Connecting Forestry Learning Objectives to Urban Forest Types

Journal of Forestry ◽

10.1093/jofore/fvab053 ◽

2021 ◽

Author(s):

Kathleen Coupland ◽

Juliana Magalhães ◽

Verena C Griess

Keyword(s):

Undergraduate Students ◽

Forest Canopy ◽

Urban Forest ◽

Canopy Cover ◽

Urban Forests ◽

Learning Objectives ◽

Educational Opportunities ◽

Walking Distance ◽

Forest Types ◽

Individual Tree

Abstract Applied educational opportunities in forestry undergraduate curricula are essential for a complete postsecondary degree program. Walking distance to local urban forests present a way to teach forestry students in applied settings, while reducing the time, cost, and travel logistics. A case study at a Canadian university (University of British Columbia) was used to connect urban forest canopy cover to forestry learning objectives and walking time to the main teaching building. Individual tree canopies were identified with light detection and ranging data and aggregated to 0.05 ha grid sections. Using canopy cover and forest arrangement, the urban forest was classified into closed, open, small, sparse, or non- forest classifications. Forestry learning objectives were matched with each forest classification in conjunction with walkability to identify critical local location for forestry education. Results identified key areas suitable for teaching forestry and for linking forestry educational values with easily accessible high value locations. Study Implications: Applied educational opportunities for undergraduate forestry students are critical for ensuring hands-on, real world experiences and essential in postsecondary forestry degrees. Local urban forests present an opportunity to allow students access to these experiences regularly. Connecting forestry learning objectives with local urban forest types allowed for the identification of key, high-value learning locations. The information and methodology from this research provide insight into explicitly classifying areas for forestry educational purposes with the goal of promoting outdoor applied educational opportunities for forestry undergraduate students.

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Applying Spatial Analysis Techniques to Assess Tennessee Urban Forests

Southern Journal of Applied Forestry ◽

10.1093/sjaf/32.4.184 ◽

2008 ◽

Vol 32 (4) ◽

pp. 184-186 ◽

Cited By ~ 2

Author(s):

Christopher A. Bridges

Keyword(s):

Spatial Analysis ◽

Urban Areas ◽

Forest Canopy ◽

Community Forestry ◽

Urban Forest ◽

Canopy Cover ◽

Urban Forests ◽

Remote Sensing Data ◽

Technical Note ◽

Analysis Techniques

Abstract Although the social, economic, and ecological benefits of urban forests have been well-documented, fewer efforts have been made to conduct landscape level assessments of urban forest canopy. This technical note describes how spatial analysis techniques were used to evaluate urban forest canopy cover in 133 municipal urban areas across Tennessee. Municipalities were compared based on participation in the Tree City USA program. Although urban forests vary greatly, results indicated that cities participating in this community forestry initiative exhibit higher levels of urban forest canopy cover. The integration of geographic information systems and remote sensing data presents new opportunities for community foresters to efficiently and effectively monitor urban ecosystems and formulate appropriate policy responses that can help to ensure forest sustainability across the urban–rural interface.

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Effects of Urban Forest Types and Traits on Soil Organic Carbon Stock in Beijing

Forests ◽

10.3390/f12040394 ◽

2021 ◽

Vol 12 (4) ◽

pp. 394

Author(s):

Xinhui Xu ◽

Zhenkai Sun ◽

Zezhou Hao ◽

Qi Bian ◽

Kaiyue Wei ◽

...

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Carbon Stock ◽

Urban Forest ◽

Coniferous Forest ◽

Predictor Variable ◽

Urban Forests ◽

Least Squares Regression ◽

Forest Types ◽

Organic Carbon Stock

Forests can affect soil organic carbon (SOC) quality and distribution through forest types and traits. However, much less is known about the influence of urban forests on SOC, especially in the effects of different forest types, such as coniferous and broadleaved forests. Our objectives were to assess the effects of urban forest types on the variability of SOC content (SOC concentration (SOCC) and SOC density (SOCD)) and determine the key forest traits influencing SOC. Data from 168 urban forest plots of coniferous or broadleaved forests located in the Beijing urban area were used to predict the effects of forest types and traits on SOC in three different soil layers, 0–10 cm, 10–20 cm, and 20–30 cm. The analysis of variance and multiple comparisons were used to test the differences in SOC between forest types or layers. Partial least squares regression (PLSR) was used to explain the influence of forest traits on SOC and select the significant predictors. Our results showed that in urban forests, the SOCC and SOCD values of the coniferous forest group were both significantly higher than those of the broadleaved group. The SOCC of the surface soil was significantly higher than those of the following two deep layers. In PLSR models, 42.07% of the SOCC variance and 35.83% of the SOCD variance were explained by forest traits. Diameter at breast height was selected as the best predictor variable by comparing variable importance in projection (VIP) scores in the models. The results suggest that forest types and traits could be used as an optional approach to assess the organic carbon stock in urban forest soils. This study found substantial effects of urban forest types and traits on soil organic carbon sequestration, which provides important data support for urban forest planning and management.

