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Jurnal Penelitian Hutan Tanaman Vol 9, No 3 (2012): JURNAL PENELITIAN HUTAN TANAMAN
Publisher : Pusat Penelitian dan Pengembangan Peningkatan Produktivitas Hutan

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ABSTRACTEstimation of carbon stock has direct limitations related to the speed of obtaining results, area coverage and high cost. Remote sensing can be used to estimate carbon stocks with an adequate level of accuracy. The objectives of this study are 1) to obtain the potential carbon storage of A. mangium, 2) to estimate model of carbon stocks based on a radar image (backscatter value of Alos Palsar), and 3) to map potential carbon stock distribution of A. mangium at PT. Inhutani II, South Kalimantan. The method used was a direct inventory of carbon stocks by making 69 measuring plots (0.1 ha area of each plot) spread across several age. The field inventory results were used to formulate a relationship with the polarization values of the Alos Palsar. The results showed that the potential surface carbon deposit varied from 32.03 to 46.10 tons/ha with an average value of 39.06 tons/ha. The total potential carbon stock per ha ranged from 35.48 to 51.01 tons/ha with an average of 43.24 tons/ha. The best allometric relationship between carbon stock and the polarization values HH and HV of the Alos Palsar image was Carbon Deposit = 292 + 2.00 HH2 + 27.1 HV with the R2  = 40.9%. Potential carbon storage based on Alos Palsar image ranged between 40 - 80 tons/ha. The result of Alos Palsar predicton is accurate so the technology can be used for measuring or monitoring of carbon stocks in plantation forest.ABSTRAKPendugaan persediaan karbon secara langsung mempunyai keterbatasan terkait dengan kecepatan memperoleh hasil, cakupan luasan yang terbatas dan biaya yang mahal. Penginderaan jarak jauh dapat dimanfaatkan untuk menduga persediaan karbon dengan akurasi yang cukup memadai. Tujuan penelitian ini yakni: 1) mendapatkan potensi simpanan karbon jenis A. mangium, 2) mendapatkan model penduga simpanan karbon berdasarkan citra Radar (nilai backscatter citra Alos Palsar), 3) mendapatkan peta sebaran potensi simpanan karbon jenis A. mangium di PT. Inhutani II, Kalimantan Selatan. Metode yang digunakan dengan melakukan inventarisasi persediaan karbon secara langsung yakni pembuatan plot pengukuran sebanyak 69 plot dengan luas masing-masing plot seluas 0,1 Ha tersebar pada beberapa umur. Hasil inventarisasi tersebut digunakan untuk membentuk hubungan dengan nilai polarisasi dari citra Alos Palsar. Hasil penelitian menunjukkan potensi simpanan karbon permukaan sebesar 32,03 - 46,10 ton/ha dengan rata-rata 39,06 ton/ha. Potensi simpanan karbon total per Ha berkisar antara 35,48 -51,01 ton/ha dengan rata-rata 43,24 ton/ha. Model alometrik terbaik hubungan antara simpanan karbon dan nilai polarisasi HH dan HV dari citra Alos Palsar adalah Simpanan karbon = 292 + 2,00 HH2 + 27,1 HV dengan koefisien determinasi sebesar 40,9%. Potensi sebaran simpanan karbon total terbesar berdasarkan aplikasi citra Alos Palsar yakni berkisar antara 40 - 80 ton/Ha. Penggunaan Alos Palsar untuk menduga simpanan karbon menghasilkan dugaan yang cukup akurat sehingga teknologi ini dapat digunakan untuk mengukur atau monitoring persediaan karbon pada tegakan hutan tanaman.
Spatial Model of Deforestation in Jambi Province for The Periode 1990–2011 Putu Ananta Wijaya; Muhammad Buce Saleh; Tatang Tiryana
Jurnal Manajemen Hutan Tropika Vol. 21 No. 3 (2015)
Publisher : Institut Pertanian Bogor (IPB University)

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In the last 2 decades, deforestation had been an international issue due to its effect to climate change. This study describes a spatial modelling for predicting deforestation in Jambi Province. The main study objective was to find out the best spatial model for predicting deforestation by considering the spatial contexts. The main data used for the analysis were multitemporal Landsat TM images acquired in 1990, 2000, and 2011, the existing land cover maps published by the Ministry of Forestry, statistical data and ground truth.  Prior to any other analyses, all districts within the study area were classified into 2 typologies,  i.e., low-rate and high-rate deforestation districs on the basis of social and economic factors by using clustering approaches.  The spatial models of deforestation were developed by using least-square methods. The study found that the spatial model of deforestation for low-rate deforestation area  is Logit (Deforestation) = -2.7046 – 0.000397*JH90(distance from forest edge) + 0.000002*JJ(distance from road) – 0.000111*JKBN90 (distance from estate crop edge) + 0.000096 *JP90(distance from agricultural crop edge) + 0.044227*PDK90(population density) + 0.148187 *E(elevation) – 0.131178*S(slope); while for the high-speed deforestation area is Logit (Deforestation) = 9.1727 – 0.000788*JH90(distance from forest edge) – 0.000065 *JJ(distance from road) – 0.000091*JKBN90(distance from estate crop edge) + 0.000005 *JP90(distance from agricultural crop edge) – 0.070372*PDK90(population density) + 11.268539*E(elevation) – 1.495198*S(slope). The low-rate and high-rate deforestation models had relatively good ROC (Relative Operating Characteristics) values of 91.32% and 99.08%, respectively. The study concludes that the deforestation rate was significantly affected by accessibility (distance from forest edge, distance from estate crop edge, edge from agricultural land), biophysical condition (elevation and slope) as well as population density.  
Spatial Modeling for Determining Managerial Options for Structuring Productivity in KPH Bogor Ricca Rohani Hutauruk; Nining Puspaningsih; Muhammad Buce Saleh
Jurnal Manajemen Hutan Tropika Vol. 22 No. 3 (2016)
Publisher : Institut Pertanian Bogor (IPB University)

