Preliminary study on integrated wireless smart terminals for leaf area index measurement
Introduction
Vegetation constitutes an important component in land surface ecosystems, and leaves are vital organs of vegetation that interact with the outside environment. Leaf area index (LAI), which is defined as half the vegetation canopy leaf area per unit of surface area (Chen and Black, 1992), is an important parameter for quantitative description of photosynthesis (Wit, 1965), respiration, and transpiration of vegetation leaves (Yamori, 2016). Accurate and fast measurement of LAI is of fundamental importance for quantitative analysis of many physical and biological processes related to vegetation dynamics and their effects on the global carbon cycle and climate (Chen et al., 2002). Ground LAI measurement techniques can be divided into direct and indirect methods, the advantages, weaknesses, and working conditions of which have been conducted in many comprehensive reviews (Bréda, 2003, Jonckheere et al., 2004, Weiss et al., 2004).
Among ground measurement methods, photographic imaging techniques constitute an important branch. The basic principle of the photographic method is to take photographs of a canopy at single (Liu and Pattey, 2010, Liu et al., 2013, Ryu et al., 2012) or multiple shooting angles (Demarez et al., 2008, Leblanc et al., 2005, Leblanc and Fournier, 2014). The captured images are then segmented into leaf and background, which is known as gap classification, to obtain the gap fraction of the canopy at single or multiple angles. The LAI can then be computed on the basis of a gap fraction model (Nilson, 1971). In the technical solutions to obtain LAI by photographic imaging, diverse types of available imaging devices are available such as professional single-lens reflex (SLR) cameras, common digital cameras, and pre-1970 film cameras. Therefore, photographic imaging is a flexible LAI ground measurement technique.
In addition to traditional imaging devices, new instrumentation has become available to measure LAI. For example, some researchers have used smartphones to obtain LAI. Confalonieri et al. launched a mobile application based on smartphones that can be run on Android and iOS operating systems (Confalonieri et al., 2013, Confalonieri et al., 2014). Known as PocketLAI, their application computes LAI by obtaining the gap fraction of a canopy at a specific angle of 57.5°. They initially conducted tests on a scatter-seeded rice crop and then performed comparative tests on other vegetation types (Francone et al., 2014, Orlando et al., 2015). Their findings provided an excellent example of achieving professional LAI measurement based on easily accessible smartphones.
Although PocketLAI is based on a portable device compared with the heavier professional SLR cameral, there is still much room for improvement. First, in the PocketLAI framework, LAI is calculated by using the gap fraction at a shoot angle of 57.5°. In such an assumption, PocketLAI ignores the fact that most mobile cameras have a large field of view (FOV). Consequently, when the image is captured at 57.5°, most pixels have viewing zeniths up to 90°. Larger zenith means larger risk of capture space that does not belong to the vegetation. Second, it may be easy to take a photograph at the configuration of zenith 57.5° in a tall forest, where the user can hold the device while standing. However, we found this task to be quite difficult among short plants, where the user must crawl on the ground to aim the vegetation at zenith 57.5°. Moreover, a mobile phone screen cannot be easily monitored by the operator in such situations; therefore, capturing the photograph at such a specific angle is virtually impossible when working among short vegetation.
This paper proposes an LAI measurement system known as LAISmart, which is based on integrated smart terminals such as smartphones. In the designing of LAISmart, we provided flexible options on the shoot angle and the algorithm of image processing. Another improvement of the LAISmart is that it separates the function of PocketLAI into a sensing component and a monitoring component by using an additional smartphone employed as the user operating terminal; these two components are then connected via a wireless network. Such improvement facilitates the operation on different vegetation types.
The primary aim of this paper is to present the design of LAISmart from the aspects of its hardware and its algorithm on calculating the gap fraction and LAI parameter. An additional aim is to validate the assumption on the leaf angle distribution when calculating LAI without the knowledge of leaf angle. The validation work was supported by eight datasets collected on four types of vegetation from different shooting directions.
Section snippets
Composition of the LAISmart system
LAISmart is composed of two smartphones connected by a support system and communicating by a wireless network. The frontend terminal serves as a data collection terminal, and the backend terminal serves as a user operation console (Fig. 1).
Currently, both the backend and frontend smart terminals are run on the Android operation system, and their hardware configuration must include a Global Positioning System (GPS) sensor, a gyro sensor, an imaging sensor, and a Wireless Fidelity (WIFI) sensor.
Measurement results
In summary, 114 pairwise data of LAISmart and LAI-2000 were acquired in the eight datasets. For brevity, we arranged the datasets into three groups, i.e., DNF and DBF as Group 1 of deciduous leaf forest, because they share the common feature of leaf growing and defoliating process with time; ENF as Group 2 of evergreen leaf forests; and maize and wheat crops as Group 3. The statistical characteristics of the LAI values in these groups are shown in Table 2.
