| General Information: |
| Title: | Field data for satellite validation and forest structure modeling in a pure and sparse forest of Picea glehnii in northern Hokkaido |
| Identifier: | ERDP-2020-06.1.1 |
| Abstract: |
To validate and to improve ecological products obtained from satellites, such as a leaf area index (LAI), above-ground biomass (AGB), and a fraction of photosynthetically active radiation (fAPAR), in-situ accurate data are indispensable. They must be not a single point-data but an areal data representing the satellite footprint. Their accuracy needs to be much higher than the required accuracy for the satellite products. The quantitative assessment of their error is necessary for evaluating the satellite products' error from the discrepancy between the satellite products and the in-situ data. However, such data had not been available. In particular, there had been few data of LAI in a sparse evergreen needle-leaved forest, because of difficulty of accuracy control of in-situ observation in such a forest. To overcome the difficulty and to obtain the representative LAI, we made an allometric equation to estimate the leaf mass of Picea glehnii in northern Hokkaido. We report the allometric equations of leaf mass and above-ground biomass of P. glehnii, its leaf mass per area (LMA), its leaf life span, its leaf distribution, its crown shapes, its wood specific gravity, and tree locations. We also report LAI, AGB, and fAPAR within the 500 m × 500 m area, which is the footprint scale of the Global Change Observation Mission-Climate (GCOM-C) satellite, in a pure and sparse forest of P. glehnii in northern Hokkaido. These precise data are useful for validation of other satellite data, especially with higher spatial resolution, and forest structure modeling.
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| Keywords: |
- Satellite validation data set
- Leaf area index
- Above-ground biomass
- fAPAR
- Allometric equation of Picea glehnii
- Leaf mass per area
- Wood specific gravity
- Leaf distribution
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Involved Parties
| Data Set Owners: |
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Individual: | Tomoko AKITSU |
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Organization: | Faculty of Life and Environmental Sciences, University of Tsukuba |
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Address: |
| 1-1-1, Tennodai, |
| Tsukuba, 305-8572 Japan |
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Email Address:
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Individual: | Toshiya YOSHIDA |
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Organization: | Nayoro Research Office, Field Science Center for Northern Biosphere, Hokkaido University |
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Address: |
| 250 Tokuda, |
| Nayoro, Hokkaido 096-0071 Japan |
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Email Address:
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| Data Set Contacts: |
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Individual: | Tomoko AKITSU |
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Organization: | Faculty of Life and Environmental Sciences, University of Tsukuba |
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Address: |
| 1-1-1, Tennodai, |
| Tsukuba, 305-8572 Japan |
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Email Address:
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| Associated Parties: |
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Individual: | Tomoko Akitsu |
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Organization: | Faculty of Life and Environmental Sciences, University of Tsukuba |
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Address: |
| 1-1-1 Tennodai, |
| Tsukuba, Ibaraki 305-8572 Japan |
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Phone:
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Phone:
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Email Address:
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Individual: | Tatsuro Nakaji |
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Organization: | Uryu Experimental Forest, Field Science Center for Northern Biosphere, Hokkaido University |
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Address: |
| Moshiri, |
| Horokanai, Hokkaido 074-0741 Japan |
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Individual: | Toshiya YOSHIDA |
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Organization: | Nayoro Research Office, Field Science Center for Northern Biosphere, Hokkaido University |
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Address: |
| 250 Tokuda, |
| Nayoro, Hokkaido 096-0071 Japan |
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Email Address:
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Individual: | Rei Sakai |
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Organization: | Uryu Experimental Forest, Field Science Center for Northern Biosphere, Hokkaido University |
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Address: |
| Moshiri, |
| Horokanai, Hokkaido 074-0741 Japan |
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Individual: | Wataru Mamiya |
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Organization: | Uryu Experimental Forest, Field Science Center for Northern Biosphere, Hokkaido University |