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Detecting Long-Term Urban Forest Cover Change and Impacts of Natural Disasters Using High-Resolution Aerial Images and LiDAR Data

Remote Sensing ◽

10.3390/rs12111820 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1820

Author(s):

Raoul Blackman ◽

Fei Yuan

Keyword(s):

Remote Sensing ◽

Ecosystem Services ◽

Forest Canopy ◽

High Spatial Resolution ◽

Urban Forest ◽

Urban Canopy ◽

Canopy Cover ◽

Random Point ◽

Aerial Images ◽

Lidar Data

Urban forests provide ecosystem services; tree canopy cover is the basic quantification of ecosystem services. Ground assessment of the urban forest is limited; with continued refinement, remote sensing can become an essential tool for analyzing the urban forest. This study addresses three research questions that are essential for urban forest management using remote sensing: (1) Can object-based image analysis (OBIA) and non-image classification methods (such as random point-based evaluation) accurately determine urban canopy coverage using high-spatial-resolution aerial images? (2) Is it possible to assess the impact of natural disturbances in addition to other factors (such as urban development) on urban canopy changes in the classification map created by OBIA? (3) How can we use Light Detection and Ranging (LiDAR) data and technology to extract urban canopy metrics accurately and effectively? The urban forest canopy area and location within the City of St Peter, Minnesota (MN) boundary between 1938 and 2019 were defined using both OBIA and random-point-based methods with high-spatial-resolution aerial images. Impacts of natural disasters, such as the 1998 tornado and tree diseases, on the urban canopy cover area, were examined. Finally, LiDAR data was used to determine the height, density, crown area, diameter, and volume of the urban forest canopy. Both OBIA and random-point methods gave accurate results of canopy coverages. The OBIA is relatively more time-consuming and requires specialist knowledge, whereas the random-point-based method only shows the total coverage of the classes without locational information. Canopy change caused by tornado was discernible in the canopy OBIA-based classification maps while the change due to diseases was undetectable. To accurately exact urban canopy metrics besides tree locations, dense LiDAR point cloud data collected at the leaf-on season as well as algorithms or software developed specifically for urban forest analysis using LiDAR data are needed.

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Characterizing Tree Spatial Distribution Patterns Using Discrete Aerial Lidar Data

Remote Sensing ◽

10.3390/rs12040712 ◽

2020 ◽

Vol 12 (4) ◽

pp. 712 ◽

Cited By ~ 3

Author(s):

Xiaofei Wang ◽

Guang Zheng ◽

Zengxin Yun ◽

L. Monika Moskal

Keyword(s):

Spatial Distribution ◽

Point Processes ◽

Forest Canopy ◽

Forest Ecosystems ◽

Urban Forest ◽

Pair Correlation ◽

Distribution Patterns ◽

Individual Tree ◽

Spatial Distribution Patterns ◽

Water Cycles

Tree spatial distribution patterns such as random, regular, and clustered play a crucial role in numerical simulations of carbon and water cycles and energy exchanges between forest ecosystems and the atmosphere. An efficient approach is needed to characterize tree spatial distribution patterns quantitatively. This study aims to employ increasingly available aerial laser scanning (ALS) data to capture individual tree locations and further characterize their spatial distribution patterns at the landscape or regional levels. First, we use the pair correlation function to identify the categories (i.e., random, regular, and clustered) of tree spatial distribution patterns, and then determine the unknown parameters of statistical models used for approximating each tree spatial distribution pattern using ALS-based metrics. After applying the proposed method in both natural and urban forest sites, our results show that ALS-based tree crown radii can capture 58%–77% (p < 0.001) variations of visual-based measurements depending on forest types and densities. The root mean squared errors (RMSEs) of ALS-based tree locations increase from 1.46 m to 2.51 m as the forest densities increasing. The Poisson, soft-core, and hybrid-Gibbs point processes are determined as the optimal models to approximate random, regular, and clustered tree spatial distribution patterns, respectively. This work provides a solid foundation for improving the simulation accuracy of forest canopy bidirectional reflectance distribution function (BRDF) and further obtain a better understanding of the processes of carbon and water cycles of forest ecosystems.

