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All Journal MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management INVOTEC Jurnal Pengabdian Kepada Masyarakat (JPKM) Jurnal Teknologi Jurnal Ilmiah Fakultas Sains dan Teknik Universitas Nusa Cendana Kelimutu Journal of Community Service Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital
I Made Parsa
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Social Sciences Other

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Journal : Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital

 1 
OPTIMALISASI PARAMETER SEGMENTASI UNTUK PEMETAAN LAHAN SAWAH MENGGUNAKAN CITRA SATELIT LANDSAT (STUDI KASUS PADANG PARIAMAN, SUMATERA BARAT DAN TANGGAMUS, LAMPUNG) Parsa, I Made
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 10 No. 1 (2013)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v10i1.3270

Abstract

Pixel-based digital-image classification results often contain of salt and pepper effects, while the visual classification has weakness because frequently provide inconsistent results. Due to the above, this study describes "Segmentation Parameter Optimization for Wetland Mapping Using Landsat Satellite Image" with object-based classification. The main objective of this study is to find out the optimal combination of segmentation parameters for paddy field mapping. The study was carried out in two sites namely in Pariaman, -West Sumatera Province and in Tangamus, -Lampung Province using segmentation of Landsat acquired in 2008 and visual interpretation of multitemporal Landsat images acquired in 2000-2009. Landsat segmentation covers two steps, firstly segmentation to optimize the parameter of color and compactness values, secondly to optimize the segmentation scale parameter. For validation, the study used both the visual-based and quantitative-based classification results of 2005 and 2007 derived from Quickbird image. Qualitative test includes object separation and segmentation accuracy of the first step of segmentation, while quantitative test is performed using confusion matrix on the second step of segmentation. This study results show that within the combinations of parameter values analyzed, the combination of parameter color value of 0.9, shape of 0.1, compactness of 0.5, and smoothness of 0.5 provides the most similar segmentation to the data reference. Meanwhile, the best case that the rules of cartography is scale of 8 for Pariaman study area and scale of 6 for Tangamus study area having accuracy ranges from 90.7% to 96.3%. This study concluded that the effect of the uncertainty of geometry of Landsat images against Quickbird images shows the maximum error of segment tolerance the origin of 4 ha to 16.70 ha for Pariaman site and 13.32 ha for Tangamus site. This results are still acceptable in segmentation results. Finally it was found that, the most optimal combination of parameters for mapping paddy field is at a scale of 1:1, color of 0.9.
supabase Please extract the text as it is Here is the extracted text from the image: DENOSING OF HIGH RESOLUTION REMOTE SENSING DATA USING STATIONARY WAVELET TRANSFORM Danang Surya Candra Peneliti Bidang Jianta, Pusdata, LAPAN e-mail: thedananx@yahoo. Parsa, I Made
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 10 No. 2 (2013)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v10i2.3279

Abstract

Land cover change, from bare land, water and to vegetation, or vice versa can be used as the basis for paddy fields mapping using the theory of probability, that is probability a land cover can be regarded as an paddy fields if a sequence of land cover changes of water, vegetation and bare land or vice versa on multitemporal images have been detected. The data being used were Landsat multitemporal imagery, while the methods being used in this analysis is the transformation of vegetation index and converted to land covers (bare land, water and vegetation). Detection of three types of land covers (bare land-water_vegetasi or viceversa) at sample area is assumed to have a probability 1 as paddy fields, if only two of the land cover types were detected (water and bare land , or water and vegetation , or vegetation or bare land ) the land cover of that pixel is assumed to have the probability as paddy fields 0.67, whereas if only one land cover types were detected for example only of water, or bare land or vegetation only, then the probability as paddy fields is assumed to be just 0.33. The results of the study showed that multitemporal Landsat of the study area is adequate for paddy fields mapping with accuracy of 91.2%.
UJICOBA MODEL PEMETAAN LAHAN SAWAH BERBASIS PERUBAHAN PENUTUP LAHAN CITA LANDSAT MOSAIK TAHUNAN DI JAWA BARAT Parsa, I Made
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 11 No. 1 (2014)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v11i1.3297

Abstract

Land cover changes of bare land, water and vegetation can be used as a basis for paddy field mapping by using probability theory approach, that is, the probability of one area can be determined as paddy field if the changes of water, bare and vegetation in multi time series can be detected. The results of preliminary studies that have been done on Tenggamus region – Lampung showed that probability theory approach produces a mapping accuracy reaches 91.2%. Based on this results, it has been carried out the model of validation for the wide region for some districts in Province West Java. The data used in this study are multitemporal Landsat 2000-2009. Data processing methods include: 1. Unsupervised digital classification of global land cover to map the bare land, vegetation and water from Landsat images, 2. Merger of each two multitemporal land cover so that the three spatial information obtained: bare land, vegetation and water 2000-2009. The validation of land cover changes made by overlaying the three spatial information. The evaluation results conducted by the confusion matrix (error matrix) by using reference paddy field 1:50,000 scale in 2010. Results of the testing showed that the average mapping accuracy of this probability model reaches 65.5%.
PENGUJIAN MODEL PENDEKATAN PROBABILITAS BERBASIS PERUBAHAN PENUTUP LAHAN CITA LANDSAT TUNGGAL MULTIWAKTU UNTUK PEMETAAN LAHAN SAWAH Parsa, I Made
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 12 No. 1 (2015)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v12i1.3308