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Address: |
| Moshiri, |
| Horokanai, Hokkaido 074-0741 Japan |
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Individual: | Terigele |
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Organization: | Graduate School of Life and Environmental Science, University of Tsukuba |
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Address: |
| 1-1-1 Tennodai, |
| Tsukuba, Ibaraki 305-8572 Japan |
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Individual: | Kentaro Takagi |
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Organization: | Teshio Experimental Forest, Field Science Center for Northern Biosphere, Hokkaido University |
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Address: |
| Toikanbetsu, |
| Horonobe, Hokkaido 098-2943 Japan |
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Individual: | Honda Yoshiaki |
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Organization: | Center for Environmental Remote Sensing, Chiba University |
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Address: |
| 1-33, |
| Yayoi-cho, Inage-ku, Chiba 263-8522 Japan |
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Individual: | Koji Kajiwara |
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Organization: | Center for Environmental Remote Sensing, Chiba University |
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Address: |
| 1-33, |
| Yayoi-cho, Inage-ku, Chiba 263-8522 Japan |
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Individual: | Kenlo Nasahara |
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Organization: | Faculty of Life and Environmental Sciences, University of Tsukuba |
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Address: |
| 1-1-1, Tennodai, |
| Tsukuba, 305-8572 Japan |
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Data Set Characteristics
| Geographic Region: |
| Geographic Description: | Moshiri, Horokanai, Hokkaido, Japan |
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Bounding Coordinates:
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| West: | 142.19970314 degrees
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| East: | 142.206662672 degrees
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| North: | 44.402076437 degrees
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| South: | 44.397768101 degrees
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Sampling, Processing and Quality Control Methods
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Step by Step Procedures
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| Step 1: |
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Description:
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Study sites
This study was mainly conducted within Dorogawa watershed at Uryu Experimental Forest, Hokkaido University, located in the northern part of Hokkaido, Japan (44.40°N, 142.20°E, at approximately 295 m above sea level; Fig. 1). The annual mean temperature and precipitation for the period from 1956 to 2014 are 3.1 °C and 1390 mm, respectively. Approximately 50% of the precipitation falls as rain between May and November and the remainder as snow. The ground is covered with snow approximately for 7 months (from November to the following May), and the maximum snow height reaches 3 m (Xu et al., 2007). Our study site, which has flat topography and homogeneous vegetation cover over a 500 m × 500 m square, was established in a wetland area, where a pure and sparse forest of Picea glehnii is performed naturally. The understory vegetation was a dense dwarf bamboo, Sasa senanensis (Franch. et Sav.) Rehder.
To make an allometric equation of P. glehnii, 7 trees were cut down: 6 trees were cut down at the Uryu site and 1 tree at Teshio Experimental Forest, Hokkaido University, located in the northern part of Hokkaido, Japan (45.03°N, 142.07°E, at approximately 90 m above sea level; Fig. 1).
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| Step 2: |
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Description:
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Sampling design
Five parallel transects were placed at 100-m intervals, and each transect was 400-m long (Fig. 2). Along each transect, observation points of a leaf area index (LAI) and a fraction of absorbed photosynthetically active radiation (fAPAR) were placed at 20-m intervals and tree census points to observe above-ground biomass (AGB) were placed at 100-m intervals. In addition, tree census points were placed at the corners of the 500 m × 500 m square. Tree census area was established in a 1000-m2 circle area centered on each AGB observation point. Furthermore, four fixed observation points of photosynthetically active radiation (PAR) and four temporal observation points of understory vegetation were established in the 500 m × 500 m area.
The observation point ID was defined as ABCD, where A is a transect line ID, B is a direction from the center of the line (E or W meaning east or west, respectively), CD (10 m) is a distance from the center of the line (see Fig. 2). In the case of the four corner of the 500 m × 500 m area, since there were no transect lines, A is a location of the edge of south or north (S or N). For instance, the 2E10 indicates the observation point of 100 m east from the center of the line 2, and the SE25 indicates the observation point of the southeast edge.
