Lana Landra Narine

Forests worldwide are impacted by tropical cyclones which alter their structure and ecological functions. In this study, we investigated repeat track data from ICESat-2’s (Ice, Cloud and land Elevation Satellite-2’s) land and vegetation height product (ATL08) to quantify structural changes in forests, with a focus on coastal forests in Alabama and Florida affected by Hurricane Sally (2020). We evaluated pre-hurricane ATL08 along-track canopy estimates at the ATL08 100 m segment scale and 20 m sub-segment scale and quantified structural canopy changes using exact pre- and post-repeated tracks. Results demonstrated strong agreement between ATL08’s 98th percentile canopy height (RH98) and reference airborne LiDAR-derived RH98 at both spatial scales, with improved performance at the 20 m sub-segment scale (mean bias: −1.16 m; MAE: 2.28 m; RMSE: 3.44 m; r: 0.80). Samples over evergreen forests provided reduced bias (−2 m to −0.55 m), reduced RMSE (4.02 m to 2.96 m), and improved correlation (0.77 to 0.83) than woody wetlands for canopy height acquisition. Post-hurricane analyses revealed height reductions in tall canopy (20–30 m) of 1.51 m, while smaller trees (0–10 m) increased by 0.77 m, reflecting growth. Overall, findings highlight ICESat-2’s ability to monitor canopy height changes and offer prospects for integrating ICESat-2 data for damage assessments.

Headwater wetlands in the southeast United States are a ubiquitous feature in the Coastal Plain but difficult to map because of their transitional nature. These wetlands are important for regional drainage and there is a need to better document their occurrence on the landscape. To improve mapping and detection, we evaluated the Wetland Intrinsic Potential (WIP) tool due to its use of multi-scale topographic indices, hydrologic proxies, and random forest procedures that contribute to ’cryptic’ wetland detection. Our resulting WIP model identified metrics that most accurately depicted the wetland-upland matrix within the Bushy Creek – Dyas Creek watershed, near Bay Minette, Alabama, USA. An initial model was trained and validated with an out of bag (OOB) accuracy assessment on a spatial subset of the watershed to predict wetland presence, absence, and extent. Our model resulted in an OOB accuracy rate of 96.0%, with wetland omission and commission errors of 7.0% and 2.5%, respectively. The model was then applied to the remaining extent of the watershed for a secondary validation assessment. Overall accuracy for the secondary validation dataset was 92.3%, with wetland omission and commission errors of 14.0% and 4.5%, respectively. Thus, WIP reliably discerned wetlands from uplands, with an AUC of 0.98 and kappa coefficient of 0.83. Maps generated with WIP had 89.1% agreement with National Wetland Inventory maps, although they increased wetland area by as much as 8.7%. Our findings demonstrate the applicability and limitations of this method for wetland mapping and supports future models which explore land use/cover and hydrogeomorphic relationships with wetlands.

The extension of NASA’s Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission beyond the cryosphere to include the study of vegetation and the land surface has significantly advanced global Earth observation. This review synthesizes findings from 293 peer-reviewed articles and provides a comprehensive assessment of the mission’s contributions to terrestrial ecosystem science. We begin by outlining the mission’s objectives, instrumentation, and core data products—particularly ATL03 (geolocated photons) and ATL08 (terrain and canopy heights)—which have supported a wide array of applications in forestry, terrain analysis, and environmental monitoring. Our analysis shows that most studies focus on temperate and tropical broadleaf forests, with a strong emphasis on estimating canopy height, terrain elevation, and forest structure. Terrain metrics derived from ICESat-2 products typically achieve high accuracy, while vegetation variables, such as canopy height, cover, leaf area index (LAI), and aboveground biomass, demonstrate moderate to strong accuracies across biomes. While parametric models remain the most used approach, machine learning methods are expanding, and more than one third of the reviewed literature incorporates synergistic analyses with other satellite missions. Despite its versatility, ICESat-2 remains underutilized in several key domains—including nonforest vegetation, fire ecology, wildlife habitat assessment, urban monitoring, and disturbance detection—mainly due to the scarcity of standardized algorithms and validated reference datasets. However, the growing availability of open source processing tools presents a significant opportunity for expanding the user base and fostering innovation in these emerging areas. Future advancements, such as the forthcoming ATL18 gridded canopy height and terrain products, alongside synergies with missions like the European Space Agency (ESA)’s BIOMASS and the NASA–Indian Space Research Organisation (ISRO) Synthetic Aperture Radar (NISAR), promise to enhance multisensor integration and ecosystem monitoring. Overall, ICESat-2 continues to evolve as a powerful resource for characterizing vegetation structure, ecosystem dynamics, and land surface processes on a global scale.

