Despite the importance of Hyrcanian forests for biodiversity conservation, a few studies with biomass destruction has been done to predict biomass and carbon pools from this forest and there is a lack of knowledge in our country. Biomass and leaf area index (LAI) are important variables in many ecological and environmental applications and forest management. In this paper, allometric biomass and leaf area equations were developed for three common Hyrcanian tree species, Oriental Beech (Fagus orientalis Lipsky), Hornbeam (Carpinus Betulus Lipsky) and Chestnut- leaved Oak (Quercus castaneifolia C. A. Mey). To evaluate and estimate the leaf biomass and leaf area index of Oriental Beech, Horbeam and Chestnut-leaved Oak, 21, 27 and 17 individuals were selected and felled down, respectively. Tree characteristics such as diameter at breast height, total height, crown length and perpendicular diameters were measured. Destructive sampling was applied for determination of leaf biomass and LAI. Allometric equations were calculated for estimation of leaf biomass and LAI using simple linear regression and nonlinear regression analysis. The equations were compared based on several modelling parameters. Model comparison and selection were based on R2, Akaike’s information criterion (AIC), prediction error sums of squares, model standard error estimate (SEE), ΔAIC, and a correction factor. Based on the results, the mean values of leaf area, leaf biomass and LAI for Oriental Beech were 53.05 cm2, 0.176 gr, 2.16, for Hornbeam were 27.2 cm2, 0.128 gr, 1.13 and for Chestnut-leaved Oak were 62.419 cm2, 0.401 gr, 2.26, respectively. The highest significant correlation for Oriental Beech was found between LAI and total height (R2adj= 0. 931), the highest significant correlation for Hornbeam was found between LAI and Dbh (R2adj = 0. 956) and the highest significance for Chestnut-leaved Oak was found between LAI and SqrtDbh (R2adj = 0. 956). Also, the best equations were obtained by means of an exponential regression model for Oriental Beech, the Log-transformed regression model for Hornbeam and of a transformed regression model for Chestnut-leaved Oak.
Leaf Biomass LAI Hyrcanian Forests Fagus Orientalis Carpinus Betulus Quercus Castaneifolia1. Introduction
Physiologists and ecologists have long recognized the importance of both leaf surface area and foliar weight as factors affecting many trees and stand-level processes and functions such as photosynthesis, gas exchange, growth, stand productivity and canopy dynamics (Johansson, 2002; Karlik and McKay, 2002; Eriksson et al., 2005; Pourhashemi et al., 2012). The leaf area is the main surface of the exchange of energy and matter, between the plant canopy and the atmosphere, and therefore a key variable in driving the biological processes of the plants (Eriksson et al., 2005; Pourhashemi et al., 2012). By measuring the leaf area, it is possible to estimate parameters like canopy structure (Welles and Norman, 1991) and canopy productivity (Dantec et al., 2000). The leaf area index, or LAI, defined as half the total leaf area per unit ground surface area (Fournier et al., 2003; Menzies et al., 2006; kigomo et al., 2013) is an important canopy variable needed for many physiological and ecosystem studies (Menzies et al., 2006), is also a useful “bulk” parameter which is a necessary input parameter in many models. It has been related to canopy interception, transpiration, net photosynthesis and net primary productivity (Botkin, 1986; Pierce & Running, 1988; Gholz et al., 1991; Gower & Norman, 1991; Kigomo et al., 2013). LAI is related to atmospheric gas exchange that controls local carbon cycling. Leaf retention of gases such as CO2 in the canopy is directly related to the amount of carbon released into the atmosphere (White et al., 1997; Menzies et al., 2006). LAI is also a key variable in analyzing energy absorption for photosynthesis (Cournac et al., 2002; Menzies et al., 2006), rates of photosynthesis, and for estimating primary and net primary production (Jensen, 2000; Menzies et al., 2006). Hyrcanian forests which are located in the north of Iran stretch up to an altitude of 2800 m above sea level. They are constituted of different forest stands with about 80 woody species. The communities of oriental beech (Fagus orientalis Lipsky) forests are the most important parts of the Hyrcanian forests due to their valuable ecological characteristics and their commercial value. Traditionally, measuring LAI was highly destructive because trees had to be felled for accurate measurements (Menzies et al., 2006).The aim of this work is to evaluate and estimate the leaf biomass and LAI of Oriental Beech and Chestnut-leaved Oak. So allometric leaf biomass and leaf area index equations were developed for Oriental Beech (Fagus orientalis Lipsky), Hornbeam (Carpinus Betulus Lipsky) and Chestnut-leaved Oak (Quercu scastaneifolia C. A. Mey).
