Spatial Scale and Landscape Heterogeneity Effects on ... - IEEE Xplore

564

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 11, NO. 2, FEBRUARY 2014

Spatial Scale and Landscape Heterogeneity Effects on FAPAR in an Open-Canopy Black Spruce Forest in Interior Alaska Hideki Kobayashi, Rikie Suzuki, Shin Nagai, Taro Nakai, and Yongwon Kim

Abstract—Black spruce forests dominate the land cover in interior Alaska. In this region, satellite remote sensing of ecosystem productivity is useful for evaluating black spruce forest status and recovery processes. The fraction of absorbed photosynthetically active radiation (FAPAR) by green leaves is a particularly important input parameter for ecosystem models. FAPAR1d is computed as the ratio of absorbed photosynthetically active radiation (APAR3d ) to the incident photosynthetically active radiation at the horizontal plane above the canopy (PAR1d , FAPAR1d = APAR3d /PAR1d ). The parameter FAPAR1d is scale dependent and can be larger than 1 as a result of laterally incident PAR. We investigated the dependence of FAPAR1d on spatial scale in an opencanopy black spruce forest in interior Alaska. We compared FAPAR1d with FAPAR3d (= APAR3d /PAR3d ), the latter of which considers incident PAR as actinic flux (spheradiance) (PAR3d ). Our results showed the following: 1) landscape scale FAPAR3d (30 × 30 m2 ) was always larger (0.39–0.43) than FAPAR1d (0.19–0.27) due to the landscape heterogeneity and incident PAR regime, and 2) at the individual tree scale, FAPAR1d was highly variable, with 34% (day of year [DOY] 180) to 52% (DOY 258) of FAPAR1d > 1, whereas FAPAR3d varied across a much narrower range (0.2–0.5). The spatial-scale dependence of the ratio of PAR3d to PAR1d converged at the pixel size larger than 5 m. Thus, a 5-m or coarser resolution was necessary to ignore the lateral PAR effect in the open-canopy black spruce forest. Index Terms—Black spruce forest, fraction of absorbed photosynthetically active radiation (FAPAR), heterogeneity, interior Alaska, radiative transfer.

I. I NTRODUCTION

B

LACK spruce forests are a major landscape cover in interior Alaska. However, the cold climate and the existence of permafrost impede the growth of black spruce trees. Consequently, Alaskan black spruce trees form open sparse canopy forests [1]. Although black spruce trees have limited commercial uses, they provide a variety of ecosystem services. For example, black spruce trees act as watershed controls and limit erosion by stabilizing the soil surface [2]. They also mainManuscript received September 28, 2012; revised April 11, 2013 and August 8, 2013; accepted August 9, 2013. Date of publication September 6, 2013; date of current version November 25, 2013. This work was supported by the Environment Research and Technology Fund (Rfa1201) of the Ministry of Environment and by the JAMSTEC-IARC Collaboration Study and was partly conducted under the JAXA GCOM-C/SGLI project (PI-301). H. Kobayashi, R. Suzuki, and S. Nagai are with the Research Institute for Global Change (RIGC), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokosuka 237-0061, Japan (e-mail: [email protected]). T. Nakai and Y. Kim are with the International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AK 99775-7340 USA. Digital Object Identifier 10.1109/LGRS.2013.2278426

Fig. 1. Schematics of incident PAR at (a) the horizontal plane (PAR1d ), (b) the actinic flux (PAR3d ), and (c) the regularly placed box approximation of PAR3d . The box canopy completely absorbs incident radiation. hc : canopy height, hl1 : horizontal projection of crown area, hl2 : open space, d: pixel size, and θs : solar zenith angle. Solid arrows indicate the direct solar radiation horizontally incident at the top of canopy, and dotted arrows indicate the direct solar radiation that is incident laterally.

