リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。

リケラボ 全国の大学リポジトリにある学位論文・教授論文を一括検索するならリケラボ論文検索大学・研究所にある論文を検索できる

リケラボ 全国の大学リポジトリにある学位論文・教授論文を一括検索するならリケラボ論文検索大学・研究所にある論文を検索できる

大学・研究所にある論文を検索できる 「Analyses of spatial dynamics and photosynthesis in kudzu community by remote sensing (リモートセンシングによるクズ群落の空間動態および光合成の解析)」の論文概要。リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。

コピーが完了しました

URLをコピーしました

論文の公開元へ論文の公開元へ
書き出し

Analyses of spatial dynamics and photosynthesis in kudzu community by remote sensing (リモートセンシングによるクズ群落の空間動態および光合成の解析)

岩本, 啓己 信州大学

2021.11.01

概要

クズ(Pueraria lobata (Willd.)Ohwi)は農業や植生管理の場面において,経済的な損失および生態学的な課題を引き起こしている.侵略的な雑草の群落動態を管理スケールでモニタリングおよびモデリングすることは,拡大を抑制するための最適なリソース配分を検討する上で実用的な知見をもたらすと考えられる.無人航空機(UAV: unmanned aerial vehicle)による空撮画像は,雑草群落の空間分布のモニタリングを可能にする.時期ごとの高解像度の画像から特定の種の情報を効率よく取り出すには,機械学習による画像分類が有効と考えられる.また,携帯型のクロロフィル蛍光センサーで測定したパラメータは,野外の雑草群落の光合成機能の評価に適する.本研究では,これらの新しいセンシング技術を活用して,クズ群落の生態学的特性と最適な管理方法を検討した.

経時的なUAV画像に基づいて,クズ群落の空間分布の推移を評価した.画像を50cmグリッドに分割し,RGB画像の輝度値に基づいてサポートベクトルマシン(SVM: support vector machine)を用いて各グリッドのクズの在/不在を判別した.6月は判別が困難であったが,群落表層がクズに被覆された7月以降は,全グリッドの5%を教師データとして用いることで正解率0.9以上,F値0.9以上での判別が可能であった.群落の端部は7月の葉群展開期に急速に拡大していたが,7月下旬の除草作業で地上部が取り除かれた範囲では,その後の8~10月の養分貯蔵期にも群落が拡大していた.また,3種類の植生指数から各調査日におけるクズ群落のLAIを推定する重回帰式を得た.その結果,10月の推定精度は低かったが(調整済み決定係数<0.2),6~8月では調整済み決定係数が0.42~0.57の範囲でLAIを植生指数から推定することができた.クズが存在すると判別されたグリッドのLAIを推定したところ,LAIは7~8月に群落中央部で減少しており,その間に分布の中心は群落の中央部から周縁部に移動していた.この変化は夏季に葉を更新する際,より高次の分枝に新たに葉を展開する過程で生じたものと考えられた.この群落中央から周縁部への葉の移動は,群落の光合成効率を高めると同時に,群落の占有範囲の拡大にも寄与するものと考えられた.

また,グリッドごとの占有状態に対し隠れマルコフモデル(HMM: hidden Markov model:)を適用することで,時期ごとのクズ群落の空間動態を評価した.モデルは各グリッドの占有状態の判別過程を表す観測モデルと占有状態の時間的変化を表すシステムモデルからなり,クズの出現および拡大に関するパラメータをマルコフ連鎖モンテカルロ(MCMC: Markov chain Monte Carlo)法によって推定した.パラメータの事後確率分布に基づいて刈り取り後の群落の拡大を評価したところ,7月の刈り取り後は8月の刈り取り後より群落の拡大しやすい傾向にあった.クズの空間占有動態からは,残存した越年茎や根冠から生じた茎が急速に成長し,除草などによる撹乱のダメージを補償することが示唆された.以上からクズ群落の拡大を抑制するには,盛夏期に特に群落周縁部の高次の分枝を除去することによって,着根および貯蔵器官への養分の蓄積を減らすことが有効と考えられた.

加えて,葉の調位運動がクズ群落の受光態勢および光合成効率に及ぼす影響について,携帯型の蛍光測定器を用いて検討した.野外の群落表層をナイロンネットで覆うことで調位運動を阻害し,群落内の層位ごとの光強度および光合成パラメータを測定した.群落の表層葉を太陽光に対して平行に向けることで,表層葉が直達光を回避するとともに,群落内部への光の透過量は増加した.下位葉のリニア電子伝達(LEF: linear electron flow)が増加した一方で表層葉のLEFの減少は比較的小さく,光の透過によって群落としての光合成効率が高まることが示唆された.また,直達光を受けた下位葉は,非光化学的消光(NPQ: non-photochemical quenching)によって過剰なエネルギーを消去していた.これらの証拠から,クズの調位運動には表層葉の光阻害を回避するとともに,群落としての光合成効率を高める効果があるものと考えられた.

