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APLIKASI MODEL GEOBIOFISIK NDVI UNTUK IDENTIFIKASI HUTAN PADA DATA SATELIT LAPAN-A3 Samsul Arifin; Ita Carolita; Tatik Kartika
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 16 No. 2 Desember 2019
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

ABSTRAKSatelit LAPAN-A3/IPB merupakan satelit mikro yang dibuat anak bangsa dalam rangka membangun kemandirian bangsa bidang Keantariksaaan. Satelit ini memiliki 4 band diantaranya adalah 3 gelombang tampak dan 1 inframerah dekat. Mengingat merupakan satelit baru, perlu dilakukan kajian dan penelitian terhadap kemampuan karakteristik sensor untuk mengidentifikasi sumberaya alam, salah satunya hutan. Pada penelitian ini selain menggunakan data satelit LAPAN-A3, juga digunakan data Landsat-8 sebagai data pembanding untuk pengujian tingkat akurasi ketelitian. Penentuan ekstraksi parameter geobiofisik identifikasi hutan menggunakan model Normalized Difference Vegetation Index (NDVI) dengan nilai ambang batas untuk identifikasi hutan. Hasil penelitian dengan data satelit LAPAN-A3 menujukkan bahwa kisaran ambang batas untuk indentifikasi hutan adalah di atas 0,65 pada skala indeks vegetasi -1 (minus satu) sampai +1 (plus satu), dengan tangkat akurasi 60% setelah dibandingkan dengan nilai NDVI pada data Landsat-8.

DETERMINATION OF FOREST AND NON-FOREST IN SERAM ISLAND MALUKU PROVINCE USING MULTI-YEAR LANDSAT DATA Tatik Kartika; Ita Carolita; Johannes Manalu
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 13, No 1 (2016)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Seram 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.

DETERMINATION OF STRATIFICATION BOUNDARY FOR FOREST AND NON FOREST MULTITEMPORAL CLASSIFICATION TO SUPPORT REDD+ IN SUMATERA ISLAN Tatik Kartika; Inggit Lolita Sari; Bambang Trisakti
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 10, No 1 (2013)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Multi-temporal classification is a method to determine forest and non-forest by considering a missing data, such as cloud cover using correlations value from the other data. This circumstances is frequently occured in a tropical area such as in Indonesia. To gain an optimum result of forest and non-forest classification, it is needed a stratification zone that describes the difference of vegetation condition due to different of vegetation type, soil type, climate, and land use/cover associations. This stratification zone will be useful to indicate the different biomass volume relating to carbon content for supporting the REDD+ project. The objective of this study was to determine stratification boundary by performing multi temporal classification in Sumatera Island using Landsat imagery in 25 meter resolution and Quick Bird imagery in 0.6 meter. Rough stratification was made by considering land use/cover, DEM and landform, using visual interpretation of moderate spatial resolution of satellitedata. High spatial resolution data was also provided in some areas to increase the accuracy level of stratification zone. The stratification boundary was evaluated using forest classification indices, and it was redetermined to obtain the final stratification zone. The indices was generated by CanonicalVariate Analysis (CVA) method, which was depend on training samples of forest and non-forest in each previous stratification zone. The amount of indices used in each zone were two or three indices depending on the separability of the forest and non-forest classification. The suitable indices used in each zone described forest as 100, non-forest as 0, and uncertain forest between 50-99. The result showed 20 stratification zones in Sumatera spreading out in coastal, mountain, flat area, and group of small islands. The stratification zone will improve the accuracy of forest and non-forest classification result and their change based on multi temporal classification.

