Abstract
Background To better understand the impacts of storms on urban forests and to develop effective management strategies, it is essential to collect accurate and consistent data on urban forests before and after a storm. However, there is often limited time for researchers to establish sampling protocols and gather data before cleanup efforts erase key visual information, such as failure modes and damage severity. Moreover, storms differ in how frequently and predictably they impact regions, potentially limiting some researchers’ opportunities to gain experience with storm assessment methods.
Methods This paper presents a standardized protocol for collecting pre- and post-storm data on urban forests based on previous studies and the authors’ experiences collecting urban tree data following severe storms.
Results The protocol covers a wide range of data, including tree species, size, and condition; risk factors; and damage type. The protocol also includes instructions for post-storm data collection using a variety of methods, including field surveys, remote sensing, and citizen science efforts.
Conclusions A standardized protocol will help researchers collect consistent data on urban forests before and after storms, while also making the findings more relevant to urban forest managers, as the data will align with what they already collect.
Introduction
Urban forests are essential components of urban infrastructure. Like other infrastructure, the services provided by urban trees can be compromised by severe and extreme weather events such as ice or windstorms (Cadaval et al. 2024). While infrastructure elements like electrical utilities can be intentionally engineered to enhance their resilience to climate challenges (Stewart 2014), urban foresters must work with the biomechanical limits of the trees they manage when preparing for severe and extreme weather. However, research indicates that proper species selection (Duryea et al. 2007a; Duryea et al. 2007b; Salisbury et al. 2025), pruning (Duryea et al. 2007b; Klein et al. 2020), and preventing construction damage (Johnson et al. 2019) are effective strategies for reducing storm-related losses.
Many of these findings are based on research conducted through observations of actual storm events. In this work, researchers typically assess urban forests in the aftermath of severe and extreme weather to determine if trees or tree parts failed under the storm conditions, considering a range of intrinsic and extrinsic risk factors. When available, pre-storm inventory data (collected by urban tree managers or the researchers themselves) can enhance sampling designs and help assess pre-storm conditions that may no longer be evident due to tree removal or site cleanup (Bloniarz et al. 2001; Bond 2005).
Modern urban forestry is a data-driven management process that involves tracking, analyzing, and reporting tree assets, site characteristics, and work orders (Nitoslawski et al. 2019). Recognizing the potential scientific importance of this data and the disparate nature in which it currently exists, urban forestry researchers have worked to develop data standards for general inventories (Urban Forestry Data Standards Development Team 2010) as well as those specifically related to the assessment of tree growth and longevity over time (Roman et al. 2020).
The earlier effort by the Urban Forestry Data Standards Development Team (2010) aimed to create a comprehensive standard for all inventory data, encompassing general tree metrics such as tree taxa, trunk diameter and tree height, methods for recording tree location and site variables, as well as tree maintenance activities and defects related to risk. The more recent effort by Roman et al. (2020), while focusing on data most relevant to tracking tree condition and survival over time, addressed many of the same data inputs. The Urban Forestry Data Standards Development Team’s (2010) work is reflected in the International Society of Arboriculture’s Best Management Practices Tree Inventories (Wiseman et al. 2025).
Data standards ensure consistent terminology, structure, and organization of datasets, as well as guidelines for how data should be stored and used (Gal and Rubinfeld 2019). Standardization allows data to be easily interpreted and used by others (Gal and Rubinfeld 2019), facilitates the aggregation of datasets into largescale models, supports both longitudinal (Roman et al. 2020) and meta-analyses, ensures that key data omissions are avoided, and helps create greater research transparency (AgMIP 2023). Standardization also facilitates meta-analyses that will support comparisons of urban tree databases across multiple regions.
Data standards can also serve as a roadmap for scientists exploring new research areas or for practitioners seeking guidance on data collection options for storm preparation and response. For many researchers and industry professionals, severe and extreme weather such as windstorms and ice storms are relatively infrequent events (Hauer et al. 1993; Hauer et al. 2011; Hauer and Schulz 2023). As a result, when a storm strikes a nearby community, they may lack the experience needed to make quick decisions about data collection. For researchers, this can result in post-storm assessments being conducted after cleanup efforts have already removed critical observable failures or in rushed data collection that leads to omissions, preventing important questions from being properly articulated and addressed.
Additionally, data collection efforts that lack randomized or stratified sampling may, at best, limit the ability to generalize findings to a larger urban tree population and, at worst, result in a biased understanding of tree storm damage. Such missteps can undermine the value of data and findings derived from extensive fieldwork. Beyond research, hastily constructed datasets can hinder the recovery efforts of local governments and lead to issues when applying for disaster relief funds.
