The data was collected on the site of ECOSENSE project, a multiscale research initiative focused on quantifying and modeling spatio-temporal dynamics of ecosystem processes through sensor networks. The site is located in a mixed forest system dominated by beech with patches of conifer trees near Ettenheim, Germany . The site is located at 48.2679° N, 7.8783° E with elevation ranging from around 450 to 520 ma.s.l. (Fig. a). The basal area of the forest site is 30.9 m2ha-1. The diameter at breast heights vary with a mean = 23 cm, median = 16 cm, standard deviation 15 cm and maximum of 76 cm.

The sample points for DHP and LT measurements were setup in a regular grid of 50 m distance. Since litter traps are restricted to deciduous stands, sample points containing coniferous trees were excluded, resulting in 15 sample points (Fig. b). The average canopy coverage of the sample points amounts to 97.6 % and varies between 93.6 % to 98.8 % and average canopy heights of the sample points vary between 22 to 27 m (Fig. ) . Multi-temporal LAI estimates with LTs and DHP were acquired before the start to the end of the leaf fall between 20 September to 16 December 2024 (Fig. ).

At each sample point, a LT (40 cm × 60 cm × 30 cm) was installed to systematically collect leaf litter (Fig. d). All litter traps were placed directly on the ground surface. Each LT was equipped with a drainage fleece to prevent leaf loss and to minimize potential soil–fauna interference. No evidence of litter removal or seed predation was observed during the study period. The trap height of 30 cm was chosen to prevent the escape of leaves due to wind, ensuring accurate collection within the box. We used a surface area of 0.24 m2, as a compromise between efficiency and ensuring that enough leaf material was sampled. These design choices follow established best practices , ensuring that the data collected on leaf litterfall dynamics are reliable and that external factors have minimal influence on LAI estimation. For all sample points, LTs were emptied eight times in an approximately bi-weekly setting throughout the leaf fall phase (Fig. ), resulting in a high-resolution time series.

Many studies estimate leaf area from LTs by determining the specific leaf area (SLA) of subsamples and scaling these values to the total collected biomass . However, this approach introduces uncertainties due to species-specific and seasonal variability in SLA. To minimize these sources of error, we directly measured the area of all collected leaves by scanning them from photographs.

We systematically arranged all collected leaves from LTs at each time step on a 2 m × 1 m white background. The leaves were carefully pressed flat using a Plexiglass sheet to minimize distortions caused by folds or other deformations that could affect the accuracy of leaf area measurements. Subsequently, photographs were taken from a top-down perspective using a Sony Alpha 7 camera equipped with a 35 mm lens. The images were automatically rectified orthographically using ArUco markers placed in the corner of the white background (Fig. 1). The leaves were identified using pixel-wise segmentation, applying grayscale thresholding (threshold value: 240) to generate a binary mask that effectively distinguished leaf regions from the background (Fig. 1) . The total leaf area was calculated by summing the detected pixels and converting them to  m2. These values were then summed over time. For each subsequent sampling period, the LT leaf area was calculated by subtracting the cumulative LT leaf area of all previous sampling periods from the total and dividing it by the LT area (Fig. ).

We compared the LT-derived LAI with DHP-derived LAI estimates obtained with varying VZA of the photographs. In this study, the term VZA refers to the range from 0° to the specified VZA value. Specifically, a VZA of 20° represents the range 0–20°, while a VZA of 30° corresponds to 0–30°, and so forth for all VZA values. This definition applies throughout the study whenever VZA is mentioned (Fig. ). Additionally, we tried the Hinge-angle method which assumes that canopy gaps are more evenly distributed around 57.5°, minimizing the impact of clumping and leaf distribution irregularities . The Hinge-angle method was implemented in hemispheR using a small ring range of 55 to 60°, which limits the calculation of clumping due to the insufficient number of rings and segments for a comprehensive assessment. We compared the LT and DHP-derived LAI using all combinations of clumping methods, including no clumping, and VZA. The agreement for each site and across the time series was assessed using absolute error, slope, and coefficient of determination (R2), derived from a linear regression model.

Furthermore, we evaluated the ability of DHP to represent actual LAI for different leaf fall phases using the best-fitting VZA and clumping indices. To classify the phases we investigated the temporal patterns into three phases: onset, peak, and end of leaf fall, using a breakout calculation with the segmented package in R (Fig. ). This approach identified breakpoints based on significant changes in LAI values.

