On-orbit rapid detection of land surface anomalies is important for ensuring ecological security and human safety. Land surface anomalies (e.g., fire, industrial heat source, and deforestation, etc.) are often accompanied by different degrees of thermal anomalies. Existing methods for detecting thermal anomalies have focused primarily on high-temperature anomalies, without available approach for detecting widespread low-temperature anomalies. Here, a Novel Method based on Constructed Reference land surface temperatures (LST) for on-orbit remote sensing detection of various Thermal Anomalies (NMCRTA) is proposed and further evaluated using Landsat 8 LST product. In this method, we first construct an fitted reference temperature based on LST spatiotemporal trend surface modeling and a real reference temperature based on contextual averaging. Then, the difference (including the step of removing atmospheric effects) between on-orbit observed LST and fitted reference LST, and the difference between on-orbit observed LST and real reference LST are calculated, respectively. Finally, these two temperature differences are utilized to detect thermal anomalies using corresponding thresholds. The results indicate that the NMCRTA can effectively detect deforestation, newly constructed buildings, and river drying, with an overall F1-score of 0.867, in a 100 × 100 km region scale. Meanwhile, the NMCRTA exhibited excellent accuracy in detecting fires, deforestation, and landslides at the 15 × 15 km scene scale, achieving F1-scores of 0.943, 0.857, and 0.791, respectively. Furthermore, the NMCRTA can continuously capture different thermal anomaly events associated with a newly constructed industrial heat source and perform well in nighttime. The NMCRTA is promising for future on-orbit remote sensing detection of various land surface anomalies, as a valuable supplement to optical on-orbit detection method.

Photon recollision probability p serves as a critical bond between the spectral characteristics of the canopy at any wavelength and the reflectance, transmittance, or absorptance of the vegetation canopy. However, the estimation of p is predominantly accomplished through canopy structural parameters. In this work, we provide a method to estimate canopy p-value directly from Airborne Laser Scanning (ALS) data. The method was evaluated both in virtual experiments and field measurements. The virtual experiments employed the large-scale remote sensing data and image simulation model (LESS) to simulate virtual ALS scanning data based on three RAdiation transfer Model Intercomparison (RAMI) actual canopies. Our findings showed that p-values can be accurately estimated from ALS point clouds.

3D radiative transfer models (3D RTMs) serve as essential tools for accurately retrieving forest products from multi-source earth observation data and comprehending complex radiation signals from various forest elements, such as branches, leaves, soil and their underlying physical interactions. However, due to the extensive parameter inputs required by the 3D RTM, especially structural parameters, it has become a hindrance to the development of such processes. Although LIDAR provides precise structural information about vegetation, the construction of forest canopy geometric models from LIDAR data for the simulation of reflectance has not been thoroughly investigated. In this study, A method for constructing an explicit forest geometric structure is proposed from ALS point cloud data, and the fitted tree crown's Plant Area Density (PAD) is subsequently estimated by utilizing point cloud intensity information. The various representations of tree crowns (alphashape, voxel, ellipsoid) from ALS are analyzed and compared. Our results demonstrate that the geometric model constructed based on ALS point cloud data can robustly simulate reflectance using 3D RTMs. This approach also holds the potential for relatively high-resolution applications.

Chlorophyll fluorescence is closely related to the process of vegetation photosynthesis. However, the radiative transfer of chlorophyll fluorescence in complex canopies distorts the coupling relationship between the remotely sensed fluorescence signals and photosynthesis. The current 3D fluorescence radiative transfer models struggle to strike a balance between the canopy complexity and the computational efficiency, thus limiting their applications in interpreting fluorescence at various scales. This article proposes a 3D canopy fluorescence radiative transfer method using multiple representations of canopies, allowing for simulations at scales ranging from individual tree level to satellite pixel level. This method is applied to the LargE-Scale remote sensing data and image Simulation framework (LESS) model, maintaining the simplicity and efficiency of the LESS model. The simulated results show good consistency with both state-of-art models and field measurement.