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ASSESSING URBAN FOREST CANOPY COVER IN GREAT PLAIN CONSERVATION AREA (DÜZCE CITY, TURKEY) BETWEEN 1984 AND 2015

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlvi-4-w5-2021-539-2021 ◽

2021 ◽

Vol XLVI-4/W5-2021 ◽

pp. 539-544

Author(s):

Z. Uçar ◽

R. Eker ◽

A. Aydin

Keyword(s):

Ecosystem Services ◽

Forest Canopy ◽

Urban Forest ◽

Canopy Cover ◽

Tree Canopy ◽

Urban Trees ◽

Conservation Area ◽

Urban Tree ◽

Tree Canopy Cover ◽

Urban Tree Canopy

Abstract. Urban trees and forests are essential components of the urban environment. They can provide numerous ecosystem services and goods, including but not limited to recreational opportunities and aesthetic values, removal of air pollutants, improving air and water quality, providing shade and cooling effect, reducing energy use, and storage of atmospheric CO2. However, urban trees and forests have been in danger of being lost by dense housing resulting from population growth in the cities since the 1950s, leading to increased local temperature, pollution level, and flooding risk. Thus, determining the status of urban trees and forests is necessary for comprehensive understanding and quantifying the ecosystem services and goods. Tree canopy cover is a relatively quick, easy to obtain, and cost-effective urban forestry metric broadly used to estimate ecosystem services and goods of the urban forest. This study aimed to determine urban forest canopy cover areas and monitor the changes between 1984–2015 for the Great Plain Conservation area (GPCA) that has been declared as a conservation Area (GPCA) in 2017, located on the border of Düzce City (Western Black Sea Region of Turkey). Although GPCA is a conservation area for agricultural purposes, it consists of the city center with 250,000 population and most settlement areas. A random point sampling approach, the most common sampling approach, was applied to estimate urban tree canopy cover and their changes over time from historical aerial imageries. Tree canopy cover ranged from 16.0% to 27.4% within the study period. The changes in urban canopy cover between 1984–1999 and 1999–2015 were statistically significant, while there was no statistical difference compared to the changes in tree canopy cover between 1984–2015. The result of the study suggested that an accurate estimate of urban tree canopy cover and monitoring long-term canopy cover changes are essential to determine the current situation and the trends for the future. It will help city planners and policymakers in decision-making processes for the future of urban areas.

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Assessing Urban Forest Canopy Cover Using Airborne or Satellite Imagery

Arboriculture & Urban Forestry ◽

10.48044/jauf.2008.046 ◽

2008 ◽

Vol 34 (6) ◽

pp. 334-340

Author(s):

Jeffrey Walton ◽

David Nowak ◽

Eric Greenfield

Keyword(s):

Remote Sensing ◽

New York ◽

Forest Canopy ◽

Urban Forest ◽

Canopy Cover ◽

Classification Methods ◽

Remote Sensing Techniques ◽

Many Sources ◽

Forest Managers

With the availability of many sources of imagery and various digital classification techniques, assessing urban forest canopy cover is readily accessible to most urban forest managers. Understanding the capability and limitations of various types of imagery and classification methods is essential to interpreting canopy cover values. An overview of several remote sensing techniques used to assess urban forest canopy cover is presented. A case study comparing canopy cover percentages for Syracuse, New York, U.S. interprets the multiple values developed using different methods. Most methods produce relatively similar results, but the estimate based on the National Land Cover Database is much lower.

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Tree Growth and Resilience to Extreme Drought Across an Urban Land-use Gradient

Arboriculture & Urban Forestry ◽

10.48044/jauf.2013.036 ◽

2013 ◽

Vol 39 (6) ◽

Author(s):

Robert Fahey ◽

Margaret Bialecki ◽

David Carter

Keyword(s):

Land Use ◽

Tree Growth ◽

Urban Forest ◽

Urban Forests ◽

Urban Land Use ◽

Tree Canopy ◽

Extreme Drought ◽

Land Uses ◽

Individual Tree ◽

Extreme Climatic Events

Understanding the response of urban forests to extreme climatic events, such as drought, will be essential to predicting impacts of climate change on the urban tree canopy and related ecosystem services. This study evaluated variation in tree growth and drought resistance (growth during drought) and resilience (growth in period following drought) across four land-use categories (built, transportation, park, and semi-natural forest) and four species (Acer saccharum, Gymnocladus dioicus, Liriodendron tulipifera, and Pinus strobus) at The Morton Arboretum in suburban Lisle, Illinois, U.S. Tree growth and resistance to drought both varied as an interaction between landuse and species (F15, 100 = 5.25, p < 0.001; F15, 100 = 2.42, p = 0.005). Resilience of tree growth to extreme drought was generally high and did not vary across species and land-uses. In this study, individual tree species responses to drought varied across land-uses, illustrating the difficulty of predicting the reaction of urban forests to projected increases in the frequency of extreme climatic events. Tree growth response to drought varied even across the relatively narrow range of growing conditions studied here. Investigation of a broader range of sites, encompassing the full urban forest continuum, would likely demonstrate even greater variation in tree response to extreme climatic events.

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