Digital Hemispherical Photography Research Articles

A spectral–structural characterization of European temperate, hemiboreal, and boreal forests

Abstract. Radiative transfer models of vegetation play a crucial role in the development of remote sensing methods by providing a theoretical framework to explain how electromagnetic radiation interacts with vegetation in different spectral regions. A limiting factor in model development has been the lack of sufficiently detailed ground reference data on both the structural and spectral characteristics of forests needed for testing and validating the models. In this data description paper, we present a dataset on the structural and spectral properties of 58 stands in temperate, hemiboreal, and boreal European forests. It is specifically designed for the development and validation of radiative transfer models for forests but can also be utilized in other remote sensing studies. It comprises detailed data on forest structure based on forest inventory measurements, terrestrial and airborne laser scanning, and digital hemispherical photography. Furthermore, the data include spectral properties of the same forests at multiple scales: reflectance spectra of tree leaves and needles (based on laboratory measurements), the forest floor (based on in situ measurements), and entire stands (based on airborne measurements), as well as transmittance spectra of tree leaves and needles and entire tree canopies (based on laboratory and in situ measurements, respectively). We anticipate that these data will have wide use in testing and validating radiative transfer models for forests and in the development of remote sensing methods for vegetation. The data can be accessed at Hovi et al. (2024a, https://doi.org/10.23729/9a8d90cd-73e2-438d-9230-94e10e61adc9) (for laboratory and field data) and Hovi et al. (2024b, https://doi.org/10.23729/c6da63dd-f527-4ec9-8401-57c14f77d19f) (for airborne data).

Accuracy assessment of LAI, PAI and FCOVER from Sentinel-2 and GEDI for monitoring forests and their disturbance in Central Germany

ABSTRACT Forest monitoring benefits from biophysical parameters such as Leaf Area Index (LAI), Plant Area Index (PAI) and fractional vegetation cover (FCOVER) and can be obtained from optical and LiDAR remote sensing, such as Sentinel-2 (S2) and the Global Ecosystem Dynamics Investigation (GEDI). While GEDI-derived products consider all phyto-elements, those from S2 refer to green elements only. Apart from individual accuracies, systemic deviations among products are thus expectable. However, products from S2 and GEDI lack inter-comparison. We evaluated S2 and GEDI-derived LAI, PAI and FCOVER with digital hemispherical photography observations (DHP) in a forest disturbance hotspot in Germany across various forest conditions, including vital stands, standing deadwood and clearings. We found moderate to high agreement with in-situ data, with highest accuracy for S2-derived LAI (R2 = 0.54) and GEDI-derived FCOVER (R2 = 0.73). Agreements between S2 and GEDI products were low, which we attribute to systematic influences of woody components, GEDI’s limitations in sloped terrain, and saturation of optical signals in dense canopy. In conclusion, findings suggest that while GEDI is effective in dense canopies, S2 products are beneficial for monitoring forest recovery. We also see potential for synergistic use in monitoring standing deadwood for habitat mapping and fire risk assessment.

Validation of the vertical canopy cover profile products derived from GEDI over selected forest sites

Canopy cover (CC) quantifies the proportion of canopy materials projected vertically onto the ground surface. CC is a crucial canopy structural variable and is commonly used in many ecological and climatic models. The vertical CC profile product is currently available from the Global Ecosystem Dynamics Investigation (GEDI). However, detailed information about the accuracy and uncertainty of the GEDI vertical CC profile product remains limited. The objective of this study is to validate the GEDI CC product over selected forest sites using reference values derived from digital hemispherical photography (DHP), airborne laser scanning (ALS) point clouds, and simulated waveforms. The accuracy of CC was quantified and analyzed regarding GEDI observation conditions, waveform processing, and estimation methods. The results show that the GEDI total CC correlates well with those estimated from DHP, ALS, and simulated waveform data (r2 = 0.65, 0.71, and 0.71, respectively) but is systematically underestimated (bias = −0.05, −0.11, and −0.07, respectively) based on reference data. Compared with the ALS-estimated CC, needleleaf forest shows the highest correlation for vertical CC (r2 ≥ 0.65) and shrubland shows the lowest bias for total CC (bias = −0.13). The mean absolute error (MAE) of the GEDI CC decreases from 0.15 to 0.09 as the estimation height increases from ground to 35 m. The GEDI total CCs derived from the waveform interpretation algorithms A2 and A6 display the highest r2 (≥ 0.6) and smallest RMSE (≤ 0.23) compared to those of the other algorithms. The CC accuracy increases with beam sensitivity and decreases with increasing canopy cover. The GEDI CC was improved at moderate CC values using a canopy-to-ground backscattering coefficient ratio (ρv/ρg) determined with the regression method. The partial difference between GEDI CC and ALS CC is attributed to definitional discrepancies. Further improvement of the CC algorithm can be made by using vegetation-specific waveform processing algorithms and realistic ρv/ρg values.

