Introduction
The concept of urban forestry has been defined as “the art, science, and technique related to trees and forest resources’ management in urban and peri-urban areas in order to provide communities with psychological, sociological, economic, and aesthetical benefits” ([51]). According to this definition, the term urban forestry includes not only forests within urban areas, but also trees grown along roads, in parks, squares, and graveyards, among other places ([78], [39]). There are many benefits to human well-being that trees bring to our cities. Trees and other vegetation play an important role in reducing air pollution, thereby decreasing the incidence of respiratory diseases ([59]). Appropriate placement of trees can reduce “heat island” effects, helping urban communities adapt to the effects of climate change by reducing heat stress ([88], [110]). Access to parks and nature can have a positive influence on physical activity ([38]). Giles-Corti et al. ([22]) proved that people who use green spaces in cities attain recommended levels of physicals activity more easily than non-users. Stigsdotter et al. ([86]) found that people who lived beyond 1 km from a green zone (i.e., park) had a worse score on the dimensions of general and mental health and vitality, as well as higher levels of stress, than people who lived within 1 km of a green zone. Forest, parks and trees also influence real estate value and urban landscape aesthetics, and can play a role in storm water management and wind speed reduction.
As reported by Tate ([90]), and Bickmore & Hall ([11]), inventory is the tool that can be used for the efficient management of greening in urban areas at various scales. A tree inventory collects accurate information on the condition, diversity and spatial distribution of trees in urban areas. Inventory results may constitute a starting point for a number of different analyses, such as variations in tree structure and species within urban areas ([82]), how trees shape the urban microclimate ([60]), the impact of trees on reduction of air pollution and CO2 accumulation ([47], [61]), the impact of greening on the value of real estates ([4]), and monitoring the risk of damage to vegetation and vegetation health ([46], [40]).
Three types of inventory can be used to assess the vegetation in urban areas: partial, total, and random (statistical). Partial inventories are applied to selected urban areas, e.g., a park, a square or a tree avenue. A total inventory is a comprehensive description of all trees, groups of trees and bushes, including the identification of possible areas for new planting within the borders of streets, parks, squares, and other public spaces. Statistical methods are based on random selection and measure small parts of the area of interest, with sample plots being measured in the field ([18], [14]). The size of the field sampling area is selected in order to cover 5-10% of all trees or entire area, depending on spatial coverage and species variability. Inventories are usually carried out according to inventory key which include e.g. the list of tree species. Until now, inventories have been carried out mainly along roads, and less frequently for urban forests or parks ([91], [82]). The main aims of the studies conducted in urban environment were the maintenance of road safety, the appropriate selection of species for planting, and the monitoring of changes in urban forestry ([36]).
The varying aims of urban resources monitoring, as well as the diversity of data recipients, results in a large number of parameters being acquired for each single tree. For example, research carried out using the Delphi method (a method used to identify the most reliable responses to questions from a group of experts in Scandinavia - [66]) ended with 148 parameters assigned to single trees, all of which were considered to be important. Such a number of variables are difficult to acquire and harmonize for practical use. To increase ease of use, various attempts have been made to normalize inventory rules at the local or national level ([64], [97]). As a result, with the help of practitioners, the ten most important tree parameters have been defined: genus and species, health condition, trunk position, class of damage hazard, presence of fruit bodies, disease treatments, conservation value, location (i.e., street/park), age class, and finally, stem circumference at 1 meter height at time of planting ([66]). From an urban forest management perspective, information about tree height, crown parameters, and diameter at breast height (DBH) is also important ([80], [43]).
The high costs associated with inventory data collection, as well as the wide range of acquired parameters and data applications, have increased the interest in finding alternative methods to perform urban greening inventories ([58], [43]). To determine parameters for single trees or tree groups, or to acquire information regarding vegetation structure, remote sensing (RS) methods have been used with increasing frequency. The most commonly-used remote sensing methods include airborne (ALS), terrestrial (TLS) and mobile laser-scanning (MLS - [101], [89]), satellite imaging ([5]), aerial photography ([103]), and more recently unmanned aerial vehicle technology ([76]). RS data can be expensive to acquire, and each of the aforementioned technologies has limitations and advantages. The aims of this paper are therefore: (i) to present a systematic overview of scientific publications in which RS methods were used to determine the most important parameters of single trees, in particular: location, species, health condition, tree height, DBH, crown span (extent), height of tree crown base, and crown projection surface; (ii) to analyze the accuracy of individual parameters estimated through RS, and their viability as an alternative to field measurements; (iii) to specify which parameters of single trees can be determined using different spatial data, and how well they can be determined; (iv) to specify the opportunities and limitations associated with the various RS techniques; and (v) to identify further research directions in the subject area.