Vegetation Leaf Area Index (LAI) and Leaf Chlorophyll Content (LCC) are crucial indicators for monitoring vegetation growth. Remote sensing combined with radiative transfer models (RTMs) is the primary method for estimating vegetation parameters. However, traditional 1D RTMs make idealized assumptions that are not suitable for fine-scale inversion. Although existing 3D RTMs can intricately consider the radiative propagation mechanisms of complex vegetation structures, they require high computational costs, making it challenging to conduct fine-scale vegetation parameter inversion at a regional scale. In this study, a mixed forest scene is utilized as the foundational application scenario. A semi-empirical 3D acceleration model, named Semi-LESS, is used for fast simulation to generate a large dataset of 3m spatial resolution multispectral imagery. A residual network is used to encode vegetation structure and establish relationships between multi-band reflectance and vegetation parameters for vegetation parameter inversion. Our results indicate favorable outcomes for LAI and LCC inversion in the mixed forest, with RMSE values of 0.24 and 3.68ug/cm2, respectively. The conclusion highlights the capability of quickly achieving fine-scale inversion of vegetation parameters using the coupled Semi-LESS and deep learning. Our approach also holds potential for achieving rapid fine-scale vegetation parameter mapping in orchards and agricultural scenes.

Early warning systems and emergency management for disasters, environmental pollution, and illegal development require timely and accurate Earth surface anomaly detection (ESAD). Remote sensing, which uses satellites to observe the Earth’s surface, is an emerging approach to address this need. However, current remote sensing methods for ESAD are limited by their focus on specific anomalies, reliance on high-level satellite data, and the demand for significant computational and storage resources. In this article, we present a novel framework for general and on-orbit ESAD, which combines large vision models and lightweight priors. Our method characterizes images with a large vision model that is highly generalizable, reducing the dependency on high-level data, and compressing the prior base with sampling techniques, facilitating transmission and on-orbit storage. For on-orbit detection, we use a dictionary look-up style method for efficient anomaly prediction, enabling detection on satellites with limited computation resources. We evaluate our framework on typical scenarios and compare it with popular change detection (CD)-based and anomaly detection (AD)-based methods. Our results show that our framework achieves good performance while reducing the prior size by at least 95.56 times. Moreover, our framework can handle unpaired data, providing a chance to detect anomalies in the absence of near-term and paired images. Our framework has the potential to support the development and applications of general, on-orbit ESAD. The code and dataset are available at the following site:https://github.com/YummyWaffle/ESAD.

Estimating change in tree growth is important for monitoring forest dynamics, even the whole terrestrial ecosystem. Multi-temporal airborne laser scanning data had been used to accurately assess and predict change in forest attributes such as aboveground biomass (AGB), aboveground carbon density (ACD) and forest growth. In this study, we assessed the ability of dual-temporal airborne laser scanning data and dual-date to analyze forest growth change in Greater Khingan Mountains, Northeastern China.

Monitoring insect and disease disturbances in the lower parts of the forest has always been a hot research topic. Hyperspectral LiDAR (HSL), a new sensor, makes it possible to monitor these pest disturbances. In this study, we used the 3D radiative transfer model LESS to simulate AHSL point cloud data and conducted a sensitivity analysis of the point cloud data for monitoring forest insect and disease. A virtual forest scene was first reconstructed with explicit geometric structures using terrain laser scanning (TLS) data and ground measurement data. Based on measured optical properties of damaged foliage, some different damage scenarios with different stress levels were defined. AHLS and hyperspectral imagery (HI) were simulated for different forest pest disturbance scenarios. For the AHSL point cloud data, we select a commonly used stress index, RENDVI, as the evaluation vegetation index for assessing the sensitivity of the hyperspectral LiDAR signal in monitoring insect and disease disturbances. According to different damaged locations, different damage scenarios were rasterized into images. The corresponding hyperspectral images were also compared with the AHSL data. The result show that AHSL has great potential for monitoring forest insect and disease disturbances compared to HI. This study demonstrates that AHSL has great potential in responding to spectral signal changes in the lower and middle parts of the forest, and which may also be a powerful tool for early detection of forest pest disturbances.

