Study on LAI estimation of broadleaf forests in arid areas using digital hemispherical photography

A case study in the Tarim River Basin, China

MA Zhongguo1, 2, CHEN Xi1, Guli Japper1, CHANG Cun1, 2

1. Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences Urumqi 830011, China;

2. Graduate University, Chinese Academy of Sciences Beijing 100039, China e-mail: mazhongguo@126.com

Abstract—The values of leaf area index (LAI) were optically measured with digital hemispherical photography in deciduous Populus euphratica Oliv forests in the Tarim River Basin, China, and then they are estimated and analyzed. Circular fisheye photography was compared with another alternative indirect method (cover photography) for estimating LAI,. The purpose of the study was to develop a practical estimation mode for measuring and estimating LAI in arid areas. The results derived by the rapid and automatic analysis of cover images using WinSCANOPY 2006 are similar to that derived by the manual image analysis using Adobe Photoshop. Estimated results of LAI from cover photography are more ideal than that from circular fisheye photography to correlate with LAI from destructive sampling.

Keywords- leaf area index (LAI); hemispherical photography; Populus euphratica; WinSCANOPY

I. INTRODUCTION Leaf area index (LAI) was defined by Watson (1947)

as the total one-sided area of leaf tissue per unit ground surface area, and was also defined by Chen and Black (1992) as half of the total leaf area per unit ground surface area. It characterizes the canopy-atmosphere interface, where most of the energy fluxes exchange, and therefore it is a key variable in driving the biological processes of plants.

There are several means of estimating LAI. Direct methods include the destructive sampling and litter collection. LAI is also estimated indirectly from the incident radiation transmitted through the canopy at a given zenith angle. This method is known as the gap fraction method and is used with a number of instruments (LAI-2000, DEMON, Ceptometer, hemispherical photography [1].

Fisheye photography (and related hemispheric view analysis) has been used for decades to describe canopy structure, chart and quantify radiation microclimate under canopies, calculate solar radiation indices, and, afterwards, estimate the canopy leaf area index.

With the development of affordable digital cameras, fisheye photography has become as a rapid and cheap

method for estimating LAI. However, fisheye photography is sensitive to photographic exposure, the gamma function of the film or camera, the method of pixel classification and image resolution [2, 3].

In theory, it is possible to simultaneously estimate both LAI and the foliage angle distribution by measuring the gap fraction at multiple zenith angles, which should be the biggest advantage of fisheye method. However, the foliage angle distribution calculated from fisheye photography was as much affected by canopy structure as by the actual foliage angle distribution, despite corrections for foliage clumping.

Another alternative method, circular fisheye photography, was used to test digital photographs of the canopy obtained by pointing a 70 mm (35 mm format equivalent) focal length lens upwards to estimate crown cover and foliage cover. After correcting for foliage clumping they estimated LAI using the Beer-Lambert law. A single brightness threshold to separate sky from foliage was used in an automatic method of analyzing cover images has been incorporated in WinSCANOPY, unlike the color-based pixel classification used by Macfarlane [2].

The main objectives of this study are to compare the circular fisheye photography and cover photography with the destructive LAI measurement, and quantify the effect of variation in the gamma function on LAI estimated from single threshold photographic methods. In order to develop a practical LAI estimation mode suitable to the Tarim River Basin based on the hemispherical photography, LAI estimations from ten independent stands with varying stand density and LAI were compared rather than thinning a single stand to obtain LAI.

II. MATERIALS AND METHODS

A. The study area The Tarim River is the longest continental river in China,

and the “Green Corridor” is located in its lower reaches between the Taklimakan Desert (the second largest mobile desert in the world) and the Kumtag Desert. Populus euphratica is the edificator of the “Green Corridor”, and its community propagation has a significant effect in conserving and improving the ecology and environment in the lower reaches of the Tarim River. Field measurements were carried out at Aqik and Yingsu.

2009 International Conference on Environmental Science and Information Application Technology

978-0-7695-3682-8/09 $25.00 © 2009 IEEEDOI 10.1109/ESIAT.2009.308

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2009 International Conference on Environmental Science and Information Application Technology

978-0-7695-3682-8/09 $25.00 © 2009 IEEEDOI 10.1109/ESIAT.2009.308

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2009 International Conference on Environmental Science and Information Application Technology

978-0-7695-3682-8/09 $25.00 © 2009 IEEEDOI 10.1109/ESIAT.2009.308

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B. Photographic methods and image analysis All digital photographs were collected as the Fine

JPEG images with maximum resolution (3, 871, 488 pixels in total) using a Nikon Coolpix S5 camera in the WinSCANOPY system. Cover images were collected on grids in each plot at sunrise and sunset when the sky was clear and uniform.

