研究生: |
陳冠宇 Chen, Kuan-Yu |
---|---|
論文名稱: |
應用多時期航拍影像探討1980-2021年間臺灣兩高山地區森林範圍的擴展 Assessment of the Alpine Forest Expansion in Two Mountain Areas in Taiwan between 1980 and 2021 Using Multi-temporal Aerial Photographs |
指導教授: |
林登秋
Lin, Teng-Chiu |
口試委員: |
林登秋
Lin, Teng-Chiu 張仲德 Chang, Chung-Te 林政道 Lin, Cheng-Tao |
口試日期: | 2025/01/15 |
學位類別: |
碩士 Master |
系所名稱: |
生命科學系 Department of Life Science |
論文出版年: | 2025 |
畢業學年度: | 113 |
語文別: | 中文 |
論文頁數: | 96 |
中文關鍵詞: | 氣候變遷 、航拍影像 、U-Net 、高山樹木界線 、森林擴展 |
英文關鍵詞: | climate change, aerial photographs, U-Net, alpine treeline, forest expansion |
研究方法: | 遙測分析 、 深度學習方法 |
論文種類: | 學術論文 |
相關次數: | 點閱:4 下載:0 |
分享至: |
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
氣候變遷可能導致物種遷移,進而改變生態系的生物多樣性、結構和功能。高山生態系為對氣候變遷尤為敏感,其中高山森林海拔分布上限的變遷是氣候變遷生態學研究的焦點之一。因為低溫限制減少而使植群往更高海拔移動,但高海拔範圍受限使高山植群分布範圍逐漸縮減,可能使得部分物種滅絕、生態系統服務改變等風險。然而,臺灣針對高山森林海拔變化的研究相對有限,且主要集中於合歡山地區。考量臺灣高山環境的多樣性與區域性條件的差異,需有涵蓋更多地點的研究,才能更全面理解氣候變遷對臺灣高山森林分布的影響。本研究利用1980、2001及2021年的航空照片,結合U-Net深度學習模型進行影像分割,分析玉山與中央山脈地區過去41年間高山森林分布的變化趨勢,並使用空間迴歸分析探討氣候與地形因子對森林擴展的影響。研究發現,使用較小影像尺寸進行模型訓練的影像分割成果最佳。1980至2021年間,玉山與中央山脈的高山森林覆蓋面積淨增約197公頃與158公頃,且樹木界線平均每年向更高海拔推進約1.44至1.55公尺。森林擴展模式呈現出顯著的空間異質性,和過往研究發現的合歡山區的森林擴展以樹木界線推移帶緻密化為主不同,本研究的玉山與中央山脈區的森林擴展以樹木界線推進為主。迴歸分析結果顯示,森林面積擴展與海拔推移的調控因素有所不同,特別是海拔推移上受到更多限制。溫度相關變量及雨量變異為解釋高山森林擴展的主要因子。未來若氣溫持續上升,可能加速森林擴展,而乾旱則可能成為其主要限制條件。地形上,海拔高度為森林擴展與推移的關鍵限制因子,應是因為高海拔環境條件更為嚴苛;此外,坡度與地勢起伏亦為影響海拔推移的重要因子。綜上所述,本研究結合氣候與地形因素,深入分析臺灣高山森林擴展的趨勢與驅動機制,期望能更進一步了解氣候變遷對於臺灣高山森林生態系影響的全貌,以供未來制定高山森林保育與氣候調適策略相關研究資訊參考。
Climate change may lead to species migration, subsequently altering biodiversity and ecosystem, structure and function. Alpine ecosystems are particularly sensitive to climate change, and shifts in the upper altitudinal limits of alpine forests have become a focus in climate change ecology. Most alpine forests have expanded as the constraints of low temperatures ease, leading to a gradual contraction of alpine vegetation zones above treelines. This trend poses risks such as potential species extinctions and alterations in ecosystem services. However, studies on altitudinal shifts of alpine forests in Taiwan remain relatively limited and have primarily focused on the Hehuan Mountain area. Given the diversity of Taiwan's alpine environments and the significant regional variations in conditions, research encompassing a broader range of locations is necessary to gain a more comprehensive understanding of the impacts of climate change on the distribution of Taiwan's alpine forests. This study utilizes aerial photographs from 1980, 2001, and 2021, combined with a U-Net deep learning model for image segmentation, to analyze the trends in alpine forest distribution in the Yushan and Central Mountain Range regions over the past 41 years. Spatial regression analysis is used to explore the impact of climatic and topographic factors on forest expansion. The study found that training the model with smaller image sizes yielded the best image segmentation results. Between 1980 and 2021, the alpine forest cover in the Yushan and Central Mountain Range study areas increased by approximately 197 hectares and 158 hectares, respectively. The treeline advanced to higher elevations at an average rate of 1.44 to 1.55 meters per year. The patterns of forest expansion exhibited significant spatial heterogeneity. Unlike in the Hehuan Mountain where previous studies indicated that forest expansion was dominated by tree densification in the ecotone, treeline advancement was the primary characteristic in Yushan and the Central Mountain Range. Regression model indicated that the main factors explaining area expansion and treeline elevation shifts were different, with treeline elevation shifts constrained by more factors. If temperature continues to rise in the future, forest expansion may accelerate, while drought will be a main limiting factor. Topographically, elevation is a critical constraint on both forest expansion and treeline shifts, possibly because environmental conditions are more challenging at higher altitudes. Additionally, slope and terrain variability significantly influenced treeline advancement. In summary, this study integrates climatic and topographical factors to comprehensively analyze the trends and potential mechanisms of alpine forest expansion in Taiwan. The findings help to provide a thorough understanding of the impacts of climate change on Taiwan's alpine forest ecosystems and serve as a reference for future research on conservation and climate adaptation strategies for alpine forests.
