Effect of check dams on riparian vegetation cover: A multiscale approach based on field measurements and satellite images for Leaf Area Index assessment

2019 ◽  
Vol 657 ◽  
pp. 827-838 ◽  
Author(s):  
Giovanni Francesco Ricci ◽  
Giovanni Romano ◽  
Vincenzo Leronni ◽  
Francesco Gentile
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Cover ◽  
Riparian Vegetation ◽  
Satellite Images ◽  
Field Measurements ◽  
Multiscale Approach ◽  
Area Index ◽  
Check Dams

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 Spatial variation and seasonal dynamics of leaf-area index in the arctic tundra-implications for linking ground observations and satellite images

2017 ◽  
Vol 12 (9) ◽  
pp. 095002 ◽  
Author(s):  
Sari Juutinen ◽  
Tarmo Virtanen ◽  
Vladimir Kondratyev ◽  
Tuomas Laurila ◽  
Maiju Linkosalmi ◽  
...  
Keyword(s):  
Spatial Variation ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Seasonal Dynamics ◽  
Satellite Images ◽  
Arctic Tundra ◽  
The Arctic ◽  
Area Index

Multiscale approach for fusing leaf area index estimates from multiple sensors

2007 ◽  
Author(s):  
Zhiqiang Xiao ◽  
Jindi Wang ◽  
Huawei Wan ◽  
Xiaoque Jiang ◽  
Kai Xu
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Multiscale Approach ◽  
Area Index ◽  
Multiple Sensors

scholarly journals Combination of Linear Regression Lines to Understand the Response of Sentinel-1 Dual Polarization SAR Data with Crop Phenology—Case Study in Miyazaki, Japan

2020 ◽  
Vol 12 (1) ◽  
pp. 189 ◽  
Author(s):  
Emal Wali ◽  
Masahiro Tasumi ◽  
Masao Moriyama
Keyword(s):  
Linear Regression ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Cover ◽  
Polynomial Regression ◽  
Backscattering Coefficient ◽  
Biophysical Parameters ◽  
Area Index ◽  
Dry Biomass ◽  
Green Vegetation

This study investigated the relationship between backscattering coefficients of a synthetic aperture radar (SAR) and the four biophysical parameters of rice crops—plant height, green vegetation cover, leaf area index, and total dry biomass. A paddy rice field in Miyazaki, Japan was studied from April to July of 2018, which is the rice cultivation season. The SAR backscattering coefficients were provided by Sentinel-1 satellite. Backscattering coefficients of two polarization settings—VH (vertical transmitting, horizontal receiving) and VV (vertical transmitting, vertical receiving)—were investigated. Plant height, green vegetation cover, leaf area index, and total dry biomass were measured at ground level, on the same dates as satellite image acquisition. Polynomial regression lines indicated relationships between backscattering coefficients and plant biophysical parameters of the rice crop. The biophysical parameters had stronger relationship to VH than to VV polarization. A disadvantage of adopting polynomial regression equations is that the equation can have two biophysical parameter solutions for a particular backscattering coefficient value, which prevents simple conversion from backscattering coefficients to plant biophysical parameters. To overcome this disadvantage, the relationships between backscattering coefficients and the plant biophysical parameters were expressed using a combination of two linear regression lines, one line for the first sub-period and the other for the second sub-period during the entire cultivation period. Following this approach, all four plant biophysical parameters were accurately estimated from the SAR backscattering coefficient, especially with VH polarization, from the date of transplanting to about two months, until the mid-reproductive stage. However, backscattering coefficients saturate after two months from the transplanting, and became insensitive to the further developments in plant biophysical parameters. This research indicates that SAR can effectively and accurately monitor rice crop biophysical parameters, but only up to the mid reproductive stage.

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