BibTeX
@article{IJASEIT12514,
author = {Projo Danoedoro and Diwyacitta Dirda Gupita},
title = {Combining Pan-Sharpening and Forest Cover Density Transformation Methods for Vegetation Mapping using Landsat-8 Satellite Imagery},
journal = {International Journal on Advanced Science, Engineering and Information Technology},
volume = {12},
number = {3},
year = {2022},
pages = {881--891},
keywords = {Image processing; radiometric correction; pan-sharpening; vegetation mapping; forest cover density.},
abstract = {Forest cover density (FCD) transformation is an 8-bit Landsat imagery-based method for vegetation mapping, which uses a set of indices comprising vegetation, soil, shadow, and thermal components. With the advent of 16-bit Landsat-8 imagery, radiometric correction and pan-sharpening methods could be applied to generate new datasets with different spectral and spatial characteristics. This study combined several methods of pan-sharpening and FCD transformations for mapping vegetation density in Salatiga and Ambarawa region, Indonesia, based on Landsat-8 dataset. The imagery was treated differently to constitute five new datasets, i.e., original multispectral imagery (30 m), radiometrically corrected multispectral imagery (30 m), and three pan-sharpening datasets generated using Gram-Schmidt (GS), Principal Component Analysis (PCA), and Hyper-spherical Color Space (HCS) methods (15 m). Each dataset was then processed using FCD transformation as a basis for vegetation density and structural composition mapping. Field observation and vegetation density measurement using high-spatial-resolution imagery was used as a reference for accuracy assessment. This study found that the pan-sharpening methods produced new datasets with various correlation coefficients with their corresponding original bands, affecting the accuracy of spectral modeling in FCD. Moreover, the generated FCD models were found less accurate as compared to that of the original one. However, the accuracy could be increased by rescaling the original DNs and regrouping the original classes into simpler categorization. Besides the problem of data characteristics, all FCD models were also found inaccurate compared to previous studies due to the landscape complexity of the study area.},
issn = {2088-5334},
publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12514},
doi = {10.18517/ijaseit.12.3.12514}
}
EndNote
%A Danoedoro, Projo
%A Gupita, Diwyacitta Dirda
%D 2022
%T Combining Pan-Sharpening and Forest Cover Density Transformation Methods for Vegetation Mapping using Landsat-8 Satellite Imagery
%B 2022
%9 Image processing; radiometric correction; pan-sharpening; vegetation mapping; forest cover density.
%! Combining Pan-Sharpening and Forest Cover Density Transformation Methods for Vegetation Mapping using Landsat-8 Satellite Imagery
%K Image processing; radiometric correction; pan-sharpening; vegetation mapping; forest cover density.
%X Forest cover density (FCD) transformation is an 8-bit Landsat imagery-based method for vegetation mapping, which uses a set of indices comprising vegetation, soil, shadow, and thermal components. With the advent of 16-bit Landsat-8 imagery, radiometric correction and pan-sharpening methods could be applied to generate new datasets with different spectral and spatial characteristics. This study combined several methods of pan-sharpening and FCD transformations for mapping vegetation density in Salatiga and Ambarawa region, Indonesia, based on Landsat-8 dataset. The imagery was treated differently to constitute five new datasets, i.e., original multispectral imagery (30 m), radiometrically corrected multispectral imagery (30 m), and three pan-sharpening datasets generated using Gram-Schmidt (GS), Principal Component Analysis (PCA), and Hyper-spherical Color Space (HCS) methods (15 m). Each dataset was then processed using FCD transformation as a basis for vegetation density and structural composition mapping. Field observation and vegetation density measurement using high-spatial-resolution imagery was used as a reference for accuracy assessment. This study found that the pan-sharpening methods produced new datasets with various correlation coefficients with their corresponding original bands, affecting the accuracy of spectral modeling in FCD. Moreover, the generated FCD models were found less accurate as compared to that of the original one. However, the accuracy could be increased by rescaling the original DNs and regrouping the original classes into simpler categorization. Besides the problem of data characteristics, all FCD models were also found inaccurate compared to previous studies due to the landscape complexity of the study area.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12514
%R doi:10.18517/ijaseit.12.3.12514
%J International Journal on Advanced Science, Engineering and Information Technology
%V 12
%N 3
%@ 2088-5334
IEEE
Projo Danoedoro and Diwyacitta Dirda Gupita,"Combining Pan-Sharpening and Forest Cover Density Transformation Methods for Vegetation Mapping using Landsat-8 Satellite Imagery," International Journal on Advanced Science, Engineering and Information Technology, vol. 12, no. 3, pp. 881-891, 2022. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.12.3.12514.