tain regional water resources by insulating the permafrost [3], [4]. Black spruce forests are used locally for making wooden products, provide habitat for wildlife, and contribute to fish and plant resources [2]. Occasional forest fires are an important aspect in the succession of these forests [5]. Remote sensing observations of the spatial and temporal changes in ecosystem productivity and hydrologic behavior are of particular importance in monitoring current black spruce forest conditions and their changes over time [6]. The fraction of absorbed photosynthetically active radiation (FAPAR = APAR3d /PAR) by green leaves is a widely used input parameter in satellite-based ecosystem models. Several algorithms have been proposed to produce global time-series FAPAR maps at approximately 1-km spatial resolution [7]–[9], and the validation of these mapping products has also been performed [10]–[12]. At the individual tree level, FAPAR can be computed from the incoming PAR actinic flux (spheradiance) (PAR3d ); thus, FAPAR3d = APAR3d /PAR3d [Fig. 1(b)]. However, satellite-derived FAPAR products usually employ FAPAR1d (= APAR3d /PAR1d ), where only incident PAR from the horizontal plane at the top of the plant canopy is considered [i.e., PAR1d ; Fig. 1(a)] [13]. This assumption is valid when the pixel size is much larger than the canopy height and the plant canopy is considered homogeneous. Since the effect of laterally incident PAR flux is not negligible at high spatial resolutions [14], [15], FAPAR1d can be larger than one and significantly different from FAPAR3d Care must be taken when applying satellite FAPAR1d algorithms to high-resolution satellite images. For example, FAPAR1d is often estimated by the normalized difference vegetation index (NDVI), but the NDVI and FAPAR1d relationship is not always unique regardless of spatial resolution.

1545-598X © 2013 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

KOBAYASHI et al.: SPATIAL SCALE AND LANDSCAPE HETEROGENEITY EFFECTS ON FAPAR

The scale dependence of FAPAR1d and its subsequent biases can cause errors in gross primary production (GPP) estimation because most of the satellite-based GPP estimation approaches rely on radiation use efficiency (ε) models, in which GPP is computed as the product of FAPAR1d and PAR1d (GPP = ε × FAPAR1d × PAR1d )[16]. In particular, at the spatial scale where FAPAR1d can exceed 1, GPP will be underestimated. Our research objective was to investigate the spatial scale and landscape heterogeneity effect on FAPAR1d and FAPAR3d in a mature, open-canopy, and black spruce forest in the interior of Alaska. We conducted radiative transfer simulations based on a plant canopy radiative transfer model that requires detailed canopy structure parameters such as individual tree position, canopy height, and canopy diameter. We next analyzed radiative transfer simulations to compare the differences between FAPAR1d and FAPAR3d at different spatial resolutions. II. S TUDY S ITE AND DATA S ETS Our study site, the Poker Flat Research Range (65◦ 07 N, 147◦ 30 W, 250 m a.s.l.), is located 50 km from Fairbanks, AK, USA [17]. Black spruce (Picea mariana) is the dominant species (98% of the overstory) and an evergreen needleleaf tree. Thus, the overstory leaf area index (LAI) has weak seasonality throughout the growing season. The aboveground biomass around the study site is 6.6–50.7 Mg DW ha−1 [18]. The understory is covered with blueberry (Vaccinium corymbosum), moss, and lichen [4]. A 30 × 30 m2 plot was established, and the horizontal positions and diameters at breast height (DBH) of individual trees were measured for all existing stems with a height >1.3 m. The stand density was 3978 trees ha−1 (358 trees in the 30 × 30 m2 plot), and the average tree height and DBH were 2.4 ± 1.1 m and 1.2 ± 0.079 cm, respectively. Within the plot, 19 trees were randomly chosen with DBH ranging from 0.4 to 7.6 cm, and the crown maximum widths toward the north, south, east, and west directions were measured. The average maximum crown diameter (average width over the four (N, S, W, and E) directions) computed from these samples was 0.80 ± 0.20 m. To estimate the crown diameter of all trees in our study plot, we determined a linear relationship between the DBH and the crown diameter using 19 crown width measurements (crown diameter [m] = 0.044 DBH + 0.66, r2 = 0.23). The root-mean-square error and the mean bias between the true crown diameters and the estimated value from the aforementioned regression were 0.072 m (14.5%) and − 0.069 m (− 13.8%), respectively. We used this relationship to estimate the crown diameters of all trees in the plot. The plant area index (PAI) in the 30 × 30 m2 plot was estimated using a pair of Plant Canopy Analyzers (LAI-2000, LiCOR, Inc.); however, the number of gap fraction measurements within the 30 × 30 m2 plot was insufficient. In order to collect a statistically sufficient number of gap fraction measurements, we established three 220-m transects: one north to south and two east to west, all of which partially overlapped with the 30 × 30 m2 plot. We assumed the PAI calculated from these transects to be equal to that of the 30 × 30 m2 plot. One sensor was set at the top of the eddy covariance flux observation tower (height 17 m), which was located approximately 150 m from the plot, and the gap fractions below the canopy were

