scholarly journals Canopy structure and topography jointly constrain the microclimate of human-modified tropical landscapes

2018 ◽  
Vol 24 (11) ◽  
pp. 5243-5258 ◽  
Author(s):  
Tommaso Jucker ◽  
Stephen R. Hardwick ◽  
Sabine Both ◽  
Dafydd M.O. Elias ◽  
Robert M. Ewers ◽  
...  
Keyword(s):  
Canopy Structure ◽  
Tropical Landscapes

Canopy structure at the edge of seagrass affects sea urchin distribution

2013 ◽  
Vol 485 ◽  
pp. 47-55 ◽  
Author(s):  
S Pinna ◽  
N Sechi ◽  
G Ceccherelli
Keyword(s):  
Sea Urchin ◽  
Canopy Structure

Population Persistence, Pollination Mutualisms, and Figs in Fragmented Tropical Landscapes

1998 ◽  
Vol 12 (6) ◽  
pp. 1416-1420 ◽  
Author(s):  
Nick A. Mawdsley ◽  
Stephen G. Compton ◽  
Robert J. Whittaker
Keyword(s):  
Population Persistence ◽  
Tropical Landscapes

Herbage accumulation, canopy structure and tiller morphology of marandu palisadegrass growing in open pasture and in silvopasture

2021 ◽  
Author(s):  
H. L. B. Nascimento ◽  
B. C. Pedreira ◽  
L. E. Sollenberger ◽  
D. H. Pereira ◽  
C. A. S. Magalhães ◽  
...  
Keyword(s):  
Canopy Structure

scholarly journals Effect of flag leaf length of erect panicle rice on the canopy structure and biomass production after heading

2021 ◽  
pp. 1-10
Author(s):  
Yuki Makino ◽  
Yoshihiro Hirooka ◽  
Koki Homma ◽  
Rintaro Kondo ◽  
Tian-Sheng Liu ◽  
...  
Keyword(s):  
Biomass Production ◽  
Flag Leaf ◽  
Canopy Structure ◽  
Leaf Length ◽  
Flag Leaf Length

scholarly journals Plant ecophysiological processes in spectral profiles: perspective from a deciduous broadleaf forest

2021 ◽  
Author(s):  
Hibiki M. Noda ◽  
Hiroyuki Muraoka ◽  
Kenlo Nishida Nasahara
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Spectral Reflectance ◽  
Canopy Structure ◽  
Terrestrial Ecosystems ◽  
Optical Remote Sensing ◽  
Canopy Reflectance ◽  
Broadleaf Forest ◽  
Leaf Optical Properties ◽  
Deciduous Broadleaf Forest

AbstractThe need for progress in satellite remote sensing of terrestrial ecosystems is intensifying under climate change. Further progress in Earth observations of photosynthetic activity and primary production from local to global scales is fundamental to the analysis of the current status and changes in the photosynthetic productivity of terrestrial ecosystems. In this paper, we review plant ecophysiological processes affecting optical properties of the forest canopy which can be measured with optical remote sensing by Earth-observation satellites. Spectral reflectance measured by optical remote sensing is utilized to estimate the temporal and spatial variations in the canopy structure and primary productivity. Optical information reflects the physical characteristics of the targeted vegetation; to use this information efficiently, mechanistic understanding of the basic consequences of plant ecophysiological and optical properties is essential over broad scales, from single leaf to canopy and landscape. In theory, canopy spectral reflectance is regulated by leaf optical properties (reflectance and transmittance spectra) and canopy structure (geometrical distributions of leaf area and angle). In a deciduous broadleaf forest, our measurements and modeling analysis of leaf-level characteristics showed that seasonal changes in chlorophyll content and mesophyll structure of deciduous tree species lead to a seasonal change in leaf optical properties. The canopy reflectance spectrum of the deciduous forest also changes with season. In particular, canopy reflectance in the green region showed a unique pattern in the early growing season: green reflectance increased rapidly after leaf emergence and decreased rapidly after canopy closure. Our model simulation showed that the seasonal change in the leaf optical properties and leaf area index caused this pattern. Based on this understanding we discuss how we can gain ecophysiological information from satellite images at the landscape level. Finally, we discuss the challenges and opportunities of ecophysiological remote sensing by satellites.

scholarly journals Laser scanning reveals bryophyte canopy structure

2005 ◽  
Vol 166 (2) ◽  
pp. 695-704 ◽  
Author(s):  
Steven K. Rice ◽  
Claudia Gutman ◽  
Nicholas Krouglicof
Keyword(s):  
Laser Scanning ◽  
Canopy Structure

scholarly journals Detection and Analysis of Degree of Maize Lodging Using UAV-RGB Image Multi-Feature Factors and Various Classification Methods

2021 ◽  
Vol 10 (5) ◽  
pp. 309
Author(s):  
Zixu Wang ◽  
Chenwei Nie ◽  
Hongwu Wang ◽  
Yong Ao ◽  
Xiuliang Jin ◽  
...  
Keyword(s):  
Crop Production ◽  
Accurate Determination ◽  
Management Strategies ◽  
Canopy Structure ◽  
Object Oriented ◽  
Texture Features ◽  
Sowing Date ◽  
Kappa Coefficient ◽  
Surface Model ◽  
Field Management

Maize (Zea mays L.), one of the most important agricultural crops in the world, which can be devastated by lodging, which can strike maize during its growing season. Maize lodging affects not only the yield but also the quality of its kernels. The identification of lodging is helpful to evaluate losses due to natural disasters, to screen lodging-resistant crop varieties, and to optimize field-management strategies. The accurate detection of crop lodging is inseparable from the accurate determination of the degree of lodging, which helps improve field management in the crop-production process. An approach was developed that fuses supervised and object-oriented classifications on spectrum, texture, and canopy structure data to determine the degree of lodging with high precision. The results showed that, combined with the original image, the change of the digital surface model, and texture features, the overall accuracy of the object-oriented classification method using random forest classifier was the best, which was 86.96% (kappa coefficient was 0.79). The best pixel-level supervised classification of the degree of maize lodging was 78.26% (kappa coefficient was 0.6). Based on the spatial distribution of degree of lodging as a function of crop variety, sowing date, densities, and different nitrogen treatments, this work determines how feature factors affect the degree of lodging. These results allow us to rapidly determine the degree of lodging of field maize, determine the optimal sowing date, optimal density and optimal fertilization method in field production.

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