RefMan/ProCite (RIS)
TY - JOUR
AU - Danoedoro, Projo
AU - Gupita, Diwyacitta Dirda
PY - 2022
TI - Combining Pan-Sharpening and Forest Cover Density Transformation Methods for Vegetation Mapping using Landsat-8 Satellite Imagery
JF - International Journal on Advanced Science, Engineering and Information Technology; Vol. 12 (2022) No. 3
Y2 - 2022
SP - 881
EP - 891
SN - 2088-5334
PB - INSIGHT - Indonesian Society for Knowledge and Human Development
KW - Image processing; radiometric correction; pan-sharpening; vegetation mapping; forest cover density.
N2 - Forest cover density (FCD) transformation is an 8-bit Landsat imagery-based method for vegetation mapping, which uses a set of indices comprising vegetation, soil, shadow, and thermal components. With the advent of 16-bit Landsat-8 imagery, radiometric correction and pan-sharpening methods could be applied to generate new datasets with different spectral and spatial characteristics. This study combined several methods of pan-sharpening and FCD transformations for mapping vegetation density in Salatiga and Ambarawa region, Indonesia, based on Landsat-8 dataset. The imagery was treated differently to constitute five new datasets, i.e., original multispectral imagery (30 m), radiometrically corrected multispectral imagery (30 m), and three pan-sharpening datasets generated using Gram-Schmidt (GS), Principal Component Analysis (PCA), and Hyper-spherical Color Space (HCS) methods (15 m). Each dataset was then processed using FCD transformation as a basis for vegetation density and structural composition mapping. Field observation and vegetation density measurement using high-spatial-resolution imagery was used as a reference for accuracy assessment. This study found that the pan-sharpening methods produced new datasets with various correlation coefficients with their corresponding original bands, affecting the accuracy of spectral modeling in FCD. Moreover, the generated FCD models were found less accurate as compared to that of the original one. However, the accuracy could be increased by rescaling the original DNs and regrouping the original classes into simpler categorization. Besides the problem of data characteristics, all FCD models were also found inaccurate compared to previous studies due to the landscape complexity of the study area.
UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12514
DO - 10.18517/ijaseit.12.3.12514
RefWorks
RT Journal Article
ID 12514
A1 Danoedoro, Projo
A1 Gupita, Diwyacitta Dirda
T1 Combining Pan-Sharpening and Forest Cover Density Transformation Methods for Vegetation Mapping using Landsat-8 Satellite Imagery
JF International Journal on Advanced Science, Engineering and Information Technology
VO 12
IS 3
YR 2022
SP 881
OP 891
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Image processing; radiometric correction; pan-sharpening; vegetation mapping; forest cover density.
AB Forest cover density (FCD) transformation is an 8-bit Landsat imagery-based method for vegetation mapping, which uses a set of indices comprising vegetation, soil, shadow, and thermal components. With the advent of 16-bit Landsat-8 imagery, radiometric correction and pan-sharpening methods could be applied to generate new datasets with different spectral and spatial characteristics. This study combined several methods of pan-sharpening and FCD transformations for mapping vegetation density in Salatiga and Ambarawa region, Indonesia, based on Landsat-8 dataset. The imagery was treated differently to constitute five new datasets, i.e., original multispectral imagery (30 m), radiometrically corrected multispectral imagery (30 m), and three pan-sharpening datasets generated using Gram-Schmidt (GS), Principal Component Analysis (PCA), and Hyper-spherical Color Space (HCS) methods (15 m). Each dataset was then processed using FCD transformation as a basis for vegetation density and structural composition mapping. Field observation and vegetation density measurement using high-spatial-resolution imagery was used as a reference for accuracy assessment. This study found that the pan-sharpening methods produced new datasets with various correlation coefficients with their corresponding original bands, affecting the accuracy of spectral modeling in FCD. Moreover, the generated FCD models were found less accurate as compared to that of the original one. However, the accuracy could be increased by rescaling the original DNs and regrouping the original classes into simpler categorization. Besides the problem of data characteristics, all FCD models were also found inaccurate compared to previous studies due to the landscape complexity of the study area.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12514
DO - 10.18517/ijaseit.12.3.12514
