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Auswahl der wissenschaftlichen Literatur zum Thema „Leaf area estimation“
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Zeitschriftenartikel zum Thema "Leaf area estimation"
Fanourakis, Dimitrios, Filippos Kazakos und Panayiotis A. Nektarios. „Allometric Individual Leaf Area Estimation in Chrysanthemum“. Agronomy 11, Nr. 4 (18.04.2021): 795. http://dx.doi.org/10.3390/agronomy11040795.
Der volle Inhalt der QuelleS, THIMMEGOWDA. „ESTIMATION OF LEAF AREA IN WHEAT GENOTYPES“. Madras Agricultural Journal 73, May (1986): 278–80. http://dx.doi.org/10.29321/maj.10.a02268.
Der volle Inhalt der QuelleCargnelutti Filho, Alberto, Rafael Vieira Pezzini, Ismael Mario Márcio Neu und Gabriel Elias Dumke. „Estimation of buckwheat leaf area by leaf dimensions“. Semina: Ciências Agrárias 42, Nr. 3Supl1 (22.04.2021): 1529–48. http://dx.doi.org/10.5433/1679-0359.2021v42n3supl1p1529.
Der volle Inhalt der QuelleSilva, Jocélia Rosa da, Arno Bernardo Heldwein, Andressa Janaína Puhl, Adriana Almeida do Amarante, Daniella Moreira Salvadé, Cadmo João Onofre Gregory dos Santos und Mateus Leonardi. „Leaf Area Estimation in Chamomile“. Journal of Agricultural Science 11, Nr. 2 (15.01.2019): 429. http://dx.doi.org/10.5539/jas.v11n2p429.
Der volle Inhalt der QuelleDeng, Yangbo, Kunyong Yu, Xiong Yao, Qiaoya Xie, Yita Hsieh und Jian Liu. „Estimation of Pinus massoniana Leaf Area USING Terrestrial Laser Scanning“. Forests 10, Nr. 8 (06.08.2019): 660. http://dx.doi.org/10.3390/f10080660.
Der volle Inhalt der QuelleToebe, M., P. J. Melo, R. R. Souza, A. C. Mello und F. L. Tartaglia. „Leaf area estimation in triticale by leaf dimensions“. Revista Brasileira de Ciências Agrárias - Brazilian Journal of Agricultural Sciences 14, Nr. 2 (30.06.2019): 1–9. http://dx.doi.org/10.5039/agraria.v14i2a5656.
Der volle Inhalt der QuelleK, BALAKRISHNAN, NATARAJARATNAM N und SUNDARUM K.M. „A RAPID METHOD FOR THE ESTIMATION OF LEAF AREA IN FIELD BEAN“. Madras Agricultural Journal 72, November (1985): 633–35. http://dx.doi.org/10.29321/maj.10.a02415.
Der volle Inhalt der QuelleMack, Laura, Filippo Capezzone, Sebastian Munz, Hans-Peter Piepho, Wilhelm Claupein, Tim Phillips und Simone Graeff-Hönninger. „Nondestructive Leaf Area Estimation for Chia“. Agronomy Journal 109, Nr. 5 (September 2017): 1960–69. http://dx.doi.org/10.2134/agronj2017.03.0149.
Der volle Inhalt der QuelleÇi̇rak, C., M. Odabaş, A. Ayan, B. Sağlam und K. Kevseroğlu. „Estimation of leaf area in selectedHypericumspecies“. Acta Botanica Hungarica 50, Nr. 1-2 (März 2008): 81–91. http://dx.doi.org/10.1556/abot.50.2008.1-2.5.
Der volle Inhalt der QuelleJiang, Ni, Wanneng Yang, Lingfeng Duan, Guoxing Chen, Wei Fang, Lizhong Xiong und Qian Liu. „A nondestructive method for estimating the total green leaf area of individual rice plants using multi-angle color images“. Journal of Innovative Optical Health Sciences 08, Nr. 02 (März 2015): 1550002. http://dx.doi.org/10.1142/s1793545815500029.
