Sky Luminance Measurements Using CCD Camera and Comparisons with Calculation Models for Predicting Indoor Illuminance

Yun, Su In; Kim, Kang Soo

doi:10.3390/su10051556

Cited by 17 publications

(9 citation statements)

References 36 publications

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“…In the case of anisotropic surfaces, surface luminance depends on the properties of the surface, the location and type of light and the conditions of observation [54][55][56]. Nicodemus [57] described the characteristics of the light reflection for a specific surface by the BRDF (sr −1 ).…”

Section: Brdf Correctionmentioning

confidence: 99%

Mapping Plantations in Myanmar by Fusing Landsat-8, Sentinel-2 and Sentinel-1 Data along with Systematic Error Quantification

Poortinga¹,

Tenneson²,

Shapiro

et al. 2019

Remote Sensing

123

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Forests in Southeast Asia are experiencing some of the highest rates of deforestation and degradation in the world, with natural forest species being replaced by cropland and plantation monoculture. In this work, we have developed an innovative method to accurately map rubber and palm oil plantations using fusion of Landsat-8, Sentinel 1 and 2. We applied cloud and shadow masking, bidirectional reflectance distribution function (BRDF), atmospheric and topographic corrections to the optical imagery and a speckle filter and harmonics for Synthetic Aperture Radar (SAR) data. In this workflow, we created yearly composites for all sensors and combined the data into a single composite. A series of covariates were calculated from optical bands and sampled using reference data of the land cover classes including surface water, forest, urban and built-up, cropland, rubber, palm oil and mangrove. This training dataset was used to create biophysical probability layers (primitives) for each class. These primitives were then used to create land cover and probability maps in a decision tree logic and Monte-Carlo simulations. Validation showed good overall accuracy (84%) for the years 2017 and 2018. Filtering for validation points with high error estimates improved the accuracy up to 91%. We demonstrated and concluded that error quantification is an essential step in land cover classification and land cover change detection. Our overall analysis supports and presents a path for improving present assessments for sustainable supply chain analyses and associated recommendations.

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Section: Brdf Correctionmentioning

confidence: 99%

Mapping Plantations in Myanmar by Fusing Landsat-8, Sentinel-2 and Sentinel-1 Data along with Systematic Error Quantification

Poortinga¹,

Tenneson²,

Shapiro

et al. 2019

Remote Sensing

123

View full text Add to dashboard Cite

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“…Several remote sensing applications require the elimination of the influence of object reflectance anisotropy. For anisotropic surfaces, a change in any of the parameters leads to a change in surface luminance [15,16]. The characteristics of light reflectance for the given surface is described by the Bidirectional Reflectance Distribution Function (BRDF), first defined by Nicodemus [17].…”

Section: Introductionmentioning

confidence: 99%

Influence of Lower Atmosphere on the Radiometric Quality of Unmanned Aerial Vehicle Imagery

et al. 2019

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Unmanned aerial vehicle (UAV) imagery has been widely used in remote sensing and photogrammetry for some time. Increasingly often, apart from recording images in the red-green-blue (RGB) range, multispectral images are also recorded. It is important to accurately assess the radiometric quality of UAV imagery to eliminate interference that might reduce the interpretation potential of the images and distort the results of remote sensing analyses. Such assessment should consider the influence of the atmosphere and the seasonal and weather conditions at the time of acquiring the imagery. The assessment of the radiometric quality of images acquired in different weather conditions is crucial in terms of improving the interpretation potential of the imagery and improving the accuracy of determining the indicators used in remote sensing and in environmental monitoring. Until now, the assessment of radiometric quality of UAV imagery did not consider the influence of meteorological conditions at different times of year. This paper presents an assessment of the influence of weather conditions on the quality of UAV imagery acquired in the visible range. This study presents the methodology for assessing image quality, considering the weather conditions characteristic of autumn in Central and Eastern Europe. The proposed solution facilitates the assessment of the radiometric quality of images acquired in the visible range. Using the objective indicator of quality assessment developed in this study, images were classified into appropriate categories, allowing, at a later stage, to improve the results of vegetation indices. The obtained results confirm that the proposed quality assessment methodology enables the objective assessment of the quality of imagery acquired in different meteorological conditions.

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“…When considering a beam of light incident on a surface and its intensity, it may be stated that it depends on the properties of the surface, the location, and type of the light source as well as the conditions of observation. In case of anisotropic surfaces, a change of any of these parameters results in a change of the surface luminance [13,14]. The characteristics of the light reflection for a specific surface are described by BDRF defined by Nicodemus [15].…”

Section: Characteristic Radiometric Correction With the Brdf Functionmentioning

confidence: 99%

Quality Assessment of the Bidirectional Reflectance Distribution Function for NIR Imagery Sequences from UAV

et al. 2018

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Imaging from low altitudes is nowadays commonly used in remote sensing and photogrammetry. More and more often, in addition to acquiring images in the visible range, images in other spectral ranges, e.g., near infrared (NIR), are also recorded. During low-altitude photogrammetric studies, small-format images of large coverage along and across the flight route are acquired that provide information about the imaged objects. The novelty presented in this research is the use of the modified method of the dark-object subtraction technique correction with a modified Walthall’s model for correction of images obtained from a low altitude. The basic versions of these models have often been used to radiometric correction of satellite imagery and classic aerial images. However, with the increasing popularity of imaging from low altitude (in particular in the NIR range), it has also become necessary to perform radiometric correction for this type of images. The radiometric correction of images acquired from low altitudes is important from the point of view of eliminating disturbances which might reduce the capabilities of image interpretation. The radiometric correction of images acquired from low altitudes should take into account the influence of the atmosphere but also the geometry of illumination, which is described by the bidirectional reflectance distribution function (BRDF). This paper presents a method of radiometric correction for unmanned aerial vehicle (UAV) NIR images. The study presents a method of low-altitude image acquisition and a fusion of the method of the dark-object subtraction technique correction with a modified Walthall’s model. The proposed solution performs the radiometric correction of images acquired in the NIR range with the root mean square error (RMSE) value not exceeding 10% with respect to the original images. The obtained results confirm that the proposed method will provide effective compensation of radiometric disturbances in UAV images.

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Sky Luminance Measurements Using CCD Camera and Comparisons with Calculation Models for Predicting Indoor Illuminance

Cited by 17 publications

References 36 publications

Mapping Plantations in Myanmar by Fusing Landsat-8, Sentinel-2 and Sentinel-1 Data along with Systematic Error Quantification

Mapping Plantations in Myanmar by Fusing Landsat-8, Sentinel-2 and Sentinel-1 Data along with Systematic Error Quantification

Influence of Lower Atmosphere on the Radiometric Quality of Unmanned Aerial Vehicle Imagery

Quality Assessment of the Bidirectional Reflectance Distribution Function for NIR Imagery Sequences from UAV

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