Leaf Area Index (LAI), a measure of the amount of plant leaf material in an ecosystem, is defined as one half of the total green leaf area per unit ground surface area (Chen & Black, 1992; Fernandes
The Global Climate Observing System (GCOS) has specified the need to update and validate global LAI predictions systematically. The Land Product Validation (LPV) sub-group of the Committee for Earth Observation Satellites (CEOS) developed a strategy for validating the predictions (Fernandes
The objective of this study is to compare the three different schemes adopted within the ICOS, NEON, and TERN networks instead of searching for the most optimal, new sampling scheme. We carried out the intercomparison measurements with digital hemispherical photography during diffuse light conditions, adopting the established ICOS, NEON, and TERN sampling schemes. We use data from a measurement campaign conducted in 2021 at the Järvselja Radiation Transfer Model Intercomparison (RAMI) (Widlowski
The measurements were conducted in a 63-year-old, mature fertile silver birch (
Three sampling schemes corresponding to the ICOS, NEON, and TERN schemes (each consisting of 36 individual sampling points) were used in this study (Figure 1). The ICOS (Gielen
Three camera setups were used (Table 1), with each operated by a different individual. At each sampling point, the DHP images were taken in immediate sequence with the three different camera setups under overcast, diffuse illumination conditions on August 30, 2021. All the images were stored in a camera-specific raw format. The cameras were fixed to a tripod approximately 1.3 m above the ground at each measurement point. The top of the image was oriented to the magnetic north using a compass. Each camera was pointed upwards and levelled using a two-axis bubble level, the focus setting on the lenses was set to infinity. The weather was dry, the ambient temperature was 15–20 °C, with almost no wind.
Camera setup specifications.
Variable | Camera setup | ||
---|---|---|---|
I | II | III | |
Camera model | Canon EOS 5D | Canon EOS 600D | Canon EOS 2000D |
Fisheye Lens | Sigma 8 mm F3.5 EX DG | Sigma 4.5 mm F2.8 EX | Sigma 4.5 mm F2.8 EX |
Camera height | 1.3 m | 1.3 m | 1.3 m |
Camera orientation | magnetic north | magnetic north | magnetic north |
Initial ISO | 640 | 400 | 100 |
Initial exposure compensation | 0 | 0 | −1 |
Initial shutter speed | 1/32 | 1/60 | Aperture priority |
Image aquisition | single | single | auto-bracketing |
F-stop | f/8 | f/8 | f/8 |
Light metering | Spot | Evaluative | Evaluative |
Sensor size, pixels | 12.8×106 | 18.0×106 | 24.1×106 |
Raw image radius* | 1386 | 1450 | 1730 |
Image radius on senor (pixels for 90 deg. zenith angle).
For camera setups I and II, the camera settings (shutter speed and ISO) were adjusted manually at each sampling point to take images with no overexposure and to keep pixel maximum value within the interquartile range of the camera's dynamic range (Lang
All acquired DHP images were processed with the free image processing software Hemispherical Project Manager (
The gap fraction function
The layout of image locations, i.e. sampling grid has some effect on the gap fraction estimate at stand level (Figure 2). The most significant differences between the sampling schemes occur near the zenith and smaller viewing angles. The differences in camera setups can also be noticed. Results from all setups correlate well with each other with R2 values around 0.99, but setups I and II give a more similar gap fraction across view angles (average difference 0.005), whereas setup III deviates more from setups I and II (average difference 0.01 from setup I). Gap fractions from setups I and II consistently differ a little. Setup I almost always has a slightly smaller value for the gap fraction in all viewing zenith angles except the very small and the very large ones (Figure 2).
Based on the gap fraction, PAIe estimates also vary between schemes and camera setups. The overall PAIe, for the test site based on all schemes and all camera setups, comprising a total of 324 images, was 3.09 with a standard error (SE) of 0.02. Following Majasalmi
Estimations of effective plant area index PAIe for each sampling scheme taken with each setup and their corresponding standard errors (SE).
Camera | Sampling network | |||||
---|---|---|---|---|---|---|
ICOS | NEON | TERN | ||||
PAIe | SE | PAIe | SE | PAIe | SE | |
Setup I | 3.11 | 0.06 | 3.38 | 0.05 | 3.11 | 0.06 |
Setup II | 2.99 | 0.06 | 3.22 | 0.05 | 2.94 | 0.05 |
Setup III | 2.90 | 0.05 | 3.21 | 0.04 | 3.08 | 0.06 |
All three cameras took images at the same marked locations within a few minutes. Still, there are always slight deviations in positioning the cameras precisely at the same spot with identical levelling and orientation. This causes some variability in the recording of the scene content. Each camera operator processed the images taken with a particular camera. This also introduces some small variability into the results because (1) the markers of open sky pixel sampling markers can be freely chosen and (2) identification of canopy gaps at larger view zenith angles for extracting sky pixel samples requires some practicing and experience. However, Lang
As the forest stand is natural, i.e. not perfectly homogeneous, it can be expected that the PAIe obtained at different measurement points would vary, for example for setup I, the PAIe calculated from single images ranges from 2.45 to 4.06 (Figures 4, 5, 6). However, the PAIe includes both – the true value and uncertainty that is introduced during the measurements and data processing.
