Aggregation Methods for Assesing The Sustainability of Forest Management

Kelestarian pengelolaan hutan merupakan konsep yang samar dan kompleks, oleh karena itu tidak ada satupun alat ukur yang dapat mengukurnya secara jelas. Sertifikasi hutan digunakan sebagai instrumen untuk mengukur kelestarian pengelolaan hutan yang didasarkan atas kelestarianproduksi, ekologi dan sosial. Kriteria dan Indikator (C & I) untuk kelestarian hutan alam produksi dalam sistem sertifikasi di Indonesia (Lembaga Ekolabel Indonesia) menggunakan Analytical Hierarchy Process (AHP) sebagai alat dalam proses pengambilan keputusannya. AHP telah lama dikritisi, antara lain karena pendekatan kompensatori menggunakan modellinier additive utilitas untuk mengintegrasikan -nilai baku. Riset ini bertujuan untuk menganalisa beberapa metoda aggregasi nilai baku sebagai alternatif untuk menilai kelestarian pengelolaan hutan. Fuzzy AHP dan Rule Base (Fuzzy Reasoning Method) dipelajari sebagai metode untuk mengatasi kekurangmampuan AHP dalam menangani secara tepat peubah-peubah linguistik. Data hasil proses penilaian sertifikasi Unit Pengelolaan Hutan Labanan, Kalimantan Timur,Indonesia digunakan untuk menilai kelestarian pengelolaan hutan dengan tiga metode tersebut. Hasil Fuzzy AHP dibanding dengan Normal AHP menunjukkan hasil yeng lebih jelas dan sudah menampung ketidakpastian justifikasi ekspert yang tidak terdapat dalam Normal AHP. Metode Rule Base, yang sangat tergantung kepada pengetahuan dan pengalaman ekspertnya, memberikan hasil yang lebih berarti dan transparan dalam proses penilaian dibanding kedua metode lainnya, yaitu Normal AHP dan Fuzzy AHP.Keywords:  SFM assessment, forest certification, fuzzy decision making, AHP, Fuzzy AHP, Fuzzy Rule Bas

Biomass estimation model for peat swamp forest ecosystem using light detection and ranging

Peat swamp forest plays a very important role in absorbing and storing large amounts of terrestrial carbon, both above ground and in the soil. There has been a lot of research on the estimation of the amount of biomass above the ground, but a little on peat swamp ecosystems using light detection and ranging (LiDAR) technology, especially in Indonesia. The purpose of this study is to build a biomass estimation model based on LiDAR data. This technology can obtain information about the structure and characteristics of any vegetation in detail and in real time. Data was obtained from the East Kotawaringin Regency, Central Kalimantan. Biomass field was generated from the available allometry, and Point cloud of LiDAR was extracted into canopy cover (CC), and data on tree height, using the FRCI and local maxima (LM) method, respectively. The CC and tree height data were then used as independent variables in building the regression model. The best-fitted model was obtained after the scoring and ranking of several regression forms such as linear, quadratic, power, exponential and logarithmic. This research concluded that the quadratic regression model, with R2 of 72.16 % and root mean square error (RMSE) of 0.0003% is the best-fitted estimation model (BK). Finally, the biomass value from the models was 244.510 tons/ha