2. Material and Methods2.1. Study Area
The study was carried out in Chamestan Forest Districts (36˚22'00'' - 36˚27'35''N and 52˚02'30'' - 52˚07'30''E) located in northern forests of Iran (Figure 1). The study land has a total area of 2762 ha with altitudes which range between 250 and 1000 m a.s.l. The studied forests belong to the Beech community widely distributed in most parts of the district. The abundant tree species in the studied forests are F. orientalis L., C. betulus L. and Q. scastaneifolia C.A.M. According to data of the Nowshahr Meteorological Station, the mean annual precipitation and temperature of this area were 866 mm and 16.1˚C, respectively.
2.2. Sampling and Data Analysis
A total of 65 sample trees belonging to F. orientalis L. (21 individuals), C. betulus L. (27 individuals) and Quercu scastaneifolia C. A. Mey (17 individuals) were selected and felled down for leaf biomass and LAI estimation and allometric equation parameterization in the study area. All the trees were selected following lines of exploitation carried out by the forestry department of Nowshahr. The selection of each individual tree was based on diameter at breast height (1.3 m above the ground). The individuals were grouped into three DBH classes: 30 - 60, 60 - 80 and ≥80 cm, which is the diameter classification system commonly used in the Hyrcanian forests of Iran (Vahedi et al., 2013). Tree characteristics such as diameter at breast height, total height, crown length and perpendicular diameters were measured. Destructive sampling was applied for determination of leaf biomass and LAI. Gravimetric method was applied for calculating LAI (Adl, 2007). Allometric equations were calculated for estimation of leaf biomass and LAI using simple linear regression and nonlinear regression analysis. The equations were compared based on several modelling parameters. Model comparison and selection were based on R2, Akaike’s Information Criterion (AIC), prediction error sums of squares, model standard error estimate (SEE), ΔAIC, and correction factor (Sohrabi & Shirvani, 2012; Vahedi et al., 2013).
3. Results and Discussion
The mean values of DBH, height and crown area as measurable characteristics related to the subset of trees that was destructively sampled for leaf biomass and LAI estimation are summarized in Table 1. The diameter (DBH) range of the felled trees spanned from 17.19 to 108.5 cm so as to represent the diameter distribution reported in the studied stands. Based on the results, the mean values of leaf area, leaf biomass and LAI for Oriental Beech were 53.05 cm2, 0.176 gr, 2.16, for Hornbeam were 27.2 cm2, 0.128 gr, 1.13 and for Chestnut-leaved Oak were 62.419 cm2, 0.401 gr, 2.26, respectively (Table 1).
Regression between the Leaf biomass (gr) and the Leaf Area (cm2) of sampled trees are shown in Figure 2. A simple linear regression model using leaf biomass as independent variable showed significant correlation and produced R2 of 0.931, 0.969 and 0.981 for Oriental Beech, Hornbeam and Chestnut-leaved Oak, respectively (Figure 2).
Univariate linear and non-linear regression models were developed using measured variables. Linear and non-linear regression analysis were parameterized to predict the values of LAI using a set of allometric parameters including DBH, height, crown area and their combinations of sampling trees of Oriental Beech, Hornbeam and Chestnut-leaved Oak (Table 2 and Table 3).
Location of the study area.
