Journal articles on the topic 'Canopy'

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1

Chopping, Mark. "CANAPI: canopy analysis with panchromatic imagery." Remote Sensing Letters 2, no. 1 (March 2011): 21–29. http://dx.doi.org/10.1080/01431161.2010.486805.

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2

Arevalo, Jose Ramon, J. D. Delgado, and J. M. Fernandez-Palacios. "Regeneration of potential laurel forest under a native canopy vs. exotic canopy, Tenerife (Canary Islands)." Forest Systems 20, no. 2 (July 10, 2011): 255. http://dx.doi.org/10.5424/fs/2011202-10921.

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3

Jun-Wu Zhai, Jun-Wu Zhai, Yu-Chen Tian Jun-Wu Zhai, Wen-Tao Li Yu-Chen Tian, and Kun Liang Wen-Tao Li. "Canopy-MMD Text Clustering Algorithm Based on Simulated Annealing and Canopy Optimization." 電腦學刊 34, no. 1 (February 2023): 075–86. http://dx.doi.org/10.53106/199115992023023401006.

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<p>Aiming at the problems that traditional K-means text clustering cannot automatically determine the number of clusters and is sensitive to initial cluster centers, this paper proposes a Canopy-MMD text clustering algorithm based on simulated annealing and silhouette coefficient optimization. The algorithm uses the simulated annealing algorithm combined with the silhouette coefficient to optimize the Canopy algorithm to find the optimal number of clusters, and uses the optimal number of clusters to determine the scale coefficient in the MMD algorithm, and finally achieves a better text clustering effect. The Sohu News dataset of Sogou Lab is experimentally analyzed and compared with the clustering results obtained by traditional K-means and algorithms in the literature. The experimental results show that the clustering performance of the algorithm is better than the traditional K-means algorithm and the algorithm in the literature, and the accuracy, precision, recall and F value are improved by 8.02%, 8.91%, 8.02%, 9.51% compared with the traditional K-means algorithm, which can be widely used in fields such as text mining, knowledge graph and natural language processing.</p> <p>&nbsp;</p>
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4

Luis, V. C., M. S. Jiménez, D. Morales, J. Kucera, and G. Wieser. "Canopy transpiration of a Canary Islands pine forest." Agricultural and Forest Meteorology 135, no. 1-4 (December 2005): 117–23. http://dx.doi.org/10.1016/j.agrformet.2005.11.009.

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5

Brenner, Brian. "The Canopy." Leadership and Management in Engineering 10, no. 1 (January 2010): 41–42. http://dx.doi.org/10.1061/(asce)lm.1943-5630.0000037.

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6

Nadkarni, Nalini M., and Geoffrey Parker. "Canopy network." Nature 366, no. 6455 (December 1993): 502. http://dx.doi.org/10.1038/366502c0.

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7

STORK, N. "Canopy science." Trends in Ecology & Evolution 20, no. 6 (June 2005): 284. http://dx.doi.org/10.1016/j.tree.2005.03.016.

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8

Grosset, D. G. "Words (canopy)." BMJ 297, no. 6655 (October 22, 1988): 1047. http://dx.doi.org/10.1136/bmj.297.6655.1047-a.

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9

McDonald, Clement J., J. Marc Overhage, Paul R. Dexter, Lonnie Blevins, Jim Meeks-Johnson, Jeffrey G. Suico, Mark C. Tucker, and Gunther Schadow. "Canopy Computing." JAMA 280, no. 15 (October 21, 1998): 1325. http://dx.doi.org/10.1001/jama.280.15.1325.

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10

J. L. Hatfield, D. F. Wanjura, and G. L. Barker. "Canopy Temperature Response to Water Stress under Partial Canopy." Transactions of the ASAE 28, no. 5 (1985): 1607–11. http://dx.doi.org/10.13031/2013.32485.

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11

Nelson, Ross. "Modeling forest canopy heights: The effects of canopy shape." Remote Sensing of Environment 60, no. 3 (June 1997): 327–34. http://dx.doi.org/10.1016/s0034-4257(96)00214-3.

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12

White, Hilary. "Under a canopy." Early Years Educator 22, no. 11 (June 2, 2021): S14—S15. http://dx.doi.org/10.12968/eyed.2021.22.11.s14.

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Use the rainforest as inspiration for investigating the colours of nature and creating a variety of different patterns and textures. Use creative resources to help children understand why it must be protected.
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13

L. Springer, Brian. "Acoustical Canopy System." Journal of the Acoustical Society of America 130, no. 5 (2011): 3176. http://dx.doi.org/10.1121/1.3662360.

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14

Okri, Ben. "Canopy: A Stoku." Callaloo 38, no. 5 (2015): 1027–28. http://dx.doi.org/10.1353/cal.2015.0175.

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15

Anderson, Elijah. "The Cosmopolitan Canopy." ANNALS of the American Academy of Political and Social Science 595, no. 1 (September 2004): 14–31. http://dx.doi.org/10.1177/0002716204266833.

