Modeling and Simulation of Deciduous Forest Canopy and Its Anisotropic Reflectance Properties Using the Digital Image and Remote Sensing Image Generation (DIRSIG) Tool

Rengarajan, Rajagopalan; Schott, John R.

doi:10.1109/jstars.2017.2751539

Cited by 6 publications

(6 citation statements)

References 44 publications

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“…Our approach to evaluate the effects of the factors can be summarized in these steps: (1) Model the forest canopy for different levels of defoliation and evaluate the BRDF for each of these varying canopies, (2) Model the sensor and atmospheric characteristics in the DIRSIG tool, (3) Characterize the level of defoliation for different types of data products such as at-sensor radiance, top of atmosphere (TOA) reflectance, surface reflectance, and NDVI, and (4) Vary the factors one at a time and evaluate its effects. An approach to model the geometry and optical properties of a forest canopy using the DIRSIG tool was presented in [13]. The study further showed that the simulated BRDF, using this approach, was found to agree with the Landsat 8 surface reflectance product to within 2% in both the NIR and red spectral bands.…”

Section: Methodsmentioning

confidence: 94%

“…The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model, a synthetic image generation tool developed by Rochester Institute of Technology (RIT), is used for simulating the interactions between the sensor, atmosphere and the canopy. The DIRSIG tool has been widely used in many research projects and its capabilities to accurately model the real-world scenes have been validated and compiled in [13,17,18]. The DIRSIG tool was also compared and validated against a wide range of canopy radiative transfer models used in the Radiation Model Intercomparison (RAMI-III) studies and was found to be consistent with the majority of the models such as FLIGHT, DART, Rayspread, etc.…”

Section: Methodsmentioning

confidence: 97%

“…Furthermore, the paradigm shift to the "big data in remote sensing" using Graphic Processing Unit (GPU) programming, cloud computing, and cloud storage supports new applications that rely on simulation environments to model complex phenomenon. For example, the generation of hyperspectral canopy BRDF measurements for different levels of defoliation requires a large number of canopy radiative transfer simulations, which are best solved using cloud computing techniques [13,14]. The simulation process, as outlined in this study can be used as a template for similar studies where ground targets cannot be varied in a controlled setting.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Sensor and Environmental Factors Impacting the Use of Multiple Sensor Data for Time-Series Applications

Rengarajan

Schott

2018

Remote Sensing

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Many remote sensing sensors operate in similar spatial and spectral regions, which provides an opportunity to combine the data from different sensors to increase the temporal resolution for short and long-term trend analysis. However, combining the data requires understanding the characteristics of different sensors and presents additional challenges due to their variation in operational strategies, sensor differences and environmental conditions. These differences can introduce large variability in the time-series analysis, limiting the ability to model, predict and separate real change in signal from noise. Although the research community has identified the factors that cause variations, the magnitude or the effect of these factors have not been well explored and this is due to the limitations with the real-world data, where the effects of the factors cannot be separated. Our work mitigates these shortcomings by simulating the surface, atmosphere, and sensors in a virtual environment. We modeled and characterized a deciduous forest canopy and estimated its at-sensor response for the Landsat 8 (L8) and Sentinel 2 (S2) sensors using the MODerate resolution atmospheric TRANsmission (MODTRAN) modeled atmosphere. This paper presents the methods, analysis and the sensitivity of the factors that impacts multi-sensor observations for temporal analysis. Our study finds that atmospheric compensation is necessary as the variation due to the atmosphere can introduce an uncertainty as high as 40% in the Normalized Difference Vegetation Index (NDVI) products used in change detection and time-series applications. The effect due to the differences in the Relative Spectral Response (RSR) of the two sensors, if not compensated, can introduce uncertainty as high as 20% in the NDVI products. The view angle differences between the sensors can introduce uncertainty anywhere from 9% to 40% in NDVI depending on the atmospheric compensation methods. For a difference of 5 days in acquisition, the effect of solar zenith angle can vary between 4% to 10%, depending on whether the atmospheric attenuations are compensated or not for the NDVI products.

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Section: Methodsmentioning

confidence: 94%

Section: Methodsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Sensor and Environmental Factors Impacting the Use of Multiple Sensor Data for Time-Series Applications

Rengarajan

Schott

2018

Remote Sensing

Self Cite

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“…This result is comparable to the accuracy of Rengarajan and Schott. [45] when they used the Digital Image and Remote Sensing Image Generation tool to simulate the NIR band reflectance of Landsat-8 surface reflectance product. Usually, at short wavelengths, the simulation error is expected to be larger due to the influence of path-radiance.…”

Section: Discussionmentioning

confidence: 99%

Simulation of Sentinel-2 Bottom of Atmosphere Reflectance Using Shadow Parameters on a Deciduous Forest in Thailand