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| Step 3: |
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Description:
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Observation methods
C-1 Allometric equation to estimate leaf mass and above-ground biomass
The 6 trees were cut down outside but near the 500 m × 500 m observation area at the Uryu site (the 2016_A, 2017_A, 2017_B, 2017_C, 2018_A, and 2018_B trees shown in Table 1), and the 1 tree was cut down at the Teshio site (the 2008_A tree shown in Table 1). The dry mass of each organ (leaves, branches, and trunk) of the trees was measured using the methods described in the Table 1.
Table 1: The property of the cut trees (Picea glehnii).
NA means not available.
* Procedure A: All leaves were clipped. The mass of oven-dried leaves and branches were observed. The mass of trunk was estimated using the ratio of dry mass to fresh mass of the 3-cm thick of the trunk disk obtained from each part.
* Procedure B: The mass of all leaves and all branches were observed using a random sampling method. The mass of trunk was estimated using the (oven-dried) volume density of the 3-cm thick of the trunk disk obtained from each part.
The allometric equation of AGB and that of leaf mass of P. glehnii (R2 = 0.994 and R2 = 0.990, respectively; Fig. 3) were made as
(1),
(2),
where B (kg) is AGB, D (cm) is a diameter at breast height (DBH), L (kg) is a leaf mass.
Fig 3: Allometric relationships between a diameter at breast height (DBH) and a dry mass of Picea glehnii. The lines denote the allometric equations of P. glehnii. The dashed line denotes that of Ishihara et al. (2015): For above-ground biomass (AGB), the cubic polynomial model with a DBH and the wood specific gravity (0.465 g cm-3, this study) was shown. For a leaf mass, the model for evergreen gymnosperm was shown.
C-2 Tree census
The species name of each tree, its tree height, its DBH, its crown width, and its location (except for trees with less than 10 cm of DBH) were censused at 29 observation points on May 2016. Each survey area was 1000 m2, which was a circle with a 17.84-m radius. The tree height was measured using a ultrasonic hypsometer Vertex IV (Haglof inc.). The crown width was measured at 4 directions (north, east, south, and west) from the trunk to the far end of the branches using a measuring tape. The location of each tree was surveyed by the angle and distance from the center of each tree census area as local coordinates using a compass Expedition 54 (SILVA) and the Vertex IV, respectively. The local coordinates of each tree was converted to the Japanese Geodetic Datum 2000 (XII).
C-3 Above-ground biomass (AGB)
The AGB of each tree i (Bi; kg) was estimated by following two steps. First, the ln(Bi) was calculated using the tree census data and the allometric equation as follows:
(3),
where H (m) is a tree height, and a, b, and c are parameters which depend on tree species (see Table 2). Second, to eliminate the bias introduced by inverse logarithmic transformation, Bi was obtained as
(4),
where is the natural logarithm value of the estimated above ground biomass Bi using the equation (3), and CF is a correction factor (Sprugel, 1983). CF was calculated as follows:
(5),
where SEE is the standard error of the estimate when the logarithm allometric equation was made. SEE was calculated as
(6),
where ln(bi) is the natural logarithm value of the measured above ground biomass bi (kg) of a tree, is the natural logarithm value of the estimated above ground biomass Bi using the equation (3), n is the total number of trees which were cut and surveyed to make the allometric equation, and p is the number of parameters in the allometric equation. In this study, we used three allometric equations depending on tree species (Table 2).
Table 2: Parameters, coefficient of determination (R2), and correction factor (CF) of allometric equations of above-ground biomass (AGB).
NA means not available.
AGB in a 1000-m2 tree census area (B; Mg/ha) was calculated as
(7).
AGB for understory was measured at 4 points in the 500 m × 500 m area. The observation points for understory are intentionally avoided from the LAI and fAPAR observation points shown in Fig. 2. Three observation points were set up in the dense dwarf bamboo area, and one point was set up in the area dominated by Asian white skunk cabbage, Lysichiton camtschatcensis. All understory vegetation was cut in a 1 m × 1 m square. The cut vegetation was completely dried in an oven at 80 °C more than 24 hours, and their dry weights (Wu) were measured.