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16

Cato, Beth. "Canopy of skulls." Nature 495, no. 7439 (March 2013): 134. http://dx.doi.org/10.1038/495134a.

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17

Nizigama, Isaac. "The Sacred Canopy." Studies in Religion/Sciences Religieuses 45, no. 1 (February 26, 2016): 6–25. http://dx.doi.org/10.1177/0008429815622745.

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Peter L. Berger’s sociology of religion is one of the most studied and quoted in the contemporary social science of religions. Nevertheless, it is also one of the most discussed, notably because of the changes of position by the author with regard to his thought on the secularization of the modern world, and on the relationship between his theses of a sociological nature and his reflections on Protestant theology. The present article questions his global epistemological framework by placing that problematic within the framework of the criticisms which have been directed at ‘absolute functionalism,’ notably by the structuralists or moderate functionalists. By linking it with the prospect of going beyond the opposition between methodological holism and methodological individualism and between substantivism and functionalism, we propose a multidimensional approach to the religious, which seems to lead to a better understanding of the latter in its transformations and metamorphoses into modernity.
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18

DEERING, D. W., and T. F. ECK. "Plant canopy radiance." International Journal of Remote Sensing 8, no. 6 (June 1987): 797–98. http://dx.doi.org/10.1080/01431168708948690.

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19

Ryan, M. G. "Canopy processes research." Tree Physiology 22, no. 15-16 (November 1, 2002): 1035–43. http://dx.doi.org/10.1093/treephys/22.15-16.1035.

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20

Wesseling-Perry, Katherine. "The BRC Canopy." American Journal of Pathology 184, no. 4 (April 2014): 924–26. http://dx.doi.org/10.1016/j.ajpath.2014.01.004.

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21

McLaurin, Wayne J., and Stanley J. Kays. "SWEETPOTATO CANOPY GEOMETRY." HortScience 28, no. 5 (May 1993): 458a—458. http://dx.doi.org/10.21273/hortsci.28.5.458a.

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The sweetpotato, unlike most vegetable crops, exhibits a vining growth habit where vertical development is sacrificed for rapid radial expansion. Considerable genetic diversity is present in vine length within the sweetpotato genepool. To test the relationship between the degree of vining (land area covered during the growing season) and yield, 5 vine length types (dwarf, bunch, normal, long and very long) were grafted on the same root stock (`Jewel'). At harvest, canopy diameter and area, root fwt and number, total vine length, and number of vines, leaves, missing leaves, nodes and flowers were determined as well as root, vine, leaf, petiole and flower dwt. Individual parameters were related to storage root development and harvest index. Total vine length ranged from 5.0m to 73.8m/plant, while vine number varied from 12.6 to 117.8 vines/plant. The total number of leaves/plant varied from 595 to 2680 while the percent leaf loss ranged from 17 to 38%. Root yield (fwt) was lowest for the dwarf vine type (593 g/plant) alnd highest for the longest vine type (2716 g/plant).
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22

Hardiman, Brady, Gil Bohrer, Christopher Gough, and Peter Curtis. "Canopy Structural Changes Following Widespread Mortality of Canopy Dominant Trees." Forests 4, no. 3 (July 8, 2013): 537–52. http://dx.doi.org/10.3390/f4030537.

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23

Kim, Sunwoo, Sylvie Lorente, and Adrian Bejan. "Vascularized materials: Tree-shaped flow architectures matched canopy to canopy." Journal of Applied Physics 100, no. 6 (September 15, 2006): 063525. http://dx.doi.org/10.1063/1.2349479.

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24

Paletto, Alessandro, and Vittorio Tosi. "Forest canopy cover and canopy closure: comparison of assessment techniques." European Journal of Forest Research 128, no. 3 (February 21, 2009): 265–72. http://dx.doi.org/10.1007/s10342-009-0262-x.

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25

Tombesi, S., and D. Farinelli. "Canopy management in super high-density olive orchards: relationship between canopy light penetration, canopy size and productivity." Acta Horticulturae, no. 1177 (November 2017): 87–92. http://dx.doi.org/10.17660/actahortic.2017.1177.9.

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26

Arya*, Neeta, and Jeet Ram. "Influence of canopy cover on vegetation in P. roxburghii sarg (chir-pine) dominated forests in Uttarakhand Himalaya, India." International Journal of Bioassays 5, no. 06 (May 31, 2016): 4617. http://dx.doi.org/10.21746/ijbio.2016.06.006.