Three-dimensional (3D) radiative transfer (RT) models are frequently recognized as a prerequisite when using high spatial resolution remote sensing data in heterogeneous surfaces. However, most studies of 3D RT models have been restricted to limited applications due to the low computational efficiency. Therefore, this study proposed a graphic processing unit (GPU)-based solution for the ray tracing 3D RT model. A state-of-the-art graphics and compute application programming interface, Vulkan, was introduced to implement the RT process. A bounding box method was adopted for the computation acceleration. By comparison with a central processing unit (CPU)-based solution, the performance efficiency of the proposed solution is significantly better: the simulation time of a GPU model is significantly reduced by more than 99% when facing a large-scale simulation mission. The simulation accuracy of the two solutions is similar, with root mean squared errors (RMSEs) lower than 0.005, 0.032 and 0.31 K for the red, near-infrared (NIR) and brightness temperature images, respectively. An evaluation based on airborne multiangle measurements also indicated that the accuracy of the proposed solution was satisfactory for simulating the red and NIR bidirectional reflectance factor and brightness temperature directional anisotropies, with RMSEs lower than 0.003, 0.020 and 0.20 K, respectively, when treating the whole scene as a pixel. Considering the simulation accuracy and efficiency, a GPU-based model will be an important supplement to the CPU model.

The timely and accurate assessment of changes in mountain vegetation biomass and other parameters is of great importance to mountain ecosystem conservation. With the rapid development of remote sensing technology, hyperspectral remote sensing images have facilitated the large-scale and long-time series monitoring of environmental changes in mountainous areas. However, topographic effects cause remote sensing images of mountainous areas to be prone to spectral variations within the same land cover and spectral confusion among different land covers. This phenomenon seriously affects the accuracy of remote sensing inversions and hinders the development and application of remote sensing in mountainous areas. Numerous scholars have established various topographic correction models (TCMs) to eliminate the influence of topographic effects. Comparative evaluation of the performance of different TCMs allows us to better understand their characteristics. Most previous evaluation studies have directly applied in remote sensing images, which were limited by the changing conditions of the study area. Therefore, this letter used computer simulations to controllably evaluate six popular TCMs on hyperspectral images. The results showed that their performance varied with the spectral band, and overall, the best performance was achieved by the C correction model, followed by the sun-canopy-sensor (SCS) + C model. This letter provides a basis for the optimal selection of TCMs in complex terrains.

Remote sensing estimation based on the dimidiate pixel model (DPM) using vegetation indices (VIs) is a common approach for mapping fractional vegetation cover (FVC). The major drawback of DPM is that it does not consider real endmember conditions and multiple scattering between soil and vegetation. An analysis of FVC uncertainties caused by these model deficiencies is still lacking. Here, we first calculated the FVC theoretical uncertainty caused by reflectance uncertainties based on the law of prapagation of uncertainty (LPU). Then, we tested the performance of DPM using six VIs over 3-D forest scenes. We simulated both Aqua-MODIS and Landsat-OLI surface reflectance (SR) at their corresponding spatial resolutions and spectral response functions (SRFs) using a well-validated 3-D radiative transfer (RT) model which helps to separate the model and input uncertainties. We found that ratio vegetation index (RVI)- and enhanced vegetation index (EVI)-based models were most affected by sensors, followed by the normalized difference vegetation index (NDVI)-, enhanced vegetation index 2 (EVI2)-, renormalized difference vegetation index (RDVI)-, and difference vegetation index (DVI)-based models. Without considering SR uncertainties, the DVI-based model performed best (FVC absolute difference < 0.1); however, the commonly used NDVI model reached a maximum difference of 0.35. At the same time, input uncertainty increased the uncertainty of FVC retrieval. We noticed that the increase of solar zenith angle (SZA) resulted in a clear increase of retrieved FVC under the uniform distribution, which can be explained by the increased shadow proportion. Besides, model accuracy was dominated by the purity of soil (vegetation) endmember in low (high) vegetation cover area. This study provides a reference for the selection of the optimal VI for FVC retrieval based on the DPM.