In addition to the direct comparison between DHP- and LT-derived LAI, we examined potential confounding effects related to the VZA. Specifically, we tested whether canopy structural metrics (gap fraction (GF), clumping index (CI) and G(θ)) were correlated with VZA. To evaluate the relative influence of these factors compared to the influence of the spatial footprint on LAI estimation accuracy, we applied a random forest model with absolute error as the response variable and GF, CI, G(θ), and the sampling radius as predictors (Eq. ). 3abs_error=f(GF,CI,G(θ),r)

The corresponding sampling radius for each VZA was calculated geometrically from the camera height (hc) and mean canopy height (hcanopy) using the upper limit of the respective VZA range (θvza): 4r=(hcanopy-hc)×tan⁡(θvza)

Random forest models were implemented using the randomForest package in R .

We evaluated a location-specific calibration of DHP-based LAI estimates using the LT-derived LAI to evaluate if local biases can be overcome for an efficient and accurate long-term monitoring. We implemented the location-specific calibration using a generalized linear mixed model (GLMM). Since the data were not normally distributed and exhibited left skewness, the GLMM was particularly well-suited for handling these distributional characteristics. Additionally, by including a random intercept, we aimed to minimize the influence of sample point-specific errors and capture the underlying location-specific differences more accurately. Specifically, we applied a GLMM with an inverse Gaussian distribution and a log link function using the glmer function from the lme4 package . This approach allowed us to incorporate LAI values adjusted for the LXG1 clumping index, further refining our location-specific calibration. The following equation describes the GLMM used in the analysis: 5LT LAI∼DHP LAI×leaf fall phase+(1|sample point)

This formulation incorporates a random intercept for each individual sample point, recognizing that each sample point may have its own unique random influences (such as local variations in tree density, tree sizes, or growth conditions) while preserving the overall relationship between LT and DHP derived LAI across different leaf fall phases as fixed effects. The interaction between leaf fall phase and DHP LAI allows for phase-specific slopes, capturing differences in the relationship between DHP and LT LAI across different stages of leaf fall. The location-specific calibration aims to reduce local biases caused by stand heterogeneity and should not be considered a universal correction applicable to other forest types. All analysis was done in R programming language version 4.2.2 .

We determined the spatial footprint represented by LT using different combinations of VZA and clumping indices. The Hinge method consistently demonstrated a lower R2 value and a reduced slope (0.3), as well as a higher absolute error (1.73), compared to the optimal combination of the clumping method and VZA. Consequently, it was excluded from further analysis. VZA between 20 and 50° exhibited the lowest absolute error, with a minimum at 20° for LXG1 (0.574), corresponding to 6 % of the highest measured LAI for this clumping index. The slope between LT and DHP-based LAI gradually decreased with increasing view zenith angle. The lowest systematic bias was observed between 20 and 30°, where slopes were closest to 1. At 10°, there was a tendency for overestimation, while for VZA above 30°, LAI estimates increasingly underestimated true values. LXG1 showed the least systematic bias at 20° (slope = 0.942). In contrast, R2 showed a strong increase from 10 to 30°, after which it remained at a high level. The maximum R2 was observed for effective LAI at 60° (0.885), with similar peak values for LXG1 (0.884), LX (0.875), and LXG2 (0.872) (Fig. ).

We observed that lower VZA values were associated with stronger agreement between DHP- and LT-derived LAI (Fig. ). Other canopy structure parameters such as gap fraction decreased VZA, while clumping index increased with VZA and the projection function G(θ) showed no consistent trend across the range VZA (Fig. ). To quantify the relative influence of these parameters on LAI estimation error, we applied a random forest model with absolute error as the response variable and GF, CI, G(θ), and sampling radius as predictors (Eq. ). The model revealed that the sampling radius had the highest variable importance (52.96), followed by the clumping index (40.26), gap fraction (39.20), and G(θ) (37.95).

