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EVALUASI REHABILITASI LAHAN KRITIS BERDASARKAN TREND NDVI LANDSAT-8 (Studi Kasus: DAS Serayu Hulu) Tatik Kartika; Dede Dirgahayu; Inggit Lolita Sari; I Made Parsa; Ita Carolita
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 16 No. 2 Desember 2019
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

Pemanfaatan penginderaan jauh dalam memantau vegetasi sudah banyak dilakukan, tetapi pemanfaatannya untuk mengevaluasi rehabilitasi di lahan kritis masih sangat jarang. Kegiatan rehabiliatsi hutan dan lahan dilakukan karena makin meningkatnya lahan kritis. Kegiatan rehabilitasi tersebut perlu dievaluasi, mengingat banyak sekali dana, waktu, dan tenaga yang diperlukan. Selama ini evaluasi dilakukan dengan cara langsung mendatangi lokasi rehabilitasi dengan memantau pertumbuhan tanaman pada setiap akhir tahun sampai akhir tahun ketiga. Menurut ketentuan peraturan yang berlaku, rehabilitasi dapat dikatakan berhasil apabila 90% vegetasi yang ditanam bisa tumbuh di akhir tahun ketiga. Kegiatan evaluasi dengan cara memantau kondisi vegetasi atau kerapatannya dapat dilaksanakan dengan memanfaatkan data penginderaan jauh, karena data tersebut mempunyai sifat multi temporal dan cakupan yang luas dan ketersediannya yang berlimpah dan mudah didapat. Data penginderaan jauh yang digunakan adalah Landsat-8 tahun 2013 sampai dengan 2018 dan metode evaluasi adalah analisis NDVI dari waktu ke waktu menggunakan SIG. Hasilnya adalah bahwa dari hasil survey yang diperoleh di kawasan APL terdapat lokasi rehabilitasi di lahan tidak kritis, agak kritis, kritis, dan sangat kritis dan berturut-turut keberhasilan rehabilitasi untuk APL_TK; APL_K; APL_AK; APL_SK jika NDVI melampaui nilai 0,337; 0,465; 0,493; 0,490 setelah bulan ke 21,8; 24,5; 26, dan 25,8.

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.

GROWTH PROFILE ANALYSIS OF OIL PALM BY USING SPOT 6 THE CASE OF NORTH SUMATRA Ita Carolita; J. Sitorus; Johannes Manalu; Dhimas Wiratmoko
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 12, No 1 (2015)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Oil Palm (Elaeis guineensis Jack.) is one of the world’s most important tropical tree crops. Its expansion has been reported to cause widespread environment impacts. SPOT 6 data is one of high resolution satellite data that can give information more detail about vegetation and the age of oil palm plantation. The objective of this study was to analyze the growth profile of oil palm and to estimate the productivity age of oil palm. The study area is PTP N 3 in Tebing Tinggi North Sumatera Indonesia. The method that used is NDVI analysis and regression analysis for getting the model of oil palm growth profile. Data from the field were collected as the secondary data to build that model. The data that collected were age of oil palm and diameters of canopy for every age. Results indicate that oil palm growth can be explained by variation of NDVI with formula y = -0.0004x2 + 0.0107x + 0.3912, where x is oil palm age and Y is NDVI of SPOT, with R² = 0.657. This equation can be used to predict the age of oil palm for range 4 to 11 years with R2 around 0.89.

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

GROWTH PROFILE ANALYSIS OF OIL PALM BY USING SPOT 6 THE CASE OF NORTH SUMATRA Ita Carolita; J. Sitorus; Johannes Manalu; Dhimas Wiratmoko
International Journal of Remote Sensing and Earth Sciences Vol. 12 No. 1 (2015)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2015.v12.a2669

Oil Palm (Elaeis guineensis Jack.) is one of the worldâ€™s most important tropical tree crops. Its expansion has been reported to cause widespread environment impacts. SPOT 6 data is one of high resolution satellite data that can give information more detail about vegetation and the age of oil palm plantation. The objective of this study was to analyze the growth profile of oil palm and to estimate the productivity age of oil palm. The study area is PTP N 3 in Tebing Tinggi North Sumatera Indonesia.Â The method that used is NDVI analysis and regression analysis for getting the model of oil palm growth profile. Data from the field were collected as the secondary data to build that model. The data that collected were age of oil palm and diameters of canopy for every age.Â Â Results indicate that oil palm growth can be explained by variation of NDVI with formula y = -0.0004x2 + 0.0107x + 0.3912, where x is oil palm age andÂ Y is NDVI of SPOT, with RÂ² = 0.657. This equation can be used to predict the age of oil palm for range 4 to 11 years with R2 around 0.89.

