Evaluation of atmospheric correction models and Landsat surface reflectance product in Daerah Istimewa Yogyakarta, Indonesia

Yusuf, Febrina Ramadhani; Santoso, Kurniawan Budi; Ningam, Muhammad Ulul Lizamun; Kamal, Muhammad; Wicaksono, Pramaditya

doi:10.1088/1755-1315/169/1/012004

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…Though the overall percentage of the bare soil pixel selection using spectral indices between ATCOR method and FLAASH method was similar (Tables 4 and 5), FLAASH method showed more consistency than the ATCOR method. The study conducted by Yusuf et al 2018 [74] showed that for rural aerosol model FLAASH and ATCOR showed similar Standard Error of Estimate for surface types such as vegetation, sand, and water bodies. In the study conducted by Marcello et al [75] on soil using worldview imagery using for a rural aerosol model, FLAASH gave an RMSE of 0.0398 and ATCOR gave an RMSE of 0.0406, which is line with the superior RMSE of FLAASH over ATCOR (Tables 4 and 5).…”

Section: Atcor Versus Flaash For Soc Predictionsmentioning

confidence: 95%

Impact of Atmospheric Correction Methods Parametrization on Soil Organic Carbon Estimation Based on Hyperion Hyperspectral Data

et al. 2022

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Visible Near infrared and Shortwave Infrared (VNIR/SWIR, 400–2500 nm) remote sensing data is becoming a tool for topsoil properties mapping, bringing spatial information for environmental modeling and land use management. These topsoil properties estimates are based on regression models, linking a key topsoil property to VNIR/SWIR reflectance data. Therefore, the regression model’s performances depend on the quality of both topsoil property analysis (measured on laboratory over-ground soil samples) and Bottom-of-Atmosphere (BOA) VNIR/SWIR reflectance which are retrieved from Top-Of-Atmosphere radiance using atmospheric correction (AC) methods. This paper examines the sensitivity of soil organic carbon (SOC) estimation to BOA images depending on two parameters used in AC methods: aerosol optical depth (AOD) in the FLAASH (Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes) method and water vapor (WV) in the ATCOR (ATmospheric CORrection) method. This work was based on Earth Observing-1 Hyperion Hyperspectral data acquired over a cultivated area in Australia in 2006. Hyperion radiance data were converted to BOA reflectance using seven values of AOD (from 0.2 to 1.4) and six values of WV (from 0.4 to 5 cm), in FLAASH and ATCOR, respectively. Then a Partial Least Squares regression (PLSR) model was built from each Hyperion BOA data to estimate SOC over bare soil pixels. This study demonstrated that the PLSR models were insensitive to the AOD variation used in the FLAASH method, with R2cv and RMSEcv of 0.79 and 0.4%, respectively. The PLSR models were slightly sensitive to the WV variation used in the ATCOR method, with R2cv ranging from 0.72 to 0.79 and RMSEcv ranging from 0.41 to 0.47. Regardless of the AOD values, the PLSR model based on the best parametrization of the ATCOR model provided similar SOC prediction accuracy to PLSR models using the FLAASH method. Variation in AOD using the FLAASH method did not impact the identification of bare soil pixels coverage which corresponded to 82.35% of the study area, while a variation in WV using the ATCOR method provided a variation of bare soil pixels coverage from 75.04 to 84.04%. Therefore, this work recommends (1) the use of the FLAASH AC method to provide BOA reflectance values from Earth Observing-1 Hyperion Hyperspectral data before SOC mapping or (2) a careful selection of the WV parameter when using ATCOR.

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Section: Atcor Versus Flaash For Soc Predictionsmentioning

confidence: 95%

Impact of Atmospheric Correction Methods Parametrization on Soil Organic Carbon Estimation Based on Hyperion Hyperspectral Data

et al. 2022

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“…Systematic geometric rectifications were imperative to rectify inherent distortions resulting from Earth's rotation, sensor viewing angles, vehicular speed, trim distortion, and sprinkler line inclinations [22]. The objective behind atmospheric corrections was two-fold: to negate atmospheric interferences on sensor-recorded remote sensing data [23,24], and to counteract spectral reflections induced by atmospheric perturbations during the image capture process [25]. Cropping was administered by delineating a set of coordinates within the image domain, with two primary coordinates defining the span of the cropped image.…”