Sample trees and leaf characteristics of the oriental beech, hornbeam and chestnut-leaved Oak
Species
Dbh (cm)
H (m)
Leaf Biomass (gr)
Mean Leaf Area (cm2)
CA (m2)
LAI
Oriental Beech
Minimum
22.63
19.10
0.044
44.15
37.82
0.64
Maximum
108.50
34.85
0.355
66.66
120
3.66
Mean
66.56
28.94
0.176
53.05
84.43
2.16
Hornbeam
Minimum
22.14
13.60
0.028
14.36
24.90
0.25
Maximum
106.50
29.05
0.289
37.64
119.54
2.78
Mean
58.59
22.57
0.128
27.20
82.46
1.13
Chestnut-leaved Oak
Minimum
17.19
16.27
0.139
42.20
25.86
0.58
Maximum
72.85
29.83
0.867
88.05
99.30
4.27
Mean
42.49
24.60
0.401
62.419
58.57
2.26
Linear Regression and model description based on Dbh, height and crown area for the estimation of LAI of oriental beech, hornbeam and chestnut-leaved oak
Sig
RMSE
Std Error
CF
DW
b
a
R2(adj)
R
Allometric model
Species
***
0.5500
0.30249
-
1.190
−0.548
0.041
0.912
0.957
LAI = a(Dbh) + b
Oriental Beech
***
0.5681
0.32272
-
1.403
−3.726
0.203
0.900
0.951
LAI = a(h) + b
***
0.6775
0.45901
-
1.039
−1.692
0.046
0.798
0.898
LAI = a(CA) + b
***
0.5468
0.29902
-
1.247
0.136
0.001
0.914
0.958
LAI = a(Dbh × h) + b
***
0.5648
0.31897
-
1.345
0.52
2.95 × 10−5
0.903
0.952
LAI = a(Dbh × h2) + b
***
0.6583
0.43337
-
1.533
1.051
2.19 × 10−7
0.820
0.909
LAI = a((Dbh × h)2) + b
***
0.5673
0.32182
-
1.258
−2.871
0.628
0.901
0.951
***
0.5908
0.34903
-
1.464
−9.098
2.101
0.883
0.942
***
0.5451
0.29711
-
1.237
−1.527
0.085
0.916
0.959
***
0.5327
0.28375
-
1.213
−0.787
0.012
0.923
0.962
***
0.4169
0.17384
1.0152
1.097
−4.810
1.320
0.909
0.955
Ln(LAI) = a Ln(Dbh) + b
***
0.3995
0.15957
1.0128
1.173
−9.861
3.131
0.924
0.963
Ln(LAI) = a Ln(h) + b
***
0.4082
0.16661
1.0140
1.120
−6.331
0.931
0.917
0.959
Ln(LAI) = a Ln(Dbh × h) + b
***
0.4046
0.16368
1.0135
1.133
−7.151
0.719
0.920
0.961
Ln(LAI) = a Ln(Dbh × h2) + b
***
0.4082
0.16661
1.0140
1.120
−6.331
0.466
0.917
0.959
Ln(LAI) = a Ln((Dbh × h)2) + b
***
0.5592
0.31266
-
2.152
−0.457
0.027
0.823
0.910
LAI = a(Dbh) + b
Hornbeam
***
0.6346
0.40277
-
1.974
−2.217
0.148
0.706
0.846
LAI = a(h) + b
***
0.5950
0.35408
-
2.468
−0.020
0.014
0.773
0.884
LAI = a(CA) + b
***
0.5522
0.30496
-
2.223
−0.041
0.001
0.832
0.915
LAI = a(Dbh × h) + b
***
0.5451
0.29715
-
2.323
0.179
2.69 × 10−5
0.840
0.919
LAI = a(Dbh × h2) + b
***
0.5388
0.29031
-
2.594
0.465
2.48 × 10−7
0.847
0.923
LAI = a((Dbh × h)2) + b
***
0.3853
0.15258
-
2.060
−1.899
0.405
0.956
0.976
***
0.6487
0.42082
-
1.944
−5.269
1.353
0.679
0.831
***
0.5853
0.34257
-
2.066
−1.030
0.060
0.788
0.891
***
0.5804
0.33687
-
2.092
−0.576
0.010
0.795
0.895
***
0.3801
0.15009
1.0318
2.110
−5.605
1.388
0.956
0.978
Ln(LAI) = a Ln(Dbh) + b
***
0.5318
0.28278
1.0408
1.806
−9.548
3.054
0.820
0.909
Ln(LAI) = a Ln(h) + b
***
0.5068
0.25686
1.0335
1.995
−6.872
0.959
0.851
0.925
Ln(LAI) = a Ln(Dbh × h) + b
***
0.5116
0.26173
1.0348
1.942
−7.527
0.732
0.846
0.922
Ln(LAI) = a Ln(Dbh × h2) + b
***
0.5068
0.25686
1.0335
1.995
−6.872
0.480
0.851
0.925
Ln(LAI) = a Ln((Dbh × h)2) + b
***
0.4824
0.23267
-
1.836
−0.221
0.058
0.948
0.975
LAI = a(Dbh) + b
Chestnut-leaved Oak