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Increasing anthropogenic pressure and dependence on plant products have led to widespread exploitation of natural forests in the Uttaranchal Himalaya. The present study was carried out to study the influence of canopy cover on tree, shrub and herb vegetation. For this three different canopy types, open canopy (<30%, cover), moderate canopy (30-60%, cover) and close canopy (>60%, cover) were identified in Pinus roxburghii (chir-pine) dominated forests. The study area is located between 290 20’and 290 30’ N latitude and 790 23’ and 790 42’ E longitude between 1350-2000m elevations in Uttarakhand a newly created hill state. Total tree density was high in close canopy sites basal area was greater in open canopy sites. Total shrub density varied from 26107 to 28560 shrub/ha. It was maximum for open canopy sites and minimum for moderate canopy sites. Total shrubs cover varied from 45.8 to 50.6%. Shrubs cover was maximum for moderate canopy sites and minimum for open canopy sites. Herbs density was greater in open canopy and total herbs cover was greater in close canopy. Tree and shrub diversity was high in close canopy sites and herbs diversity in open canopy sites.
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27

Elsherif, A., R. Gaulton, J. P. Mills, and E. Sharaf El Din. "MEASURING FOREST CANOPY WATER MASS IN THREE DIMENSIONS USING TERRESTRIAL LASER SCANNING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 13, 2023): 721–26. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-721-2023.

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Abstract. Canopy water mass is an important plant characteristic that can indicate the water status of vegetation. However, the parameter remains under-investigated because measuring it requires defoliating the canopy. This study introduced a non-destructive approach to estimate canopy water mass using terrestrial laser scanning data. Tree 3D models were generated from dual-wavelength TLS data for six forest canopies, then the models were utilized in estimating the canopy LAI, total leaf area, and vertical profiles of canopy leaf area. The estimates were then coupled with canopy equivalent water thickness estimates and vertical profiles of canopy water mass were generated. The results revealed some over- and underestimation in the estimated LAI, but the obtained accuracy was considered sufficient as leaf-on point clouds were used to generate the 3D models. The vertical profiles of canopy water mass showed that the leaf area distribution within the canopy, and the canopy architecture were the main parameters affecting the water mass distribution within the canopy, with mid canopy layers having higher water mass than the other canopy layers. This study showed the potential of TLS to estimate canopy water mass, but controlled experiments that include defoliating canopies are still needed for a direct and accurate validation of the TLS estimates of canopy water mass.
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28

Delgado, Juan D., Natalia L. Arroyo, José R. Arévalo, and José M. Fernández-Palacios. "Edge effects of roads on temperature, light, canopy cover, and canopy height in laurel and pine forests (Tenerife, Canary Islands)." Landscape and Urban Planning 81, no. 4 (July 2007): 328–40. http://dx.doi.org/10.1016/j.landurbplan.2007.01.005.

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29

Monje, Oscar, and Bruce Bugbee. "Radiometric Method for Determining Canopy Stomatal Conductance in Controlled Environments." Agronomy 9, no. 3 (February 27, 2019): 114. http://dx.doi.org/10.3390/agronomy9030114.

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Canopy stomatal conductance is a key physiological factor controlling transpiration from plant canopies, but it is extremely difficult to determine in field environments. The objective of this study was to develop a radiometric method for calculating canopy stomatal conductance for two plant species—wheat and soybean from direct measurements of bulk surface conductance to water vapor and the canopy aerodynamic conductance in controlled-environment chambers. The chamber provides constant net radiation, temperature, humidity, and ventilation rate to the plant canopy. In this method, stepwise changes in chamber CO2 alter canopy temperature, latent heat, and sensible heat fluxes simultaneously. Sensible heat and the radiometric canopy-to-air temperature difference are computed from direct measurements of net radiation, canopy transpiration, photosynthesis, radiometric temperature, and air temperature. The canopy aerodynamic conductance to the transfer of water vapor is then determined from a plot of sensible heat versus radiometric canopy-to-air temperature difference. Finally, canopy stomatal conductance is calculated from canopy surface and aerodynamic conductances. The canopy aerodynamic conductance was 5.5 mol m−2 s−1 in wheat and 2.5 mol m−2 s−1 in soybean canopies. At 400 umol mol−1 of CO2 and 86 kPa atmospheric pressure, canopy stomatal conductances were 2.1 mol m−2 s−1 for wheat and 1.1 mol m−2 s−1 for soybean, comparable to canopy stomatal conductances reported in field studies. This method measures canopy aerodynamic conductance in controlled-environment chambers where the log-wind profile approximation does not apply and provides an improved technique for measuring canopy-level responses of canopy stomatal conductance and the decoupling coefficient. The method was used to determine the response of canopy stomatal conductance to increased CO2 concentration and to determine the sensitivity of canopy transpiration to changes in canopy stomatal conductance. These responses are useful for improving the prediction of ecosystem-level water fluxes in response to climatic variables.
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30

Ouyang, Jingyun, Roberta De Bei, Sigfredo Fuentes, and Cassandra Collins. "UAV and ground-based imagery analysis detects canopy structure changes after canopy management applications." OENO One 54, no. 4 (November 23, 2020): 1093–103. http://dx.doi.org/10.20870/oeno-one.2020.54.4.3647.