The authors have conducted numerous pre- and post-storm assessments, which has allowed them to refine their approaches and increase efficiency in data collection. Additionally, several of us recently conducted an international, systematic literature review based on post-storm assessments (Salisbury et al. 2023). Through this effort, we observed the wide range of data researchers have collected in an attempt to predict tree failure during tropical windstorms. This combination of experience and knowledge is presented here to outline best practices and data standards for pre- and post-storm assessment research in urban forestry. These standards can be used by urban forest managers in storm preparation and response efforts, as well as by researchers when planning fieldwork. Moreover, they can help facilitate mutually beneficial partnerships between managers and researchers— creating scenarios where insights can be gleaned by those working directly on recovery from adverse weather events while also benefiting the broader industry as a whole.
Methods
For this paper, we examined existing efforts to standardize data collection in urban forestry and past predictors from storm assessments to develop a master list of potential variables for assessing the impacts of storms on trees. The range of data options for pre- and post-storm data collection came from 3 key sources. The first is a comprehensive literature review conducted by Salisbury et al. (2023). In this work, the authors (many of whom are contributors to this paper) performed a systematic review of tropical cyclone pre- and post-storm assessments to determine which intrinsic (tree related) and extrinsic (environment related) factors predicted tree failure. The review, conducted internationally, involved searching peerreviewed literature in English, French, Japanese, Mandarin, Portuguese, and Spanish. It followed the PRISMA-ScR guidelines (Tricco et al. 2018) and utilized the search string “forest AND (hurricane OR cyclone OR typhoon)”, along with translations and synonyms in 6 languages. The search covered peerreviewed research and dissertations published between 1900 and 2022 across multiple databases, with additional studies identified through backward and forward chaining (Salisbury et al. 2023).
The second source, Urban Forestry Data Standards Development Team (2010), provided guidelines for assessing species composition, ecosystem services, tree defects, and maintenance issues. These data standards were developed through a collaboration between the IUFRO Urban Forestry Working Group, the International Society of Arboriculture (ISA), the USDA Forest Service, and the Urban Natural Resources Institute (UNRI), with contributions from over 180 team members across 14 countries. The final source, Roman et al. (2020), outlined procedures for collecting core field data necessary for the long-term monitoring of urban trees. This field guide was authored by 17 members of ISA’s Urban Tree Growth and Longevity Working Group (UTGL). In addition to integrating these foundational references, the authors drew upon their own collective expertise, which encompasses more than a century of experience in data collection, analysis, and science communication, resulting in over 2 dozen peer-reviewed publications focused on trees and storms.
Data collection options were arranged into 3 categories: metadata, pre-storm data, and post-storm data. Within each category, we outlined the minimum dataset requirements necessary for meaningful analysis and data sharing. Additionally, we include optional metadata, response, and predictor variables that can be considered when developing predictive models for tree failure. Each variable is accompanied by a description, the applicable values or units, and the source (Urban Forestry Data Standards Development Team 2010; Roman et al. 2020; Salisbury et al. 2023) from which the data is derived.
Results and Discussion
Sampling Design
To avoid bias, sampling activities related to the selection of trees, plots, or both should be completed before any observations are made. If a community has an existing inventory, selecting trees or plots can be a relatively straightforward remeasurement of all trees or a random sample. If the inventoried population is manageable and resources allow, a complete reassessment of all trees is ideal. When this is not practical, sampling is appropriate. Completely random sampling may not be as effective as stratified sampling because the latter can result in more representative data (Jaenson et al. 1992). Stratification can be across neighborhoods or street sections, or proportional to land uses (e.g., residential, commercial, parks). Crews should avoid ad hoc data collection and refrain from adjusting sampling to focus solely on areas most impacted by the storm. Such practices introduce bias and increase the risk of over-reporting storm-related failures (Edberg and Berry 1999).
Most importantly, damaged and undamaged trees should be measured. There is a tendency to focus data collection efforts solely on failed trees (Chen 2024), but this severely limits the utility of the data. Failures may be rare or represent a large portion of the tree population, but without knowing the total number of trees (damaged and undamaged), there is no context to assess the likelihood of failure. Without understanding the likelihood of failure, it is impossible to determine which factors—such as species, specific environmental conditions (e.g., wind exposure, soil conditions, associated precipitation), or other variables—increase or decrease the potential for failure. A condition may appear to be associated with a higher likelihood of failure simply because it is more commonly observed in the sample, not because it compromises the tree’s structural integrity (e.g., Koeser et al. 2023). Lastly, there is tremendous value in understanding what factors are present in trees that are strong enough to resist severe weather, as these trees may represent optimal tree selection, planting, and management practices.