The camera, without fisheye converter, was set to F2 lens, automatic exposure, aperture-priority mode and minimum aperture (f/9.6). The lens was pointed directly upward and the camera lens was leveled using a self-leveling system.

WinSCANOPY (Regent Instruments Inc., Quebec, Canada) is a canopy analysis system based on colored hemispherical images. Its standard system includes a digital camera, a calibrated fisheye lens on the standard camera and a self-leveling system. Images are taken in the field and processed in laboratory using the specific software from the company. The canopy parameters, such as LAI, leaf-angle distribution and mean leaf angle, angular distribution of gap frequencies, and site factors, are calculated according to Norman and Campbell [4]. The radiation values beneath the canopy are also predicted.

In WinSCANOPY, the blue channel of the sharpened RGB images was analyzed. Sky pixels were separated from canopy pixels using a threshold brightness value that was automatically set by algorithms in the software. The large gaps were separated from the small gaps using WinSCANOPY based on their areas; the gaps larger than 50,000 pixels (1.3% of the total image area) were classified as the large gaps.

In Photoshop the colored images were analyzed; the large and between-canopy gaps were visually selected using the ‘‘Wand’’ tool, and the number of pixels was recorded in the histogram. All sky pixels were selected using the “select similar” command, and the total sky pixels were recorded.

The data derived from both WinSCANOPY and Photoshop were used to calculate foliage cover( ), crown

cover ( ), crown porosity ( , Eq. (1)), plant area index

( , Eq. (2)) and zenithal clumping index (

ff

cf

tL )( , Eq. (3)) followed Macfarlane, et al. (2007). A light extinction coefficient ( ) at the zenith was assumed 0.5 for calculation of LAI from cover images, and it was also calculated using Eq. (4). and LAI were estimated from destructive sampling for comparing with crown cover ( ).

k

cf

c

f

ff

1 (1)

kfL c

t)ln(

(2)

f

f

ff

/)ln()1ln()1(

)0( (3)

L

fk c ln (4)

C. Statistical analysis Estimated results of LAI from destructive sampling

were initially compared with Lt from photography based on the root mean-squared error (RMSE). Methods with a small RMSE were then compared using the reduced major axis (RMA) regression because the measurement errors were similar in both approaches [5]. RMA regression was also used to compare with other methods where it was judged appropriately. RMA regressions were calculated using PAST 1.40 [6].

III. RESULTS LAI ranged from 1.62 to 2.34, and the basal area ranged

from 36.3 to 53.8 m2 ha-1 (Tab. 1). The height of trees sampled in the study ranged from 3.6 to 9.6 m. One-way ANOVA and Tukey’s test revealed that the stands with the widest space had a smaller LAI than that of the other three treatments (P < 0.05).

Generally, similar RMSEs were obtained from fisheye images using WinSCANOPY regardless of whether the images were gamma-corrected or not, and regardless of which clumping correction was used. Using WinSCANOPY, the sensitivity to the choice of clumping correction of images with gamma of 2.2 was less than the images with gamma of 1.0 (Tab. 2)

Estimated results of LAI from all the photographic methods were good for the plots of 4×4 m in tree space but overestimated for the plots of 1×1 m spaced trees (Tab. 3). LAIs of the plots of 2×2 and 2×4 m in tree space were underestimated using the fisheye method (Tab. 3). The LAIs

TABLE I. TREE HEIGHT, BASAL AREA (AB) AND LAI OF EACH STAND ESTIMATED FROM DESTRUCTIVE SAMPLING

TABLE II. ROOT MEAN-SQUARED ERRORS IN EACH PHOTOGRAPHY METHOD

Software Gamma Image NC CC LX CLX

Photoshop 2.2 Cover 0-15° 0.72 0.48

WinS-CANOPY 1.0 Cover 0-

15° 0.76 0.54

WinS-CANOPY 2.2 Cover 0-

15° 0.79 0.53

Plot Tree space (m) Height (m) AB (m2 ha-1) LAI1 1×1 4.3-8.9 46.1 1.832 3.6-9.6 52.8 2.343 3.9-7.5 41.9 1.694 2×2 4.4-6.9 38.5 1.745 5.0-7.2 40.3 1.856 5.3-7.6 44.6 2.027 2×4 4.7-6.5 36.3 1.668 5.2-7.4 48.4 1.889 4×4 5.5-8.4 53.8 1.62

