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At least 289 records · Page 16

Milwaukee Urban Development: Assessing Climate Vulnerability through the InVEST Model on Urban Cooling in Milwaukee Using NASA Earth Observations

Milwaukee’s neighborhoods experience increased social, health, and ecological stress from the Urban Heat Island (UHI) effect due to changing land cover and climate. Extreme urban heat disproportionately affects Black and Latine communities due to systematic disinvestment in infrastructure and lack of resources to cope with heat. Our partner, Groundwork Milwaukee, supports Environmental Justice organizing around urban heat in historically redlined neighborhoods. This study explores heat mitigation in Metcalfe Park, one such neighborhood that is a focus area for our partner, as well as across the city and county of Milwaukee. Our team utilized the Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Model for urban cooling to model heat mitigation scenarios for Milwaukee in support of Groundwork’s climate resilience and heat mitigation work. The data inputs used in our analysis were Land Use Land Cover (LULC), evapotranspiration, and surface temperature derived from NASA Earth observations, as well as a biophysical table containing land cover class attributes. We developed a heat vulnerability index from American Community Survey datasets and satellite imagery to identify areas in most need of heat mitigation intervention. We found that central and northwest city areas, home to a majority of Milwaukee’s Black and Latine population, have low heat mitigation and high vulnerability to heat impacts compared to the rest of the county. We also found that tree canopy increases across scales are effective in promoting heat mitigation. Our results and end products will bolster the efforts of our partners to make decisions on heat mitigation strategies, build community resilience, and reverse legacies of environmental racism in the face of extreme heat and climate change.

urban heat island↗

California Trees Seasonally Use Augmented Water Sources: Water Isotope Tracking in a Groundwater‐Dependent Ecosystem

Sustainable groundwater management must account for the needs of groundwater dependent ecosystems. To understand the relationship of ecosystems and seasonal water use, we studied the stable isotope composition (δ 18 O and δ 2 H) of water in streamside trees in a semi-arid streamside environment (Livermore, California, USA). We sampled seven trees at two sites every other month from April 2024 through April 2025 for tree xylem stable water isotope signatures. These data were compared to potential source waters: precipitation, imported surface water, soil water and regional groundwaters. Large daily precipitation events were found to be isotopically similar to regional groundwater and were thus treated as one water source. A Bayesian mixing model using stable water isotopes was used to determine the ratios of these three potential source waters (small daily precipitation events, groundwater/large daily precipitation events and imported water) present in tree xylem water. On average, tree water sources include 32% imported water (SD = 9%), 35% small daily precipitation events (SD = 10%) and 33% groundwater (SD = 3%), with significant seasonal variation (t summer-winter = 30.8, p < 0.01), particularly drawing more imported water (more than 55%) in the summer. While small daily precipitation events contribute only 10% of the total precipitation in our dataset, it represents a third of water used by trees. In addition, while these ecosystems are designated as groundwater dependent ecosystems, the trees use approximately one third imported water and even more during dry summer months. This approach provides water managers with a practical tool for quantifying ecosystem water needs, supporting data-driven decisions and regulatory compliance. While California's Sustainable Groundwater Management Act emphasizes supporting groundwater dependent ecosystems, it allows flexibility in demonstrating benefits. In conclusion, our methodology provides a way to document that management actions (such as managed aquifer recharge with imported water) deliver measurable co-benefits to GDEs.

Geosciences↗

Testing of Advanced Capabilities to Enable In-time Safety Management and Assurance for Future Flight Operations

