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Journal articles on the topic 'Leaf area index'

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

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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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Borghetti, M., G. G. Vendramin, and R. Giannini. "Specific leaf area and leaf area index distribution in a young Douglas-fir plantation." Canadian Journal of Forest Research 16, no. 6 (December 1, 1986): 1283–88. http://dx.doi.org/10.1139/x86-227.

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The spatial distribution of specific leaf area and leaf area index of needles in different age classes has been investigated in a young and unthinned Douglas-fir (Pseudotsugamenziesii (Mirb.) Franco) plantation in Central Italy through the destructive analysis of 12 trees sampled in four diameter size classes. Specific leaf area decreased with leaf age and from crown base to apex. A clear interaction between the effects of age and position on specific leaf area was demonstrated. For the whole canopy the vertical distribution of leaf area was well fitted by a normal curve equation, which explained 97% of the variation. The midpoint of the leaf area distribution, estimated as a parameter of the normal curve, was found to be 1.2 m below the mean canopy depth. The standard deviation of leaf area with respect to height was 16.4%. The midpoint of leaf area distribution decreased as leaf age increased and increased as diameter size class increased. Strong and significant linear relationships were found between leaf biomass, leaf area, sapwood area, and diameter at breast height.
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12

Caldararu, S., P. I. Palmer, and D. W. Purves. "Inferring Amazon leaf demography from satellite observations of leaf area index." Biogeosciences Discussions 8, no. 5 (October 25, 2011): 10389–421. http://dx.doi.org/10.5194/bgd-8-10389-2011.

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Abstract. Seasonal and year-to-year variations in leaf cover imprint significant spatial and temporal variability on biogeochemical cycles, and affect land-surface properties related to climate. We develop a demographic model of leaf phenology based on the hypothesis that trees seek an optimal Leaf Area Index (LAI) as a function of available light and soil water, and fitted it to spaceborne observations of LAI over the Amazon Basin, 2001–2005. We find the model reproduces the spatial and temporal LAI distribution whilst also predicting geographic variation in leaf age from the basin center (2.1 ± 0.2 yr), through to the lowest values over the deciduous Eastern Amazon (6 ± 2 months). The model explains the observed increase in LAI during the dry season as a net addition of leaves in response to increased solar radiation. We anticipate our work to be a starting point from which to develop better descriptions of leaf phenology to incorporate into more sophisticated earth system models.
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13

Caldararu, S., P. I. Palmer, and D. W. Purves. "Inferring Amazon leaf demography from satellite observations of leaf area index." Biogeosciences 9, no. 4 (April 16, 2012): 1389–404. http://dx.doi.org/10.5194/bg-9-1389-2012.

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Abstract. Seasonal and year-to-year variations in leaf cover imprint significant spatial and temporal variability on biogeochemical cycles, and affect land-surface properties related to climate. We develop a demographic model of leaf phenology based on the hypothesis that trees seek an optimal leaf area index (LAI) as a function of available light and soil water, and fit it to spaceborne observations of LAI over the Amazon basin, 2001–2005. We find the model reproduces the spatial and temporal LAI distribution whilst also predicting geographic variation in leaf age from the basin centre (2.1 ± 0.2 years), through to the lowest values over the deciduous eastern and southern Amazon (6 ± 2 months). The model explains the observed increase in LAI during the dry season as a net addition of leaves in response to increased solar radiation. We anticipate our work to be a starting point from which to develop better descriptions of leaf phenology to incorporate into more sophisticated earth system models.
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14

Shaikh Abdullah Al Mamun, Hossain, Wang Lixue, Chen Taotao, and Li Zhenhua. "Leaf area index assessment for tomato and cucumber growing period under different water treatments." Plant, Soil and Environment 63, No. 10 (November 2, 2017): 461–67. http://dx.doi.org/10.17221/568/2017-pse.

