Yazar "Peduzzi, Alicia" seçeneğine göre listele

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    Estimating forest inventory information for the talladega national forest using airborne laser scanning systems
    (Multidisciplinary Digital Publishing Institute (MDPI), 2024) Lee, Taeyoon; Vatandaşlar, Can; Merry, Krista; Bettinger, Pete; Peduzzi, Alicia; Stober, Jonathan
    Accurately assessing forest structure and maintaining up-to-date information about forest structure is crucial for various forest planning efforts, including the development of reliable forest plans and assessments of the sustainable management of natural resources. Field measurements traditionally applied to acquire forest inventory information (e.g., basal area, tree volume, and aboveground biomass) are labor intensive and time consuming. To address this limitation, remote sensing technology has been widely applied in modeling efforts to help estimate forest inventory information. Among various remotely sensed data, LiDAR can potentially help describe forest structure. This study was conducted to estimate and map forest inventory information across the Shoal Creek and Talladega Ranger Districts of the Talladega National Forest by employing ALS-derived data and aerial photography. The quality of the predictive models was evaluated to determine whether additional remotely sensed data can help improve forest structure estimates. Additionally, the quality of general predictive models was compared to that of species group models. This study confirms that quality level 2 LiDAR data were sufficient for developing adequate predictive models (R2adj. ranging between 0.71 and 0.82), when compared to the predictive models based on LiDAR and aerial imagery. Additionally, this study suggests that species group predictive models were of higher quality than general predictive models. Lastly, landscape level maps were created from the predictive models and these may be helpful to planners, forest managers, and landowners in their management efforts.
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    Mapping ecological condition classes of a natural pine-dominated national forest in the southeastern US
    (Institute of Electrical and Electronics Engineers Inc., 2023) Vatandaşlar, Can; Lee, Taeyoon; Peduzzi, Alicia; Bettinger, Pete; Merry, Krista; Stober, Jonathan
    Accurate knowledge of ecological condition (EC) is crucial to evaluate the deviation of a given ecosystem from a reference condition, and is of interest to forest managers as it helps them prioritize management activities. The present study aimed to estimate EC classes of the Talladega National Forest (NF) using NAIP images, and airborne laser scanning (ALS) data, as well as field measurements from 255 plots. The results indicated that the EC classes could be distinguished using zq25, imean, and p5th metrics from ALS, as well as Enhanced Vegetation Index. Among them, we used zq25 to generate a map for the entire study area. Accordingly, the dominant EC was class 3, suggesting that almost half of the forestland is composed of young and dense stands with woody understory. This cover type is not desirable in terms of wildfire risk and far from the historical conditions. Thus, NF managers might thin and/or prescribed burn these areas to improve EC.
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    Mapping percent canopy cover using individual tree- and area-based procedures that are based on airborne LiDAR data: Case study from an oak-hickory-pine forest in the USA
    (Elsevier B.V., 2024) Vatandaşlar, Can; Lee, Taeyoon; Bettinger, Pete; Uçar, Zennure; Stober, Jonathan; Peduzzi, Alicia
    Canopy cover (CC) is the proportion of a forest floor covered by the vertical projection of tree crowns. Recently, it has become common to utilize LiDAR (light detection and ranging) canopy metrics to estimate CC over large areas. However, these metrics are primarily related to canopy density rather than the specific definition of CC. Here, two processes that employed individual tree segmentation (ITS) and area-based procedures based on LiDAR data are presented to estimate CC across the Talladega Division of the Talladega National Forest (93,694 ha) at the plot, stand, and landscape levels. The two analytical procedures were assessed using the results of a plot/grid method as a reference dataset, which focused on CC estimates within 255 field measurement fixed-area sample plots. The accuracy of a third process, employing an imagery-based visual CC assessment, was also compared against the two procedures and the reference dataset. The LiDAR-based analytical procedures were able to provide estimates of CC with an RMSE of approximately 15 %, which is acceptable for landscape-level assessments. Based on the results of this study, we conclude that CC maps, when created using LiDAR data, may be suitable for various operational tasks such as assessing the impact of forest disturbances and helping to determine the habitat suitability for certain wildlife species.