MONITORING MODEL OF LAND COVER CHANGE FOR THE INDICATION OF DEVEGETATION AND REVEGETATION USING SENTINEL-2 Samsul Arifin; Tatik Kartika; Dede Dirgahayu; Gatot Nugroho
International Journal of Remote Sensing and Earth Sciences Vol. 17 No. 2 (2020)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2020.v17.a3385

IInformation on land cover change is very important for various purposes, including the monitoring of changes for environmental sustainability. The objective of this study is to create a monitoring model of land cover change for the indication of devegetation and revegetation usingdata fromSentinel-2 from 2017 to 2018 of the Brantas watershed.This is one of the priority watersheds in Indonesia, so it is necessary to observe changes in its environment, including land cover change. Such change can be detected using remote sensing data. The method used is a hybrid between Normalized Difference Vegetation Index(NDVI) and Normalized Burn Ratio (NBR) which aims to detect land changes with a focus on devegetationand revegetation by determining the threshold value for vegetation index (Î”NDVI) and open land index (Î”NBR).The study found that the best thresholds to detect revegetation were Î”NDVI > 0.0309 and Î”NBR < 0.0176 and to detect devegetation Î”NDVI < -0.0206 and Î”NBR > 0.0314.It is concluded that Sentinel-2 data can be used to monitor land changes indicating devegetation and revegetation with established NDVI and NBR threshold conditions.

IDENTIFICATION AND CLASSIFICATION OF FOREST TYPES USING DATA LANDSAT 8 IN KARO, DAIRI, AND SAMOSIR DISTRICTS, NORTH SUMATRA Heru Noviar; Tatik Kartika
International Journal of Remote Sensing and Earth Sciences Vol. 13 No. 2 (2016)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2016.v13.a2477

Forests 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.

DETERMINATION OF FOREST AND NON-FOREST IN SERAM ISLAND MALUKU PROVINCE USING MULTI-YEAR LANDSAT DATA Tatik Kartika; Ita Carolita; Johannes Manalu
International Journal of Remote Sensing and Earth Sciences Vol. 13 No. 1 (2016)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2016.v13.a2699

Seram 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 Â 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 IslandÂ 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.Â Â The thresholdÂ ofÂ index 1, 2, and 3 ranged between -60 and 0, 61 and 104, and 45 and 105, respectively. The lowest boundaryÂ 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

DETERMINATION OF STRATIFICATION BOUNDARY FOR FOREST AND NON FOREST MULTITEMPORAL CLASSIFICATION TO SUPPORT REDD+ IN SUMATERA ISLAN Tatik Kartika; Inggit Lolita Sari; Bambang Trisakti
International Journal of Remote Sensing and Earth Sciences Vol. 10 No. 1 (2013)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2013.v10.a1843

Multi-temporal classification is a method to determine forest and non-forest by considering a missing data, such as cloud cover using correlations value from the other data. This circumstances is frequently occured in a tropical area such as in Indonesia. To gain an optimum result of forest and non-forest classification, it is needed a stratification zone that describes the difference of vegetation condition due to different of vegetation type, soil type, climate, and land use/cover associations. This stratification zone will be useful to indicate the different biomass volume relating to carbon content for supporting the REDD+ project. The objective of this study was to determine stratification boundary by performing multi temporal classification in Sumatera Island using Landsat imagery in 25 meter resolution and Quick Bird imagery in 0.6 meter. Rough stratification was made by considering land use/cover, DEM and landform, using visual interpretation of moderate spatial resolution of satellitedata. High spatial resolution data was also provided in some areas to increase the accuracy level of stratification zone. The stratification boundary was evaluated using forest classification indices, and it was redetermined to obtain the final stratification zone. The indices was generated by CanonicalVariate Analysis (CVA) method, which was depend on training samples of forest and non-forest in each previous stratification zone. The amount of indices used in each zone were two or three indices depending on the separability of the forest and non-forest classification. The suitable indices used in each zone described forest as 100, non-forest as 0, and uncertain forest between 50-99. The result showed 20 stratification zones in Sumatera spreading out in coastal, mountain, flat area, and group of small islands. The stratification zone will improve the accuracy of forest and non-forest classification result and their change based on multi temporal classification.

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