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The aim of this study was to assess the leaf area index (LAI) of tomato and cucumber using an AccuPAR-LP-80-ceptometer to find the influence of irrigation. LAI was also determined by destructive sampling for comparison. The research was conducted at the Liaoning Water Conservancy Institute, North China in 2016. A randomized block design was used to test the influence of four treatments corresponding to field water capacity. Full irrigation (W<sub>1.0</sub>), 15% (W<sub>0.85</sub>), 25% (W<sub>0.75</sub>) and 35% (W<sub>0.65</sub>) water deficit were applied using the drip system. Regression model was developed to estimate LAI in response to irrigation. The results show that there is no difference between the two methods. The highest LAI obtained for tomato and cucumber was 5.21 and 3.21 m<sup>2</sup>/m<sup>2</sup>, respectively, with W<sub>0.85</sub> at 70-days after transplanting, which corresponds with destructive results. This result was found 11% higher and equal compared with W<sub>1.0</sub> for tomato (4.62) and cucumber (3.21), respectively. For both crops, LAI was found significantly influenced at 50-days after transplanting. It also indicated that LAI significantly influenced (by 15%) deficit irrigation for both crops and methods that achieved the highest yield. The predicted LAI was obtained best-fitting with the observed values, which indicated that the AccuPAR-ceptometer is suitable to be used.
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15

Klima, K., and B. Wiśniowska-Kielian. "Anti-erosion effectiveness of selected crops and the relation to leaf area index (LAI)." Plant, Soil and Environment 52, No. 1 (November 15, 2011): 35–40. http://dx.doi.org/10.17221/3343-pse.

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This paper presents results of an experiment carried out in 2000&ndash;2003 in the mountain region (southern Poland, 545 m a.s.l.) to determine the effect of over-ground parts growth of fodder beet, winter triticale and horse bean on the intensity of soil losses. The research was conducted on the hillside with a 16% slope with the simulated rainfall (105 mm; 1.75 mm/min) applied at seven developmental stages of the plants. It was stated that soil protective efficiency of the fodder beet, horse bean and winter triticale started at about 60, 30 and 15% of covering the soil surface, respectively. The influence of over-ground parts of the plants (x) on the soil erosion (y) can describe the following regression equations: for fodder beet: y = &ndash;9.37x + 29.4 (R<sup>2</sup> =&nbsp;0.677; n = 82); for horse bean: y = &ndash;8.44x + 26.41 (R<sup>2</sup> = 0.698; n = 96); for winter triticale: y = &ndash;4.98x + 15.61 (R<sup>2</sup> = 0.66; n&nbsp;= 112). The obtained results made possible verification of the nomograms determining the value of the C&nbsp;indicator (cropping factor, i.e. index of soil coverage and cultivation calculated as a ratio of soil mass eroded from the field covered with specific crop to mass of soil eroded from black fallow with a 9% slope angle) present in USLE equation (Universal Soil Losses Equation, method commonly recommended by FAO for studies on erosion) for tested plants under similar conditions.
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16

Xiao, Chun-Wang, I. A. Janssens, J. Curiel Yuste, and R. Ceulemans. "Variation of specific leaf area and upscaling to leaf area index in mature Scots pine." Trees 20, no. 3 (March 3, 2006): 304–10. http://dx.doi.org/10.1007/s00468-005-0039-x.

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17

Firman, D. M., and E. J. Allen. "Estimating individual leaf area of potato from leaf length." Journal of Agricultural Science 112, no. 3 (June 1989): 425–26. http://dx.doi.org/10.1017/s0021859600085889.

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Measurements of the area of individual leaves in crops are useful in the analysis of canopy architecture as they allow determination of the structure of leaf area index in a vertical profile. This information may be of use in modelling leaf growth and the assessment of photosynthetic potential of different strata of the canopy with ontogeny (cf. Firman & Allen, 1988).
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18

Doring, J., M. Stoll, R. Kauer, M. Frisch, and S. Tittmann. "Indirect Estimation of Leaf Area Index in VSP-Trained Grapevines Using Plant Area Index." American Journal of Enology and Viticulture 65, no. 1 (November 7, 2013): 153–58. http://dx.doi.org/10.5344/ajev.2013.13073.

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19

Smith, N. J., and D. R. Clark. "Estimating salal leaf area index and leaf biomass from diffuse light attenuation." Canadian Journal of Forest Research 20, no. 9 (September 1, 1990): 1265–70. http://dx.doi.org/10.1139/x90-168.

