The occurrence of hurricanes in the southern U.S. is on the rise, and assessing the damage caused to forests is essential for implementing protective measures and comprehending recovery dynamics. This work aims to create a novel data integration framework that employs LANDSAT 8, drone-based images, and geographic information system data for change detection analysis for different forest types. We propose a method for change vector analysis based on a unique spectral mixture model utilizing composite spectral indices along with univariate difference imaging to create a change detection map illustrating disturbances in the areas of McDowell County in western North Carolina impacted by Hurricane Helene. The spectral indices included near-infrared-to-red ratios, a normalized difference vegetation index, Tasseled Cap indices, and a soil-adjusted vegetation index. In addition to the satellite imagery, the ground truth data of forest damage were also collected through the field investigation and interpretation of post-Helene drone images. Accuracy assessment was conducted with geographic information system (GIS) data and maps from the National Land Cover Database. Accuracy assessment was carried out using metrics such as overall accuracy, precision, recall, F score, Jaccard similarity, and kappa statistics. The proposed composite method performed well with overall accuracy and Jaccard similarity values of 73.80% and 0.6042, respectively. The results exhibit a reasonable correlation with GIS data and can be employed to assess damage severity.

In recent decades, increases in severe drought, heat extremes, and pest burden have contributed to increased global tree mortality. These risks are expected to be exacerbated under projected climate change. So far, observations of tree mortality are mainly based on manual field surveys with limited spatial coverage. The lack of accurate tree mortality data over large areas has limited the development and applications of tree mortality models. However, a combination of high-resolution remote sensing data, such as aerial imagery and automated imagery analysis, may provide a solution to this problem. In this study, we analysed the dynamics and drivers of forest canopy mortality in 117 366 ha of boreal forest in Southeast Finland, between 2017 and 2023. For this purpose, we first developed a fully convolutional semantic segmentation model to automatically segment forest canopy mortality from aerial imagery in 2017, 2020, and 2023 with a spatial resolution of 0.5 m. Secondly, we trained the model using a dataset consisting of 32555 canopy mortality segments manually delineated from aerial imagery from various geographic regions in Finland. The trained model showed high accuracy in detecting forest canopy mortality (with an F1 score of 0.86-0.93) when tested using an independent test set. To estimate standing deadwood volume, we combined the observed yearly forest canopy mortality with open forest resource information based on extensive field campaigns and airborne laser scanning. In our study area, forest canopy mortality increased from 23.4 ha (0.02 % of the study area) to 207.8 ha (0.18 %) between 2017 and 2023. Consequently, standing deadwood volume was estimated to increase from 5192 m3 (0.04 m3/ha) to 52800 m3 (0.45 m3/ha) during the study period. Both the volume of standing deadwood and the extent of forest canopy mortality increased exponentially. The majority of the forest canopy mortality occurred in Norway sprucedominated forests (64.1-77.3 %) on relatively fertile soils (81.6-84.7 %) while 20-25 % of the forest canopy mortality occurred in Scots pine-dominated forests. The average age of stands where mortality was observed was between 60 and 70 years old (2017 = 69.7 years and 2023 = 62.6 years), indicating that mature forests were more susceptible to mortality than younger stands. Our findings highlight an exponential increase in forest canopy mortality over a relatively short time span (6 years). The increasing risk of tree mortality in boreal forests underlines the urgent need for large-scale and spatially accurate monitoring to keep up to date with fast-paced changes in boreal forest mortality. As climate change increases drought, extreme heat and bark beetle outbreaks, consistent canopy mortality mapping is essential for implementing timely risk management measures in forestry.