Brown spot needle blight (BSNB), caused by the fungus Lecanosticta acicola , is a widely distributed foliar disease of pines that causes needle necrosis, premature defoliation, and growth loss in both natural forests and commercial plantations. Although L. acicola has been reported from more than 50 pine species worldwide, recent large‐scale outbreaks on native loblolly ( Pinus taeda ) and eastern white pine ( P. strobus ) in North America highlight emerging management challenges within the pathogen's presumed native range. Here, we synthesise current knowledge of L. acicola taxonomy, biology, pathogenicity, and global distribution, with particular emphasis on how recent North American epidemics fit into the broader invasion history and what they reveal about host–pathogen–environment interactions. We review how taxonomic instability and cryptic diversity complicate species delimitation and surveillance, and evaluate diagnostic and monitoring approaches spanning field sampling, microscopy, culture‐based isolation, spore trapping, species‐specific PCR and LAMP assays, and the emerging roles of environmental DNA and remote sensing. We then assess control and management options, including fungicides where permitted, silvicultural and hygiene practices, and resistance breeding in pine improvement programs, and place these in the context of projected climate‐driven range expansion and ecosystem impacts. Finally, we highlight key research and policy priorities, including coordinated long‐term monitoring, genomics‐informed resistance screening, and international collaboration and data standards, that are needed to support integrated management strategies and mitigate the growing threat of BSNB to pine‐dominated forests in North America and globally.

Coastal forests worldwide are vulnerable to hurricanes, which cause significant canopy loss and long-term disruption of ecosystem services. Scalable methods for assessing hurricane-driven forest damage are critical for ecosystem recovery, yet conventional field-based approaches are time-consuming, costly and challenging to acquire over large areas. The Ice Cloud and land Elevation Satellite-2 land and vegetation height product (ATL08) provides three-dimensional information, and its capability of measuring forest structure is already well demonstrated. However, its integration with satellite imagery for mapping hurricane-induced canopy height changes remains limited. In this study, we analyzed coastal forest canopy losses and thematic transitions using ICESat-2, Sentinel-2, and ancillary predictors. We used Random Forest (RF) and Extreme Gradient Boosted (XGB) regression models to extrapolate ATL08 pre-hurricane canopy heights and applied the better-performing model for post-hurricane mapping. The resulting canopy height maps were combined with existing land cover products to assess structural and thematic transitions across the impacted landscape. RF outperformed XGB (R ² = 0.44, RMSE = 4.30 m vs. R ² = 0.41, RMSE = 4.76 m). Landcover shifts included transitions from evergreen to herbaceous, scrub, and barren classes, and from woody wetlands to emergent herbaceous wetlands, with mean canopy height losses of 2.3–5.2 m. Canopy cover analysis showed dense (> 60%) and sparse (< 30%) cover experienced greatest losses (up to 8.3 m), while moderate covers (30–60%) were resilient. This study demonstrates the potential of integrating ICESat-2 and Sentinel-2 for assessing structural and thematic changes and informing adaptive strategies in hurricane-prone coastal ecosystems.

