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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Geography Jurnal Ilmu Lingkungan TELKOMNIKA (Telecommunication Computing Electronics and Control) Geoplanning : Journal of Geomatics and Planning Majalah Geografi Indonesia JTSL (Jurnal Tanah dan Sumberdaya Lahan) Forum Geografi Indonesian Journal of Science and Technology JURNAL ILMIAH GEOMATIKA Jurnal Geografi Jurnal Penginderaan Jauh Indonesia

Projo Danoedoro

Fakultas Geografi Universitas Gadjah Mada, Yogyakarta, Indonesia

Author-ID : 249626

Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Mathematics Social Sciences Other

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FUSI CITRA LANDSAT 7 ETM+ DAN CITRA ASTER G-DEM UNTUK IDENTIFIKASI ZONA ALTERASI HYDROTHERMAL TERKAIT MINERAL DI SEBAGIAN KALIMANTAN BARAT Ananda, Irvan Nurrahman; Danoedoro, Projo
GEOMATIKA Vol 20, No 2 (2014)
Publisher : Badan Informasi Geospasial in Partnership with MAPIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24895/JIG.2014.20-2.153

Indonesia sebagai negara kepulauan dengan potensi sumber daya mineral yang melimpah. Salah satunya adalah mengindikasikan terdapat batuan teralterasi hydrothermal. Batuan teralterasi hydrothermal dapat digunakan sebagai indikator berbagai macam mineral. Data penginderaan jauh dengan teknik pengolahan citra banyak digunakan untuk melihat potensi mineral melalui pendekatan fisik medan. Pada penelitian ini, aspek fisik medan diperoleh melalui interpretasi visual LANDSAT 7 ETM+ dan ASTER G-DEM yang telah diolah menggunakan tiga metode fusi yaitu Principal Component (PC), Intensity Hue and Saturation (IHS), dan fusi hasil Band Ratioing. Selain itu, dilakukan juga proses pemfilteran spasial. Analisis yang digunakan adalah petrografi untuk mengetahui kandungan mineral pada batuan terkait zona alterasi hydrothermal. Hasil penelitian ini menunjukkan bahwa Principal Component memiliki akurasi bentuklahan tertinggi sebesar 71,15%. Akurasi tertinggi untuk parameter batuan (litologi) sebesar 70,98% yang diperoleh dari Intensity, Hue, and Saturation. Pemetaan zona alterasi hydrothermal Â ini menghasilkan empat zona yaitu Argilik 1399,42 km2, Potasik 2913,46 km2, Propilitik 1160,54 km2, dan Serisitik 946,38 km2.Kata Kunci: mineral, fusi, pemfilteran spasial, alterasi hydrothermal, interpretasi visual, petrografiABSTRACTIndonesia as an archipelagic country has huge potentials of mineral resources. One of them is an indication of hydrothermal alteration rocks. Hydrothermal alteration rocks can be used for indicating various type of minerals. Remote sensing data with image processing techniques have been frequently used to determine the mineral potentials through terrain analysis approach. In this study, physical aspects of terrain parameters were obtained using visual interpretation of LANDSAT 7 ETM+ and ASTER G-DEM imagery, which have been processed using three fusion methods, i.e. Principal Component (PC), Intensity, Hue, and Saturation (IHS), and image fusion from Band Ratioing techniques. In addition spatial filtering was also applied. Laboratory analysis of rock petrographic analysis was conducted to identity the mineral content of the rocks in order to determine the hydrothermal alteration zones. Results of this study showed that Principal Component (PC) fusion techniques have the highest accuracy for landform identification with 71.15%. Highest accuracy for rocks (lithology) is 70.98%, which was obtained from Intensity, Hue, and Saturation fusion techniques. Mapping of hydrothermal alteration zones showed four hydrothermal alterated zones, i.e. Argilic alteration zone with an area of 1399,42 km2, 2913,46 km2 zone of potassic alteration, Propilitic alteration zone 1160,54 km2, and 946,38 km2 zone of Serisitic alteration.Keyword: mineral, image fusion, spatial filtering, hydrothermal alteration, visual interpretation, petrographic

Modeling of Percentage of Canopy in Merawu Catchment Derived From Various Vegetation Indices of Remotely Sensed Data Sulistyo, Bambang; Gunawan, Totok; Hartono, H; Danoedoro, Projo
Forum Geografi Vol 27, No 1 (2013): July 2013
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.v27i1.5075

