVID-19 outbreak in Oslo, Norway , 2020 .
[6] Alireza Shams,et al. Automated street tree inventory using mobile LiDAR point clouds based on Hough transform and active contours , 2021 .
[7] Nora Fagerholm,et al. Outdoor recreation and nature’s contribution to well-being in a pandemic situation - Case Turku, Finland , 2021, Urban Forestry & Urban Greening.
[8] Juha Hyyppä,et al. Comparison of Laser and Stereo Optical, SAR and InSAR Point Clouds from Air- and Space-Borne Sources in the Retrieval of Forest Inventory Attributes , 2015, Remote. Sens..
[9] Jean-Michel Guldmann,et al. Creating 3D city models with building footprints and LIDAR point cloud classification: A machine learning approach , 2019, Comput. Environ. Urban Syst..
[10] Christophe Girot,et al. Point cloud modeling as a bridge between landscape design and planning , 2020 .
[11] Joanne C. White,et al. Estimating Changes in Forest Attributes and Enhancing Growth Projections: a Review of Existing Approaches and Future Directions Using Airborne 3D Point Cloud Data , 2021, Current Forestry Reports.
[12] Joanne C. White,et al. Comparing ALS and Image-Based Point Cloud Metrics and Modelled Forest Inventory Attributes in a Complex Coastal Forest Environment , 2015 .
[13] J. Schipperijn,et al. Tools for mapping social values of urban woodlands and other green areas , 2007 .
[14] Jacob B. Slyder,et al. Individual tree segmentation from a leaf-off photogrammetric point cloud , 2018 .
[15] Ulrika K. Stigsdotter,et al. The relation between perceived sensory dimensions of urban green space and stress restoration , 2010 .
[16] Juudit Ottelin,et al. Carbon sequestration and storage potential of urban green in residential yards: A case study from Helsinki , 2021, Urban Forestry & Urban Greening.
[17] Alessandro Rigolon,et al. “We're not in the business of housing:” Environmental gentrification and the nonprofitization of green infrastructure projects , 2018, Cities.
[18] Fang Qiu,et al. Individual Tree Segmentation from LiDAR Point Clouds for Urban Forest Inventory , 2015, Remote. Sens..
[19] Jing Zhang,et al. One-Class Classification of Airborne LiDAR Data in Urban Areas Using a Presence and Background Learning Algorithm , 2017, Remote. Sens..
[20] Andreas Baumann,et al. Comparison of Three Innovative Technologies for 3D-Acquisition, Modelling, and Visualization of an Underground Mine , 2018 .
[21] Ziyue Chen,et al. Urban landscape pattern analysis based on 3D landscape models , 2014 .
[22] Juha Hyyppä,et al. Evaluating the Quality of TLS Point Cloud Colorization , 2020, Remote. Sens..
[23] Juha Hyyppä,et al. Under-canopy UAV laser scanning for accurate forest field measurements , 2020, ISPRS Journal of Photogrammetry and Remote Sensing.
[24] Naser El-Sheimy,et al. Using an Unmanned Aerial Vehicle-Based Digital Imaging System to Derive a 3D Point Cloud for Landslide Scarp Recognition , 2016, Remote. Sens..
[25] Diego González-Aguilera,et al. Use of a Wearable Mobile Laser System in Seamless Indoor 3D Mapping of a Complex Historical Site , 2018, Remote. Sens..
[26] M. Vastaranta,et al. Terrestrial laser scanning in forest inventories , 2016 .
[27] Juha Hyyppä,et al. Factors Affecting Object-Oriented Forest Growth Estimates Obtained Using Laser Scanning , 2003 .
[28] Lincoln R. Larson,et al. Outdoor Activity Participation Improves Adolescents’ Mental Health and Well-Being during the COVID-19 Pandemic , 2021, International journal of environmental research and public health.
[29] J. Wolch,et al. Urban green space, public health, and environmental justice: The challenge of making cities ‘just green enough’ , 2014 .
[30] T. Lakes,et al. Integrating the third dimension into the concept of urban ecosystem services: A review , 2017 .
[31] Valentina Bonora,et al. Examination of Indoor Mobile Mapping Systems in a Diversified Internal/External Test Field , 2018 .
[32] Kavisha Kumar,et al. Utilities of Virtual 3D City Models Based on CityGML: Various Use Cases , 2018, Journal of the Indian Society of Remote Sensing.
[33] Yoshifumi Miyazaki,et al. Physiological and psychological effects of viewing urban forest landscapes assessed by multiple measurements , 2013 .
[34] M. Vastaranta,et al. Revealing Changes in the Stem Form and Volume Allocation in Diverse Boreal Forests Using Two-Date Terrestrial Laser Scanning , 2021, Forests.
[35] J. Bush,et al. Integrating Green Infrastructure into Urban Planning: Developing Melbourne’s Green Factor Tool , 2021, Urban Planning.
[36] M. Keeley. The Green Area Ratio: an urban site sustainability metric , 2011 .
[37] Carlos Cabo,et al. An algorithm for automatic detection of pole-like street furniture objects from Mobile Laser Scanner point clouds , 2014 .
[38] Thomas P. Kersten,et al. Methods for Geometric Accuracy Investigations of Terrestrial Laser Scanning Systems , 2009 .
[39] Baohua Zhang,et al. Mobile LiDAR Scanning System Combined with Canopy Morphology Extracting Methods for Tree Crown Parameters Evaluation in Orchards , 2021, Sensors.
[40] François Jonard,et al. Estimating Forest Structure from UAV-Mounted LiDAR Point Cloud Using Machine Learning , 2021, Remote. Sens..
[41] S. Juhola. Planning for a green city: The Green Factor tool , 2018, Urban Forestry & Urban Greening.
[43] Michael Weinmann,et al. A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas , 2017, Remote. Sens..
[44] Guido Morgenthal,et al. Quality Assessment of Unmanned Aerial Vehicle (UAV) Based Visual Inspection of Structures , 2014 .
[45] B. Henricot,et al. The domestic garden – Its contribution to urban green infrastructure , 2012 .
[46] Juha Hyyppä,et al. Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging , 2017, Remote. Sens..
[47] Bisheng Yang,et al. Dense 3D surface reconstruction of large-scale streetscape from vehicle-borne imagery and LiDAR , 2020, Int. J. Digit. Earth.
[48] Juha Hyyppä,et al. An International Comparison of Individual Tree Detection and Extraction Using Airborne Laser Scanning , 2012, Remote. Sens..
[49] K. Gaston,et al. Urban domestic gardens (X): the extent & structure of the resource in five major cities , 2007, Landscape Ecology.
[50] Roland Siegwart,et al. Challenging data sets for point cloud registration algorithms , 2012, Int. J. Robotics Res..
[51] C. Ordóñez,et al. Private tree removal, public loss: Valuing and enforcing existing tree protection mechanisms is the key to retaining urban trees on private land , 2020 .
[52] Michael W. Strohbach,et al. Challenges of urban green space management in the face of using inadequate data , 2017 .
[53] Perry Hystad,et al. Evaluating street view exposure measures of visible green space for health research , 2019, Journal of Exposure Science & Environmental Epidemiology.
[54] F. Nex,et al. UAV for 3D mapping applications: a review , 2014 .
[55] Terje Gobakken,et al. Assessing 3D point clouds from aerial photographs for species-specific forest inventories , 2017 .