Material and methods
The review of international literature presented here is based on the search results of two databases: Scopus® and Science Direct®. The literature search was carried out pursuant to the guidelines formulated by Pullin & Stewart ([75]). Three keywords were defined in a browser window, i.e., urban*, forest *, and inventory*, which were searched for in categories such as title, keywords, and abstract. The search was conducted for papers published between January 2000 and February 2017, and 543 records were retrieved. Search results were refined based on title and abstract content. Only those articles concerned with tree inventories in urban environment were selected for further analysis. Detailed analysis of the selected articles showed that nine of the studies were conducted using only field measurements. These articles were also removed from the analysis ([15], [12], [16], [91], [83], [56], [14], [111], [87]). The final database included 86 scientific papers. The selected papers fulfilled the following criteria: the study was published in English, the scope of the study was urban forestry, and the main focus of the study related to greening inventory methods at the level of individual trees and tree groups. The following information was recorded in the metadata table constructed for the search results: journal name; year of publication; the country where the research was carried out; the inventory methodology; and a detailed synopsis of the study content.
Results
Overview of scientific papers
The analyzed studies were carried out in 25 countries on all continents, except Africa and Antarctica. Most papers were performed in the United States (38 papers), followed by Germany (9 papers), Finland (8 papers), and the United Kingdom (5 papers - Fig. 1). This distribution reflects the general number of studies performed on urban forestry worldwide. As reported by Bentsen et al. ([9]), in the principal journal in this subject area, Urban Forestry and Urban Greening, 59% of papers in the years 2000-2008 were performed in North America and Scandinavia.
Enlarge this image.Fig. 1 - Number of publications per country, retrieved in the Scopus® and ScienceDirect® databases. (*): The number of papers published in Polish magazines was 12.
The number of scientific papers whose focus is the determination of greening parameters has trended upward over time. The increase has been shown to be a result of improved access to RS data acquisition technologies; the growth of forestry-focused scientific research, the results of which can be used for analyses of greening in urbanized areas (see Fig. S1 in Supplementary material); and the increased availability of spatial data, resulting from the creation of free-of-charge spatial data repositories ([6], [13], [102], [37]). A diverse range of RS technologies have been developed to determine individual tree parameters and forest stand characteristics (Fig. 2). Since 2010, data from laser scanning has been acquired in multiple studies (ALS and TLS: 19 published studies). Researchers have also often performed analyses from the acquisition and combination of several sets of spatial data (data fusion, DF: 32 published studies).
Enlarge this image.Fig. 2 - Number of publications retrieved from Scopus® and ScienceDirect® databases per year and the remote sensing technology used.
Conclusions
The use of remote sensing data makes it possible to determine the characteristics of urban vegetation at various levels of detail and at different scales. The accuracy of analyses depend on the type and quality of the RS data used, and the environment in which the analyses were carried out.
Laser scanning is a technology that collects the most versatile RS data on the characteristics of trees. TLS has the highest precision of measurement, while ALS has the largest operating system.
Spectral data, in particular hyperspectral data, allow the classification of up to several dozen species of tree in urban areas. The integration of many datasets, particularly spectral data (aerial images and satellite images) and structural data (LIDAR), facilitates the most complex use of RS data and helps to improve tree species classification estimates.
To estimate the largest possible number of significant parameters from RS data, it is necessary to apply data that have been integrated from multiple sources.
The most important research challenges in urban vegetation monitoring (apart from the development of data processing and data integration methods) are identified as refining species classification methods, tree segmentation methods, and methods of determining specific tree characteristics. There are no studies on these topics that have been performed on large datasets, carried out on a wide geographic scale or on a homogeneous set of remote sensing data.
Despite the fact that this review summarized and attempted to compare the results of the different methods and technologies used in the estimation of tree features and species, it is difficult to state clearly which of them are the most accurate. This is mainly due to the enormous variety of data usage, processing methods, ground data volumes and methods of integrating different types of remote sensing data.