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Salal (Gaultheriashallon Pursh) leaf area index and leaf biomass were estimated from 37 quadrat samples in 13 stands dominated by Douglas-fir (Pseudotsugamenziesii (Mirb.) Franco) on eastern Vancouver Island, British Columbia. Leaf area index and biomass were predicted from a Beer's Law light attenuation model using diffuse photosynthetically active radiation (400–700 nm wavelength). The extinction coefficients, determined using reduced major axis maximum likelihood, were 0.8055 m2/m2 for leaf area index and 0.0069 g/m2 for leaf biomass. Salal leaf area index and biomass were then predicted for any convenient height in the understory canopy using a cumulative Weibull model based on dominant salal height per quadrat. The models are of use for objectively assessing the amount of Columbian black-tailed deer (Odocoileushemionuscolumbianus Richardson) winter browse and to quantify competitive leaf area.
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20

Chen, Wei, and Chunxiang Cao. "Topographic correction-based retrieval of leaf area index in mountain areas." Journal of Mountain Science 9, no. 2 (March 21, 2012): 166–74. http://dx.doi.org/10.1007/s11629-012-2248-2.

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21

Heitholt, J. J., and W. R. Meredith. "Yield, Flowering, and Leaf Area Index of Okra‐Leaf and Normal‐Leaf Cotton Isolines." Crop Science 38, no. 3 (May 1998): 643–48. http://dx.doi.org/10.2135/cropsci1998.0011183x003800030003x.

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22

Hardin, Perry J., and Ryan R. Jensen. "Neural Network Estimation of Urban Leaf Area Index." GIScience & Remote Sensing 42, no. 3 (September 2005): 251–74. http://dx.doi.org/10.2747/1548-1603.42.3.251.

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23

Neinavaz, Elnaz, Andrew K. Skidmore, Roshanak Darvishzadeh, and Thomas A. Groen. "LEAF AREA INDEX RETRIEVED FROM THERMAL HYPERSPECTRAL DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 20, 2016): 99–105. http://dx.doi.org/10.5194/isprs-archives-xli-b7-99-2016.

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Leaf area index (LAI) is an important essential biodiversity variable due to its role in many terrestrial ecosystem processes such as evapotranspiration, energy balance, and gas exchanges as well as plant growth potential. A novel approach presented here is the retrieval of LAI using thermal infrared (8–14 μm, TIR) measurements. Here, we evaluate LAI retrieval using TIR hyperspectral data. Canopy emissivity spectral measurements were recorded under controlled laboratory conditions using a MIDAC (M4401-F) illuminator Fourier Transform Infrared spectrometer for two plant species during which LAI was destructively measured. The accuracy of retrieval for LAI was then assessed using partial least square regression (PLSR) and narrow band index calculated in the form of normalized difference index from all possible combinations of wavebands. The obtained accuracy from the PLSR for LAI retrieval was relatively higher than narrow-band vegetation index (0.54 < R<sup>2</sup> < 0.74). The results demonstrated that LAI may successfully be estimated from hyperspectral thermal data. The study highlights the potential of hyperspectral thermal data for retrieval of vegetation biophysical variables at the canopy level for the first time.
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24

Kiniry, Jim, Mari-Vaughn Johnson, Robert Mitchell, Ken Vogel, Jerry Kaiser, Steve Bruckerhoff, and Ron Cordsiemon. "Switchgrass Leaf Area Index and Light Extinction Coefficients." Agronomy Journal 103, no. 1 (January 2011): 119–22. http://dx.doi.org/10.2134/agronj2010.0280.

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25

Kuusk, Andres, Mait Lang, Ave Kodar, and Allan Sims. "Estimation of Leaf Area Index of Hemiboreal Forests." Open Remote Sensing Journal 6, no. 1 (April 10, 2015): 1–10. http://dx.doi.org/10.2174/1875413901506010001.