王俊能 (2023)。暖化使高山特有植物瀕危、臺灣人,你不該冷漠。生態台灣, (81),34-43。https://www.airitilibrary.com/Article/Detail?DocID=19914903-N202310130007-00006
王思皓 (2013)。應用合歡山冷杉樹輪穩定氧同位素重建台灣高山232年氣候[碩士論文,國立臺灣大學]。華藝線上圖書館。https://doi.org/10.6342/NTU.2013.02055
王禹翔、吳笙緯、魏擇壹、鄭錦桐、鍾智昕、吳淑華、鄧國楨、黃宗仁 (2023)。應用卷積神經網絡於自動化森林覆蓋型辨識工作。航測及遙測學刊,28 (2),125-139。https://doi.org/10.6574/JPRS.202306_28 (2).0004
史培軍、陳彥強、馬恒、葉濤、唐海萍、王靜愛 (2021)。再論青藏高原近地表大氣相對氧含量影響因素的貢獻率。科學通報,66 (31),4028-4035。https://doi.org/10.1360/TB-2021-0072
林奐宇、陳建帆、黃婉如 (2022)。臺灣森林植群調查與成果的應用。林業研究專訊,29 (2),6-13。https://www.airitilibrary.com/Article/Detail?DocID=16056922-202204-202206150014-202206150014-6-13
林奐宇、謝長富、陳子英、石芝菁、羅秀雲 (2021)。臺灣山地森林在氣候變遷下的可能變化。林業研究專訊,28 (6),1-8。https://www.airitilibrary.com/Article/Detail?DocID=16056922-202112-202203310024-202203310024-1-8
吳兆鴻、徐逸祥、張晏菁 (2019)。利用Landsat 8 OLI影像反演氣溶膠光學厚度之成果論證臺中市交通流量對PM 2.5之影響。航測及遙測學刊,24 (1),59-77。https://doi.org/10.6574/JPRS.201903_24 (1).0005
宋承恩、王韻皓、林國聖、王培蓉、詹進發、陳毅青、王素芬 (2022)。運用空載高光譜及光達資料建立森林覆蓋分類判釋模型。台灣林業科學,37 (2),121-143。https://doi.org/10.7075/TJFS.202206_37 (2).0003
李昱祺、王嘉琪、翁叔平、陳正達、鄭兆尊 (2019)。臺灣氣象乾旱特性未來趨勢推估。大氣科學,47 (1),66-93。 https://doi.org/10.3966/025400022019034701003
邱清安、林鴻志、廖敏君、曾彥學、歐辰雄、呂金誠、曾喜育 (2008)。臺灣潛在植群形相分類方案。林業研究季刊,30 (4),89-111。 https://doi.org/10.29898/SHBQ.200812.0008
邱清安、曾彥學、王志強、廖敏君、曾喜育 (2010)。臺灣高山寒原植群之商榷及其在生態氣候觀點下的潛在位置。林業研究季刊,32 (3),89-102。 https://doi.org/10.29898/SHBQ.201009.0008
陳姿彤 (2011)。以臺灣中部雲杉樹輪重建三百年古氣候:利用傳統樹輪及總體經驗模態分解法[碩士論文,國立臺灣大學]。華藝線上圖書館。https://doi.org/10.6342/NTU.2011.01116
陳信雄、魏聰輝 (2005)。塔塔加地區表層土壤熱通量特性之研究。中華水土保持學報,36 (3),249-265。https://doi.org/10.29417/JCSWC.200509_36 (3).0004
陳建璋、葉日嫈、鄧國禎、陳朝圳 (2014)。臺灣12種重要針葉樹種之數位立體像對判釋。中華林學季刊,47 (2),193-213。https://www.airitilibrary.com/Article/Detail?DocID=05781345-201406-201503020016-201503020016-193-213
陳朝圳、王慈憶 (2009)。氣候變遷對台灣森林之衝擊評估與因應策略。林業研究專訊,16 (5),1-5。https://doi.org/10.29953/FRN.200910.0001
徐國士 (1996)。全球氣候變遷之樹木年輪指標。經濟部水資局。
徐健榮 (2014)。合歡山地區臺灣冷杉枯立木空間分布型態與枯死之影響因素[碩士論文,國立屏東科技大學]。華藝線上圖書館。https://doi.org/10.6346/NPUST.2014.00240
張仲德、黃倬英 (2016)。以MODIS 時序資料分析台灣植被物候空間分布。航測及遙測學刊,20 (1),1-14。https://doi.org/10.6574/JPRS.2016.20 (1).1
張惠珠、古心蘭 (2010)。合歡山台灣冷杉永久樣區之植群分析。太魯閣國家公園。https://taroko.gov.tw/News_Content.aspx?n=5522&sms=10330&s=227226
許欣湄 (2016)。雪山及南湖大山地區玉山圓柏遺傳分布及遺傳結構之探討[碩士論文,國立屏東科技大學]。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0042-1805201714154733
莊貴瑜 (1998)。合歡山台灣冷杉群落樹齡結構與草原推移之研究[碩士論文,國立東華大學]。臺灣博碩士論文知識加值系統。 https://hdl.handle.net/11296/h4e7zp
曾彥倫 (2015)。應用航空照片探討氣候變遷對合歡山地區臺灣冷杉植群消長之影響[碩士論文,國立屏東科技大學]。華藝線上圖書館。https://doi.org/10.6346/NPUST.2015.00120
斯煒 (1948)。玉山之植物社會。氣象局玉山科學調查。
黃凱易、李旻旻 (1998)。應用DTM資料探討合歡山地區台灣冷杉空間分布之特性。國立中興大學實驗林研究彙刊,20 (2),111-124。https://tpl.ncl.edu.tw/NclService/JournalContentDetail?SysId=A98021999
黃靜宜、林文和 (2014)。應用遙測技術評估玉山國家公園之植生退化潛勢。台灣生物多樣性研究,16 (4),379-391。https://www.airitilibrary.com/Article/Detail?DocID=20766971-201410-201411250011-201411250011-379-391
楊承道、張容慈、翁叔平 (2023)。臺灣地區日溫度網格化資料庫之建置和長期變化趨勢分析。地理研究,(78),75-109。https://doi.org/10.6234/JGR.202311_(78).0004
詹明勳、王亞男、葉永廉 (2005)。台灣中部塔塔加地區台灣雲杉樹輪氣候學研究過去245年氣溫與降雨量趨勢。中華林學季刊,38 (1),67-82。 https://doi.org/10.30064/QJCF.200503.0005
雷祖強、李哲源 (2013)。ADS-40數值航照影像中雲蔽區多重階層式資訊還原模式之研究-以濁水溪農業區為例。航測及遙測學刊,17 (4),237-250。 https://doi.org/10.6574/JPRS.2013.17 (4).1
廖敏君、蔡尚悳、王偉、曾喜育、歐辰雄 (2013)。雪山主峰線臺灣冷杉族群結構研究。林業研究季刊,35 (1),1-13。https://www.airitilibrary.com/Article/Detail?DocID=16068351-201303-201306180010-201306180010-1-13
劉棠瑞、蘇鴻傑 (1978)。大甲溪上游台灣二葉松天然林之植群組成及相關環境因子之研究。國立台灣大學農學院實驗林研究報告,121,207-239。
劉業經、呂福原、歐辰雄、賴國祥 (1984)。臺灣高山箭竹草生地之植物演替與競爭機制。中華林學季刊,17 (1),1-32。
賴國祥 (1992)。臺灣亞高山針葉林與草生地間推移帶動態結構之探討[博士論文,國立中興大學]。臺灣博碩士論文知識加值系統。 https://hdl.handle.net/11296/aq5sj9
Ameztegui, A., Coll, L., Brotons, L., & Ninot, J. M. (2015). Land-use legacies rather than climate change are driving the recent upward shift of the mountain tree line in the Pyrenees. Global Ecology and Biogeography, 25 (3), 263-273. https://doi.org/10.1111/geb.12407
Anselin, L. (1988). Spatial econometrics: methods and models. Springer Science & Business Media. https://doi.org/10.1007/978-94-015-7799-1
Anselin, L. (1999). The future of spatial analysis in the social sciences. Geographic Information Sciences, 5 (2), 67-76. https://doi.org/10.1080/10824009909480516
Anselin, L. (2005). Exploring spatial data with GeoDa: A workbook. Center for Spatially Integrated Social Science. https://www.geos.ed.ac.uk/~gisteac/fspat/geodaworkbook.pdf