Mapping canopy cover for municipal forestry monitoring: Using free Landsat imagery and machine learning

Trees across the urban-rural continuum are recognized for their ecological importance and ecosystem services. Municipalities often utilize spatial canopy cover data for monitoring this resource. Monitoring frameworks typically rely on fine-scale maps derived from very high spatial resolution sensors, which are high quality but expensive and unwieldy for consistent wide-area monitoring. In this paper, we explore how free Landsat imagery, supported by very high-resolution imagery interpretation and/or digital hemispherical photographs, can be used to effectively map canopy cover at a scale appropriate for municipal monitoring. We compare linear models and random forest machine learning for predicting canopy cover across a landscape (general) and within specific land covers (specialized). We create 2018 canopy cover maps and track progress towards forestry objectives in a region of southern Ontario, Canada. Random forest models using all reference data perform best for general use (R2: 0.90, RMSE: 10.1 %), separating non-canopy vegetation (e.g., agricultural fields) from tree canopy. Specialized models are useful in forest land cover patches, where hemispherical photographs relate with Landsat at a moderate strength (R2: 0.67, RMSE: 2.73 %), and in residential areas, capturing the totality of canopy cover variation (R2: 0.85, RMSE: 5.66 %). Accuracy was assessed with standard cross-validation, which is useful given limited resources. However, following best practice, an independent reference sample was also leveraged to assess the best general model (R2: 0.86, RMSE: 11.4 %), indicating that cross-validation was slightly overoptimistic. Results show that Caledon, a rural-dominant municipality within the study area, is the greenest (34 % canopy cover). The two cities (Brampton and Mississauga) have 15.9 % and 17.5 % canopy cover. Residential canopy criteria indicate “Good” performance in Caledon, “Moderate” in Mississauga, and “Low” in Brampton based on our 2018 assessment. The methods described here can provide municipalities with a low-cost approach for tree canopy monitoring across complex landscapes. Data availabilityThe data used for this paper are available at: https://zenodo.org/records/12549244. The code created for this paper are available at: https://github.com/ZZMitch/PredictTreeCC_Landsat_1972to2020.

Optimization of leaf area index measurement method and correction of green plot ratio formula based on regional plant characteristics-a study in Chongqing, China.

The quantification of green space green plot ratio (GPR) is mostly based on estimation formulas, and the leaf area index (LAI) estimation values in these estimation formulas have not been well verified by measured LAI values, resulting in errors and uncertainties in GPR quantification results. This study aims to address this gap by measuring the LAI of 113 regional plants in Chongqing, China, following a standardized measurement path for digital hemispherical photography (DHP). The results indicate that the optimal relative exposure value (REV) was - 1 under overcast conditions and - 2 under sunny and cloudy conditions. Among the threshold algorithms for hemispherical images, the Intermodes algorithm in ImageJ was the best. The LAI of regional plants is highest in summer, followed by spring and autumn, and lowest in winter. Tree height (h) and crown width (w) are key factors affecting LAI, but the LAI also varies with plant species. Overall, the LAI of evergreen trees is higher than that of deciduous trees. The LAI of evergreen trees and shrubs with a height shorter than 5m is the largest, and that of deciduous trees and shrubs with a crown width larger than 8m is the largest. The study further verified that the existing GPR estimation formula exhibited large errors in Chongqing, while there was a strong correlation (R2 = 0.973) between the GPR estimation value and the measured value. A conversion formula was developed to reduce estimation biases, and the corrected formula is capable of estimating GPR values more accurately when actual LAI measurements are insufficient. Overall, this study verifies the significance of measuring localized LAI values, promotes the understanding of LAI suitability for GPR calculations, and provides an empirical formula for GPR estimation in Chongqing, China.