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26

ZHANG Zhengyang, 张正杨, 马新明 MA Xinming, 贾方方 JIA Fangfang, 乔红波 QIAO Hongbo, and 张营武 ZHANG Yingwu. "Hyperspectral estimating models of tobacco leaf area index." Acta Ecologica Sinica 32, no. 1 (2012): 168–75. http://dx.doi.org/10.5846/stxb201011051586.

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27

Neinavaz, Elnaz, Andrew K. Skidmore, Roshanak Darvishzadeh, and Thomas A. Groen. "LEAF AREA INDEX RETRIEVED FROM THERMAL HYPERSPECTRAL DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 20, 2016): 99–105. http://dx.doi.org/10.5194/isprsarchives-xli-b7-99-2016.

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Leaf area index (LAI) is an important essential biodiversity variable due to its role in many terrestrial ecosystem processes such as evapotranspiration, energy balance, and gas exchanges as well as plant growth potential. A novel approach presented here is the retrieval of LAI using thermal infrared (8–14 μm, TIR) measurements. Here, we evaluate LAI retrieval using TIR hyperspectral data. Canopy emissivity spectral measurements were recorded under controlled laboratory conditions using a MIDAC (M4401-F) illuminator Fourier Transform Infrared spectrometer for two plant species during which LAI was destructively measured. The accuracy of retrieval for LAI was then assessed using partial least square regression (PLSR) and narrow band index calculated in the form of normalized difference index from all possible combinations of wavebands. The obtained accuracy from the PLSR for LAI retrieval was relatively higher than narrow-band vegetation index (0.54 &lt; R&lt;sup&gt;2&lt;/sup&gt; &lt; 0.74). The results demonstrated that LAI may successfully be estimated from hyperspectral thermal data. The study highlights the potential of hyperspectral thermal data for retrieval of vegetation biophysical variables at the canopy level for the first time.
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28

Palán, Ladislav, Josef Křeček, and Yoshinobu Sato. "Leaf area index in a forested mountain catchment." Hungarian Geographical Bulletin 67, no. 1 (March 31, 2018): 3–11. http://dx.doi.org/10.15201/hungeobull.67.1.1.

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29

DANSON, F. M., and S. E. PLUMMER. "Red-edge response to forest leaf area index." International Journal of Remote Sensing 16, no. 1 (January 1995): 183–88. http://dx.doi.org/10.1080/01431169508954387.

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30

CHEN, J. M., and T. A. BLACK. "Defining leaf area index for non-flat leaves." Plant, Cell and Environment 15, no. 4 (May 1992): 421–29. http://dx.doi.org/10.1111/j.1365-3040.1992.tb00992.x.

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31

Aboelghar, M., S. Arafat, A. Saleh, S. Naeem, M. Shirbeny, and A. Belal. "Retrieving leaf area index from SPOT4 satellite data." Egyptian Journal of Remote Sensing and Space Science 13, no. 2 (December 2010): 121–27. http://dx.doi.org/10.1016/j.ejrs.2010.06.001.

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32

Shih, S. F., and G. H. Snyder. "Leaf Area Index and Evapotranspiration of Taro 1." Agronomy Journal 77, no. 4 (July 1985): 554–56. http://dx.doi.org/10.2134/agronj1985.00021962007700040012x.

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33

Gordon, R., D. M. Brown, and M. A. Dixon. "Estimating potato leaf area index for specific cultivars." Potato Research 40, no. 3 (September 1997): 251–66. http://dx.doi.org/10.1007/bf02358007.

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34

Patočka, Zdeněk, Kateřina Novosadová, Pavel Haninec, Radek Pokorný, Tomáš Mikita, and Martin Klimánek. "Comparison of LiDAR-based Models for True Leaf Area Index and Effective Leaf Area Index Estimation in Young Beech Forests." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 68, no. 3 (2020): 559–66. http://dx.doi.org/10.11118/actaun202068030559.