AGB in a 1-m2 square (Bu; Mg/ha) was calculated as
(8).
The wood specific gravity (ρ; g cm−3), which is the most important predictor for AGB (Ishihara et al., 2015), was measured from 31 trunk disks with approximately 3-cm thick which were obtained from 9 trees (the 2017_A, 2017_B, 2017_C, 2018_A, 2018_B, 2018_C, 2018_D, 2018_E, and 2018_F trees shown in Table 1). The trunk disks were completely dried in an oven, and the dry weights were measured. The volume of each disk was measured as follows: (1) The trunk disk was completely submerged in water in a large plastic box. (2) Water was added to the upper line. (3) The disk was removed as soon as possible. (4) Water was added to the upper line again. (5) The volume of the added water in (4) was measured. The ρ of each disk was calculated as the dry weight divided by the volume. The mean wood specific gravity () was calculated as follows:
(9),
(10),
where Wi, h (g) is the dry weight of the trunk from the height h (m) to the height h+1 (m) of the tree i, h+1 is the trunk disk's height next above the trunk disk at h, ρi, h (g cm−3) is the ρ of the trunk disk at h of the tree i, ρi, h+1 (g cm−3) is the ρ of the trunk disk at h+1 of the tree i, Vi, h (cm−3) is the volume of the trunk from h to h+1. In the case that there is no trunk disk next above the trunk disk at h, the tree height was adopted as the h+1. As a result, the ρ of P. glehnii was 0.462 g cm−3.
C-4 Leaf mass per area (LMA) and leaf distribution
LMA (g m−2) of P. glehnii was measured from two logged trees, which were the 2018_A and 2018_B trees shown in Table 1. LMA of current leaves and that of old leaves were separately measured using 60 samples each. The samples were obtained using a random sampling method:
(1) The logged tree i was cut into 3 parts: each part (j) was upper, middle, and bottom part.
(2) Each branch was cut at the base where it was branching from the trunk. The height from the bottom of each tree was marked on each branch. If the full length of a branch exceeded 2 m, the branch was cut to approximately 1.5-m length each. Accordingly, the length of branches was adjusted from 1.0 m to 2.0 m. The number of branches with 1.5-m length (Ni, j) was counted.
(3) Ten branches were selected from the adjusted branches in each part j using a random sampling method.
(4) The selected branch k was cut into short branches approx. 0.15-m length each. The number of branches with 0.15 length (Ni, j, k) was counted.
(5) Ten short branches were selected using a random sampling method.
(6) The selected short branches were divided into bare (leafless) parts and leafy parts.
(7) The leafy parts of the branches were cut at every node which indicated the age of leaves. The tiny pieces of the leafy parts of the branches were divided into those with current leaves and those with old leaves.
(8) A fistful of the tiny pieces with current leaves and a fistful of those with old leaves were randomly selected, respectively.
(9) All current and old leaves were separately clipped from the selected tiny pieces of the leafy parts of the branches. Their area (LAi, j, k, l, s; cm2) and their corresponding dry weight (LWi, j, k, l, s; g) were measured, where l is the leaf-age category (current or old leaves).
(10) All current and old leaves were also separately clipped from the non-selected tiny pieces. Dry weight of the clipped leaves from the non-selected tiny pieces (LWi, j, k, l, n; g) were separately measured.
LMA of the branch k (LMAi, j, k, l; g m-2) was estimated as follows:
(11).
The number of leaves in each LMA sample group was more than 200 leaves. The total dry weight of leaves of leaf-age l of the selected 10 short branches of the branch k (; g) was obtained as:
(12).
The total dry weight of leaves of leaf-age l of the branch k [LWi, j, k, l; g] was obtained as:
(13),
where Ni, j, k is the total number of short branches obtained from the branch k. The mean LMA of leaf-age l of the part j of the tree i (LMAi, j, l; g m-2) was calculated as:
(14).
