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Letteratura scientifica selezionata sul tema "Leaf area"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 25 luglio 2025

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Articoli di riviste sul tema "Leaf area"

1

Payne, W. A., C. W. Wendt, L. R. Hossner, and C. E. Gates. "Estimating Pearl Millet Leaf Area and Specific Leaf Area." Agronomy Journal 83, no. 6 (1991): 937–41. http://dx.doi.org/10.2134/agronj1991.00021962008300060004x.

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2

S, 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.

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Leaf product cons ants for night wheat genotypes, ware investigated. The test product constants varied significantly 10.729 to 0.709 for flag-leat, 0.737 10 0.864. for other than flag leaf and 0 740 to 0.831 for all leaves) among the genotypes. This indicated that a singla constant for whost cras as such cannot be accepted." However, a lea! product constant of flag leaf only could be used for estimating leaf area in wheat genotypes as the high releationship existed between the actual leal area and the estimated leaf area in all the genotypes as compared to other than flag leaf and all leaves

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3

Chinnamuthu, C. R., C. Kailasam, and Dr S. Sankaran. "Sorghum Leaf Area as a Function of Sixth Leaf Area." Journal of Agronomy and Crop Science 162, no. 5 (1989): 300–304. http://dx.doi.org/10.1111/j.1439-037x.1989.tb00720.x.

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4

K, BALAKRISHNAN, NATARAJARATNAM N, and 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.

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The present investigation aimed to establish a relationship between leaf length x leal breadth and leat area in field bean CV. Co. 1. The regression equation fitted against leaf area and the product between terminal leaf length and breadth was Y = 3 09+1.63 (X) (r = 0.9647**), where Y = leaf area (trifoliate leaf) per leaf. X = Length X Breadth of the terminal leaf let of the trifoliate leaf (L x B). The leaf area was also predicted by using the formula A = 1,685 (L x B). A significant correlation (r=0.9630) was also obtained with actual and predicted leaf area by using the above constants. It

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5

Awal, M. A., Wan Ishak ., J. Endan ., and M. Haniff . "Determination of Specific Leaf Area and Leaf Area-leaf Mass Relationship in Oil Palm Plantation." Asian Journal of Plant Sciences 3, no. 3 (2004): 264–68. http://dx.doi.org/10.3923/ajps.2004.264.268.

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6

Pierce, Lars L., Steven W. Running, and Joe Walker. "Regional-Scale Relationships of Leaf Area Index to Specific Leaf Area and Leaf Nitrogen Content." Ecological Applications 4, no. 2 (1994): 313–21. http://dx.doi.org/10.2307/1941936.

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7

Smith, Duncan D., John S. Sperry, and Frederick R. Adler. "Convergence in leaf size versus twig leaf area scaling: do plants optimize leaf area partitioning?" Annals of Botany 119, no. 3 (2016): 447–56. http://dx.doi.org/10.1093/aob/mcw231.

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Background and Aims Corner’s rule states that thicker twigs bear larger leaves. The exact nature of this relationship and why it should occur has been the subject of numerous studies. It is obvious that thicker twigs should support greater total leaf area (Atwig) for hydraulical and mechanical reasons. But it is not obvious why mean leaf size (A-) should scale positively with Atwig. We asked what this scaling relationship is within species and how variable it is across species. We then developed a model to explain why these relationships exist. Methods To minimize potential sources of variabil

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8

Tony, K. Hariadi, Fadholi Zulfan, NN Chamim Anna, A. Utama Nafi, Prabasari Indira, and Riyadi Slamet. "Development of Leaf Area Meter Using Open CV for Smartphone Application." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 4 (2018): 1857–63. https://doi.org/10.12928/TELKOMNIKA.v16i4.8608.