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Aim: To analyse unmanned aerial vehicle (UAV)-based imagery to assess canopy structural changes after the application of different canopy management practices in the vineyard.Methods and results: Four different canopy management practices: i–ii) leaf removal within the bunch zone (eastern side/both eastern and western sides), iii) bunch thinning and iv) shoot trimming were applied to grapevines at veraison, in a commercial Cabernet-Sauvignon vineyard in McLaren Vale, South Australia. UAV-based imagery captures were taken: i) before the canopy treatments, ii) after the treatments and iii) at harvest to assess the treatment outcomes. Canopy volume, projected canopy area and normalized difference vegetation index (NDVI) were derived from the analysis of RGB and multispectral imagery collected using the UAV. Plant area index (PAI) was calculated using the smartphone app VitiCanopy as a ground-based measurement for comparison with UAV-derived measurements. Results showed that all three types of UAV-based measurements detected changes in the canopy structure after the application of canopy management practices, except for the bunch thinning treatment. As expected, ground-based PAI was the only technique to effectively detect internal canopy structure changes caused by bunch thinning. Canopy volume and PAI were found to better detect variations in canopy structure compared to NDVI and projected canopy area. The latter were negatively affected by the interference of the trimmed shoots left on the ground.Conclusions: UAV-based tools can provide accurate assessments to some canopy management outcomes at the vineyard scale. Among different UAV-based measurements, canopy volume was more sensitive to changes in canopy structure, compared to NDVI and projected canopy area, and demonstrated a greater potential to assess the outcomes of a range of canopy management practices. Significance and impact of the study: Canopy management practices are widely applied to regulate canopy growth, improve grape quality and reduce disease pressure in the bunch zone. Being able to detect major changes in canopy structure, with some limitations when the practice affects the internal structure (i.e., bunch thinning), UAV-based imagery analysis can be used to measure the outcome of common canopy management practices and it can improve the efficiency of vineyard management.
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31

Sands, PJ. "Modelling Canopy Production. I. Optimal Distribution of Photosynthetic Resources." Functional Plant Biology 22, no. 4 (1995): 593. http://dx.doi.org/10.1071/pp9950593.

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On the basis of detailed numerical simulations, Field (1983. Oecologia 56, 341-347) stated that total canopy photosynthesis will be a maximum for a fixed total canopy leaf nitrogen provided the derivative δA/δN, where A is photosynthetic rate and N is leaf nitrogen concentration, has the same value throughout the canopy. This paper uses the calculus of variations to formally prove Field's assertion. It shows that if the single-leaf light response is a first-degree homogeneous function of both light-saturated photosynthetic rate Amax and intensity I of photosynthetically active radiation and if Amax is linearly related to N, then the optimal distribution of leaf nitrogen is linearly related to the decline in I with canopy depth, and Amax is proportional to this decline. The nature of photosynthetic gains due to optimisation of canopy nitrogen distribution is illustrated numerically for a simple model canopy. It is found that, for canopies with fixed mean leaf nitrogen, canopy photosynthesis is approximately proportional to canopy leaf area index (LAI), and the gain due to canopy optimisation compared with a uniform canopy is small for shallow canopies but pronounced for deep canopies. It is also found that, for canopies with fixed total leaf nitrogen, there is a canopy LAI which maximises canopy photosynthesis, and that this LAI and the corresponding canopy photosynthesis are approximately proportional to total canopy nitrogen.
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32

Prasolova, Nina V., Zhihong Xu, Graham D. Farquhar, Paul G. Saffigna, and Mark J. Dieters. "Canopy carbon and oxygen isotope composition of 9-year-old hoop pine families in relation to seedling carbon isotope composition, growth, field growth performance, and canopy nitrogen concentration." Canadian Journal of Forest Research 31, no. 4 (April 1, 2001): 673–81. http://dx.doi.org/10.1139/x00-207.

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Carbon isotope composition (δ13C), oxygen isotope composition (δ18O), and nitrogen concentration (Nmass) of branchlet tissue at two canopy positions were assessed for glasshouse seedlings and 9-year-old hoop pine (Araucaria cunninghamii Ait. ex D. Don) trees from 22 open-pollinated families grown in 5 blocks of a progeny test at a water-limited and nitrogen-deficient site in southeastern Queensland, Australia. Significant variations in canopy δ13C, δ18O, and Nmass existed among the 9-year-old hoop pine families, with a heritability estimate of 0.72 for branchlet δ13C from the upper inner canopy position. There was significant variation in canopy δ13C of glasshouse seedlings between canopy positions and among the families, with a heritability estimate of 0.66. The canopy δ13C was positively related to canopy Nmass only for the upper outer crown in the field (R = 0.62, p < 0.001). Phenotypic correlations existed between tree height and canopy δ13C (R = 0.37–0.41, p < 0.001). Strong correlations were found between family canopy δ13C at this site and those at a wetter site and between field canopy δ13C and glasshouse seedling δ13C. The mechanisms of the variation in canopy δ13C are discussed in relation to canopy photosynthetic capacity as reflected in the Nmass and stomatal conductance as indexed by canopy δ18O.
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33

Lamm, Freddie R., James P. Bordovsky, and Terry A. Howell Sr. "A Review of In-Canopy and Near-Canopy Sprinkler Irrigation Concepts." Transactions of the ASABE 62, no. 5 (2019): 1355–64. http://dx.doi.org/10.13031/trans.13229.