In order to refine an initial Concept of Operations, explore Concepts of Use, and expose/validate requirements for future In-Time Aviation Safety Management Systems (IASMS), testing architectures were created, along with a set of capabilities and underlying information exchange protocols. These systems were conceived and developed based on hazards associated with two envisioned urban area flight domains: (1) highly autonomous small uncrewed aerial systems (sUAS) operating at low altitudes, and (2) highly autonomous air taxis. The initial scope of this development is described in [1]; this report provides an update, focusing on the subsequent developments and test activities. As stated in [1], it is important to note that there are many capabilities already in use by the industry (or soon to be in use) that will play critical roles in future IASMS designs. Those reported here were developed to address a gap in the current state-of-the-art regarding specific hazards/risks, and/or to allow for investigation of the interplay between and across hazard types — particularly regarding how overall safety risk can be reduced or managed effectively. Results of testing and development activities are organized by the operational phase wherein a particular capability would be employed (i.e., preflight, in-flight, and post-flight/off-line). Pre-flight: A set of capabilities were developed to help mitigate safety risk prior to flight (e.g., during flight and mission planning). Results of testing summarize (1) validation activities to raise the Technology Readiness Level (TRL) and (2) evaluation activities where the capabilities were applied to flight/mission planning procedures and used by operators/pilots. For the latter, flight plans were automatically assessed, and operators/pilots were notified of hazardous flight segments so as to enable adjustment of the flight plan and re-evaluation, and/or to better inform go/no-go decisions. Capabilities addressed hazards associated with power consumption, third-party risk, wind, navigation system performance, radiofrequency interference, and proximity to geo-spatial threats (e.g., buildings, trees, and no-fly zones). In-flight: Flight experiments tested capabilities that detect and respond to hazards encountered during flight. In the first series, safety hazards were monitored and assessed onboard, and system-generated mitigation maneuvers were recorded (but not acted upon by the vehicle). In the second series, mitigation maneuver commands directed the aircraft in response to safety hazards (i.e., auto-mitigation). The sUAS used for testing is described in full, as is the test architecture, which included commercial avionics, research avionics, and onboard software designed to detect, assess, and respond to hazards. The onboard system was designed as a run-time assurance framework, consistent with [2] and supportive of both supervisory and automated modes. The primary functions included: real-time risk assessment (RTRA), auto-pilot monitoring, constraint monitoring, and contingency select/triggering. RTRA performs integrated risk assessment considering data from several hazard-related monitors (e.g., battery, motors, navigation, communications, population density, and loss-of-control). Post-flight/off-line: Data monitored and recorded during flights can enable IASMS capabilities that execute after flights have completed (or “off-line”). These include: (1) the ability to identify anomalies and trends that may only be observable when comparing data spanning a number of similar flights; (2) the ability to update and validate pre-flight and in-flight capabilities and any underlying models to improve their performance; (3) the ability to report anomalies/off-nominals that may indicate design changes or maintenance actions are needed; and (4) the ability for humans involved in operations to report safety-relevant observations to help in understanding the flight data and/or the operational context of a flight. Progress on three such capabilities is summarized; the first investigates anomaly detection given a limited set of flight logs and applies an approach previously used for space operations. The second explores what could be identified using a larger set of flight logs, including from web-based forums where flight logs are posted by sUAS autopilot users. The third creates a new means of collecting information on UAS incidents and accidents via the Aviation Safety Reporting System (ASRS).

sUAS↗

Prediction of Weather Impacts on Airport Arrival Meter Fix Capacity

This paper introduces a data driven model for predicting airport arrival capacity with a look-ahead time 2-8 hour forecast. The model is suitable for air traffic flow management by explicitly investigating the impact of convective weather on airport arrival meter fix throughput. Estimation of the arrival airport capacity under arrival meter fix flow constraints due to severe weather is an important part of Air Traffic Management (ATM). Airport arrival capacity can be reduced if one or more airport arrival meter fixes are partially or completely blocked by convective weather. When the predicted airport arrival demands exceed the predicted available airport's arrival capacity for a sustained period, Ground Delay Program (GDP) operations will be triggered by ATM system. Serious imbalances between demand and capacity occur most frequently when the airport capacity is severely degraded due to either bad airport terminal surface weather or inclement convective weather around airport arrival fixes. A model that predicts the weather-impacted airport arrival meter fix throughput may help ATM personnel to plan GDP operations more efficiently. This paper identifies the characteristics of air traffic flow across arrival meter fixes at Newark Liberty International Airport (EWR). The proposed approach, based on machine-learning methods, is developed to predict the weather impacted EWR arrival Meter Fix (MF) throughput. Sector forecast coverage is used to envision the weather impact on airport arrival MF flow, and the validation is accomplished by using Convective Weather Avoidance Model (CWAM) 0.5 to 2-hour and Collaborative Convective Forecast Product (CCFP) 4 to 8-hour look-ahead forecast data for the period of April-September in 2014. Furthermore, the regression tree ensemble learning of random forests approach for translating a sector forecast coverage model to an EWR arrival meter fix throughput model is examined. The results suggest that ATM decision makers in charge of MF flow control and GDP planning may benefit from adopting the airport arrival meter capacity prediction models to estimate the inclement weather impacts.