Occlusion effect, an inherent problem of terrestrial laser scanning (TLS) measurements, limits the potential of TLS data in tree attribute estimation. Multiple scans seek to mitigate this effect to provide enhanced scan completeness. However, the numbers and locations of the scans (i.e., the scan design) are usually determined via a subjective assessment of the tree density, spatial patterns of trees, and attributes to be derived. These could cause suboptimal scan completeness and limit tree attribute estimation. This study proposed an iterative-mode scan design to minimize the occlusion effect. First, we introduced a PoTo index based on visibility analysis to evaluate how many trees can be scanned from a location and to select effective candidates for the optimal TLS location. Second, we introduced a cumulative degree of ring closure (CDRC) to quantify the scan completeness for each candidate and determine the optimal TLS location. The TLS data sets of virtual forests with field-measured and synthetic plot parameter settings were simulated according to iterative- and regular-mode designs by using a Heidelberg light detection and ranging (LiDAR) Operations Simulator (HELIOS). The results demonstrated that an iterative-mode design can improve the scan completeness of trees compared to the regular-mode design. The tree attribute (diameter at breast height (DBH), tree height, stem curve, and crown volume) estimates of the iterative-mode design were less erroneous than those of the regular-mode design (e.g., the root-mean-square error (RMSE) could decrease the stem curve estimation by 38% and the crown volume estimation by 15%). This study suggests that the iterative-mode design can obtain an improved quality of the TLS data, especially for dense stands.

Estimation of downward shortwave radiation (DSR) is of great importance in global energy budget and climatic modeling. Although various algorithms have been proposed, effective validation methods are absent for rugged terrains due to the lack of rigorous methodology and reliable field measurements. We propose a two-step validation method for rugged terrains based on computer simulations. The first step is to perform point-to-point validation at local scale. Time-series measurements were applied to evaluate a three-dimensional (3-D) radiative transfer model. The second step is to validate the DSR at pixel-scale. A semiempirical model was built up to interpolate and upscale the DSR. Key terrain parameters were weighted by empirical coefficients retrieved from ground-based observations. The optimum number and locations of ground stations were designed by the 3-D radiative transfer model and Monte Carlo method. Four ground stations were selected to upscale the ground-based observations. Additional three ground stations were set up to validate the interpolated results. The upscaled DSR was finally applied to validate the satellite products provided by MODIS and Himawari-8. The results showed that the modeled and observed DSR exhibited good consistency at point scale with correlation coefficients exceeding 0.995. The average error was around 20 W/m2for the interpolated DSR and 10 W/m2for the upscaled DSR in theory. The accuracies of the satellite products were acceptable at most times, with correlation coefficients exceeding 0.94. From an operational point of view, our method has an advantage of using small amount of ground stations to upscale DSR with relatively high accuracy over rugged terrains.

Physical models simulating the radiative budget (RB) and remote sensing (RS) observation of three-dimensional (3D) landscapes are critical to better understand human and natural components of the Earth system and further develop RS technology. DART is one of the most comprehensive 3D models of Earth-atmosphere optical radiative transfer (RT), from ultraviolet (UV) to thermal infrared (TIR). It simulates the optical signal of proximal, aerial and satellite imaging spectrometers and laser scanners, the 3D RB and solar induced chlorophyll fluorescence (SIF) signal, for any urban or natural landscape and any experimental or instrument configuration. It is freely available for research and teaching activities (https://dart.omp.eu). Here, five recent advances are presented. 1) Atmosphere RT. 2) RT in non repetitive topography. 3) Monte Carlo modelling for fast RS image simulation of large landscapes. 4) SIF modelling for vegetation simulated as facets and turbid cells. 5) RS image inversion for mapping the optical properties of urban material and the urban radiative budget.