At the moment, no “best practice” methodologies for the use of RS in urban forestry do exist. Such guidelines would address issues such as the selection of RS data for specific purposes, or the technical specifications necessary to achieve particular objectives. In addition, there are no dedicated user-friendly applications designed for the use of civil servants, who do not necessarily have extensive knowledge about remote sensing, but are responsible for acquiring spatial information.
Acknowledgements
This article was written under the project (281502 and 240412) financed by the Polish Ministry of Science and Higher Education.
(1) Abd-Elrahman A, Thornnill M, Andreu M, Escobedo F (2010). A community-based urban forest inventory using online mapping services and consumer-grade digital images. International Journal of Applied Earth Observation and Geoinformation 12: 249-260.
(2) Alonzo M, Bookhagen B, Roberts D (2014). Urban tree species mapping using hyperspectral and lidar data fusion. Remote Sensing of Environmental 148: 70-83.
(3) Alonzo M, McFadden J, Nowak D, Roberts D (2016). Mapping urban forest structure and function using hyperspectral imagery and lidar data. Urban Forestry and Urban Greening 17: 135-147.
(4) Anderson L, Cordell H (1988). Influence of trees on residential property values in Athens Georgia (USA): a survey based on actual sales prices. Landscape and Urban Planning 15 (1-2): 153-164.
(5) Ardila J, Bijker W, Tolpekin V, Stein A (2012). Context-sensitive extraction of tree crown objects in urban areas using VHR satellite images. International Journal of Applied Earth Observation and Geoinformation 15: 57-69.
(6) Banskota A, Kayastha N, Falkowski M, Wulder M, Forese R, White J (2014). Forest monitoring using Landsat time series data: a review. Canadian Journal of Remote Sensing 40 (5): 362-384.
(7) Banzhaf E, Kollai H (2015). Monitoring the urban tree cover for urban ecosystem services - The case of Leipzig, Germany. In: Proceedings of the “36th International Symposium on Remote Sensing of Environment”, Berlin (Germany) 11-15 May 2015. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3: 301-305.
(8) Bazezew M (2018). Integrating airborne LiDAR and terrestrial laser scanner forest parameters for accurate above-ground biomass/carbon estimation in Ayer Hitam tropical forest, Malaysia. International Journal of Applied Earth Observation and Geoinformation 73: 638-652.
(9) Bentsen P, Lindholst A, Konijnendijk CC (2010). Reviewing eight years of urban forestry and urban greening: taking stock, looking ahead. Urban Forestry and Urban Greening 9 (4): 273-280.
(10) Berland A, Lange D (2017). Google Street View shows promise for virtual street tree surveys. Urban Forestry and Urban Greening 21: 11-15.
(11) Bickmore CJ, Hall T (1983). Computerisation of tree inventories. AB Academic Publisher, Berkhamsted, UK. Gscholar
(12) Brack CL (2006). Updating urban forest inventories: an example pf the DISMUT model. Urban Forestry and Urban Greening 5: 189-194.
(13) Chi M, Plaza A, Benediktsson J, Sun Z, Shen J, Zhu Y (2016). Big data for remote sensing: challenges and opportunities. Proceedings of the IEEE 104 (11): 2207-2219. Online | Gscholar
(14) Clarke L, Jenerette D, Davila A (2013). The luxury of vegetation and the legacy of tree biodiversity in Los Angeles, CA. Landscape and Urban Planning 116: 48-59.
(15) Cumming A, Galvin M, Rabaglia R, Cumming J, Twardus D (2001). Forest health monitoring protocol applied to roadside trees in Maryland. Journal of Arboriculture 27 (3): 126-138. Online | Gscholar
(16) Cumming A, Twardus D, Nowak D (2008). Urban forest health monitoring: large-scale assessments in the United States. Arboriculture and Urban Forestry 34 (6): 341-346. Online | Gscholar
(17) Dian Y, Pang Y, Dong Y, Li Z (2016). Urban tree species mapping using airborne LiDAR and hyperspectral data. Journal of the Indian Society of Remote Sensing 44 (4): 595-603.
(18) Escobedo F, Andreu M (2008). A community guide to urban forest inventories. IFAS Extension, University of Florida, Gainesville, FL, USA, pp. 1-4. Online | Gscholar
(19) Forzieri G, Tanteri L, Moser G, Catani F (2013). Mapping natural and urban environments using airborne multi-sensor ADS40-MIVIS-LiDAR synergies. International Journal of Applied Earth Observation and Geoinformation 23: 313-323.