The research was aimed at studying Percentage of Canopy mapping derived from various vegetation indices of remotely-sensed data int Merawu Catchment. Methodology applied was by analyzing remote sensing data of Landsat 7 ETM+ image to obtain various vegetation indices for correlation analysis with Percentage of Canopy measured directly on the field (PTactual) at 48 locations. These research used 11 (eleven) vegetation indices of remotely-sensed data, namely ARVI, MSAVI, TVI, VIF, NDVI, TSAVI, SAVI, EVI, RVI, DVI and PVI. The analysis resulted models (PTmodel) for Percentage of Canopy mapping. The vegetation indices selected are those having high coefficient of correlation (>=0.80) to PTactual. Percentage of Canopy maps were validated using 39 locations on the field to know their accuracies. Percentage of Canopy map (PTmodel) is said to be accurate when its coefficient of correlation value to PTactual is high (>=0.80). The research result in Merawu Catchment showed that from 11 vegetation indices under studied, there were 6 vegetation indices resulted high accuracy of Percentage of Canopy maps (as shown in the value of coefficient of correlation as >=0.80), i.e. TVI, VIF, NDVI, TSAVI, RVI dan SAVI, while the rest, namely ARVI, PVI, DVI, EVI and MSAVI, have r values of < 0.80.

Developing an Extraction Method of Urban Built-Up Area Based on Remote Sensing Imagery Transformation Index Hidayati, Iswari Nur; Suharyadi, R; Danoedoro, Projo
Forum Geografi Vol 32, No 1 (2018): July 2018
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.v32i1.5907

Studying urban areas using remote sensing imagery has become a challenge, both visually and digitally. Supervised classification, one of the digital classification approaches to differentiate between built-up and non-built-up area, used to be leading in digital studies of urban area. Then the next generation uses index transformation for automatic urban data extraction. The extraction of urban built-up land can be automatically done with NDBI although it has one limitation on separating built-up land and bare land. The previous studies provide opportunities for further research to increase the accuracy of the extraction, particularly using index transformation. This study aims to obtain the maximum accuracy of the extraction by merging several indices including NDBI, NDVI, MNDWI, NDWI, and SAVI. The merging of the indices is using four stages: merging of two indices, three indices, four indexes and five indices. Several operations were experimented to merge the indices, either by addition, subtraction, or multiplication. The results show that merging NDBI and MNDWI produce the highest accuracy of 90.30% either by multiplication (overlay) or reduction. Application of SAVI, NDBI, and NDWI also gives a good effect for extracting urban built-up areas and has 85.72% mapping accuracy.

The Development of Interpretataion Method For Remote Sensing Imagery In Determining The Candidate of Landslide In Leitimur Paninsula, Ambon Island Puturuhu, Ferad; Danoedoro, Projo; Sartohadi, Junun; Srihadmoko, Danang
Jurnal Ilmu Lingkungan Vol 15, No 1 (2017): April 2017
Publisher : School of Postgraduate Studies, Diponegoro Univer