Wang, Yao X.↗

Adaptive Stress Testing of Trajectory Predictions in Flight Management Systems

To find failure events and their likelihoods in flight-critical systems, we investigate the use of an advanced black-box stress testing approach called adaptive stress testing. We analyze a trajectory predictor from a developmental commercial flight management system which takes as input a collection of lateral waypoints and en-route environmental conditions. Our aim is to search for failure events relating to inconsistencies in the predicted lateral trajectories. The intention of this work is to find likely failures and report them back to the developers so they can address and potentially resolve shortcomings of the system before deployment. To improve search performance, this work extends the adaptive stress testing formulation to be applied more generally to sequential decision-making problems with episodic reward by collecting the state transitions during the search and evaluating at the end of the simulated rollout. We use a modified Monte Carlo tree search algorithm with progressive widening as our adversarial reinforcement learner. The performance is compared to direct Monte Carlo simulations and to the cross-entropy method as an alternative importance sampling baseline. The goal is to find potential problems otherwise not found by traditional requirements-based testing. Results indicate that our adaptive stress testing approach finds more failures and finds failures with higher likelihood relative to the baseline approaches.

adaptive stress testing↗

Ground Operations Autonomous Control and Integrated Health Management

An intelligent autonomous control capability has been developed and is currently being validated in ground cryogenic fluid management operations. The capability embodies a physical architecture consistent with typical launch infrastructure and control systems, augmented by a higher level autonomous control (AC) system enabled to make knowledge-based decisions. The AC system is supported by an integrated system health management (ISHM) capability that detects anomalies, diagnoses causes, determines effects, and could predict future anomalies. AC is implemented using the concept of programmed sequences that could be considered to be building blocks of more generic mission plans. A sequence is a series of steps, and each executes actions once conditions for the step are met (e.g. desired temperatures or fluid state are achieved). For autonomous capability, conditions must consider also health management outcomes, as they will determine whether or not an action is executed, or how an action may be executed, or if an alternative action is executed instead. Aside from health, higher level objectives can also drive how a mission is carried out. The capability was developed using the G2 software environment (www.gensym.com) augmented by a NASA Toolkit that significantly shortens time to deployment. G2 is a commercial product to develop intelligent applications. It is fully object oriented. The core of the capability is a Domain Model of the system where all elements of the system are represented as objects (sensors, instruments, components, pipes, etc.). Reasoning and decision making can be done with all elements in the domain model. The toolkit also enables implementation of failure modes and effects analysis (FMEA), which are represented as root cause trees. FMEA's are programmed graphically, they are reusable, as they address generic FMEA referring to classes of subsystems or objects and their functional relationships. User interfaces for integrated awareness by operators have been created.