以上のように新しいセンシング技術によって,野外のクズ群落の空間動態や光合成機能に関する重要な知見が得られた.本研究のセンシングおよびモデリングは他の雑草種にも拡張できる.機械学習を含む空撮画像処理技術は,特定の種の空間分布を評価するのに有用である.また,様々なセンシングデータを統合し,生態特性を包括的に理解には柔軟な階層モデルが重要である.生態特性に関する知見とデータ処理の枠組みを蓄積することで,管理スケールでの雑草群落動態のシミュレーションが可能となるであろう.

参考文献

Alexandridis, T. K., Tamouridou, A. A., Pantazi, X. E., Lagopodi, A. L., Kashefi, J., Ovakoglou, G. V., ... & Moshou, D. (2017). Novelty detection classifiers in weed mapping: Silybum marianum detection on UAV multispectral images. Sensors 17, 2007.

Anselin, L. (1995). Local indicators of spatial association – LISA. Geographical Analysis 27, 93–115.

Araki, M. (2014). Introduction of machine learning with free software. Morikita Press, Tokyo, pp. 115-127. [in Japanese]

Arase et al. (1999). Growth and seed yield of Amphicarpaea edgeworthii Benth. in a field cropping. Japanese Journal of Crop Science 68, 83-90.

Asada, M., Osada, Y., Fukasawa, K. & Ochiai, K. (2014). Bayesian estimation of reeves’ muntjac (Muntiacus reevesi) populations using state-space models. Mammalian Science 54, 53-72.

Auld, B. A. & Johnson, S. B. (2014). Invasive alien plant management. CAB reviews 9, 37.

Aurambout, J. P. & Endress, A. G. (2018). A model to simulate the spread and management cost of kudzu (Pueraria montana var. lobata) at landscape scale. Ecological Informatics 43, 146-156.

Bakhshipour, A. & Jafari, A. (2018). Evaluation of support vector machine and artificial neural networks in weed detection using shape features. Computers and Electronics in Agriculture 145, 153-160.

Bastiaans, L., Kropff, M. J., Goudriaan, J. & van Laara, H. H. (2000). Design of weed management systems with a reduced reliance on herbicides poses new challenges and prerequisites for modeling crop-weed interactions. Field Crops Research 67, 161-179.

Bivand, R. S. & Wong, D. W. S. (2018). Comparing implementations of global and local indicators of spatial association. TEST 27, 716–748

Bonsi, C., Rhoden, E., Woldeghebriel, A., Mount, P., Solaiman, S., Noble, R., Paris, G., McMahon, C., Pearson, H. & Cash, B. (1992). Kudzu-goat interactions ̶ A pilot study. In: Solaiman, S. G. & Hill, W. A. (Eds.) Using goats to manage forest vegetation, a regional inquiry. Tuskegee, AL: Tuskegee University Agricultural Experiment Station. pp. 84-88.

Boyne, R., Osunkoya, O. O. & Scharaschkin, T. (2013). Variation in leaf structure of the invasive Madeira vine (Anredera cordifolia, Basellaceae) at different light levels. Australian Journal of Botany 61, 412–417.

Bray, J. R. & Curtis, J. T. (1957). An ordination of upland forest communities of southern Wisconsin. Ecological Monographs 27, 325-349.

Chabot, D., Dillon, C., Shemrock, A., Weissflog, N. and Sager, E. P. S. (2018). An ObjectBased Image Analysis Workflow for Monitoring Shallow-Water Aquatic Vegetation in Multispectral Drone Imagery. International Journal of Geo-Information 7, 294.

Choi, E. & Inoue, Y. (2004). Relationship of transpiration and evapotranspiration to solar radiation and spectral reflectance in soybean canopies ̶ A simple method for remote sensing of canopy transpiration —. Journal of Agricultural Meteorology 60, 43-53.

Cruz, J. A., Sacksteder, C. A., Kanazawa, A. & Kramer, D. M. (2001). Contribution of electric field (∆Ψ) to steady-state transthylakiod proton motive force (pmf) in vitro and in vivo. Control of pmf parsing into ∆Ψ and ∆pH by ionic strength. Biochemistry 40, 1226-1237.

Dejong, T. M., Day, K. R. & Johnson, R. S. (1989). Seasonal relationships between leaf nitrogen content (photosynthetic capacity) and leaf canopy light exposure in peach (Prunus persica). Trees 3, 89-95.

