Smart green infrastructure in a smart city – the case study of ecosystem services evaluation in Krakow based on i-Tree Evo software

It is a common perception that urban greenery does not bring any rational benefits, while profits from real estates are obvious. Therefore, the cities green infrastructure (urban forests, parks, trees, lawns, meadows, etc.) are constantly threatened with housing and development. However, urban greenery plays a substantial role in improving the quality of urbanites’ life, which is particularly significant in terms of predicted 70% urbanization rate by 2050. Healthy and well managed city green infrastructure can improve air quality, remove particulate matters (PM) and CO2 sequestrate carbon, cool down temperature or protect against winds. These functions of vegetation are known as ecosystem services (ES). Recognizing the value of ES provided by green infrastructure is crucial for urban planning and management in terms of assuring sustainable urban development. In our study we used the i-Tree Eco (USDA Forest Service) software, which quantifies vegetation structure, environmental effects and values of ES. The i-Tree Eco model is based on air pollution and local meteorological data along with the field data from inventory of city vegetation. Requiring easy to collect (e.g. based on LiDAR 3D point clouds) input data and having user-friendly interface, the i-Tree Eco has a potential of becoming a very useful tool for planners and managers in their everyday work. In this paper we present a case study of ES evaluation for the “Krakowski Park” in Krakow (582 trees on 4.77 hectares, with domination of Fraxinus excelsior, Ulmus laevis and Betula pendula). For the analysed 2015 year, the Krakowski Park trees stored in total 441.59 t of carbon, removed 184 kg of air pollutants and contributed to 220 m3 of avoided runoff. Total value of ecosystem services provided by the Krakowski Park in year 2015 was EUR 5.096 (EUR 8.76 tree/year). In our further work we intend to expand the ES evaluation on other green areas in Krakow and on a wider range of ES

Methods of Landscape Valorization and Possibilities of Its Application in Hunting Area Categorisation

As a result of environmental changes, assessment indexes for the agricultural landscape have been changing dramatically. Being at the interface of human activity and the natural environment, hunting is particularly sensitive to environmental changes, such as increasing deforestation or large-scale farming. The classical categorisation of hunting grounds takes into account the area, forest cover, number of forest complexes, fertility of forest habitats, lack of continuity of areas potentially favourable to wild animals. Landscape assessment methods used in architecture often better reflect the actual breeding and hunting value of a given area, especially in relation to fields and forests. The forest-field mosaic, large spatial fragmentation as well as interweaving of natural environment elements with buildings do not have to be the factors that limit the numbers of small game. Identification of the constituents of architectural-landscape interiors: content and significance assessment, determination of the functional role or assessment based on the general environmental values being represented take into account factors important for the existence of game, in particular small game

Can iPad Pro be as reliable as TLS for urban forest inventories?

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Promising Uses of the iPad Pro Point Clouds: The Case of the Trunk Flare Diameter Estimation in the Urban Forest

The rule of thumb “the right tree in the right place” is a common idea in different countries to avoid damages caused by trees on sidewalks. Although many new planting techniques can be used, the estimation of the trunk flare diameter (TFD) could help the planning process to give tree roots more space to grow over the years. As such, we compared the applicability of point clouds based on iPad Pro 2020 image processing and a precise terrestrial laser scanner (TLS FARO) for the modeling of the TFD using different modeling procedures. For both scanning methods, 100 open-grown and mature trees of 10 different species were scanned in an urban park in Cracow, Poland. To generate models, we used the PBH (perimeter at breast height) and TFD variables and simple linear regression procedures. We also tested machine learning algorithms. In general, the TFD value corresponded to two times the size of a given DBH (diameter at breast height) for both methods of point cloud acquisition. Linearized models showed similar statistics to machine learning techniques. The random forest algorithm showed the best fit for the TFD estimation, R2 = 0.8780 (iPad Pro), 0.8961 (TLS FARO), RMSE (m) = 0.0872 (iPad Pro), 0.0702 (TLS FARO). Point clouds generated from iPad Pro imageries (matching approach) promoted similar results as TLS FARO for the TFD estimations