(20) Franke J, Roberts D, Halligan K, Menz G (2009). Hierarchical Multiple Endmember Spectral Mixture Analysis (MESMA) of hyperspectral imagery for urban environments. Remote Sensing of Environmental 113: 1712-1723.
(21) Ghosh A, Fassnacht F, Joshi PK, Koch B (2014). A framework for mapping tree species combining hyperspectral and LiDAR data: role of selected classifiers and sensor across three spatial scales. International Journal of Applied Earth Observation and Geoinformation 26: 49-63.
(22) Giles-Corti B, Broomhall M, Knuiman M, Collins C, Douglas K, Ng K, Lange A, Donovan R (2005). Increasing walking: how important is distance to, attractiveness, and size of public open space? American Journal of Preventive Medicine 28: 169-176.
(23) Gill SJ, Biging GS, Murphy EC (2000). Modelling conifer tree crown radius and estimating canopy cover. Forest Ecology and Management 126: 2416-2433.
(24) Gupta K, Kumar P, Pathan SK, Sharma KP (2012). Urban neighbourhood green index - a measure of green spaces in urban areas. Landscape and Urban Planning 105: 325-335.
(25) Hanou I (2010). 2010 Town of Oakville hyperspectral EAB analysis. Town of Oakville Parks and Open Space, Denver, CO, USA, pp. 9. Gscholar
(26) Holopainen M, Leino O, Kämäri H, Talvitie M (2006). Drought damage in the park forests of the city of Helsinki. Urban Forestry and Urban Greening 4: 75-83.
(27) Holopainen M, Kankare V, Vastaranta M, Liang X, Lin Y, Vaaja M, Yu X, Hyyppä J, Hyyppä H, Kaartinen H (2013). Tree mapping using airborne, terrestrial and mobile laser scanning: a case study in a heterogeneous urban forest. Urban Forestry and Urban Greening 12: 546-553.
(28) Horbaczewska J (2016). Wykonanie inwentaryzacji dendrologicznej wybranych terenów zieleni miasta Kolobrzeg oraz ekspertyzy dendrologicznej wskazanych egzemplarzy drzew [Making a dendrological inventory of selected green areas of the city of Kolobrzeg and dendrological expertise of selected specimens of trees]. Documents of City of Kolobrzeg, Poland, pp. 5-6. [in Polish] Gscholar
(29) Hu B, Li J, Wang J, Hall B (2014). The early detection of the Emerald Ash Borer (EAB) using advanced geospatial technologies. In: Proceedings of the “ISPRS Technical Commission II Symposium”, Toronto (Canada) 6-8 Oct 2014. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol XL-2, pp. 213-219. Online | Gscholar
(30) Höfle B, Hollaus M, Hagenauer J (2012). Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data. ISPR Journal of Photogrammetry and Remote Sensing 67: 134-147.
(31) Iovan C, Boldo D, Cord M (2008). Detection, characterization, and modeling vegetation in urban areas from high-resolution aerial imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 1 (3): 206-213.
(32) Jarocinska A, Robak A, Kopeć D, Niedzielko J, Slawik L, Ochtyra A, Marcinkowska-Ochtyra A, Zagajewski B, Walesiak J (2016). Ocena i monitorowanie stanu zieleni miejskiej badana metodami teledetekcyjnymi. Informacja o efektywnosci funkcjonowania przestrzeni zielonej w miescie [Assessment and monitoring of the urban greenery condition by remote sensing methods. Information on the effectiveness of green space in the city]. In: Proceedings of the “Information Environment Conference”. Warsaw (Poland) 21-22 Nov 2016. Ekoportal, Poland, pp. 13-14. [in Polish] Gscholar
(33) Ju J, Roy D (2008). The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally. Remote Sensing of Environment 112: 1196-1211.
(34) Kaartinen H, Hyyppä J, Yu X, Vastaranta M, Hyyppä H, Kukko A, Holopainen M, Heipke C, Hirschugl M, Morsdorf F, Naesset E, Pitkänen J, Popescu S, Solberg S, Bernd M, Wu J (2012). An international comparison of individual tree detection and extraction using airborne laser scanning. Remote Sensing 4 (4): 950-974.
(35) Kaminska A, Sterenczak K, Lisiewicz M, Kraszewski B, Sadkowski R (2018). Species-related single dead tree detection using multi-temporal ALS data and CIR imagery. Remote Sensing of Environment 219: 31-43.
