Found 4 Documents
Journal : Geoplanning Journal

Geoplanning: Journal of Geomatics and Planning Vol 4, No 1 (2017): April-Accepted papers (in proofreading process)
Publisher : Department of Urban and Regional Planning, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/geoplanning.0.0.%p


Image-sharpening process integrates lower spatial resolution multispectral bands with higher spatial resolution panchromatic band to produce multispectral bands with finer spatial detail called pan-sharpened image. Although the pan-sharpened image can greatly assist the process of information extraction using visual interpretation, the benefit and setback of using pan-sharpened image on the accuracy of digital classification for mapping remain unclear. This research aimed at 1) highlighting the issue of using pan-sharpened image to perform benthic habitats mapping and 2) comparing the accuracy of benthic habitats mapping using original and pan-sharpened bands. Quickbird image was used in this study and Kemujan Island was selected as the study area. Two levels of hierarchical classification scheme of benthic habitats were constructed based on the composition of benthic habitats insitu. PC Spectral sharpening method was applied on Quickbird image. Image radiometric corrections, PCA transformation, and image classifications were performed on both original and pan-sharpened image. The results show that the accuracy of benthic habitats classification of pan-sharpened image (maximum overall accuracy 64.28% and 73.30% for per-pixel and OBIA respectively) is lower than the original image (73.46%, 73.10%). The main setback of using pan-sharpened image is the inability to correct the sunglint, hence adversely affects the process of water column correction, PCA transformation and image classification. This is mainly because sunglint do not only affect object’s spectral response but also the texture of the object. Nevertheless, the pan-sharpened image can still be used to map benthic habitats using visual interpretation and digital image processing. Pan-sharpened image will deliver better classification accuracy and visual appearance especially when the sunglint is low.
Geoplanning: Journal of Geomatics and Planning Vol 3, No 2 (2016): (October 2016)
Publisher : Department of Urban and Regional Planning, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1334.232 KB) | DOI: 10.14710/geoplanning.3.2.107-116


The Karimunjawa Islands mangrove forest has been subjected to various direct and indirect human disturbances in the recent years. If not properly managed, this disturbance will lead to the degradation of mangrove habitat health. Assessing forest canopy fractional cover (fc) using remote sensing data is one way of measuring mangrove forest degradation. This study aims to (1) estimate the forest canopy fc using a semi-empirical method, (2) assess the accuracy of the fc estimation and (3) create mangrove forest degradation from the canopy fc results. A sample set of in-situ fc was collected using the hemispherical camera for model development and accuracy assessment purposes. We developed semi-empirical relationship models between pixel values of ALOS AVNIR-2 image (10 m pixel size) and field fc, using Enhanced Vegetation Index (EVI) as a proxy of the image spectral response. The results show that the EVI provides reasonable estimation accuracy of mangrove canopy fc in Karimunjawa Island with the values ranged from 0.17 to 0.96 (n = 69). The low fc values correspond to vegetation opening and gaps caused by human activities or mangrove dieback. The high fc values correspond to the healthy and dense mangrove stands, especially the Rhizophora sp formation at the seafront. The results of this research justify the use of simple canopy fractional cover model for assessing the mangrove forest degradation status in the study area. Further research is needed to test the applicability of this approach at different sites.
TOTAL SUSPENDED SOLID DISTRIBUTION ANALYSIS USING SPOT-6 DATA IN SEGARA ANAKAN, CILACAP Dhannahisvara, Aisya Jaya; Harjo, Hartono; Wicaksono, Pramaditya; Nugroho, Ferman Setia
Geoplanning: Journal of Geomatics and Planning Vol 5, No 2 (2018): (October 2018)
Publisher : Department of Urban and Regional Planning, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1152.851 KB) | DOI: 10.14710/geoplanning.5.2.177-188


Spatial distribution and concentration of Total Suspended Solid (TSS) is one of the coastal parameters which are required to be examined in order to understand the quality of the water. Rapid development of remote sensing technology has resulted in the emergence of various methods to estimate TSS concentration. SPOT-6 data has spatial, spectral, and temporal characteristics that can be used to estimate TSS concentration. The purposes of this research are (1) to determine the best method for estimating TSS concentration, (2) to map TSS distribution, and (3) to determine the correlation between TSS concentration and chlorophyll-a concentration using SPOT-6 data in Segara Anakan. The estimation of TSS concentration in this research was performed using empirical model built from SPOT-6 and TSS field data. Bands used in this research are single band data (blue, green, red, and near infrared) and transformed bands such as band ratio (12 combinations), Normalized Difference Suspended Solid Index (NDSSI), and Suspended Solid Concentration Index (SSC). The result shows that blue, green, red, and near infrared bands and SSC index significantly correlated to TSS. Afterwards, regression analysis was performed to determine the function that can be used to predict TSS concentration using SPOT-6 data. Regression function used are linear and non-linear (exponential, logarithmic, 2nd order polynomial, and power). The best model was chosen based on the accuracy assessment using Standard Error of Estimate (SE). The selected model was used to calculate total TSS concentration and was correlated with chlorophyll-a field data. The result of accuracy test shows that the model from blue band has an accuracy of 70.68 %, green band 70.68 %, red band 75.73 %, near infrared band 65.58 %, and SSC 73.67 %. The accuracy test shows that red band produced the best prediction model for mapping TSS concentration distribution. The total TSS concentration, which was calculated using red band empirical model, is estimated to be 6.13 t. According to the correlation test, TSS concentration in Segara Anakan has no significant correlation with chlorophyll-a concentration, with a coefficient correlation value of -0.265.
Geometric Accuracy Assessment for Shoreline Derived from NDWI, MNDWI, and AWEI Transformation on Various Coastal Physical Typology in Jepara Regency using Landsat 8 OLI Imagery in 2018 Wicaksono, Arief; Wicaksono, Pramaditya
Geoplanning: Journal of Geomatics and Planning Vol 6, No 1 (2019): (August 2019)
Publisher : Department of Urban and Regional Planning, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1996.46 KB) | DOI: 10.14710/geoplanning.6.1.55-72


Landsat 8 OLI imagery and water index utilization is expected to be able to complete the shoreline data that is difficult to obtain by using terrestrial and hydrographic surveys. In fact, coastal areas in Indonesia have a variety of coastal physical typology so that each water index characteristic in obtaining shoreline data needs to be understood in order to use water index method effectively. The objectives of this study are to map the shoreline using NDWI, MNDWI, and AWEI transformations and assess the shoreline geometric accuracy on various coastal physical typology. The shoreline derived from water index is obtained from Landsat 8 OLI imagery, while the reference shoreline for accuracy assessment is obtained from visual interpretation on Planet Scope imagery. Threshold 0 and subjective threshold based on per coastal physical typology sample experiments are used to separate land-sea. The horizontal accuracy standard of the shoreline derived from water index uses the regulation from Geospatial Information Agency of Indonesia No.15 in 2014 on technical guidelines for basic map accuracy. The results consisted of 1:100,000 scale shoreline map and shoreline geometric accuracy per coastal physical typology. Based on the shoreline geometry accuracy assessment, NDWI has the lowest shoreline geometry accuracy on artificial coast (RMSE=24.13 m). MNDWI has the lowest shoreline geometry accuracy on land deposition coast (RMSE=15.84 m), marine deposition coast (RMSE=29.53 m), and volcanic coast (RMSE=10 m). AWEIsh has the lowest shoreline geometry accuracy on the organic coast (RMSE=13.47 m), while AWEI does not superior to any coastal physical typology.