Evers, J. B. & Bastiaans, L. (2016). Quantifying the effect of crop spatial arrangement on weed suppression using functional-structural plant modelling. Journal of Plant Research 129, 339-351.

Ferguson, P. F. B., Conroy, M. J. & Hepinstall-Cymerman, J. (2015). Occupancy model for data with false positive and false negative errors and heterogeneity across sites and surveys. Methods in Ecology and Evolution 6, 1395-1406.

Fernández-Calleja, M., Monteagudo, A., Casas, A.M., Boutin, C., Pin, P.A., Morales, F. & Igartua, E. (2020). Rapid on-site phenotyping via field fluorimeter detects differences in photosynthetic performance in a hybrid - parent barley germplasm set. Sensors 20, 1486.

Field, C. (1983). Allocating leaf nitrogen for the maximization of carbon gain: Leaf age as a control on the allocation program. Oecologia. 56, 341-347.

Forseth, I. N. & Teramura, A. H. (1986). Kudzu leaf energy budget and calculated transpiration: the influence of leaflet orientation. Ecology 67, 564-571.

Forseth, J. I. N. & Innis, A. F. (2004). Kudzu (Pueraria montana): History, physiology, and ecology combine to make a major ecosystem threat. Critical Reviews in Plant Sciences 23, 401-413.

Frankenberg, C., Butz, A. & Toon, G. C. (2011), Disentangling chlorophyll fluorescence from atmospheric scattering effects in O2 A-band spectra of reflected sun-light. Geophysical Research Letters 38, L03801.

Frye, M. J., Hough-Goldstein, J., & Sun, J. H. (2007). Biology and preliminary host range assessment of two potential kudzu biological control agents. Envionmental Entomology 36, 1430-1440.

Frye, M. J., Hough-Goldstein, J. & Kidd, K. A. (2012). Response of kudzu (Pueraria montana var. lobata) to different types and levels of simulated insect herbivory damage. Biological Control 61, 71-77.

Frye, M. J. & Hough-Goldstein, J. (2013). Plant architectute and growth response of kudzu (Fabales: Fabaceae) to simulated insect herbivory. Environmental Entomology

42, 936-941.

Fukuda, E. (2014). Influences of clear-cutting, burning and sheep grazing on the dynamics of buried seeds of kudzu (Pueraria lobata (Willd.) Ohwi) in abandoned land. Japanese Journal of Grassland Science 60, 1-9.

Gamon, J. A., Penuelas, J. & Field, C. B. (1992). A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment 41, 35-44.

Gelman, A. & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science 7, 457-472.

Geographical Survey Institute. (2019). Conversion into plane rectangular coordinate system. https://vldb.gsi.go.jp/sokuchi/surveycalc/surveycalc/bl2xyf.html (Access confirmed on 2021. 3. 1.)

Getis, A. & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis 24, 189-205.

Gitelson, A. A., Merzlyak, M. N. & Lichtenthaler, H. K. (1996). Detection of red edge position and chlorophyll content by reflectance measurements near 700nm. Journal of Plant Physiology 148, 501-508.

Gitelson, A. A. & Merzlyak, M. N. (1997). Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing 18, 2691-2697.

Gitelson, A. A., Gritz, Y. & Merzlyak, M. N. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology 160, 271-282.

Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J. & Strachan, I. B. (2004).

Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment 90, 337–352.

Hikosaka, K., Terashima, I. & Katoh, S. (1994). Effects of leaf age, nitrogen nutrition and photon flux density on the distribution of nitrogen among leaves of a vine Ipomoea tricolor Cav.) grown horizontally to avoid mutual shading of leaves. Oecologia 97, 451-457.

Hikosaka, K. (1996). Effects of leaf age, nitrogen nutrition and photon flux density on the organization of the photosynthetic apparatus in leaves of a vine (Ipomoea tricolor Cav.) grown horizontally to avoid mutual shading of leaves. Planta 198, 144-150.

Hikosaka, K. (2003).A model of dynamics of leaves and nitrogen in a plant canopy: an integration of canopy photosynthesis, leaf life span, and nitrogen use efficiency. The American Naturalist 162,149-164.

Hoffberg, S. L., Mauricio, R. & Hall, R. J. (2018). Control or re-treat? Model-based guidelines for managing established plant invasions. Biological Invasions 20, 1387- 1402.

Horler, D. N., Dockray, M. & Barber, J. (1983). The red edge of plant leaf reflectance. International Journal of Remote Sensing 4, 273-288.