(36) Keller J, Konijnendijk CC (2012). A comparative analysis of municipal urban tree inventories of selected major cities in North America and Europe. Arboriculture and Urban Forestry 38 (1): 24-30. Online | Gscholar
(37) Kempeneers P, Soille P (2017). Optimizing Sentinel-2 image selection in a big data context. Big Earth Data 1 (1-2): 145-158.
(38) Kerr J, Sallis J, Saelens B, Cain K, Conway T, Frank L, King A (2012). Outdoor physical activity and self-rated health in older adults living in two regions of the US. International Journal of Behavioral Nutrition and Physical Activity 9 (1): 89.
(39) Konijnendijk CC, Ricard R, Kenney A, Randrup T (2006). Defining urban forestry - A comparative perspective of North America and Europe. Urban Forestry and Urban Greening 4 (3-4): 93-103.
(40) Kronenberg J (2012). Bariery dla utrzymania drzew w miastach i sposoby pokonywania tych barier [Barriers for maintaining trees in cities and ways to overcome these barriers]. Zrównowazony rozwój - zastosowania 3 (2012): 31-49. [in Polish] Gscholar
(41) Lamprecht S, Stoffels J, Dotzler S, Ha E, Udelhoven T (2012). aTrunk - An ALS-based trunk detection algorithm. Remote Sensing 7 (8): 9975-9997.
(42) Latif Z, Zamri I, Omar H (2012). Determination of tree species using Woldview-2 data. In: Proceedings of the “IEEE 8th International Colloquium on Signal Processing and its Applications”. Melaka (Malaysia), 23-25 Mar 2012. IEEE Explore Digital Library, Piscataway, NJ, USA, pp. 383-387.
(43) Lee J-H, Ko Y, McPherson E (2016). The feasibility of remotely sensed data to estimate urban tree dimensions and biomass. Urban Forestry and Urban Greening 16: 208-220.
(44) Liu L, Coops N, Aven N, Pang Y (2017). Mapping urban tree species using integrated airborne hyperspectral and LiDAR remote sensing data. Remote Sensing of Environmental 200: 170-182.
(45) Malthus T, Younger C (2000). Remote sensing stress in street trees using high spatial resolution data. In: Proceedings of the “2nd International Geospatial Information in Agriculture and Forestry Conference”. Lake Buena Vista (FL, USA) 10-12 Jan 2000. pp. 326-333. Gscholar
(46) Maruthaveeran S, Yaman A (2010). The identification of criteria and indicators to evaluate hazardous street trees of Kuala Lumpur, Malaysia: a Delphi study. Journal of Forestry 108 (7): 360-364. Online | Gscholar
(47) McPherson E, Nowak D, Heisler G, Grimmond S, Souch C, Grant R, Rowntree R (1997). Quantifying urban forest structure, function, and value: the Chicago Urban Forest Climate Project. Urban Ecosystems 1 (1): 49-61.
(48) MIAA (2011). Regulation of the Minister of Internal Affairs and Administration of November 9, 2011 on technical standards of performing detailed surveys and working out and sending results of these surveys to National Geodetic and Cartographic Database. Journal of Laws No. 263, item 1572, Dziennik Ustaw Rzeczypospolitej Polskiej, Warsaw, Poland. Gscholar
(49) Mielcarek M, Balazy R, Zawila-Niedzwiecki T (2015). Comparison of the accuracy of remote methods tree-height estimation. Sylwan 159 (9): 714-721. Gscholar
(50) Mielcarek M, Sterenczak K, Khosravipour A (2018). Testing and evaluating different LiDAR - derived canopy height model generation methods for tree height estimation. International Journal of Applied Earth Observation and Geoinformation 71: 132-143.
(51) Miller R (1997). Urban forestry: planning and managing urban green spaces (2nd edn). Prentice Hall, New Jersey, USA, pp. 4-8. Gscholar
(52) Mincey S, Schmitt-Harsh M, Thurau R (2013). Zoning, land use, and urban tree canopy cover: the importance of scale. Urban Forestry and Urban Greening 12: 191-199.
(53) Morgenroth J, Gomez C (2014). Assessment of tree structure using a 3D image analysis technique - a proof of concept. Urban Forestry and Urban Greening 13: 198-203.
(54) Morsdorf F, Frey O, Meier E, Itten K, Allgöwer B (2008). Assessment of the influence of flying altitude and scan angle on biophysical vegetation products derived from airborne laser scanning. International Journal of Remote Sensing 29 (5): 1387-1406.