***
0.5305
0.28142
-
2.843
−3.837
0.248
0.924
0.964
LAI = a(h) + b
***
0.5215
0.27201
-
2.172
−0.135
0.041
0.929
0.966
LAI = a(CA) + b
***
0.4971
0.24708
-
1.711
0.399
0.002
0.941
0.972
LAI = a(Dbh × h) + b
***
0.5200
0.27043
-
1.579
0.746
5.18 × 10−5
0.930
0.966
LAI = a(Dbh × h2) + b
***
0.6065
0.36784
-
1.486
1.260
6.49 × 10−7
0.870
0.937
LAI = a((Dbh × h)2) + b
***
0.4621
0.21355
-
2.403
−2.609
0.762
0.956
0.976
***
0.5535
0.30636
-
2.787
−9.510
2.382
0.910
0.957
***
0.4640
0.21533
-
2.430
−1.306
0.111
0.955
0.979
***
0.4676
0.21863
-
2.339
−0.582
0.018
0.954
0.978
***
0.5060
0.25604
1.0333
1.586
−0.557
0.029
0.790
0.896
Ln(LAI) = a Ln(Dbh) + b
***
0.3965
0.15725
1.0124
2.163
−2.644
0.136
0.921
0.962
Ln(LAI) = a Ln(h) + b
***
0.5175
0.26782
1.0365
1.557
−0.237
0.001
0.770
0.886
Ln(LAI) = a Ln(Dbh × h) + b
***
0.5338
0.28499
1.0414
1.531
−0.054
2.55 × 10−5
0.739
0.869
Ln(LAI) = a Ln(Dbh × h2) + b
***
0.5871
0.34463
1.0612
1.539
0.224
3.04 × 10−7
0.619
0.802
Ln(LAI) = a Ln((Dbh × h)2) + b
Nonlinear regression and Model description based on DBH, height and crown area for the estimation of LAI of oriental beech, hornbeam and Chestnut-leaved Oak
Sig
F
RMSE
Std. Error
CF
b
a
R2(adj)
R
Allometric Model
Xvar
Species
***
158.861
0.6197
0.384
1.0765
−7.216
2.274
0.859
0.930
LAI = alog(Dbh) + b
Dbh
Oriental Beech
***
261.509
0.4171
1.174
1.0153
0.008
1.320
0.909
0.955
LAI = a(Dbh)b
***
196.767
0.4450
0.198
1.0198
0.417
0.023
0.883
0.942
LAI = aexp[a(Dbh)]
***
163.790
0.6156
0.379
1.0745
−15.822
5.367
0.862
0.931
LAI = a log(h) + b
h
***
315.045
0.4000
0.160
1.0129
5.22 × 10−5
3.131
0.924
0.963
LAI = a(h)b
***
352.636
0.3886
0.151
1.0115
0.064
0.117
0.931
0.966
LAI = a exp[a(h)]
***
64.211
0.7430
0.522
1.1646
−11.754
3.160
0.709
0.848
LAI = alog(CA) + b
CA
***
84.068
0.5310
0.282
1.0406
0.001
1.846
0.762
0.878
LAI = a(CA)b
***
100.171
0.5128
0.263
1.0352
0.215
0.026
0.792
0.895
LAI = aexp[a(CA)]
***
85.473
0.6164
0.380
1.0749
−4.597
1.440
0.738
0.864
LAI = alog(Dbh) + b
Dbh
Hornbeam
***
183.917
0.5000
0.250
1.0317
0.004
1.388
0.859
0.929
LAI = a(Dbh)b
***
156.202
0.5177
0.268
1.0366
0.219
0.024
0.838
0.918
LAI = aexp[a(Dbh)]
***
56.598
0.6633
0.440
1.1016
−8.316
3.049
0.650
0.813
LAI = alog(h) + b
h
***
137.539
0.5320
0.283
1.0409
7.14 × 10−5
3.054
0.820
0.909
LAI = a(h)b
***
157.306
0.5167
0.267
1.0363
0.035
0.144
0.839
0.919
LAI = aexp[a(h)]
***
67.107
0.6442
0.415
1.0899
−3.196
1.020
0.668
0.836
LAI = alog(CA) + b
CA
***
113.763
0.5523
0.305
1.0476
0.977
0.015
0.790
0.893
LAI = a(CA)b
***
96.039
0.5710
0.326
1.0546
0.331
0.012
0.760
0.876
LAI = aexp[a(CA)]
***
288.591
0.4837
0.234
1.0278
−6.460
2.377
0.947
0.975
LAI = alog(Dbh) + b
Dbh
Chestnut-leaved Oak
***
139.439
0.4243
0.180
1.0163
0.019
1.269
0.896
0.950
LAI = a(Dbh)b
***
61.031
0.506
0.256
1.0333
0.573
0.029
0.790
0.896
LAI = aexp[a(Dbh)]
***
132.074
0.5797
0.336
1.0581
−15.794
5.662
0.891
0.948
LAI = alog(h) + b
h
***
247.238
0.3715
0.138
1.0096
7. 92 × 10−5
3.179
0.939
0.971
LAI = a(h)b
***
186.564
0.3962
0.157
1.0124
0.071
0.136
0.921