Figueroa, Fernando↗

CropManage Application for Vineyard Irrigation Decision-Support

CropManage is a free web-application developed by U.C. Cooperative Extension to support evapotranspiration based irrigation scheduling and nutrient management for major specialty crops. Prescribed phenology curves are used to develop daily estimates of canopy cover within a given field, based on days since planting (annual crops) or budbreak (trees, vines). These curves are modulated by a MaxCan parameter representing seasonal maximum canopy cover. Crop development observations can be used to adjust for such factors as weather anomalies or non-standard agronomic practice, as needed. Canopy cover is converted to crop coefficient and combined with reference evapotranspiration to derive daily water consumption. Guidance on crop water requirement is then conveyed to users in terms of system runtime issued on-demand for a given date, largely based on total evapotranspiration since last irrigation event. In this study, CropManage was adapted to vineyards by adding modules accounting for early-season soil moisture depletion and cover crop presence. A crop stress parameter was added to accommodate deficit irrigation practice, allowing the user to specify percentage departure from full water requirement along with start/stop dates. An initial verification exercise was performed on three winegrape vineyards located in California’s Central Coast (2020), North Coast (2020) and Central Valley (2019). Daily crop evapotranspiration was monitored by eddy-covariance fluxtowers. MaxCan was measured by ground and satellite observation. Stress regime was specified by grower practice where available, otherwise stress levels were inferred from applied water records. Mean absolute error and mean bias error of modeled cumulative evapotranspiration were computed with respect to the eddy covariance measurements collected throughout the growing season. Results indicate the modified CropManage water management module performs reasonably well for winegrape. Additional effort is planned to modify the nutrient module for vineyard use.

CropManage↗

Seeing the forest for the trees: implementing dynamic representation of forest management and forest carbon in a long-term global multisector model

Abstract Studies have found that understanding forest management is critical in understanding the interaction between the carbon cycle and the integrated human-Earth system. This makes effectively representing forest management decisions such as planting and harvesting important. Here, we implement a novel dynamic forest harvest model in a global state of the art multi-sector dynamics model, namely the Global Change Analysis Model (GCAM). We implement an approach that explicitly tracks forest age and generates rotation ages for forest harvest that are responsive to changes in wood prices, changes in forest age and regional preferences for forest rotation. Furthermore, the forest sector in GCAM competes for investment with other land use types in the future years based on expected profit. Our baseline scenario results indicate that with the new forest harvest model, the current global wood product demand in GCAM can be met with minimal loss of old growth forest through the age-based harvest decisions. We find that economic pressure for deforestation and consequent loss of forest carbon is a bigger driver of global forest change than wood harvests, especially in developing regions. Under alternative scenarios where an economic value is placed on carbon across the terrestrial and energy systems, while there is an increase in forest plantations, there can be corresponding decreases in forest cover in some regions as forest land competes with land for bio-energy crops. When the carbon in forests is assigned a price, we find that the average rotation age for wood harvests can be reduced across regions to harvest forests in a more carbon efficient manner.

54 ENVIRONMENTAL SCIENCES↗

Fault Tree Based Diagnosis with Optimal Test Sequencing for Field Service Engineers

When field service engineers go to customer sites to service equipment, they want to diagnose and repair failures quickly and cost effectively. Symptoms exhibited by failed equipment frequently suggest several possible causes which require different approaches to diagnosis. This can lead the engineer to follow several fruitless paths in the diagnostic process before they find the actual failure. To assist in this situation, we have developed the Fault Tree Diagnosis and Optimal Test Sequence (FTDOTS) software system that performs automated diagnosis and ranks diagnostic hypotheses based on failure probability and the time or cost required to isolate and repair each failure. FTDOTS first finds a set of possible failures that explain exhibited symptoms by using a fault tree reliability model as a diagnostic knowledge to rank the hypothesized failures based on how likely they are and how long it would take or how much it would cost to isolate and repair them. This ordering suggests an optimal sequence for the field service engineer to investigate the hypothesized failures in order to minimize the time or cost required to accomplish the repair task. Previously, field service personnel would arrive at the customer site and choose which components to investigate based on past experience and service manuals. Using FTDOTS running on a portable computer, they can now enter a set of symptoms and get a list of possible failures ordered in an optimal test sequence to help them in their decisions. If facilities are available, the field engineer can connect the portable computer to the malfunctioning device for automated data gathering. FTDOTS is currently being applied to field service of medical test equipment. The techniques are flexible enough to use for many different types of devices. If a fault tree model of the equipment and information about component failure probabilities and isolation times or costs are available, a diagnostic knowledge base for that device can be developed easily.