ABSTRAKPenginderaa jauh merupakan salah satu metode yang digunakan untuk menjawab permasalahan penelitian tentang teknologi perolehan data spasial dan sekaligus permasalahan kewilayahan serta manajemen sumber daya laha. Pemanfaatan metode penginderaan jauh untuk penelitian landslide dianataranya metode interpretasi citra secara visual dan digital. Tujuan penelitian ini adalah membandingkan akurasi metode interpretasi dan menentukan lokasi kejadian landslide. Citra yang digunakan dalam penelitian ini adalah citra Landsat 8, Quickbird dan SRTM. Metode yang digunakan untuk menentukan kandidat landslide adalah interpretasi visual berlapis, Interpretasi citra digital dengan NDVI, OBIA, Toposhape, dan kombinasi NDVI-OBIA, dan NDVI-OBIA-Toposhape. Penggunaan metode interpretasi kejadian landslide yang terbaik adalah interpretasi visual berlapis dengan presentase 90 %. Interpretasi digital dengan NDVI mempunyai ketelitian 47 %, OBIA ketelitiannya 45 %, Toposhape 47 %, kombinasi NDVI-OBIA 47 %, dan Kombinasi NDVI-OBIA-Toposhape 53 %. Dari interpretasi visual berlapis dan pengamatan lapangan diperoleh tipe landslide yang ditemukan yaitu nendatan/slump (soil rotational slide) dalam jumlah yang banyak 7 titik (38.9%), rayapan tanah (soil creep), aliran bahan rombakan (debris flow), longsor translasi dengan material tanah (earths Slide), dan nendatan majemuk (multiple rotational slide).Kata kunci: Pengembanga, Metode, Interpretasi Citra, Penginderaan Jauh, Kandidat, Landslide, Paninsula LeitimurABSTRACTRemote sensing is one of the methods used to address the problem of research on spatial data acquisition technologies and is also acquiring the problems of territorial and land resource management. The utilization of remote sensing method for the landslide research is visual and digital imagery interpretation. The purpose of this study was to compare the accuracy of the method of interpretation and determine the location of the landslide event. The imagery that used in this study was Landsat 8, Quickbird and SRTM. The method that used to determine the candidate of landslide was the layered visual interpretation, digital imagery interpretation with NDVI, OBIA, Toposhape, and combination-OBIA NDVI and NDVI-OBIA-Toposhape. The use of the interpretation method for the landslide event is the best of layered-visual interpretation with a percentage of 90%. Digital interpretation with NDVI has a 47% of its accuracy, thoroughness OBIA 45%, Toposhape 47%, the combination of NDVI-OBIA 47%, and the combination of NDVI-OBIA-Toposhape 53%. From the layered-visual interpretation and field observations were obtained type of landslide found that soil rotational slide in large quantities 7 points (38.9%), creep soil (soil creep), the flow of material destruction (debris flow), landslides translation with soil materials (earths slide) and multiple rotational slide.Keywords: Development, Method, Imagery Interpretation, Remote Sensing, Candidate of Landslide, Landslide and Leitimur JaizirahCitation: Puturuhu, F., Danoedoro, P., Sartohadi, J. and Srihadmoko, D. (2017). The Development of Interpretataion Method for Remote Sensing Imagery In Determining The Candidate of Landslide In Leitimur Paninsula, Ambon Island. Jurnal Ilmu Lingkungan, 15(1), 20-34, doi:10.14710/jil.15.1.20-34

Erosion Prediction Model using Fractional Vegetation Cover Arif, Nursida; Danoedoro, Projo; Hartono, Hartono; Mulabbi, Andrew
Indonesian Journal of Science and Technology Vol 5, No 1 (2020): IJOST: VOLUME 5, ISSUE 1, 2020
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/ijost.v5i1.21060

The purpose of this study was toÂ create an erosion prediction model in Serang Watershed, Indonesia. The erosion model used two input data, namely the slope derivied from Digital Elevation Model (DEM) data, and Fractional Vegetation Cover (FVC) from SPOT images. Assessment of the model was carried out using questionnaires and interviews with several experts by presenting the results of the model and its supporting data. Based on the DEM data, the level of slope steepness in the study area is very varied namely; flat (52.77%), sloping (7.62%), and rather steep to very steep (39.59%). Vegetation density according to the FVC results is dominated by medium density. The results of the analysis of the two input models can provide predictions of the level of erosion with an accuracy of 67.92%. Evaluation of the model was done by experts with conclusions that the method was very flexible and can be adapted to similar watersheds elsewhere.

KLASIFIKASI TUTUPAN LAHAN DATA LANDSAT-8 OLI MENGGUNAKAN METODE RANDOM FOREST Zulfajri, Zulfajri; Danoedoro, Projo; Murti, Sigit Heru
Jurnal Penginderaan Jauh Indonesia Vol 3 No 01 (2021)
Publisher : Masyarakat Ahli Penginderaan Jauh Indonesia (MAPIN) /Indonesian Society of Remote Sensing (ISRS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Informasi penggunaan dan tutupan lahan terbaru sangat diperlukan dalam perencanaan pembangunan wilayah dan pemantauan lingkungan. Salah satu cara untuk memperoleh informasi tersebut yaitu melalui pengolahan data citra satelit penginderaan jauh. Citra Landsat-8 OLI merupakan salah citra satelit penginderaan jauh yang mempunyai resolusi spasial multispektral 30 m dan resolusi temporal 16 hari. Penelitian ini bertujuan untuk melakukan klasifikasi tutupan lahan di sebagian wilayah Kabupaten Pidie menggunakan metode random forest berdasarkan citra Landsat-8 OLI dan menghitung nilai akurasi dari hasil klasifikasi tersebut. Ekstraksi informasi tutupan lahan dilakukan dengan menggunakan metode random forest dengan proporsi 70% untuk data training dan 30% untuk data testing. Kemudian uji akurasi dari hasil klasifikasi yang dilakukan menggunakan metode confusion matrix. Hasil pemetaan tutupan lahan di sebagian wilayah Kabupaten Pidie menunjukkan bawah kelas tutupan lahan sawah mendominasi daerah penelitian dengan luas sebesar 22.598,20 ha (29,22% dari total luas daerah penelitian). Hasil klasifikasi tutupan lahan menghasilkan nilai akurasi keseluruhan sebesar 89,53% dan nilai kappa 0,91.