In general, mean and median were highly
Conclusions
This paper proposed the use of LAISmart, a portable LAI measurement system based on a wireless smart terminal platform, and its performance was preliminarily validated. Moreover, the design and implementation of the hardware and software architecture of LAISmart was described. For LAISmart, an application for image acquisition and wireless transmission, automatic segmentation and LAI calculation was developed on the Android mobile platform. Validation of the LAISmart results was performed on
Acknowledgements
This work was supported in part by the National Basic Research Program of China (2013CB733403) and the National Natural Science Foundation of China (41271348, 41531174). We also thank Mrs Yanbo Sui who helped us collecting part of the field data.
All the supporting material in this paper, including original LAISmart photos and the derived LAI data together with the data of LAI-2000 instrument can be available on the request by email to corresponding author Y. Qu ([email protected]).
References (25)
- et al.
Image analysis compared with other methods for measuring ground cover
Arid Land Res. Manage.
(2005) Ground-based measurements of leaf area index: a review of methods, instruments and current controversies
J. Exp. Bot.
(2003)Optically-based methods for measuring seasonal variation of leaf area index in boreal conifer stands
Agric. Forest Meteorol.
(1996)- et al.
Defining leaf area index for non-flat leaves
Plant, Cell Environ.
(1992) - et al.
Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements
Remote Sens. Environ.
(2002) - et al.
Development of an app for estimating leaf area index using a smartphone. Trueness and precision determination and comparison with other indirect methods
Comput. Electron. Agric.
(2013) - et al.
The PocketLAI smartphone app: an alternative method for leaf area index estimation
- et al.
Estimation of leaf area and clumping indexes of crops with hemispherical photographs
Agric. Forest Meteorol.
(2008) - et al.
Comparison of leaf area index estimates by ceptometer and PocketLAI smart app in canopies with different structures
Field Crop. Res.
(2014) The bare bones of leaf-angle distribution in radiation models for canopy photosynthesis and energy exchange
Agric. Forest Meteorol.
(1988)
Review of methods for in situ leaf area index determination: Part I. Theories, sensors and hemispherical photography
Agric. Forest Meteorol.
Methodology comparison for canopy structure parameters extraction from digital hemispherical photography in boreal forests
Agric. Forest Meteorol.
Cited by (22)
LAI-NOS: An automatic network observation system for leaf area index based on hemispherical photography
2022, Agricultural and Forest MeteorologyEstimation of leaf area index using inclined smartphone camera
2021, Computers and Electronics in AgricultureCitation Excerpt :As a result, assumptions of LAD or G should be made before LAI can be estimated. LAISmart (a LAI application using smartphone), which uses images captured in the vertical upward mode, assumes that LAD follows a spherical distribution, and in this mode, the G value is a constant of 0.5 (Qu et al., 2017, 2016). PocketLAI, which calculates LAI from images captured at a 57.5° view angle, takes the G value of 0.5 in this so-called hinge angle (Confalonieri et al., 2013).
Leaf area index estimation using top-of-canopy airborne RGB images
2021, International Journal of Applied Earth Observation and GeoinformationEstimation model of canopy stratification porosity based on morphological characteristics: A case study of cotton
2020, Biosystems EngineeringCitation Excerpt :9). Based on Beer's Law in optics, relating the absorption of light to the properties of a material through which the light travels (Houghton, 2002), optical porosity can be obtained by various methods, such as hemispherical photography (Qu, Meng Wan & Li, 2016; Zarate-Valdez et al., 2012), ultrasonic system (Palleja & Landers, 2017), laser point cloud (Zhang et al., 2017), hyperspectral data from fixed-wing aircraft (Ali, Darvishzadeh, Skidmore, & Duren, 2017) or unmanned aerial vehicle (Roosjen et al., 2018) and ground short-wave or thermal infrared hyperspectral data (Neinavaz, Skidmore, Darvishzadeh, & Groen, 2016). Meanwhile, physico-mathematical models have also been used to characterise the vegetation (Giusti & Marsili-Libelli, 2006; Giusti, Marsili-Libelli, Renzi, & Silvano, 2010).
Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives
2019, Agricultural and Forest MeteorologyCitation Excerpt :Great attention should, therefore, be paid to get the best quality photographs for dense canopies (Baret et al., 2010). Some smartphone applications proposed in recent years, such as PocketLAI and LAISmart, are interesting for their portable and fast measurement characteristics (Confalonieri et al., 2013; Qu et al., 2016), while they should be further examined because they face the same issues as photography methods (Fang et al., 2018; Francone et al., 2014). Ground-based or handheld LiDAR will be of great interest to improve the measurement of shrubs and row crops due to its good penetration ability and unique 3D information (Baret et al., 2010; Hu et al., 2018a).