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Abstract. The use of in-canopy and near-canopy sprinkler application with mechanical-move systems is prevalent in the U.S. Great Plains. These systems can reduce evaporative losses by nearly 15%, but they introduce a much greater potential for irrigation non-uniformity and other water losses. This article is a review of these application technologies for mechanical-move sprinkler irrigation systems that have been widely adopted in the region, where irrigation capacities are typically less than those required to meet “fully irrigated” crop water demand and there is limited seasonal precipitation. Close attention to the design, installation, management, and operating guidelines for these systems can prevent many of the non-uniformity and water loss issues that reduce system performance and crop water productivity. Keywords: Center pivot, In-canopy sprinkler application, LEPA, LESA, LPIC, MESA, PARM, Sprinkler irrigation.
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34

Gara, Tawanda W., Andrew K. Skidmore, Roshanak Darvishzadeh, and Tiejun Wang. "Leaf to canopy upscaling approach affects the estimation of canopy traits." GIScience & Remote Sensing 56, no. 4 (October 30, 2018): 554–75. http://dx.doi.org/10.1080/15481603.2018.1540170.

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35

Song, Bo, Jiquan Chen, Paul V. Desander, David D. Reed, Gay A. Bradshaw, and Jerry F. Franklin. "Modeling canopy structure and heterogeneity across scales: From crowns to canopy." Forest Ecology and Management 96, no. 3 (September 1997): 217–29. http://dx.doi.org/10.1016/s0378-1127(97)00021-2.

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36

Barradas, Victor L., Hamlyn G. Jones, and Jerry A. Clark. "Sunfleck dynamics and canopy structure in a Phaseolus vulgaris L. canopy." International Journal of Biometeorology 42, no. 1 (September 9, 1998): 34–43. http://dx.doi.org/10.1007/s004840050081.

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37

Jacobs, A. F. G., J. H. van Boxel, and R. H. Shaw. "The dependence of canopy layer turbulence on within-canopy thermal stratification." Agricultural and Forest Meteorology 58, no. 3-4 (April 1992): 247–56. http://dx.doi.org/10.1016/0168-1923(92)90064-b.

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38

Sauer, T. J., and J. M. Norman. "Simulated canopy microclimate using estimated below-canopy soil surface transfer coefficients." Agricultural and Forest Meteorology 75, no. 1-3 (June 1995): 135–60. http://dx.doi.org/10.1016/0168-1923(94)02208-2.

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39

Sola-Guirado, Rafael R., David Ceular-Ortiz, and Jesús A. Gil-Ribes. "Automated system for real time tree canopy contact with canopy shakers." Computers and Electronics in Agriculture 143 (December 2017): 139–48. http://dx.doi.org/10.1016/j.compag.2017.10.011.

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40

Chen, Xuelong, William J. Massman, and Zhongbo Su. "A Column Canopy‐Air Turbulent Diffusion Method for Different Canopy Structures." Journal of Geophysical Research: Atmospheres 124, no. 2 (January 17, 2019): 488–506. http://dx.doi.org/10.1029/2018jd028883.

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41

Kikuzawa, Kihachiro, Makoto Yagi, Yuji Ohto, Kiyoshi Umeki, and Martin J. Lechowicz. "Canopy ergodicity: can a single leaf represent an entire plant canopy?" Plant Ecology 202, no. 2 (August 29, 2008): 309–23. http://dx.doi.org/10.1007/s11258-008-9486-y.

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42

Zimba, Henry, Miriam Coenders-Gerrits, Banda Kawawa, Hubert Savenije, Imasiku Nyambe, and Hessel Winsemius. "Variations in Canopy Cover and Its Relationship with Canopy Water and Temperature in the Miombo Woodland Based on Satellite Data." Hydrology 7, no. 3 (August 16, 2020): 58. http://dx.doi.org/10.3390/hydrology7030058.