(55) Moskal M, Zheng G (2012). Retrieving forest inventory variables with terrestrial laser scanning (TLS) in urban heterogeneous forest. Remote Sensing 4: 1-20.
(56) Muthulingam U, Thangavel S (2012). Density, diversity and richness of woody plants in urban green spaces: a case study in Chennai metropolitan city. Urban Forestry and Urban Greening 11: 450-459.
(57) Myint S, Gober P, Brazel A, Grossman-Clarke S, Weng Q (2011). Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment 115: 1145-1161.
(58) Nielsen A, Östberg J, Delshammar T (2014). Review of urban tree inventory methods used to collect data at single-tree level. Arboriculture and Urban Forestry 40 (2): 96-111. Online | Gscholar
(59) Nowak D, Heisler G (2010). Air quality effects of urban trees and parks. National Recreation and Park Association, Ashburn, VA, USA, pp. 6. Online | Gscholar
(60) Nowak D, Noble N, Sisinni S, Dwyer J (2001). People and trees assessing the US urban forest resource. Journal of Forestry 99 (3): 37-42. Online | Gscholar
(61) Nowak D, Crane D, Stevens J (2006). Air pollution removal by urban trees and shrubs in the United States. Urban Forestry and Urban Greening 4: 111-123.
(62) Olofsson K, Holmgren J, Olsson H (2014). Tree stem and height measurements using terrestrial laser scanning and the RANSAC algorithm. Remote Sensing 6 (5): 4323-4344.
(63) Orka H, Bollandsås O (2010). Effects of different sensors and leaf-on and leaf-off canopy conditions on echo distributions and individual tree properties derived from airborne laser scanning. Remote Sensing of Environment 114 (7): 1445-1461.
(64) Östberg J, Delshammar T, Fransson A, Nielsen A (2012a). Standard for tree inventories in urban environments. Department of Landscape Management, Design and Construction, Alnarp, Sweden, pp. 4-7. Gscholar
(65) Östberg J, Martinsson M, Stål O, Fransson A (2012b). Risk of root intrusion by tree and shrub species into sewer pipes in Swedish urban areas. Urban Forestry and Urban Greening 1: 65-71.
(66) Östberg J, Delshammar T, Wiström B, Nielsen A (2013). Grading of parameters for urban tree inventories by city officials, arborists, and academics using the Delphi method. Environmental Management 51: 694-708.
(67) Patterson M, Wiseman E, Winn M, Lee S-M, Araman P (2011). Effects of photographic distance on tree crown attributes calculated using UrbanCrowns image analysis software. Arboriculture and Urban Forestry 37 (4): 137-179. Online | Gscholar
(68) Phu La H, Dam Eo Y, Chang A, Kim CH (2015). Extraction of individual tree crown using hyperspectral image and LiDAR data. KSCE Journal of Civil Engineering 19 (4): 1078-1087.
(69) Plowright A, Coops N, Eskelson B, Sheppar S, Aven N (2016). Assessing urban tree condition using airborne light detection and ranging. Urban Forestry and Urban Greening 19: 140-150.
(70) Pontius J, Hanavan R, Hallett R, Cook B, Corp L (2017). High spatial resolution spectral unmixing for mapping ash species across urban environment. Remote Sensing of Environment 199: 360-369.
(71) Popescu SC, Wynne RH, Nelson RF (2003). Measuring infividual tree crown diameter with LiDAR and assessing its influence on estimating forest volume and biomass. Canadian Journal of Remote Sensing 29: 564-577.
(72) Pu R, Liu D (2011). Segmented canonical discriminant analysis of in situ hyperspectral data for identifying 13 urban tree species. International Journal of Remote Sensing 32: 2207-2226.
(73) Pu R, Landry S (2012). A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species. Remote Sensing of Environment 124: 516-533.
(74) Pueschel P, Newnham G, Rock G, Udelhoven T, Werner W, Hill J (2013). The influence of scan mode and circle fitting on tree stem detection, stem diameter and volume extraction from terrestrial laser scans. ISPRS Journal of Photogrammetry and Remote Sensing 77: 44-56.
(75) Pullin A, Stewart G (2006). Guidelines for systematic review in conservation and environmental management. Conservation Biology 20 (6): 1647-1656.
