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All Journal JURNAL ILMIAH GEOMATIKA MAJALAH ILMIAH GLOBE Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital
Ita Carolita, Ita
Remote Sensing Application Center, Indonesian National Aeronautic and Space Institute, Jakarta,13721, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Mathematics

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UJI AKURASI TRAINING SAMPEL BERBASIS OBJEK CITRA LANDSAT DI KAWASAN HUTAN PROVINSI KALIMANTAN TENGAH Noviar, Heru; Carolita, Ita; Cahyono, Joko Santo
GEOMATIKA Vol 18, No 2 (2012)
Publisher : Badan Informasi Geospasial in Partnership with MAPIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (944.692 KB) | DOI: 10.24895/JIG.2012.18-2.190

Abstract

Teknik klasifikasi citra digital telah berkembang, dari berbasis pixel menjadi klasifikasi berbasis objek, dimana citra sebelumnya dibuat dalam bentuk segmentasi/poligon yang bias diatur homogenitasnya. Tetapi dalam proses klasifikasi baik dengan berbasis pixel dengan metode Maximum Likelihood maupun dengan berbasis objek tetap harus ditentukan training sampel untuk mengidentifikasi objek yang akan diklasifikasi. Dalam pengambilan training sampel dengan berbasis pixel, poligon yang dibuat, diambil sehomogen mungkin sedangkan dalam metode berbasis objek, training sampel dibuat berdasarkan poligon-poligon yang sudah terbentuk hasil segmentasi yang dibuat berdasarkan parameter scale, shape, compactness yang telah ditentukan.  Penelitian ini bertujuan untuk menguji akurasi hasil training sampel yang dibuat berdasarkan polygon hasil segmentasi dengan training sampel yang dibuat berbasis pixel dengan studi kasus kawasan hutan di PLG Kapuas, Kalimantan Tengah dan citra yang digunakan citra Landsat. Akurasi diuji dengan melihat percampuran antar kelas (dengan Scatterplot) dan keterpisahan antar kelas dengan metode Confusion Matrix (nilai overall accuracy dan nilai kappa). Hasil memperlihatkan bahwa uji keakuratan training sampel berbasis objek pada lokasi lebih rendah ini jika dibandingkan dengan training sampel berbasis pixel, terlihat dari nilai Overall Accuracy dan nilai Kappanya. Grafik Scatterplot menunjukkan masih ada ketercampuran antar kelas (hutan, non hutan, non vegetasi dan tubuh air) pada kedua hasil dan lebih banyak terjadi pada training sampel hasil segmentasi.Kata kunci: training sampel, uji keakuratan, segmentasi, klasifikasi berbasis objek dan pixel, hutan dan non hutan, citra Landsat.ABSTRACTDigital image classification techniques have been developed from a pixel-based to an object-based classification, where the previous image is created in the form of segmentation/polygons whose homogenity can be set based on scale, shape, and compactness. However, in the classification process, either using pixel-based or object-based, several training samples still need to be determined in advance to identify objects that will be classified. In the pixel-based, while generating training samples, created polygons were made as homogeneous as possible. On the other hand, in the object-based method, training samples were made based on polygons from the results from segmentation process based on scale, shape, and compactness parameter. The research aim is to test  the accuracy of training samples from the object-based method, which is compared with the ones from the pixel-based method. As the case study was forest areas around PLG Kapuas, Central Kalimantan. Landsat imagery was used as material. The accuracy was tested by looking at the values of inter-class mixture (using scatterplot) and of class-separation (using confusion matrix to gain overall accuracy and kappa value). The results show that the accuracy of pixel-based training samples is better, which can be seen from the Kappa value and Overall AccuracyScatterplot graphic shows that there are mixed-classes (forest, non-forest, non-vegetation, and water bodies) on both samples test result, although there are more in the segmentation process rather than in the training samples made from manual delineationKey words: training samples, test accuracy, segmentation, object and pixel-based classification, forest and non forest, Landsat imagery
KLASIFIKASI PENUTUP LAHAN BERBASIS OBJEK PADA CITRA SATELIT SPOT DENGAN MENGGUNAKAN METODE TREE ALGORITHM Julzarika, Atriyon; Carolita, Ita
MAJALAH ILMIAH GLOBE Vol 17, No 2 (2015)
Publisher : Badan Informasi Geospasial