A Minimum Cloud Cover Mosaic Image Model of the Operational Land Imager Landsat-8 Multitemporal Data using Tile based Ratih Dewanti Dimyati; Projo Danoedoro; Hartono Hartono; Kustiyo Kustiyo
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 1: February 2018
Publisher : Institute of Advanced Engineering and Science

The need for remote sensing minimum cloud cover or cloud free mosaic images is now increasing in line with the increased of national development activities based on one map policy. However, the continuity and availability of cloud and haze free remote sensing data for the purpose of monitoring the natural resources are still low. This paper presents a model of medium resolution remote sensing data processing of Landsat-8 uses a new approach called mosaic tile based model (MTB), which is developed from the mosaic pixel based model (MPB) algorithm, to obtain an annual multitemporal mosaic image with minimum cloud cover mosaic imageries. The MTB model is an approach constructed from a set of pixels (called tiles) considering the image quality that is extracted from cloud and haze free areas, vegetation coverage, and open land coverage of multitemporal imageries. The data used in the model are from Landsat-8 Operational Land Imager (OLI) covering 10 scenes area, with 2.5 years recording period from June 2015 to June 2017; covered Riau, West Sumatra and North Sumatra Provinces. The MTB model is examined with tile size of 0.1 degrees (11x11 km2), 0.05 degrees (5.5x5.5 km2), and 0.02 degrees (2.2x2.2 km2). The result of the analysis shows that the smallest tile size 0.02 gives the best result in terms of minimum cloud cover and haze (or named clear area). The comparison of clear area values to cloud cover and haze for three years (2015, 2016 and 2017) for the three mosaic images of MTB are 68.2%, 78.8%, and 86.4%, respectively.

Interpretability Evaluation of Annual Mosaic Image of MTB Model for Land Cover Changes Analysis Muhammad Dimyati; Ratih Dewanti Dimyati; Kustiyo Kustiyo; Projo Danoedoro; Hartono Hartono
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 3: June 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i3.9331

To verify whether the annual mosaic image of MTB model is acceptable for further digital analysis, it is necessary to evaluate the visual interpretability. The MTB model is an effort to integrate multi-scene and multi-temporal data, to obtain a minimum cloud cover mosaic image in locations that are often covered by clouds and haze. This study is to evaluate the interpretability of the annual mosaic image for analysis of the land cover changes. The data used are the images of 2015, 2016, and 2017 covers a part of central Sumatra. Visual interpretations with a series of steps are used, starting with identification of the objects using interpretation keys, followed by spectral band correlations, scattergram analysis, and ended by consistency assessment. The consistency assessment step is performed to determine the level of clearness and easiness of the object recognition in the annual mosaic images. The results showed that the most optimal spectral bands used for RGB combinations for visual interpretation were Band SWIR-1, Band NIR, and Band Red. Based on the evaluation results, the annual mosaic image of MTB model performed the consistent results of the clearness objects and the easiness of the object recognition. Thus the annual mosaic image of MTB model of 0.02x0.02 degree tile is acceptable for further digital processing as well as digital land cover analysis.

Penentuan Tingkat Kekeringan Lahan Berbasis Analisa Citra Aster Dan Sistem Informasi Geografi Alfian Pujian Hadi; Projo Danoedoro; Sudaryatno Sudaryatno
Majalah Geografi Indonesia Vol 26, No 1 (2012): Majalah Geografi Indonesia
Publisher : Fakultas Geografi, Universitas Gadjah Mada