Metadata
Metadata plays an essential role in science and data sharing, as it provides context about the data, such as how it was collected, processed, formatted, and organized. This information ensures that datasets are easily understandable, reproducible, and usable by other researchers, facilitating collaboration and enhancing the transparency of scientific work. Well-documented metadata helps prevent misinterpretation, enables effective data integration across studies, and improves the long-term usability of data, making it a valuable resource for future research. Metadata standards such as ISO 19115 (https://www.fgdc.gov/metadata/iso-standards) could be used to ensure documentation that meets federal requirements in the US, but we recommend several metadata elements as a minimum metadata set when working with pre-and post-storm inventory data (Table 1).
Recommended metadata to accompany publicly available datasets collected after a storm has damaged trees in the urban forest. These data may be recorded as variables within the dataset itself. Data should be reported in the methods section of a research paper documenting storm-related tree failures.
The first 2 metadata elements suggested for the minimum dataset in Table 1 provide information about the inventory crews who conduct field work. First, field crew identification should include details on whether volunteers, students, or professionals gathered the data. Information such as education, training, or industry credentials (especially those related to tree risk assessment) should also be included. In addition to these descriptions, an ordinal rating of experience level (novice, intermediate, expert) allows others to gauge data quality.
Most other metadata elements suggested for the minimum dataset are intended to provide details about the storm (Table 1). They include its date, type (e.g., hurricane, tornado, ice), and severity (e.g., hurricane category level, tornado EF or F level, measured wind speed, measured ice accumulation), and the source of the storm information. Weather associated with a storm, even in the same region, is often characterized differently (Landry et al. 2021) depending on the geographic focus. Furthermore, details on storm events can be difficult to obtain, and researchers may draw from a variety of sources, including local weather stations, news outlets, weather services, and national agencies—each of which may provide slightly different estimates of storm intensity. Storm data should be reported from reputable sources close to the tree population being assessed. Depending on the size of the storm, conditions may differ dramatically, even a few kilometers apart. When available, report relevant storm variables such as precipitation received, average and maximum sustained wind speeds, average and maximum gust speeds, storm duration, storm surge, flooding, and average, maximum, and minimum temperatures. To provide context for researchers in other regions, average climate across the area should be reported, both annually and for the month that the storm occurred. This presents the baseline or normative conditions experienced by the tree population. In some cases it may be appropriate to include information about precipitation immediately prior to the storm event. Areas that had received high levels of precipitation which saturated the soil may experience high levels of tree blowdown even in less severe wind events (Kamimura et al. 2012).
Response Variables
The response variables collected determine the research questions that can be asked. In some cases, researchers have the opportunity to form their hypotheses first and then gather the most appropriate data to address those questions. Other times, researchers will be working with data previously collected during routine inventories or post-storm urban or utility forestry operations (Urban Forest Strike Teams 2024). In this latter scenario, the available data will limit the range of possible research directions. Additionally, data quality can vary. However, using existing data sources, when available, can help conserve resources and offer a snapshot of the immediate aftermath of a storm and the associated cleanup efforts.
Dead trees present a particular challenge because it is sometimes difficult or impossible to determine the cause of mortality. Large scale damage, data gaps, and delayed mortality after a storm can increase this uncertainty (Armentano et al. 1995). Depending on the timing of the post-storm assessment, crews may encounter a standing or fallen dead tree or find only a stump or vacant planting site. While all these scenarios can be categorized as “dead,” it is often impossible to attribute urban tree mortality to a specific storm with certainty, especially if there is a time gap between the pre- and post-storm assessments and the actual storm. When trees are missing, crews might consider contacting the property owner or manager to confirm whether the tree died or failed during the storm (by knocking on the door or leaving a door hanger). For example, post-storm assessment crews followed this approach to disregard missing trees that had been removed for site development prior to Hurricane Irma (Landry et al. 2021).
While a simple assessment of whether a tree survives or not is useful, data on partial branch failure can be highly valuable from a risk assessment or utility management perspective. In these cases, damage could be evaluated using a simple ordinal rating system. For example, Roman et al. (2020) include a “Crown vigor” variable, described as “a holistic assessment of overall crown health, reflecting the proportion of the crown with foliage problems and major branch loss,” with 5 classes ranging from healthy to dead. Additionally, other existing crown assessment techniques, typically used for evaluating condition, health, or estimating ecosystem services, could be applied to document tree damage that is not an immediate source of mortality (Bond 2012; i-Tree 2021).