0.962
LAI = aexp[a(h)]
***
190.223
0.5329
0.284
1.0412
−6.782
2.270
0.922
0.963
LAI = alog(CA) + b
CA
***
91.014
0.4658
0.217
1.0238
0.017
1.197
0.849
0.927
LAI = a(CA)b
***
54.773
0.5167
0.267
1.0363
0.600
0.021
0.771
0.886
LAI = aexp[a(CA)]
Regression between the leaf biomass (gr) and the Leaf Area (cm<sup>2</sup>) of: (a) Oriental Beech; (b) Hornbeam and (c) Chestnut-leaved Oak.
The best-fit equations had adjusted R2 values between 0.709 to 0.931 and RMSE values between 0.7430 and 0.3886 for Oriental Beech, R2 values between 0.650 to 0.956 and RMSE values between 0.6633 and 0.3801 for Hornbeam and R2 values between 0.619 to 0.956 and RMSE values between 0.5871 and 0.4621 for Chestnut-leaved Oak (Table 1 and Table 2).
Allometric equations for estimating LAI from DBH and height and their combinations were derived. Based on results the highest significant correlation for Oriental Beech was found between LAI and total height (R2adj = 0. 931), the highest significant correlation for Hornbeam was found between LAI and Dbh (R2adj = 0. 956) and the highest significance for Chestnut-leaved Oak was found between LAI and SqrtDbh (R2adj = 0. 956). Also, the best equations were obtained by means of an exponential regression model for Oriental Beech, the Log-transformed regression model for Hornbeam and of a transformed regression model for Chestnut-leaved Oak.
4. Conclusions
In the presence of complex environmental gradients, allometric equations can provide additional information to improve the knowledge of LAI distribution.
Allometric equations presented in this study associated with oriental beech, Hornbeam and Chestnut-leaved oak, the three common species in the Hyrcanian forests, may bring additional information for the LAI patterns and distribution. The allometric equations provide improved methods for predicting LAI, leaf biomass and also carbon storage in Hyrcanian forests from standard forestry measurements, which means these equations may be applied to historical and new forest data.
Cite this paper
Saleh Kahyani,Hormoz Sohrabi,Seyyed Mohsen Hosseini,Jerry Vanclay, (2016) LAI and Leaf Biomass Allometric Equations for Three Common Tree Species in a Hyrcanian Temperate Forest. Open Journal of Forestry,06,1-7. doi: 10.4236/ojf.2016.62B001
NOTESReferencesAdl H. R. ,et al. (2007). Estimation of Leaf Biomass and Leaf Area Index of Two Major Species in Yasuj Forests Iranian Journal of Forest and Poplar Research 15, 417-426.Botkin, D. B. (1986). Remote Sensing of the Biosphere (135 p). National Academy of Sciences, Report of the Committee on Planetary Biology, Washington, DC: National Research Council.Cournac, L., Dubois, M., Chave, J., & Riera, B. (2002). Fast Determination of Light Availability and Leaf Area Index in Tropical Forests. Journal of Tropical Ecology, 18, 295-302. http://dx.doi.org/10.1017/S0266467402002201Dantec, V. L., Dufrêne, E., & Saugier, B. (2000). Interannual and Spatial Variation in Maximum Leaf Area Index of Temperate Deciduous Stands. Forest Ecology and Management, 134, 71-81.