(76) Putut Ash Shidiq I, Wibowo A, Kusratmoko E, Indratmoko S, Ardhianto R, Prasetyo Nugroho B (2017). Urban forest topographical mapping using UAV Lidar. In: Proceedings of the “5th Geoinformation Science Symposium (GSS 2017)”. Yogyakarta (Indonesia) 27-28 Sept 2017. IOP Conference Series: Earth and Environmental Science 98: 012034.
(77) Rahman M, Rashed T (2015). Urban tree damage estimation using airborne laser scanner data and geographic information systems: an example from 2007 Oklahoma ice storm. Urban Forestry and Urban Greening 14: 562-572.
(78) Randrup T, Konijnendijk CC, Dobbertin M, Prüller R (2005). The concepts of urban forestry in Europe. In: “Urban Forests and Trees” (Konijnendijk CC, Nilsson K, Randrup TB, Schipperijn J eds). Springer, Berlin, Heidelberg, pp. 9-21.
(79) Rutzinger M, Pratihast A, Elberink S (2011). Tree modeling from mobile laser scanning data-sets. The Photogrammetric Record 26 (135): 361-372.
(80) Saarinen N, Vastaranta M, Kankare V, Tanhuanpää T, Holopainen M, Hyyppä J, Hyyppä H (2014). Urban-tree-attribute update using multisource single-tree inventory. Forests 5: 1032-1052.
(81) Shrestha R, Wynne R (2012). Estimating biophysical parameters of individual trees in an urban environment using small footprint discret-return imaging Lidar. Remote Sensing 4: 484-508.
(82) Sjöman H, Östberg J, Bühler O (2012). Diversity and distribution of the urban tree population in ten major Nordic cities. Urban Forestry and Urban Greening 11 (1): 1-39.
(83) Sobczynski L, Sitarski M, Stawowska-Carewicz N, Pstragowska M (2011). Inventory of green areas on the example of Warsaw and Gliwice - field measurements. Czlowiek i Srodowisko 35 (3-4): 123-132. Gscholar
(84) Sterenczak K, Bedkowski K, Weinacker H (2008). Accuracy of crown segmentation and estimation of selected trees and forest stand parameters in order to resolution of used DSM and nDSM models generated from dense small footprint LIDAR data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Youth Forum, vol. XXXVIII, Part B6b, pp. 27-33. Online | Gscholar
(85) Sterenczak K (2013). Czynniki warunkujace proces detekcji pojedynczych drzew w oparciu o dane pozyskane w wyniku lotniczego skanowania laserowego [Factors influencing individual tree crowns detection based on airborne laser scanning data]. Lesne Prace Badawcze 74 (4): 323-333. [in Polish] Gscholar
(86) Stigsdotter U, Ekholm O, Schipperijn J, Toftager M, Kamper-Jorgensen F, Randrup T (2010). Health promoting outdoor environments - associations between green space, and health, health related quality of life and stress based on a Danish national representative survey. Scandinavian Journal of Public Health 38: 411-417.
(87) Strunk J, Mills J, Ries P, Temesgen H, Jeroue L (2016). An urban forest-inventory-and-analysis investigation in Oregon and Washington. Urban Forestry and Urban Greening 18: 100-109.
(88) Takebayashi H, Moriyama M (2007). Surface heat budget on green roof and high reflection roof for mitigation of urban heat island. Building and Environment 42: 2971-2979.
(89) Tanhuanpää T, Vastaranta M, Kankare V, Holopainen M, Hyppä J, Hyppä H, Alho P, Raisio J (2014). Mapping of urban roadside trees - A case study in the tree register update process in Helsinki city. Urban Forestry and Urban Greening 13: 562-570.
(90) Tate R (1985). Uses of street tree inventory data. Journal of Arboriculture 11 (7): 210-213. Gscholar
(91) Thaiutsa B, Puangchit L, Kjelgren R, Arunpraparut W (2008). Urban green space, street tree and heritage large tree assessment in Bangkok, Thailand. Urban Forestry and Urban Greening 7: 219-229.
(92) Tigges J, Lakes T, Hostert P (2013). Urban vegetation classification: benefits of multitemporal RapidEye satellite data. Remote Sensing of Environment 136: 66-75.