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KPH Bogor Ricca Rohani Hutauruk1, 2*, Nining Puspaningsih3, Muhammad Buce Saleh31Graduate School of  Bogor Agricultural University, Dramaga Main Road, Campus IPB Dramaga, Bogor,Indonesia 16680 2Trainer, Environment and Forestry Education and Training Bogor Agency,The Ministry of Environment and Forestry, Jl. Prada Samlawi Rumpin, Bogor, Indonesia 3Department of Forest Management, Faculty of Forestry, Bogor Agricultural University, Academic Ring Road,Campus IPB Dramaga, PO Box 168, Bogor, Indonesia 16680Received Agustus 23, 2016/Accepted October 20, 2016AbstractIn the past few years, forest management unit (KPH) Bogor has experienced many problems, technical, environmental and social, affecting the company's finances. This condition requires new breakthroughs in the form of managerial options in managing the forests of KPH Bogor. At present, KPH Bogor has formulated 12 managerial options. The purpose of this study is to build a spatial model in selecting managerial options at site level. The spatial models were built based on the score of each land unit which was obtained from expert judgment using an intensity scale, while weight was obtained using a pairwise comparison, resulting in the following equation: total score = 0.14 (0.06x1 + 0.11x2 + 0.09x3 + 0.08x4 + 0.10yx5 + 0.31x6 + 0.25x7) + 0.72 (0.08y1 + 0.22y2 +  0.46y3 + 0.13y4 + 0.12y5) +0.14 (0.45z1 + 0.05z2 + 0.44z3 + 0.06z4). The resulting total score was then divided into 5 classes using the equal interval method. The results for each of the managerial options were then aggregated using GIS to create KPH Bogor's management pattern. In areas where there was an overlap due to the similarity in options, a decision support system using neighboring similarity spatial analysis was used. This step allowed the spatial model to be built with many biophysical, social, and economic variables. This spatial model could map 12 types of managerial options at site level in the production structuring in KPH Bogor.
Quick Tecniques in Indentifying Open Area by the Use of Multi Spatial and Multidate Imageries Ahyar Gunawan; I Nengah Surati Jaya; Muhammad Buce Saleh
Jurnal Manajemen Hutan Tropika Vol. 16 No. 2 (2010)
Publisher : Institut Pertanian Bogor (IPB University)

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This study describes the use of multitemporal principal component analysis (MPCA) and vegetation index differencing (VIDN) techniques in identifying open area on post-coal mining sites using multi spatial and multidate of Landsat TM and SPOT 4 XS imageries. The study revealed that the synthetic images derived from stable brightness, stable greenness,s and delta brightness of MPCA summarize information on post-coal-mining opened areas provided overall accuracy of 76.47% for the new ex mining area and 32.69% for old ex mining area. The VIDN method provided relatively lower accuracy than those from MPCA i.e. 58.87% for new ex mining and 13.25% for old ex-mining areas. The study also concluded that identifying open area on post-coal-mining sites using imageries was more efficient than using only ground survey, providing cost efficiency of 29%. Thisindicates that the cost required using satellite image is only 29% of the cost required for ground survey. The study concluded that MPCA is better than VIDN for identifying open area on post-coal-mining sites.
Deteksi Kondisi Hutan Paska Kebakaran Melalui Citra Multisensor MOS-MESSR dan Landsat TM: Studi Kasus di Areal PT. MHP Sumatera Selatan I Nengah Surati Jaya; Endang Pujiastuti; Muhammad Buce Saleh
Jurnal Manajemen Hutan Tropika Vol. 6 No. 2 (2000)
Publisher : Institut Pertanian Bogor (IPB University)