Hosoi, F. & Omasa, K. (2006). Voxel-based 3-D modeling of individual trees for estimating leaf area density using high-resolution portable scanning lidar. IEEE Transactions on Geoscience and Remote Sensing 44, 3610-3618.

Huete, A. R. 1988. A soil vegetation adjusted index (SAVI). Remote Sensing of Environment 25, 295-309.

Hughes, M. J., Johnson, E. G. & Armsworth, P. R. (2014). Optimal spatial management of an invasive plant using a model with above- and below-ground components. Biological Invasions 16, 1009-1020.

Inoue, Y., Miah, G., Sakaiya, E., Nakano, K. & Kawamura, K. (2008). NDSI map and IPLS using hyperspectral data for assessment of plant and ecosystem variables: with a case study on remote sensing of grain protein content, chlorophyll content and biomass in rice. Journal of The Remote Sensing Society of Japan 28, 317-330.

Ishida, A., Toma, T. & Marjenah (1999). Leaf gas exchange and chlorophyll fluorescence in relation to leaf angle, azimuth, and canopy position in the tropical pioneer tree, Macaranga conifera. Tree Physiology 19, 117-124.

Isoda, A., Yoshimura, T., Ishikawa, T., Nojima, H. & Takasaki, Y. (1993a). Effects of leaf movement on radiation interception in field grown leguminous crops. I. Peanut (Arachis hypogaea L.). Japanese Journal of Crop Science 62, 300-305.

Isoda, A., Yoshimura, T., Ishikawa, T., Wang, P., Nojima, H. & Takasaki, Y. (1993b). Effects of leaf movement on radiation interception in field grown leguminous crops.

II. Soybean (Glycine max Merr.). Japanese Journal of Crop Science 62, 306-312.

Isoda, A., Misa, A. L., Nojima, H. & Takasaki, Y. (1996). Effects of leaf movement on radiation interception in field grown leguminous crops. IV. Relation to leaf temperature and transpiration among peanut cultivars. Japanese Journal of Crop Science 65, 700-706.

Iwasa, Y. & Teramoto, E. (1977). A mathematical model for the formation of a distributional pattern and an index of aggregation. Japanese Journal of Ecology 27, 117-124.

Jones, M. & Harper, J. L. (1987a). The influence of neighbours on the growth of trees I. The demography of buds in Betula pendula. Proceedings of the Royal Society London

232, 1-18.

Jones, M. & Harper, J. L. (1987b). The influence of neighbours on the growth of trees II. The fate of buds on long and short shoots in Betula pendula. Proceedings of the Royal Society London 232, 19-33.

Kameyama, A. (1978). Kudzu community in vegetation succession on the slope of expressway. Journal of Japanese Revegetational Technology Society 5, 36-42. [in Japanese]

Karatzoglou, A., Smola, A., Hornik, K. & Zeileis, A. (2004). kernlab - An S4 Package for

Kernel Methods in R. Journal of Statistical Software 11(9), 1–20.

Katul, G. G., Porporato, A., Nathan, R., Siqueira, M., Soons, M. B., Poggi, D., ... & Levin, S. A. (2005). Mechanistic analytical models for long-distance seed dispersal by wind. The American Naturalist 166, 368-381.

Kawamoto, A. (2018). A story of kudzu starch: the past, present and future of Yoshinohonkudzu crafts. Weeds and Vegetation management 10 (Special Issue), 15-21.

Kawamura, K. & Akiyama, T. (2012). Remote sensing for precision grassland management. Journal of The Remote Sensing Society of Japan 32, 232-244.

Kawashima, R. (1969). Studies on the leaf orientation-adjusting movement in soybean plants I. The leaf orientation-adjusting movement and light intensity on leaf surface. Japanese Journal of Crop Science 38, 718-729.

Kellner, K. (2017). jagsUI: a wrapper around ‘rjags’ to streamline ‘JAGS’ analyses. R package version 1.4.4. https://cran.r-project.org/web/packages/jagsUI/index.html (Access confirmed on 2021. 3. 1.)

Kin, M. (2017). Data science by R (Second edition). Morikita Press, Tokyo, pp. 206–215. [in Japanese]

Kim, E., Lee, W. K., Yoon, M., Lee, J. Y., Son, Y., & Salim, K. A. (2016). Estimation of voxel-based above-ground biomass using airborne LiDAR data in an intact tropical rain forest, Brunei. Forests 7, 259.

Kitagawa, M. (2017). Efficient grassland management method using the grassland management support system. Journal of Japanese Grassland Science 62, 212-215.

Komuro, T. & Koike, F. (2005). Colonization by woody plants in fragmented habitats of a suburban landscape. Ecologocal Applications 15, 662-673.