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This study aimed to design an accurate and practical system of leaf area determination using a smartphone. A software application for leaf area computation was developed using Open CV (Open Source Computer Vision) library. Open CV software was tested to estimate the accuracy of leaf area calculation. Leaf area calculations were undertaken using three different image resolutions to compare their accuracy. The results of the software calculations were then compared with the results of the laboratory leaf area meter to identify any errors. The results showed that higher image resolutions improved

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9

CURRAN, P. J., and N. W. WARDLEY. "Radiometric leaf area index." International Journal of Remote Sensing 9, no. 2 (1988): 259–74. http://dx.doi.org/10.1080/01431168808954850.

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10

Cargnelutti Filho, Alberto, Rafael Vieira Pezzini, Ismael Mario Márcio Neu, and Gabriel Elias Dumke. "Estimation of buckwheat leaf area by leaf dimensions." Semina: Ciências Agrárias 42, no. 3Supl1 (2021): 1529–48. http://dx.doi.org/10.5433/1679-0359.2021v42n3supl1p1529.

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The objective of this work was to model and identify the best models for estimating the leaf area, determined by digital photos, of buckwheat (Fagopyrum esculentum Moench) of the cultivars IPR91-Baili and IPR92-Altar, as a function of length (L), width (W) or length x width product (LW) of the leaf blade. Ten uniformity trials (blank experiments) were carried out, five with IPR91-Baili cultivar and five with IPR92-Altar cultivar. The trials were performed on five sowing dates. In each trial and cultivar, expanded leaves were collected at random from the lower, middle and upper segments of the

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Tesi sul tema "Leaf area"

1

Back, Merri, and A. K. Dobrenz. "Increasing the Leaf Area of Alfalfa." College of Agriculture, University of Arizona (Tucson, AZ), 1985. http://hdl.handle.net/10150/200495.

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Malone, Sean M. "Assessment of Soybean Leaf Area for Redefining Management Strategies for Leaf-Feeding Insects." Diss., Virginia Tech, 2001. http://hdl.handle.net/10919/29252.

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Commercially available leaf area index (LAI) meters are tools that can be used in making insect management decisions. However, proper technique must be determined for LAI estimation, and accuracy must be validated for the meters. Full-season soybean require LAI values of at least 3.5 to 4.0 by early to mid-reproductive developmental stages to achieve maximum yield potential, but the relationship between double-crop soybean LAI and yield is unknown. This research (1) evaluated minimum plot size requirements for mechanically defoliated soybean experiments using the LAI-2000 Plant Canopy Analy

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3

Duffy, Natasha Michelle. "Design limitations to potential leaf area in urban forests." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ46004.pdf.

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4

Ebodaghe, Denis Abumere. "Estimating daily green leaf area index for corn in Virginia." Diss., Virginia Polytechnic Institute and State University, 1986. http://hdl.handle.net/10919/74731.

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A model to predict the daily green leaf area index (GLAI) for corn has been developed for Indiana conditions. Using daily maximum and minimum temperatures the GLAI was predicted for the vegetative stage, reproductive and grain filling stage, and the leaf senescing stage of corn. Predictions of GLAI for corn can be made on a daily basis from the day corn is planted until it is harvested for grain. The GLAI model was tested under Virginia conditions using green leaf area measurements collected from corn plants grown on Davidson silty clay loam, Davidson silty clay, and Mayodan sandy loam soils

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5

Coker, Graham William Russell. "Leaf Area Index in Closed Canopies: An indicator of site quality." Thesis, University of Canterbury. School of Forestry, 2006. http://hdl.handle.net/10092/1128.

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This study examined leaf area index (LAI) and relationships with corresponding tree growth, climate and soil characteristics across New Zealand forest plantations. The aim of this study was to determine if quick measures of projected leaf area across environmental gradients of New Zealand were an accurate indicator of site quality. Projected leaf areas of Pinus radiata D Don and Cupressus lusitanica Mills seedlings were measured using a Li-Cor LAI-2000 plant canopy analyser at 22 locations representing the soil and climatic diversity across New Zealand plantation forests. Seedlings planted at

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6

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.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2009.<br>Thesis 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.