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Understanding the canopy cover relationship with canopy water content and canopy temperature in the Miombo ecosystem is important for studying the consequences of climate change. To better understand these relationships, we studied the satellite data-based land surface temperature (LST) as proxy for canopy temperature, leaf area index (LAI), and the normalized difference vegetation index (NDVI) as proxies for canopy cover. Meanwhile, the normalized difference infrared index (NDII) was used as a proxy for canopy water content. We used several statistical approaches including the correlated component regression linear model (CCR.LM) to understand the relationships. Our results showed that the most determinant factor of variations in the canopy cover was the interaction between canopy water content (i.e., NDII) and canopy temperature (i.e., LST) with coefficients of determination (R2) ranging between 0.67 and 0.96. However, the coefficients of estimates showed the canopy water content (i.e., NDII) to have had the largest percentage of the interactive effect on the variations in canopy cover regardless of the proxy used i.e., LAI or NDVI. From 2009–2018, the NDII (proxy for canopy water content) showed no significant (at alpha level 0.05) trend. However, there was a significant upward trend in LST (proxy for canopy temperature) with a magnitude of 0.17 °C/year. Yet, the upward trend in LST did not result in significant (at alpha level 0.05) downward changes in canopy cover (i.e., proxied by LAI and NDVI). This result augments the observed least determinant factor characterization of temperature (i.e., LST) on the variations in canopy cover as compared to the vegetation water content (i.e., NDII).
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43

Wang, Yujie, and Christian Frankenberg. "On the impact of canopy model complexity on simulated carbon, water, and solar-induced chlorophyll fluorescence fluxes." Biogeosciences 19, no. 1 (January 3, 2022): 29–45. http://dx.doi.org/10.5194/bg-19-29-2022.

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Abstract. Lack of direct carbon, water, and energy flux observations at global scales makes it difficult to calibrate land surface models (LSMs). The increasing number of remote-sensing-based products provide an alternative way to verify or constrain land models given their global coverage and satisfactory spatial and temporal resolutions. However, these products and LSMs often differ in their assumptions and model setups, for example, the canopy model complexity. The disagreements hamper the fusion of global-scale datasets with LSMs. To evaluate how much the canopy complexity affects predicted canopy fluxes, we simulated and compared the carbon, water, and solar-induced chlorophyll fluorescence (SIF) fluxes using five different canopy complexity setups from a one-layered canopy to a multi-layered canopy with leaf angular distributions. We modeled the canopy fluxes using the recently developed land model by the Climate Modeling Alliance, CliMA Land. Our model results suggested that (1) when using the same model inputs, model-predicted carbon, water, and SIF fluxes were all higher for simpler canopy setups; (2) when accounting for vertical photosynthetic capacity heterogeneity, differences between canopy complexity levels increased compared to the scenario of a uniform canopy; and (3) SIF fluxes modeled with different canopy complexity levels changed with sun-sensor geometry. Given the different modeled canopy fluxes with different canopy complexities, we recommend (1) not misusing parameters inverted with different canopy complexities or assumptions to avoid biases in model outputs and (2) using a complex canopy model with angular distribution and a hyperspectral radiation transfer scheme when linking land processes to remotely sensed spectra.
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44

Wu, Chunyan, Dongsheng Chen, Guowei Xia, Xiaomei Sun, and Shougong Zhang. "Response Characteristics of Photosynthetic Productivity to the Canopy Spatial Distribution Pattern of Larix kaempferi." Forests 14, no. 6 (June 6, 2023): 1171. http://dx.doi.org/10.3390/f14061171.

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The spatial distribution of the forest canopy plays an important role in the transpiration and photosynthetic capacity of trees, ultimately affecting their growth and biomass production. Despite its importance, how canopy photosynthetic productivity enhancement depends on canopy spatial distribution remains unclear. To address this knowledge gap, we conducted a study on Larix kaempferi (Lamb.) Carrière (L. kaempferi) plantations in Gansu, China, investigating the relationship between canopy height, leaf area, seasonal variations in canopy spatial distribution, and photosynthetic parameters. The results showed that the net photosynthetic rate, stomatal conductance, and transpiration rate of L. kaempferi increase with greater canopy height, while photosynthetically active radiation shows the opposite trend. Canopy photosynthetic productivity peaked in April, May, and June when the height in the canopy was 40%, followed by 20%, and then 30% from the perspective of spatiotemporal canopy spatial distribution. Maximum leaf area (10.7 m2) and photosynthesis productivity (919.6 mg·C·h−1) were observed when the height in the canopy ranged from 48%–59%. The changes increased sunlight exposure (75%–88%, 88%–100%) in different canopy areas. Additionally, there was a decrease in the amount of space covered by shade (25%–38%, 50%–63%, and 63%–75%), depending on the specific region within the canopy. By scientifically managing stand density, the canopy spatial distribution can be optimized for photosynthesis, resulting in maximum light interception rates, enhanced photosynthetic capacity, and reduced “non-functional canopy”. These findings offer effective and scientifically informed management strategies for the forestry industry. By optimizing the structure of the canopy, specifically in L. kaempferi, these strategies aim to maximize photosynthetic productivity.
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45

Ashapure, Akash, Jinha Jung, Anjin Chang, Sungchan Oh, Murilo Maeda, and Juan Landivar. "A Comparative Study of RGB and Multispectral Sensor-Based Cotton Canopy Cover Modelling Using Multi-Temporal UAS Data." Remote Sensing 11, no. 23 (November 23, 2019): 2757. http://dx.doi.org/10.3390/rs11232757.

