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Respons Hidrologi Akibat Deforestasi di DAS Barito Hulu, Kalimantan Tengah Moch. Anwar; Hidayat Pawitan; Kukuh Murtilaksono; I Nengah Surati Jaya
Jurnal Manajemen Hutan Tropika Vol. 17 No. 3 (2011)
Publisher : Institut Pertanian Bogor (IPB University)

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The landcover change, particularly deforestation, can to affect of hydrological response on watershed.Deforestation of natural forest is identical to degradation of watershed functions, such as loos of land productivity due to increased soil erosion, runoff, evapotranspiration, and decreased of water storage. The main objective of this study is describes the hydrologic response on the Barito Hulu watershed due to landcover change (particularly deforestation) by the use Soil and Water Assessment Tool (SWAT) model. The result of the study showed that decreasing of forest area was 9.51% from initial condition can increase water yield, evapotranspiration, and erosion as much as 8.52%, 5.94%, and 1.73 t ha-1 year-1, respectively. Otherwise, water storage decreased as much as14.46%. The landcover on 1990 with rainfall 3,117.10 mm year-1 provided water yield of approximately 1,387.13 mm, evapotranspiration of 1,152.10 mm, water storage of 577.87 mm, and erosion of about 20.52 t ha-1 year-1. The landcover on 2003 with rainfall 2,613.2 mm year-1 provided water yield of approximately 1,385.50 mm, evapotranspiration of 1,121.10 mm, water storage of 106.60 mm, and erosion of about 33.15 t ha-1 year-1 with erosion tolerance of about 43.57 t ha-1 year-1.

Spatial Model of Deforestation in Sumatra Islands Using Typological Approach Nurdin Sulistiyono; I Nengah Surati Jaya; Lilik Budi Prasetyo; Tatang Tiryana
Jurnal Manajemen Hutan Tropika Vol. 21 No. 3 (2015)
Publisher : Institut Pertanian Bogor (IPB University)

High rate of deforestation occurred in Sumatra Islands had been allegedly triggered by various factors. This study examined how the deforestation pattern was related to the typology of the area, as well as how the deforestation is being affected by many factors such as physical, biological, and socio-economic of the local community. The objective of this study was to formulate a spatial model of deforestation based on triggering factors within each typology in Sumatra Islands. The typology classes were developed on the basis of socio-economic factors using the standardized-euclidean distance measure and the memberships of each cluster was determined using the furthest neighbor method. The logistic regression method was used for modeling and estimating the spatial distribution of deforestation. Two deforestation typologies were distinguished in this study, namely typology 1 (regencies/cities with low deforestation rate) and typology 2 (regencies/cities with high deforestation rate). The study found that growth rate of farm households could be used to assign each regencies or cities in Sumatra Islands into their corresponding typology. The resulted spatial model of deforestation from logistic regression analysis were logit (deforestation) = 1.355 + (0.012*total of farm households) – (0.08*elevation) – (0.019*distance from road) for typology 1 and logit (deforestation) = 1.714 + (0.007*total of farm households) – (0.021*slope) – (0.051*elevation) – (0.038* distance from road) + (0.039* distance from river) for typology 2, respectively. The accuracy test of deforestation model in 2000–2006 showed overall accuracy of 68.52% (typology 1) and 74.49% (typology 2), while model of deforestation in 2006–2012 showed overall accuracy of 65.37% (typology 1) and 72.24% (typology 2), respectively.

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

Identifying The Key Variables for Assessing The Reclamation Success on Early Growth Vegetation in Ex-exploration of Oil and Gas Mining Areas Tirta Negara; I Nengah Surati Jaya; Cecep Kusmana; Irdika Mansur; Nitya Ade Santi
Jurnal Manajemen Hutan Tropika Vol. 26 No. 3 (2020)
Publisher : Institut Pertanian Bogor (IPB University)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.7226/jtfm.26.3.222