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7

Johnson, Ryan L. "Airborne remote sensing of forest leaf area index in mountainous terrain." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ49131.pdf.

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Johnson, Ryan L., and University of Lethbridge Faculty of Arts and Science. "Airborne remote sensing of forest leaf area index in mountainous terrain." Thesis, Lethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2000, 2000. http://hdl.handle.net/10133/90.

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Leaf area index (LAI) provides forestry information that is important for regional scale ecological models and in studies of global change. This research examines the effects of mountainous terrain on the radiometric properties of multispectral CASI imagery in estimating ground-based optical measurements of LAI, obtained using the TRAC and LAI- 2000 systems. Field and image data were acquired summer 1998 in Kananaskis, Alberta, Canada. To account for the influence of terrain a new modified approach using the Li and Strahler Geometric Optical Mutual Shadowing (GOMS) model in 'multiple forward

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9

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.

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Mestrado Vinifera Euromaster - Viticulture and Enology - Instituto Superior de Agronomia - UL / Institut National D'Etudes Superieures Agronomiques de Montpellier<br>Estimating 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

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10

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.

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Mestrado Vinifera EuroMaster - Instituto Superior de Agronomia<br>Empirical 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 Le

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Libri sul tema "Leaf area"

1

Deddy, Hadrijanto, ed. Penelitian tentang arsitektural model pohon jenis pioner dalam hubungannya dengan liaf [i.e. leaf] area index (LAI) di Bukit Suharto, Kalimantan Timur. Proyek Peningkatan & Pengembangan Perguruan Tinggi, Universitas Mulawarman, 1989.

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2

Jing, Chen. BOREAS RSS-7 LAI, gap fraction, and FPAR data. National Aeronautics and Space Administration, Goddard Space Flight Center, 2000.

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3

Jing, Chen. BOREAS RSS-7 LAI, gap fraction, and FPAR data. National Aeronautics and Space Administration, Goddard Space Flight Center, 2000.

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4

Jing, Chen. BOREAS RSS-7 LAI, gap fraction, and FPAR data. National Aeronautics and Space Administration, Goddard Space Flight Center, 2000.

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5

Jing, Chen. BOREAS RSS-7 LAI, gap fraction, and FPAR data. National Aeronautics and Space Administration, Goddard Space Flight Center, 2000.

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6

Warchałowski, Andrzej. Chrysomelidae: The leaf-beetles of Europe and the Mediterranean area. Natura Optima dux Foundation, 2003.

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7

Sarabandi, J. Effect of curvature on the backscattering from leaves. National Aeronautics and Space Administration, 1988.

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8

Feiveson, A. H. Error analysis of leaf area estimates made from allometric regression models. National Aeronautics and Space Administration, 1987.

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9

Plummer, Stephen E. BOREAS RSS-4 1994 jack pine leaf biochemistry and modeled spectra in the SSA. National Aeronautics and Space Administration, Goddard Space Flight Center, 2000.

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10

Terry, Dawson, and Goddard Space Flight Center, eds. BOREAS RSS-4 1994 jack pine leaf biochemistry and modeled spectra in the SSA. National Aeronautics and Space Administration, Goddard Space Flight Center, 2000.

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Capitoli di libri sul tema "Leaf area"

1

Liang, Shunlin, Xiaotong Zhang, Zhiqiang Xiao, Jie Cheng, Qiang Liu, and Xiang Zhao. "Leaf Area Index." In Global LAnd Surface Satellite (GLASS) Products. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02588-9_2.

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2

Keane, Robert E. "LAI: Leaf Area Index." In Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-51727-8_237-1.

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Keane, Robert E. "LAI: Leaf Area Index." In Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-52090-2_237.