http://dx.doi.org/10.1016/S0378-1127(99)00246-7Eriksson, H., Eklundh, L., Hall, K., & Lindroth, A. (2005). Estimating LAI in Deciduous Forest Stands. Agricultural and Forest Meteorology, 129, 27-37. http://dx.doi.org/10.1016/j.agrformet.2004.12.003Fournier, R. A., Mailly, D., Walter, J. N., & Soudan, K. (2003). Indirect Measurements of Forest Canopy Structure from in Situ Optical Sensors. In M. A. Wulber, & S. E. Franklin (Eds.), Remote Sensing of Forest Environments: Concepts and Case Studies, Massachusetts: Kluwer Academic Publishers, 77-113. http://dx.doi.org/10.1007/978-1-4615-0306-4_4Gholz, H. L., Vogel, S. A., Cropper, W. P., McKelvey, K., Ewel, K. C., Teskey, R. O., & Curran, P. J. (1991). Dynamics of Canopy Structure and Light Interception in Pinus elliottii Stands, North Florida. Ecological Monographs, 61, 33-51.
http://dx.doi.org/10.2307/1942998Gower, S. T., & Norman, J. M. (1991). Rapid Estimation of Leaf Area Index in Conifer and Broadleaf Plantations. Ecology, 72, 1896-1900. http://dx.doi.org/10.2307/1940988Jensen, R. R. (2000), Measurement Comparison and Use of Remotely Derived Leaf Area Index Predictors (133 p). Ph.D. Dissertation, University of Florida.Johansson, T., (2002). Increment and Biomass in 26- to 91-Year-Old European Aspen and Some Practical Implications. Journal of Biomass and Bioenergy, 23, 245-255. http://dx.doi.org/10.1016/S0961-9534(02)00056-9Karlik, J. F., & McKay, M. K. (2002). Leaf Area Index, Leaf Mass Density, and Allometric Relationships Derived from Harvest of Blue Oaks in a California Oak Savanna. In R. B. Standiford et al. (Eds.), Proceedings of the Fifth Symposium on Oak Woodlands: Oaks in California's Challenging Landscape (pp. 719-729). General Technical Report, PSW-GTR-184, Albany, CA: Pacific Southwest Research Station, Forest Service, U.S. Department of Agriculture.Kigomo J. N., Sweda T., & Tsuzuki H. ,et al. (2013). Improved Model for Estimation of Leaf Area Index Using Airborne Laser Profiling Journal of Horticulture and Forestry 5, 53-60.Menzies J., Jensen R., Brondizio E., Moran E. F., & Mausel P. ,et al. (2006). Accuracy of Neural Network and Regression Leaf Area Estimators for the Amazon Basin GIScience & Remote Sensing 43, 1-11.Pierce, L. L., & Running, S. W. (1988). Rapid Estimation of Coniferous Forest Leaf Area Index Using Portable Integrating Radiometer. Ecology, 69, 1762-1767. http://dx.doi.org/10.2307/1941154Pourhashemi M., Eskandari S., Dehghani M., Najafi T., Asadi A., & Panahi P. ,et al. (2012). Biomass and Leaf Area Index of Caucasian Hackberry (Celtis caucasica Willd.) in Taileh Urban Forest, Sanandaj, Iran Iranian Journal of Forest and Poplar Research 19, 609-620.Sohrabi H., & Shirvani A. ,et al. (2012). Allometric Equations for Estimating Standing Biomass of Atlantic Pistache (Pistacia atlantica var. mutica) in Khojir National Park Iranian Journal of Forest 4, 55-64.Vahedi A., Mataji A., Babayi-Kafaki S., Eshaghi-Rad J., & Hodjati S. M. ,et al. (2013). Modeling the Bole Mass of Beech (Fagus Orientalis Lipsky) through Allometric Equations within Hyrcanian Forests Iranian Journal of Forest 5, 309-322.Welles, J. M., & Norman, J. M. (1991). Instrument for Indirect Measurement of Canopy Architecture. Agronomy Journal, 83, 818-825. http://dx.doi.org/10.2134/agronj1991.00021962008300050009xWhite, J., Running, S., Nemani, R., Keane, R., & Ryan, K. (1997). Measurement of Remote Sensing of LAI in Rocky Mountain Montane Ecosytems. Canadian Journal of Forest Research, 27, 1714-1727. http://dx.doi.org/10.1139/x97-142