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京都大学 / 新制・課程博士 / 博士(農学) / 甲第23946号 / 農博第2495号 / 新制||農||1090(附属図書館) / 学位論文||R4||N5381(農学部図書室) / 京都大学大学院農学研究科森林科学専攻 / (主査)教授 舘野 隆之輔, 教授 北島 薫, 教授 德地 直子 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DGAM

Forests have the ability to sequester carbon from our atmosphere. Soil respiration (Rs) plays a role in a forest's ability to do so as it is a significant source of carbon dioxide back to the atmosphere. Therefore, understanding the process of Rs under varying conditions is gaining more attention. As of now we have a relatively good understanding of Rs under managed forest ecosystems such as pine plantations. This particular study examined Rs under different overstories and understories in a high elevation Southern Appalachian forest in order to get a better understanding of Rs under a natural hardwood system. The four vegetation types under consideration were an eastern hemlock (Tsuga canadensis L. Carriere) dominated overstory, a hardwood overstory with little to no understory, a mountain laurel (Kalmia latifolia L.) dominated understory, and a cinnamon fern (Osmundastrum cinnamomeum (L.) C.Presl) dominated understory. Differing temporal variations of Rs were observed under the vegetation types. We found monthly differences in rates among vegetation type however, an overall annual difference in Rs rates between vegetation types was not observed. This simply indicates the importance of observing Rs under different time scales to get a better understanding of its variation. We also calculated vegetation indices from remotely-sensed data to explore any relationships to Rs as well as if the indices themselves could improve out model. A vegetation index is a number that is calculated for every pixel in a remotely sensed image and represents plant vigor or abundance. Few significant relationships were found between the indices and Rs. Future work may want to better understand vegetation indices' spatial extent and accuracy in order to find whether they may be beneficial in Rs estimation. Understanding the influence of varying vegetation type and soil temperature and moisture on Rs will ultimately improve our ability to predict what drives changes in carbon fluxes. / Master of Science / Forests have the ability to sequester carbon from our atmosphere. Soil respiration (Rs) plays a role in a forest’s ability to do so as it is a significant source of carbon dioxide back to the atmosphere. Therefore, understanding the process of Rs under varying conditions is gaining more attention. As of now we have a relatively good understanding of Rs under managed forest ecosystems such as pine plantations. This particular study examined Rs under different overstories and understories in a high elevation Southern Appalachian forest in order to get a better understanding of Rs under a natural hardwood system. The four vegetation types under consideration were an eastern hemlock (Tsuga canadensis L. Carriere) dominated overstory, a hardwood overstory with little to no understory, a mountain laurel (Kalmia latifolia L.) dominated understory, and a cinnamon fern (Osmundastrum cinnamomeum (L.) C.Presl) dominated understory. Differing temporal variations of Rs were observed under the vegetation types. We found monthly differences in rates among vegetation type however, an overall annual difference in Rs rates between vegetation types was not observed. This simply indicates the importance of observing Rs under different time scales to get a better understanding of its variation. We also calculated vegetation indices from remotely-sensed data to explore any relationships to Rs as well as if the indices themselves could improve out model. A vegetation index is a number that is calculated for every pixel in a remotely sensed image and represents plant vigor or abundance. Few significant relationships were found between the indices and Rs. Future work may want to better understand vegetation indices’ spatial extent and accuracy in order to find whether they may be beneficial in Rs estimation. Understanding the influence of varying vegetation type and soil temperature and moisture on Rs will ultimately improve our ability to predict what drives changes in carbon fluxes.

Studien har använt ny metod för att klassificera vitryggig hackspetts skogarförutsättningar för häckande revir. 287 hektar skog värdeklassades med minstav värdeklass 1 (42 ha) och mest av värdeklass 2 (195 ha). Död ved måste ökaför att populationen av vitryggig hackspett ska öka. De åtgärder som behövergöras är ringbarkning och att minska granvolymen för att värdeklasserna ska nånästa värdeklass. Resultatet tyder på att förutsättningarna för häckningsrevir ärgoda, dock måste vissa restaureringar göras för att vitryggig hackspett skaetablera sig i området.

The purpose of this research was to model the growth and yield of bottomland hardwood forests of the Lower Mississippi Alluvial Valley and to explain the economic tradeoffs of even- and uneven-aged management. The US Forest Service (USFS) Forest Vegetation Simulator was used to model growth and yield for four different bottomland hardwood forest types using USFS inventory data. Even- and uneven-aged management scenarios were optimized for timber revenue maximization using the Land Expectation Value formula. Analyses suggested that growth and yield of even-aged and uneven-aged management approaches differ in terms of end products and harvesting time. The even-aged management scenarios performed better over the uneven-aged management scenarios with few exceptions; however, the magnitude of the economic tradeoff depended upon initial stand conditions and required rates of return. These analyses will allow landowners to understand how much economic gain or loss they may realize by adopting an alternative management.