Der volle Inhalt der QuelleDissertationen zum Thema "Leaf area estimation"
Wang, Dongdong. „Improving satellite Leaf Area Index estimation based on various integration methods“. College Park, Md.: University of Maryland, 2009. http://hdl.handle.net/1903/9872.
Der volle Inhalt der QuelleThesis research directed by: Dept. of Geography. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
Winkler, Tobias. „Empirical models for grape vine leaf area estimation on cv. Trincadeira“. Master's thesis, ISA-UL, 2016. http://hdl.handle.net/10400.5/13008.
Der volle Inhalt der QuelleEstimating a Vineyard’s leaf area is of great importance when evaluating the productive and quality potential of a vineyard and for characterizing the light and thermal microenvironments of grapevine plants. The aim of the present work was to validate the Lopes and Pinto method for determining vineyard leaf area in the vineyards of Lisbon’s wine growing region in Portugal, with the typical local red grape cultivar Trincadeira, and to improve prediction quality by providing cultivar specific models. The presented models are based on independent datasets of two consecutive years 2015 and 2016. Fruiting shoots were collected and analyzed during all phenological stages. Primary leaf area of shoots is estimated by models using a calculated variable obtained from the average of the largest and smallest primary leaf area multiplied by the number of primary leaves, as presented by Lopes and Pinto (2005). Lateral Leaf area additionally uses the area of the biggest lateral leaf as predictor. Models based on Shoot length and shoot diameter and number of lateral leaves were tested as less laborious alternatives. Although very fast and easy to assess, models based on shoot length and diameter were not able to predict variability of lateral leaf area sufficiently and were susceptible to canopy management. The Lopes and Pinto method is able to explain a very high proportion of variability, both in primary and lateral leaf area, independently of the phenological stage, as well as before and after trimming. They are inexpensive, universal, practical, non-destructive methods which do not require specialized staff or expensive equipment
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Phinopoulos, Victoras Georgios. „Estimation of leaf area in grapevine cv. Syrah using empirical models“. Master's thesis, ISA/UL, 2014. http://hdl.handle.net/10400.5/8631.
Der volle Inhalt der QuelleEmpirical models for the estimation of the Area of single Primary and Lateral leaves, and total Primary and Lateral Leaf Area of a shoot, are presented for the grapevine cv. Syrah (Vitis vinifera L.). The Area of single Leaves is estimated with models using the sum of the lengths of the two lateral veins of each leaf, with logarithmic transformation of both variables. Separate models are proposed for Primary and Lateral Leaves. Models based on the Lopes and Pinto (2005) method, using Mean Leaf Area multiplied by the number of Leaves as predictors, are proposed for the estimation for Total Primary and Lateral Leaf Area. It is suggested, that failure to locate the Largest Leaf of a Primary or Lateral shoot, would not significantly impair the accuracy of the models. All models explain a very high proportion of variability in Leaf Area and they can by applied in research and viticulture for the frequent estimation of Leaf Area in any phase of the growing cycle. They are inexpensive, practical, non-destructive methods which do not require specialised staff or expensive equipment
Fang, Hongliang. „Improving the estimation of leaf area index from multispectral remotely sensed data“. College Park, Md. : University of Maryland, 2003. http://hdl.handle.net/1903/304.
Der volle Inhalt der QuelleThesis research directed by: Geography. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
Pacheco, Anna. „Contribution of hyperspectral remote sensing to the estimation of leaf area index in the context of precision agriculture“. Thesis, University of Ottawa (Canada), 2004. http://hdl.handle.net/10393/26734.
Der volle Inhalt der QuelleBanskota, Asim. „The discrete wavelet transform as a precursor to leaf area index estimation and species classification using airborne hyperspectral data“. Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/39188.
Der volle Inhalt der QuellePh. D.
Kandasamy, Sivasathivel. „Leaf Area Index (LAI) monitoring at global scale : improved definition, continuity and consistency of LAI estimates from kilometric satellite observations“. Phd thesis, Université d'Avignon, 2013. http://tel.archives-ouvertes.fr/tel-00967319.
Der volle Inhalt der QuelleMazumdar, Deepayan Dutta. „Multiangular crop differentiation and LAI estimation using PROSAIL model inversion“. Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Geography, c2011, 2011. http://hdl.handle.net/10133/3103.