Section: Data Pre-processingmentioning

confidence: 99%

Temporal-Spatial Impact of the COVID-19 Pandemic on Land Surface Temperatures in Kuta, Bali: An Analysis Using Landsat 8 Satellite Imagery

Wiguna,

Sutari

2023

ATG

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In 2020, the world witnessed an unprecedented event: the outbreak of the COVID-19 pandemic, leading to significantly curtailed human activities. This study sought to elucidate the potential spatial ramifications of this on land surface temperatures (LSTs) in the renowned tourist locale of Kuta, Bali, Indonesia. Landsat 8 satellite imagery from 2019-2021, complemented by spatial data from local agencies, was employed for this analysis. LST processing was achieved through the calculation of Spectral Radiance/Top of Atmosphere, Brightness Temperature, and the conversion of Brightness Temperature to actual LST. In 2019, observed LSTs in Kuta District varied from 20.1℃ to over 32℃, with the predominant temperature range being 28.1℃ -31.99℃, covering an expansive 1487.03 ha or 70.26% of the entire area. By 2020, a notable decline was discerned with temperatures peaking at 27.99 ℃ and the most prevalent temperature range being 24.1℃ -27.99℃, encompassing an area of 1105.46 ha (52.23%). Contrarily, 2021 experienced an upswing, with the apex temperature touching 31.99℃, and the dominant temperature bracket being 28.1℃ -31.99℃, spanning 974.90 ha (46.06%). A discernable correlation was identified between tourism activities and LST fluctuations, with temperature reductions conspicuous in zones endowed with tourism amenities.

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“…Applying image classi cation based on the Mahalanobis distance method is the same as the minimum distance method. The only difference is that; the basis of classi cation based on the minimum distance is the minimum Mahalanobis distance and not the minimum Euclidean distance (Yusuf et al, 2018). This method was performed assuming that the histogram bands were normal (Shang et al, 2020).…”

Section: Mahalanobismentioning

confidence: 99%

Evaluation of the ability to measure morphological structures of plants obtained from tissue culture applying image processing techniques

Aliabad,

aliabad,

Habbab

et al. 2023

Preprint

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Biotechnological approaches, for instance, plant tissue culture, can be used to improve and accelerate the reproduction of plants. A single portion of a plant can produce many plants throughout the year in a relatively short period of laboratory conditions. Monitoring and recording plant morphological characteristics such as root length and shoot length in different conditions and stages are necessary for tissue culture. These features were measured using graph paper in a laboratory environment and sterile conditions. This research investigated the ability to use image processing techniques in determining the morphological features of plants obtained from tissue culture. In this context RGB images were prepared from the plants inside the glass, and different pixel-based and object-based classification methods were applied to an image as a control. The accuracy of these methods was evaluated using the kappa coefficient, and overall accuracy was obtained from Boolean logic. The results showed that among pixel-based classification methods, the maximum likelihood method with a kappa coefficient of 87% and overall accuracy of 89.4 was the most accurate, and the Spectral angle mapper method (SAM) method with a kappa coefficient of 58% and overall accuracy of 54.6 was the least accurate. Also, among object-based classification methods, Support Vector Machine (SVM), Naïve Bayes, and K-nearest neighbors algorithm (KNN) techniques, with a Kappa coefficient of 88% and overall accuracy of 90, can effectively distinguish the cultivation environment, plant, and root. Comparing the values of root length and shoot length estimated in the laboratory culture environment with the values obtained from image processing showed that the use of the SVM image classification method, which is capable of estimating root length and shoot length with RMSE 2.4, MAD 3.01 and R2 0.97, matches the results of manual measurements with even higher accuracy.

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Evaluation of atmospheric correction models and Landsat surface reflectance product in Daerah Istimewa Yogyakarta, Indonesia

Cited by 4 publications

References 5 publications

Impact of Atmospheric Correction Methods Parametrization on Soil Organic Carbon Estimation Based on Hyperion Hyperspectral Data

Impact of Atmospheric Correction Methods Parametrization on Soil Organic Carbon Estimation Based on Hyperion Hyperspectral Data

Temporal-Spatial Impact of the COVID-19 Pandemic on Land Surface Temperatures in Kuta, Bali: An Analysis Using Landsat 8 Satellite Imagery

Evaluation of the ability to measure morphological structures of plants obtained from tissue culture applying image processing techniques

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