The total dry weight of leaves of leaf-age l of the part j of the tree i [LWi, j, l; g] was obtained as:
(15),
where Ni, j is the total number of branches obtained from the part j of the tree i. The mean LMA of P. glehnii of the leaf-age l (LMAl; g m-2) was calculated as:
(16).
As a result, the mean LMA of P. glehnii of current leaves and that of old leaves were 329.039 g m-2 and 330.340 g m-2, respectively. However, these values should be used with caution, since the ranges of LMA of current and old leaves were varied depending on a tree height from 181.507 g m-2 to 498.099 g m-2 and from 210.027 g m-2 to 427.689 g m-2, respectively (Fig. 4).
Fig 4: Leaf mass per area (LMA) of Picea glehnii.
In addition, the mean leaf life-span of P. glehnii was calculated either based on leaf weight or on leaf area (LLSw and LLSa, respectively) as follows:
(17),
(18).
As a result, there was no difference in the two estimates and the mean leaf life-span of P. glehnii was estimated to be 4.0 years. The leaf age of current leaves were counted as 0 years.
Leaf distribution of P. glehnii was observed from five logged trees, which are the 2016_A, 2017_A, 2017_B, 2017_C, and 2018_B trees shown in Table 1 (Fig. 5).
Fig 5: Leaf distribution of Picea glehnii.
C-5 Leaf area index (LAI)
To obtain LAI, leaf mass of each tree i (LWi; kg) was estimated by following two steps. First, the ln(LWi) was estimated using the tree census data and the allometric equation as follows:
(19),
where a and b are parameters which depend on tree species (see Table 3). Second, to eliminate the bias introduced by inverse logarithmic transformation, LWi was obtained as
(20),
where CF is a correction factor obtained using Equations (5) and (6), in which the above-biomass terms were replaced on the leaf mass terms.
Table 3: Parameters, determination coefficient (R2), and correction factor (CF) of allometric equations of leaf mass.
NA means not available.
Basically, except for P. glehnii, the whole leaf area of each tree i (LAi, m−2) was calculated as
(21),
where LMA (g m−2) is leaf mass per area. The used LMA depends on tree species (see Table 4). For P. glehnii, the leaf area of current leaves and that of old leaves were separately estimated and summed as follows:
(22),
(23),
(24),
where LMAcurrent (g m−2) and LMAold (g m−2) are the mean LMA of current leaves and that of old leaves, respectively. LWi, current (kg) and LWi, old (kg) are the total leaf mass of current leaves and that of old leaves of the tree i, respectively.
Table 4: Leaf mass per area (LMA) of each tree species used in this study.
LAI of canopy in a 1000-m2 tree census area (LAIc; m2 m−2) was calculated as
(25).
In addition, using LAI-2200Cs (LI-COR), LAI of canopy (LAIc-2200; m2 m−2) and that of understory (LAIu-2200; m2 m−2) were observed in 2016 and in 2018. On August 2016, LAIc-2200 and LAIu-2200 were observed twice (after sunset on August 02 and before sunrise on August 03) at 105 observation points. The LAIc-2200 and LAIu-2200 at each observation point were obtained by the means obtained from twice observations. Since the LAI-2200C's data is a set of five light data obtained from five rings, if a set of the data contained one or more zero data, the set was excluded from the analysis. On June 23, 2018, LAIc-2200 and LAIu-2200 were also observed under near-perfect cloudy sky.
At additional 4 points, LAI of understory was observed using both clipping and LAI-2200C methods (LAIu_clip and LAIu_2200, respectively). The 4 observation points for understory was the same as described in C-3. All of understory vegetation was cut in a 1 m × 1 m quadrat. The leaves were clipped from the vegetation, and the leaf area was measured. After determining the quadrat location and before setting the quadrat, LAIu_2200 was also observed at the center of the quadrat and at the one small step left and right from the center of the quadrat. LAIu_2200 at each quadrat q was obtained by their mean. Correction factor of LAIu_2200 to LAIu_clip (CFLAIu) was calculated as
(26),
where LAIu_clip_q is LAIu_clip at the quadrat q, and LAIu_2200_q is LAIu_2200 at the quadrat q. As a result, CFLAIu was 0.790. LAIu_2200 obtained at each of 105 observation points (LAIu_2200_o) were corrected as
(27),
where LAIu_2200_corrected is the corrected LAI of understory.