To assess the ability of DHP to accurately predict temporal LAI dynamics, the mean values from all plots for the LT were compared to the mean values from all plots for DHP using the LXG1 clumping index, which exhibited the closest to 1:1 slope with minimal error (Fig. ). The comparison reveals that the temporal trends of DHP-derived LAI and LT-derived LAI are similar, with the mean values closer together at the end of the time series and an increased spread at the onset of leaf fall. Moreover, the 5th–95th percentile range is broader at the start of leaf fall with a slight underestimation and becomes narrower towards the end of leaf fall (Fig. ).

In order to further investigate the LAI estimation over time, we set up three phases (onset, peak and end) of the leaf fall to assess those phases separately (Fig. ). We analyzed the differences of leaf fall phases based on the LXG1 LAI (with clumping). The analysis shows almost no systematic over- or underestimation (Fig. ).

We selected the LXG1 clumping index for the site specific calibration because it produced the lowest absolute error while maintaining almost no systematic over- or underestimation with high correlation in comparison to LXG2 which had higher systematic over-/underestimation with similar correlation. At a VZA of 20°, the site-specific calibration reduced the mean absolute error (MAE) by 52 %, from 0.574 to 0.275, demonstrating a substantial improvement in accuracy. The greatest error reduction within the phases was observed during the onset phase, where MAE decreased by 68 % from 0.81 to 0.26. The systematic over- underestimation and correlation stayed at a similar level with a slope increase of 0.03 and an increase in R2 from 0.88 to 0.97 (marginal R2=0.94, conditional R2=0.97) (Fig. ).

Our findings demonstrate that the spatial footprint of LTs corresponds well with specific VZA ranges, enhancing our understanding of LAI estimation using DHP. By correlating LT-derived LAI with DHP-derived LAI across multiple VZA ranges (0–10 to 0–90° in 10° increments), we identified an optimal VZA range (20°) for DHP analysis. The Hinge Angle method, which has shown strong performance in other studies , did not align as well with LT-derived LAI in our dataset and was therefore less suitable for our analysis, although no clumping index was applied with the implementation using HemispheR. Instead, clumping indices proved crucial in improving DHP accuracy, particularly at lower VZA values, where they helped reduce LAI underestimation compared to effective LAI without clumping corrections (Fig. ). Among the tested clumping indices, LXG1 exhibited the best performance, as it is particularly effective in accounting for smaller canopy gaps and performing well in dense broadleaf forests .

We observed that the correlation between DHP- and LT-derived LAI was highest at lower VZA values (< 30°) and declined with increasing VZA. This trend corresponds to reduced compensation of clumping effects and a decrease of gap fraction with increasing VZA (Fig. ). However, the random forest model indicates that the spatial footprint, represented by the sampling radius, shows the strongest influence on absolute error. While clumping and gap fraction also contribute, as reflected by their variable importance, the spatial footprint remains the dominant factor. These findings suggest that the LTs are best represented at a VZA around 20°, supporting previous recommendations to use a VZA range of 20–40° for DHP-based LAI estimation in temperate forests .

In contrast to our results of an ideal VZA range around 0–20°, recommended a VZA range of 30–60°. At 30–60° VZA, we observe an underestimation of LAI, which suggests that further investigation into the impact of VZA range on estimated LAI from DHP would be valuable. Our finding that LAI underestimation increased with higher VZA values is consistent with previous research attributing this effect to larger canopy gaps and overestimated openness . We show that decreasing clumping correction influences the underestimation at higher VZA. Also note that a small VZA range offers to resolve spatial LAI variation within small spatial gradients. Such focused areas are well-suited for validating LAI products from high-resolutions satellite data, which often have spatial resolutions around 10–30 m (e.g. Sentinel-2 or Landsat data). As shown by our results, the choice of clumping index remains critical, as effective LAI values without clumping corrections showed significant underestimation and higher absolute error. Here, the best performing index was the LXG1. This clumping index is similar to the CLX index , which incorporates gap fraction averaging as a core principle and has shown good results for seasonality. The key difference between the two indices is that LXG1 focuses on refining weighting functions for segment-based calculations, while CLX emphasizes gap fraction averaging.

Overall, our findings emphasize the importance of carefully selecting VZA ranges and incorporating canopy structure corrections in DHP-based studies. While VZA is often underemphasized in DHP research, our results demonstrate that it has a substantial impact on absolute error and systematic bias, and strongly influences the estimation of clumping effects and gap fraction. Future studies should prioritize VZA optimization and clumping index selection to enhance the accuracy of DHP-derived LAI estimates.