Wykorzystanie danych Lidar do określenia znaczenia struktury przestrzennej drzewostanów sosnowych w zachowaniu borów chrobotkowych na terenie Parku Narodowego „Bory Tucholskie”

Celem badań realizowanych w roku 2018 finansowanych z Funduszu Leśnego, była analiza cech biometrycznych i parametrów drzewostanów sosnowych na terenie Parku Narodowego "Bory Tucholskie" (PNBT), w których w 2017 roku zainicjowano program ochronny czynnej borów chrobotkowych. Analizy środowiskowe prowadzono w odniesieniu do wybranych cech biometrycznych drzew i drzewostanów z wykorzystaniem chmur punktów ze skanowania laserowego (LiDAR), w tym bezzałogowych platform ULS (RiCopter + VUX-1 RIEGL) oraz naziemnych skanerów TLS (FARO FOCUS 3D; X130). Dzięki zastosowaniu technologii LiDAR, w precyzyjny sposób opisano strukturę drzewostanów sosnowych poprzez szeregi statystyk opisowych charakteryzujących strukturę przestrzenną 3D roślinności. Wykorzystując Model Koron Drzew (CHM) dokonano analizy objętości koron drzew oraz objętości przestrzeni podokapowej. Dla analizowanych wydzieleń przeprowadzono analizy solarne GIS pod kątem sumarycznej energii słonecznej docierającej do okapu drzewostanu oraz bezpośrednio do poziomu gruntu co ma duże znaczenie dla ochrony czynnej chrobotków. Dla celów projektu pozyskano także zdjęcia wielospektralne przy wykorzystaniu specjalistycznej kamery RedEdge-M (MiceSense) zamontowanej na platformie BSP wielowirnikowca Typhoon H520 (Yuneec). Przeprowadzono też naloty z kamerą termalną w celu detekcji miejsc z wysoką temperaturą na gruncie, odpowiednich na pionierskich gatunków porostów. Dla wydzieleń leśnych obliczono także wskaźniki roślinne: NDVI, NDRE, GNDVI oraz GRVI. Dane pozyskane w 2017 oraz 2018 roku były podstawą analiz przestrzenno-czasowych 4-D zmian w drzewostanach jakie miały związek z usunięciem części drzew oraz warstwy organicznej (ścioła, warstwa mszaków).The aim of the research carried out in 2018 and financed by the Forest Fund was the analysis of biometric features and parameters of pine stands in the area of the "Bory Tucholskie" National Park (PNBT), where a program of active protection of lichen was initiated in 2017. Environmental analyses were conducted in relation to selected biometric features of trees and stands using laser scanning (LiDAR), including ULS (Unmanned Laser Scanning; RIEGL VUX-1) and TLS (Terrestrial Laser Scanning; FARO FOCUS 3D; X130). Thanks to the application of LiDAR technology, the structure of pine stands was precisely determined by means of a series of descriptive statistics characterizing the 3D spatial structure of vegetation. Using the Trees Crown Model (CHM), the analysis of the volume of tree crowns and the volume of space under canopy was performed. For the analysed sub-compartments, GIS solar analyses were carried out for the solar energy reaching the canopy and the ground level due to active protection of lichen. Multispectral photos were obtained using a specialized RedEdge-M camera (MicaSense) mounted on the UAV multi rotor platform Typhoon H520 (Yuneec). Flights with a thermal camera were also performed in order to detect places on the ground with high temperature. Plant indices: NDVI, NDRE, GNDVI and GRVI were also calculated for sub-compartments. The data obtained in 2017 and 2018 were the basis for spatial and temporal analyses of 4-D changes in stands which were related to the removal of some trees and organic layer (litter, moss layer)