Iverson, David L.↗

Accounting for Point Estimate Uncertainty in Space Systems Reliability and Risk Analysis

Understanding and accounting for uncertainty in risk analysis is a critical step in the management and communication of risk in engineered systems. The component and system-level analysis to determine the probability of a negative outcome and its consequence is often quantified by a point estimate. Many Program and Enterprise decisions involving technical concerns and issues rely on reliability engineering activities to produce quantified risk analysis to inform the decision making process. At NASA, it is common to use a Probabilistic Risk Analysis (PRA) to inform the overall risk to Loss of Mission or Loss of Crew that involves integration across all spacecraft subsystem fault trees to produce an overall probability of mission failure. The point estimate is an estimate of this overall probability and is an immediate result of a fault tree model. It is the result of a model where the probability of each event is taken to be equal to its mean. The value provides an approximation of the overall mean without running any uncertainty calculations (e.g., no sampling). Using only the point estimate can lead to a false sense of precision and the point estimate may not match the resulting mean when uncertainty is taken into consideration. This paper will explore five conditions that can cause the PRA model mean to diverge from the point estimate and will provide engineers and managers insight into the importance of understanding uncertainty in the elements of PRA models.

Paul J Collier↗

Generalized Symbolic Execution for Model Checking and Testing

Modern software systems, which often are concurrent and manipulate complex data structures must be extremely reliable. We present a novel framework based on symbolic execution, for automated checking of such systems. We provide a two-fold generalization of traditional symbolic execution based approaches: one, we define a program instrumentation, which enables standard model checkers to perform symbolic execution; two, we give a novel symbolic execution algorithm that handles dynamically allocated structures (e.g., lists and trees), method preconditions (e.g., acyclicity of lists), data (e.g., integers and strings) and concurrency. The program instrumentation enables a model checker to automatically explore program heap configurations (using a systematic treatment of aliasing) and manipulate logical formulae on program data values (using a decision procedure). We illustrate two applications of our framework: checking correctness of multi-threaded programs that take inputs from unbounded domains with complex structure and generation of non-isomorphic test inputs that satisfy a testing criterion. Our implementation for Java uses the Java PathFinder model checker.

Khurshid, Sarfraz↗

Success Path Method: Introduction to the Success Path Method Software Tool©

As part of its commitment to advancing safety and reliability assessment methodologies, Argonne National Laboratory pioneered the use of an evaluation method called the Success Path Method (SPM) to improve risk management for offshore oil and gas operations. The development of the SPM at Argonne has been driven by the need to improve existing risk assessment methodologies by focusing on the steps necessary for success rather than failure modes alone. This is particularly important for industrial environments like offshore facilities that perform multiple functions under a continuously evolving set of operational conditions – such as water depth and temperature, currents, and weather conditions. In these dynamic environments, the traditional Probabilistic Risk Assessment (PRA) approach is far too complex as it focuses on what can go wrong – which comprises an infinite failure space that must be fully explored and understood. By shifting the focus to a finite space of success paths, the SPM enables operators and decision makers to prioritize a manageable number of steps that must go right to ensure success. Building on its five decades of experience in safety assessments for the nuclear industry, Argonne made major adaptations to existing risk assessment methods utilizing features similar to fault trees that are traditionally used in PRA to map all pathways in which the system can malfunction. In contrast, SPM identifies the components and processes that must function correctly to achieve specific outcomes – such as preventing the uncontrolled release of hydrocarbons during drilling operations. The SPM framework integrates equipment, procedures, software, processes, and human actions to ensure that physical barriers meet critical safety functions in dynamic operational conditions. This approach helps identify failure modes and improve operational risk management by narrowing the focus to key success elements, which in turn reduces uncertainty and helps users understand, manage, and respond to failures.

97 MATHEMATICS AND COMPUTING↗

EVI-EnSitePy (Electric Vehicle Infrastructure – Energy Estimation and Site Optimization Tool in Python) [EVI-X Modeling Suite] [SWR-25-07]