Der volle Inhalt der Quellexiii, 161 leaves : ill., map ; 29 cm
Soma, Maxime. „Estimation de la distribution spatiale de surface et de biomasse foliaires de couverts forestiers méditerranéens à partir de nuages de points acquis par un LIDAR terrestre“. Thesis, Aix-Marseille, 2019. http://www.theses.fr/2019AIXM0111.
Der volle Inhalt der QuelleTo better understand functioning of forest ecosystems at fine scale, ecophysiological model attempt to include energy and material fluxes. Such exchanges depend on the distribution of vegetation. Hence, these models require a tridimensional (3D) description of vegetation structure, at a level of detail which can only be retrieve with remote sensing at large scale. Terrestrial LiDAR (Light Detection And Ranging) have a great potential to provide 3D description of vegetation elements in canopy. Previous studies established promising relations between the point density and quantity of vegetation. This work develop these statistical methods, focusing on source of errors. Systematic biases are corrected at branch, tree and plot scales. This study relies on both numerical simulations and field experiments. First, we test estimators on branches in laboratory conditions. On this natural vegetation, estimators are sensitive to voxel size and distance from instrument with phase-shift LiDAR. Developed corrections from this branch experiment are valid at tree scale. However, difficulties arising from sampling limitations due to occlusion and instrument sampling pattern cause negative biases in dense areas. Specific investigations are conducted to identify source of errors and to optimize multiscan estimations. A statistical method called LAD-kriging, based on spatial correlation within vegetation, improves local accuracy of estimations and limits underestimations due to occlusion. The tools produced in this work allow to map vegetation at plot scale by providing unbiased estimator of leaf area. Some of these tools are currently implemented within open access Computree software
Pinjuv, Guy L. „Hybrid forest modelling of Pinus Radiata D. Don in Canterbury, New Zealand“. Thesis, University of Canterbury. New Zealand School of Forestry, 2006. http://hdl.handle.net/10092/1102.
Der volle Inhalt der QuelleBücher zum Thema "Leaf area estimation"
Frazer, G. W. A method for estimating canopy openness, effective leaf area index, and photosynthetically active photon flux density using hemispherical photography and computerized image analysis techniques. Victoria, B.C: Pacific Forestry Centre, 1997.
Den vollen Inhalt der Quelle findenStolker, Robert Jan, und Felix van Lier. Choice and interpretation of preoperative investigations. Herausgegeben von Jonathan G. Hardman. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199642045.003.0041.
Der volle Inhalt der QuelleAddison, Tony, und Atanu Ghoshray. Pandemics and their impact on oil and metal prices. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/914-3.
Der volle Inhalt der QuelleBuchteile zum Thema "Leaf area estimation"
Raj, Rahul, Saurabh Suradhaniwar, Rohit Nandan, Adinarayana Jagarlapudi und Jeffrey Walker. „Drone-Based Sensing for Leaf Area Index Estimation of Citrus Canopy“. In Lecture Notes in Civil Engineering, 79–89. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37393-1_9.
Der volle Inhalt der QuelleKorhonen, Lauri, und Felix Morsdorf. „Estimation of Canopy Cover, Gap Fraction and Leaf Area Index with Airborne Laser Scanning“. In Forestry Applications of Airborne Laser Scanning, 397–417. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-017-8663-8_20.
Der volle Inhalt der QuelleYing-Ying, Dong, Wang Ji-Hua, Li Cun-Jun, Wang Qian und Huang Wen-Jiang. „Integration of Ground Observations and Crop Simulation Model for Crop Leaf Area Index Estimation“. In Advances in Intelligent and Soft Computing, 831–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29637-6_112.
Der volle Inhalt der QuelleFeng, Haikuan, Fuqin Yang, Guijun Yang und Haojie Pei. „Hyperspectral Estimation of Leaf Area Index of Winter Wheat Based on Akaike’s Information Criterion“. In Computer and Computing Technologies in Agriculture X, 528–37. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-06155-5_54.