Kramer D. M., Sacksteder, C. A. & Cruz, J. A. (1999). How acidic is the lumen? Photosynthesis Research 60, 151-163.

Kosaki, T. & Ito, K. (2018). A story of kudzu fiber textiles: the past, present and future of

Kakegawa crafts. Weeds and Vegetation management 10 (Special Issue), 22-27.

Kuhlgert, S., Austic, G., Zegarac, R., Osei-Bonsu, I., Hoh, D., Chilvers, M. I., ... &

Kramer, D. M. (2016). MultispeQ Beta: a tool for large-scale plant phenotyping connected to the open PhotosynQ network. Royal Society Open Science 3, 160592.

Lang, A. R. G. (1973). Leaf orientation of a cotton plant. Agricultural Meteorology 11, 37-51.

Lamiter, A. M., Wu, S., Gelfand, A. E. & Silander Jr., J. A. (2006). Building statistical models to analyze species distributions. Ecological Applications 16, 33-50.

Lewis, P. (1999). Three-dimensional plant modelling for remote sensing simulation studies using the Botanical Plant Modelling System. Agronomie 19, 185-210.

Liu, H. K., Gyokusen, K., & Saito, A. (1997a). Studies on leaf orientation movements in kudzu (Pueraria lobata) (I): Diurnal changes of leaflet azimuth and leaf temperature. Bulletin of the Kyushu University Forest 76, 11-24.

Liu, H. K., Gyokusen, K., & Saito, A. (1997b). Studies on leaf orientation movements in kudzu (Pueraria lobata) (II): Effects of leaf orientation movements on photosynthetic rate. Bulletin of the Kyushu University Forest 77, 1-12.

Lopez-Granados, F., Torres-Sanchez, J., De Castro, A.-I., Serrano-Perez, A., MesasCarrascosa, F.-J. & Pena, J.-M. (2016). Object-based early monitoring of a grass weed in a grass crop using high resolution UAV imagery. Agronomy for Sustainable Development 36, 67.

Louargant, M., Villette, S., Jones, G., Vigneau, N., Paoli, J. N. & Gee, C. (2017). Weed detection by UAV: simulation of the impact of spectral mixing in multispectral images. Precision Agriculture 18, 932-951.

Louvrier, J., Chambert, T. Marboutin, E. & Gimenez, O. (2018). Accounting for misclassification and heterogeneity in occupancy studies using hidden Markov models. Ecological modelling 387, 61-69.

McClintock, B. T., Langrock, R., Gimenez, O., Cam, E., Borchers, D. L., Glennie, R. & Patterson, T. A. (2020). Uncovering ecological state dynamics with hidden Markov models. Ecological Letters 23, 1878-1903.

Miller, J. H. & Edwards, B. (1983). Kudzu: where did it come from? And how can we stop it? Southern Journal of Applied Forestry 7, 165-169.

Miller, D. A. W., Weir, L. A., McClintock, B. T., Campbell Grant, E. H., Bailey, L. L. & Simons, T. R. (2012). Experimental investigation of false positive errors in auditory species occurrence surveys. Ecological Applications 22, 1665–1674.

Monteith, J. L. (1977). Climate and the efficiency of crop production in Britain. Philosophical Transactions of the Royal Society B 281, 277-294.

Monzeglio, U. & Stoll, P. (2005). Spatial patterns and species performances in experimental plant communities. Oecologia 145, 619-628.

Muranaka, T. & Washitani, I. (2003). The population expansion predicted by a simulation model of an invasive alien species, Eragrostis curvula, in a middle reach floodplain. Japanese Journal of Conservation Ecology 8, 51-62.

Nathan, R., Klein, E., Rebledo-Arnuncio, J. J. and Revilla, E. (2012). Dispersal kernels: review. Oxford University Press. pp. 189-210.

Nemoto, M. (2001). Method for weed community structure. in Weed Science Society of Japan (eds.) “Method in Weed Science”, Weed Science Society of Japan, Tokyo, pp. 63-75.

Nichol et al. (2000). Remote sensing of photosynthetic-light-use efficiency of boreal forest. Nichol, C. J., Lloyd, J., Shibistova, O., Arneth, A., Röser, C., Knohl, A., Matsubara, S. & Grace, J. (2002). Remote sensing of photosynthetic-light-use efficiency of a Siberian boreal forest. Tellus B: Chemical and Physical Meteorology 54, 677-687.

Nishimura, K. & Ide, Y. (2017). Development of “grassland management support system” to rationalize ranch management. Journal of Japanese Grassland Science 62, 207-211.

