Relevant bibliographies by topics / Leaf area index

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Leaf area index'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Leaf area index"

1

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

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Balakrishnan, K., N. Natarajaratnam, and C. Rajendran. "Critical Leaf Area Index in Pigeonpea." Journal of Agronomy and Crop Science 159, no. 3 (September 1987): 164–66. http://dx.doi.org/10.1111/j.1439-037x.1987.tb00081.x.

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ZHONG, X., S. PENG, J. E. SHEEHY, R. M. VISPERAS, and H. LIU. "Relationship between tillering and leaf area index: quantifying critical leaf area index for tillering in rice." Journal of Agricultural Science 138, no. 3 (May 2002): 269–79. http://dx.doi.org/10.1017/s0021859601001903.

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A field study was conducted at the International Rice Research Institute (IRRI), Philippines during the dry seasons of 1997 and 1998 under irrigated conditions. The objectives of this study were to quantify the critical leaf area index (LAIc) at which tillering stops based on the relationship between tillering rate and LAI, and to determine the effect of nitrogen (N) on LAIc in irrigated rice (Oryza sativa L.) crop. Results showed that the relative tillering rate (RTR) decreased exponentially as LAI increased at a given N input level. The coefficient of determination for the equation quantifying the RTR-LAI relationship ranged from 0·87 to 0·99. The relationship between RTR and LAI was affected by N input level, but not by planting density. The N input level had a significant effect on LAIc with a high N input level causing an increase in LAIc. Tillering stopped at LAI of 3·36 to 4·11 when N was not limiting. Under N limited conditions LAIc reduced to as low as 0·98. Transplanting spacing and number of seedlings per hill had little effect on LAIc. Results from this study suggest that LAI and plant N status are two major factors that influence tiller production in rice crops. The possibility that LAI influences tillering by changing light intensity and/or light quality at the base of the canopy where tiller buds and young tillers are located is discussed.
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Zhang, Hu, Jing Li, Qinhuo Liu, Yadong Dong, Songze Li, Zhaoxing Zhang, Xinran Zhu, Liangyun Liu, and Jing Zhao. "Estimating Leaf Area Index with Dynamic Leaf Optical Properties." Remote Sensing 13, no. 23 (December 2, 2021): 4898. http://dx.doi.org/10.3390/rs13234898.

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Leaf area index (LAI) plays an important role in models of climate, hydrology, and ecosystem productivity. The physical model-based inversion method is a practical approach for large-scale LAI inversion. However, the ill-posed inversion problem, due to the limited constraint of inaccurate input parameters, is the dominant source of inversion errors. For instance, variables related to leaf optical properties are always set as constants or have large ranges, instead of the actual leaf reflectance of pixel vegetation in the current model-based inversions. This paper proposes to estimate LAI with the actual leaf optical property of pixels, calculated from the leaf chlorophyll content (Chlleaf) product, using a three-dimensional stochastic radiative transfer model (3D-RTM)-based, look-up table method. The parameter characterizing leaf optical properties in the 3D-RTM-based LAI inversion algorithm, single scattering albedo (SSA), is calculated with the Chlleaf product, instead of setting fixed values across a growing season. An algorithm to invert LAI with the dynamic SSA of the red band (SSAred) is proposed. The retrieval index (RI) increases from less than 42% to 100%, and the RMSE decreases to less than 0.28 in the simulations. The validation results show that the RMSE of the dynamic SSA decreases from 1.338 to 0.511, compared with the existing 3D-RTM-based LUT algorithm. The overestimation problem under high LAI conditions is reduced.
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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 (May 1994): 313–21. http://dx.doi.org/10.2307/1941936.

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Antognozzi, E., A. Tombesi, and A. Palliotti. "RELATIONSHIP BETWEEN LEAF AREA, LEAF AREA INDEX AND FRUITING IN KIWIFRUIT (ACTINDIA DELICIOSA)." Acta Horticulturae, no. 297 (April 1992): 435–42. http://dx.doi.org/10.17660/actahortic.1992.297.57.

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Anderson, Martha C. "Simple method for retrieving leaf area index from Landsat using MODIS leaf area index products as reference." Journal of Applied Remote Sensing 6, no. 1 (July 18, 2012): 063554. http://dx.doi.org/10.1117/1.jrs.6.063554.