Bader, M. Y., Llambí, L. D., Case, B. S., Buckley, H. L., Toivonen, J. M., Camarero, J. J., Cairns, D. M., Brown, C. D., Wiegand, T., & Resler, L. M. (2020). A global framework for linking alpine-treeline ecotone patterns to underlying processes. Ecography, 44 (2), 265-292. https://doi.org/10.1111/ecog.05285
Bader, M. Y., & Ruijten, J. J. A. (2008). A topography-based model of forest cover at the alpine tree line in the tropical Andes. Journal of Biogeography, 35 (4), 711-723. https://doi.org/10.1111/j.1365-2699.2007.01818.x
Bailey, S. N., Elliott, G. P., & Schliep, E. M. (2021). Seasonal temperature-moisture interactions limit seedling establishment at upper treeline in the Southern Rockies. Ecosphere, 12 (6), e03568. https://doi.org/10.1002/ecs2.3568
Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W., & Courchamp, F. (2012). Impacts of climate change on the future of biodiversity. Ecology Letters, 15 (4), 365-377. https://doi.org/10.1111/j.1461-0248.2011.01736.x
Beloiu, M., Poursanidis, D., Tsakirakis, A., Chrysoulakis, N., Hoffmann, S., Lymberakis, P., Barnias, A., Kienle, D., & Beierkuhnlein, C. (2022). No treeline shift despite climate change over the last 70 years. Forest Ecosystems, 9, 100002. https://doi.org/10.1016/j.fecs.2022.100002
Bivand, R. S., Pebesma, E., & Gómez-Rubio, V. (2013). Applied spatial data analysis with R (2nd ed.). Springer. https://doi.org/10.1007/978-1-4614-7618-4
Blumthaler, M., Ambach, W., & Ellinger, R. (1997). Increase in solar UV radiation with altitude. Journal of Photochemistry and Photobiology B: Biology, 39 (2), 130-134. https://doi.org/10.1016/S1011-1344 (96)00018-8
Boguszewski, A., Batorski, D., Ziemba-Jankowska, N., Dziedzic, T., & Zambrzycka, A. (2021). LandCover. ai: Dataset for automatic mapping of buildings, woodlands, water and roads from aerial imagery. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 1102-1110. https://doi.org/10.1109/CVPRW53098.2021.00121
Cao, K., & Zhang, X. (2020). An improved Res-UNet model for tree species classification using airborne high-resolution images. Remote Sensing, 12 (7), 1128. https://doi.org/10.3390/rs12071128
Cazzolla Gatti, R., Callaghan, T., Velichevskaya, A., Dudko, A., Fabbio, L., Battipaglia, G., & Liang, J. (2019). Accelerating upward treeline shift in the Altai Mountains under last-century climate change. Scientific Reports, 9 (1), 7678. https://doi.org/10.1038/s41598-019-44188-1
Chen, C., Jing, L., Li, H., & Tang, Y. (2021). A new individual tree species classification method based on the ResU-Net model. Forests, 12 (9), 1202. https://doi.org/10.3390/f12091202
Chen, C. S., & Chen, Y. L. (2003). The rainfall characteristics of Taiwan. Monthly Weather Review, 131 (7), 1323-1341. https://doi.org/10.1175/1520-0493 (2003)131<1323:TRCOT>2.0.CO;2
Chen, I. C., Hill, J. K., Ohlemüller, R., Roy, D. B., & Thomas, C. D. (2011). Rapid range shifts of species associated with high levels of climate warming. Science, 333 (6045), 1024-1026. https://doi.org/10.1126/science.1206432
Chen, S., Zhang, M., & Lei, F. (2023). Mapping vegetation types by different fully convolutional neural network structures with inadequate training labels in complex landscape urban areas. Forests, 14 (9), 1788. https://doi.org/10.3390/f14091788
Chen, Y. M., Chen, C. W., Chao, Y. C., Tung, Y. S., Liou, J. J., Li, H. C., & Cheng, C. T. (2020). Future landslide characteristic assessment using ensemble climate change scenarios: a case study in Taiwan. Water, 12 (2), 564. https://doi.org/10.3390/w12020564
Chiu, C. A., Tzeng, H. Y., Lin, C. T., Chang, K. C., & Liao, M. C. (2022). Spatial distribution and climate warming impact on Abies kawakamii forest on a subtropical island. Plants, 11 (10), 1346. https://doi.org/10.3390/plants11101346
Chou, C. H., Huang, T. J., Lee, Y. P., Chen, C. Y., Hsu, T. W., & Chen, C. H. (2011). Diversity of the alpine vegetation in central Taiwan is affected by climate change based on a century of floristic inventories. Botanical Studies, 52 (4), 503-516.
Chung, M. E., Doyog, N. D., & Lin, C. (2021). Monitoring of the trend of timberlines in Taiwan amidst climate change through multi-temporal satellite images. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 6488-6491. https://doi.org/10.1109/IGARSS47720.2021.9553538