The southeastern United States (US) is known for its highly productive forests, but they are under intense threat from increasing climate-induced windstorms like hurricanes and tornadoes. This study explored the effectiveness of unmanned aerial vehicles (UAVs) equipped with Light Detection and Ranging (LiDAR) to detect, classify, and map windstorm damage in ten pine-dominated forest stands (10–20 acres each). Three classification techniques, Random Forest (RF), Maximum Likelihood (ML), and Decision Tree (DT), were tested on two datasets: RGB imagery integrated with LiDAR-derived Canopy Height Model (CHM) and without LiDAR-CHM. Using LiDAR-CHM integrated datasets, RF achieved an average Overall Accuracy (OA) of 94.52% and a kappa coefficient (k) of 0.92, followed by ML (average OA = 89.52% and k = 0.85), and DT (average OA = 81.78% and k = 0.75). The results showed that RF consistently outperformed ML and DT in classification accuracy across all sites. Without LiDAR-CHM, the performance of all classifiers significantly declined, underscoring the importance of structural data in distinguishing among the classification categories (downed trees, standing trees, ground, and water). These findings highlight the role of UAV-derived LiDAR-CHM in improving classification accuracy for assessing the impact of windstorm damage on forest stands.

This review presents a synthesis of global research on precision forestry, a field that integrates advanced technologies to enhance—rather than replace—established tools and methods used in the operational forest management and the wood products industry. By evaluating 210 peer-reviewed publications indexed in Web of Science (up to 2025), the study identifies six main categories and eight components of precision forestry. The findings indicate that “forest management and planning” is the most common category, with nearly half of the studies focusing on this topic. “Remote sensing platforms and sensors” emerged as the most frequently used component, with unmanned aerial vehicle (UAV) and light detection and ranging (LiDAR) systems being the most widely adopted tools. The analysis also reveals a notable increase in precision forestry research since the early 2010s, coinciding with rapid developments in small UAVs and mobile sensor technologies. Despite growing interest, robotics and real-time process control systems remain underutilized, mainly due to challenging forest conditions and high implementation costs. The research highlights geographical disparities, with Europe, Asia, and North America hosting the majority of studies. Italy, China, Finland, and the United States stand out as the most active countries in terms of research output. Notably, the review emphasizes the need to integrate precision forestry into academic curricula and support industry adoption through dedicated information and technology specialists. As the forestry workforce ages and technology advances rapidly, a growing skills gap exists between industry needs and traditional forestry education. Equipping the next generation with hands-on experience in big data analysis, geospatial technologies, automation, and Artificial Intelligence (AI) is critical for ensuring the effective adoption and application of precision forestry.

Spatially continuous and accurate estimation of forest aboveground biomass (AGB) is essential for understanding carbon storage, ecosystem health, and biodiversity. Forests of the southeastern United States (US) represent about 40% of the nation’s forest area and one of the most significant carbon sequestration and storage potentials in the US. The availability of data from more recent and long-standing Earth-observing missions, like spaceborne light detection and ranging data from NASA’s Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) and imagery from Landsat satellites, present an exemplary opportunity to characterize vegetation structure and AGB. Despite this potential, the extent to which data from these ongoing missions can be used synergistically for AGB estimation at the regional scale is not well known. This study served to better understand the combined utility of Landsat and ICESat-2 for developing a large-area AGB mapping framework. Specifically, this work served to: (1) determine the best modeling technique for estimating field-derived AGB using ICESat-2 and Landsat-derived variables, among machine learning (random forest (RF) and support vector machine (SVM)) and geostatistical approaches (random forest regression kriging (RFRK) and support vector machine regression kriging (SVMRK)), and (2) create a high-resolution (30 m) baseline AGB map for the year 2020 across ~254,266 km² of forests of the southeastern US. Canopy height information from ICESat-2, Landsat-8 imagery and imagery-derived variables, digital elevation models, and canopy cover were used to model AGB. Resulting models yielded R² values ranging from 0.34 to 0.61, and RMSEs between 22 and 31 Mg/ha. Evidently, AGB estimated using the SVMRK model was substantially better than the other models (R² = 0.61 and RMSE = 23.99 Mg/ha), highlighting its potential for broad-scale AGB mapping. Overall, this work highlights a feasible approach for deriving spatially comprehensive AGB information for southeastern US forests and provides a high-resolution AGB baseline product to support regional-scale monitoring.