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The leaf area index (LAI) is one of the most common leaf area and canopy structure quantifiers. Direct LAI measurement and determination of canopy characteristics in larger areas is unrealistic due to the large number of measurements required to create the distribution model. This study compares the regression models for the ALS-based calculation of LAI, where the effective leaf area index (eLAI) determined by optical methods and the LAI determined by the direct destructive method and developed by allometric equations were used as response variables. LiDAR metrics and the laser penetration index (LPI) were used as predictor variables. The regression models of LPI and eLAI dependency and the LiDAR metrics and eLAI dependency showed coefficients of determination (R2) of 0.75 and 0.92, respectively; the advantage of using LiDAR metrics for more accurate modelling is demonstrated. The model for true LAI estimation reached a R2 of 0.88.
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35

Nel, Elizabeth M., and Carol A. Wessman. "Canopy transmittance models for estimating forest leaf area index." Canadian Journal of Forest Research 23, no. 12 (December 1, 1993): 2579–86. http://dx.doi.org/10.1139/x93-319.

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Leaf area index was estimated in old-growth and young post-fire coniferous forests in northwestern Colorado. A line quantum sensor was used to measure canopy transmittance at different solar zenith angles. Leaf area indices were estimated from canopy transmittance data according to three different models and were subsequently compared with leaf area indices derived from existing allometric equations. Of the three canopy transmittance methods evaluated, a Beer–Lambert model adjusted for diffuse light and solar zenith angle was in closest agreement with allometric leaf area index estimates (11.5% average difference), followed closely by the Beer–Lambert model (14.4% average difference). Leaf area index predicted by a one-dimensional inversion model did not agree well with allometric estimates (30.6% average difference). Differences in methods of data processing were found to have significant effects on final results. Subtraction of diffuse photosynthetically active radiation increased the leaf area indices. Calculation of leaf area index at each sampled point and determination of a final mean leaf area index approximated the allometrically derived values more closely than did derivation of leaf area index only once from an averaged gap-fraction value. Leaf area index estimates varied with sun angle.
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36

Kucharik, Christopher J., John M. Norman, and Stith T. Gower. "Measurements of branch area and adjusting leaf area index indirect measurements." Agricultural and Forest Meteorology 91, no. 1-2 (May 1998): 69–88. http://dx.doi.org/10.1016/s0168-1923(98)00064-1.

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37

Abdul Manan, Faid, Muhammad Buce Saleh, I. Nengah Surati Jaya, and Uus Saepul Mukarom. "Algorithm for assessing forest stand productivity index using leaf area index." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 3 (December 1, 2019): 1311. http://dx.doi.org/10.11591/ijeecs.v16.i3.pp1311-1319.

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This paper describes a development of an algorithm for assessing stand productivity by considering the stand variables. Forest stand productivity is one of the crucial information that required to establish the business plan for unit management at the beginning of forest planning activity. The main study objective is to find out the most significant and accurate variable combination to be used for assessing the forest stand productivity, as well as to develop productivity estimation model based on leaf area index. The study found the best stand variable combination in assessing stand productivity were density of poles (X2), volume of commercial tree having diameter at breast height (dbh) 20-40 cm (X16), basal area of commercial tree of dbh &gt;40 cm (X20) with Kappa Accuracy of 90.56% for classifying into 5 stand productivity classes. It was recognized that the examined algorithm provides excellent accuracy of 100% when the stand productivity was classified into only 3 classes. The best model for assessing the stand productivity index with leaf area index is y = 0.6214x - 0.9928 with R2= 0.71, where y is productivity index and x is leaf area index.
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38

Gower, Stith T., and John M. Norman. "Rapid Estimation of Leaf Area Index in Conifer and Broad-Leaf Plantations." Ecology 72, no. 5 (October 1991): 1896–900. http://dx.doi.org/10.2307/1940988.

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39

Xuan-Ran, LI, LIU Qi-Jing, CAI Zhe, and MA Ze-Qing. "SPECIFIC LEAF AREA AND LEAF AREA INDEX OF CONIFER PLANTATIONS IN QIANYANZHOU STATION OF SUBTROPICAL CHINA." Chinese Journal of Plant Ecology 31, no. 1 (2007): 93–101. http://dx.doi.org/10.17521/cjpe.2007.0012.