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (635.217 KB) | DOI: 10.24895/MIG.2015.17-2.220

Abstract

Perkembangan klasifikasi berbasis objek sudah dikenal sejak kemajuan recognition pada bidang fotogrametri kedokteran sekitar tahun 1970. Klasifikasi berbasis objek ini kemudian digunakan pada bidang penginderaan jauh. Klasifikasi ini diaplikasikan pada klasifikasi penutup lahan dengan berbagai pendekatan metode. Pada penelitian ini, penutup lahan berbasis objek dilakukan menggunakan pendekatan region growing dan teknik klasifikasi dengan menggunakan metode tree algorithm. Klasifikasi ini menggunakan citra satelit SPOT wilayah Danau Limboto. Proses pertama yang dilakukan adalah melakukan segmentasi dengan penentuan parameter skala 15, shape 0,1 dan compactness 0,5. Pembuatan tree algorithm ini didasarkan pada jenis sampel yang dipilih sesuai dengan jenis klas objek. Kemudian hasil klasifikasi ini dilakukan uji geostatistik berupa classification stability, best classification result, error matrix based on TTA Mask, dan error matrix based on samples. Tulisan ini bertujuan untuk menentukan teknik klasifikasi penutup lahan berbasis objek pada citra satelit SPOT menggunakan metode tree algorithm. Teknik klasifikasi ini diharapkan bisa meningkatkan ketelitian (akurasi dan presisi) klasifikasi penutup lahan serta dapat menjadi alternatif metode klasifikasi yang telah tersedia saat ini.Kata kunci: objek, segmentasi, tree algorithm, uji geostatistik, Danau LimbotoABSTRACTThe development of object-based classification has been known since the recognition progress in the field of medical photogrammetry in about the year of 1970. Object-based classification is then also used in the field of remote sensing. This classification was applied to land cover with various approach of methods. In this study, object-based land cover classification used an approach of region growing and classification technique by tree algorithm. This classification used SPOT satellite imagery of Lake Limboto. The first process was determined the segmentation with scale parameter 15, shape 0.1 and compactness 0.5. The making of treealgorithm was based on the type of sample selected according to the type of objects class. Then the results of the classification has been used to perfom geostatistical tests of classification stability, best classification result, error matrix based on TTA Mask, and error matrix based on samples. This paper aims to determine the land cover objects based classification technique on the SPOT satellite imagery using tree algorithm method. This classification technique is expected to increase accuracy and precision of land cover classification and can be used as an alternative method of classification that has been available at this time.Keywords: object, segmentation, tree algorithm, geostatistical test, Lake Limboto
APLIKASI DATA LANDSAT DAN SIG UNTUK POTENSI LAHAN TAMBAK DI KABUPATEN BANYUWANGI Parwati, Ety; Carolita, Ita; Effendy, Iskandar
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 1 No. 1 (2004)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v1i1.3092

Abstract

The application of Remote Sensing and Geographic Information System are used to evaluate land potential that is suitable for cultivation of fishpond. The parameter that is used in this research is the existing land use by remote sensing and analysis process, topographic/land slope,kind of land, climate data (such as; rainfall and amount of dry season). The evaluation of land potential gives 4 land suitability levels, they are 1)Suitable level:2)Rather suitable level:3)Less suitable level and:4)Non suitable level. The analysis shows that are three areas in Banyuwangi sub-province that is suitable for fishpond cultivation: they are Muncar, Rogojampi, and Pesanggrahan districts.
ANALISIS HUBUNGAN PENUTUP/PENGGUNAAN LAHAN DENGAN TOTAL SUSPENDED MATTER (TSM) KAWASAN PERAIRAN SEGARA ANAKAN MENGGUNAKAN DATA INDERAJA Parwati, Ety; Trisakti, Bambang; Carolita, Ita; Kartika, Tatik; Harini, Sri; Dewanti, Ratih
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 3 No. 1 (2006)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v3i1.3183

