The heterogeneity and 3-dimensional (3D) organization of forest canopy elements is highly linked with the spatial variability of within and below canopy light. Using terrestrial LiDAR we studied the influence of several parameters in efficiently building 3D canopy models, and quantified below canopy light in 2 forest stands using ray-tracing. A voxel-based approach was used for canopy modeling, and a series of forest scenes were built for calculation of simulated structural variables (e.g., leaf area index, canopy openness). Through hypothesis testing, we found that simulated variables were consistent with the observed ones depending on: forest type, voxel size utilized in 3D modeling, and the zenith angle ranges used for calculations. Following below canopy light simulations were performed considering these 3 aspects. On average, estimates of light being transmitted overestimated measured light, and variance in below canopy light was maximum at lower values of measured light. This study presented a method to objectively define 3D modeling parameters for an efficient characterization of canopy structure, allowing to simulate trends in radiation flux transmitted to the forest floor. Improvements in the modeling process and ray-tracing parameterization were suggested.

In this analysis, a method for construction of forest canopy three-dimensional (3D) models from terrestrial LiDAR was used for assessing the influence of structural changes on reflectance for an even-aged forest in Belgium. The necessary data were extracted by the developed method, as well as it was registered the adjacent point-clouds, and the canopy elements were classified. Based on a voxelized approach, leaf area index (LAI) and the vertical distribution of leaf area density (LAD) of the forest canopy were derived. Canopy–radiation interactions were simulated in a ray tracing environment, giving suitable illumination properties and optical attributes of the different canopy elements. Canopy structure was modified in terms of LAI and LAD for hyperspectral measurements. It was found that the effect of a 10% increase in LAI on NIR reflectance can be equal to change caused by translating 50% of leaf area from top to lower layers. As presented, changes in structure did affect vegetation indices associated with LAI and chlorophyll content. Overall, the work demonstrated the ability of terrestrial LiDAR for detailed canopy assessments and revealed the high complexity of the relationship between vertical LAD and reflectance.

Terrestrial laser scanning (TLS) data makes possible to directly characterize the three-dimensional (3D) distribution of canopy foliage elements. The scanned edges of these elements may result in incorrectly point measurements (i.e., “ghost points”) impacting the quality of point cloud data. Therefore, estimation of the ghost points’ spatial visibilities, measurement of their characteristics and their removal are essential. In order to quantify the improvements on data quality, a method is developed in this study to efficiently correct for ghost points. Since the occurrence of ghost points is governed by a number of factors, (e.g., scanning resolution and distance, object properties, incident angle); the developed method is based on the analysis of the effects of these factors under controlled conditions where canopy-like objects (i.e., leaves, branches and layers of leaves) were scanned using a continuous-wave TLS system that employs phase-shift technology. Manual extraction of ghost points was done in order to calculate the relative amount of ghost points per scan, or ghost points ratio (gpr). The gpr values were computed in order to: (i) analyze their relationships with variables representing the above factors; and (ii) be used as a reference to evaluate the performance of filters used for extraction of ghost points. The ghost points’ occurrence was modeled by fitting regression models using different predictor variables that represent the variables under study. The obtained results indicated that reduced models with three predictors were suitable for gpr estimation in artificial leaves and in artificial branches, with a relative root mean squared error (RMSE) of 4.7% and 3.7%, respectively; while the full model with four predictors was appropriate for artificial layers of leaves, with relative RMSE of 4.5%. According to the statistical analysis, scanning distance was identified as the most important variable for modeling ghost points occurrence. Results indicated that optimized distance-based filters relative to the scanning distance have improved the outcomes in ghost points detection, in comparison to standard filtering criteria. These results suggest that more accurate characterization of forest canopy 3D structures can be achieved by removing ghost points using the new developed method.

The Araucaria araucana is an endemic species from Chile and Argentina, which has a high biological, scientific and cultural value and since 2016 has shown a severe affection of leaf damage in some individuals, causing in some cases their death. The purpose of this research was to detect, from hyperspectral images, the individuals of the Araucaria species (Araucaria araucana (Molina and K. Koch)) and its degree of disease, by isolating its spectral signature and evaluating its physiological state through indices of vegetation and positioning techniques of the inflection point of the red edge, in a sector of the Ralco National Reserve, Biobío Region, Chile. Seven images were captured with the HYSPEX VNIR-1600 hyperspectral sensor, with 160 bands and a random sampling was carried out in the study area, where 90 samples of Araucarias were collected. In addition, from the remote sensing techniques applied, spatial data mining was used, in which Araucarias were classified without symptoms of disease and with symptoms of disease. A 55.11% overall accuracy was obtained in the classification of the image, 53.4% in the identification of healthy Araucaria and 55.96% in the identification of affected Araucaria. In relation to the evaluation of their sanitary status, the index with the best percentage of accuracy is the MSR (70.73%) and the one with the lowest value is the SAVI (35.47%). The positioning technique of the inflection point of the red edge delivered an accuracy percentage of 52.18% and an acceptable Kappa index.

Land surface temperature (LST) is of fundamental importance to many aspects of geosciences, such as, net radiation budget, evaluation and monitoring of forest and crops. The LST is also a key-parameter to derive the Surface Urban Heat Island (SUHI) or health related indices, for example, the Discomfort Index (DI). It is therefore an essential prerequisite to understand the urban climate and to support the definition of mitigation strategies, health risk management plans, public policies among other initiatives to effectively address the adverse effects of heat. While the LST is commonly retrieved from data acquired in the TIR (Thermal InfraRed) spectral domain by remote multispectral sensors with about 1K accuracy for natural surfaces, its retrieval over urban areas is not trivial. Urban landscapes possess tremendous challenges in LST estimates due to its high heterogeneity of surfaces and materials, and the three-dimensional (3D) configuration of the elements that are present on urban areas.The Thermal InfraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA) is planned for launch in 2025 and features a TIR instrument that will image the Earth every three days, at 57 m resolution, providing the research community with critical information to understand the radiative interactions and impacts al local level. This new satellite mission will offer an unprecedented opportunity to support urban microclimate studies. Based on extensive radiative transfer simulations using the Discrete Anisotropic Radiative Transfer Model (DART) and sensitivity analysis, this work investigates the impacts of 3D urban structures (e.g., road width, building height, building density) and materials with different optical properties on LST estimation at the TRISHNA spatial resolution. In fine, the idea is to develop a method to minimize these impacts on LST estimated from the TRISHNA data.First, a processing chain has been set up to simulate TRISHNA LST with DART, by using as inputs i) the configuration of the sensor and ii) 3D urban forms with different geometric and optical properties. Second, the radiative transfer modeling for simulation of the TIR remote sensing signal is performed. Finally, by correlating the simulated TRISHNA LST and the surface characteristics for each scene, the main parameters impacting the LST in urban environments have been identified. From these results, a correction method at satellite scale to minimize the impacts of urban 3D variables on LST will be formulated.