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Hirose, T., D. D. Ackerly, M. B. Traw, D. Ramseier, and F. A. Bazzaz. "CO2ELEVATION, CANOPY PHOTOSYNTHESIS, ANDOPTIMAL LEAF AREA INDEX." Ecology 78, no. 8 (December 1997): 2339–50. http://dx.doi.org/10.1890/0012-9658(1997)078[2339:cecpal]2.0.co;2.

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Price, J. C. "Estimating leaf area index from satellite data." IEEE Transactions on Geoscience and Remote Sensing 31, no. 3 (May 1993): 727–34. http://dx.doi.org/10.1109/36.225538.

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Abuelgasim, Abdelgadir A., and Sylvain G. Leblanc. "Leaf area index mapping in northern Canada." International Journal of Remote Sensing 32, no. 18 (July 4, 2011): 5059–76. http://dx.doi.org/10.1080/01431161.2010.494636.

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Dissertations / Theses on the topic "Leaf area index"

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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 in the Piedmont region of the State. Maximum and minimum temperature data were also collected at the three sites. Measurements were made for two growing seasons using corn hybrid Pioneer 3369A, three plant population densities and two irrigation schedules. Short duration temperature data were also collected to compare with the daily maximum and minimum temperature data for the Mayodan site. Also a combination of soil temperature at 10 cm depth and air temperatures were used for the temperature functions accumulated from date of planting at the Mayodan site. Results of this study show that the predicted and measured GLAI values compare favorably under irrigated conditions on the Davidson soil. The results were not as favorable on the irrigated corn on the Mayodan soil. When the corn is subjected to severe moisture stress on either soil, GLAI cannot be predicted with this model. Short duration temperature data resulted in a better prediction of GLAI on the Mayodan soil. When applying nitrogen fertilizer to the corn through the irrigation system through the grain filling stage, the measured GLAI values compared favorably with the predicted GLAI values. However, the application of nitrogen and sulfur fertilizer together resulted in GLAI being maintained above that predicted for a longer period of time during the grain filling stage before its decline.
Ph. D.
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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 Analyzer, (2) compared LAI estimates among LAI-2000 detector types which respond to different wavelengths of light, (3) compared LAI-2000 estimates with directly determined LAI values for 0, 33, 66, and 100% mechanical defoliation levels, (4) used linear and non-linear models to describe the response of full-season and double-crop soybean yields to reductions in LAI through mechanical defoliation, and (5) evaluated the response of double-crop soybean yields to reductions in LAI through insect defoliation. The minimum plot size for obtaining accurate LAI estimates of defoliated canopies in soybean with 91 cm row centers is four rows by 2 m, with an additional 1 m at the ends of the two middle rows also defoliated. The wide-blue detector, which is found in newer LAI-2000 units and responds to wavelengths of light from 360 to 460 nm, gave higher LAI estimates than the narrow-blue detector, which responds to light from 400 to 490 nm. The unit with the narrow-blue detector gave estimates equal to directly determined LAI in two of three years for 0, 33, and 66% defoliation levels, while the units with the wide-blue detectors gave estimates higher than directly determined LAI in the two years that they were studied, except for a few accurate 33% defoliation estimates. Therefore, the LAI-2000 usually provides reasonable estimates of LAI. Yield decreased linearly with LAI when LAI values were below 3.5 to 4.0 by developmental stages R4 to R5 in both full-season and double-crop soybean. Usually, there was no relationship between yield and LAI at LAI values greater than 4.0. There was an average yield reduction of 820 ± 262 kg ha-1 for each unit decrease in LAI below the critical 3.5 to 4.0 level; maximum yields ranged from 1909 to 3797 kg ha-1. Insect defoliators did not defoliate double-crop soybean plots to LAI levels less than 4.0, and there was no yield difference between insect-defoliated and control plots. Therefore, double-crop soybean that maintains LAI values above the 3.5 to 4.0 critical level during mid-reproductive developmental stages is capable of tolerating defoliating pest
Ph. D.
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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 40 000 stems per hectare were used to test treatment effects of fertiliser, site disturbance and species over a 4 year period. It was hypothesised that collected climate and soil information would explain differences in LAI development patterns across sites as the canopies approached site and seasonal maxima. Averaged across sites Cupressus lusitanica 7.28 (± 2.59 Std.) m2 m-2 had significantly (p = 0.0094) greater projected LAIs than Pinus radiata 6.47 (± 2.29) m2m-2. Maximum site LAI (LAImax) varied from 2.9 to 11.8 m2 m-2 for Pinus radiata and from 3.1 to 12.6 m2 m-2 for Cupressus lusitanica. LAImax of both species was significantly and positively correlated with vapour pressure deficit, soil carbon, nitrogen, phosphorous and CEC, but negatively with solar radiation, temperature and soil bulk density. A seasonal model of LAI across sites illustrated an 8.5% fluctuation in LAI of established canopies over the course of a year. Despite considerable variation in climate and soil characteristics across sites the combined effects of LAI at harvest and temperature were significantly correlated with site productivity (r2 = 0.84 and 0.76 for Pinus radiata and Cupressus lusitanica respectively). A national model of LAImax (r2 = 0.96) was proposed for Pinus radiata across climate and soil environments and the significance of LAImax as a component of site quality monitoring tools is discussed.
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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.
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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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 mode' (MFM) was developed. This new methodology was evaluated against four traditional radiometric corrections used in comination with spectral mixture analysis (SMA) and NDVI. The MFM approach provided the best overall predictions of LAI measured with ground-based optical instruments, followed by terrain normalized SMA, SMA without terrain normalization and NDVI.
xiv, 151 leaves : ill. (some col.), map ; 29 cm.
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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.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2003.
Thesis 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.
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Bowyer, P. "Estimating leaf area index in savanna vegetation using remote sensing and inverse modelling." Thesis, University of Salford, 2005. http://usir.salford.ac.uk/2234/.