Post-storm work orders from local urban forestry programs can serve as a proxy for graduated damage assessment, particularly if they are generated as part of a systematic evaluation. Trees that suffer little to no damage will be skipped when determining the need for care after the storm, while those with more significant damage may require pruning to remove broken branches and restore crown structure. Trees with severe crown damage, trunk failure, or root failure will be scheduled for removal. This dual-purpose approach benefits both practical management and research. With this in mind, data collection crews should seek opportunities to collaborate with local urban foresters and other urban vegetation managers to generate work orders or damage reports from post-storm assessments conducted for research.
Most post-storm research focuses on tree damage and death, and the models generated from this research can help managers predict losses in urban forest value and benefits after a storm event. From an urban tree risk perspective, post-storm tree damage is a critical response that helps inform likelihood of failure, but it does not provide a complete picture. Risk also includes the likelihood of impact and consequences of failure. To gain a better understanding of the risks posed by trees in a population, researchers must conduct surveys of property owners and managers to assess the consequences of failure to targets such as structures, utilities, vehicles, and people.
Pre-Storm Inventory Data—Minimum Dataset
In addition to the metadata listed in Table 1, the minimum pre-storm dataset includes 5 other variables (Table 2). The first is the date the tree was inventoried or last updated. The second is a unique identifier of each tree that allows users to combine pre- and post-storm datasets. Additionally, location information sufficient for reassessment should be recorded— whether this is (in order of greatest to least useful) coordinates stored in a geographic information system (GIS), an address, or a unique identifier of the planting space. Species identification, according to internationally accepted taxonomic databases like the Royal Botanic Garden, KEW’s International Plant Names Index, and Plants of the World Online (POWO 2024) should also be recorded to both aid in relocation and to identify species specific likelihood of failure. Taxon is consistently one of the most significant predictors of failure (Salisbury et al. 2025).
Pre-storm inventory data—minimum dataset.
Lastly, trunk diameter should be recorded to aid in relocation efforts, especially when groups of a single species exist in a small area. When measuring trunk diameter, the height at which the trunk diameter was measured should be included. In the US, diameter is typically measured at 4.5 ft (1.4 m) versus 4.3 ft (1.3 m) outside of the USA (Magarik 2021; Wiseman et al. 2025). Municipalities may also establish local policies or practices for the height at which trunk diameter is measured, which can differ from the more common 1.4- or 1.3-m heights (Wiseman et al. 2025). The consistent height for trunk diameter measurements should therefore be recorded, as should the protocol for measuring diameter when a tree consists of multiple trunks (Table 2).
Pre-Storm Inventory Data—Optional Data
While the minimum dataset described above is sufficient for creating random or stratified samples, additional pre-storm data collection can be valuable when addressing specific questions related to risk assessment and tree biomechanics (Table 3). Some of these variables are best collected prior to a storm if they are to be included in the analysis. These include components of visual risk assessments (i.e., qualitative safety assessments of a tree and its surroundings using industry best practices), which could be biased by the hindsight that occurs during post-storm evaluations. Additionally, aspects of tree conditions, such as opacity and quality (Bond 2012), may be affected by the storm. While many measurements are easier to take before a storm, most can still be measured or estimated afterward, as explained below.
Understandably, urban forest managers may be hesitant to make tree risk and condition assessments publicly available, as residents might be alarmed to learn that a tree near them poses an elevated risk. Moreover, public risk records could lead to future litigation if a tree identified as high-risk fails before mitigation can occur. If data will be published in a paper or posted in a repository, one strategy is to remove these risk assessment ratings; alternatively, the data can be anonymized by eliminating identifying location information (e.g., addresses, coordinates, storm names). Researchers affiliated with a university should consult with their research offices or libraries to discuss specific requirements for sharing sensitive data. These offices can also assist in creating data management and sharing plans, which may be required by funding sources to demonstrate to reviewers that these issues have been thoroughly considered.
Post-Storm Assessment—Minimum Dataset
Several parameters collected during the pre-storm assessment (Tables 2 and 3) are also collected during the post-storm assessment (Table 4). First, if a pre-storm assessment was conducted, use the unique identifiers of each tree to pair pre- and post-storm data collection. If a pre-storm assessment was not conducted, the post-storm dataset will require unique tree identifier numbers. Similarly, location, species, and trunk diameter are necessary for both datasets in order to ensure pre- and post-storm data are appropriately matched. Lastly, the minimum post-storm dataset should include a measured response related to tree damage. This could be as simple as a binary variable (1 = damage, 0 = no damage), or more detailed information could be collected, such as the location of damage (e.g., roots, trunk, crown) and the severity of failure within the tree as described in above.