Kekeringan lahan yang melanda suatu daerah menimbulkan dampak yang besar terhadap produktivitas lahan pertanian. Terjadinya kekeringan ini disebabkan oleh defisit air akibat kurangnya hujan yang jatuh, laju infiltrasi air yang tinggi serta jenis tanaman yang tidak sesuai dengan ketersediaan air. Untuk meminimalkan dampak yang terjadi akibat kekeringan lahan maka perlu dilakukan antisipasi dengan mengetahui defisit dan surflus air lahan melalui data curah hujan serta kemampuan tanah menahan air (water holding capasity). Untuk keperluan analisis kekeringan lahan dapat menggunakan citra penginderaan jauh dan neraca air lahan sebagai pengetahuan awal guna perencanaan antisipasi kekeringan lahan sehingga kebutuhan air bagi tanaman dapat terpenuhi setiap saat. Penelitian ini dilakukan di sebagian wilayah Kabupaten Gunung Kidul. Tujuan penelitian ini adalah : (1) Mengkaji akurasi berbagai saluran TIR Citra Aster untuk mendapatkan informasi sebaran suhu permukaan, (2) Mengkaji sebaran kekeringan melalui indeks TVDI (Temperature Vegetation Dryness Indeks) yang diekstrak dari suhu permukaan (Land Surface Temperature) dan indeks NDVI. (3) Mengkaji tingkat kekeringan lahan dengan menggunakan metode Thornthwaite-Mather, (4) Mengkaji pola tanam yang sesuai diterapkan di wilayah penelitian. Hasil penelitian menunjukkan bahwa saluran 13 Citra Aster memiliki akurasi paling tinggi jika dibandingkan dengan saluran 10,11,12, serta 14 Citra Aster karena memiliki selisih paling kecil dengan suhu permukaan lapangan. Berdasarkan analisis RMS difference diperoleh nilai 1,140. Luas sebaran kekeringan berdasarkan indeks TVDI pada seluruh penggunaan lahan dengan tingkat kekeringan tinggi, sedang dan rendah masingmasing melanda daerah seluas 2.922,8 Ha (4,6%), 20.286,16 Ha (32,11%) serta 39.962,72 Ha (63,26%). Dari total luas 2.922,8 Ha lahan yang dilanda kekeringan dengan tingkat kekeringan tinggi (kering/kurang air) seluas 2.069,47 Ha merupakan sawah tadah hujan. Analisis hubungan indeks TVDI dengan kadar lengas tanah menunjukkan hubungan yang tidak terlalu kuat sebesar 53,7%. Tingkat kekeringan lahan dengan analisis neraca air Thornthwaite-Mather menunjukkan indeks kekeringan (aridity index) berada dalam tingkat kekeringan sedang dan berat. Kekeringan sedang terjadi pada satuan lahan yang terpengaruh stasiun hujan Giriwungu (Panggang), Kedung Keris, Gedangan serta sebagian Playen. Kekeringan berat terjadi pada satuan lahan yang terpengaruh stasiun hujan Wonosari, Tepus dan sebagian Playen. Pola tanam berdasarkan agroklimat Oldeman dikelompokkan ke dalam pola tanam Padi Gogo (Palawija) -Palawija - Bero, Padi sawah - Palawija - Bero, Palawija – Palawija - Bero. Pola tanam Padi Gogo (Palawija)-Palawija-Bero diterapkan di sawah tadah hujan dan tegalan pada satuan lahan yang terpengaruh stasiun hujan Tepus dan Panggang dengan musim tanam 1 terjadi bulan Oktober–Januari dan musim tanam 2 terjadi pada bulan Februari-Mei, pola tanam Padi Sawah-Palawija-Bero diterapkan di sawah dan sawah tadah hujan pada satuan lahan yang terpengaruh stasiun hujan Wanagama (Playen), Kedung Keris dan Gedangan dengan musim tanam 1 terjadi pada bulan November-Februari dan musim tanam 2 terjadi pada bulan Maret-Juni sedangkan pola tanam Palawija-Palawija-Bero diterapkan di kebun campuran pada satuan lahan yang terpengaruh stasiun hujan Kedung Keris, Panggang, Playen, Gedangan, serta Wonosari untuk sawah tadah hujan dimana musim tanam 1 terjadi pada bulan November-Februari dan musim tanam 2 terjadi pada bulan Maret-Juni.

Pendugaan Cadangan Karbon pada Perkebunan Tanaman Teh (Ca-Mellia Sinensis) melalui Citra Penginderaan Jauh Alos Avnir-2 Karen Slamet Hardjo; Projo Danoedoro; Zuharnen Zuharnen
Majalah Geografi Indonesia Vol 28, No 1 (2014): Majalah Geografi Indonesia
Publisher : Fakultas Geografi, Universitas Gadjah Mada