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4

Wirth, Rainer, Hubert Herz, Ronald J. Ryel, Wolfram Beyschlag, and Bert Hölldobler. "The Study Area — Barro Colorado Island." In Herbivory of Leaf-Cutting Ants. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05259-4_3.

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5

Sánchez-de-Miguel, Patricia, Pilar Baeza, Pedro Junquera, and José Ramón Lissarrague. "Vegetative Development: Total Leaf Area and Surface Area Indexes." In Methodologies and Results in Grapevine Research. Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9283-0_3.

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6

Chen, Baisong, Zhuo Fu, Yuchun Pan, Jihua Wang, and Zhixuan Zeng. "Single Leaf Area Measurement Using Digital Camera Image." In Computer and Computing Technologies in Agriculture IV. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18336-2_64.

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Han, Dianyuan, and Fengqing Zhang. "Leaf Area Measurement Embeded in Smart Mobile Phone." In Advances in Intelligent Systems. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27869-3_27.

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Qu, Yonghua. "Leaf Area Index: Advances in Ground-Based Measurement." In Observation and Measurement of Ecohydrological Processes. Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-48297-1_11.

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9

Strachan, I. B., D. W. Stewart, and E. Pattey. "Determination of Leaf Area Index in Agricultural Systems." In Agronomy Monographs. American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, 2015. http://dx.doi.org/10.2134/agronmonogr47.c9.

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10

Fujita, K., T. Oka, H. Sato, et al. "Factors controlling leaf area development in husk leaf of flint corn (Zea mays L.)." In Plant Nutrition for Sustainable Food Production and Environment. Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-009-0047-9_293.

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Atti di convegni sul tema "Leaf area"

1

Xie, Tian, Xuewei Hu, Shiyu Chen, et al. "Estimating the Leaf Area of Urban Individual Trees from Single-Scan Terrestrial Laser Scanner Based on Slant Leaf Area Index." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10641382.

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2

Wang, Dong, Guoyuan Zhou, Jinglue Xu, João Eduardo Batista, Jia Guo, and Hitoshi Iba. "A PSO-CNN-BiLSTM Model for Predicting Leaf Area Index." In 2024 IEEE Cyber Science and Technology Congress (CyberSciTech). IEEE, 2024. https://doi.org/10.1109/cyberscitech64112.2024.00086.

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3

Polimenov, Ventsislav, Krassimira Ivanova, Mihaela Tsvetkova, Elena Anastasova, and Katya Dimitrova. "Calculating Leaf Area Index Using Neural Network and WorldView 3 Multispectral Imagery." In 2024 59th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST). IEEE, 2024. http://dx.doi.org/10.1109/icest62335.2024.10639753.

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Chang, Haoyu, Yulong Liu, Hui Zhang, and Shizhuang Weng. "Detection of Rice Leaf Area Index Based on Multispectral Images of UAV." In 2025 5th International Conference on Neural Networks, Information and Communication Engineering (NNICE). IEEE, 2025. https://doi.org/10.1109/nnice64954.2025.11064324.

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5

BURG, Patrik, Jana BURGOVÁ, Vladimír MAŠÁN, and 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.

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Experimental measurements focused on evaluation of grapevine leaf surface area development in nine varieties, in the viticultural conditions of South Moravia. The dynamics of leaf surface area development was measured by using a device called leaf area meter AM 300. The device operates on the principle of a scanner and the resulting values are expressed through the leaf area index - LAI. The measurements were carried out in five dates during phenophases of growth, flowering, initial development of fruits, and ripening of berries. The results show a significant differences in increase in leaf a

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Ghazal, Mohammed, and 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.

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Lü, Chaohui, Hui Ren, Yibin Zhang, and Yinhua Shen. "Leaf Area Measurement Based on Image Processing." In 2010 International Conference on Measuring Technology and Mechatronics Automation (ICMTMA 2010). IEEE, 2010. http://dx.doi.org/10.1109/icmtma.2010.141.

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Lee, Sang-Ho, Myung-Min Oh, and 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.

