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RISS 인기검색어
- 검색키워드
- 저자 : Eko Pujiono (검색결과 8 건)
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Pujiono,Eko Mulyanto Yuniarno,I Ketut Eddy Purnama,Mochamad Hariadi 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.11
Low quality of underwater image due to color spread and absorption deals with the propagation of light wave length. In this paper we propose exponential equation approach to enhance underwater image color and maintaining color constancy. Exponential approach is conducted through two steps: first, determining the relation between the color intensity of an image underwater and the color intensity of an image in a certain depth; second, determining the coefficient of underwater image color absorption by using least square. The result of exponential approach is measured by using Peak Signal to Noise Ratio, yielding an average value of 19.18 and visually the result of the image color approximates its original color. We concluded that exponential approach can determine the color constancy level which in turns can enhance the underwater image just as its original color.
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Assessing the Extent and Rate of Deforestation in the Mountainous Tropical Forest
Pujiono, Eko,Lee, Woo-Kyun,Kwak, Doo-Ahn,Lee, Jong-Yeol The Korean Society of Remote Sensing 2011 大韓遠隔探査學會誌 Vol.27 No.3
Landsat data incorporated with additional bands-normalized difference vegetation index (NDVI) and band ratios were used to assess the extent and rate of deforestation in the Gunung Mutis Nature Reserve (GMNR), a mountainous tropical forest in Eastern of Indonesia. Hybrid classification was chosen as the classification approach. In this approach, the unsupervised classification-iterative self-organizing data analysis (ISODATA) was used to create signature files and training data set. A statistical separability measurement-transformed divergence (TD) was used to identify the combination of bands that showed the highest distinction between the land cover classes in training data set. Supervised classification-maximum likelihood classification (MLC) was performed using selected bands and the training data set. Post-classification smoothing and accuracy assessment were applied to classified image. Post-classification comparison was used to assess the extent of deforestation, of which the rate of deforestation was calculated by the formula suggested by Food Agriculture Organization (FAO). The results of two periods of deforestation assessment showed that the extent of deforestation during 1989-1999 was 720.72 ha, 0.80% of annual rate of deforestation, and its extent of deforestation during 1999-2009 was 1,059.12 ha, 1.31% of annual rate of deforestation. Such results are important for the GMNR authority to establish strategies, plans and actions for combating deforestation.
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Assessing the Extent and Rate of Deforestation in the Mountainous Tropical Forest
Eko Pujiono,Woo Kyun Lee,Doo Ahn Kwak,Jong Yeol Lee 大韓遠隔探査學會 2011 大韓遠隔探査學會誌 Vol.27 No.3
Landsat data incorporated with additional bands-normalized difference vegetation index (NDVI) and band ratios were used to assess the extent and rate of deforestation in the Gunung Mutis Nature Reserve (GMNR), a mountainous tropical forest in Eastern of Indonesia. Hybrid classification was chosen as the classification approach. In this approach, the unsupervised classification-iterative self-organizing data analysis (ISODATA) was used to create signature files and training data set. A statistical separability measurement-transformed divergence (TD) was used to identify the combination of bands that showed the highest distinction between the land cover classes in training data set. Supervised classification-maximum likelihood classification (MLC) was performed using selected bands and the training data set. Post-classification smoothing and accuracy assessment were applied to classified image. Post-classification comparison was used to assess the extent of deforestation, of which the rate of deforestation was calculated by the formula suggested by Food Agriculture Organization (FAO). The results of two periods of deforestation assessment showed that the extent of deforestation during 1989-1999 was 720.72 ha, 0.80% of annual rate of deforestation, and its extent of deforestation during 1999-2009 was 1,059.12 ha, 1.31% of annual rate of deforestation. Such results are important for the GMNR authority to establish strategies, plans and actions for combating deforestation.
