scholarly journals Estimation of Leaf Area Index of Table Grapes Using a Digital Camera Equipped with Fisheye Lens and the Image Processing Software “Fiji-ImageJ”

2020 ◽  
Vol 19 (1) ◽  
pp. 83-88
Author(s):  
Michio Hamada ◽  
Mikio Shiraishi
Keyword(s):  
Image Processing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Digital Camera ◽  
Fisheye Lens ◽  
Area Index ◽  
Table Grapes ◽  
Processing Software ◽  
Image Processing Software

scholarly journals Automated computation of leaf area index from fruit trees using improved image processing algorithms applied to canopy cover digital photograpies

2016 ◽  
Vol 123 ◽  
pp. 195-202 ◽  
Author(s):  
Marco Mora ◽  
Felipe Avila ◽  
Marcos Carrasco-Benavides ◽  
Gonzalo Maldonado ◽  
Jeissy Olguín-Cáceres ◽  
...  
Keyword(s):  
Image Processing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Canopy Cover ◽  
Fruit Trees ◽  
Area Index ◽  
Image Processing Algorithms ◽  
Processing Algorithms

scholarly journals An automated procedure for estimating the leaf area index (LAI) of woodland ecosystems using digital imagery, MATLAB programming and its application to an examination of the relationship between remotely sensed and field measurements of LAI

2008 ◽  
Vol 35 (10) ◽  
pp. 1070 ◽  
Author(s):  
Sigfredo Fuentes ◽  
Anthony R. Palmer ◽  
Daniel Taylor ◽  
Melanie Zeppel ◽  
Rhys Whitley ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Gap Analysis ◽  
Digital Camera ◽  
Field Measurements ◽  
Accurate Estimation ◽  
Image Capture ◽  
Area Index ◽  
Modis Lai ◽  
Matlab Programming

Leaf area index (LAI) is one of the most important variables required for modelling growth and water use of forests. Functional–structural plant models use these models to represent physiological processes in 3-D tree representations. Accuracy of these models depends on accurate estimation of LAI at tree and stand scales for validation purposes. A recent method to estimate LAI from digital images (LAID) uses digital image capture and gap fraction analysis (Macfarlane et al. 2007b) of upward-looking digital photographs to capture canopy LAID (cover photography). After implementing this technique in Australian evergreen Eucalyptus woodland, we have improved the method of image analysis and replaced the time consuming manual technique with an automated procedure using a script written in MATLAB 7.4 (LAIM). Furthermore, we used this method to compare MODIS LAI values with LAID values for a range of woodlands in Australia to obtain LAI at the forest scale. Results showed that the MATLAB script developed was able to successfully automate gap analysis to obtain LAIM. Good relationships were achieved when comparing averaged LAID and LAIM (LAIM = 1.009 – 0.0066 LAID; R2 = 0.90) and at the forest scale, MODIS LAI compared well with LAID (MODIS LAI = 0.9591 LAID – 0.2371; R2 = 0.89). This comparison improved when correcting LAID with the clumping index to obtain effective LAI (MODIS LAI = 1.0296 LAIe + 0.3468; R2 = 0.91). Furthermore, the script developed incorporates a function to connect directly a digital camera, or high resolution webcam, from a laptop to obtain cover photographs and LAI analysis in real time. The later is a novel feature which is not available on commercial LAI analysis softwares for cover photography. This script is available for interested researchers.

Early regeneration conifer identification and competition cover assessment using airborne digital camera imagery

2000 ◽  
Vol 76 (6) ◽  
pp. 915-928 ◽  
Author(s):  
K. A. Haddow ◽  
D. J. King ◽  
D. A. Pouliot ◽  
D. G. Pitt ◽  
F. W. Bell
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Cover ◽  
Early Stage ◽  
Digital Camera ◽  
Vegetation Management ◽  
Forest Vegetation ◽  
Crop Species ◽  
Area Index ◽  
Forest Vegetation Management

The potential of low cost, high-resolution airborne digital camera imagery for use in early stage forest regeneration assessment was investigated. Airborne imagery with 2.5-cm pixel size was acquired near Sault Ste. Marie, Ontario, over a forest vegetation management research site to: i) evaluate capabilities for identification and stem counting of two-year old conifer crop species under leaf-off and leaf-on conditions using classification of spectral and textural image information, and ii) develop models relating vegetation cover parameters to image spectral and texture information. Results indicate strong potential for identification and counting of conifer trees when competing vegetation cover is low or in leaf-off condition. However, systematic decreases in class separability and conifer count accuracy were observed with increasing competition. In image modelling of competition Leaf Area Index and Cover, statistically significant relations were found using primarily spectral measures. Stratification by competition species improved model fits and included texture measures in some models. Key words: airborne remote sensing, forest vegetation management, regeneration, digital cameras, leaf area index, cover, tree classification

scholarly journals 3D Digital modeling for archeology using close range photogrammetry

2018 ◽  
Vol 162 ◽  
pp. 03027
Author(s):  
Abbas Khalaf ◽  
Tariq Ataiwe ◽  
Israa Mohammed ◽  
Ali Kareem
Keyword(s):  
Image Processing ◽  
Total Error ◽  
Three Dimensional ◽  
Digital Camera ◽  
Control Points ◽  
Close Range Photogrammetry ◽  
Digital Modeling ◽  
Close Range ◽  
Processing Software ◽  
Image Processing Software

This research is to evaluate the feasibility of applying three-dimensional modelling of the close-range photogrammetry in documenting archaeological monuments by using digital photogrammetry image processing software and digital consumer camera. The digital camera used was Nikon D3100, the processing software was (AgiSoft PhotoScan) and (ArcGIS, ArcScene extension). The study area was selected in the centre of Baghdad province by choosing one of the archeological monuments in it, namely the Abbasid alace. A set of camera locations represent the locations of the images, and as a result of the processing, 81 digital images were arranged in a sequence in which the results of this step were verified. The points cloud after processing were 1,082,617 points. Six control points were selected, used as distances constrained. The validity of the fixed location of the points can be ascertained by checking the data. The program provide the error and accuracy for each image, where a total error in the scale bar was 0.005253 meters, a total error of marks points was 0.010957 meters and the accuracy for all six points was 0.005 meters.

scholarly journals Estimation of Leaf Area Index by Using a Digital Camera in Delaware Grapes Cultivated in a Plastic Greenhouse

2012 ◽  
Vol 11 (2) ◽  
pp. 251-255
Author(s):  
Yoshinari Tateishi ◽  
Haruhiko Yamamoto ◽  
Kiyoshi Iwaya ◽  
Yasushi Tuchiya ◽  
Takao Kurahashi ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Digital Camera ◽  
Area Index

An Estimation of Crop Leaf Area Index Using Image Processing and GSM Technology

2015 ◽  
Vol 8 (1) ◽  
pp. 315-319 ◽  
Author(s):  
P. P. V. Sudheer ◽  
R. Tejas ◽  
M. R. Ebenezar Jebarani
Keyword(s):  
Image Processing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index

Leaf area index and standard error of pine forests estimated with common digital camera

2013 ◽  
Vol 21 (5) ◽  
pp. 638-644 ◽  
Author(s):  
Zhang-Hua XU ◽  
Jian LIU ◽  
Kun-Yong YU ◽  
Cong-Hong GONG ◽  
Wan-Jun XIE ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Standard Error ◽  
Digital Camera ◽  
Pine Forests ◽  
Area Index
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