Digital hemispherical photographs and Sentinel-2 multi-spectral imagery for mapping leaf area index at regional scale over a tropical deciduous forest

Digital hemispherical photographs and Sentinel-2 multi-spectral imagery for mapping leaf area index at regional scale over a tropical deciduous forest

Near-infrared digital hemispherical photography enables correction of plant area index for woody material during leaf-on conditions

Indirect optical measurement techniques enable efficient and non-destructive estimation of plant area index (PAI). However, because they cannot distinguish between foliage and other canopy elements, corrections are needed to determine leaf area index (LAI), which is typically the property of interest. In this study, we investigate near-infrared digital hemispherical photography (DHP) as a means of estimating and correcting for woody material. Using data collected at a deciduous broadleaf forest site, we show that near-infrared DHP could successfully estimate effective wood area index (WAIe) and wood area index (WAI) during leaf-on conditions, providing similar mean values (WAIe = 0.88, WAI = 1.53) to those determined from visible DHP during leaf-off conditions (WAIe = 0.87, WAI = 1.38). This information was used to correct estimates of effective PAI (PAIe) and PAI, enabling effective LAI (LAIe) and LAI to be derived with low RMSD (0.33 for LAIe and 0.76 for LAI), NRMSD (12% for LAIe and 19% for LAI), and bias (−0.01 for LAIe and −0.16 for LAI). Not correcting for woody material led to overestimation of LAIe by 31% on average and 46% in the worst observed case, and the degree of overestimation was further enlarged for LAI (42% on average and 61% in the worst observed case). In agreement with previous studies, the effects of clumping and woody area were found to be partly compensatory. On average, PAIe provided a reasonable approximation of LAI without correction, though overestimation of 52% and underestimation of 20% occurred at the lowest and highest LAI values, respectively. Compared to WAIe and WAI measurement using leaf-off visible DHP, near-infrared DHP offers two crucial advantages: i) data collection can be conducted at the same time as leaf-on PAIe and PAI measurements, and ii) it is likely that the approach could provide an indirect WAIe and WAI measurement option for evergreen species.

Combining Texture, Color, and Vegetation Index from Unmanned Aerial Vehicle Multispectral Images to Estimate Winter Wheat Leaf Area Index during the Vegetative Growth Stage

Leaf Area Index (LAI) is a fundamental indicator of plant growth status in agronomy and environmental research. With the rapid development of drone technology, the estimation of crop LAI based on drone imagery and vegetation indices is becoming increasingly popular. However, there is still a lack of detailed research on the feasibility of using image texture to estimate LAI and the impact of combining texture indices with vegetation indices on LAI estimation accuracy. In this study, two key growth stages of winter wheat (i.e., the stages of green-up and jointing) were selected, and LAI was calculated using digital hemispherical photography. The feasibility of predicting winter wheat LAI was explored under three conditions: vegetation index, texture index, and a combination of vegetation index and texture index, at flight heights of 20 m and 40 m. Two feature selection methods (Lasso and recursive feature elimination) were combined with four machine learning regression models (multiple linear regression, random forest, support vector machine, and backpropagation neural network). The results showed that during the vegetative growth stage of winter wheat, the model combining texture information with vegetation indices performed better than the models using vegetation indices alone or texture information alone. Among them, the best prediction result based on vegetation index was RFECV-MLR at a flight height of 40 m (R2 = 0.8943, RMSE = 0.4139, RRMSE = 0.1304, RPD = 3.0763); the best prediction result based on texture index was RFECV-RF at a flight height of 40 m (R2 = 0.8894, RMSE = 0.4236, RRMSE = 0.1335, RPD = 3.0063); and the best prediction result combining texture and index was RFECV-RF at a flight height of 40 m (R2 = 0.9210, RMSE = 0.3579, RRMSE = 0.1128, RPD = 3.5575). The results of this study demonstrate that combining vegetation indices and texture from multispectral drone imagery can improve the accuracy of LAI estimation during the vegetative growth stage of winter wheat. In addition, selecting a flight height of 40 m can improve efficiency in large-scale agricultural field monitoring, as this study showed that drone data at flight heights of 20 m and 40 m did not significantly affect model accuracy.