565

TABLE I O PTICAL PARAMETERS U SED IN THE R ADIATIVE T RANSFER S IMULATION

measured using the other sensor. All below-canopy gap fraction measurements were conducted approximately 50 cm above the ground surface. A 45◦ field view cap was used on all sensors. The sampling interval of gap fractions was 20 m. The PAI and apparent clumping index were estimated by the inversion of the gap fraction equation [19] using two different averaging approaches [20], [21]. The PAI and apparent clumping index obtained at this site were 0.62 and 0.74, respectively. Assuming that the shoot-level clumping (1.4) and woody fraction to total plant area ratio (0.16) were the same as in Canadian old black spruce forests [22], we obtained an LAI value of 0.73. We used this LAI as a representative value for the 30 × 30 m study plot. The color of mosses and lichens changes seasonally from spring (reddish or yellow-green) to summer (green) to autumn (reddish or yellow-green). This color change should be considered in terms of seasonal reflectance changes in the background. Spectral hemispherical-directional reflectance factors (HDRF) of the black spruce forest floor were measured at two different times. One was in summer, July 7, 2010 (clear sky), and the other was in fall, October 5, 2011 (clear with some clouds). We used a spectrometer (MS-720, Eiko, Inc.) that measures from 350 to 1050 nm with a 3-nm spectral resolution and a field of view of 25◦ . We took six forest floor spectra on July 7, 2010, and 36 spectra on October 5, 2011, selecting various floor conditions. The measured spectral HDRFs were averaged over the PAR spectral domain (400–700 nm) and weighted for incoming spectral solar fluxes. Stem HDRF was also measured at the field site by slicing bark from some stems and placing them in a flat open space. We assumed this stem HDRF to be the bihemispherical reflectance. Because we could not measure the black spruce needle bihemispherical reflectance and transmittance in this study, we used the black spruce needle bihemispherical reflectance and transmittance values reported by Hall et al. [23]. Table I summarizes the optical data measured in this study. The shoot structure was also measured to incorporate the effect of shoot-level clumping on light penetration in the crown. Eleven shoots were clipped from three black spruce trees: three shoots from a younger tree (less than 0.5-m canopy height) and eight shoots from two mature trees. For each sampled tree, we took one shoot from the lower crown, another one from the upper crown, and other shoots from the middle of the crown. The length of these shoots ranged from 6 mm (young shoot) to 146 mm (old shoot), and the total number of needles on one shoot ranged from 14 to 299. The black spruce forest stand was sparse, and the average canopy height was low (2.4 m). Therefore, there were no clear differences in sunlit and shaded shoots. We measured the shoot length and number of needles. The average needle density of shoots was 2.22 ± 0.21 needles mm−1 . From these shoot samples, we randomly selected 31 needles and measured their length (8.1 ± 0.7 mm),