EVI-EnSitePy is a comprehensive agent-based tool designed for the analysis and design of high-power charging sites, encompassing a wide array of site agents including Electric Vehicles (EVs), chargers, energy storage units (ESS), renewable energy resources (DER), and loads. This versatile tool offers diverse functionalities and a modular modeling approach, allowing detailed configuration of agents based on power ratings, port numbers, energy capacities, demand requirements, charger interfaces, and flexibility to customize the tool for project specific goals. By simulating agent interactions and employing various metrics, EVI-EnSitePy enables the assessment of site performance, exploration of energy management systems (EMS), and implementation of innovative EV charging policies. Utilizing EV charge schedules and arrival states, the tool performs thorough charging site simulations, with outputs consisting of agent and site-level power profiles and statistical metrics. Employing a tree graph structure, EVI-EnSitePy supports nested site structures and power distribution modeling. The tool's ability to generate charging schedules deterministically or via stochastic analysis further enhances its versatility. Through its features and capabilities, EVI-EnSitePy offers a powerful platform for informed decision-making in the realm of high-power charging site design and operation.

Jackson, Derek [National Renewable Energy Laborato↗

Growth–Mortality Coordination Differs Among Xerophytic Versus Mesophytic Tree Species During Severe Drought

ABSTRACT Forest composition is changing, yet the consequences for terrestrial carbon cycling are unclear. In the eastern United States, water‐demanding “mesophytic” tree species are replacing “xerophytic” oaks (Quercusspp.) and hickories (Caryaspp.), raising concerns that forest productivity will become increasingly sensitive to more frequent and severe drought conditions predicted for the region. However, we have a limited understanding of the extent to which the mortality risk of xerophytes versus mesophytes is coordinated with their growth sensitivity during drought. Here, we evaluated growth and mortality dynamics for 20 abundant eastern United States tree species following a severe drought in the summer of 2012. We synthesized data from ~4500 forest inventory plots and used an approach that quantified relative drought responses between co‐located trees to minimize impacts from environmental heterogeneity. We found that mesophytes were just as likely to perish as co‐occurring xerophytes but were more sensitive to drought in terms of diminished growth. These findings suggest that xerophytic decline is likely to lead to reduced carbon uptake during drought and that management efforts to conserve oak‐hickory stands will be decisive to sustain the carbon mitigation potential of these forests. However, we also found that growth‐mortality relationships differed between functional groups. Among xerophytes, growth and survival during drought were decoupled. Among mesophytes, there was a high degree of coordination, where species that experienced greater mortality also experienced greater growth reductions. Therefore, mesophytes with high growth sensitivity to water deficits are likely to be the most vulnerable to drought‐driven die‐off events moving forward.

Biodiversity & Conservation↗

Adaptive Stress Testing: Finding Likely Failure Events with Reinforcement Learning

Finding the most likely path to a set of failure states is important to the analysis of safety-critical systems that operate over a sequence of time steps, such as aircraft collision avoidance systems and autonomous cars. In many applications such as autonomous driving, failures cannot be completely eliminated due to the complex stochastic environment in which the system operates.As a result, safety validation is not only concerned about whether a failure can occur, but also discovering which failures are most likely to occur. This article presents adaptive stress testing (AST), a framework for finding the most likely path to a failure event in simulation. We consider a general black box setting for partially observable and continuous-valued systems operating in an environment with stochastic disturbances. We formulate the problem as a Markov decision process and use reinforcement learning to optimize it. The approach is simulation-based and does not require internal knowledge of the system, making it suitable for black-box testing of large systems. We present different formulations depending on whether the state is fully observable or partially observable. In the latter case, we present a modified Monte Carlo tree search algorithm that only requires access to the pseudorandom number generator of the simulator to overcome partial observability. We also present an extension of the framework, called differential adaptive stress testing (DAST), that can find failures that occur in one system but not in another. This type of differential analysis is useful in applications such as regression testing, where we are concerned with finding areas of relative weakness compared to a baseline. We demonstrate the effectiveness of the approach on an aircraft collision avoidance application, where a prototype aircraft collision avoidance system is stress tested to find the most likely scenarios of near mid-air collision.

