Arboriculture & Urban Forestry 48(2): March 2022 carry a payload of a spectral sensor and visual cam- era; collecting spectral data to analyze baseline tree health and classify land cover; and visual data for mapping and as a visual aid for stakeholders. Utiliz- ing existing GIS land cover data will be necessary to age the inventory and precisely locate sites for test planting (Boucher 2016). Google Earth View or Street View imagery can be accessed and analyzed externally (Meunpong et al. 2019) using basic ML algorithms (Li 2020) or analyzed in the Google Earth Enterprise (Google Earth Engine 2021) to estimate parameters such as DBH class and identify suitable planting sites (Li et al. 2015; Boucher 2016; Berland and Lange 2017), as well as obtain tree leaf imagery to assist in verifying species. An early example of this effort is Google Earth Enterprise and AI creating a Los Angeles, California, USA Tree Canopy visual- ization tool in 2020 for public use and engagement (Calma 2020). In the near future, to analyze tree health and prog- ress of species tests, ML routines and analytics will be available to run preprogrammed sets of algorithms to collect appropriate data to assist in the analysis of urban forests and climate adaptation. ML routines will take any number of data sets and determine tree or canopy health without human intervention. ML algorithms will access hyper- or multispectral data and determine the optimal bands for a particular anal- ysis to save computing time and reduce errors. ML routines will be used in Google Earth Engine that can look for any visual tree parameters necessary for analysis: DBH, open sites, leaf shapes, flowers, even visual health assessment cues. Weather and agricul- tural data will be mined for trends, as will construc- tion permits and other sources the ML algorithms deem necessary. Urban forest professionals and managers will use these data and analysis outputs to notify stakeholders, decision makers, and citizens of findings, implica- tions, and plans to remedy climate change impacts. New analysis outputs can be used to make compel- ling visual images to help stakeholders understand and act, and savvy communicators will utilize avail- able communication platforms to disseminate infor- mation about threats to the urban forest and measures being taken to adapt to these threats. Urban Heat Island Mitigation The urban heat island (UHI) is a phenomenon of increased heat in urban environments due to changes 155 in land surfaces, the heat storing properties of build- ing materials, and waste heat of human activity (Akbari et al. 2015; Mohajerani et al. 2017). Urban heat disasters are increasing in frequency due to urbanization and increasing temperatures from cli- mate change (Corburn 2009). Now, disaster manage- ment professionals across the world have plans for urban heat events that include urban greening strate- gies (Wong et al. 2021), because urban forests are recognized as an important component of mitigating the UHI. Urban heat event disaster management plans (hereafter both combined as “UHI plans”) are dependent upon urban forest management plans and thus are closely aligned with UHI plans. Today, collecting modern technology data for UHI plans is straightforward. Inventory data that comprise data that include digitized tree crowns, species, loca- tion, and condition are an excellent start for UHI managers. Having digitized data of empty and avail- able planting spaces, growth and change over time, and species distribution change are important UHI plan components as well (Li 2020). At a minimum, having medium- to high-resolution visual satellite data (Duan et al. 2019) integrated with traditional inven- tory data is an excellent UHI plan component. Tools exist today to extract total canopy cover from satellite images (PlanIT Geo 2021). Collecting high-resolution spectral data from aircraft or RPAs over multiple years can assist urban foresters in predicting future urban heat event severity for UHI forecasters by having a better mortality model to predict areas of increasing heat. Creating good collaboration and sharing net- works today is essential to the success of UHI plans, which are complicated logistically and are for, cur- rently, rare events. In the near future, ML algorithms that model can- opy growth and decline based on plant health— obtained from spectral and visual data and canopy crown extent over time—will be used by UHI man- agers and forecasters to manage assets and resources to prepare for urban heat events. High-resolution visual and spectral satellite images used by urban for- est managers will also be used by UHI managers in their short-term plans. New methods of measuring temperature (Allen et al. 2018) that benefit urban for- est managers and UHI plans will produce fine-grained data that can be mined by ML algorithms tracking microclimates for tree growth (as in the climate change scenario above). Simple coordination and data sharing by forestry and disaster management ©2022 International Society of Arboriculture
March 2022
| Title Name |
Pages |
Delete |
Url |
| Empty |
Ai generated response may be inaccurate.
Search Text Block
Page #page_num
#doc_title
Hi $receivername|$receiveremail,
$sendername|$senderemail wrote these comments for you:
$message
$sendername|$senderemail would like for you to view the following digital edition.
Please click on the page below to be directed to the digital edition:
$thumbnail$pagenum
$link$pagenum
Your form submission was a success.
Downloading PDF
Generating your PDF, please wait...
This process might take longer please wait