Setup III had a problem with overexposure due to the semiautomatic imaging mode. Some images were out of focus – possibly due to accidental turning of the focus wheel, resulting in loss of detail in smaller gaps, leaves and branches (Figure 8). Even though setup III had the highest pixel resolution, the loss of small details due to the wrong focus setting combined with overexposure did contribute to inconsistent results at many sampling points demonstrated by PAIe values compared to setups I and II (Figure 3B, C). We identified overexposed measurements of setup III based on possible maximum pixel values according to the camera radiometric resolution (also known as bit depth) and found that the concordance of the rest (i.e. correct measurements with setup III) with the other measurements was strong (Figure 3B, C).
The impact of image overexposure on the measurements of the canopy gap fraction and consequently PAIe was not constant. The PAIe of some of the overexposed measurements was not much different from setup I and setup II, which can be attributed to the magnitude of overexposure and also to the amount of mixed pixels in the scene where “burn-in” effects occur first. Additionally, the out-out-of-focus problem tended to fill small canopy gaps of few pixels in size, effectively decreasing the canopy gap fraction. Therefore, it is the responsibility of the camera operator to keep the camera in the linear sensitive range of the sensor to avoid overexposure and systematic errors in the canopy gap fraction and PAIe.
The PAIe values obtained at each sampling point correlated well between setups I and II with R2 values of 0.97 for ICOS, 0.89 for NEON, and 0.92 for TERN (Table 3). The differences in correlation are probably due to the uncertainty of measurements and cannot be attributed to a particular sampling scheme. Overexposed measurements decreased substantially the correlation of setup III with the others. However, in the subset of correct measurements that did not suffer from overexposure, the correlations were of the same strength than between setup I and setup II (Table 3).
Linear relationships between effective plant area index between three camera setups by sampling schemes and using only correct (C) i.e. non-overexposed images of setup III or all (A). RSE is model residual error and N is the number of observations.
Camera setup | Sampling | Linear regression |
||||||
---|---|---|---|---|---|---|---|---|
Network | Subset | R2 | a | b | RSE | N | ||
I | II | ICOS | A | 0.97 | 0.21 | 0.89 | 0.06 | 36 |
I | II | NEON | A | 0.90 | 0.05 | 0.94 | 0.10 | 36 |
I | II | TERN | A | 0.92 | 0.21 | 0.88 | 0.09 | 36 |
I | III | ICOS | A | 0.28 | 1.55 | 0.45 | 0.27 | 36 |
I | III | ICOS | C | 0.92 | 0.10 | 0.96 | 0.10 | 21 |
I | III | NEON | A | 0.23 | 1.90 | 0.40 | 0.23 | 36 |
I | III | NEON | C | 0.84 | −0.52 | 1.15 | 0.12 | 23 |
I | III | TERN | A | 0.71 | 0.55 | 0.81 | 0.18 | 36 |
I | III | TERN | C | 0.84 | 0.10 | 0.97 | 0.13 | 24 |
II | III | ICOS | A | 0.29 | 1.44 | 0.50 | 0.27 | 36 |
II | III | ICOS | C | 0.93 | 0.02 | 1.02 | 0.09 | 21 |
II | III | NEON | A | 0.17 | 2.15 | 0.35 | 0.24 | 36 |
II | III | NEON | C | 0.85 | −0.73 | 1.28 | 0.12 | 23 |
II | III | TERN | A | 0.77 | 0.36 | 0.92 | 0.16 | 36 |
II | III | TERN | C | 0.87 | 0.16 | 1.01 | 0.12 | 24 |
As we noticed some systematic differences in camera setups I and II, we conducted an experiment where the images of the ICOS scheme were cross-processed by the camera operators to see if there was some human error or bias. We found a small difference in the estimated gap fraction around the large viewing angles (Figure 8) but overall, the results are not statistically different from each other with a one-tail t-test p-value of 0.486.
An estimation of plant or leaf area index for a test site using optical measurements of canopy gap fraction or gap size distribution is influenced by many factors that introduce uncertainty into the results. In our study, we focused on the sampling design, field measurements, and image processing. Sampling design, in an ideal case, (1) provides that enough measurements are made to obtain sufficiently narrow confidence intervals for the estimates and (2) ensures that repeated measurements in time series are comparable by fixing, for example, the arrangement of permanent and random sampling points. The stage of field measurements involves uncertainties related to configuring cameras, locating measurement points, setting up the camera on the point, observing illumination conditions according to the measurement protocol, determining exposure settings to keep the camera in its linear sensitivity range, and the physical process of taking images. Image processing involves extracting the data from camera output files, applying radiometric and geometric corrections, selecting the image processing method and its parameters and software, and calculating the gap fraction according to the view zenith angle from the processed images for the Equation (2). Prediction of the true green leaf area index further from the canopy gap fraction includes another wide range of models and methods.