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Eko Pujiono,이우균,곽두안,Sulistyanto,김소라,이종열,이승호,박태진,김문일 한국산림과학회 2013 Forest Science And Technology Vol.9 No.4
The Maubesi Nature Reserve (MNR) is a protected lowland area in eastern Indonesia that mainly consists of mangroveforest. The objective of this paper was to demonstrate a simple technique to visualize and quantify the change in mangrovearea using a 3-year dataset of Landsat TM images acquired in 1989, 2003 and 2009. The normalized difference vegetationindex (NDVI) was calculated to determine high and low vegetation biomass in each image. Each NDVI extracted byLandsat image in 1989, 2003 and 2009 was assigned to red, green and blue (RGB) color, respectively, and then combinedto make color composites. Additive color theory was applied to interpret mangrove changes within the MNR area on theRGB-NDVI color composite. Changed areas were quantified by performing an unsupervised classification on theRGB-NDVI image with 45 classes that were grouped into eight major mangrove change categories. An Indonesian landcover map was used to assess the accuracy of the classified image. The result showed that 77.13% of the MNR area wasunchanged and 22.87% of the MNR area changed over 20 years (1989–2009).
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Ronggo Sadono,Eko Pujiono,Linda Lestari 한국산림과학회 2020 Forest Science And Technology Vol.16 No.3
This study investigated the land cover changes, carbon storage dynamics and their underlying socio-economic processes before and after a community forest permit in Bleberan village, Gunungkidul, Indonesia, during 1999–2018. We used a combination of the forest canopy density model, carbon conversion and socio-economic related data to analyze land cover classes in the periods of 1999–2003, 2003–2009, and 2009–2018, representing the phases of several years before community forestry permit, initial phase of community forestry establishment and several years after community forestry permit, respectively. Results showed that at baseline (1999), where illegal logging was started on the ground, the condition of the 40 ha investigated area was dominated by non-forest in the form of open land by 54% with an amounted carbon storage of 1352.62 ton. In the phase of before community forestry permit (1999–2003), when there was continuous illegal logging, the open land rise quickly achieved to 83%, with only 312.09 ton of carbon storage remaining. In the initial phase of community forestry establishment (2003–2009), when the government issued a legalization of community forest, the mixed dryland agriculture shifted to dominate the area by 55%, with the carbon storage being increased to 1840.94 ton. The last phase, several years after community forestry permit (2009–2018), which characterized by active engagement of the community in forest rehabilitation, the area was altered to fully stocked teak plantation forest by 82%, with a carbon storage enhancement of 3379.16 ton or two times higher than that at baseline. Such results are important for the forest community authority and related stakeholder for designing appropriate forest-related policies and supporting REDD þ implementation.
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이우균,박태진,이종열,변우혁,곽두안,Guishan Cui,김문일,정래선,Eko Pujiono,오수현,변정연,남기준,조현국,이정수,정동준,김성호 한국산림과학회 2012 Forest Science And Technology Vol.8 No.2
The importance of estimating forest volume has been emphasized by increasing interest on carbon sequestration and storage which can be converted from volume estimates. With importance of forest volume, there are growing needs for developing efficient and unbiased estimation methods for forest volume using reliable data sources such as the National Forest Inventory (NFI) and supplementary information. Therefore, this study aimed to develop a forest plot volume model using selected explanatory variables from each data type (only Forest Type Map (FTM), only airborne LiDAR and both datasets), and verify the developed models with forest plot volumes in 60 test plots with the help of the NFI dataset. In linear regression modeling, three variables (LiDAR height sum, age, and crown density class) except diameter class were selected as explanatory independent variables. These variables generated the four forest plot volume models by combining the variables of each data type. To select an optimal forest plot volume model, a statistical comparing process was performed between four models. In verification, Model no. 3 constructed by both FTM and airborne LiDAR was selected as an optimal forest plot volume model through comparing root mean square error (RMSE) and coefficient of determination (R^2). The selected best performance model can predict the plot volume derived from NFI with RMSE and R2 at 50.41 (m^3) and 0.48, respectively.
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