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Leaf area index (LAI), defined as the one sided green leaf area per unit ground area, is a key parameter in ecosystem process models. Owing to the large area of the earth's surface that they occupy, savanna ecosystems represent the third largest terrestrial carbon sink. There is considerable uncertainty however, as to the functioning of these ecosystems, particularly as they respond to land cover changes. Consequently, ecosystem process models constitute one of the best methods available for investigating the effect this may have on terrestrial carbon cycling. If these models are to be used over large areas however, they need to be parameterised. This thesis develops a methodology to estimate LAI in savanna ecosystems, using remotely sensed earth observation (EO) data, laboratory bidirectional reflectance measurements (BRDF), physically based canopy reflectance models (CRMs), and artificial neural networks (ANN). First, the scattering behaviour of Kalahari soils was characterised, by making laboratory BRDF measurements. Soils were shown to be highly non-Lambertian. These measurements were then used to parameterise three different CRMs. Modelled reflectances were assessed with respect to Landsat ETM+ and Terra-MODIS reflectances. Results showed that a 1-D turbid medium provided the closest fit to the measurements. A series of model sensitivity analyses (SA) were performed, and it was shown that reflectance in the red and shortwave infrared displayed greatest sensitivity to LAI, sensitivity in the near-infrared was negligible. Model inversions were performed with ANN and different waveband combinations, and LAI was estimated. The results showed that LAI could be estimated with high accuracy, an RMSE of 0.3 1, and 0.18, from ETM+ and MODIS measurements, respectively. These results were promising, and with further improvements to models, coupled with more accurate input data, will see the use of EO data play an increasingly important role in understanding the functioning of these savanna ecosystems.
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May, David Z. "A MULTISPECTRAL REMOTE SENSING INVESTIGATION OF LEAF AREA INDEX AT BLACK ROCK FOREST, NY." Ohio University / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1157569101.

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Hirooka, Yoshihiro. "Evaluation of Rice Growth Characteristics Based on Non-destructive Measurements of Leaf Area Index." Kyoto University, 2016. http://hdl.handle.net/2433/215581.

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Kyoto University (京都大学)
0048
新制・課程博士
博士(農学)
甲第19755号
農博第2151号
新制||農||1038(附属図書館)
学位論文||H28||N4971(農学部図書室)
32791
京都大学大学院農学研究科農学専攻
(主査)教授 白岩 立彦, 教授 奥本 裕, 教授 稲村 達也
学位規則第4条第1項該当
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Books on the topic "Leaf area index"

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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. [Samarinda]: Proyek Peningkatan & Pengembangan Perguruan Tinggi, Universitas Mulawarman, 1989.

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

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

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

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

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

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

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

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A, Morrissey Leslie, Livingston Gerald P, and United States. National Aeronautics and Space Administration., eds. Microwave backscatter and attenuation dependence of leaf area index for flooded rice fields. [Washington, D.C: National Aeronautics and Space Administration, 1995.

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Chen, Jing. BOREAS RSS-7 Landsat TM LAI images of the SSA and NSA. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 2000.