566


short diameter (0.9 ± 0.11 mm), and long diameter (1.43 ± 0.18 mm). We found a negative correlation between the needle densities (ρ) and shoot lengths (l; ρ = 2.39 − 0.0033l, r2 = 0.52). However, the difference in needle density between 6and 146-mm shoots was only 10% of the average needles per shoot. Therefore, we used the average needle density of 2.22 ± 0.21 needles mm−1 to determine the spherical average shoot silhouette area to the total needleleaf area (STAR), representative of the black spruce trees. III. M ETHOD We conducted a detailed radiative transfer analysis to evaluate the spatial-scale dependence of FAPAR. We used the Forest Light Environmental Simulator (FLiES) to simulate FAPAR [24], [25]. FLiES is a 3-D Monte Carlo ray-tracing radiative transfer model. Previous studies have focused on FLiES validation in regard to simulated light transmittance through a canopy and bidirectional reflectance factors, as well as their spatial variation [25]. Each tree object can be divided into several domains, which contain leaves and/or woody materials. Based on the measured forest optical and structural data, we constructed a 30 × 30 m2 model forest canopy landscape. To avoid edge effects near the boundary of our 30 × 30 m2 landscape, a periodic boundary condition was applied in order to consider outgoing photons from the lateral boundaries as incoming photons from the opposite side of the forest landscape boundary. In our study area, the crown is thin, and the maximum crown width usually appears in the middle range of the crown length. Therefore, we modeled individual black spruce crowns using spheroid shapes with two domains. The outer domain was occupied by clumped black spruce needles, and the inner domain was occupied by randomly distributed woody elements. The size (height and diameter) of the inner domain was set to 50% of the crown size. Additionally, we assumed constant needle and woody area densities over all crowns. The stems were modeled as cylinders. The shoot-level clumping effect was modeled using Smolander and Stenberg’s approach [26]. The method used to incorporate shoot-level clumping in FLiES has been described by Kobayashi et al. [27]. Computation of the extinction coefficient was improved by replacing the spherical average shoot silhouette area by the total needleleaf area (STAR) and introducing the effect of photon recollision within a shoot. Based on the measurements of needle density, needle length, and diameter data described in Section II, we created four modeled shoots with 20, 50, 100, and 150 mm lengths. The value of STAR was then determined by the ray-tracing method, in which the projected shoot areas from all spherical directions were simulated and integrated over the sphere. The average STAR value over four different shoot lengths was 0.158 ± 0.008. We used this value in the radiative transfer simulation and assumed that shoot clumping within the crowns was the same over all trees. We conducted radiative transfer simulations using a 1 × 1 m2 grid size over the landscape. In total, 107 photons were fired into the 30 × 30 m2 landscape plot. Hence, approximately 110 000 photons on average were incident to each 1 × 1 m2 grid cell. The FAPAR outputs were computed at coarser spatial resolutions (3 × 3 m2 , 5 × 5 m2 , and 10 × 10 m2 ) by resam-

Fig. 2. Seasonal course of average FAPAR1d and FAPAR3d over the 30 × 30 m plot. FAPAR1d was computed by the simulated APAR divided by the incoming PAR flux at the horizontal plant above the canopy [PAR1d ; see Fig. 1(a)], and FAPAR3d was computed by the simulated APAR divided by the sum of incident PAR3d to the individual tree crowns [see Fig. 1(b)].

pling the simulated results of the 1 × 1 m2 grid-cell outputs. The statistical error was less than 1% for the FAPAR simulation. This error did not impact any results of the comparison between FAPAR1d and FAPAR3d . FAPAR was also simulated for individual trees. The individual tree case was equivalent to the pixel size of about 0.5 × 0.5 m2 . We simulated FAPAR under clearsky conditions. The fraction of diffuse radiation was computed by the atmospheric module in FLiES, using the high-latitude summer atmospheric profile and continental aerosol type (optical thickness at 550 nm = 0.1). Four different seasonal timings were considered to address the effects of seasonal changes on forest floor color and solar zenith angles. Specifically, we used day of year (DOY) 140 (spring, after snowmelt, and before floor greening), DOY 180 (early summer, green forest floor), DOY 211 (mid-summer), and DOY 258 (fall, before snowfall and after floor senescence). The solar zenith angles at noontime were 46.45◦ (DOY 140), 42.94◦ (DOY 180), 47.38◦ (DOY 211), and 62.62◦ (DOY 258), which were close to the Terra-MODIS overpass time. The fractions of diffuse radiation in each day near the Terra-MODIS overpass time were 0.193 (DOY 140), 0.188 (DOY 180), 0.192 (DOY 211), and 0.270 (DOY 258).