Verification and Validation↗

Satellite sound broadcast propagation studies and measurements

Satellite Sound Broadcasting is an attractive satellite application. Before regulatory decisions can be made in 1992, the propagation effects encountered have to be characterized. The Electrical Engineering Research Laboratory has nearly completed a system which will allow amplitude measurements to be made over 10 MHz bandwidths in the 800 to 1800 MHz frequency range. The system uses transmission from a transportable tower, and reception inside buildings or in the shadow of trees or utility poles. The goal is to derive propagation models for use by systems engineers who are about to design satellite broadcast systems. The advance of fiber-optics technology has helped to focus future development of satellite services into areas where satellites are uniquely competitive. One of these preferred satellite applications is the broadcasting of high-quality sound for stationary or mobile reception by listeners using low-cost, consumer-grade receivers. Before such services can be provided, however, the political hurdles of spectrum allocation have to be surmounted and the technical questions of standardization for world-wide compatibility have to be resolved. In order to arrive at an optimal system design, efficient in the use of our scarce spectral resources, affordable both to the broadcaster and the listener, and providing predictable performance, the propagation effects to which the service is subjected have to be characterized. Consequently, the objective of the research project is to make basic propagation measurements for direct Satellite Sound Broadcasting Service (SSBS). The data obtained should allow the development of propagation models to be used by communications engineers designing the operational systems. Such models shall describe the effects of shadowing and multipath propagation on SSBS receivers operating in a specified environment, such as inside commercial or residential buildings of various construction and also in the shadow of trees or utility poles as might be encountered by transporting or mobile listeners.

Vogel, Wolfhard J.↗

Central Park Ecological Conservation: Assessing Tree Health Conditions in New York City’s Central Park with NASA Earth Observation Data

The Central Park Conservancy stewards New York City’s iconic Central Park with a mission to preserve the park for all. This mission is complicated by the spread of Dutch elm disease (DED) which has threatened the culturally and ecologically significant American elm tree (Ulmus americana). Central Park is home to one of the largest and last remaining urban concentrations of American elm and the Conservancy currently protects them through integrated pest management. This paper discusses an interdisciplinary feasibility study that assessed the application of NASA Earth observations from 2014 to 2023 to detect changes in forest phenology possibly related to DED. Landsat 8 and 9 imagery was used to calculate multiyear time series of the Normalized Difference Vegetation Index (NDVI) and quantify changes in land surface phenology for a given year. A pixel-based logistic regression analysis was performed using changes in NDVI, tree site locations, and recorded occurrences of trees infected with DED as inputs. The results of this analysis show that changes in NDVI derived from Landsat data are capable of detecting unhealthy tree canopies with 71% precision and healthy tree canopies with 41% precision. The study had uncertainties and limitations due to the spatial and temporal resolutions of Landsat, the natural variability in land surface phenology and NDVI, and the attempt to detect disease impacts while disease prevention and mitigation is occurring. As is, the findings of this study and its methods provide managers with an approach for integrating Earth observations to make more informed decisions in the application and timing of urban forest management activities.

Central Park↗

Assessing Tree Health Conditions in New York City’s Central Park with Earth Observation Data

The Central Park Conservancy stewards New York City’s iconic Central Park with a mission to preserve the park for all. This mission is complicated by the spread of Dutch elm disease (DED) which has threatened the culturally and ecologically significant American elm tree (Ulmus americana). Central Park is home to one of the largest and last remaining urban concentrations of American elm and the Conservancy currently protects them through integrated pest management. This project is an interdisciplinary feasibility study that assessed the application of NASA Earth observations from 2014 to 2023 to detect changes in forest phenology possibly related to DED. Landsat 8 and 9 imagery was used to calculate a multiyear time series of the Normalized Difference Vegetation Index (NDVI) and quantify changes in land surface phenology. A pixel-based logistic regression analysis was performed using changes in NDVI, tree site locations, and recorded occurrences of trees infected with DED as inputs. The results of this analysis show that changes in NDVI derived from Landsat data are capable of detecting unhealthy tree canopies with 71% precision and healthy tree canopies with 41% precision. The study had uncertainties and limitations due to the spatial and temporal resolutions of Landsat, the natural variability in land surface phenology and NDVI, and the attempt to detect disease impacts while disease prevention and mitigation are occurring. As is, the findings of this study and its methods provide managers with an approach for integrating Earth observations to make more informed decisions in the application and timing of urban forest management activities.

John Hocknell↗