We used three different sampling schemes and obtained the PAIe in the range of 2.99–3.26 for the Järvselja RAMI birch stand. The result is close to Kuusk
Proper camera configuration is essential to get consistent and comparable measurements. As our goal was to measure incident radiation as precisely as possible, it was necessary to avoid over- or underexposing the image. In our testing, setup III illustrated possible issues with overexposed and out-of-focus images where smaller details of the canopy were often lost (Figure 7C). This resulted in occasional inconsistent results when compared to setups I and II, which had a very good correlation with each other (Figure 3A). When tested afterwards, reducing the focus setting of the camera decreased the effective PAIe by 1.3% on average when the focus was turned down from infinity to 0.2 meters. When intentionally overexposing the images the PAIe decreased significantly. When an image with no overexposed pixels was compared to another one that had 0.1% of pixels overexposed to the maximum digital value of the sensor, the PAIe decreased by around 1.7%. When the amount of overexposed pixels was around 0.4%, the PAIe decreased substantially by 14%. Here we see that for obtaining data for the canopy gap fraction it is crucial to operate hemispherical cameras in their linear sensitive range.
Our intercomparison test allowed us to search for possible influences of cameras and image analysts on the canopy gap fraction and calculated PAIe. There was very strong correlation between the PAIe of setups I and II with a small systematic difference. In a time series this could be interpreted as a possible change in the PAIe, but in our experiment the measurements were done almost within a few minutes in time that excludes the change in the PAIe. For image processing we used a well-established method LinearRatioSC that, by its principle, is an analogue for LAI-2000 when compared to various threshold-based and pixel classification-based algorithms. To apply LinearRatioSC the image analyst has to place sky pixel sampling marks for constructing open sky reference which involves a subjective decision to some extent.
We tested the influence of operator subjectivity by cross-reprocessing the ICOS scheme images that were taken with setup I and II by corresponding operators. However, no statistically relevant differences were found in the gap fraction calculated from the same set of images processed by two different people (Figure 8). This indicates that there is a more fundamental reason stemming from the raw images or cameras themselves. We also tested the effects of vignetting and projection models, subtracting the dark images, and the effects of different versions of dcraw. They all had negligible or no impact on the results. Setup I consistently had a little but smaller gap fraction than setup II. Addressing all these possible sources of uncertainty, we conclude that the difference may be caused by light scattering and colour crosstalk within cameras. Another possible reason could be a small systematic difference in camera height over the ground or vertical levelling during field measurements. For example, a two-degree error in camera levelling changes the path length at 57° view zenith angle up to three metres (10%) in the stand. As a conclusion, we recommend always to carry out intercomparison measurements with old and new cameras when devices are upgraded.
Overall, we illustrate that the three different employed schemes produce comparable results for estimating the gap fraction and PAIe. Although different setups and schemes gave differing results, with Anova Two-Factor test p-values <0.001, there was no connection between the camera setup used and the scheme (p-value 0.178). Our testing shows that more evenly spread out sampling schemes are more consistent with each other, exemplified by ICOS and TERN, and could be more representative of the overall conditions of the forest. The results vary when different cameras with different operators are used, but it appears that 36 images employed by all three setups are enough to deliver an average with sufficiently narrow confidence limits. It depends on the application what type of cameras and how high accuracy of calibration is required. From this study we cannot conclude which scheme or camera setup is best used in the field, because all these established schemes vary slightly in their intended use and purpose and are not necessarily intended to yield site level mean estimates. But we did conclude that some layout schemes may have inherited problems when applied to estimate the PAIe for our 1-hectare plot. The CEOS Land Product Validation Subgroup (Morisette
The CEOS LPV is currently in the process of selecting supersites with a fully characterized land surface and vegetation cover for the parameterization of 3D radiative transfer models. Our study contributes towards establishing the procedures for uncertainty estimation by evaluating potential systematic errors stemming from collecting in situ measurements with DHP using different scheme designs, methods and devices currently in use at most LPV Supersites.
Future work can include efforts to investigate the impact of sampling schemes on the LAI estimation of forests from other indirect LAI measurement methods (e.g. terrestrial laser scanner and LAI-2200). Ground-Based Observations for Validation (GBOV) of Copernicus Global Land Products (GBOV, 2023) uses schemes studied in this paper to validate its decametric and hectometric satellite-derived LAI products (Brown