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This study presents a comparative study of multispectral and RGB (red, green, and blue) sensor-based cotton canopy cover modelling using multi-temporal unmanned aircraft systems (UAS) imagery. Additionally, a canopy cover model using an RGB sensor is proposed that combines an RGB-based vegetation index with morphological closing. The field experiment was established in 2017 and 2018, where the whole study area was divided into approximately 1 x 1 m size grids. Grid-wise percentage canopy cover was computed using both RGB and multispectral sensors over multiple flights during the growing season of the cotton crop. Initially, the normalized difference vegetation index (NDVI)-based canopy cover was estimated, and this was used as a reference for the comparison with RGB-based canopy cover estimations. To test the maximum achievable performance of RGB-based canopy cover estimation, a pixel-wise classification method was implemented. Later, four RGB-based canopy cover estimation methods were implemented using RGB images, namely Canopeo, the excessive greenness index, the modified red green vegetation index and the red green blue vegetation index. The performance of RGB-based canopy cover estimation was evaluated using NDVI-based canopy cover estimation. The multispectral sensor-based canopy cover model was considered to be a more stable and accurately estimating canopy cover model, whereas the RGB-based canopy cover model was very unstable and failed to identify canopy when cotton leaves changed color after canopy maturation. The application of a morphological closing operation after the thresholding significantly improved the RGB-based canopy cover modeling. The red green blue vegetation index turned out to be the most efficient vegetation index to extract canopy cover with very low average root mean square error (2.94% for the 2017 dataset and 2.82% for the 2018 dataset), with respect to multispectral sensor-based canopy cover estimation. The proposed canopy cover model provides an affordable alternate of the multispectral sensors which are more sensitive and expensive.
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46

Lynch, Ryan L., Laura A. Brandt, Hongjun Chen, Danielle Ogurcak, Ikuko Fujisaki, and Frank J. Mazzotti. "Recruitment and Growth of Old World Climbing Fern in Hurricane-Caused Canopy Gaps." Journal of Fish and Wildlife Management 2, no. 2 (December 1, 2011): 199–206. http://dx.doi.org/10.3996/062011-jfwm-040.

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Abstract Following 2 y of severe hurricanes in 2004 and 2005, we examined the role of canopy gaps in promoting recruitment and growth of the exotic fern, Old World climbing fern Lygodium microphyllum (hereafter Lygodium), on tree islands of the Arthur R. Marshall Loxahatchee National Wildlife Refuge, Florida. We selected 12 sample tree islands, on which we placed three 1-m2 plots in a hurricane-caused canopy gap and three plots in an adjacent closed canopy area. Spore traps were placed in canopy gaps and closed canopy areas to quantify the number of spores reaching the forest floor on each island. In addition, in each plot occurrence and growth of Lygodium was measured across four height classes (recruitment class, understory, midstory, and canopy). We predicted that recruitment and growth of Lygodium would be higher in canopy gaps than in closed canopy areas. After 3 y of biannual monitoring, a significantly greater number of spores were foundin canopy gaps (4,804 spores·m2·d) than in closed canopy areas (4,288 spores·m2·d). Furthermore, we observed significantly greater recruitment and growth in canopy gaps compared with closed canopy areas in the recruitment class only. Presence of recruitment-class Lygodium in canopy gaps increased from four to five treatment areas and decreased from 1 to 0 treatment areas in closed canopy areas. These results suggest differences in recruitment and growth of Lygodium between canopy gaps and closed canopy areas on tree islands after severe hurricanes. However, despite the large number of spores in both canopy gaps and closed canopy areas, recruitment and growth were much lower than expected, with only two treatment areas having an average percent cover greater than 10% in any height class. If conducted within several years after a hurricane, focused monitoring efforts on hurricane-impacted tree islands may allow managers to detect and treat new infestations before they are able to overrun tree islands.
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47

Dow, R. L., N. L. Powell, and D. M. Porter. "Effects of Modification of the Plant Canopy Environment on Sclerotinia Blight of Peanut." Peanut Science 15, no. 1 (January 1, 1988): 1–5. http://dx.doi.org/10.3146/i0095-3679-15-1-1.

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Abstract The development of Sclerotinia blight, caused by Sclerotinia minor Jagger under various environmental conditions, was studied in field plots of peanuts (Arachis hypogaea L.). The peanut plant canopy was modified to produce desired environmental parameters. The modifications included the thinning of canopy foliage to allow air circulation that would decrease canopy humidity and the addition of water-filled troughs under an unthinned canopy that would increase humidity. Canopy relative humidity and soil moisture under the canopy was decreased by canopy thinning. Following infection by S. minor, the number of infection foci and disease development was reduced in the thinned canopy; however, thinning also reduced pod yield. Disease development was not increased, nor was yield affected by the addition of the water-filled troughs which increased humidity levels in the canopy. Soil moisture and canopy light interception were important variables in multiple linear regression models for the disease severity index and longest lesion length in the thinned and unthinned-trough plots.
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48

Andreu, Anne G., John I. Blake, and Stanley J. Zarnoch. "Estimating canopy fuel characteristics for predicting crown fire potential in common forest types of the Atlantic Coastal Plain, USA." International Journal of Wildland Fire 27, no. 11 (2018): 742. http://dx.doi.org/10.1071/wf18025.