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Book chapters on the topic "Leaf area index"

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Liang, Shunlin, Xiaotong Zhang, Zhiqiang Xiao, Jie Cheng, Qiang Liu, and Xiang Zhao. "Leaf Area Index." In Global LAnd Surface Satellite (GLASS) Products, 3–31. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02588-9_2.

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Keane, Robert E. "LAI: Leaf Area Index." In Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires, 1–8. Cham: 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, 730–37. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-52090-2_237.

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Qu, Yonghua. "Leaf Area Index: Advances in Ground-Based Measurement." In Observation and Measurement of Ecohydrological Processes, 359–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-48297-1_11.

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Strachan, I. B., D. W. Stewart, and E. Pattey. "Determination of Leaf Area Index in Agricultural Systems." In Agronomy Monographs, 179–98. Madison, WI, USA: 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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Raj, Rahul, Saurabh Suradhaniwar, Rohit Nandan, Adinarayana Jagarlapudi, and 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.

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Cao, Hongxin, Chunlei Zhang, Guangming Li, Baojun Zhang, Suolao Zhao, Baoqing Wang, Zhiqing Jin, Dawei Zhu, Juanjuan Zhu, and Xiufang Wei. "RESEARCHES OF OPTIMUM LEAF AREA INDEX DYNAMICMODELS FOR RAPE(BRASSICA NAPUS L.)." In Computer and Computing Technologies in Agriculture II, Volume 3, 1585–94. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0213-9_8.

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Rahimi, Hamid, Shahnaz Karami Sorkhalije, and Hajar Marabi. "Determining the Yield of Rice Using the Leaf Area Index (LAI) in Iran." In Application of Remote Sensing and GIS in Natural Resources and Built Infrastructure Management, 123–42. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14096-9_7.

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Gerasko, Tatyana, Lyudmila Velcheva, Liudmyla Todorova, Lyubov Pokoptseva, and Iryna Ivanova. "Effect of Living Mulch on Chlorophyll Index, Leaf Moisture Content and Leaf Area of Sweet Cherry (Prunus avium L.)." In Modern Development Paths of Agricultural Production, 681–88. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14918-5_66.

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Smethurst, P., C. Baillie, and M. Cherry. "Nutritional effects on leaf area index and growth of a young Eucalyptus nitens plantation." In Plant Nutrition, 928–29. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/0-306-47624-x_452.

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Conference papers on the topic "Leaf area index"

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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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Verma, Bhagyashree, Rajendra Prasad, Prashant K. Srivastava, and Prachi Singh. "Retrieval of Leaf Area Index Using Inversion Algorithm." In 2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2022. http://dx.doi.org/10.1109/whispers56178.2022.9955056.

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Qiu, Zhengjun, Hui Fang, Yun Zhang, and Yong He. "Measurement of leaf area index using image-processing technology." In MIPPR 2005 SAR and Multispectral Image Processing, edited by Liangpei Zhang, Jianqing Zhang, and Mingsheng Liao. SPIE, 2005. http://dx.doi.org/10.1117/12.652409.

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Huang, Wenjiang, Yi Tang, Rongyuan Liu, Guijun Yang, and Xiaoyu Song. "Estimating leaf area index considering the crop geometry effection." In Remote Sensing, edited by Christopher M. U. Neale and Antonino Maltese. SPIE, 2010. http://dx.doi.org/10.1117/12.865967.

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Cai, Erli, Xiuhong Li, Qiang Liu, Baocheng Dou, Chongyan Chang, Hailin Niu, Xingwen Lin, and Jialin Zhang. "Preliminary validation of leaf area index sensor in Huailai." In International Conference on Intelligent Earth Observing and Applications, edited by Guoqing Zhou and Chuanli Kang. SPIE, 2015. http://dx.doi.org/10.1117/12.2207616.

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Wang, Xiuzhen, Jingfeng Huang, Yunmei Li, and Renchao Wang. "Rice Leaf Area Index (LAI) estimates from hyperspectral data." In Third International Asia-Pacific Environmental Remote Sensing Remote Sensing of the Atmosphere, Ocean, Environment, and Space, edited by Xiaoling Pan, Wei Gao, Michael H. Glantz, and Yoshiaki Honda. SPIE, 2003. http://dx.doi.org/10.1117/12.466486.

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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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Huang, Linsheng, Jing Jiang, Furan Song, Jinling Zhao, and Wenjiang Huang. "Estimation of the Leaf Area Index Using a Modified Triangular Difference Vegetation Index." In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019. http://dx.doi.org/10.1109/igarss.2019.8898763.