This paper examines the identification of key indicators that could be used to measure the success of reclamation plants in post-exploration oil and gas mining areas. The main objective of this research was to find key indicators or variables for evaluating the level success of reclamation results in the post-mining of oil and gas area. In this study, 44 environmental variables of the physical, biological, soil, water and air indicators were analyzed from 70 field plots of 6 reclamation and 2 natural forest sites. The analysis methods included (1) cluster analysis using the Agglomerative Hierarchical Clustering method with the Ward's method, and (2) quadratic discriminant analysis. The results of the clustering analysis showed that there were some clusters due to variation of biomass, water, soil and air conditions. The three clusters developed based on water and/or air variables provided high cophenetic correlation (0.80) with low within-cluster (14.5%) and high between-cluster variations (85.5%). Based on the multicollinearity analysis, average vector difference test, variance matrix variance test, unidimensional test of each variable and quadratic discriminant function, this study found that there were 3 key indicators determining variations of the quality of the reclamation plantations within the study sites, namely, biological indicator of biomass volume (Bio_B); soil indicator of P content in the soil (Tnh_P), saturation base of soil (Tnh_Kb), Manganese (Mn) content in the soil (Tnh_Mn), Sulfur content in the soil (Tnh_S), percentage of ash in the soil (Tnh_Ab), percentage of clay in the soil (Tnh_Li), and water indicator of chloride content in the surface water (Air_Cl). The examination on four classes of the reclamation quality showed that the classes were successfully classified having excellent cross-validation error matrix with overall accuracy more than 90%.

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

Between 1992-2005 there were 8 tsunamis occurred in Indonesia. At 17 July 2006 tsunami reached the southern coast of West Java, Cilacap and Yogyakarta. Tsunamis had caused heavy destruction and death. The aim of the study is to find out spatially the potential of inundation area by tsunami wave based on the distribution of inundation extent of different run up. The run up of 7.5 m flooded 4% from whole study area. 36% of the area was flooded by 15 m run up. When the run up was 7,5 m, some area Anita Zaitunah; Cecep Kusmana; I Nengah Surati Jaya; Oteng Haridjaja
Forum Pasca Sarjana Vol. 34 No. 4 (2011): Forum Pascasarjana
Publisher : Forum Pasca Sarjana

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Between 1992-2005 there were 8 tsunamis occurred in Indonesia. At 17 July 2006 tsunami reached the southern coast of West Java, Cilacap and Yogyakarta. Tsunamis had caused heavy destruction and death. The aim of the study is to find out spatially the potential of inundation area by tsunami wave based on the distribution of inundation extent of different run up. The run up of 7.5 m flooded 4% from whole study area. 36% of the area was flooded by 15 m run up. When the run up was 7,5 m, some areas were flooded reaching 200 m from the coast and there were also some points going further to 1 km. The run up of 15 m moved further to 4,5 km. The information on the distance and extent of inundation from some possible run up of tsunami become an input on potential areas flooded by water and having destruction. Key words: tsunami, inundation, coastal area, West Java

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

Restoration Priority Index Development of Degraded Tropical Forest Landscape in Batang Toru Watershed, North Sumatera Indonesia Samsuri Samsuri; I Nengah Surati Jaya; Cecep Kusmana; Kukuh Murtilaksono
BIOTROPIA - The Southeast Asian Journal of Tropical Biology Vol. 21 No. 2 (2014)
Publisher : SEAMEO BIOTROP

Forest degradation has been important in the context of global issues since it contributes to the global climate change significanly.Â Forest degradation of tropical rain forest area reduces global biological resources and has impact on occurence of poverty in community.Â It also increases forest fragmentation and decreases forest connectivity as wildlife habitat.Â To improve the connectivity, landscape restoration approach is used. Â This paper describes the forest landscape restoration priority index to decide where the site gets restoration priority. Â Restoration index is constructed by four factors indices that are index of forest degradation, forest connectivity, forest fragmentation, and socio economic of forest communities. Batang Toru forest landscape was getting pressure during the period 1989-2013. Â Forest landscape fragmentation degree increased in the period 1989-2013, while the degree of connectivity tends to increase. Â Forest connectivity index effects the restoration index more than other. Â It implies that connectivity of Batang Toru forest landscape needs to be maintained and enhanced. It will provide proper habitat and reduce loss of biodiversity. Restoration index classifies Batang Toru forest landscape into low, medium and high priority for restoration. Sub-watershed of Sarula has high restoration index value so that it must be restored firstly.

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