C-6 Fraction of absorbed photosynthetically active radiation (fAPAR)
Photosynthetically active radiation (PAR) was observed using PAR sensors: LI-190R (LI-COR, Inc.) and the PAR-02D (Prede Co., Ltd.). Above the canopy, we observed incoming and reflecting PAR (PARabove↓ and PARabove↑, respectively) using two PAR-02D mounted on a drone which has gimbals to keep the sensors level. In the forest, we observed downward and upward PAR under the canopy (PARmiddle↓ and PARmiddle↑, respectively) and downward and upward PAR under the understory (PARunder↓ and PARunder↑, respectively) at 24 points: At four points, we observed PAR using a set of four PAR-02D fixed on a single pipe. Each set consisting of two PAR-02D for observing PARmiddle↓ and PARmiddle↑ which were installed at 2.2-m height and the other two PAR-02Ds for observing PARunder↓ and PARunder↑ which were installed at 0.2-m height. The PAR data were recorded at 10-seconds intervals using the CR-1000 (Campbell Scientific, Inc.) at each observation point. PARabove↓ and PARabove↑ were observed just above each PAR observation point using the drone. At the remaining 20 points, we observed PAR (PARmiddle↓, PARmiddle↑, PARunder↓, and PARunder↑) using the LI-190Rs. Two LI-190Rs were equipped with an LAI-2200C. We used two LI-2200C and four LI-190R. In order to keep a distance from just below the drone during the flight for safety, PARabove↓ and PARabove↑ were not observed just above each observation point but observed above the canopy within the 500 m × 500 m area. The PAR observation using the LI-190Rs was conducted at each observation point as the following sequences: (a) PARmiddle↓ and PARmiddle↑ in synchronization with a drone PAR observation, (b) PARmiddle↓ and PARmiddle↑ over the target understory, (c) PARunder↓ and PARunder↑ under the target understory, and (d) PARmiddle↓ and PARmiddle↑ over the target understory.
All of used PAR sensors were calibrated with the accurate PAR which was observed using two spectroradiometers (MS-700, EKO instruments B.V.) applying the direct and diffuse separate observation method (Akitsu et al., 2015a).
Fraction of absorbed PAR (fAPAR) was calculated at each observation point. fAPAR of the canopy (fAPARcanopy; unitless) was calculated as
(28).
fAPAR of the understory (fAPARunder; unitless) at each observation point was calculated as
(29).
Total fAPAR (fAPARtotal; unitless) at each observation point was calculated as
(30).
C-7 Location survey
The location of the 29 observation points was determined using a Global Positioning System (GPS) receiver Pathfinder Pro XR (Trimble) for navigation. After determining the location, it was surveyed using the a Global Navigation Satellite System (GNSS) handheld mapping devices MobileMapper 100 (Ashtech) and a GNSS antenna A-325 (Hemisphere GNSS, inc.). In addition, the location of PAR observation points where four PAR-02D were installed on a single pipe was surveyed by a GPS antenna A-100 (Hemisphere GNSS, inc.).
C-8. Sample mean and standard error of the mean within the 500 m × 500 m area
The sample means of LAI, AGB, and fAPAR were calculated as follows:
(31),
where is the sample mean, xi is the each sample data, n is the number of the sample. The standard error of the mean (SEM) indicates how far the sample mean of the data may be from the true population mean. It was calculated as:
(32),
(33),
where s is the standard deviation.
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Data Set Usage Rights
| This dataset is provided under a Creative Commons Attribution 4.0 International license (CC-BY 4.0) (https://creativecommons.org/licenses/by/4.0/). |
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