Takumi

Takeuchi

2020

IJGI

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The shadow fraction of the canopy is an important factor in Bidirectional Reflectance Distribution Function (BRDF) and in estimating physical quantities, such as tree height and biomass. Shadows are used as a shielding ratio for direct solar irradiance, but, at shorter wavelengths, the amount of diffuse solar irradiance is greater, so the shielding ratio cannot be ignored. The shielding ratio of direct and diffuse solar irradiance is called Cast Shadow (CS) and Self Cast Shadow (SCS), respectively; however, it has been pointed out that the effect of these shadows is greater at higher resolutions, such as Sentinel-2. In addition, the Bottom Of Atmosphere (BOA) reflectance is greatly affected by shadows, because it is corrected for atmospheric effects. Therefore, the main purpose of this study is to investigate the spatial variability of CS and SCS and simulate the Sentinel-2BOA reflectance with these shadows. The target forest was a greenness season of a deciduous broadleaf forest in Thailand. First, we obtained a point cloud of the forest by Structure from Motion while using the Unmanned Aerial Vehicle. Next, we created a voxel model with CS and SCS as attributes. CS was calculated as the percentage of area where the plane that is assumed per voxel is shielded from direct solar irradiance by other voxels. SCS was calculated as the percentage of area where the hemispheric radiant environment is shielded by other voxels. Subsequently, using solar irradiance and leaf spectral reflectance data, the reflectance of each band of Sentinel-2 was simulated. Nine leaves were used to investigate the effect of leaf species on the simulation. The reflectance acquired by Sentinel-2 is not at the leaf level; however, we used this spectral reflectance data because the reflectance was simulated at the same spatial resolution as the voxel size. Voxel sizes of 20 cm, 50 cm, 100 cm, and 200 cm were used. Our result showed that (1) the spatial variability of SCS was smaller than that of CS when the sun position is fixed and the view zenith angle is changed. SCS was mostly 0.12 at different zenith angles, while the CS had a maximum value of 0.45 and a minimum value of 0.15. (2) The accuracy of the simulations was evaluated using the Root Mean Square Error (RMSE). The best RMSE is 0.020 ± 0.015 and the worst one is 0.084 ± 0.044. It was found that the error is larger in short wavelength infrared bands. (3) In this forest, the relative reflectance changed only about 1.2 times as much, as the voxel size was increased from 20 cm to 200 cm. In this study, we have simulated a single Sentinel-2 image. In the future, we will simulate multi-temporal images in order to investigate the effects of phenology and shadow changes on the reflectance that was observed by optical sensors.

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“…Canopy structures vary from horizontally spreading to erect types, each with unique light absorption and reflection patterns, posing challenges in spectral analysis. The implementation of advanced preprocessing methods such as BRDF corrections is crucial [116]. These methods account for the leaf orientation and density-related spectral effects by normalizing reflectance data to a standard geometrical perspective, thus minimizing the variability caused by different viewing angles or solar positions.…”

Section: Correcting Canopy Structural Effects On Uav-derived Nue Esti...mentioning

confidence: 99%

Meta-Analysis Assessing Potential of Drone Remote Sensing in Estimating Plant Traits Related to Nitrogen Use Efficiency

Zhang,

Hu,

et al. 2024

Remote Sensing

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Unmanned Aerial Systems (UASs) are increasingly vital in precision agriculture, offering detailed, real-time insights into plant health across multiple spectral domains. However, this technology’s precision in estimating plant traits associated with Nitrogen Use Efficiency (NUE), and the factors affecting this precision, are not well-documented. This review examines the capabilities of UASs in assessing NUE in crops. Our analysis specifically highlights how different growth stages critically influence NUE and biomass assessments in crops and reveals a significant impact of specific signal processing techniques and sensor types on the accuracy of remote sensing data. Optimized flight parameters and precise sensor calibration are underscored as key for ensuring the reliability and validity of collected data. Additionally, the review delves into how different canopy structures, like planophile and erect leaf orientations, uniquely influence spectral data interpretation. The study also recognizes the untapped potential of image texture features in UAV-based remote sensing for detailed analysis of canopy micro-architecture. Overall, this research not only underscores the transformative impact of UAS technology on agricultural productivity and sustainability but also demonstrates its potential in providing more accurate and comprehensive insights for effective crop health and nutrient management strategies.

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Modeling and Simulation of Deciduous Forest Canopy and Its Anisotropic Reflectance Properties Using the Digital Image and Remote Sensing Image Generation (DIRSIG) Tool

Cited by 6 publications

References 44 publications

Evaluation of Sensor and Environmental Factors Impacting the Use of Multiple Sensor Data for Time-Series Applications

Evaluation of Sensor and Environmental Factors Impacting the Use of Multiple Sensor Data for Time-Series Applications

Simulation of Sentinel-2 Bottom of Atmosphere Reflectance Using Shadow Parameters on a Deciduous Forest in Thailand

Meta-Analysis Assessing Potential of Drone Remote Sensing in Estimating Plant Traits Related to Nitrogen Use Efficiency

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