Arboriculture & Urban Forestry 48(2): March 2022 151 Collection is finding relevant existing data (“dis- Figure 2. Pest detection using spectral reflectance algorithms. Proprietary plant health algorithm developed specifically to detect emerald ash borer (EAB). Three Fraxinus pennsylvanica attacked by EAB and in various stages of early stress are denoted by yellow arrows. 1 = best condition and 3 = worst condition. In this image, colors are only relevant for Fraxinus: blue is healthy foliage, and orange is thinning foliage due to EAB attack. At the time of imaging, only crown thinning was evident visually, and no other indicators of attack were visible. Woodpeckers were found in these trees several months after this imaging mission. Location: Boulder, Colorado, USA. Collecting or accessing remotely sensed data, ana- lyzing on software platforms, integrating new infor- mation into action, and communicating results to users and clients will soon be a standard task for the urban forestry professions. Urban forestry routinely using RS data and acting on it will be an important inflection point in the direction and history of urban forestry. “Digital urban foresters” collecting, harvesting, curat- ing, and analyzing data will be a new specialty, because current and emergent threats to urban ecosystems—and the citizens who depend on urban forests—require utilization of all available data in an increasingly complex world. Now is the time to begin the transi- tion to the era of Big Data-driven urban forestry. The coming era of urban forest technology can be characterized as a “Big Data Urban Forestry” infor- mation environment. This new era will require new ways of organizing some traditional tasks and devel- oping new tasks arising from the proliferation of data. The CACI Model The CACI model in this paper refers to the process of urban forest RS data Collection, Analysis, Commu- nication, and Implementation. Key to this process is a specialized branch of artificial intelligence comput- ing called machine learning (ML), discussed below. covery”), and/or physically collecting needed RS data. RS data discovered or collected may be from any platform and curated in the public or private sphere (open-source or fee-based data). Analysis requires an understanding of both the applicability and relevance of remote sensing out- puts, such as vegetation indices, LiDAR point clouds, Digital Evaluation Models (DEMs), 3D models, and digital orthophotos. Applicability and relevance of sensor outputs are key considerations for choosing a particular sensor for urban forest analysis. Spectral imagery can be processed into vegetation indices to indicate tree health and help determine canopy extent (Jensen et al. 2012; Alonzo et al. 2016), as well as attempt to determine genus and species. It is import- ant to understand that, currently, analysis of spectral data using existing commercial agriculture software platforms on the internet does not sufficiently analyze trees for accurate pest and disease tracking (Staley et al. 2019) and requires human intervention using a platform such as ENVI (Harris Geospatial Solutions Inc. 2021), ArcGIS (ESRI 2009), or QGIS (QGIS 2021). Choosing data types will be an important anal- ysis and budget consideration in the future: just because data exist, does not mean those data are rele- vant to a particular analysis (Li et al. 2019). For exam- ple: dense point clouds of LiDAR data are excellent for three-dimensional representations of tree crowns (Gülçin and Konijnendijk van den Bosch 2021) but currently struggle to resolve parameters such as leaf chlorophyll content or presence of pests or disease (Fahey et al. 2021). Paying for the right data and anal- ysis will be an important discussion in many urban forests soon. Communication, for purposes of this paper, includes written, spoken, and digital communication. Exam- ples of communication types relevant to urban forestry include tasks such as explaining to decision makers the merits of a project or a proposal, advising third- party contractors on the validity of an output or con- clusion, notifying citizens of the findings of a project, writing urban forest plans based on RS data and anal- ysis, asking for money to act on a proposal or analysis, explaining to a contractor the scope of project require- ments and expectations, and answering questions or relaying findings to the public via social media and web pages. Communication skills and coalition building will be even more important in an information-rich ©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
