scholarly journals A Prior Knowledge-Based Method to Derivate High-Resolution Leaf Area Index Maps with Limited Field Measurements

2016 ◽  
Vol 9 (1) ◽  
pp. 13 ◽  
Author(s):  
Yuechan Shi ◽  
Jindi Wang ◽  
Jian Wang ◽  
Yonghua Qu
Keyword(s):  
High Resolution ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Prior Knowledge ◽  
Field Measurements ◽  
Area Index ◽  
Knowledge Based ◽  
Index Maps

scholarly journals A Comparison of Leaf Area Index Maps Derived from Multi-Sensor Optical Data Acquired over Agricultural Areas

2010 ◽  
Vol 5 (2) ◽  
pp. 167 ◽  
Author(s):  
Giuseppe Satalino ◽  
Francesco Mattia ◽  
Anna Balenzano ◽  
Michele Rinaldi ◽  
Sergio Ruggieri ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Optical Data ◽  
Area Index ◽  
Agricultural Areas ◽  
Index Maps

scholarly journals Wheat Leaf Area Index Prediction Using Data Fusion Based on High-Resolution Unmanned Aerial Vehicle Imagery

2021 ◽  
Author(s):  
Shuang Wu ◽  
Lei Deng ◽  
Lijie Guo ◽  
Yanjie Wu
Keyword(s):  
High Resolution ◽  
Data Fusion ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Unmanned Aerial Vehicle ◽  
Prediction Accuracy ◽  
Sensor Data ◽  
Support Vector ◽  
Area Index ◽  
Aerial Vehicle

Abstract Background: Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion.Methods: To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.Result: The results show that: (1) the soil background reduced the accuracy of the LAI prediction, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data improved LAI prediction accuracy and achieved the best accuracy (R2 = 0.815 and RMSE = 1.023). (3) When compared to other variables, 23 CHM, NRCT, NDRE, and BLUE are crucial for LAI estimation. Even the simple Multiple Linear Regression model could achieve high prediction accuracy (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction.Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.

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.

scholarly journals Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing

2020 ◽  
Vol 57 (7) ◽  
pp. 943-964
Author(s):  
Aleksi Räsänen ◽  
Sari Juutinen ◽  
Margaret Kalacska ◽  
Mika Aurela ◽  
Pauli Heikkinen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Biomass Estimation ◽  
Area Index
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