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Reforestation Achievement Monitoring at Mining Area through Soil Index Model Nining Puspaningsih; Kukuh Murtilaksono; Naik Sinukaban; I Nengah Surati Jaya; Yadi Setiadi
Jurnal Manajemen Hutan Tropika Vol. 16 No. 2 (2010)
Publisher : Institut Pertanian Bogor (IPB University)

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The achievement of the reforestation is expected to reach a climax forest ecosystem. The objectives of this studywas to develop soil index model on monitoring of reforestation achievement. The study used a statistical approach to obtain soil index model to determine the achievement level of reforestation in mining area. The achievement indices for each variable were derived from the best regression model developed, while the weights of eachvariable were computed based on magnitude of regression coefficient for each indicator. The level of reforestationachievement index was initially developed by the use of 4 indicators, i.e. physical soil, biological soil, chemical soil, and litter index. Of those indicators, the study revealed that the heights weight for reforestation monitoring was chemical soil, which is composed pH, cation exchange capacity (CEC), macro-micro nutrient, and base saturation.

Studi Perencanaan Pengelolaan Lahan di Sub DAS Cisadane Hulu Kabupaten Bogor Nining Puspaningsih
Jurnal Manajemen Hutan Tropika Vol. 5 No. 2 (1999)
Publisher : Institut Pertanian Bogor (IPB University)

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The research area is an upper watershed and is a land culture for the peasant its self. Then in the plan of land management have to participate the social economic life of community in this area. The alternative plan of land management based on the erosion prediction (A) and to enable erosion (T). The land management and the techniques of land conservation have to arranged if A value more high then T value. This problem happened because the negative impact of land changes. These values must be manipulated in order to A value less than T value. The selection of CP value have to less then and equal with the T/RKLS ratio or the CP maximum value. The must of part of area research have A value more high then T value and plan alternative is rice field, dry field and mixed field with system tumpang sari, agro tourism and protection forest areas.

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.

MODEL SPASIAL DEFORESTASI DI KABUPATEN KONAWE UTARA DAN KONAWE PROVINSI SULAWESI TENGGARA Hariaji Setiawan; I Nengah Surati Jaya; Nining Puspaningsih
Media Konservasi Vol 20 No 2 (2015)
Publisher : Department of Forest Resources Conservation and Ecotourism - IPB University

Deforestation is now becoming a global concern due to its effect on the global warming. This paper describes a dynamic change of deforestation and spatial modeling for predicting deforestation in North Konawe and Konawe Districts, Southeast Sulawesi Porvince. The study objective is to examine and analyze the variety of explanatory variables related to the process of deforestation at each deforestation typology. The data used for the analysis include Multitemporal Landsat images acquired in 1997, 2000, 2005, 2010 and 2013, the existing land cover maps published by the Ministry of Forestry, statistical data and ground truth. All district within the study area were classified into two typologies on the basis of social and economic factors by using clustering approaches, i.e., low-speed and high-speed deforestation district. To analyze model and predictions using land cover data in 2005, 2010 and 2013. The study found that the spatial model of deforestation for low-speed deforestation area is Logit (Deforestation) =– 1.0998 – 0.017031*Kpd05(population density) – 0.000095*JJ(distance from road) – 0.000419*JS(distance from the river) – 0.002057*JH05(distance from forest edge) – 0.00001*JPmk05(distance from settlements) – 0.000019*JPlc05(distance to the mixture of dry land agriculture)+0.016305*S(slope)+0.084348*E(elevation), high-speed deforestation area is Logit (Deforestation) =– 1.2361– 0.062622*Kpd05(population density) – 0.000008*JJ(distance from road) – 0.00001*JS(distance from the river) – 0.005443*JH05(distance from forest edge) – 0.000077*JPmk05(distance from settlements) – 0.000067*JPlc05(distance to the mixture of dry land agriculture)+0.469883*S(slope)+0.300739*E(elevation). The low-speed and high-speed deforestation models had ROC (Relative Operating Characteristics) of 93.48% and 97.71%, respectively. The study concludes that typology could be made on the basis of population density and the amount of dry land with wetland. The results of this study showed that there are eight explanatory variables that significantly affect deforestation probability, namely population density, distance from road, distance to the river distance from the forest edge, distance to settlement, distance to the mixture of dryland agriculture, slope, elevation and. Keywords: deforestation, konawe, logistic model, spatial model, typology