<p dir="ltr">Forest ecosystems play a pivotal role in climate change mitigation. Sustainable forest management practices necessitate accurate quantification of forest aboveground carbon stocks (FACS). In the first part of this study, I compared the 13-year changes in FACS across three silvicultural systems, including even-aged management (EA), uneven-aged management (UEA), and non-harvested controls (NH), in Indiana's hardwood forests. Forest stands within each silvicultural system were assigned with one of the six treatment types, including clearcutting, shelterwood, or prescribed burning for EA, single-tree selection or patch cutting for UEA, or untreated controls. From 2008 to 2021, the FACS of the study area exhibited an increase from 91.5 ± 9.0 Mg/ha to 115.3 ± 2.1 Mg/ha. Single-tree selection, shelterwood, and prescribed burning were found to have minimal impacts on FACS. However, clearcutting and patch cutting resulted in a significant reduction in FACS, with subsequent recovery reaching only 30-37% of their pre-treatment levels after 13 years. Further investigations may use long-term inventory data to analyze the chronic recovery patterns on these sites.</p><p dir="ltr">In the second part of this study, I evaluated the feasibility of using 3DEP LiDAR in conjunction with the random forest algorithm for multiscale FACS prediction. It was found that the stand-scale model outperformed the plot-scale model, primarily due to a stand’s higher positioning accuracy and reduced boundary effects than the plot-scale model. This led to a reduction in RMSE from 25.43 Mg/ha (26%) to 16.74 Mg/ha (20%). Moreover, the stand-scale model exhibited robust landscape-level prediction performance even in scenarios where point density decreased from 7.7 points/m² to 2.0 points/m². However, the partitioned model including solely clearcut and patch sites produced a higher RMSE of 59% (17.82 Mg/ha) due to inaccurate LiDAR return classification and biased canopy height metrics extraction. Future research should delve into the mechanisms of point cloud classification to improve the FACS prediction accuracy for clearcut forest monitoring.</p><p dir="ltr">Overall, this thesis contributed to a deeper understanding of carbon dynamics in managed hardwood forests, highlighted the potential of using LiDAR technology for improved landscape-level carbon monitoring, and informed the decision-making processes in the context of climate change mitigation.</p><p><br></p>

In order to assess the enhancement of seedling survival and growth during drought conditions, five-hundred bare-root seedlings each of Shumard oak (Quercus shumardii Buckl.) and green ash (Fraxinus pennsylvanica Marsh.) were planted each with four soil amendments at a Wildlife Management Area in Lewisville, Texas. The treatments were a mycorrhizal inoculant, mulch fabric, and two superabsorbent gels (TerraSorb® and DRiWATER®). Survival and growth measurements were assessed periodically for two years. Research was conducted on vegetation, soil, and site history for baseline data. Both superabsorbent gels gave significant results for Shumard oak survival, and one increased green ash diameter. For overall growth, significant results were found among DRiWATER®, mycorrhizae, and mulch treatments.

Recently, products of Unmanned Aerial System (UAS) integrated through SIFT algorithm and dense cloud matching using structure from motion has gained prominence with tree-level inventory maintenance in forestry. Various studies have been carried out by using UAS imagery to quantify and map forest structure of simple coniferous stands. However, most of the previous works employ methodologies that require manual inputs and lack of reproducibility to other forest systmes. Manual detection of trees and calculation of their attributes can be a time-consuming and complicated process which can be overcome with an automated technique applied by forest managers and/or landowners is highly desired to take full advantage of the readily available UAS remote sensing images. This study presents a methodology for automated measurements of tree height, crown area and crown diameter of hardwood species using UAS images. Different UAS platforms were employed to gather digital data of two hardwood plantations at Martell, Indiana. The resulting aerial images were used to generate the Digital Surface Model (DSM) and Digital Elevation Model (DEM) for the forest stand from which the Crown Height Model (CHM) was derived. The canopy height model can be inputted to the web platform deployed through shiny server (https://feilab.shinyapps.io/Crown/) to derive individual tree parameters automatically. The results show that this automated method provides a high accuracy in individual tree identification (F-score> 90%) and tree-level measurements (RMSEht<1.2m and RMSEcrn<1m). Moreover, tree-level parameter estimation for 4,600 trees were calculated in less than 30 minutes based on a post-processed DSM from UAS-SfM derived images with minimal manual inputs. This study demonstrates the feasibility of automated inventory and measure of tree-level attributes in hardwood plantations with UAS images.

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