Abstract

Segara Anakan and its surrounding which is located in Cilacap Regency Central of Java, is the study area for this research. This region, like other estuaries, has a unique ecosystem which is protected and surrounded by the mangrove forest that can cause very dynamic development. In the upland, there are three big rivers flow; Citanduy, Cibeureum, and Cimeng. The main issue in this region is that the lagoon to become narrowing because of rapid sedimentation process. Landsat MSS, TM, and ETM of the years 1978, 1995, 1998, and 2003 are remote sensing data used in this research. An analysis in term of correlation between landuse/landcover changes and sedimentation was carried out by looking at their changes in the upper land especially along the rivers that have big contribution to the sedimentation in the lagoon. The result shows that there is high relation between landuse/landcover changes in the upper land and sedimentation around the lagoon.
SIMULASI JALUR EVAKUASI UNTUK BENCANA TSUNAMI BERBASIS DATA PENGINDERAAN JAUH (STUDI KASUS: KOTA PADANG, PROPINSI SUMATERA BARAT) Trisakti, Bambang; Carolita, Ita; Nur, Mawardi
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 4 No. 1 (2007)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v4i1.3191

Abstract

Tsunami disaster caused great damages and very large victims especially when occurs in urban area along. Therefore information of evacuation in a map is very important for disaster preparedness in order to minimize the number of victims in affected area. Here, information generated from remote sensing satellite data (Landsat, SPOT-5 nad DEM) and secondary data (administration boundary and field survey data) are used ti simulate evacuation route and to produce a map for Padang City. Vulnerability and evacuation areas are determined based on information of maximum tsunami height in shoreline and topography condition. Landuse/landcover and infrastructure (road, bridge and building) are extracted from SPOT data. All the data obtained from remote sensing and secondary data are integrated using geospatial modeling to simulate tsunami evacuation routes. The simulation of evacuation route in Padang City for tsunami preparedness is provided by considering river line, shelters and save zone, available infrastructur (road), the shortest distance (to shelters and save zone) and local community experiences.
KAJIAN PEMANFAATAN DATA ALOSPALSAR DALAM PEMETAAN KELEMBABAN TANAH Prasasti, Indah; Carolita, Ita; Ramdani, A. E.; Risdiyanto, Idung
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 9 No. 2 (2012)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v9i2.3263

Abstract

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.
APLIKASI MODEL GEOBIOFISIK NDVI UNTUK IDENTIFIKASI HUTAN PADA DATA SATELIT LAPAN-A3 Arifin, Samsul; Carolita, Ita; Kartika, Tatik
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.v16i2.3356

Abstract

The LAPAN-A3 / IPB satellite is a micro satellite created by the nation's children in order to build the nation's independence in the field of Space. This satellite has 4 bands including 3 visible waves and 1 near infrared. Given that it is a new satellite, it is necessary to do a study and research on the ability of sensor characteristics to identify natural resources, one of which is forests. In this study besides using LAPAN-A3 satellite data, Landsat-8 data is also used as comparative data for testing the similarity of forest object classification results. Determination of extraction of geobiophysical parameters of forest identification using the Normalized Difference Vegetation Index (NDVI) model with a threshold value for forest identification. The results of the study with LAPAN-A3 satellite data show that the threshold range for forest identification is above 0.65 on the vegetation index scale -1 (minus one) to +1 (plus one). The results of the study after comparing NDVI values with Landsat-8 data have a 60% similarity.
Co-Authors Bambang Trisakti Dede Dirgahayu Effendy, Iskandar Ety Parwati I Made Parsa Idung Risdiyanto Indah Prasasti, Indah Inggit Lolita Sari, Inggit Lolita Johannes Manalu, Johannes Joko Santo Cahyono, Joko Santo Julzarika, Atriyon KH, Wawan Noviar, Heru Nur, Mawardi Ramdani, A. E. Ratih Dewanti -Hariyadi Samsul Arifin Sri Harini Tatik Kartika, Tatik