Cira, C. I., Manso Callejo, M. Á., Alcarria, R., Iturrioz, T., & Arranz Justel, J. J. (2024). Insights into the effects of tile size and tile overlap levels on semantic segmentation models trained for road surface area extraction from aerial orthophotography. Remote Sensing, 16 (16), 2954. https://doi.org/10.3390/rs16162954
Cira, C. I., Manso Callejo, M. Á., Yokoya, N., Sălăgean, T., & Badea, A. C. (2024). Impact of tile size and tile overlap on the prediction performance of convolutional neural networks trained for road classification. Remote Sensing, 16 (15), 2818. https://doi.org/10.3390/rs16152818
Coumou, D., & Rahmstorf, S. (2012). A decade of weather extremes. Nature Climate Change, 2 (7), 491-496. https://doi.org/10.1038/nclimate1452
Cudlín, P., Klopčič, M., Tognetti, R., Máliš, F., Alados, C. L., Bebi, P., Grunewald, K., Zhiyanski, M., Andonowski, V., Porta, N. L., Bratanova Doncheva, S., Kachaunova, E., Edwards Jonáová, M., Ninot, J. M., Rigling, A., Hofgaard, A., Hlásny, T., Skalák, P., & Wielgolaski, F. E. (2017). Drivers of treeline shift in different European mountains. Climate Research, 73 (1-2), 135-150. https://doi.org/10.3354/cr01465
Dar, F. A., Hamid, M., Malik, R. A., Wani, S. A., Singh, C. P., Shah, M. A., & Khuroo, A. A. (2024). ToTE: A global database on trees of the treeline ecotone. Ecology, 105 (6), e4309. https://doi.org/10.1002/ecy.4309
Davis, E. L., Brown, R., Daniels, L., Kavanagh, T., & Gedalof, Z. (2020). Regional variability in the response of alpine treelines to climate change. Climatic Change, 162 (3), 1365-1384. https://doi.org/10.1007/s10584-020-02743-0
Demir, B., Bovolo, F., & Bruzzone, L. (2013). Updating land-cover maps by classification of image time series: a novel change-detection-driven transfer learning approach. IEEE Transactions on Geoscience and Remote Sensing, 51 (1), 300-312. https://doi.org/10.1109/TGRS.2012.2195727
Dvořák, J., Potůčková, M., & Treml, V. (2022). Weakly supervised learning for treeline ecotone classification based on aerial orthoimages and an ancillary dsm. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3, 33-38. https://doi.org/10.5194/isprs-annals-V-3-2022-33-2022
Elliott, G. P., Bailey, S. N., & Cardinal, S. J. (2020). Hotter drought as a disturbance at upper treeline in the southern Rocky Mountains. Annals of the American Association of Geographers, 111 (3), 756–770. https://doi.org/10.1080/24694452.2020.1805292
Elliott, G. P., & Petruccelli, C. A. (2018). Tree recruitment at the treeline across the continental divide in the northern Rocky Mountains, USA: the role of spring snow and autumn climate. Plant Ecology and Diversity, 11 (3), 319-333. https://doi.org/10.1080/17550874.2018.1487475
Fazlollahi Mohammadi, M., Jalali, S. G. H., Kooch, Y., & Said Pullicino, D. (2016). Slope gradient and shape effects on soil profiles in the northern mountainous forests of Iran. Eurasian Soil Science, 49 (12), 1366-1374. https://doi.org/10.1134/S1064229316120061
Feuillet, T., Birre, D., Milian, J., Godard, V., Clauzel, C., & Serrano Notivoli, R. (2020). Spatial dynamics of alpine tree lines under global warming: What explains the mismatch between tree densification and elevational upward shifts at the tree line ecotone? Journal of Biogeography, 47 (5), 1056-1068. https://doi.org/10.1111/jbi.13779
Giang, T. L., Dang, K. B., Toan Le, Q., Nguyen, V. G., Tong, S. S., & Pham, V. M. (2020). U-Net convolutional networks for mining land cover classification based on high-resolution UAV imagery. IEEE Access, 8, 186257-186273. https://doi.org/10.1109/ACCESS.2020.3030112
Gobiet, A., Kotlarski, S., Beniston, M., Heinrich, G., Rajczak, J., & Stoffel, M. (2014). 21st century climate change in the European Alps—A review. Science of The Total Environment, 493, 1138-1151. https://doi.org/10.1016/j.scitotenv.2013.07.050
Gottfried, M., Pauli, H., Futschik, A., Akhalkatsi, M., Barančok, P., Benito Alonso, J. L., Coldea, G., Dick, J., Erschbamer, B., Fernández Calzado, M. R., Kazakis, G., Krajči, J., Larsson, P., Mallaun, M., Michelsen, O., Moiseev, D., Moiseev, P., Molau, U., Merzouki, A., … Grabherr, G. (2012). Continent-wide response of mountain vegetation to climate change. Nature Climate Change, 2, 111-115. https://doi.org/10.1038/nclimate1329
Grabherr, G., Gottfried, M., & Pauli, H. (2010). Climate change impacts in alpine environments. Geography Compass, 4 (8), 1133-1153. https://doi.org/10.1111/j.1749-8198.2010.00356.x