Our results demonstrate that DHP with LXG1 effectively captured seasonal LAI trends, accurately reflecting variations throughout the leaf fall. Discrepancies decreased throughout the leaf fall period, ultimately resulting in no discrepancies at the final sampling period due to the imposed value of 0 for both DHP and LT LAI. This reduction results from subtracting woody material from the PAI like previously done from , ensuring that only the LAI is analyzed. The fraction of woody material is known to be the main cause of error in deciduous stands shown . As we substracted the LAI from the final time step after the leaf fall, showing only woody material, our results are not affected by systematic overestimation. In addition, we observe higher variability in the onset phase due to the increased structural complexity of fully developed canopies, leading to greater variability in LAI estimates due to the heterogeneous light environment and mutual shading effects, which cannot be fully accounted for by clumping alone .

To address local variability in DHP-derived LAI estimates, we implemented a location-specific calibration using a generalized linear mixed model. This model incorporated the LXG1-adjusted LAI as a fixed effect, with individual sample points included as random intercepts to account for spatial heterogeneity. The LXG1 clumping index was selected based on its superior performance in minimizing systematic error across all VZA configurations (Fig. ).

Importantly, we integrated the leaf fall phase as an interaction term, allowing the model to adjust slope parameters depending on the temporal dynamics of leaf fall. This phase-specific correction significantly improved model fit, particularly during the early stages of leaf fall where variability in canopy structure is highest. At a VZA of 20°, the calibrated model reduced the absolute error by 52 %, with the largest improvements observed during the onset phase, particularly for LAI values between 4 and 6 (Fig. ).

These results underscore the value of combining DHP-derived LAI with LT reference data in a mixed-effects modeling framework. By explicitly modeling spatial and temporal sources of variation, the approach enhances the robustness and accuracy of LAI estimation across different forest conditions. This location-specific model can be extended to similar temperate deciduous forests, provided that data collection includes temporal labeling of leaf fall phase. Notably, since the leaf fall phases align with distinct LAI value ranges, phase classification could also be approximated directly from observed LAI trends in future applications.

Fisheye imagery is particularly sensitive to sky conditions and exposure settings, which can alter gap fraction retrieval and thereby bias LAI estimates . Most images in this study were acquired under diffuse or moderately bright conditions using fixed settings (f/5.6 , 1/100 s exposure), with visual inspection by experienced operators to minimize overexposure. Nevertheless, occasional variations in illumination and exposure could have influenced canopy–sky classification, particularly under high-contrast sky conditions. While we attempted to maintain consistent settings, logistical constraints prevented complete standardization across sampling dates. Despite these potential limitations, the consistency of DHP–LT correlations across measurement dates suggests that exposure variability introduced only minor bias in our dataset. Future applications should consider capturing and processing RAW fisheye images and aperture priority mode with -1 EV exposure compensation. This ensures linear radiometric scaling and reduces sensitivity to overexposed images.

The parameterization of azimuthal segments and zenith rings influences clumping index calculations, as these parameters define foliage distribution patterns and serve as the basis for clumping correction . However, determining optimal parameterization remains challenging. In later leaf fall stages, a reduced number of azimuth segments may be sufficient, as increased canopy openness diminishes the need for detailed segmentation .

LT data are primarily suitable for deciduous forest stands, as they accurately capture seasonal leaf shedding but do not effectively represent evergreen canopies . In this study, sample points containing conifer trees were excluded due to the lack of validation data for DHP. While DHP methods are highly reliable for deciduous forests, they can also be adapted for coniferous stands. However, an alternative validation approach is required, as LT-based LAI estimation is unsuitable for conifers, given that litter traps primarily capture fallen foliage and do not account for retained needles.

Future research should explore allometric approaches for validating DHP-derived LAI in evergreen forests. One promising method involves deriving LAI for individual branches through direct, destructive measurement, and subsequently upscaling these estimates to the entire canopy by counting branches per tree . This upscaling can be assisted by structural data from Terrestrial Laser Scanning (TLS) or photogrammetry, which allows for the detailed quantification of branching architecture. Such an approach provides a non-destructive, yet scalable reference for validating DHP in coniferous stands and could greatly enhance LAI estimation in evergreen-dominated ecosystems.