IV. R ESULTS AND D ISCUSSION Fig. 2 shows the simulated FAPAR1d and FAPAR3d over the 30 × 30 m2 plot. In the simulation of the black spruce landscape used here, there were two noticeable differences between the plot-scale FAPAR1d and FAPAR3d outputs. First, the seasonal course of FAPAR3d was opposite to that of FAPAR1d . The values of FAPAR1d showed a bowl shape related to the solar zenith angle changes. In contrast, FAPAR3d was largest when the solar zenith angle was the lowest (= 42.94◦ ). This was caused by differences in the incident PAR value used for the denominator when computing FAPAR (PAR1d or PAR3d in Fig. 1), where PAR3d is the actinic flux. Our study site had an open canopy, and the black spruce crowns were vertically elongated so that, as the solar zenith angle became large, more PAR3d became incident to the black spruce crowns. Eventually, FAPAR3d decreased. The other noticeable difference is the absolute value of FAPAR. Here, FAPAR3d was always larger (0.39–0.43) than FAPAR1d (0.19–0.27). This was caused by the definition of incident PAR. Whereas PAR3d is actinic flux

KOBAYASHI et al.: SPATIAL SCALE AND LANDSCAPE HETEROGENEITY EFFECTS ON FAPAR

567

Fig. 3. Individual-tree-based comparison of FAPAR1d and FAPAR3d in four different growing seasons. (a) DOY = 140. (b) DOY = 180. (c) DOY = 211. (d) DOY = 258. The gray lines are one-to-one lines. Fig. 5. Ratio of PAR3d to PAR1d simulated by (1) and the simulated results by the 3-D radiative transfer model at the study plot. The x-axis, d, is pixel size as shown in (1). The parameters used were θs = 42.94◦ (DOY 180), hc = 2.4 m, hl1 ∼ = 0.8 m, and hl2 ∼ = 4.2 m.

Fig. 4. Contributions of laterally incident PAR (P ARl ) relative to PAR1d at four different pixel sizes (1 × 1 m2 , 3 × 3 m2 , 5 × 5 m2 , and 10 × 10 m2 ). PAR1d is incident PAR passing through the horizontal plane at the canopy top. The gray-colored box corresponds to the solar zenith angle range in the growing season at the study site.

that always enters the canopy [Fig. 1(b)], some PAR1d passes through the crown spaces and gaps without ever interacting with the black spruce trees stems, branches, or needles. To understand the variability in FAPAR at a high spatial resolution, we computed FAPAR1d and FAPAR3d for individual black spruce trees (Fig. 3). We found FAPAR1d to be highly variable, and 34% (DOY 180) to 52% (DOY 258) of FAPAR1d was > 1. Comparatively, FAPAR3d varied across a much narrower range (0.2–0.5). The scatter plots can be split into two domains (FAPAR3d ≤ 0.35 and FAPAR3d > 0.35; Fig. 3). The domains of FAPAR3d ≤ 0.35 correspond to the part of the 30 × 30 m plot area where the black spruce crown densities were high. In these areas, the horizontal component of PAR3d was low, and PAR3d became closer to PAR1d . In the domain where FAPAR3d > 0.35, FAPAR1d became up to 3.5–4.8. This domain corresponded to the part of the 30 × 30 m plot where the trees were spatially isolated and the laterally incident PAR was dominant. FAPAR1d and FAPAR3d differences occurred due to the incident PAR (PAR1d or PAR3d ). In order to evaluate the spatial-scale dependence of PAR fluxes, we investigated the laterally incident component of PAR3d for four pixel sizes (1 × 1 m2 , 3 × 3 m2 , 5 × 5 m2 , and 10 × 10 m2 ) and a height of 2.4 m (average canopy height). In this evaluation, we sampled laterally incident PAR (PARl ) as it entered each pixel domain. The value of PAR3d was separately sampled by the following: 1) incident PAR at the horizontal plane above the average canopy height (= PAR1d ) and 2) the laterally incident PAR3d component (PARl ). Although there is solar zenith angle dependence in the contributions of PARl relative to PAR1d , PARl /PAR1d gradually decreased with an increase in pixel size (Fig. 4). This analysis suggests that a 5-m or coarser