Note on the compatibility of ICOS, NEON, and TERN sampling designs, different camera setups for effective plant area index estimation with digital hemispherical photography

Abstract Environmental monitoring networks such as the Integrated Carbon Observation System (ICOS) in Europe, the National Ecological Observatory Network (NEON) in the U.S., or the Terrestrial Ecosystem Research Network (TERN) in Australia deploy different sampling schemes for in situ measurements. We report on the intercomparison of measurements of the canopy gap fraction with different digital hemispherical photography setups adopting ICOS, NEON, and TERN sampling schemes. The test was carried out at the Järvselja Radiation Transfer Model Intercomparison (RAMI) birch stand. Results show that spreading out sampling points which cover more of the plot is important for a good representation of the forest as a whole. The NEON tower plot layout scheme may be more prone to errors in overall canopy properties estimation than ICOS or TERN due to its compact sampling layout and should always be used in conjunction with its distributed plots. Different camera setups involving different camera operators, camera bodies, lenses and settings yield slightly varied results, and it is important to ensure that the images are taken in such a way that they would not be over or underexposed, or out of focus. As a conclusion we recommend always to carry out intercomparison measurements with old and new cameras when devices are upgraded. Our study contributes towards establishing the uncertainty and evaluating potential error budget stemming from collecting in situ measurements using different sampling schemes and camera setups.

Biophysical Variable Retrieval of Silage Maize with Gaussian Process Regression and Hyperparameter Optimization Algorithms

Quantification of vegetation biophysical variables such as leaf area index (LAI), fractional vegetation cover (fCover), and biomass are among the key factors across hydrological, agricultural, and irrigation management studies. The present study proposes a kernel-based machine learning algorithm capable of performing adaptive and nonlinear data fitting so as to generate a suitable, accurate, and robust algorithm for spatio-temporal estimation of the three mentioned variables using Sentinel-2 images. To this aim, Gaussian process regression (GPR)–particle swarm optimization (PSO), GPR–genetic algorithm (GA), GPR–tabu search (TS), and GPR–simulated annealing (SA) hyperparameter-optimized algorithms were developed and compared against kernel-based machine learning regression algorithms and artificial neural network (ANN) and random forest (RF) algorithms. The accuracy of the proposed algorithms was assessed using digital hemispherical photography (DHP) data and destructive measurements performed during the growing season of silage maize in agricultural fields of Ghale-Nou, southern Tehran, Iran, in the summer of 2019. The results on biophysical variables against validation data showed that the developed GPR-PSO algorithm outperformed other algorithms under study in terms of robustness and accuracy (0.917, 0.931, 0.882 using R2 and 0.627, 0.078, and 1.99 using RMSE in LAI, fCover, and biomass of Sentinel-2 20 m, respectively). GPR-PSO also possesses the unique ability to generate pixel-based uncertainty maps (confidence level) for prediction purposes (i.e., estimated uncertainty level <0.7 in LAI, fCover, and biomass, for 96%, 98%, and 71% of the total study area, respectively). Altogether, GPR-PSO appears to be the most suitable option for mapping biophysical variables at the local scale using Sentinel-2 images.

Quantifying vegetation indices using terrestrial laser scanning: methodological complexities and ecological insights from a Mediterranean forest

Abstract. Accurate measurement of vegetation density metrics including plant, wood and leaf area indices (PAI, WAI and LAI) is key to monitoring and modelling carbon storage and uptake in forests. Traditional passive sensor approaches, such as digital hemispherical photography (DHP), cannot separate leaf and wood material, nor individual trees, and require many assumptions in processing. Terrestrial laser scanning (TLS) data offer new opportunities to improve understanding of tree and canopy structure. Multiple methods have been developed to derive PAI and LAI from TLS data, but there is little consensus on the best approach, nor are methods benchmarked as standard. Using TLS data collected in 33 plots containing 2472 trees of 5 species in Mediterranean forests, we compare three TLS methods (lidar pulse, 2D intensity image and voxel-based) to derive PAI and compare with co-located DHP. We then separate leaf and wood in individual tree point clouds to calculate the ratio of wood to total plant area (α), a metric to correct for non-photosynthetic material in LAI estimates. We use individual tree TLS point clouds to estimate how α varies with species, tree height and stand density. We find the lidar pulse method agrees most closely with DHP, but it is limited to single-scan data, so it cannot determine individual tree properties, including α. The voxel-based method shows promise for ecological studies as it can be applied to individual tree point clouds. Using the voxel-based method, we show that species explain some variation in α; however, height and plot density were better predictors. Our findings highlight the value of TLS data to improve fundamental understanding of tree form and function as well as the importance of rigorous testing of TLS data processing methods at a time when new approaches are being rapidly developed. New algorithms need to be compared against traditional methods and existing algorithms, using common reference data. Whilst promising, our results show that metrics derived from TLS data are not yet reliably calibrated and validated to the extent they are ready to replace traditional approaches for large-scale monitoring of PAI and LAI.