Nishimuta, K. & Yanase, T. (2016). The model using the plant height to determine the timing to mow vegetation on embankment slopes. Journal of Japanese Society of Revegetation Technology 42. 98-103.

Nishino, A., Maebara, Y., Hasimoto, K., Uchida, T. and Hayasaka, D. (2019). Search for the eradication techniques on the noxious liana kudzu (Pueraria lobata (Willd.) Ohwi) in consideration of cut-slope vegetation recovery. Journal of Japanese Society Revegetation Technology 44, 596-605.

Obayashi, K. (1979). Ecology of kudzu from the viewpoint of forest nursery. Ringyo Gijutu 444, 231-235. [in Japanese]

Oguchi, R., Hikosaka, K., Hiura, T. & Hirose, T. (2006). Leaf anatomy and light acclimation in woody seedlings after gap formation in a cool-temperate deciduous forest. Oecologia 149, 571-582.

Oguma, H., Ide, R. & Isagi, Y. (2016). Automatic detection of the flowers of endangered vegetation by unmanned aerial vehicle observation. Journal of The Remote Sensing Society of Japan 36, 72-80.

Okazaki (Tanaka), M., Hanawa, S. & Masaru Ogasawara, M. (2018). Growth of climbing vines and pod production of Kudzu (Pueraria lobata (Willd.) Ohwi.). Journal of the Japanese Society of Revegetation Technology 43, 620-622.

Osawa, T., Okawa, S., Kurokawa, S. & Ando, S. (2016). Generating an agricultural risk map based on limited ecological information: A case study using Sicyos angulatus. Ambio 45, 895–903.

Pagel, J. & Schurr, F. M. (2012). Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics. Global Ecology and Biogeography 21, 293-304.

Pereira-Netto, A. B., Gabriele, A. C. & Pinto, H. S. (1999). Aspects of leaf anatomy of kudzu (Pueraria lobata, Leguminosae-Faboideae) related to water and energy balance.

Pesquisa Agropecuária Brasileira 34, 1361-1365.

Plummer, M. (2003). JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. Proceedings of the 3rd international workshop on distributed statistical computing 124, 125.

Poggi, G., Scarpa, G. & Zerubia, J. B. (2005). Supervised segmentation of remote sensing images based on a tree-structured MRF model. IEEE Transactions on Geoscience and Remote Sensing 43, 1901–1911.

Prusinkiewicz, P. (2004). Modeling plant growth and development. Current Opinion in Plant Biology 7, 79–83.

QGIS.org. (2021). QGIS Geographic Information System. QGIS Association.http://www.qgis.org (Access confirmed on 2021. 3. 1.) Rashid, Md. H., Uddin, Md. N., Asaeda, T. & Robinson, R. W. (2017). Seasonal variations of carbohydrates in Pueraria lobata related to growth and phenology. Weed Biology and Management 17, 103-111.

Rasmussen, J., Georgios, N. Nielsen, J., Svesgaard, J., Poulsen, R. N. & Christensen, S. (2016). Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots? European Journal of Agronomy 74, 75-92.

R Core Team (2019). R: A language and environment for statistical computing. R

Foundation for Statistical Computing, Vienna, Austria. URL https://www.Rproject.org/ (Access confirmed on 2021. 3. 1.)

Roberts, S., Osborne, M., Ebden, M., Reece, S., Gibson, N. & Aigrain, S. (2013). Gaussian processes for time-series modelling. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371.

Rouse, J. W., R. H. Haas, J. A. Schell, D. W. Deering 1974. Monitoring vegetation systems in the great plains with ERTS. Proceedings of the Third Earth Resources Technology Satellite-1 Symposium; NASA SP-351. pp. 309-317.

Royle, J. A. & Link, W. A. (2006). Generalized site occupancy models allowing for false positive and false negative errors. Ecology 87, 835–841.

Schindelin, J., Arganda-Carreras, I., Frise, E. Kaynig, V., Longair, M., Pietzsch, T., ... & Cardona, A. (2012). Fiji: an open-source platform for biological-image analysis. Nature methods 9, 676-682.

Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal 27, 379–423.

Sharif, B., Lundin, R., Morgan, P., Hall, J., Dhadda, A., Mann, C., Donoghue, D., Brownlow, E., Hill, F. Carr, G., Turley, H., Hassall, J., Atkinson, M., Jones, M., Martin, R., Rollason, S., Ibrahim, Y., Kopczynska, M. & Szakmany, T. (2016). Developing a digital data collection platform to measure the prevalence of sepsis in Wales. Journal of the American Medical Informatics Association 23, 1185–1189.