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We computed four stand-level canopy stratum variables important for crown fire modelling – canopy cover, stand height, canopy base height and canopy bulk density – from forest inventory data. We modelled the relationship between the canopy variables and a set of common inventory parameters – site index, stem density, basal area, stand age or stand height – and number of prescribed burns. We used a logistic model to estimate canopy cover, a linear model to estimate the other canopy variables, and the information theoretic approach for model selection. Coefficients of determination across five forest groups were 0.72–0.91 for stand height, 0.36–0.83 for canopy base height, 0.39–0.80 for canopy cover, and 0.63–0.78 for canopy bulk density. We assessed crown fire potential (1) for several sets of environmental conditions in all seasons, and (2) with increasing age, density and number of prescribed burns using our modelled canopy bulk density and canopy base height variables and local weather data to populate the Crown Fire Initiation and Spread model. Results indicated that passive crown fire is possible in any season in Atlantic coastal plain pine stands with heavy surface fuel loads and active crown fire is most probable in infrequently burned, dense stands at low fuel moistures.
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49

Zhou, Huitao, Weidong Jia, Yong Li, and Mingxiong Ou. "Method for Estimating Canopy Thickness Using Ultrasonic Sensor Technology." Agriculture 11, no. 10 (October 16, 2021): 1011. http://dx.doi.org/10.3390/agriculture11101011.

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The accurate detection of canopy characteristics is the basis of precise variable spraying. Canopy characteristics such as canopy density, thickness and volume are needed to vary the pesticide application rate and adjust the spray flow rate and air supply volume. Canopy thickness is an important canopy dimension for the calculation of tree canopy volume in pesticide variable spraying. With regard to the phenomenon of ultrasonic waves with multiple reflections and the further analysis of echo signals, we found that there is a proportional relationship between the canopy thickness and echo interval time. In this paper, we propose a method to calculate canopy thickness using echo signals that come from ultrasonic sensors. To investigate the application of this method, we conducted a set of lab-based experiments with a simulated canopy. The results show that we can accurately estimate canopy thickness when the detection distance, canopy density, and canopy thickness range between 0.5and 1.5 m, 1.2 and 1.4, and 0.3and 0.6 m, respectively. The relative error between the estimated value and actual value of the simulated canopy thickness is no higher than 8.8%. To compare our lab results with trees in the field, we measured canopy thickness from three naturally occurring Osmanthus trees (Osmanthus fragrans Lour). The results showed that the mean relative errors of three Osmanthus trees are 19.2%, 19.4% and 18.8%, respectively. These results can be used to improve measurements for agricultural production that includes both orchards and facilities by providing a reference point for the precise application of variable spraying.
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Xu, X., C. Yi, and E. Kutter. "Stably stratified canopy flow in complex terrain." Atmospheric Chemistry and Physics 15, no. 13 (July 10, 2015): 7457–70. http://dx.doi.org/10.5194/acp-15-7457-2015.

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Abstract. Stably stratified canopy flow in complex terrain has been considered a difficult condition for measuring net ecosystem–atmosphere exchanges of carbon, water vapor, and energy. A long-standing advection error in eddy-flux measurements is caused by stably stratified canopy flow. Such a condition with strong thermal gradient and less turbulent air is also difficult for modeling. To understand the challenging atmospheric condition for eddy-flux measurements, we use the renormalized group (RNG) k–&amp;varepsilon; turbulence model to investigate the main characteristics of stably stratified canopy flows in complex terrain. In this two-dimensional simulation, we imposed persistent constant heat flux at ground surface and linearly increasing cooling rate in the upper-canopy layer, vertically varying dissipative force from canopy drag elements, buoyancy forcing induced from thermal stratification and the hill terrain. These strong boundary effects keep nonlinearity in the two-dimensional Navier–Stokes equations high enough to generate turbulent behavior. The fundamental characteristics of nighttime canopy flow over complex terrain measured by the small number of available multi-tower advection experiments can be reproduced by this numerical simulation, such as (1) unstable layer in the canopy and super-stable layers associated with flow decoupling in deep canopy and near the top of canopy; (2) sub-canopy drainage flow and drainage flow near the top of canopy in calm night; (3) upward momentum transfer in canopy, downward heat transfer in upper canopy and upward heat transfer in deep canopy; and (4) large buoyancy suppression and weak shear production in strong stability.
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