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40

Bouriaud, O., K. Soudani, and N. Bréda. "Leaf area index from litter collection: impact of specific leaf area variability within a beech stand." Canadian Journal of Remote Sensing 29, no. 3 (January 2003): 371–80. http://dx.doi.org/10.5589/m03-010.

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Ghadami Firouzabadi, Ali, Mahmoud Raeini-Sarjaz, Ali Shahnazari, and Hamid Zareabyaneh. "Non-destructive estimation of sunflower leaf area and leaf area index under different water regime managements." Archives of Agronomy and Soil Science 61, no. 10 (February 23, 2015): 1357–67. http://dx.doi.org/10.1080/03650340.2014.1002776.

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Klubertanz, T. H., L. P. Pedico, and R. E. Carlson. "Reliability of Yield Models of Defoliated Soybean Based on Leaf Area Index Versus Leaf Area Removed." Journal of Economic Entomology 89, no. 3 (June 1, 1996): 751–56. http://dx.doi.org/10.1093/jee/89.3.751.

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43

Klima, K., B. Wiśniowska-Kielian, and A. Lepiarczyk. "The interdependence between the leaf area index value and soil-protecting effectiveness of selected plants." Plant, Soil and Environment 62, No. 4 (June 6, 2016): 151–56. http://dx.doi.org/10.17221/639/2015-pse.

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44

Ramirez-Garcia, J., P. Almendros, and M. Quemada. "  Ground cover and leaf area index relationship in a grass, legume and crucifer crop." Plant, Soil and Environment 58, No. 8 (August 21, 2012): 385–90. http://dx.doi.org/10.17221/195/2012-pse.

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Canopy characterization is essential for describing the interaction of a crop with its environment. The goal of this work was to determine the relationship between leaf area index (LAI) and ground cover (GC) in a grass, a legume and a crucifer crop, and to assess the feasibility of using these relationships as well as LAI-2000 readings to estimate LAI. Twelve plots were sown with either barley (Hordeum vulgare L.), vetch (Vicia sativa L.), or rape (Brassica napus L.). On 10 sampling dates the LAI (both direct and LAI-2000 estimations), fraction intercepted of photosynthetically active radiation (FIPAR) and GC were measured. Linear and quadratic models fitted to the relationship between the GC and LAI for all of the crops, but they reached a plateau in the grass when the LAI &gt; 4. Before reaching full cover, the slope of the linear relationship between both variables was within the range of 0.025 to 0.030. The LAI-2000 readings were linearly correlated with the LAI but they tended to overestimation. Corrections based on the clumping effect reduced the root mean square error of the estimated LAI from the LAI-2000 readings from 1.2 to less than 0.50 for the crucifer and the legume, but were not effective for barley. &nbsp;
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Jůzl, M., and M. Štefl. "The effect of leaf area index on potatoes yield in soils contaminated by some heavy metals." Plant, Soil and Environment 48, No. 7 (December 21, 2011): 298–306. http://dx.doi.org/10.17221/4369-pse.

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A&nbsp;method of growth analysis was used to evaluate the yield results in experiments conducted during years 1999&ndash;2001 on School co-operative farm in Žabčice. In sequential terms of sampling from two potato varieties with different duration of growing season, the effect of leaf area index (L, LAI), on yield of tubers in soils contaminated by cadmium, arsine and beryllium, was evaluated. From a&nbsp;growers view the phytotoxic influence on development of assimilatory apparatus and yields during the growth of a&nbsp;very-early variety Rosara and a&nbsp;medium-early Korela were evaluated. These varieties were grown under field conditions in soils contaminated by graded levels of cadmium, arsenic and beryllium. The yields of tubers were positively influenced by duration of growing season and increased of leaf area index during three experimental years. On the contrary, graded levels of heavy metals had negative influence on both chosen varieties. The highest phytotoxic influence was recorded of arsine and the lowest of cadmium. Significant influence of arsenic and beryllium on size of leaf area index in the highest applied variants was found. The influence of experimental years on tuber yields was also statistically significant.
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Khosravi, S., M. Namiranian, H. Ghazanfari, and A. Shirvani. " Estimation of leaf area index and assessment of its allometric equations in oak forests: Northern Zagros, Iran." Journal of Forest Science 58, No. 3 (March 27, 2012): 116–22. http://dx.doi.org/10.17221/18/2011-jfs.