The potential of solar-induced chlorophyll fluorescence (SIF) to monitor photosynthesis and plant stress has attracted considerable interest in SIF remote sensing (RS). However, canopy SIF and RS observations are impacted by topography, vegetation three dimension (3D) structure, leaf orientation, non foliar elements (e.g., tree woody skeleton), ... Physically based downscaling of canopy SIF RS data to leaf-level (i.e., to leaf photosynthesis) requires 3D radiative transfer (RT) models simulating canopy SIF and its observation. These models are necessary to better exploit the potential of SIF, by linking leaf SIF and SIF in RS observations as a function of canopy 3D architecture and experimental configurations (sun and viewing directions, etc.). The Discrete Anisotropic Radiative Transfer (DART) model is a comprehensive 3D radiative transfer (RT) model for urban and natural landscapes. This paper presents its SIF modeling for vegetation simulated with facets, its validation with the SCOPE/mSCOPE 1D models, and its recent extension to SIF modelling for landscapes simulated with 3D turbid medium.

Freehand exercises help improve physical fitness without any requirements on devices, or places (e.g., gyms). Existing fitness assistant systems require wearing smart devices or exercising at specific positions, which compromises the ubiquitous availability of freehand exercises. This work proposes MobiFit, a contactless freehand exercise assistant using just one cellular signal receiver. MobiFit monitors the ubiquitous cellular signals sent by the base station and provides accurate repetition counting, exercise type recognition, and workout quality assessment without any attachments to the human body. To design MobiFit, we first analyze the characteristics of the received cellular signal sequence during freehand exercises through experimental studies. Based on the observation, we construct the analytic model of the received signals. Guided by the analytic model, MobiFit segments out every repetition and rest interval from one exercise session through spectrogram analysis, and extracts low-frequency features from each repetition for type recognition. We have implemented the prototype of MobiFit and collected 22,960 exercise repetitions performed by ten volunteers over six months. The results confirm that MobiFit achieves high counting accuracy of 98.6%, high recognition accuracy of 94.1%, and low repetition duration estimation error within 0.3s. Besides, the experiments show that MobiFit works both indoor and outdoor, and supports multiple users exercising together.

This work proposes a new approach to estimate two canopy structure properties: leaf area index (LAI) and leaf angle distribution (LAD) using terrestrial LiDAR system (TLS) data. Our methodology consists of two steps. First, a forward model was developed to simulate TLS observations of a vegetation scene having known structure variables (i.e. LAI and LAD) which permit obtaining 3D point cloud representing the studied scene. Second, a backward model was designed to retrieve LAI and LAD based on the relationship between light transmittance and foliage density. Our approach was validated with results obtained with different homogenous vegetation covers.

3D radiative transfer modeling in forest canopies is of great importance to upscale leaf level observations to canopy level, which, however, is particularly difficult in heterogeneous areas due to the complexity of forests. A common solution is to use physically based radiative transfer models. In this paper, we parameterized the LESS (LargE-Scale remote sensing data and image Simulation framework) model through a voxel-based reconstruction of airborne LiDAR data. For that, an airborne spectral image was simulated and compared with actual ASIA hyperspectral image. The results show a good agreement with R-squared being 0.5 and 0.56 for near infrared and red band, respectively. This demonstrates that the proposed voxel-based parameterization approach can successfully capture the fine-scale structures of forest canopies, and it can provide reliable data source for 3D radiative transfer models.

Urban areas have profound environmental impacts, while the existed products of urban areas have some issues, such as low spatial resolution and confused definition of urban. In this study, we developed a method based on image pattern recognition is developed to classify urban and rural from the artificial surfaces class in GlobaLand30. The global urban areas with 30m resolution in years 2000 and 2010 are extracted. The results are compared with the data from the China City Statistical Yearbooks (CCSY) and the US Census Bureau (USCB) in year 2010. The correlation coefficient between our urban areas and CCSY reached at 0.877. The user accuracy between our urban areas and USCB can reach at 91.82%. The major difference is from the green land and water in the urban areas and the urban fringe with more green lands, where are ignored by our data.