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Wan, H., J. Wang, S. Liang, H. Fang, and Z. Xiao. "Estimating Leaf Area Index by Fusing MODIS and MISR Data." In 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, 2006. http://dx.doi.org/10.1109/igarss.2006.470.

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Martino, Luca, Victor Elvira, and Gustau Camps-Valls. "Particle Group Metropolis Methods for Tracking the Leaf Area Index." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053962.

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Reports on the topic "Leaf area index"

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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Scurlock, JMO. Worldwide Historical Estimates of Leaf Area Index, 1932-2000. Office of Scientific and Technical Information (OSTI), February 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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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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Chen, J. M. Optically-Based Methods for Measuring Seasonal Variation of Leaf area Index in Boreal Conifer Stands. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1996. http://dx.doi.org/10.4095/218392.

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Leblanc, S. G., R. Fernandes, and J. M. Chen. Recent advancements in optical field leaf area index, foliage heterogeneity, and foliage angular distribution measurements. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/219868.

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Chen, J. M. A New Optical Instrument for Measuring Leaf area Index Based on a Canopy Gap Size Distribution Theory. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1995. http://dx.doi.org/10.4095/218506.

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Fernandes, R., M. Maloley, and F. Canisius. Relationship between leaf area index and Landsat Operational Land Imager equivalent reduced simple ratio vegetation index for the Athabasca oil sands region, northern Alberta. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2018. http://dx.doi.org/10.4095/308333.

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Leblanc, S. G., J. M. Chen, H. P. White, R. Latifovic, R. Fernandes, J. L. Roujean, and R. Lacaze. Mapping leaf area index heterogeneity over Canada using directional reflectance and anisotropy canopy reflectance models using directional reflectance and anisotropy canopy reflectance models. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/219874.

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Perkins, Dustin. Invasive exotic plant monitoring at Dinosaur National Monument: Results of the 2019 field season on the Green River, and the third completed monitoring rotation. Edited by Alice Wondrak Biel. National Park Service, December 2021. http://dx.doi.org/10.36967/nrr-2284627.

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Abstract:
Invasive exotic plant (IEP) species are a significant threat to natural ecosystem integrity and biodiversity, and controlling them is a high priority for the National Park Service. The Northern Colorado Plateau Network (NCPN) selected the early detection of IEPs as one of 11 monitoring protocols to be implemented as part of its long-term monitoring program. We also calculated a patch management index (PMI) to quantify the extent and density of invasive patches into a single value that helps identify the scale of the problem. Park managers can use this tool to help prioritize IEP treatment. At Dinosaur National Monument, the NCPN monitors IEPs in the Green and Yampa river corridors. This report summarizes data from monitoring on the Green River in 2019, and monitoring on the Yampa River in 2017, to represent the completion of the third monitoring rotation of the entire river corridor (2002–2005, 2010–2011, 2017–2019). During surveys conducted from June 26 to July 2, 2019, NCPN staff detected 12 priority IEP species and two non-priority species in a 84.6-hectare (209-acre) area along 74.4 kilometers of the Green River above (“upper”) and below (“low-er”) its confluence with the Yampa. A total of 2,535 IEP patches were detected. Of those patches, 24.2% and 15.6% were smaller than 40 m2 on the upper and lower Green River reaches, respectively. The patch management index (PMI) was low or very low for 95.7% of patches on the upper Green River and 90.9% of patches on the lower Green River. Tamarisk (Tamarix sp.), broad-leaf pepperwort (Lepidium latifolium), and yellow sweetclover (Meli-lotus officinalis) were the most widespread species. For the first time, NCPN monitoring detected teasel (Dipsacus sylvestris) on the upper Green River. Yellow sweetclover has increased on all three river reaches during the survey years. Musk thistle (Carduus nutans) was found at considerably lower levels than yellow sweetclover but has also increased on all three river reaches. Leafy spurge is increasing on the lower Green River and Yampa River. Cheatgrass was not monitored in the first rotation, but increased substantially in cover and percent frequency on all three river sections from 2010–2011 to 2017–2019. This increase may be due to a lack of recent high-flow scouring events. The highly regulated upper Green River generally has the highest number of IEPs, while the lower Green River has a moderate amount of IEPs. The largely unregulated flows of the Yampa River continue to result in a lower number of patches per kilometer, lower percent cover, and lower percent frequency than the upper or lower Green River. Network staff will return to the monument in 2022 to begin the fourth monitoring rotation.
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