Greenwood, S., Chen, J. C., Chen, C. T., & Jump, A. S. (2014). Strong topographic sheltering effects lead to spatially complex treeline advance and increased forest density in a subtropical mountain region. Global Change Biology, 20 (12), 3756-3766. https://doi.org/10.1111/gcb.12710
Greenwood, S., Chen, J. C., Chen, C. T., & Jump, A. S. (2015). Temperature and sheltering determine patterns of seedling establishment in an advancing subtropical treeline. Journal of Vegetation Science, 26 (4), 711-721. https://doi.org/10.1111/jvs.12269
Groemping, U. (2007). Relative importance for linear regression in R: the package relaimpo. Journal of Statistical Software, 17 (1), 1-27. https://doi.org/10.18637/jss.v017.i01
Hansson, A., Dargusch, P., & Shulmeister, J. (2021). A review of modern treeline migration, the factors controlling it and the implications for carbon storage. Journal of Mountain Science, 18 (2), 291-306. https://doi.org/10.1007/s11629-020-6221-1
Hansson, A., Shulmeister, J., Dargusch, P., & Hill, G. (2023). A review of factors controlling southern hemisphere treelines and the implications of climate change on future treeline dynamics. Agricultural and Forest Meteorology, 332, 109375. https://doi.org/10.1016/j.agrformet.2023.109375
Hansson, A., Yang, W. H., Dargusch, P., & Shulmeister, J. (2023). Investigation of the relationship between treeline migration and changes in temperature and precipitation for the northern hemisphere and sub-regions. Current Forestry Reports, 9 (2), 72-100. https://doi.org/10.1007/s40725-023-00180-7
Harsch, M. A., Hulme, P. E., McGlone, M. S., & Duncan, R. P. (2009). Are treelines advancing? A global meta-analysis of treeline response to climate warming. Ecology Letters, 12 (10), 1040-1049. https://doi.org/10.1111/j.1461-0248.2009.01355.x
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770-778. https://doi.org/10.1109/CVPR.2016.90
Holtmeier, F. K., & Broll, G. (2010). Wind as an ecological agent at treelines in north America, the Alps, and the European subarctic. Physical Geography, 31 (3), 203-233. https://doi.org/10.2747/0272-3646.31.3.203
Hsieh, C. (2002). Composition, endemism and phytogeographical affinities of the Taiwan flora. Taiwania, 47 (4). https://doi.org/10.6165/tai.2002.47 (4).298
Jo, W., & Park, K. H. (2022). Deep learning based land cover change detection using U-Net. Journal of the Korean Geographical Society, 57 (3), 297-306. https://doi.org/10.22776/kgs.2022.57.3.297
Jump, A. S., Huang, T. J., & Chou, C. H. (2012). Rapid altitudinal migration of mountain plants in Taiwan and its implications for high altitude biodiversity. Ecography, 35, 204-210. https://doi.org/10.1111/j.1600-0587.2011.06984.x
Klasner, F. L., & Fagre, D. B. (2002). A half century of change in alpine treeline patterns at Glacier National Park, Montana, U.S.A. Arctic, Antarctic, and Alpine Research, 34 (1), 49-56. https://doi.org/10.1080/15230430.2002.12003468
Knutzen, F., Averbeck, P., Barrasso, C., Bouwer, L. M., Gardiner, B., Grünzweig, J. M., Hänel, S., Haustein, K., Johannessen, M. R., Kollet, S., Pietikaeinen, J. P., Pietras Couffignal, K., Pinto, J. G., Rechid, D., Rousi, E., Russo, A., Suarez Gutierrez, L., Wendler, J., Xoplaki, E., & Gliksman, D. (2023). Impacts and damages of the European multi-year drought and heat event 2018-2022 on forests, a review. EGUsphere,2023, 1-56. https://doi.org/10.5194/egusphere-2023-1463
Körner, C. (1998). A re-assessment of high elevation treeline positions and their explanation. Oecologia, 115 (4), 445-459. https://doi.org/10.1007/s004420050540
Körner, C. (1999). Alpine plant life. Springer. https://doi.org/10.1007/978-3-642-98018-3
Körner, C. (2012). Alpine treelines. Springer. https://doi.org/10.1007/978-3-0348-0396-0
Körner, C., & Hoch, G. (2023). Not every high-latitude or high-elevation forest edge is a treeline. Journal of Biogeography, 50 (5), 838-845. https://doi.org/10.1111/jbi.14593
Kuo, C. C., Liu, Y. C., Su, Y., Liu, H. Y., & Lin, C. T. (2022). Responses of alpine summit vegetation under climate change in the transition zone between subtropical and tropical humid environment. Scientific Reports, 12 (1), 13352. https://doi.org/10.1038/s41598-022-17682-2
Kuo, C. C., Su, Y., Liu, H. Y., & Lin, C. T. (2021). Assessment of climate change effects on alpine summit vegetation in the transition of tropical to subtropical humid climate. Plant Ecology, 222 (8), 933-951. https://doi.org/10.1007/s11258-021-01152-2
