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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 黃倬英(Cho-ying Huang) | |
dc.contributor.author | Guan-Yu Lai | en |
dc.contributor.author | 賴冠宇 | zh_TW |
dc.date.accessioned | 2021-07-11T15:10:51Z | - |
dc.date.available | 2021-08-13 | |
dc.date.copyright | 2019-08-13 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-08-07 | |
dc.identifier.citation | Abdi, H. (2003). Partial least square regression (PLS regression). Encyclopedia for research methods for the social sciences, 6(4), 792-795.
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/78666 | - |
dc.description.abstract | 附生苔蘚植物是中海拔熱帶山地雲霧森林最具代表性的物種之一,且在森林中的水循環、蒸發散扮演關鍵角色。其中,生物量是少數可間接估測附生苔蘚攔截霧水的參數,然而,附生苔蘚生物量調查至今依然缺乏兼具高效率、低成本且不具破壞性量化生物量的方式,區域尺度的估測更是困難。已知生物量與森林結構、物理環境和微氣候有高度相關性,透過現地調查以及遙測光達資料,我們可以間接估測區域尺度的附生苔蘚生物量。本研究分析區域尺度附生苔蘚生物量與46個生物物理、地形以及氣候因子的關係,並評估在熱帶山地雲霧森林繪製區域尺度附生苔蘚生物量地圖的可行性。本研究樣區在臺灣東北部的棲蘭山 (24°35’N; 121°25’E, 16775 ha),我們在六個林份跨海拔梯度 (1200-1950 m a.s.l.),破壞性地蒐集131個100 cm^2附生苔蘚樣本,建立附生苔蘚厚度及生物量的異速生長迴歸模式。此外,我們設計一套新的高效率附生苔蘚生物量野外調查方法並使用所建立的迴歸模式,在另外21個30×30公尺樣方估測樣區尺度附生苔蘚總生物量。最後使用偏最小平方迴歸分析影響附生苔蘚生物量空間分佈的顯著變量 (主成分),並繪製區域尺度附生苔蘚生物量地圖。異速生長迴歸模式得指數為0.753有最佳結果 (r^2 = 0.72,p <0.001),樣區尺度估測平均附生苔蘚生物量 (±標準偏差) 為188.7±100.3 kg ha^-1。偏最小平方迴歸結果顯示大樹密度、宿主胸高直徑、樹冠層高度、地上部生物量、海拔高度、剖面曲率、平面曲率、氣溫顯著影響附生苔蘚生物量的空間分佈。取前四個主成份共解釋94%的樣本變異,並繪製附生苔蘚生物量地圖。在區域尺度上估測平均附生苔蘚總生物量 (±標準偏差) 為161.3±83.2 kg ha^-1,配合1 m空間解析度空載光達冠層高度模型與現地資料,估測棲蘭山的總附生苔蘚生物量為2808.6 Mg。本研究提出結合現地測量和空載光達資料的區域尺度繪圖方法,有助於評估附生苔蘚植物在熱帶山地雲霧森林水循環中的作用。 | zh_TW |
dc.description.abstract | Epiphytic bryophytes are some of the key species characterizing mid-altitude tropical montane cloud forests (TMCF) and play a pivotal role in influencing the global hydrological cycle. For epiphytic bryophytes (EB), biomass is one of the only a few measurable parameters to assess the capacity of TMCFs to intercept fog. However, carrying out field EB measurements have known to be very technically challenging and time to carry out, which makes the regional quantification impractical. The abundance of EB is highly related to the forest structure, physical environment and microclimate. Therefore, we may be able to indirectly map EB biomass at the regional scale by combining field observation and active remotely sensed light detection and ranging (lidar) spatial coverage. In this study, we investigated the relationship of the plot scale EB biomass and a comprehensive set of field and lidar derived biophysical, topographic and bioclimatic attributes and assessed the feasibility of regional mapping of EB biomass in a TMCF. The study was conducted in a 16773 ha TMCF of Chilan Mountain in the northeastern Taiwan. We destructively collected 131 100 cm^2 circular EB samples from six sites across an elevation gradient of 1200–1950 m a.s.l. to derive a general allometry of EB biomass using the central depth of each sample. Additionally, a new field instrument was designed specifically for the efficient estimation of EB biomass along tree stems, and the 30×30 m plot scale EB biomass (n = 21) was estimated with aids of previously measured in-situ field data and developed allometrics. The partial least squares regression was applied to investigate the relationship between the plot scale EB biomass and 46 field and/or lidar derived biophysical, topographic and bioclimatic attributes. The salient variables (principal components) were then selected to for regional mapping of EB biomass. The general allometric model had the best performance (r^2= 0.72, p < 0.001) with the exponent of 0.753. Estimation of mean EB biomass (±standard deviation [sd]) at the plot-scale was 188.7 ± 100.3 kg ha^-1. The partial least squares regression showed that big tree density, tree DBH, canopy height, aboveground biomass elevation, profile curvature, plan curvature and air temperature were significantly affecting spatial distribution of EB biomass. The first four principal components explaining 94% of data variation were used for regional EB biomass mapping. We estimated that the mean (± sd) EB biomass density was 161.3 ± 83.2 kg ha^-1, and total EB biomass of the TMCF of Chilan Mountain is 2808.6 Mg. Our proposed synoptic sensing approach may be feasible for regional mapping of EB biomass, thereby advancing our understanding of the role of EB plays in the hydrological cycle of TMCFs. | en |