(93) Tompalski P (2009). Naziemny skaning laserowy w inwentaryzacji zieleni miejskiej na przykladzie plant w Krakowie [Terrestrial laser scanning for an urban green inventory]. Archiwum Fotogrametrii, Kartografii i Teledetekcji 20: 421-431. [in Polish] Gscholar
(94) Tompalski P (2013). Wykorzystanie technik geomatycznych w inwentaryzacji szaty roslinnej obszarów poprzemyslowych Krakowa w aspekcie ich rewitalizacji [The use of geomatic techniques in the inventory of plant cover of post-industrial areas in Krakow in the aspect of their revitalization]. PhD thesis, Wydzial Lesny, Katedra Ekologii Lasu, Uniwersytet Rolniczy im. Hugona Kollataja, Poland, pp. 127-155. [in Polish] Gscholar
(95) Tooke T, Coops N, Goodwin N, Voogt J (2009). Extracting urban vegetation characteristics using spectral mixture analysis and decision tree classifications. Remote Sensing of Environment 113: 398-407.
(96) Ucar Z, Bettinger P, Merry K, Siry J, Bowker JM, Akbulut R (2016). A comparison of two sampling approaches for assessing the urban forest canopy cover from aerial photography. Urban Forestry and Urban Greening 16: 221-230.
(97) UNRI (2010). A field guide standards for urban forestry data collection. Draft 2.0, Web site. Online | Gscholar
(98) Verlič A, Durič N, Kokalj Z, Marsetič A, Simončič P, Oštir K (2014). Tree species classification using Worldview-2 satellite images and laser scanning data in a natural urban forest. Prethodno priopćenje - Preliminary communication. Šumarski list 9-10: 477-488. Gscholar
(99) Vonderach C, Voegtle T, Adler P (2012). Voxel-based approach for estimating urban tree volume from terrestrial laser scanning data. In: Proceedings of the “XXII ISPRS Congress”. Melbourne (Australia) 25 Aug - 1 Sept 2012. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXIX-B8: 451-456.
(100) Voss M, Sugumaran R (2008). Seasonal effect on tree species classification in an urban environment using hyperspectral data, LiDAR, and an object- oriented approach. Sensors 8: 3020-3036.
(101) Wu B, Yu B, Yue W, Shu S, Tan W, Hu Ch Huang Y, Wu J, Liu H (2013). A voxel-based method for automated identification and morphological parameters estimation of individual street trees from mobile laser scanning data. Remote Sensing 5: 584-611.
(102) Wulder M, White J, Loveland T, Woodcock C, Belward A, Cohen W, Fosnight E, Shaw J, Masek J, Roy D (2016). The global Landsat archive: status, consolidation and direction. Remote Sensing of Environment 185: 271-283.
(103) Xiao Q, McPherson E (2005). Tree health mapping with multispectral remote sensing data at UC Davis, California. Urban Ecosystems 8: 349-361.
(104) Xiao Q, Ustin SL, McPherson EG (2004). Using AVIRIS data and multiple-masking techniques to map urban forest tree species. International Journal of Remote Sensing 25: 5637-5654.
(105) Yu X, Hyppa J, Huppa H, Maltamo M (2004). Effects of flight altitude on tree height estimation using airborne laser scanning. In: Proceedings of the “ISPRS working group VIII/2”. Freiburg (Germany) Oct 3-6 2004. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 36 (8/W2): 96-101. Gscholar
(106) Zhang K, Hu B (2012). Individual urban tree species classification using very high spatial resolution airborne multi-spectral imagery using longitudinal profiles. Remote Sensing 4: 1741-1757.
(107) Zhang C, Qiu F (2012). Mapping individual tree species in an urban forest using airborne lidar data and hyperspectral imagery. Photogrammetric Engineering and Remote Sensing 78 (10): 1079-1087.
(108) Zhang K, Hu B, Robinson J (2014). Early detection of emerald ash borer infestation using multisourced data: a case study in the town of Oakville, Ontario, Canada. Journal of Applied Remote Sensing 8 (1): 083602.
(109) Zhang C, Zhou Y, Qiu F (2015). Individual tree segmentation from LiDAR point clouds for urban forest inventory. Remote Sensing 7: 7892-7913.
(110) Zupancic T, Westmacott C, Bulthius M (2015). The Impact of green space on heat and air pollution in urban communities: a meta-narrative systematic review. David Suzuki Foundation, Vancouver, BC, Canada, pp. 68. Gscholar
(111) Zygmunt R, Banas J, Zieba S (2014). Urban forests stability on the example of “Las Wolski” in Cracow. Proceedings of the Center for Nature and Forestry Education 16 (39): 106-119. Gscholar
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