Accurate and efficient measurement of leaf angle distribution (LAD) is important for characterizing canopy structures and understanding solar radiation regimes within the plant canopy. The main challenge for obtaining LAD is measuring the orientations of individual leaves rapidly and accurately in complex field conditions. In this letter, we propose an efficient and low-cost approach to estimate both leaf zenith and azimuth angles from smartphone photographs by using a structure from motion (SfM) point cloud and pyramid convolutional neural network (PCNN)-based leaf detection. This SfM-PCNN method first detects individual leaves from 2-D photographs by delineating leaf boundaries, while minimizing the influences of interior leaf textures. The segmented image with leaf annotations is then used to partition the 3-D SfM point cloud into leaf clusters, each of which is fit by a plane to calculate the leaf orientation. The method was validated with manual measurements for five plant species with different leaf sizes, leaf shapes, and leaf textures. The accuracy is satisfactory for a leaf-to-leaf comparison over a Euonymus japonicus Thunb. with R-squared values of 0.84 (RMSE = 6.27°) and 0.97 (RMSE = 12.61°) for zenith and azimuth angle estimations, respectively. The method allows researchers to efficiently acquire LADs of different plants with low cost yet high accuracy.

Models that simulate the radiative budget (RB) and remote sensing (RS) observation of landscapes with physical approaches and consideration of the three-dimensional (3-D) architecture of Earth surfaces are increasingly needed to better understand the life-essential cycles and processes of our planet and to further develop RS technology. DART (Discrete Anisotropic Radiative Transfer) is one of the most comprehensive physically based 3-D models of Earth-atmosphere optical radiative transfer (RT), from ultraviolet to thermal infrared. It simulates the optical 3-D RB and signal of proximal, aerial and satellite imaging spectrometers and laser scanners, for any urban and/ or natural landscapes and for any experimental and instrumental configurations. It is freely available for research and teaching activities. Here, an application is presented after a summary of its theory and recent advances: inversion of Sentinel 2 images for simulating time series of urban radiative budget `Q*sw' maps through the determination of maps of urban surface material. Results are very encouraging: satellite and in-situ Q*sware very close (RMSE ≈ 15W/m2; i.e., 2.7% mean relative difference).

Potential of solar-induced chlorophyll fluorescence (SIF) to track time variable environmental stress of vegetation explains high interest in SIF remote sensing. There is an increasing need for physical models that consider the 3D structure of Earth surfaces, in order to better understand the relationships between SIF, vegetation three-dimensional (3D) architecture, irradiance and remote sensing configuration at canopy level. The Discrete Anisotropic Radiative Transfer (DART) model is one of the most comprehensive physically based 3D models of Earth-atmosphere radiative transfer (RT), covering the spectral domain from ultraviolet to thermal infrared wavelengths. This paper presents the determination of the sun and shade adapted leaf elements of a 3D vegetation canopy in DART, which is required for accurate RT simulations of SIF in geometrically explicit 3D canopy representations.

Three dimensional (3D) radiative transfer simulation is becoming an important and essential tool to understand the interaction between solar radiation and forest canopies. However, conducting a 3D simulation usually needs a lot of input parameters, especially the 3D information of forest scene, which is difficult to obtain and reconstruct. This paper presents a voxel approach that derives forest mockups from LiDAR data. These 3D mock-ups are adapted to DART model, using its recently introduced data access objects (DAO) tool. Here, they are used to simulate multispectral images with DART.