Retrieval and validation of vertical LAI profile derived from airborne and spaceborne LiDAR data at a deciduous needleleaf forest site

ABSTRACT Leaf area index (LAI) is defined as one half of the total green leaf area per unit ground surface area. Its vertical profile is critical for understanding the remote sensing radiative transfer processes. LAI profile has been derived from airborne and spaceborne LiDAR data, such as the Global Ecosystem Dynamics Investigation (GEDI) installed on the International Space Station. However, the capability of various algorithms for the LAI profile estimation with airborne LiDAR is not clearly evaluated, and the estimated LAI profiles, including the GEDI LAI products, are not been fully validated. This study conducted a quantitative retrieval and validation of the LAI profiles using terrestrial and airborne laser scanning (TLS and ALS) and spaceborne GEDI data over a deciduous needleleaf forest site in northern China. The vertical LAI profile was estimated in the field using an upward digital hemispherical photography (DHP) attached to a portable measurement system in 2020 and 2021. A suite of new LiDAR indices combining both LiDAR return number and return intensity was explored for the LAI profile estimation. All LAI profiles obtained from the DHP, TLS, ALS, and GEDI during the leaf-on season and leaf-off season were compared. The DHP shows a good agreement with the TLS LAI profiles (R2 = 0.97). The LAI profile derived from the ALS data using the combined light penetration index (LPIRI) agrees well (R2 ≥0.86) with the DHP, TLS, and GEDI estimates. In general, the LPIRI is advantageous for regional LAI profile mapping from ALS. The GEDI cumulative LAI corresponds well with the DHP during the leaf-on season (R2 = 0.90, RMSE = 0.23), but underestimates during the leaf-off season (R2 = 0.70, RMSE = 0.14, bias=−0.13). The underestimation is attributed to the higher canopy and ground reflectance ratio (ρv/ρg) assigned in the algorithm and the height discrepancy between the GEDI and field measurements. For the GEDI LAI profile product, further validation and improvement are necessary for other biome types and landscape conditions, especially during the leaf-off season.

Estimating Effective Leaf Area Index of Winter Wheat Based on UAV Point Cloud Data

Leaf area index (LAI) is a widely used plant biophysical parameter required for modelling plant photosynthesis and crop yield estimation. UAV remote sensing plays an increasingly important role in providing the data source needed for LAI extraction. This study proposed a UAV-derived 3-D point cloud-based method to automatically calculate crop-effective LAI (LAIe). In this method, the 3-D winter wheat point cloud data filtered out of bare ground points was projected onto a hemisphere, and then the gap fraction was calculated through the hemispherical image obtained by projecting the sphere onto a plane. A single-angle inversion method and a multi-angle inversion method were used, respectively, to calculate the LAIe through the gap fraction. The results show a good linear correlation between the calculated LAIe and the field LAIe measured by the digital hemispherical photography method. In particular, the multi-angle inversion method of stereographic projection achieved the highest accuracy, with an R2 of 0.63. The method presented in this paper performs well in LAIe estimation of the main leaf development stages of the winter wheat growth cycle. It offers an effective means for mapping crop LAIe without the need for reference data, which saves time and cost.

HemispheR: an R package for fisheye canopy image analysis

Hemispherical photography is a relevant tool to estimate canopy attributes such as leaf area index (LAI). Advancements in digital photography and image processing tools have supported long-lasting use of digital hemispherical photography (DHP). While some open-source tools exists for DHP, very few solutions have been made available in R programming packages, and none of these allows a full processing workflow to retrieve LAI and other canopy attributes from fisheye images.To fill this gap, we developed an R package (hemispheR) to support the whole processing of DHP images in an automated, fast, and reproducible way. The package functions, which are designed for step-by-step single-image analysis, can be performed sequentially in a pipeline, while allowing inspecting the quality of each image processing step. The package allows to analyze both circular and fullframe fisheye images, collected either with upward facing (forest canopies) or downward facing (short canopies and crops) camera orientation. In addition, the package allows to implement two consolidated LAI methods (LAI-2000/2200 and 57° method).A case study is presented to demonstrate the reliability of canopy attributes derived from hemispheR in temperate deciduous forests with variable canopy density and structure. Canopy attributes were validated against either results obtained from a reference proprietary software, either by benchmarking plot-level LAI with measurements obtained from littertraps. Results indicated hemispheR provide reliable openness and leaf area index in forest canopies as compared with reference values. We also found that combining hemispheR with other R packages further advance analysis of hemispherical canopy images, by reducing the sensitivity of results to camera exposure in both raw and non-raw canopy imagery.By providing a simple, transparent, and flexible image processing procedure, hemispheR supported the use of DHP for routine measurements and monitoring of forest canopy attributes. Hosting the package in a Git repository can further support development of the package, through either collaborative coding or forking projects.