10 5.7-7.9 49.6 1.93

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WinS-CANOPY 1.0 Circular

fisheye 0-

90° 0.51 0.70 0.55 1.21

WinS-CANOPY 2.2 Circular

fisheye 0-

90° 0.53 0.56 0.43 0.77

θ is the zenith range used for the analyses, NC means no correction for foliage clumping, CC means the Chen–Cihlar method, LX means the Lang–Xiang method, and CLX means the combined method of Chen–Cihlar’s with Lang–Xiang’s.

TABLE III. ESTIMATED RESULTS OF CANOPY PARAMETERS USING S.E.M.

Tree space Method LAI Lt(P) Lt(W) fC fF ( ) k

1×1 Average 1.95 2.64 2.47 0.72 0.61 0.14 0.73 0.65 S. E. M. 0.327 0.169 0.165 0.058 0.042 0.005 0.034 0.056

2×2 Average 1.87 1.88 1.76 0.64 0.54 0.16 0.61 0.45 S. E. M. 0.279 0.292 0.183 0.062 0.059 0.006 0.020 0.029

2×4 Average 1.77 1.72 1.56 0.51 0.45 0.10 0.55 0.44 S. E. M. 0.206 0.398 0.288 0.069 0.061 0.001 0.032 0.046

4×4 Average 1.76 1.76 1.85 0.30 0.24 0.13 0.48 0.42 S. E. M. 0.293 0.361 0.402 0.091 0.073 0.012 0.036 0.043

The average and its standard error (S. E. M.) of LAI for each tree space were derived from destructive sampling, plant area indexes (Lt) were derived from either fisheye or cover photography, and crown cover ( fC), foliage cover ( fF), crown porosity ( ) and zenithal clumping index ( (θ)) were derived from cover photography.

The light extinction coefficient (k) was calculated with Eq. (4). LT(W): WinSCANOPY, fisheye images, gamma = 2.2, θ= 0–90°, LX method. Lt(P) : Photoshop, cover images, gamma = 2.2, θ= 0–15°, CC method.

at two sites (Plot 1: 1×1 m; and Plot 2: 1×1 m) were consistently overestimated using all the photographic methods, but the LAIs at another two sites (Plot 4: 2×2 m; and Plot 7: 2×4 m) was consistently underestimated using all the photographic methods.

Foliage cover ff, crown cover fc and the zenith clumping index ( ) all increased with decreasing the tree space (Tab. 3). There was a positive correlation between

( ) and foliage cover ( 37.054.0)( ff , R2 = 0.83, n = 10, P < 0.002). There was no correlation between crown and foliage cover. The zenithal extinction coefficients (k) calculated from cover, porosity and destructive LAI were slightly lower than 0.5 for the plots of

foliage cover

leaf

are

a in

dex

Figure 1. LAI estimated from destructive sampling and allometry vs. foliage cover from photography (from Photoshop). The RMA regression

equation was , (n = 12, R23.052.4 ffL2 = 0.93, P < 0.001).

2×2, 2×4 and 4×4 m in tree space, but close to 0.7 for the 1×1 m (Tab. 3). There was a good correlation between foliage cover and LAI (R2 = 0.64, P = 0.002). Their correlation was highly improved (R2 = 0.93, P < 0.001) if the data for the 1×1 m were removed and the data from Macfarlane, et al. (2007) were included (Fig. 1).

The automatic WinSCANOPY analysis of cover images performed nearly as well as the manual Photoshop method without image’s gamma correction. To compare clumping indices and mean leaf angles of cover images with that of circular fisheye images, the 2.2-gamma images and the CC clumping correction were selected because both of these had the same gamma, small RMSE (Tab. 2) and good correlations with LAI. The circular images resulted in the quite different estimated values of the average clumping index in a zenith angle range of 0–90° (Fig. 2). values from the circular images varied in a range from 0.85 to 0.95 for all the plots. The average leaf angle estimated from the circular images was about 5° larger than the average leaf angle from the cover images on average. Average leaf angle increased with increasing the tree space for both circular and cover images.