Der volle Inhalt der QuelleBrown, Daniel G. „A Spectral Unmixing Approach to Leaf Area Index (LAI) Estimation at the Alpine Treeline Ecotone“. In GIS and Remote Sensing Applications in Biogeography and Ecology, 7–21. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1523-4_2.
Der volle Inhalt der QuelleVichev, B. I., und K. G. Kostov. „Estimation of Leaf and Branch Area Indexes of Deciduous Trees Using Dual-Frequency Microwave Radiometric Data“. In Microwave Physics and Techniques, 407–12. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5540-3_41.
Der volle Inhalt der QuelleKorzukhin, Michael, und Vasily Grabovsky. „Estimation of Leaf Area Index (LAI) of Russian Forests Using a Mechanical Model and Forest Inventory Data“. In Innovations in Landscape Research, 341–61. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37421-1_18.
Der volle Inhalt der QuelleLi, Dan, Hao Jiang, Shuisen Chen, Chongyang Wang, Siyu Huang und Wei Liu. „Leaf Area Index Estimation of Winter Pepper Based on Canopy Spectral Data and Simulated Bands of Satellite“. In Communications in Computer and Information Science, 515–26. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3966-9_57.
Der volle Inhalt der QuelleUdayakumar, M., und T. Sekar. „Estimation of Leaf Area–Wood Density Traits Relationship in Tropical Dry Evergreen Forests of Southern Coromandel Coast, Peninsular India“. In Wood is Good, 169–87. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3115-1_17.
Der volle Inhalt der QuelleLeal-Ramirez, Cecilia, Héctor Echavarría-Heras und Oscar Castillo. „Exploring the Suitability of a Genetic Algorithm as Tool for Boosting Efficiency in Monte Carlo Estimation of Leaf Area of Eelgrass“. In Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization, 291–303. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17747-2_23.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Leaf area estimation"
Lee, Sang-Ho, Myung-Min Oh und Jong-Ok Kim. „Plant Leaf Area Estimation via Image Segmentation“. In 2022 37th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC). IEEE, 2022. http://dx.doi.org/10.1109/itc-cscc55581.2022.9894907.
Der volle Inhalt der QuelleGhazal, Mohammed, und Hassan Hajjdiab. „Leaf spot area index: A nondestructive mangrove leaf spot estimation technique“. In 2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES). IEEE, 2015. http://dx.doi.org/10.1109/spices.2015.7091414.
Der volle Inhalt der QuelleBURG, Patrik, Jana BURGOVÁ, Vladimír MAŠÁN und Miroslav VACHŮN. „LEAF SURFACE AREA ESTIMATION IN DIFFERENT GRAPES VARIETIES USING A AM 300 LEAF AREA METER“. In RURAL DEVELOPMENT. Aleksandras Stulginskis University, 2018. http://dx.doi.org/10.15544/rd.2017.037.
Der volle Inhalt der QuelleHajjdiab, Hassan, und Abdellatif Obaid. „A vision-based approach for nondestructive leaf area estimation“. In 2010 2nd Conference on Environmental Science and Information Application Technology (ESIAT). IEEE, 2010. http://dx.doi.org/10.1109/esiat.2010.5568973.
Der volle Inhalt der QuelleSoni, Amar Prasad, Amar Kumar Dey und Manisha Sharma. „An image processing technique for estimation of betel leaf area“. In 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO). IEEE, 2015. http://dx.doi.org/10.1109/eesco.2015.7253691.
Der volle Inhalt der QuelleWang, Peicheng, Ling Tong, Xing Zhou, Xun Gang, Bo Gao, Yuxia Li und Yuan Sun. „Estimation of Leaf Area Index Based on Hemispherical Canopy Photography“. In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9554699.
Der volle Inhalt der QuelleFu, Wenxue, Huadong Guo und Xinwu Li. „Estimation of leaf area index (LAI) using POLInSAR: Preliminary research“. In IGARSS 2011 - 2011 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2011. http://dx.doi.org/10.1109/igarss.2011.6048982.
Der volle Inhalt der QuelleGe, Yunjian, Zhenbo Liu, Jian Chen und Tao Sun. „Estimation of paddy rice leaf area index using digital photography“. In 2014 7th International Congress on Image and Signal Processing (CISP). IEEE, 2014. http://dx.doi.org/10.1109/cisp.2014.7003865.