Crop Phenology Modelling Using Proximal and Satellite Sensor Data

Understanding crop phenology is crucial for predicting crop yields and identifying potential risks to food security. The objective was to investigate the effectiveness of satellite sensor data, compared to field observations and proximal sensing, in detecting crop phenological stages. Time series data from 122 winter wheat, 99 silage maize, and 77 late potato fields were analyzed during 2015–2017. The spectral signals derived from Digital Hemispherical Photographs (DHP), Disaster Monitoring Constellation (DMC), and Sentinel-2 (S2) were crop-specific and sensor-independent. Models fitted to sensor-derived fAPAR (fraction of absorbed photosynthetically active radiation) demonstrated a higher goodness of fit as compared to fCover (fraction of vegetation cover), with the best model fits obtained for maize, followed by wheat and potato. S2-derived fAPAR showed decreasing variability as the growing season progressed. The use of a double sigmoid model fit allowed defining inflection points corresponding to stem elongation (upward sigmoid) and senescence (downward sigmoid), while the upward endpoint corresponded to canopy closure and the maximum values to flowering and fruit development. Furthermore, increasing the frequency of sensor revisits is beneficial for detecting short-duration crop phenological stages. The results have implications for data assimilation to improve crop yield forecasting and agri-environmental modeling.

Estimation of Leaf Area Index in a Typical Northern Tropical Secondary Monsoon Rainforest by Different Indirect Methods

The leaf area index (LAI) is a crucial indicator for quantifying forest productivity and community ecological processes. Satellite remote sensing can achieve large-scale LAI monitoring, but it needs to be calibrated and validated according to the in situ measurements on the ground. In this study, we attempted to use different indirect methods to measure LAI in a tropical secondary forest. These methods included the LAI-2200 plant canopy analyzer (LAI-2200), Digital Hemispherical Photography (DHP), Tracing Radiation and Architecture of Canopies (TRAC), and Terrestrial Laser Scanning (TLS) (using single-station and multi-station measurements, respectively). Additionally, we tried to correct the measured LAI by obtaining indicators of woody components and clumping effects. The results showed that the LAI of this forest was large, with estimated values of 5.27 ± 1.16, 3.69 ± 0.74, 5.86 ± 1.09, 4.93 ± 1.33, and 3.87 ± 0.89 for LAI-2200, DHP, TRAC, TLS multi-station, and TLS single-station, respectively. There was a significant correlation between the different methods. LAI-2200 was significantly correlated with all other methods (p < 0.01), with the strongest correlation with DHP (r = 0.684). TRAC was significantly correlated with TLS single-station (p < 0.01, r = 0.283). TLS multi-station was significantly correlated with TLS single-station (p < 0.05, r = 0.266). With the multi-station measurement method, TLS could maximize the compensation for measurement bias due to the shadowing effects. In general, the clumping index of this forest was 0.94 ± 0.05, the woody-to-total area ratio was 3.23 ± 2.22%, and the total correction coefficient was 1.03 ± 0.07. After correction, the LAI estimates for all methods were slightly higher than before, but there was no significant difference among them. Based on the performance assessment of existing ground-based methods, we hope to enhance the inter-calibration between methods to improve their estimation accuracy under complex forest conditions and advance the validation of remote sensing inversion of the LAI. Moreover, this study also provided a practical reference to promote the application of LiDAR technology in tropical forests.