IV. DISCUSSION AND CONCLUSIONS This study is to compare the estimated results of LAI

from digital circular fisheye photography and cover photography with destructive measurements of LAI. The cover method performed better than the fisheye photography. This result may be attributed to the higher image resolution of cover images compared to that of the circular fisheye images.

Variations in the gamma function had little effect on the results of photographic methods using a single global threshold in fisheye or cover images. Gamma Increase did increase the gap fraction based on the need for clumping corrections to accurately estimate LAIs from 2.2-gamma fisheye images.

The sensitivity of single threshold method to the camera’s gamma was low because the grey levels of the pixels were not used to calculate the gap fractions. The change of gamma altered the grey levels of the pixels with intermediate brightness [7], and many pixels brightness were between the two thresholds set by the two-value threshold method. However, the single-threshold method converted all pixels to either black or white, and was not so sensitive to modification of the grey levels of intermediate pixels.

LAI estimation from cover photography can be improved by using the batch analysis capability of WinSCANOPY, and all gaps larger than 50,000 pixels can be treated as nonrandom and between crown gaps. As evidenced by the small correlation coefficients between the two methods for crown porosity and (θ), there is little difference in estimation of cover or LAI between the two methods although the results of individual images between WinSCANOPY and Photoshop are different.

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It is confirmed that the LX method is only sensitive to

the overall gap fraction, not to the gap size distribution, and may provide more stable estimations of under the situations where the gap size distribution is inaccurate owing to the effects of photographic exposure, camera gamma function and image resolution.

A wide range of estimations of LAI and derived with different methods affects the fisheye photography method. This study supports the use of the Lang and Xiang’s method [8] for correcting foliage clumping in preference to the Chen and Cihlar’s method [9] unless the gap size distribution is accurate. Cover photography is relatively insensitive to the camera’s gamma function, and is easy to automatically derive the ideal results.

ACKNOWLEDGMENT The author would like to acknowledge Pro. Chen and Dr.

Guli for their theory guidance, and Dr. Chang for his data processing. The study was supported by the National Natural Science Foundation of China (40801146).

REFERENCES

[1] Chen, J. M., Rich, P. M., Gower, S. T., et al, 1997. Leaf area index of boreal forests: theory, techniques and measurements. J. Geophys. Res. 102, 29429–29443.

[2] Macfarlane, C., Hoffman, M., Eamus, D., et al, 2007. Estimation of leaf area index in eucalypt forest using digital photography. Agric. For. Meteorol., 143, 176–188.

[3] Wagner, S., Hagemeier, M., 2006. Method of segmentation affects leaf inclination angle estimation in hemispherical photography. Agric. For. Meteorol. 139, 12–24.

[4] Norman J. M., Campbell G. S., 1989. Canopy structure. In: Plant Physiological Ecology: Field Methods and Instrumentation. London: Chapman and Hall, 301-325.

[5] Mc Ardle, B. H., 1988. The structural relationship: regression in biology. Can. J. Zool; 66, 2329–2339.

[6] Hammer, Harper, D. A. T., Ryan, P. D., 2001. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electr., 4, 9.

[7] Cescatti, A., 2006. Indirect estimates of the canopy gap fraction based on the linear conversion of hemispherical photographs. Methodology and comparison with standard thresholding techniques. Agric. For. Meteorol. 143, 1–12.

[8] Lang, A. R. G., Xiang, Y., 1986. Estimation of leaf area index from transmission of direct sunlight in discontinuous canopies. Agric. For. Meteorol. 35, 229–243.

[9] Chen, J. M., Cihlar, J., 1995. Plant canopy gap-size analysis theory for improving optical measurements of leaf area index. Appl. Opt. 34, 6211–6222.

clumping index(circular)

clum

ping

inde

x(co

ver)

leaf area index

Figure 2. Average clumping indexes: (a) The average clumping indexes were calculated from circular fisheye photography and cover image (WinSCANOPY, gamma = 2.2); (b) The clumping indexes were calculated from the cover images (gamma = 2.2) vs. LAIs were derived from destructive sampling.

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