Lamprecht, A., Semenchuk, P. R., Steinbauer, K., Winkler, M., & Pauli, H. (2018). Climate change leads to accelerated transformation of high-elevation vegetation in the central Alps. New Phytologist, 220 (2), 447-459. https://doi.org/10.1111/nph.15290
Lee, H., Calvin, K., Dasgupta, D., Krinner, G., Mukherji, A., Thorne, P., ... & Park, Y. (2023). IPCC, 2023: climate change 2023: synthesis report, summary for policymakers. Contribution of working groups i, ii and iii to the sixth assessment report of the intergovernmental panel on climate change [core writing team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland. https://doi.org/10.59327/IPCC/AR6-9789291691647.001
Lee, Y., Sim, W., Park, J., & Lee, J. (2022). Evaluation of hyperparameter combinations of the U-Net model for land cover classification. Forests, 13 (11), 1813. https://doi.org/10.3390/f13111813
Li, X., Liang, E., Gričar, J., Rossi, S., Čufar, K., & Ellison, A. M. (2017). Critical minimum temperature limits xylogenesis and maintains treelines on the southeastern Tibetan Plateau. Science Bulletin, 62 (11), 804-812. https://doi.org/10.1016/j.scib.2017.04.025
Lin, H. Y., Li, C. F., Chen, T. Y., Hsieh, C. F., Wang, G., Wang, T., & Hu, J. M. (2020). Climate-based approach for modeling the distribution of montane forest vegetation in Taiwan. Applied Vegetation Science, 23 (2), 239-253. https://doi.org/10.1111/avsc.12485
Lindeman, R. H., Merenda, P. F., & Gold, R. Z. (1980). Introduction to bivariate and multivariate analysis. Scott, Foresman.
Liu, Q., Yang, D., Cao, L., & Anderson, B. (2022). Assessment and prediction of carbon storage based on land use/land cover dynamics in the tropics: a case study of Hainan Island, China. Land, 11 (2), 244. https://doi.org/10.3390/land11020244
Lu, M. L., & Huang, J. Y. (2023). Predicting negative effects of climate change on Taiwan’s endemic bumblebee Bombus formosellus. Journal of Insect Conservation, 27 (1), 193-203. https://doi.org/10.1007/s10841-022-00415-1
Lu, X., Liang, E., Wang, Y., Babst, F., & Camarero, J. J. (2020). Mountain treelines climb slowly despite rapid climate warming. Global Ecology and Biogeography, 30 (1), 305-315. https://doi.org/10.1111/geb.13214
Lyu, L., Zhang, Q. B., Pellatt, M. G., Büntgen, U., Li, M. H., & Cherubini, P. (2019). Drought limitation on tree growth at the Northern Hemisphere’s highest tree line. Dendrochronologia, 53, 40-47. https://doi.org/10.1016/j.dendro.2018.11.006
Mathisen, I. E., Mikheeva, A., Tutubalina, O. V., Aune, S., & Hofgaard, A. (2014). Fifty years of tree line change in the Khibiny Mountains, Russia: Advantages of combined remote sensing and dendroecological approaches. Applied Vegetation Science, 17 (1), 6-16. https://doi.org/10.1111/avsc.12038
Mienna, I. M., Klanderud, K., Ørka, H. O., Bryn, A., & Bollandsås, O. M. (2022). Land cover classification of treeline ecotones along a 1100 km latitudinal transect using spectral- and three-dimensional information from UAV-based aerial imagery. Remote Sensing in Ecology and Conservation, 8 (4), 536-550. https://doi.org/10.1002/rse2.260
Mietkiewicz, N., Kulakowski, D., Rogan, J., & Bebi, P. (2017). Long-term change in sub-alpine forest cover, tree line and species composition in the Swiss Alps. Journal of Vegetation Science, 28 (5), 951-964. https://doi.org/10.1111/jvs.12561
Mohapatra, J., Singh, C. P., Tripathi, O. P., & Pandya, H. A. (2019). Remote sensing of alpine treeline ecotone dynamics and phenology in Arunachal Pradesh Himalaya. International Journal of Remote Sensing, 40 (20), 7986-8009. https://doi.org/10.1080/01431161.2019.1608383
Morley, P. J., Donoghue, D. N. M., Chen, J. C., & Jump, A. S. (2020). Montane forest expansion at high elevations drives rapid reduction in non-forest area, despite no change in mean forest elevation. Journal of Biogeography, 47 (11), 2405-2416. https://doi.org/10.1111/jbi.13951
Nemani, R. R., Keeling, C. D., Hashimoto, H., Jolly, W. M., Piper, S. C., Tucker, C. J., Myneni, R. B., & Running, S. W. (2003). Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science, 300 (5625), 1560-1563. https://doi.org/10.1126/science.1082750
