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All Journal MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management MEDIA KONSERVASI Forum Pasca Sarjana FORESTA BIOTROPIA - The Southeast Asian Journal of Tropical Biology TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknosains Jurnal Penelitian Hutan dan Konservasi Alam Jurnal Penelitian Hutan Tanaman Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) BERITA BIOLOGI JURNAL BIOLOGI INDONESIA MAJALAH ILMIAH GLOBE International Journal of Remote Sensing and Earth Sciences (IJReSES) Journal of Sylva Indonesiana Indonesian Journal of Electrical Engineering and Computer Science Jurnal Risalah Kebijakan Pertanian dan Lingkungan International Journal of Electronics and Communications Systems Aceh International Journal of Science and Technology Jurnal Penelitian Kehutanan Wallacea

I Nengah Surati Jaya

Department Of Forest Management, Faculty Of Forestry, Bogor Agricultural University, Academic Ring Road, Campus IPB Dramaga, PO Box 168, Bogor, Indonesia, 16680

Author-ID : 291796

Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Veterinary

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Stemflow Variability in Tropical Lowland Forest Landscape Transformation System: Case Study at Jambi Province, Indonesia Bejo Slamet; I Nengah Surati Jaya; Hendrayanto Hendrayanto; Suria Darma Tarigan
Jurnal Manajemen Hutan Tropika Vol. 21 No. 1 (2015)
Publisher : Institut Pertanian Bogor (IPB University)

Land cover change may cause change on the hydrological function of an area, particularly on the distribution of rainfall that reach land surface. This study describes the characteristic of stemflow occurred within 4 ecosystems in Jambi, namely logged forest, jungle rubber, rubber plantation, and oil palm plantation. The main objective of the study was to measure the variability of stemflow in those 4 ecosystems. The main data used were rainfall and stemflow data that were directly measured for 5 months. The derived regression equation model showed that stemflow increase with rainfall depth. It was shown that values of stemflow amongs plantation types was varied indicated by the difference of its regression coefficients, as well as variations of the rainfall at the same transformation type. The percentage of stemflow to rainfall was ranging from 0.04–0.21% for rubber, 0.10–0.38% for jungle rubber, 0.28–0.54% for forest, and 0.84–3.07% for oil palm. The oil palm provided the highest stemflow volume compared to other land cover type. The uniqueness of oil palm canopy may cause the drainage of water from the canopy to the main stem that indicated by highest stemflow funneling ratio value. Rainfall significantly affected the amount of stemflow compared with the characteristics of the plant.

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 Metrics of Deforestation in Kampar and Indragiri Hulu, Riau Province Syamsu Rijal; Muhammad Buce Saleh; I Nengah Surati Jaya; Tatang Tiryana
Jurnal Manajemen Hutan Tropika Vol. 22 No. 1 (2016)
Publisher : Institut Pertanian Bogor (IPB University)

The Riau Province has been suffering from the highest deforestation rate in Sumatra, Indonesia. Many and various factors haved been discussed as causes of different deforestation types. This research is focused on evaluating the spatial pattern of deforestation in a specific location respresenting a typical deforestation in Riau. The main objective of this study was to identify spatial metrics to describe deforestation that occurred in Kampar and Indragiri Hulu regencies.The study divided the deforestation process into 3 periods of observation, e.g., 1990–2000, 2000–2010, and 2010–2014. The study based on Landsat satellite imagery aquired in 1990, 2000, 2010, and 2014 as the main data sources. The deforestation was detected using post-classification comparison (PCC) on the basis of 11 land cover classes developed prior to any further change detection. The deforestation was initially derived from reclassifying the original classes into only forest and non-forest classes, and then followed by spatial pattern analysis using Fragstat software. The study shows that 2 spatial pattern of deforestation in Kampar distinctly differs from those occurred in Indragiri Hulu Regency, particularly for the period of 1990–2014. The spatial pattern of deforestation in Kampar Regency were clumped, low contiguous between patch, and high fragmentated. Meanwhile, the spatial pattern in Indragiri Hulu Regency were clumped, high contiguous between patch, and low fragmentated. Profile of deforestation in Kampar Regency was cathegorized into early deforestation and Indragiri Hulu Regency as lately deforestation.

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)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2951.688 KB)

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

Typology of Tropical Forest Transition Model in Several Watershed, Sumatera Island Widyananto Basuki Aryono; Endang Suhendang; I Nengah Surati Jaya; Herry Purnomo
Jurnal Manajemen Hutan Tropika Vol. 24 No. 3 (2018)
Publisher : Institut Pertanian Bogor (IPB University)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1064.309 KB)

At a landscape level, forest transitions have complex spatial heterogeneity characteristics, thus the causes, driving force, typology and specific profile characteristics need to be considered for managing and mitigating forest transition. This paper describes how the diversity of forest transition characteristics was grouped and how the characteristic of group was identified. Typology classes within water catchment areas in Riau, North Sumatera and West Sumatera Provinces, Indonesia were investigated by considering social, economic and biophysical aspects. The main study objective was to develop a forest transition typology at a landscape level. The model typology was derived from a clustering method with the Standardized Euclidean Distance. The study found that the most significant factor which successfully differentiated the typology of forest transition into two typologies was the population growth having approximately 92% of overall accuracy. The first typology (typology 1) could be categorized as rapid forest transition, while the typology 2 was categorized as slow forest transition. The study suggested that the management and mitigation of the impacts of the forest transition should be conducted by considering the landscape typology as a function of the profiles for each typology.

