Leaf Area Index (LAI) is a crucial parameter in environmental, ecological and agronomic because of its importance for quantitative analysis of biophysical process. In particular, it can be an important parameter related to climate, carbon cycle and hydrological modeling studies at different spatial scale. Direct methods for LAI estimation are very accurate, but they are destructive and time consuming and hardly applicable for forest ecosystem. Therefore, it let to development of different fast and non-destructive indirect methods (e.g. LAI-2000 PCA, hemispherical photography and other methods of ceptometers). On the other hand, these instruments are expensive, low portability and in case of damage require long and expensive services. Nowadays, smartphones are become ubiquitous and their advance properties (high camera quality, GPS, and high memory capacity) have made them suitable candidate for indirect methods. The main objective of this study was to test the Pocket LAI app developed by CSIRO for LAI estimation based on the use of sensors and processing power normally present in most of the modern mobile phones. For testing the app we need to find suitable smartphone, proper height for smartphone to capture images, evaluation and check whether there is any improvement in LAI estimation by increasing number of smartphone’s measurements. After checking and testing the app on over ten locally present smartphone, the result shown Samsung Galaxy S4 mini is a suitable smartphone. The comparison of the LAI of hemispherical photography and smartphone at three heights (0.5m, 1m, and 1.5m) indicates that 0.5 meter (R2=0.7776) is the most suitable height for smartphone to capture images and indicates their comparable performance. There is no improvement in the result of LAI estimation of smartphone at 1 meter with 13 measurements in compare with 5 measurements. The comparison of the LAI of LAI-2000 PCA and smartphone at three heights for evaluation indicates that there is not significant correlation at any heights and same result between hemispherical photography and LAI-2000 PCA (R2=0.0134) . The comparison of LAI from LAI-2000 PCA and other two approaches indicates that LAI-2000 PCA underestimate the LAI. A possible reason for this can be caused by sensor position, canopy height and user error.
Download:
Année de publication
2014
Auteurs
Langue
English
Mots clés
leaf area index, remote sensing, carbon sinks, climate, technology
Géographique
Netherlands