Nguyen, H. D., Quang Thanh, B., Nguyen, Q. H., Nguyen, T. G., Pham, L. T., Nguyen, X. L., Vu, P. L., Thanh Nguyen, T. H., Nguyen, A. T., & Petrisor, A. I. (2022). A novel hybrid approach to flood susceptibility assessment based on machine learning and land use change. Case study: A river watershed in Vietnam. Hydrological Sciences Journal, 67 (7), 1065-1083. https://doi.org/10.1080/02626667.2022.2060108
Parolo, G., & Rossi, G. (2008). Upward migration of vascular plants following a climate warming trend in the Alps. Basic and Applied Ecology, 9 (2), 100-107. https://doi.org/10.1016/j.baae.2007.01.005
Pauli, H., Gottfried, M., Dullinger, S., Abdaladze, O., Akhalkatsi, M., Alonso, J. L. B., Coldea, G., Dick, J., Erschbamer, B., Calzado, R. F., Ghosn, D., Holten, J. I., Kanka, R., Kazakis, G., Kollár, J., Larsson, P., Moiseev, P., Moiseev, D., Molau, U., … Grabherr, G. (2012). Recent plant diversity changes on Europe’s Mountain summits. Science, 336 (6079), 353-355. https://doi.org/10.1126/science.1219033
Pecl, G. T., Araújo, M. B., Bell, J. D., Blanchard, J., Bonebrake, T. C., Chen, I. C., Clark, T. D., Colwell, R. K., Danielsen, F., Evengård, B., Falconi, L., Ferrier, S., Frusher, S., Garcia, R. A., Griffis, R. B., Hobday, A. J., Janion Scheepers, C., Jarzyna, M. A., Jennings, S., … Williams, S. E. (2017). Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science, 355 (6332), eaai9214. https://doi.org/10.1126/science.aai9214
Peereman, J., Hogan, J. A., & Lin, T. C. (2022). Disturbance frequency, intensity and forest structure modulate cyclone-induced changes in mangrove forest canopy cover. Global Ecology and Biogeography, 31 (1), 37-50. https://doi.org/10.1111/geb.13407
Peñuelas, J., & Boada, M. (2003). A global change-induced biome shift in the Montseny mountains (NE Spain). Global Change Biology, 9 (2), 131-140. https://doi.org/10.1046/j.1365-2486.2003.00566.x
Persson, M., Lindberg, E., & Reese, H. (2018). Tree species classification with multi-temporal sentinel-2 data. Remote Sensing, 10 (11), 1794. https://doi.org/10.3390/rs10111794
Román Palacios, C., & Wiens, J. J. (2020). Recent responses to climate change reveal the drivers of species extinction and survival. Proceedings of the National Academy of Sciences of the United States of America, 117 (8), 4211-4217. https://doi.org/10.1073/pnas.1913007117
Ronneberger, O., Fischer, P. & Brox, T. (2015). U-Net: convolutional networks for biomedical image segmentation. In N. Navab, J. Hornegger, W. M. Wells & A. F. Frangi (eds.), Medical Image Computing and Computer-Assisted Intervention- MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III (pp. 234-241). Springer International Publishing. https://doi.org/10.1007/978-3-319-24574-4_28
Sala, O. E., Chapin, F. S., Armesto, J. J., Berlow, E., Bloomfield, J., Dirzo, R., Huber Sanwald, E., Huenneke, L. F., Jackson, R. B., Kinzig, A., Leemans, R., Lodge, D. M., Mooney, H. A., Oesterheld, M., Poff, N. L., Sykes, M. T., Walker, B. H., Walker, M., & Wall, D. H. (2000). Global biodiversity scenarios for the year 2100. Science, 287 (5459), 1770-1774. https://doi.org/10.1126/science.287.5459.1770
Shi, H., Zhou, Q., He, R., Zhang, Q., & Dang, H. (2022). Climate warming will widen the lagging gap of global treeline shift relative to densification. Agricultural and Forest Meteorology, 318, 108917. https://doi.org/10.1016/j.agrformet.2022.108917
Smyčka, J., Roquet, C., Renaud, J., Thuiller, W., Zimmermann, N. E., & Lavergne, S. (2017). Disentangling drivers of plant endemism and diversification in the European Alps - A phylogenetic and spatially explicit approach. Perspectives in Plant Ecology, Evolution and Systematics, 28, 19-27. https://doi.org/10.1016/j.ppees.2017.06.004
Steinbauer, M. J., Field, R., Grytnes, J. A., Trigas, P., Ah Peng, C., Attorre, F., Birks, H. J. B., Borges, P. A. V., Cardoso, P., Chou, C. H., De Sanctis, M., de Sequeira, M. M., Duarte, M. C., Elias, R. B., Fernández Palacios, J. M., Gabriel, R., Gereau, R. E., Gillespie, R. G., Greimler, J., … Beierkuhnlein, C. (2016). Topography-driven isolation, speciation and a global increase of endemism with elevation. Global Ecology and Biogeography, 25 (9), 1097-1107. https://doi.org/10.1111/geb.12469
Su, H.J. (1984). Studies on the climate and vegetation types of the natural forests in Taiwan (2): Altitudinal vegetation zones in relation to temperature gradient. Quarterly Journal of Chinese Forestry, 17 (4), 57-73.