ABSTRAK Pemanasan global menjadi isu terkini dalam perubahan iklim, salah satu penyebabnya adalah pelepasan gas karbondioksida (CO2) ke atmosfer. Tanaman teh menyerap CO2, sehinggga mampu berperan untuk mengurangi emisi karbon. Program clean development mechanism dalam piagam Kyoto, membutuhkan perhitungan cadangan karbon yang akurat dan terkini beserta agihan secara spasial. Tujuan penelitian untuk mengetahui hubungan respon spektral citra ALOS AVNIR-2 dan nilai indeks vegetasi dengan cadangan karbon tanaman teh dan Pendugaan cadangan karbon tanaman teh beserta agihannya pada perkebunan teh PT Pagilaran. Metodenya adalah pengolahan citra digital ALOS AVNIR-2 menggunakan algoritma indeks vegetasi (NDVI, RVI, SAVI, MSAVI-2, ARVI, GEMI), pengukuran sampel cadangan karbon tanaman teh dilapangan menggunakan rumus allometrik, analisis statistik hubungan respon spektral citra dan nilai indeks vegetasi dengan nilai cadangan karbon. Nilai korelasi tertinggi digunakan menghitung dan memetakan agihan cadangan karbon tanaman teh. Hasil penelitian tidak menunjukkan hubungan korelasi yang kuat antara saluran tunggal pada citra ALOS AVNIR-2 dengan cadangan karbon, yaitu nilai korelasi (r) < 0,21. Hasil korelasi nilai indeks vegetasi dengan cadangan karbon tertinggi adalah r = 0,44, diperoleh pada indeks vegetasi RVI (koefisien determinasi/R² = 0,196). Pendugaan cadangan karbon pada perkebunan teh pagilaran sebesar 30.974,5 Ton, dengan agihan urutan terbesar dari afdeling Pagilaran, afdeling Kayulandak dan afdeling Andongsili. ABSTRACT Global warming is an issue of current climate change, one reason is the release of carbon dioxide (CO2) into the atmosphere. Tea plants absorb CO2, so can contribute to reducing carbon emissions. Programs clean development mechanism under the Kyoto charter, requiring the calculation of carbon stocks and their recent and accurate spatial. The aim of research to determine the relationship spectral response AVNIR ALOS-2 and the index vegetation with carbon stocks of tea plants and then estimated of carbon stocks of tea plant and it distributions in PT Pagilaran plantations. The method is digital image processing ALOS AVNIR-2 uses an algorithm vegetation index (NDVI, RVI, SAVI, MSAVI-2, ARVI, GEMI), sample measurements of carbon stocks of tea plants in the field using a formula allometric, statistical analysis-response relationship spectral image and index vegetation with the value of carbon stocks. The highest correlation value is used calculate and mapping the carbon stocks tea plant. Results of the study did not show a strong correlation between a single channel on AVNIR ALOS-2 with carbon stocks, ie the value of the correlation (r) <0.21. The correlation value of index vegetation with the highest carbon stocks are r = 0.44, obtained in the vegetation index RVI (coefficient of determination / R ² = 0.196). Estimation of carbon stocks in the tea plantations Pagilaran of 30,974.5 tons, the largest spread of the sequence is Afdeling Pagilaran, Afdeling Kayulandak, and Afdeling Andongsili.

Co-Authors Alfian Pujian Hadi Alfian Pujian Hadi Ananda, Irvan Nurrahman Bambang Sulistyo Bowo Susilo Christian S. Imburi Danang Sri Hadmoko Diana Wisnu Wardani Erisa Ayu Waspadi Putri Fela Pritian Cera Ferad Puturuhu H Hartono H Hartono Hartono Hartono Hartono Hartono Hartono Hartono Hartono Hartono Hasibuan, Algi Variski Iswari Nur Hidayati Junun Sartohadi Karen Slamet Hardjo Kustiyo Kustiyo Lewi Ristiyono Marfai, Muh Aris Moh Yasir Muhammad Dimyati Mulabbi, Andrew Nama Penulis Ni Kadek Oki Febrianti Nur Mohammad Farda Nursida Arif Prama Ardha Aryaguna Pramaditya Wicaksono Prima Widayani Ratih Dewanti Dimyati Ratih Dewanti Dimyati Ratih Dewanti Dimyati Retnadi Heru Jatmiko Sanjiwana Arjasakusuma Shafira Himayah Sigit Heru Murti Sri Retno Murdiyati Sudaryatno Sudaryatno Sudaryatno Sudaryatno Suharyadi, R Totok Gunawan Totok Gunawan Umarhadi, Deha Agus Yanuar Adji Nugroho Zuharnen Zuharnen Zulfajri, Zulfajri

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