Sharkey, T. D. and Loreto, F. (1993). Water stress, temperature, and light effects on the capacity for isoprene emission and photosynthesis of kudzu leaves. Oecologia 95, 328-333.

Shell, G. S. G., Lang, A. R. G. & Sale, P. J. M. (1974). Quantitative measures of leaf orientation and heliotropic response in sunflower, bean, pepper and cucumber. Agricultural Meteorology 13, 25-37.

Shigesada, N. (1992). Mathematical model for biological invasions (Up Biology 92). University of Tokyo Press, Tokyo, pp. 24-45. [in Japanese]

Shiyomi, M., Takahashi, S., Yoshimura, J., Yasuda, T., Tsutsumi, M., Tsuiki, M. & Hori, Y. (2001). Spatial heterogeneity in a grassland community: Use of power law Ecological Research 16, 487-495.

Sinclair, T. R., Shiraiwa, T. and Hammer, G. L. (1992). Variation in crop radiation use efficiency with increased diffuse radiation. Crop Science 32, 1281-1284.

Stoll, P. & Prati, D. (2001). Intraspecific aggregation alters competitive interactions in experiment plant communities. Ecology 82, 319-327.

Su, Y. & Yajima, M. (2015). R2jags: Using R to Run 'JAGS'. R package version 0.5-7. https://CRAN.R-project.org/package=R2jags (Access confirmed on 2021. 3. 1.)

Susko, D. J. & Mueller, J. P. (1999). Influence of environmental factors on germination and emergence of Pueraria lobata. Weed Science 47, 585-558.

Suzuki, Y., Okamoto, H., Hirata, T., Kataoka, T. & Shibata, Y. (2010). Estimating spatial distribution of herb species and herbage mass in cover crop field using hyperspectral imaging. Japanese Journal of Farm Work Research 45, 99-109.

Takahashi, T. & Yasuoka, Y. (2006). Estimating leaf area index of riverine vegetation using hyperspectral remote sensing and vegetation index. Proceedings in hydraulic engineering 50, 1213-1218.

Tanaka, J., Horie, N. & Hayakawa, N. (2008). The test of get rid of invaded Pueraria lobata (Willd.) Ohwi at slope planting. Journal of the Japanese Society of Revegetation Technology 34, 215-218.

Tanaka, J., Horie, N. & Hayakawa, N. (2009). The test of get rid of invaded Pueraria lobata (Willd.) Ohwi at slope planting (II): Two years after. Journal of the Japanese Society of Revegetation Technology 35, 170-173.

Tanaka, J. & Yamaguchi Y. (2016). Utilization of metal plate barrier for inhibiting growth of Pueraria lobata (Willd.) Ohwi. Journal of the Japanese Society of Revegetation Technology 41, 468-471.

Tashima, R., Sakaguti, N. & Gyokusen, K. (2004). Leaf movements in Japanese black pine (Pinus thunbergii). Kyushu Journal of Forest Research 57, 215-216.

Thanyapraneedkul, J., Muramatsu, K., Daigo, M., Furumi, S., Soyama, N., Nasahara, N. K., Muraoka, H., Noda, M. H., Nagai, S., Maeda, T., Mano, M., Mizoguchi, Y., (2012). A vegetation index to estimate terrestrial gross primary production capacity for the global change observation mission-climate (GCOM-C)/second-generation global imager (SGLI) satellite sensor. Remote Sensing 4, 3689 - 3720.

Tietz, S., Hall, C. C., Cruz, J. A. and Kramer, D. M. (2017). NPQ(T): a chlorophyll fluorescence parameter for rapid estimation and imaging of non-photochemical

quenching of excitons in photosystem-II-associated antenna complexes. Plant, Cell & Environment 40, 1243-1255.

Tsugawa, H. & Kayama, R. (1974). Studies on population structure of kudzu vine 1. On the thickening growth of internodes of stems and main roots, the number of their vascular bandle rings. Journal of Japanese Grassland Science 20, 181-188.

Tsugawa, H. & Kayama, R. (1975). Studies on population structure of kudzu vine 2. A method for classification and presentation of root systems. Journal of Japanese Grassland Science 21, 207-212.

Tsugawa, H. & Kayama, R. (1978). Studies on population structure of kudzu vine 4. The diiference in the distribution pattern of rooted nodes and of stumps classified by the developmental stage of their root system. Journal of Japanese Grassland Science 23, 307-311.

Tsugawa, H. & Kayama, R. (1981a). Studies on dry matter production and leaf area expansion of kudzu vines (Pueraria lobate Ohwi) I. The dry weight and leaf area of

the current year’s stem produced from the node of the overwintering stem. Journal of Japanese Grassland Science 27, 267-271.

