International Journal of Remote Sensing and Earth Sciences (IJReSES)
Not a member yet
334 research outputs found
Sort by
HAZE REMOVAL IN THE VISIBLE BANDS OF LANDSAT 8 OLI OVER SHALLOW WATER AREA
Get PDFHaze is one of radiometric quality parameters in remote sensing imagery. With certain atmospheric correction, haze is possible to be removed. Nevertheless, an efficient method for haze removal is still a challenge. Many methods have been developed to remove or to minimize the haze disruption. While most of the developed methods deal with removing haze over land areas, this paper tried to focus to remove haze from shallow water areas. The method presented in this paper is a simple subtraction algorithm between a band that reflected by water and a band that absorbed by water. This paper used data from Landsat 8 with visible bands as a band that reflected by water while the band that absorbed by water represented by NIR, SWIR-1, and SWIR-2 bands. To validate the method, a reference data which relatively clear of cloud and haze contamination is selected. The pixel numbers from certain points are selected and collected from data scene, results scene and reference scene. Those pixel numbers, then being compared each other to get a correlation number between data scene to reference scene and between result scene and reference scene. The comparison shows that the method using NIR, SWIR-1, and SWIR-2 all significantly improved correlations numbers between result scene with reference scene to higher than 0.9. The comparison also indicates that haze removal result using NIR band had the highest correlation with reference data.
EVALUATION OF SPOT-5 IMAGE FUSION USING MODIFIED PAN-SHARPENING METHODS
Get PDFImage fusion, commonly known as pan-sharpening, is a method that combines two data: a panchromatic image that has geometric detail information with the highest spatial resolution and multi-spectral image that has the highest color information but with the lowest resolution. Pan-sharpeningis very important for various remote sensing applications, such as to improve the image classification, to change the detection using temporal data, to increase the geometric, image segmentation, and to improve the visibility of certain object that does not appear on certain data. This study aims to compare the existing pan-sharpening methods such as Brovey, Brovey modification using green and red band, Gram-Schmidt, HPF, Multiplicative, and SFIM.The quality of the pan-sharpening result should be evaluated, this study used Universal Image Quality Index (UIQI/Q index); this evaluation methodgives the opportunity to choose which method is best to provide the most similar spectral information with the original multispectral image. A pan-sharpening qualitative analysis shows that there has been a sharpening process on all pan-sharpening images. Based on spectral visualization (color display), several pan-sharpening methods such as HPF multiplicative method provides brighter colorsand Brovey transformation method displays dark colors. Gram-Schmidt method also provides a different color from the original multispectral image. A pan-sharpening quantitative analysis shows that the best pan-sharpening method with UIQI value> 0.9 is Brovey modification using green and red band. This is due to the green band (500-590 nm) and the red band(610-680 nm) wavelength are in the panchromatic band (480-710 nm) of the SPOT-5 Data.Â
ANALYSIS OF SCENE COMPATIBILITIES FOR MOSAIC OF LANDSAT 8 MULTI-TEMPORAL IMAGES BASED ON RADIOMETRIC PARAMETER
Get PDFCloud free mosaic simplified the remote sensing imagery. Multi-temporal image mosaic needed to make a cloud free mosaic i.e. in the area covered by cloud throughout year like Indonesia. One of the satellite imagery that was widely used for various purposes was Landsat 8 image due to the temporal, spatial and spectral resolution which was suitable for many utilization themes. Landsat 8 could be used for multi-temporal image mosaic of the entire region in Indonesia. Landsat 8 had 16 days temporal resolution which allowed a region (scene image) acquired in a several times one year. However, not all the acquired Landsat 8 scene was proper when used for multi-temporal mosaic. The purpose of this work was observing radiometric parameters for scene selection method so a good multi-temporal mosaic image could be generated and more efficient processing. This study analyzed the relationship between radiometric parameters from image i.e. histogram and Scattergram with scene selection for multi-temporal mosaic purposes. Histogram and Scattergram representing radiometric imagery context such as mean, standard deviation, median and mode which was displayed visually. The data used were Landsat 8 imagery with the Area of Interest (AOI) in Kalimantan and Lombok. Then the histogram and Scattergram of the image AOI was analyzed. From the histogram and Scattergram analysis could be obtained that less shift between the data’s histogram and the more Scattergram forming 45 degree angle for distribution of the data then indicated more similar to radiometric of the image
BATHYMETRY DATA EXTRACTION ANALYSIS USING LANDSAT 8 DATA