resolution is recommended to ignore the laterally incident net PARl at our study site. Widlowski et al. [28] examined the effect of laterally incident radiation on a 3-D canopy landscape using box-shaped perfect absorbers and a black background. They determined that the maximum impact of laterally incident radiation becomes equal to hc tan θs /d, where hc , d, and θs are canopy height, pixel size, and solar zenith angle, respectively [for parameter definitions, see Fig. 1(c)]. Using this box canopy landscape, the PAR3d incident to the perfect absorbers box canopy with a pixel size of d can be computed by ⎧ hc tan θs ⎨ hl1 cos θs + hl2 sin θs Fs >1 tan θs d hl2 PAR3d = hc tan θs ⎩{hl1 cos θs +hc sin θs } Fs ≤1 d hl2 (1) where Fs is incoming direct solar radiation and pixel size d is defined as the distance from the edge of the box [Fig. 1(c)]. The first term of (1) is PAR incident to the horizontal plant above the box-shaped canopy. The second term is the laterally incident component of PAR (PARl ). Note that we defined PAR3d as the actinic flux around the box-shaped canopy; therefore, we only considered the PAR incident to the canopy. The PAR component incident to the background is not included in (1). Although (1) is a simple approximation, it is possible to understand the maximum impact of PARl on PAR3d . When hl1 hl2 or θs is small, the contribution of PARl decreases. We applied parameter values close to those of our study site [θs = 42.94◦ (DOY 180), hc = 2.4 m (mean crown height), hl1 ∼ = 0.8 m (mean crown diameter), and hl2 ∼ = 4.2 m (mean crown distance)] and computed the ratio of PAR3d to PAR1d as a function of d (Fig. 5). Fig. 5 also shows the results simulated by the 3-D radiative transfer model using the 30 × 30 m2 tree plot census data. In both cases, PAR3d was larger than PAR1d when the pixel size d was low. As d became larger, PAR3d gradually decreased. At d = 4 ∼ 5 m, the ratio of PAR3d to PAR1d became almost constant. The simulated ratio of PAR3d to PAR1d by the 3-D radiative transfer model at d = 5 was about 0.46, which was close to the 30 × 30 m2 results (FAPAR1d /FAPAR3d = PAR3d /PAR1d = 0.44 in Fig. 2, DOY = 180). These results suggest that the scale dependence of FAPAR was weak when d > 4−5 m. The ratio of PAR3d to PAR1d from the simulation was similar to, but lower than, the prediction by (1). In the black spruce forest, trees were not evenly distributed in the plot areas, and trees were clumped in some parts. Equation (1) should

568


be interpreted as the maximum effect of PAR3d for a given landscape. Currently, several satellites monitor Earth’s surface at a spatial resolution of larger than 1 m (e.g., IKONOS, Quickbird, and Worldview). However, when estimating FAPAR from these satellite data, we cannot simply apply the FAPAR1d estimation algorithm to the original image resolution because of the spatial-scale dependence of FAPAR. If ancillary plant structure data sets such as tree dimensions, density, and height are available from high-resolution satellite data, PAR3d and FAPAR3d can be approximated using (1). Black spruce forests with open canopies cover an extensive area of interior Alaska, our study site. Given the canopy architecture of this study site, a 5-m or coarser resolution is considered necessary to estimate FAPAR1d . However, this does not mean that FAPAR1d will become equal to FAPAR3d (Fig. 2), and because the appropriate spatial resolution depends on the forest structure and solar geometry, the forest-specific pixel size at which the spatialscale dependence becomes negligible should be determined. As shown in this study, radiative transfer simulation and a simple equation can help in understanding the scale dependence and landscape heterogeneity of incident PAR and thus FAPAR.

[9] [10]

[11]

[12] [13]

[14] [15]

V. C ONCLUSION

[16]

We have investigated the spatial scale and landscape heterogeneity effect on the relationships between FAPAR1d and FAPAR3d in an open-canopy black spruce forest in interior Alaska. Our results highlight the variations in FAPAR1d and FAPAR3d in high-resolution data sets. Furthermore, the effect of laterally incident PAR was found to decrease with an increase in pixel size.