Abstract

Tests on a model of probability approach to paddy field mapping based on land cover changes that has been done in several districts in West Java showed overall accuracy an average only 65.5%. It is thought to be related to the use of annual multitemporal Landsat mosaic results so often seem illogical because the image is derived from some of the data obtained at different seasons. In this regard has been done phase II trial of the model using multitemporal Landsat-8 singles data (not mosaic data). The objective of this research is to test the validity of the probability model on multitemporal land cover changes for rice field mapping. The method used in this trial is unsupervised classification methods for mapping multitemporal land cover. Merging multitemporal land cover change in the timeframe in accordance with the date of acquisition date. Analysis of probability as a rice field area, where if two of the land cover types were detected, bare land or otherwise classified as land with probability 1, if only observed one land cover change, bare land becomes water, water or bare land is classified as a probability of 0.3. Accuracy tests using confusion matrix between the field probability image and rice field reference level 1: 5,000. The evaluation results show that the rice field probability image reached 79.7% with lowest accuracy 67.7% (Babelan Sub-districts) and the highest 86.7% (Sukamandi Sub-districts). Comparison of results with previous results showed a high significant difference with an average increase of more than 600% accuracy. Based on these results it can concluded that the probability model based on multitemporal land cover changes for rice field mapping has good accuracy.
UJI MODEL FASE PERTUMBUHAN PADI BERBASIS CITRA MODIS MULTIWAKTU DI PULAU LOMBOK Parsa, I Made; Dirgahayu, Dede; Manalu, Johannes; Carolita, Ita; KH, Wawan
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 14 No. 1 (2017)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.pjpdcd.2017.v14.a2621

Abstract

Model testing is a step that must be done before operational activities. This testing aimed to test rice growth phase models based on MODIS in Lombok using multitemporal LANDSAT imagery and field data. This study was carried out by the method of analysis and evaluation in several stages, these are: evaluation of accuracy by multitemporal Landsat 8 image analysis, then evaluation by using field data, and analysis of growth phase information to calculate model consistency. The accuracy of growth phase model was calculated using Confusion Matrix. The results of stage I analysis for phase of April 30 and July 19 showed the accuracy of the model is 58-59%, while the evaluation of stage II for phase of period July 19 with survey data indicated that the overall accuracy is 53%. However, the results of model consistency analysis show that the resulting phase of the smoothed MODIS imagery shows a consistent pattern as well as the EVI pattern of rice plants with an 86% accuracy, but not for pattern data without smoothing. This testing give conclusion is the model is good, but for operational MODIS input data must be smoothed first before index value extraction.
EVALUASI REHABILITASI LAHAN KRITIS BERDASARKAN TREND NDVI LANDSAT-8 (Studi Kasus: DAS Serayu Hulu) Kartika, Tatik; Dirgahayu, Dede; Sari, Inggit Lolita; Parsa, I Made; Carolita, Ita
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 16 No. 2 (2019)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v16i2.3353

Abstract

The use of remote sensing in vegetation monitoring has been widely applied, including vegetation density monitoring. However, the use to evaluate rehabilitation program on critical land is still limited. Evaluation of forest cover and land rehabilitation activities become important due to the increase of critical land. The current method to evaluate the land condition is conducted by ground check at the rehabilitation site held at the end of the year after the initial implementation of the rehabilitation program until the third year. This method requires a lot of time, labour, and money. Based on the standard regulation to evaluate the rehabilitation program, the program is successful if 90% the new vegetation planted can grows until the third year. Therefore, this research uses an effective and efficient method for evaluating land rehabilitation programs using remote sensing data by understanding vegetation conditions and their densities using multi-temporal analysis for large areas. A multi-temporal Landsat-8 images from 2015-2018 will be used to analyze the Normalized Difference Vegetation Index (NDVI) trend in the time-based sequence method using spatial analysis. The results show that the non-forest area in Serayu Hulu Watershed consist of non-critical land, moderate critical land, critical land, and severe ciritical land. According to the ground check and NDVI trend analysis, the rehabilitation in non-critical land of the non-forest area was generally unsuccessful due to the area rehabilitation plant were harvested before the rehabilitation evaluation time ended. On the otherhand, on critical land; moderate critical land; and severe critical land of the non-forest area, the success of rehabilitation program was indicated by the achievement of the NDVI threshold value at 0.4660; 0.4947. 0.4916, respectively.
Co-Authors Abel, Alize Cormidiana Boesday, Lois F. Boesday, Louis F. Crispinus P. Tamal Damianus Dao Samo Dede Dirgahayu Dominikus, Wara Sabon Fahmi, Ichsan Hidayat Pawitan Hiwa Wonda Inggit Lolita Sari, Inggit Lolita Ita Carolita, Ita Johannes Manalu, Johannes KH, Wawan Manafe, Yestursance Y. Messakh, Jacobis J. Moldok, Renold H. Ray, Frans F. G. Renold H. Modok Ridmawan, Rahmawati Taneo, Malkisedek Tatik Kartika, Tatik Tonda, Hilarius Uup S. Wiradisastra Yetursance Y. Manafe