Get PDFThe remote sensing technique can be used to produce bathymetric map. Bathymetric mapping is important for the coastal zone and watershed management. In the previous study conducted in Menjangan Island of Bali, bathymetric extractin information from the top of the atmosphere (TOA) reflectance image of Landsat ETM+ data has R2 = 0.620. Not optimal  correlation value produced is highly influenced by the reflectance image of Landsat ETM+ data, were used, hence the lack of the research which became the basis of the present study. The study was on the Karang Lebar water of Thousand Islands, Jakarta. And the aim was to determine whether there was an increased correlation coefficient value of bathymetry extraction information generated from Surface reflectance and TOA reflectance imager of Landsat 8 data acquired on August 12, 2014. The method of extraction was done using algorithms Van Hengel and Spitzer (1991). Extraction   absolute depth information obtained from the model logarithm of Landsat 8 surface reflectance images and pictures TOA produce a correlation value of R2 = 0.663 and R2 = 0.712
DETERMINATION OF FOREST AND NON-FOREST IN SERAM ISLAND MALUKU PROVINCE USING MULTI-YEAR LANDSAT DATA
Get PDFSeram Island is one of the islands in Maluku Province. Forest in Seram Island still exists because there is Manusela National Park, but they should be monitored. The forest and non-forest information is usually obtained through the classification process from single remote sensing data, but in certain places in Indonesia it is difficult enough to get  single Landsat data with cloud free, so annual mosaic was used. The aim of this research was to analyze the stratification zone, their indices and thresholds to get spatial information of annual forest area in Seram Island using multi-year Landsat Data. The method consists of four stages: 1) analyzing the base probability result for determination of stratification zone 2) determining the annual forest probability by applying indices from stage-I, 3) determining the spatial information of forest and non-forest annual phase-I by searching the lowest boundary of forest probability, and 4) determining the spatial information of forest and non-forest annual phase-II using the method of permutation of three data and multi-year forest rules. The results of this study indicated that Seram Island could be coumpond into one stratification zone with three indices. The index equations were B2+B3-2B for index-1, B3+B4 for index-2, and -B3+B4 for index-3.  The threshold of index 1, 2, and 3 ranged between -60 and 0, 61 and 104, and 45 and 105, respectively. The lowest boundary of forest probability in Seram Island since 2006 to 2012 have a range between 46% and 60%. The last result was the annual forest spatial information phase II where the missing data on the forest spatial information phase I decreased. The information is very important to analyze forest area change, especially in Seram Island.Â
IDENTIFICATION AND CLASSIFICATION OF FOREST TYPES USING DATA LANDSAT 8 IN KARO, DAIRI, AND SAMOSIR DISTRICTS, NORTH SUMATRA
Get PDFForests have important roles in terms of carbon storage and other values. Various studies have been conducted to identify and distinguish the forest from non-forest classes. Several forest types classes such as secondary forests and plantations should be distinguished related to the restoration and rehabilitation program for dealing with climate change. The study was carried out to distinguish several classes of important forests such as the primary dryland forests, secondary dryland forest, and plantation forests using Landsat 8 to develop identification techniques of specific forests classes. The study areas selected were forest areas in three districts, namely Karo, Dairi, and Samosir of North Sumatera Province. The results showed that using composite RGB 654 of Landsat 8 imagery based on test results OIF for the forest classification, the forests could be distinguished with other land covers. Digital classification can be combined with the visual classification known as a hybrid classification method, especially if there are difficulties in border demarcation between the two types of forest classes or two classes of land covers
IN-SITU MEASUREMENT OF DIFFUSE ATTENUATION COEFFICIENT AND ITS RELATIONSHIP WITH WATER CONSTITUENT AND DEPTH ESTIMATION OF SHALLOW WATERS BY REMOTE SENSING TECHNIQUE
Get PDFDiffuse attenuation coefficient, Kd(λ), has an empirical relationship with water depth, thus potentially to be used to estimate the depth of the water based on the light penetration in the water column. The aim of this research is to assess the relationship of diffuse attenuation coefficient with the water constituent and its relationship to estimate the depth of shallow waters of Air Island, Panggang Island and Karang Lebar lagoons and to compare the result of depth estimation from Kd model and derived from Landsat 8 imagery. The measurement of Kd(λ) was carried out using hyperspectral spectroradiometer TriOS-RAMSES with range 320 – 950 nm. The relationship between measurement Kd(λ) on study site with the water constituent was the occurrence of absorption by chlorophyll-a concentration at the blue and green spectral wavelength. Depth estimation using band ratio from Kd(λ) occurred at 442,96 nm and 654,59 nm, which had better relationship with the depth from in-situ measurement compared to the estimation based on Landsat 8 band ratio. Depth estimated based on Kd(λ) ratio and in-situ measurement are not significantly different statistically. Depth estimated based on Kd(λ) ratio and in-situ measurement are not significantly different statistically. However, depth estimation based on Kd(λ) ratio was inconsistent due to the bottom albedo reflection because the Kd(λ) measurement was carried out in shallow waters. Estimation of water depth based on Kd(λ) ratio had better results compared to the Landsat 8 band ratio