Optimization of Lai Estimation Method Based on Smartphones with Fisheye Lens

Leaf area index (LAI) is an important biological factor reflecting vegetation growth and forest ecosystem. LAI can be used to obtain plant health status, carbon cycle, and surrounding ecological environment effectively. In this study, the smartphone was equipped with a fisheye lens, and the optimization method was used to estimate LAI, which was compared with digital hemispherical photography (DHP) to investigate the possibility of the new method for LAI estimation. The hemispherical image was divided into blocks, and the optimized Otsu method was used for algorithm segmentation, which can effectively distinguish vegetation from the sky. Concurrently, when the gap fraction inversion LAI was performed, the linear inversion algorithm was improved based on single-angle inversion, and the LAI was obtained by inversion through the linear fitting of the mul-tiangle gap fraction. The experimental sample was located in Olympic National Forest Park in Beijing. Three coniferous mixed forests and three broadleaved forests were selected from the experimental sample. LAI measurements from smartphones were compared with those from DHP. In the samples for mixed coniferous forests, the values for coefficients of determination R^2 were 0.835, 0.802, and 0.809, and root mean square errors (REMS) were 0.137, 0.120, and 0.147. For the broadleaf forest samples, the values for R² were 0.629, 0.679, and 0.758, and REMS were 0.144, 0.135, and 0.137. The R^2 and RMES for the overall data was 0.810 and 0.134, respectively, and a good agreement between the LAI measurements from the proposed method and those from the DHP supports an accurate estimation. The results show that the use of a fisheye lens on a smartphone can effectively and accurately obtain tree canopy LAI. This provides a fast and effective new method to measure LAI of forest vegetation near the ground, which is of great significance for studying the interaction between plant growth status, ecological environment, and phenological changes.

COMPARISON OF CANOPY COVER EFFECTS ON RAINFALL INTERCEPTION LOSS IN TROPICAL FOREST WITH HOMOGENOUS AND MIXED TREE SPECIES

This study was designed to determine the canopy cover, c of the tropical forest and explore its effects on the interception loss amount. The study area was a reserved forest Lagong Hill Forest Reserve, Kepong, Selangor, Malaysia, where two plots with an area of 400 m2 (20 m x 20 m) each have been set up to collect the data. In order to determine the measured interception loss, rainfall, throughfall, and stemflow data have been measured on site. The tree with a diameter at breast height (dbh) of more than 10 cm was selected in collecting the stemflow data. Twenty-five (25) locations have been chosen for the digital hemispherical photographs to be taken. The forest standing of the study area has been visualised by Stand Visualization System (SVS) method, and WinSCanopy 2009a and RGBFisheye.exe application software has been used to obtain the canopy cover values. In this study, the correlation between canopy cover and interception loss were obtained for both plots. It is found that the Lagong Hill Forest Reserve varied has a compact canopy density with canopy cover up to 95%. This condition will affect the canopy interception loss value ranging from 24.83% up to 64.72%. The canopy cover and interception loss correlation denote that the interception loss amount reduces whilst the canopy cover increases.

Sampling scale effects of vegetation apparent clumping index obtained by digital hemispherical photography

Sampling scale effects of vegetation apparent clumping index obtained by digital hemispherical photography

Improving methods to predict aboveground biomass of Pinus sylvestris in urban forest using UFB model, LiDAR and digital hemispherical photography

The article proposes methods for combining Airborne Laser Scanning (ALS) with Digital Hemispherical Photography (DHP) data required by the Urban Forest Biomass (UFB) model to predict the aboveground biomass (AGB) of Scotch pine (Pinus sylvestris L.) in urban forests of Lublin (Poland). The article also demonstrates the potential of ALS and DHP data in urban AGB estimation. ALS and Leaf Area Index (LAI) data were calculated using a voxels-vector approach based on the measurements taken at eight permanent sample plots (PSPs). The research was conducted in 2014 and the prediction was made until 2030. It was found that the determination coefficients (R2) for the Basal Area (BA) of the trees are 0.97, and the BA modeling parameters have a high correlation with those observed in the field (model efficiency (ME) 0.94). 83 % growth trajectory based on the measured BA was appropriately modeled using the UFB model (P > 0.9). The results for AGB show that the degree of fitting and accuracy are greatest for the Monte Carlo (MC) simulation technique based on ALS and DHP data (UBF with ALS and DHP) where R2 = 0.98, RMSE = 2.97 t/ha, MAE = 2.35 t/ha, rRMSE = 1.28 %, which performed better than MC simulation technique without ALS and DHP (UBF without ALS and DHP) where R2 = 0.94, RMSE = 4.58 t/ha, MAE = 3.64 t/ha, rRMSE = 3.29 %. The results indicate that the proposed method based on combining the UFB model, LiDAR and DHP allows us to improve the accuracy of the AGB prediction.