Light Detection And Ranging (LiDAR) techniques have been extensively applied in spaceborne, airborne and ground-based platforms. Understanding LiDAR data requires modeling approaches that can precisely account for the physical interactions between the emitted laser pulse and reflecting targets. Diverse LiDAR data types arise from different systems, platforms, and applications. However, most existing physical models consider only single pulse configurations to simulate large footprint LiDAR waveforms, which do not correspond to standard data formats. Hence, in many cases, model outputs are not well adapted to research conducted with actual LiDAR systems, especially for Aerial and Terrestrial Laser Scanning (ALS and TLS) systems. The Discrete Anisotropic Radiation Transfer (DART) model provides accurate and efficient simulations of multiple LiDAR pulses from all platform types. This paper presents the latest development of the DART LiDAR module: Gaussian decomposition of the simulated ALS and TLS waveforms followed by the provision of LiDAR point cloud and waveforms in text and standard ASPRS LAS formats.

Estimation of daily downward shortwave radiation (DSR) is of great importance in global energy budget and climatic modeling. The combination of satellite-based instantaneous measurements and temporal extrapolation models is the most feasible way to capture daily radiation variations at large scales. However, previous studies did not pay enough attention to topographic effects and simple temporal extrapolation methods were applied directly to rugged terrains which cover a large amount of the land surface. This paper, divided into two parts, aims at analyzing the topographic uncertainties of existing models and proposing a better method based on a mountain radiative transfer (MRT) model to calculate daily DSR. As the first part, this paper analyze the spatiotemporal variations of DSR influenced by topographic effects and checks the applicability of three temporal extrapolation methods on cloud-free days. Considering that clouds also have a strong influence on solar radiation, cloud-free days are chosen for targeted analysis of topographic effects on DSR. Three indices, the coefficient of variation, entropy-based dispersion coefficient (CH), and sill of semivariogram, are put forward to give a quantitative description of spatial heterogeneity. Our results show that the topography can dramatically strengthen the spatial heterogeneity of DSR. The index, CH, has an advantage for quantifying spatial heterogeneity as it offers a tradeoff between accuracy and efficiency. Spatial heterogeneity distorts the daily variation of DSR. Application of extrapolation methods in rugged terrains leads to overestimation of daily average DSR up to 60 W/m2 and a maximum 200 W/m2 error of instantaneous DSR on cloud-free days. This paper makes a quantitative analysis of topographic effects under different spatiotemporal conditions, which lays the foundation for developing a new extrapolation method.

Reconstructing a realistic and large-scale 3D forest scene has potential applications in visual representations and scientific research. Forest scene with explicitly described branches and leaves can provide a more accurate interpretation of interactions between light and canopy. In this study, a large-scale 3D forest scene reconstruction and simulation method is proposed. A series of individual trees with high level of details are generated using parameters derived from allometric equation, which populates plot leaf area index (LAI) into each individual tree. Based on the airborne laser scanning (ALS) data, the height, crown diameter and position of each individual tree are extracted by watershed segmentation algorithm. Finally, an emulation system based on ray-tracing is developed. It provides the capability to simulate RGB and multi-spectral images. These simulated datasets with “ground truth” can be used as benchmark for a variety of applications in remote sensing, forest investigation and computer graphic.

Scale effect, which is caused by a combination of model nonlinearity and surface heterogeneity, has been of interest to the remote sensing community for decades. However, there is no current analysis of scale effect in the ground-based indirect measurement of leaf area index (LAI), where model nonlinearity and surface heterogeneity also exist. This paper examines the scale effect on the indirect measurement of LAI. We built multiscale data sets based on realistic scenes and field measurements. We then implemented five representative methods of indirect LAI measurement at scales (segment lengths) that range from meters to hundreds of meters. The results show varying degrees of deviation and fluctuation that exist in all five methods when the segment length is shorter than 20 m. The retrieved LAI from either Beer's law or the gap-size distribution method shows a decreasing trend with increasing segment lengths. The length at which the LAI values begin to stabilize is about a full period of row in row crops and 100 m in broadleaf or coniferous forests. The impacts of segment length on the finite-length averaging method, the combination of gap-size distribution and finite-length methods, and the path-length distribution method are relatively small. These three methods stabilize at the segment scale longer than 20 m in all scenes. We also find that computing the average LAI of all of the short segment lengths, which is commonly done, is not as good as merging these short segments into a longer one and computing the LAI value of the merged one.