[17]

[18]

[19]

R EFERENCES [1] F. S. Chapin, III, T. Hollingworth, D. F. Murray, L. A. Viereck, and M. D. Walker, “Floristic diversity and vegetation distribution in the Alaskan boreal forest,” in Alaska’s Changing Boreal Forest, F. S. J. Chapin, III, M. Oswood, K. Van Cleve, L. A. Viereck, and D. L. Verbyla, Eds. Oxford, U.K.: Oxford Univ. Press, 2006, pp. 81–99. [2] R. A. Post, “Functional profile of black spruce wetlands in Alaska,” US Environ. Protection Agency, Seattle, WA, USA, Tech. Rep. EPA 910/ R-96-006, 1996. [3] H. Iwata, Y. Harazono, and M. Ueyama, “The role of permafrost in water exchange of a black spruce forest in interior Alaska,” Agr. Forest Meteorol., vol. 161, pp. 107–115, Aug. 2012. [4] T. Nakai, Y. Kim, R. C. Busey, R. Suzuki, S. Nagai, H. Kobayashi, H. Park, K. Sugiura, and A. Ito, “Characteristics of evapotranspiration from a permafrost black spruce forest in interior Alaska,” Polar Sci., vol. 7, no. 2, pp. 136–148, Jun. 2013. [5] J. A. Lynch, J. S. Clark, N. H. Bigelow, M. E. Edwards, and B. P. Finney, “Geographic and temporal variations in fire history in boreal ecosystems of Alaska,” J. Geophys. Res., vol. 107, no. D1, pp. FFR 8-1–FFR 8-8, Jan. 2003. [6] K. Kushida, Y. Kim, N. Tanaka, and M. Fukuda, “Remote sensing of net ecosystem productivity based on component spectrum and soil respiration observation in a boreal forest, interior Alaska,” J. Geophys. Res., vol. 109, no. D6, pp. D06108-1–D06108-11, Mar. 2004. [7] F. Baret, O. Hagolle, B. Geiger, P. Bicheron, B. Miras, M. Huc, B. Berthelot, F. Niño, M. Weiss, O. Samain, J. L. Roujean, and M. Leroy, “LAI, APAR and fCover CYCLOPES global products derived from VEGETATION,” Remote Sens. Environ., vol. 110, no. 3, pp. 275–286, Oct. 2007. [8] N. Gobron, B. Pinty, O. Aussedat, J. M. Chen, W. B. Cohen, R. Fensholt, V. Gond, K. F. Huemmrich, T. Lavergne, F. Melin, J. L. Privette, I. Sandholt, M. Taberner, D. P. Turner, M. M. Verstraete, and J.-L. Widlowski, “Evaluation of fraction of absorbed photosynthetically active radiation products for different canopy radiation transfer regimes:

[20] [21]

[22] [23]

[24]

[25]

[26] [27]

[28]

