Illumination Modelling for Topographic Correction of Landsat 8 and Sentinel-2A Imageries

Hudjimartsu, Sahid; Prasetyo, Lilik Budi; Setiawan, Yudi; Suyamto, Desi

doi:10.1109/ems.2017.27

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…Geographic correction was conducted to correct distortions and assign the properties (and practical value) of the map to the images (Green et al, 2000). It was performed by reprojecting the initial projection from WGS 1984 Zone 48 N to WGS 1984 Zone 48 S. Then, topographic correction was performed using terrain illumination correction model by combining the mathematical model and empirical rotation model (Hudjimartsu et al, 2017). The model was the improvement of topographic influence correction (Tan et al, 2013).…”

Section: Methodsmentioning

confidence: 99%

Canopy Cover Estimation in Lowland Forest in South Sumatera, Using LiDAR and Landsat 8 OLI imagery

Saleh

Dewi

Prasetyo

et al. 2021

jtfm

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Canopy cover is one of the most important variables in ecology, hydrology, and forest management, and useful as a basis for defining forests. LiDAR is an active remote sensing method that provides the height information of an object in three-dimensional space. The method allows for the mapping of terrain, canopy height and cover. Its only setback is that it has to be integrated with Landsat to cover a large area. The main objective of this study is to generate the canopy cover estimation model using Landsat 8 OLI and LiDAR. Landsat 8 OLI vegetation indices and LiDAR-derived canopy cover estimation, through First Return Canopy Index (FRCI) method, were used to obtain a regression model. The performance of this model was then assessed using correlation, aggregate deviation, and raster display. Lastly, the best canopy cover estimation was obtained using equation, FRCI = 2.22 + 5.63Ln(NDVI), with R2 at 0.663, standard deviation at 0.161, correlation between actual and predicted value at 0.663, aggregate deviation at -0.182 and error at 56.10%.

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Section: Methodsmentioning

confidence: 99%

Canopy Cover Estimation in Lowland Forest in South Sumatera, Using LiDAR and Landsat 8 OLI imagery

Saleh

Dewi

Prasetyo

et al. 2021

jtfm

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“…Classification of AUC values 5 [17] was used to assess model performance in predicting the presence of proboscis monkeys, as shown in Table 3. The MaxEnt calculation results in habitat suitability indicated by a value range of 0 (unsuitable) to 1 (high probability) [22]. The threshold value range is the probability value of suitable habitat.…”

Section: Output Format Logisticsmentioning

confidence: 99%

Habitat suitability of Proboscis Monkey Nasalis larvatus Wurmb. in Muara Kaman Sedulang Nature Reserve, East Kalimantan

Hidayat,

Sulistioadi,

Kustiawan

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Proboscis monkey (Nasalis larvatus Wurmb.) is an endemic primate species to Borneo’s island. Muara Kaman Sedulang Nature Reserve is one of protected area for this species in the island, but it’s riverine habitat is largely lost by forest fires and conversion for aquaculture, agriculture and settlements. A conservation action in the area needs spatial information regarding the habitat suitability of proboscis monkey. This study aimed to project a spatial model for habitat suitability of proboscis monkey in the reserve through species distribution modelling (SDM) using the Maximum Entropy (MaxEnt), based on the species’ occurrence records and environmental predictors. The results showed that suitable habitat for proboscis monkey was 3,410.27 ha (5.22% of the nature reserve total area). Model performance evaluation shows an accuracy of the Area Under Curve (AUC) value of 0.940, which is a very good category. Jackknife-test results showed the presence of secondary swamp forest (40.8%), distance from water bodies (25.9%), land cover (12.4%), secondary forest (6.3%) and mixed gardens (5.1%) are five variables that significantly contribute to the distribution of proboscis monkeys in the reserve. This study provides suggestions for conservation of proboscis monkey in remaining suitable habitat, which include local people involvement, monitoring and habitat restoration.

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“…The Landsat 8 OLI/TIRS Path/Row 123/64, taken on 5 July 2015 image was topographically corrected based on Tan algorithm (Tan et al, 2013), which also adopted by Hudjimartsu (2017) to reduce the effect of sun illumination difference. Illumination condition and corrected reflectance model were calculated based on equation as follows: Ic = cos ϕ cos slope+ sin ϕ sin slope cos(θ-aspect) (1) λcorrected = λuncorrectedβ(ic-ϕ)…”

Section: Topographic Correctionmentioning

confidence: 99%

AGROFORESTRY TREE DENSITY ESTIMATION BASED ON HEMISPHERICAL PHOTOS & LANDSAT 8 OLI/TIRS IMAGE: A CASE STUDY AT CIDANAU WATERSHED, BANTEN-INDONESIA

Khairiah

Prasetyo

Setiawan

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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The Cidanau watershed is the only watershed in Indonesia that implements Payment for Environmental Services (PES) for farmers who can maintain tree/stand density of 500 trees/hectare on their land. Payments are made upon the verification on the field by the project supervisor. This method requires a lot of time and costly, so it is necessary to build more efficient indirect methods, including using satellite imagery or camera data. The aim of this study is to understand Landsat OLI 8 and hemispherical photo can estimate tree density in the farmer's agroforestry stand. To obtain tree density, the number of trees with diameter more than 10 cm in 50 plots (50 m x 50 m) were counted. Some predictor variables were utilized, such as Leaf Area Index (LAI) based on hemispherical photos, Normalized Difference Vegetation Index (NDVI), Forest Cover Density (FCD), as well as NDVI and FCD which were enhanced with topographic correction. The imagery used was Landsat 8 OLI acquired on July 5, 2015, with Path/Row 123/64. The relationship between tree density and predictor variables was done using linear regression analysis. Prior to regression analysis, normality (Kolmogorov Smirnov/K-S), heteroscedasticity (Glejser test) and auto correlation (Durbin Watson) test were performed. The results of the analysis showed that tree density was estimated better with hemispherical photos-based LAI, with determination coefficient of 80.6%. Meanwhile, estimation using NDVI and FCD has lower determination coefficient. Even though, the use of topographic correction had been able to increase the determination coefficient of the regression relationship between tree density and FCD, from 4.64% to 35.18%.

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Illumination Modelling for Topographic Correction of Landsat 8 and Sentinel-2A Imageries

Cited by 5 publications

References 2 publications

Canopy Cover Estimation in Lowland Forest in South Sumatera, Using LiDAR and Landsat 8 OLI imagery

Canopy Cover Estimation in Lowland Forest in South Sumatera, Using LiDAR and Landsat 8 OLI imagery

Habitat suitability of Proboscis Monkey Nasalis larvatus Wurmb. in Muara Kaman Sedulang Nature Reserve, East Kalimantan

AGROFORESTRY TREE DENSITY ESTIMATION BASED ON HEMISPHERICAL PHOTOS & LANDSAT 8 OLI/TIRS IMAGE: A CASE STUDY AT CIDANAU WATERSHED, BANTEN-INDONESIA

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