Climate change has accelerated the frequency of catastrophic wildfires; however, the drivers that control the time-to-recover of forests are poorly understood. We integrated remotely sensed data, climate records, and landscape features to identify the causes of variability in the time-to-recover of canopy leaf area in southeast Australian eucalypt forests. Approximately 97% of all observed burns between 2001 and 2014 recovered to a pre-fire leaf area index (+/- 0.25 sd) within six years. Time-to-recover was highly variable within individual wildfires (ranging between = 5 years), across burn seasons (90% longer January to September), and year of fire (median time-to-recover varying four-fold across fire years). We used the logistic growth function to estimate the leaf area recovery rate, burn severity, and the long-term carrying capacity of leaf area. Time-to-recover was most correlated with the leaf area recovery rate. The leaf area recovery rate was largest in areas that experienced high burn severity, and smallest in areas of intermediate to low burn severity. The leaf area recovery rate was also strongly accelerated by anomalously high post-fire precipitation, and delayed by post-fire drought. Finally we developed a predictive machine-learning model of time-to-recover (R2: 0.68). Despite the exceptionally high burn severity of the 2019-2020 Australian megafires, we forecast the time-to-recover to be only 15% longer than the average of previous fire years. Australian eucalypt forests have evolved different strategies to recover from fire. While the meteorological drivers of bushfire are reasonably well understood, the various processes explaining how long a forest takes to recover from fire are not. We investigated a range of static (landscape) and dynamic (vegetation condition or meteorological) factors that could influence how long a forest's canopy leaf area would take to recover from fire. Time-to-recover after fire is highly variable, ranging from less than 1 year to more than 5 years even within an individual burn location. More intense fires cause greater forest canopy damage and generally (but not always) lead to longer recovery times, whereas wetter conditions after the fire can accelerate recovery. Using these factors and others, we developed a model capable of predicting the time-to-recover and applied it to the 2019-2020 Australian megafires. Our analysis suggests the canopy damage caused by these fires was far more severe than fires in years prior. This would normally lead to a prolonged time-to-recover, however we predict that anomalously high rainfall in the year following the fires will shorten recovery time, compensating for the high burn severity. Ultimately we predict the time-to-recover will be only slightly longer than average. Pre-fire leaf area, burn severity, and post-fire meteorological conditions combine to determine time-to-recover after fire Large geographic variation in time-to-recover can be explained by mean climate and landscape differences Time-to-recover can be predicted with high accuracy using information limited to the first year following fire

Modern forestry research emphasizes infusing management practices with an understanding of natural disturbance regimes-often called ecological forestry. Forestry practices that emulate aspects of natural disturbance regimes are considered an effective approach to balance silvicultural and ecological objectives. Silvicultural research is often available to guide successful regeneration in many forest types, but little information is available about gap patterns from common disturbances in the eastern U.S. like hurricanes. We examined the size, shape, and spatial distribution of canopy gaps formed in a longleaf pine woodland by Hurricane Michael across multiple landscape factors including stand size, composition, and soil types. We found high variation in many gap characteristics but no significant differences in gap size or shape among landscape factors. However, spatial distribution of gaps differed among landscape types in nuanced ways. We also found that stand size complexity may prevent the formation of very large gaps that can disrupt fire continuity in systems managed with frequent fire. The results highlight the ecological importance of hurricane events and provide insight into hurricane gap formation at the landscape scale. The implementation of silviculture practices that emulate a large, rapid, single disturbance event may be more practically applied than management based on disturbances such as lightning or insects which occur over longer timeframes.

Wildfires considerably disturb the structure and forest ecosystem functioning. The disturbances estimation as well as the extent of damage to the soil and vegetation soon after the fire is crucial information for planning of restoration efforts. Because of the financial resources needed for field work and the involvement of experts, remote aerospace methods and data are extensively employed in monitoring ecological research. The aim of this paper is to assess postfire forest disturbances and initial regrowth processes using the tasseled cap derived Direction Angle (DA). DA is an index introduced by the authors in previous research - the angle between the Greenness component from the TCT (tasseled cap transformation) and VIC (Vector of Instantaneous Condition). The proposed method is based on linear orthogonal transformation of multispectral satellite images and is characterized with higher accuracy compared to standard methodologies using vegetation indices. The higher accuracy of the methodology is based on the linear orthogonal transformation of multispectral satellite images (TCT), which increases the degree of identification of the three main components changing during fire - soil, vegetation, and moisture/water. The methodology proposed in this paper is characterized by high accuracy in assessing the recovery of undergrowth, that is difficult to differentiate using standard monitoring methodologies based on vegetation indices. The DA raster images show the direction of change of the green tasseled cap component (TCG) relative to the VIC, which allows to estimate the degree of recovery of the vegetation component for different moments of the study period. The variations observed in DA values illustrate the pattern of the green component at various points during the investigation period, enabling the assessment of disturbances and the monitoring of regrowth processes. The test area is located in the Middle Rhodopes, near the village of Hvoyna (Smolyan region), Bulgaria, where on 28/08/2023 a wildfire broke out. 1,500 decares have been burnt by the fire, including deciduous and coniferous forest. The wildfire affected 100-130 years old black pine (Pinus nigra) forests.