Methodology and results using Joint Research Center products derived from SeaWiFS against ground-based estimations,” J. Geophys. Res., vol. 111, no. D3, pp. D13110-1–D13110-15, Jul. 2006. R. Myneni, R. Nemani, and S. W. Running, “Estimation of global leaf area index and absorbed PAR using radiative transfer models,” IEEE Trans. Geosci. Remote, vol. 35, no. 6, pp. 1380–1393, Nov. 1997. R. Myneni, S. Hoffman, Y. Knyazikhin, J. L. Privette, J. Glassy, Y. Tian, Y. Wang, X. Song, Y. Zhang, G. R. Smith, A. Lotsch, M. Friedl, J. T. Morisette, P. Votava, R. R. Nemani, and S. W. Running, “Global products of vegetation leaf area and fraction of absorbed PAR from year one of MODIS data,” Remote Sens. Environ., vol. 83, no. 1/2, pp. 214– 231, Nov. 2002. I. McCallum, W. Wagner, C. Schmullius, A. Shvidenko, M. Obersteiner, S. Fritz, and S. Nilsson, “Comparison of four global FAPAR datasets over Northern Eurasia for the year 2000,” Remote Sens. Environ., vol. 114, no. 5, pp. 941–949, May 2010. D. C. Steinberg, S. J. Goetz, and E. Hyer, “Validation of MODIS FPAR products in boreal forests of Alaska,” IEEE Trans. Geosci. Remote, vol. 44, no. 7, pp. 1818–1828, Jul. 2006. M. C. A. Senna, M. H. Costa, and Y. E. Shimabukuro, “Fraction of photosynthetically active radiation absorbed by Amazon tropical forest: A comparison of field measurements, modeling, and remote sensing,” J. Geophys. Res., vol. 110, no. G1, pp. G01008-1–G01008-8, Sep. 2005. J.-L. Widlowski, “On the bias of instantaneous FAPAR estimates in opencanopy forests,” Agr. Forest Meteorol., vol. 150, no. 12, pp. 1501–1522, Dec. 2010. J.-L. Widlowski, B. Pinty, T. Lavergne, M. M. Verstraete, and N. Gobron, “Horizontal radiation transport in 3-D forest canopies at multiple spatial resolutions: Simulated impact on canopy absorption,” Remote Sens. Environ., vol. 103, no. 4, pp. 379–397, 2006. J. L. Monteith, “Solar radiation and productivity in tropical ecosystems,” J. Appl. Ecol, vol. 9, no. 3, pp. 744–766, Dec. 1972. K. Sugiura, R. Suzuki, T. Nakai, B. Busey, L. Hinzman, H. Park, Y. Kim, S. Nagai, K. Saito, J. Cherry, A. Ito, T. Ohata, and J. Walsh, “Supersite as a common platform for multi-observations in Alaska for a collaborative framework between JAMSTEC and IARC,” JAMSTEC Rep. Res. Dev., vol. 12, pp. 61–69, Mar. 2011. R. Suzuki, Y. Kim, and R. Ishii, “Sensitivity of the backscatter intensity of ALOS/PALSAR to the above-ground biomass and other biophysical parameters of boreal forest in Alaska,” Polar Sci., vol. 7, no. 2, pp. 100– 112, Jun. 2013. J. M. Welles and J. M. Norman, “Instrument for indirect measurement of canopy architecture,” Agron. J., vol. 83, no. 5, pp. 818–825, Sep. 1991. A. R. G. Lang and Y. Xiang, “Estimation of leaf area index from transmission of direct sunlight in discontinuous canopies,” Agr. For. Meteorol., vol. 37, no. 3, pp. 229–243, Aug. 1986. Y. Ryu, T. Nilson, H. Kobayashi, O. Sonnentag, B. E. Law, and D. D. Baldocchi, “On the correct estimation of effective leaf area index: Does it reveal information on clumping effects?” Agr. For. Meteorol., vol. 150, no. 3, pp. 463–472, Mar. 2010. J. M. Chen, P. M. Rich, S. T. Gower, J. M. Norman, and S. Plummer, “Leaf area index of boreal forests: Theory, techniques, and measurements,” J. Geophys. Res., vol. 102, no. D24, pp. 29429–29443, Dec. 1997. Oak Ridge Nat. Lab. Distrib. Active Archive Center, F. G. Hall, K. F. Huemmrich, D. E. Strebel, S. J. Goetz, J. E. Nickeson, and K. D. Woods, SNF Leaf Optical Properties: TMS. [Superior National Forest Leaf Optical Properties: Thematic Mapper Simulator]. Data Set, Oak Ridge, TN, USA 1996. [Online]. Available: http://daac.ornl.gov H. Kobayashi and H. Iwabuchi, “A coupled 1-D atmosphere and 3-D canopy radiative transfer model for canopy reflectance, light environment, and photosynthesis simulation in a heterogeneous landscape,” Remote Sens. Environ., vol. 112, no. 1, pp. 173–185, Jan. 2008. H. Kobayashi, D. Baldocchi, Y. Ryu, Q. Chen, S. Ma, J. Osuna, and S. Ustin, “Modeling energy and carbon fluxes in a heterogeneous oak woodland: A three-dimensional approach, agricultural and forest meteorology,” Agr. For. Meteorol., vol. 152, pp. 83–100, 2012. S. Smolander and P. Stenberg, “A method to account for shoot-scale clumping in coniferous canopy reflectance models,” Remote Sens. Environ., vol. 88, pp. 363–373, 2003. H. Kobayashi, N. Delbart, R. Suzuki, and K. Kushida, “A satellite-based method for monitoring seasonality in the overstory leaf area index of Siberian larch forest,” J. Geophys. Res., vol. 115, no. G1, p. G01002, Mar. 2010. J.-L. Widlowski, T. Lavergne, B. Pinty, N. Gobron, and M. M. Verstraete, “Toward a high spatial resolution limit for pixel-based interpretations of optical remote sensing data,” Adv. Space Res., vol. 14, no. 11, pp. 1724– 1732, 2008.