dc.description.provenance | Made available in DSpace on 2021-07-11T15:10:51Z (GMT). No. of bitstreams: 1 ntu-108-R06228008-1.pdf: 3800895 bytes, checksum: caecf248bfc4c9d6a117689465764ab2 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 誌謝 ....................................................................................................................... i
摘要 ...................................................................................................................... ii Abstract .............................................................................................................. iii Table of Contents .................................................................................................v List of Figures .................................................................................................... vi List of Tables ..................................................................................................... vii 1. Introduction .....................................................................................................1 2. Methods and Materials ...................................................................................4 2.1 Study area ....................................................................................................4 2.2 Field EB biomass sampling and model development ..................................6 2.3 The plot-scale EB biomass estimation .......................................................10 2.4 Airborne lidar ............................................................................................13 2.5 Field meteorological data .........................................................................14 2.6 Regional estimation of EB biomass ...........................................................15 3. Results .............................................................................................................18 3.1 Epiphytic bryophytes biomass allometry ...................................................18 3.2 The plot-scale EB biomass estimation .......................................................21 3.3 Regional EB estimation .............................................................................22 4. Discussion .......................................................................................................29 4.1 Epiphytic bryophytes depth-biomass allometry .........................................30 4.2 Scaling of EB biomass from the patch to forest stand scales ....................31 4.3 Determinants governing the abundance of EB ..........................................33 4.4 Remotely sensed regional EB biomass estimation ....................................35 5. Conclusions ....................................................................................................37 Acknowledgments ..............................................................................................38 Appendix A. Supplementary data ....................................................................39 Reference ............................................................................................................47 | |
dc.language.iso | en | |
dc.title | 區域估測熱帶山地雲霧森林苔蘚附生植物生物量 | zh_TW |
dc.title | Regional estimation of the biomass of epiphytic bryophytes in a tropical montane cloud forest | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 林登秋(Teng-Chiu Lin),徐嘉君(Chia-Chun Hsu) | |
dc.subject.keyword | 異速生長,臺灣扁柏,臺灣,遙測,光達,大尺度,偏最小平方迴歸, | zh_TW |
dc.subject.keyword | allometry,hinoki,lidar,large scale,partial least squares regression,synoptic sensing,Taiwan, | en |
dc.relation.page | 51 | |
dc.identifier.doi | 10.6342/NTU201902408 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-08-08 | |
dc.contributor.author-college | 理學院 | zh_TW |
dc.contributor.author-dept | 地理環境資源學研究所 | zh_TW |
顯示於系所單位: | 地理環境資源學系 |
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