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This paper presents how the multisensor MOS-MESSR and Landsat Thematic Mapper (TM) should be manipulated as tools for detecting land cover changes. Radiometric correction using image regression was recognized as useful approach to adjust pixel brightness value of MOS-MESSR. In this study, the standardized MPC showed comparable accuracy, similar to DMC method. Using this technique forest changes due to fire as well as land clearing were well recognized. Some recommendations and suggestions for improving classification accuracy of change detection using multisensor MOS-MESSR and Landsat TM were drawn up from this study.
Spatial Modeling of Forest Cover Change in Kubu Raya Regency, West Kalimantan Hanifah Ikhsani; I Nengah Surati Jaya; Muhammad Buce Saleh
Jurnal Manajemen Hutan Tropika Vol. 24 No. 3 (2018)
Publisher : Institut Pertanian Bogor (IPB University)

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Forest cover change is one of the environmental issues that continually gotten an international attention. This study describes how to develop a spatial model of forest cover change in each village-based typology by considering various bio-physical and social-economic factors. The village typologies were investigated by applying the clustering analysis approach. The objective of this study was to develop the spatial model and to identify the driving forces of forest cover change by village in Kubu Raya Regency of West Kalimantan. Based on proportion of forest in 2015, the study found that there are two village typologies within the study area with 81% overall accuracy (OA). The typology 1 (T1) which has low forest cover change rate of 5001.8 Ha per year consisted of 56 villages, while the typology 2 (T2) which has high rate of forest cover change of about 8050.6 Ha per year covered 34 villages. The study also recognized that the most significant driving forces of forest cover change in T1 were distance from rivers (X2) and settlements (X3), whereas in T2 were distance from roads (X1) and the edge of forest in 2015 (X9). The best spatial model of forest cover change are Y = -0.01+0.0001X2+0.0004X3 with OA of 83% and mean deviation (SR) 10.5% for T1 and Y = 0.02+0.0001X1-0.0002X9with OA 53% and SR 13.3% for T2. The study concludes that the proximity from the center of the human activities hold a significant influence to the behavior of forest cover changes
PREDIKSI PERUBAHAN TUTUPAN LAHAN DENGAN MODEL MARKOV CHAIN DAN ANN-MARKOV DI DAS KRUENG ACEH (Land cover change prediction using Markov Chain and ANN-Markov Model in Krueng Aceh Watershed) Yudi Armanda Syahputra; Muhammad Buce Saleh; Nining Puspaningsih
Jurnal Penelitian Pengelolaan Daerah Aliran Sungai (Journal of Watershed Management Research) Vol 5, No 2 (2021): Jurnal Penelitian Pengelolaan Daerah Aliran Sungai (Journal of Watershed Managem
Publisher : Center for Implementation of Standards for Environmental and Forestry Instruments Solo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20886/jppdas.2021.5.2.185-206


ABSTRACT Prediction of land cover change will be a consideration in determining the development strategy in the future. There are many methods for predicting  land cover change. It depends on data availability, model algorithms and output needed. The objective of this reasearch was to predict land cover change from 2007 to 2020 in the Krueng Aceh watershed. The method used remote sensing and GIS.  The Markov Chain (MC) and Artificial Neural Network-Markov (ANN-M) models were used to understand the spatio-temporal dynamics of land cover. The accuracy of the classified imagery was obtained from on-screen digitation using  medium resolution landsat-8 OLI image in 2020 with Kappa Accuracy around 84%. Both prediction algorithms used year 2007 (T1) and year 2017 (T2) land cover data to calculated the probability of land cover change prediction in year 2020 (T3). The Kappa Accuracy of both models shows a strong correlation between the simulated land cover maps and the results of visual interpretation (ANN=87.81% and MC=88.69%), this proves high accuracy of both models. Key words: model; ANN-Markov; landcover change prediction; Markov Chain ABSTRAKPrediksi perubahan tutupan lahan yang baik akan menjadi pertimbangan dalam menentukan strategi pembangunan di masa depan. Terdapat banyak metode dalam melakukan prediksi perubahan tutupan lahan yang tergantung pada kebutuhan data, algoritma pemodelan yang dilakukan dan output apa saja yang diperlukan. Penelitian ini dilakukan untuk mengkaji model prediksi perubahan tutupan lahan dari tahun 2007 hingga 2020 di DAS Krueng Aceh. Pendekatan yang dilakukan menggunakan penginderaan jauh dan SIG. Model Markov Chain (MC) dan Artificial Neural Network-Markov (ANN-MC) digunakan untuk memahami dinamika spatio-temporal tutupan lahan. Akurasi dari citra penginderaan jauh yang diklasifikasikan diperoleh dari hasil interpretasi visual pada citra resolusi sedang Landsat OLI tahun 2020 dengan nilai Kappa Accuracy sebesar 84%. Kedua model prediksi menggunakan data tutupan lahan tahun 2007 (T1) dan 2017 (T2) untuk membuat probabilitas perubahan yang digunakan dalam memprediksi tutupan lahan pada tahun 2020 (T3). Validasi kedua algoritma menunjukkan korelasi yang kuat dengan peta tutupan lahan 2020, hal tersebut membuktikan kehandalan model kedua simulasi (ANN=87,81% dan MC=88,69%).Kata kunci: model; ANN-Markov; prediksi tutupan lahan; Rantai Markov