ANALISIS KELEMBAGAAN DAN PERANAN KESATUAN PENGELOLAAN HUTAN PRODUKSI (KPHP) DALAM PENGEMBANGAN WILAYAH KABUPATEN KERINCI

Kerinci is one of regency with the large forest, but sub sector of forestry contributes only 0,04% of GDP Kerinci Regency. It’s may possibly by the weakness of forest management and policy of Kerinci Regency Government. Forest production management unit (KPHP) Model Kerinci establishment is one of goverment efforts to achieve sustainable forest management. Therefore, we need research with purpose: (1) to analyze the role of forest production management unit (KPHP) Model Kerinci in the regional development of Kerinci Regency; (2) to analyze the institutional of forest production management unit (KPHP) Model Kerinci; (3) to analyze region’s readiness forest production management unit (KPHP) Model Kerinci development. The study was conducted in Kerinci Regency. Data were analyzed by total economic value (TEV), institutional analysis, and analytical hierarchy process (AHP). The results showed that the total economic value of natural resources of KPHP Model Kerinci is Rp. 337.839.832.400 in a year, it’s mean that sub sector of forestry potentially to contribute about 8,38% of GDP Kerinci Regency. To realize the total economic values of natural resources of KPHP Model Kerinci, it needs strong institutions. Kerinci Regency is ready for KPHP Model Kerinci development, because it’s has the support from stakeholders

KAJIAN METODE DETEKSI DEGRAD AS I HUTAN MENGGUNAKAN CITRA SATE LIT LANDSAT DI HUTAN LAHAN KERING TAMAN NASIONAL HALIMUN SALAK

The study examined detection method of forest degradation using forest canopy density (FCD), maximum likelihood,fuzzy and belief dempster shafer classification method. Accuracy evaluation of classification and detection were based on overall accuracy which obtained from 51 ground sample plot. Canopy density, LA/, crown indicator, trees density and basalarea(Lbds) were conducted asfield indicators.Accuracy of classificationamong forest density (treesjHa) with four classification methods were FCD 61%, maximum likelihood 57%, fuzzy 51% and belief dempster shafer 49%. Based on temporal detection accuracyfrom 2003 until 2008, FCD had overall accuracy 68 %. The result of research,FCD is the best method to detect offorest degradation. Studi ini mengkaji metode deteksi degradasi hutan menggunakan metode klasifikasi forest canopy density(FCD), maximum likelihood,fuzzy, dan belief dempster shafer. Uji akurasi klasifikasi dan deteksi menggunakan overall accuracy yang didapatkan dari 51 sampel lapangan. Kerapatan kanopi, leaf area index (LAI), indikator tajuk, kerapatan pohon, dan luas bidang dasar (lbds) digunakan sebagai indikator degradasi di lapangan. HasH uji akurasi klasifikasi antara hasH klasifikasi dengan kerapatan pohon adalah FCD 61%,maximum likelihood57%, fuzzy 51%, dan beliefdempster shafer49%. Berdasarkan deteksi degradasi secara temporal dari tahun 2003 sampai 2008, FCD mempunyai akurasi 68 %.HasH penelitian ini, FCD adalah metode terbaik untuk deteksi degradasi hutan

Aggregation Methods for Assesing The Sustainability of Forest Management

Kelestarian pengelolaan hutan merupakan konsep yang samar dan kompleks, oleh karena itu tidak ada satupun alat ukur yang dapat mengukurnya secara jelas. Sertifikasi hutan digunakan sebagai instrumen untuk mengukur kelestarian pengelolaan hutan yang didasarkan atas kelestarianproduksi, ekologi dan sosial. Kriteria dan Indikator (C & I) untuk kelestarian hutan alam produksi dalam sistem sertifikasi di Indonesia (Lembaga Ekolabel Indonesia) menggunakan Analytical Hierarchy Process (AHP) sebagai alat dalam proses pengambilan keputusannya. AHP telah lama dikritisi, antara lain karena pendekatan kompensatori menggunakan modellinier additive utilitas untuk mengintegrasikan -nilai baku. Riset ini bertujuan untuk menganalisa beberapa metoda aggregasi nilai baku sebagai alternatif untuk menilai kelestarian pengelolaan hutan. Fuzzy AHP dan Rule Base (Fuzzy Reasoning Method) dipelajari sebagai metode untuk mengatasi kekurangmampuan AHP dalam menangani secara tepat peubah-peubah linguistik. Data hasil proses penilaian sertifikasi Unit Pengelolaan Hutan Labanan, Kalimantan Timur,Indonesia digunakan untuk menilai kelestarian pengelolaan hutan dengan tiga metode tersebut. Hasil Fuzzy AHP dibanding dengan Normal AHP menunjukkan hasil yeng lebih jelas dan sudah menampung ketidakpastian justifikasi ekspert yang tidak terdapat dalam Normal AHP. Metode Rule Base, yang sangat tergantung kepada pengetahuan dan pengalaman ekspertnya, memberikan hasil yang lebih berarti dan transparan dalam proses penilaian dibanding kedua metode lainnya, yaitu Normal AHP dan Fuzzy AHP.Keywords:  SFM assessment, forest certification, fuzzy decision making, AHP, Fuzzy AHP, Fuzzy Rule Bas