ENVIRONMENTAL QUALITY CHANGES OF SINGKARAK WATER CATCHMENT AREA USING REMOTE SENSING DATA Ita Carolita; Bambang Trisakti; Heru Noviar
International Journal of Remote Sensing and Earth Sciences Vol. 10 No. 2 (2013)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2013.v10.a1853

Lake Singkarak in west Sumatera is currently in very poor condition and become one of the priorities in the government lake rescue program. High sedimentation rate from soil erosion has caused siltation, decreasing of quality and quantity of lake water. Monitoring of the environment quality changes of the lake and its surrounding are required. This study used Landsat and SPOT satellite data in periods of 2000-2011 to evaluate environmental quality parameters of the lake such as land cover, lake water quality (total suspended solid), water run-off, and water discharge in Singkarak lake catchment area. Maximum likelihood classifier was used to obtain land cover. Total suspended solid was extracted using Doxaran algorithm. The look up table and rational method were used to estimate run-off and water discharge. The results showed that the decreasing of forest area and the increasing of settlement were consistent with the increasing of average run-off and water discharge in Paninggahan and Sumpur sub-catchment area. The results were also consistent with the increasing of TSS in Singkarak lake, where TSS increased from around 2-3 mg/l up to 5-6 mg/l in the periods of 2000-2011.

DIGITAL ELEVATION MODEL FROM PRISM-ALOS AND ASTER STEREOSCOPIC DATA Bambang Trisakti; Ita Carolita; Firsan Ardi Pradana
International Journal of Remote Sensing and Earth Sciences Vol. 6 (2009)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2009.v6.a1236

Digital Elevation Model (DEM) is a source to produce contour map, slope, and aspect information, which is needed for other information such as disaster and water resources management. DEM can be generated by several methods. One of them is parallax calculations from stereoscopic data of optical sensor. Panchromatic Remote-Sensing Instrument for Stereo Mapping (PRISM) sensor from Advanced LAnd Observation Satellite (ALOS) satellite and advance space borne Thermal Emission and Reflection Radiometer (ASTER) sensor from Terra Satellite is Japanese optical satellite sensor which have abilityto produce stereoscopic data. This study showed DEM generations from PRISM (2.5 m spatial resolution) and ASTER (15m spatial resolution) stereoscopic data using image matching and collinear model based on Orthobase-pro software. The Generated DEM from each sensor was compared to the DEM from Shuttle Radar Topography Mission (SRTM) X-C band with 30 m spatial resolution. The dependent on the pixel size from the DEM produced were also discussed. The result showed that both DEMs have similiar elevation and distribution pattern to the referenced DEM, but DEM for PRISM had higher relative accuracy (RMSE is 6.5 m) and Smoother pattern comparing to DEM from ASTER (RMSE is 10.2 m) Keyword : ASTER, DEM, PRISM, SRTM, Stereoscopic satellite data

COMPARISON RESULT OF DEM GENERATED FROM ASTER STEREO DATA AND SRTM Bambang Trisakti; Ita Carolita
International Journal of Remote Sensing and Earth Sciences Vol. 4 (2007)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2007.v4.a1220

This paper explains a method to generated DEM (Digital Elevation Model) from ASTER (Advanced Spaceborn Thermal Emission and Reflection Radiometer) stereo data and evaluates the generation of ASTER DEM and SRTM (Shuttle Radar Topography Mission) DEM with 90 m spatial resolution. ASTER DEM is generated from 3n (nadir looking) and 3b (backward looking) level 1b, with 10 ground control points (XYZ coordinate)derived from ASTER RGB 321 geometric-corrected image and SRTM DEM. Almost all tie points are collected automatically and several tie points is added manually. The triangulation and DEM extraction process are made automatically using ERDAS Imagine Software. DEM evaluation is carried out by comparing between ASTER DEM and SRTM DEM in the height distribution of vertical and horizontal transect lines and the height value of the whole DEM image. The process is continued by analyzing the height differences between ASTER DEM and SRTM DEM. The results shows thatASTER DEM has 15 m spatial resolution with height differnces less than 30 m for about 67 percent of total area, and absolute mean error is 27 m (compared with SRTM DEM) This absolute mean error is large enough, because the GCPs (Ground Control Point) used in this study are only in a small amount and most of study area is in the high terrain area (mountainous area) with dense vegetation coverage.

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