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The focus of the present study is the estimation of leaf area index (LAI) and the assessment of allometric equations for predicting the leaf area of Lebanon oaks (Quercus libani Oliv.) in Iran&rsquo;s northern Zagros forests. To that end, 50 oak trees were randomly selected and their biophysical parameters were measured. Then, on the basis of destructive sampling of the oak trees, their specific leaf area (SLA) and leaf area were measured. The results showed that SLA and LAI of the Lebanon oaks were 136.9 cm&middot;g<sup>&ndash;1 </sup>and 1.99, respectively. Among all the parameters we measured, the crown volume exhibited the highest correlation with LAI (r<sup>2</sup> = 0.65). The easily measured tree parameters such as diameter at breast height did not show a high correlation with leaf area (r<sup>2</sup> = 0.36). Our obtained moderate correlations in the allometric equations could be due to the fact that branches of these trees had been pollarded by the local people when the branches were only 3 or 4 years old; therefore, the natural structure of the crowns in these trees might have been damaged. &nbsp;
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Alchemi, P. J. K., and S. Jamin. "Impact Of Pestalotiopsis Leaf Fall Disease On Leaf Area Index and Rubber Plant Production." IOP Conference Series: Earth and Environmental Science 995, no. 1 (April 1, 2022): 012030. http://dx.doi.org/10.1088/1755-1315/995/1/012030.

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Abstract Currently, Pestalotiopsis leaf fall disease caused by the fungus Pestalotiopsis microspora is commonly found in Indonesian rubber plantations. The rubber defoliation period usually occurs for 1 month as a response to drought during the dry season. However, due to this disease, the rubber defoliation period occurs gradually with an earlier fall. Leaf fall can cause a decrease in the number of plant canopy which affects the leaf area index and latex production. Therefore, this study was carried out to examine the effect of Pestalotiopsis leaf fall disease on the decrease in leaf area index and latex production. The study was carried out at the Experimental Garden of the Indonesian Rubber Research Institute, Sembawa, South Sumatra by observing disease severity in RRIC 100 and GT 1 clones, measuring leaf area index, and observing latex production for 1 year. The results showed that there was a strong correlation between an increase in the Pestalotiopsis leaf fall disease severity and a decrease in leaf area index. In addition, the decrease in leaf area index affects the decrease in latex production.
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Baez-Gonzalez, Alma Delia, James R. Kiniry, Stephan J. Maas, Mario L. Tiscareno, Jaime Macias C., Jose L. Mendoza, Clarence W. Richardson, Jaime Salinas G., and Juan R. Manjarrez. "Large-Area Maize Yield Forecasting Using Leaf Area Index Based Yield Model." Agronomy Journal 97, no. 2 (March 2005): 418–25. http://dx.doi.org/10.2134/agronj2005.0418.

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Liu, Fan, Chuankuan Wang, and Xingchang Wang. "Sampling protocols of specific leaf area for improving accuracy of the estimation of forest leaf area index." Agricultural and Forest Meteorology 298-299 (March 2021): 108286. http://dx.doi.org/10.1016/j.agrformet.2020.108286.

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Penner, Margaret, and Godelieve Deblonde. "The relationship between leaf area and basal area growth in jack and red pine trees." Forestry Chronicle 72, no. 2 (April 1, 1996): 170–75. http://dx.doi.org/10.5558/tfc72170-2.

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Relationships between leaf area and sapwood area, sapwood area and basal area, and leaf area and basal area growth are determined for jack pine and red pine. The relationships vary with species and stand origin. Growth efficiency (basal area growth per unit leaf area) is relatively independent of tree size under all but the densest conditions. Observed changes in the leaf area to leaf mass ratio from July to October indicate that allometric relationships vary seasonally. A procedure is outlined for obtaining estimates of stand leaf area index (LAI). These estimates may be used to calibrate instruments that measure LAI and, subsequently, to predict forest productivity. Key words: leaf area index, basal area, growth efficiency, red pine, jack pine, sapwood area
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