Tsugawa, H. & Kayama, R. (1981b). Studies on dry matter production and leaf area expansion of kudzu vines (Pueraria lobata Ohwi) II. The difference in dry matter and leaf area productivity between the main stem and the branches of the current year’s stem. Journal of Japanese Grassland Science 27, 272-276.

Tsugawa, H., Sasek, T. W., Tange, M. & Nishikawa, K. (1987). Studies on dry matter production and leaf area expansion of kudzu vines (Pueraria lobata Ohwi) III. The emergence of current year’s stems from overwintering stems. Journal of Japanese Grassland Science 32, 337-347.

Tsugawa, H., Sasek, T. W., Komatsu, N., Tange, M. and Nishikawa, K. (1989). Seasonal changes in dry matter production and leaf area expansion of first year stands of kudzu-vine (Pueraria lobata OHWI) differing in spacing. Journal of Japanese Grassland Science 35, 193-205.

Tsutsumi, M., Shiyomi, M., Takahashi, S. & Sugawara, K. (2001). Use of beta-binomial series in occurrence counts of plant populations in sown grasslands. Grassland Science 47, 121-127.

Vehtari, A., Gelman, A. & Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross validation and WAIC. Statistics and Computing 27, 1413-1432.

Venables, W. N. & Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth Edition. Springer, New York.

Veran, S., Kleiner, K. J., Choquet, R., Collazo, J. A. & Nichols, J. D. (2012). Modeling habitat dynamics accounting for possible misclassification. Landscape Ecology 27, 943-956.

Vos, J., Evers, J. B., Buck-Sorlin, G. H., Andrieu, B., Chelle, M. & de Visser, P. H. B. (2010). Functional-structural plant modelling: a new versatile tool in crop science. Journal of Experimental Botany 61, 2101–2115.

Waite, S. (1994). Field evidence of plastic growth responses to habitat heterogeneity in the clonal herb Ranunculus repens. Ecological Research 9, 311–316.

Watanabe, O., Otani, I. & Ohara, G. (2005). Estimation of the above ground plant biomass on the paddy levees environment using the hyper-spatial image data. Journal of the Japanese Agricultural Systems Society 21, 47-57.

Watanabe, S., Nakano, Y. & Okano, K. (2001). Comparison of light interception and field photosynthesis between vertically and horizontally trained watermelon (Citrullus lanatus (Thunb.) Matsum. et Nakai) plants. Journal of Japanese Society for Horticultural Science 70, 669-674.

Weaver, M. A., Boyette, C. D. & Hoagland, R. E. (2016). Rapid kudzu eradication and switchgrass establishment through herbicide, bioherbicide and integrated programmes. Biocontrol Science and Technology 26, 640-650.

Wien, H. C. & Wallace, D. H. (1973). Light-Induced Leaflet Orientation in Phaseolus vulgaris L. Crop Science 13, 721-724.

Woebbecke, D. M., Meyer, G. E., Von Bargen, K. & Mortensen, D. A. (1995). Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the American Society of Agricultural Engineers 38, 259-269.

Xu, F., Guo, W., Wang, R., Xu, W., Du, N. & Wang, Y. (2009). Leaf movement and photosynthetic plasticity of black locust (Robinia Pseudoacacia) alleviate stress under different light and water conditions. Acta Physiologiae Plantarum 31, 553-563.

Yamamoto, Y., Wakabayashi, D., Saji, K., Kodani, T., Iwamoto, H. and Yamada, H. (2018). Guard fence for vines. Japan patent P2018-105122A. [in Japanese]

Yamori, W., Makino, A. & Shikanai, T. (2019). A physiological role of cyclic electron transport around photosystem I in sustaining photosynthesis under fluctuating light in rice. Scientific Reports 6, 20147.

Yasuda, T., Shiyomi, M. & Takahashi, S. (2003). Differences in spatial heterogeneity at the species and community levels in semi-natural grasslands under different grazing intensities. Grassland Science 49. 101-108.

Zhang, R. & Shea, K. (2012). Integrating multiple disturbance aspects: management of an invasive thistle, Carduus nutans. Annals of Botany 110, 1395-1401.

Zhang, R. & Shea, K. (2019). Working smarter, not harder: objective-dependent management of an invasive thistle, Carduus nutans. Invasive Plant Science and Management 12, 155-160.

Zheng, Y., Zhu, Q., Huang, M., Guo, Y. & Qin, J. (2017). Maize and weed classification using color indices with support vector data description in outdoor fields. Computers and Electronics in Agriculture 141, 215-222.

参考文献をもっと見る

全国の大学の
卒論・修論・学位論文

一発検索!

この論文の関連論文を見る