PENDUGAAN PERUBAHAN STOK KARBON DI TAMAN NASIONAL BROMO TENGGER SEMERU Rahimahyuni Fatmi Noor'an; I Nengah Surati Jaya; Nining Puspaningsih
Media Konservasi Vol 20 No 2 (2015)
Publisher : Department of Forest Resources Conservation and Ecotourism - IPB University

Recently a comprehensive source of data and information on carbon storage in various types of forest ecosystems and other land use in Java Island are still limited. This study was carried out in a conservation area of Bromo Tengger Semeru National Park (TNBTS) that represents the ecosystem types of lowland rain forest, sub-montane forests and mountain forests in Java. The information on carbon sequestration and carbon stocks at TNBTS becomes important. The main objective of this study was to estimate biomass and carbon storage in various types of forests in TNBTS using allometric approaches. The additional objectives were to estimate carbon storage on various land cover and to estimate the changes in carbon storage by land cover changes during the period 1990, 2000 and 2013. The measurement of forest carbon include aboveground, understorey, necromass and litter pools covering all ecosystem such as primary forest, secondary forest with high- and low- canopy density. This study found that the average of carbon stocks in primary forest were 193,49 ± 125,98 tonC/ha, and were 267,42 ± 119,25 tonC/ha in secondary forest. The total carbon stocks in the period 1990–2000 has decreased about 22.6 tonC/ha/year and in the period 2000–2013 has increased about 41.2 tonC/ha/year. The enhancement of carbon stocks in this area was driven by an intensive forest protection, good monitoring and land rehabilitation. Keywords: biomass, carbon storage, carbon stock, land cover, national park

Calculation Methods of Topographic Factors Modification Using Data Digital Elevation Model (DEM) To Predict Erosion Hengki Simanjuntak; Hendrayanto .; Nining Puspaningsih
Media Konservasi Vol 22 No 3 (2017): Media Konservasi Vol. 22 No. 3 Desember 2017
Publisher : Department of Forest Resources Conservation and Ecotourism - IPB University

Erosion is a crucial information for sustainable management of land resources within a particular watershed. The information of erosion is needed for land resource management planning, and is generally counted by USLE (Universal Soil Loss Equation). One of the parameters in USLE is topographic factor (LS). The determinations of LS in erosion estimation model are vary, both in terms of LS factor equation, as well as in terms of the length of the slope (λ) and slope (s) measurements. There are at least 3 methods used to calculate slope factors in spatial operation, i.e (1) Input of the LS Value from Table (INT), (2) Flow accumulation, and (3) Cell Size. The study was designed to obtain a method of calculation that gives the smallest topographic factor and in order to obtain a LS factors that similar to the slope information. Research location in Kampa Sub watershed, The LS determination in Kampa Sub watershed basically are with (INT) and without calculating λ and s. INT method is determination without calculating λ and s, LS value is generate from the contour map and DEM SRTM by giving LS value from table reference of LS value. The Flow Accumulation and Cell Size are determination of LS Value by calculating λ and s. The Flow Accumulation method modifies the determination of λ and s using the middle value of s, λ per land use, and λ and s per cell. Cell Size method determines λ using the amount of cell size. The results showed that the “cell size” and "INT" methods were the best method for topographic factor (LS) calculation, because LS value of “cell size” and "INT" methods are smaller than the flow accumulation method and the LS value similar to the slope information. LS value from that methods generated weighted value in average of 0,55−0,58. Keywords: cell size, flow accumulation, flow direction, the length of the slope, USLE

Study of Land Cover Change using Multi Layer Perceptron and Logistic Regression Methods in Gunung Ciremai National Park Agus Rudi Darmawan; Nining Puspaningsih; M. Buce Saleh
Media Konservasi Vol 22 No 3 (2017): Media Konservasi Vol. 22 No. 3 Desember 2017
Publisher : Department of Forest Resources Conservation and Ecotourism - IPB University