Der volle Inhalt der QuelleCheng, Yuanlei, Yunping Chen, Shuaifeng Jiao, Haichang Wei, Wangyao Shen, Yan Chen, Shilong Li und Hua Zhan. „Leaf Area Index Estimation from Hemisphere Image Based on GhostNet“. In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9884777.
Der volle Inhalt der QuelleYang, Zhiliang, Jiapei Tong, Mingchen Feng, Guoliang Hu, Jinqiao Wu und Yingchun Fan. „Soybean Leaf Segmentation and Area Estimation Based on Extreme Points“. In ICMLC 2023: 2023 15th International Conference on Machine Learning and Computing. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3587716.3587782.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Leaf area estimation"
Lockhart, Brian Roy, Emile S. Gardiner, Theran P. Stautz, Theodore D. Leininger, Paul B. Hamel, Kristina F. Connor, Nathan M. Schiff, A. Dan Wilson und Margaret S. Devall. Nondestructive estimation of leaf area for pondberry. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station, 2007. http://dx.doi.org/10.2737/srs-rn-14.
Der volle Inhalt der QuelleLockhart, Brian Roy, Emile S. Gardiner, Theran P. Stautz, Theodore D. Leininger, Paul B. Hamel, Kristina F. Connor, Nathan M. Schiff, A. Dan Wilson und Margaret S. Devall. Nondestructive estimation of leaf area for pondberry. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station, 2007. http://dx.doi.org/10.2737/srs-rn-14.
Der volle Inhalt der QuelleDuncan, A. Estimation of Leak Rate from the Emergency Pump Well in L-Area Complex Basin. Office of Scientific and Technical Information (OSTI), Dezember 2005. http://dx.doi.org/10.2172/890207.
Der volle Inhalt der QuelleAzzi, Elias S., Cecilia Sundberg, Helena Söderqvist, Tom Källgren, Harald Cederlund und Haichao Li. Guidelines for estimation of biochar durability : Background report. Department of Energy and Technology, Swedish University of Agricultural Sciences, 2023. http://dx.doi.org/10.54612/a.lkbuavb9qc.
Der volle Inhalt der QuelleEstache, Antonio, Ronaldo Seroa da Motta und Grégoire Garsous. Shared Mandates, Moral Hazard, and Political (Mis)alignment in a Decentralized Economy. Inter-American Development Bank, März 2015. http://dx.doi.org/10.18235/0011691.
Der volle Inhalt der QuelleHertel, Thomas, David Hummels, Maros Ivanic und Roman Keeney. How Confident Can We Be in CGE-Based Assessments of Free Trade Agreements? GTAP Working Paper, Juni 2003. http://dx.doi.org/10.21642/gtap.wp26.
Der volle Inhalt der QuelleGelain, Paolo, Marco Lorusso und Saeed Zaman. Oil Price Fluctuations and US Banks. Federal Reserve Bank of Cleveland, Mai 2024. http://dx.doi.org/10.26509/frbc-wp-202411.
Der volle Inhalt der QuelleGranado, Camilo, und Daniel Parra-Amado. Estimating the Output Gap After COVID: How to Address Unprecedented Macroeconomic Variations. Banco de la República, September 2023. http://dx.doi.org/10.32468/be.1249.
Der volle Inhalt der QuelleDouglas, Thomas A., Christopher A. Hiemstra, Stephanie P. Saari, Kevin L. Bjella, Seth W. Campbell, M. Torre Jorgenson, Dana R. N. Brown und Anna K. Liljedahl. Degrading Permafrost Mapped with Electrical Resistivity Tomography, Airborne Imagery and LiDAR, and Seasonal Thaw Measurements. U.S. Army Engineer Research and Development Center, Juli 2021. http://dx.doi.org/10.21079/11681/41185.
Der volle Inhalt der QuelleRodríguez, Francisco. Cleaning Up the Kitchen Sink: On the Consequences of the Linearity Assumption for Cross-Country Growth Empirics. Inter-American Development Bank, Januar 2006. http://dx.doi.org/10.18235/0011322.
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