Study of Land Cover Change using Multi Layer Perceptron and Logistic Regression Methods in Gunung Ciremai National Park

The development of land cover change is important to understand, so that the pattern of future land cover changes can be predicted and its negative impacts can be prevented or reduced. Various modeling approaches have been widely used to analyze land cover changes. The common modeling methods used for analyzing land cover changes are Multi-layer Perceptron (MLP) and Logistic Regression (Logit). This research is designed to assess the accuracy of modeling of land cover change with MLP and Logit methods in Gunung Ciremai National Park. The result indicated that the accuracy of both methods was very good with kappa values were 0,8991 and 0,8989 for MLP and Logit respectively. Therefore, the model can be applied to predict land cover change in Gunung Ciremai National Park in the future. Keywords: Gunung Ciremai National Park, land cover change, Logistic Regression, Multi-layer Perceptro

Practical Technique for Detecting Mangrove Vegetation Using Digital Mos Messr and Landsat-5 TM Images: A Case Study in Karawang Cape, West Java

Studi ini menerangkan bagaimana algoritme-algoritme indeks separabilitas dan akurasi klasifikasi seyogyanya diterapkan secara benar untuk mendeteksi obyek-obyek yang dikehendaki secara optimal. Studi ini menemukan bahwa akurasi Kappa dan kriteria Separabilitas (Transformed Divergence) harus digunakan secara simultan. Evaluasi dengan hanya menggunakan akurasi Kappa saja atau separabilitas saja akan memberikan hasil yang keliru. Algoritme-algoritme yang diterapkan diujicobakan pada data dijital MOS MESSR (Marine Observation Satellite Multispectral Self-Scanning Radiometer) dan Landsat TM (Thematic Mapper) untuk mendeteksi distribusi vegetasi mangrove. Studi ini memperlihatkan bahwa algoritme-algoritme yang diujicobakan pada MESSR dan TM berhasil mendeteksi distribusi mangrove secara baik, dengan akurasi pengguna (user accuracy) dan akurasi pembuat (producer’s accuracy) yang cukup tinggi berkisar antara 55% dan 100%

Deforestation Profile of Regency Level in Sumatra

Sumatera Islands is an island with the highest deforestation rate in Indonesia for the of period 199

Algorithm for detecting deforestation and forest degradation using vegetation indices

In forestry sector, the remote sensing technology hold a key role on forest inventory and monitoring their changes. This paper describes the algorithm for detecting deforestation and forest degradation using high resolution satellite imageries with knowledge-based approach. The main objective of the study is to develop a practical technique for monitoring deforestation and forest degradation occurred within the mangrove and swamp forest ecosystem.  The SPOT 4, 5, and 6 images acquired in 2007, 2012 and 2014 were transformed into three vegetation indices, i.e., Normalized Difference Vegetation Index (NDVI), Green-Normalized Difference Vegetation index (GNDVI) and Normalized Green-Red Vegetation index (NRGI).  The study found that deforestation was well detected and identified using the NDVI and GNDVI, however the forest degradation could be well detected using NRGI, better than NDVI and GNDVI.  The study concludes that the strategy for monitoring deforestation, biomass-based forest degradation as well as forest growth could be done by combining the use of NDVI, GNDVI and NRGI respectively