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Zhao, Chunjiang, Jihua Wang, Wenjiang Huang, and Liangyun Liu. "Influence of leaf water status on leaf area index and leaf nitrogen concentration inversion of wheat canopy." In Multispectral and Hyperspectral Remote Sensing Instruments and Applications II. SPIE, 2005. http://dx.doi.org/10.1117/12.578698.

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Han, Dianyuan. "Leaf area measurement based on Markov random field." In 2012 2nd International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2012. http://dx.doi.org/10.1109/iccsnt.2012.6526258.

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Rapporti di organizzazioni sul tema "Leaf area"

1

Wang, S. Leaf area index. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2016. http://dx.doi.org/10.4095/298882.

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2

Lockhart, Brian Roy, Emile S. Gardiner, Theran P. Stautz, et al. Nondestructive estimation of leaf area for pondberry. U.S. Department of Agriculture, Forest Service, Southern Research Station, 2007. http://dx.doi.org/10.2737/srs-rn-14.

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Lockhart, Brian Roy, Emile S. Gardiner, Theran P. Stautz, et al. Nondestructive estimation of leaf area for pondberry. U.S. Department of Agriculture, Forest Service, Southern Research Station, 2007. http://dx.doi.org/10.2737/srs-rn-14.

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Dogan, Adnan, Cuneyt Uyak, Nurhan Keskin, et al. Grapevine Leaf Area Measurements by Using Pixel Values. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2018. http://dx.doi.org/10.7546/crabs.2018.06.07.

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Scurlock, JMO. Worldwide Historical Estimates of Leaf Area Index, 1932-2000. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/814100.

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Maloley, MJ, R. Fernandes, F. Canisius, and C. Butson. Peak season leaf area index for the Nanaimo Aquifer - 2011. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2013. http://dx.doi.org/10.4095/293342.

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Kaszycki, C. A., L. A. Dredge, and H. Groom. Surficial geology and glacial history, Lynn Lake - Leaf Rapids area, Manitoba. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2008. http://dx.doi.org/10.4095/225935.

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Fernandes, R. A., and L. Sun. Monthly leaf area index of Canada from medium-resolution satellite imagery. Natural Resources Canada/CMSS/Information Management, 2024. https://doi.org/10.4095/pmrkmpbtuy.

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L'indice de surface foliaire (ISF) quantifie la densité de la végétation indépendamment du type de couverture terrestre. L'ISF mesure la surface totale du feuillage par unité de surface terrestre. L'ISF a été identifié par le Système mondial d'observation du climat comme une variable climatique essentielle nécessaire pour la modélisation et le suivi des écosystèmes, de la météo et du climat. Ce produit comprend une couverture à l'échelle nationale (Canada) de cartes mensuelles de l'ISF maximal pendant une saison de croissance (mai-juin-juillet-août-septembre) à une résolution de 20 mètres.

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Chen, J. M., and J. Cihlar. Retrieving Leaf Area Index of boreal conifer forests using Landsat TM images. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1996. http://dx.doi.org/10.4095/218507.

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Fernandes, R. A., H. Tang, and L. Brown. Proposed definition of leaf area index by the Committee of Earth Observation Satellites. Natural Resources Canada/CMSS/Information Management, 2024. https://doi.org/10.4095/p9fzmg1djq.

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Abstract (sommario):

Le Comité des Satellites d'Observation de la Terre (CEOS) est une Organisation Participante du Groupe des Observations de la Terre (GEO), organe intergouvernemental. Le Groupe de Travail sur l'Étalonnage et la Validation du CEOS est responsable de la définition des variables géophysiques produites par les systèmes satellites du CEOS. Ce document définit l'Indice de Surface Foliaire (LAI), y compris les abréviations recommandées, la symbolique et les unités. Les quantités associées, couramment dérivées tant par des systèmes in-situ que par télédétection, y compris les satellites du CEOS, sont é

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