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 Perceptron

Optimal Land Use for Rainfall-Runoff Transformation in Wae Ruhu Watershed Aly Laturua; . Hendrayanto; Nining Puspaningsih
Media Konservasi Vol 23 No 1 (2018): Media Konservasi Vol. 23 No. 1 April 2018
Publisher : Department of Forest Resources Conservation and Ecotourism - IPB University

Flooding hit the island of Ambon in 2012 and 2013. Many analyzes has been developed to estimate the cause of the flooding. The study aims topredict optimal land management for reducing run-off. The method is simulation of CN value based on spatial analysis on watershed characteristics.The rainfall can’t be managed by watershed. The level of run-off can be determined by CN value that depends on the type of land cover. The resultshows that the land cover has changed about 90 ha, with the higher rainfall intensity is 2.118 in 2013. The result of simulation indicated that tochange of shrub and bare land, mix dryland forest, and secondary dryland forest with agroforestry. Agroforestry can decrease run-off amount 0,86%.The change of land cover and high rainfall are the main factors that caused the flooding in 2012 and 2013. It is necessary to add a rainfallobservation station so that the observation of surface flow can be done well.Keywords: curve number, land cover change, watershed

Biomass Estimation Model in Revegetation Area of Nickel Post-Mining Witno Witno; Nining Puspaningsih; Budi Kuncahyo
Media Konservasi Vol 23 No 3 (2018): Media Konservasi Vol. 23 No. 3 Desember 2018
Publisher : Department of Forest Resources Conservation and Ecotourism - IPB University

Deforestation and forest degradation are one of the most crucial issues in the forestry sector. The impact of deforestation and forest degradation due to the opening of forest areas for mining activities that causes damage to sustainable forest ecology. This condition requires companies as miners to carry out revegetation activities in post-mining areas to restore forest existence. PT. Vale of Indonesia (PTVI) is a nickel mining company located in Sorowako, South Sulawesi Province, which has carried out revegetation activities and is considered quite successful. This assessment has not included biomass as an indicator of forest productivity. Biomass is one of the determinants of forest productivity in post-mining areas needs to be further investigated to maximized revegation management. The objective of this study was to measure and construct a model for estimating biomass in the revegetation of the post-mining area in PTVI. The results of this study obtained a regression model of the rank as a biomass estimator in the revegetation of the post-mining area in PTVI. The form of the selected model equation is Y= 2,59505E-13 X1 2,489 X2 3,645. The independent variable is X1 = DVI (vegetation index) and X2 = C% (percentage of canopy). The regression model chosen has a determination coefficient of 70,60% and a standard deviation of -0,33528. Keywords: biomass, post-mining, regression model, revegetation

Land rehabilitation of post-mining must be done with reforestation. Reforestation success in post-mining revegetation should to refer the characteristics of natural forests. The success of the reforestation is expected to reach a climax forest ecosystem. How much time is needed to achieve the desired condition (success reforestation), in this case described as the age of achievement expectations of reforestation success to reach a climax forest ecosystem (the basalt area). The research is aim Nining Puspaningsih; Kukuh Murtilaksono; Naik Sinukaban; I Nengah Surati Jaya; Yadi Setiadi
Forum Pasca Sarjana Vol. 33 No. 4 (2010): Forum Pascasarjana
Publisher : Forum Pasca Sarjana

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Land rehabilitation of post-mining must be done with reforestation. Reforestation success in post-mining revegetation should to refer the characteristics of natural forests. The success of the reforestation is expected to reach a climax forest ecosystem. How much time is needed to achieve the desired condition (success reforestation), in this case described as the age of achievement expectations of reforestation success to reach a climax forest ecosystem (the basalt area). The research is aimed to predict reforestation success age. The study used regression analysis for determining the reforestation success age in mining area. The measure used to determining the reforestation success age is basalt area (LBDS) of natural forests. Mathematically it can be summarized to LBDS = f (age). The study found the age of achievement expectations of reforestation success is 75 years. Over the 75 years when the efforts to improve, protect and enhance forest functions are be done well, consistently, and even continued to rise, certainly reforestation success will be achieved, or even faster. Key words: rehabilitation, mining area, reforestation, basalt area, reforestation success age

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