Taravat, A., Wagner, M. P., Bonifacio, R., & Petit, D. (2021). Advanced fully convolutional networks for agricultural field boundary detection. Remote Sensing, 13 (4), 722. https://doi.org/10.3390/rs13040722
Trant, A., Higgs, E., & Starzomski, B. M. (2020). A century of high elevation ecosystem change in the Canadian Rocky Mountains. Scientific Reports, 10 (1), 9698. https://doi.org/10.1038/s41598-020-66277-2
Treml, V., & Migoń, P. (2015). Controlling factors limiting timberline position and shifts in the Sudetes: A review. Geographia Polonica, 88, 55-70. https://doi.org/10.7163/GPol.0015
Treml, V., Šenfeldr, M., Chuman, T., Ponocná, T., & Demková, K. (2016). Twentieth century treeline ecotone advance in the Sudetes Mountains (Central Europe) was induced by agricultural land abandonment rather than climate change. Journal of Vegetation Science, 27 (6), 1209-1221. https://doi.org/10.1111/jvs.12448
Tuia, D., Roscher, R., Wegner, J. D., Jacobs, N., Zhu, X., & Camps Valls, G. (2021). Toward a collective agenda on ai for earth science data analysis. IEEE Geoscience and Remote Sensing Magazine, 9 (2), 88-104. https://doi.org/10.1109/MGRS.2020.3043504
Vali, A., Comai, S., & Matteucci, M. (2020). Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: a review. Remote Sensing, 12 (15), 2495. https://doi.org/10.3390/rs12152495
Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S (4th ed.). Springer. https://doi.org/10.1007/978-0-387-21706-2
Verrall, B., & Pickering, C. M. (2020). Alpine vegetation in the context of climate change: a global review of past research and future directions. Science of The Total Environment, 748, 141344. https://doi.org/10.1016/j.scitotenv.2020.141344
Vitali, A., Urbinati, C., Weisberg, P. J., Urza, A. K., & Garbarino, M. (2018). Effects of natural and anthropogenic drivers on land-cover change and treeline dynamics in the Apennines (Italy). Journal of Vegetation Science, 29 (2), 189-199. https://doi.org/10.1111/jvs.12598
Wagner, F. H., Sanchez, A., Tarabalka, Y., Lotte, R. G., Ferreira, M. P., Aidar, M. P. M., Gloor, E., Phillips, O. L., & Aragão, L. E. O. C. (2019). Using the U-net convolutional network to map forest types and disturbance in the Atlantic rainforest with very high resolution images. Remote Sensing in Ecology and Conservation, 5 (4), 360-375. https://doi.org/10.1002/rse2.111
Wang, Y., Pederson, N., Ellison, A. M., Buckley, H. L., Case, B. S., Liang, E., & Julio Camarero, J. (2016). Increased stem density and competition may diminish the positive effects of warming at alpine treeline. Ecology, 97 (7), 1668-1679. https://doi.org/10.1890/15-1264.1
Weiss, K., Khoshgoftaar, T. M., & Wang, D. (2016). A survey of transfer learning. Journal of Big Data, 3 (1), 9. https://doi.org/10.1186/s40537-016-0043-6
Wielgolaski, F. E., Hofgaard, A., & Holtmeier, F. K. (2017). Sensitivity to environmental change of the treeline ecotone and its associated biodiversity in European mountains. Climate Research, 73 (1-2), 151-166. https://doi.org/10.3354/cr01474
Wieser, G., Oberhuber, W., & Gruber, A. (2019). Effects of climate change at treeline: lessons from space-for-time studies, manipulative experiments, and long-term observational records in the central Austrian Alps. Forests, 10 (6), 508. https://doi.org/10.3390/f10060508
Yadava, A. K., Sharma, Y. K., Dubey, B., Singh, J., Singh, V., Bhutiyani, M. R., Yadav, R. R., & Misra, K. G. (2017). Altitudinal treeline dynamics of Himalayan pine in western Himalaya, India. Quaternary International, 444, 44-52. https://doi.org/10.1016/j.quaint.2016.07.032
Yen, M. C., & Chen, T. C. (2000). Seasonal variation of the rainfall over Taiwan. International Journal of Climatology, 20 (7), 803-809. https://doi.org/10.1002/1097-0088 (20000615)20:7<803::AID-JOC525>3.0.CO;2-4
Zhao, M., & Running, S. W. (2010). Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science, 329 (5994), 940-943. https://doi.org/10.1126/science.1192666
Zou, F., Tu, C., Liu, D., Yang, C., Wang, W., & Zhang, Z. (2022). Alpine treeline dynamics and the special exposure effect in the Hengduan Mountains. Frontiers in Plant Science, 13, 1-14. https://doi.org/10.3389/fpls.2022.861231