Pre-storm inventory data—optional data.
Post-storm assessment—minimum dataset.
Post-storm assessment—optional data.
Post-Storm Assessment—Optional Data
Optional post-storm data collection could include a wide range of response data beyond simply the presence or absence of tree damage (Table 5). Alternatives include damage expressed as a percentage of the whole tree or the crown, ordinal rankings of damage, damage in specific tree locations (e.g., roots, trunk, crown), and tree mortality. Post-storm assessments could also evaluate property damage or injuries/deaths. Work orders from post-storm cleanup efforts can also be analyzed to gauge damage severity.
Optional Data that Can Be Collected Before or After a Storm
To study the impact of storms on trees, many optional data can be collected before or after the storm to better understand tree vulnerability and resilience (Table 6). Tree characteristics such as age, species growth form, and species growth rate can provide insights into the developmental stage and overall health of trees (Salisbury et al. 2023). Defects such as leans or decay may indicate predispositions to failure during extreme weather events (Hickman et al. 1995; Kane 2008; Nelson et al. 2022). Variables such as native status, wood density, and the wood’s modulus of elasticity and rupture can affect a tree’s inherent strength (Francis 2000; Duryea et al. 2007b; Uriarte et al. 2019; Nakamura 2021). Additional structural factors like height, stem taper, and the presence of vines can influence a tree’s risk of wind damage (Allen et al. 1997; Tabata et al. 2020; Torres-Martinez et al. 2021). Pruning history and root conditions (including root loss and available rooting space) can further impact a tree’s loadbearing capacity (Kane 2008; Johnson et al. 2019; Klein et al. 2020).
Additional optional data—can be collected pre-storm, post-storm, or both.
Environmental conditions and tree site factors are also important considerations. Aspect of slope or exposure, along with site elevation and proximity to the storm center, have all been investigated to determine how environmental forces interact with individual trees or groups of trees (Salisbury et al. 2023). Grouped trees or those in protected sites might experience differing levels of damage compared to isolated trees or those in exposed sites (Duryea et al. 2007a; Duryea et al. 2007b). When considering group trees, consider the stem taper, soil drainage characteristics, height above or below the dominant canopy height, and species diversity (Mitchell 1995). Collecting such data helps researchers assess not only the structural properties of trees but also the influence of their surroundings, ultimately informing storm preparedness and mitigation strategies.
The Future of Pre- and Post-Storm Assessment
We have presented what we consider current best practices for data collection in pre- and post-storm assessments. But we acknowledge that novel attributes and methods will likely arise as researchers and practitioners learn more and new circumstances arise. As technologies like ground-based light detection and ranging (LiDAR)(Nguyen et al. 2020) and photogrammetry (Roberts et al. 2018) become more widespread, new metrics—beyond what can be measured with traditional forestry tools—may emerge as valuable predictors of storm damage.
In an effort to create a living standard that evolves as methods advance, we have established the Urban Tree Storm Data Network (UTSDN 2024). This network will include the published data standard, a bibliography of pre- and post-storm assessment research, a data repository for uploading storm data, and a wiki of metadata, response variables, and predictor variables (including descriptions, units, sources, etc.) used in past studies. We encourage both researchers and industry professionals to contribute their data using the standards documented here and draw from this resource as we work toward creating more resilient forests in a changing global climate.
Conclusion
Our work highlights the importance of developing standardized data collection methods for pre- and post-storm assessments in urban forestry. By adopting standard practices and data in community inventories and field research, scientists and practitioners can generate more reliable, comparable, and actionable information to predict tree failure and assess the health and resilience of urban forests in the face of severe and extreme weather. Consistency in data collection will improve the ability to draw meaningful conclusions from post-storm data, inform species selection and tree management practices, and ultimately contribute to more storm-resilient urban landscapes. Standardized approaches not only enhance the scientific rigor of post-storm studies but also facilitate collaboration across regions, helping cities and municipalities better prepare for and mitigate the impacts of future storms on their urban forests.
However, tree risk and condition ratings may not be publicly available to researchers. Moreover, biometric data such as stem diameter, height, and species identification may be collected in an unusable format (i.e., without units), contain errors, or utilize outdated or regionally specific taxonomic conventions. Consequently, reassessing the existing inventoried population is preferred.
Conflicts of Interest
The authors reported no conflicts of interest.
- © 2025 International Society of Arboriculture
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