The Examination of The Satellite Image-Based Growth Curve Model Within Mangrove Forest I Nengah Surati Jaya; Muhammad Buce Saleh; Dwi Noventasari; Nitya Ade Santi; Nanin Anggraini; Dewayany Sutrisno; Zhang Yuxing; Wang Xuenjun; Liu Qian
Jurnal Manajemen Hutan Tropika Vol. 25 No. 1 (2019)
Publisher : Institut Pertanian Bogor (IPB University)

Developing growth curve for forest and environmental management is a crucial activity in forestry planning. This paper describes a proposed technique for developing a growth curve based on the SPOT 6 satellite imageries. The most critical step in developing a model is on pre-processing the images, particularly during performing the radiometric correction such as reducing the thin cloud. The pre-processing includes geometric correction, radiometric correction with image regression, and index calculation, while the processing technique include training area selection, growth curve development, and selection. The study found that the image regression offered good correction to the haze-distorted digital number. The corrected digital number was successfully implemented to evaluate the most accurate growth-curve for predicting mangrove. Of the four growth curve models, i.e., Standard classical, Richards, Gompertz, and Weibull models, it was found that the Richards is the most accurate model in predicting the mean annual increment and current annual increment. The study concluded that the growth curve model developed using high-resolution satellite image provides comparable accuracy compared to the terrestrial method. The model derived using remote sensing has about 9.16% standard of error, better than those from terrestrial data with 15.45% standard of error.

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%.

Algorithm for detecting deforestation and forest degradation using vegetation indices M. Buce Saleh; I Nengah Surati Jaya; Nitya Ade Santi; Dewayany Sutrisno; Ita Carolita; Zhang Yuxing; Wang Xuenjun; Liu Qian
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 5: October 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i5.12585

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.

Drone image-based parameters for assessing the vegetation condition the reclamation success in post-mining oil exploration Tirta Negara; I Nengah Surati Jaya; Cecep Kusmana; Irdika Mansur; Nitya Ade Santi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 1: February 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i1.16663

This paper examines drone-based parameters for assessing the success of reclamation activities in post-mining oil-exploration area. The applied drone-based images were multispectral images having visible light and infrared wavelength regions with 5 cm spatial resolution. The main objective of the study is to develop a mathematical model to estimate a reclamation success, through development of success indices. The model were developed by analyzing the relationship between the vegetation success and the digital number values of original and/or synthetic images of drone-based images using 70 sample plots. The mathematical models were developed using a regression analysis, where responses are biomass, volume, and basal area, while the independent variables were original digital number value of images and their derivative synthetic images. The study found that there is a close relationship between parameter biomass stock (ton/ha) and basal area (cm) with both, i.e., original digital number and vegetation indices.

Quantitative approach for reclassification of the spatial cluster of archipelagos in Maluku Province for the basis of forest development Patrich Papilaya; Endang Suhendang; I Nengah Surati Jaya; Teddy Rusolono
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 5: October 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i5.17041

In natural resource management, it is necessary to group regions based on the similarity of their spatial and non-spatial characteristics, to efficiency and effectiveness Therefore, this study describes the re-grouping of the twelve island clusters established by the provincial government of Maluku into more homogeneous classes. The re-grouping was carried out based on the biophysical conditions of the regions, therefore, it could be used as the basis for determining the forest management units. The results showed that the twelve designated island clusters could be simplified to eight more homogeneous island clusters with 86.4% accuracy and 82.2 validation. It also showed that there were thirteen significant changes in the grouping of clusters of the island, including the horticultural crop area (Bf) and horticultural crop production (E). Moreover, when the island cluster is reclassified into 5 classes, the grouping would be more accurate, with 94.9% accuracy and 92.4% validation. This study concludes that there are two dominant factors in the classification of the island cluster in Maluku province namely, biophysical and social.

Co-Authors Abdul Rosyid Adelia Juli Kardika Agung Budi Cahyono Agung Budi Cahyono Agus P. Kartono Ahyar Gunawan Andry Indrawan Anita Zaitunah Anita Zaitunah Antonius B Wijanarto Antonius B Wijanarto Antonius B Wijanarto Bambang Hero Saharjo Bambang Sapto Pratomosunu Bejo Slamet Beni Iskandar Boedi Tjahjono Bramasto Nugroho Budi Kuncahyo Cecep Kusmana Dahlan Dahlan Dahlan Dahlan Darwo Darwo Darwo Darwo Dede Dirgahayu Dewayany Sutrisno Dewayany Sutrisno Diana Septriana Dito Cahya Renaldi Dito Cahya Renaldi Dwi Noventasari Dwi Putra Apriyanto Dwi Shanty Apriliani Gunadi Elias Elias Ema Kurnia ENDANG SUHENDANG Endang Suhendang Endang Suhendang Suhendang Endes Nurfilmarasa Dahlan Eva Achmad F Gunarwan Suratmo Fahmi Amhar Faid Abdul Manan Fairus Mulia Fairus Mulia Farida H. Susanty Farida Herry Susanty Farida Herry Susanty Florentina Sri Hardiyanti Purwadhi Hanifah Ikhsani Hardian, Dwika Hardjanto Hardjanto Hardjanto Hariaji Setiawan Haryo Tabah Wibisono Hasriani Muis Hendrayanto . Hendri Nurwanto Hermanu Triwidodo Herry Purnomo Herry Purnomo Hidayat Pawitan I Gusti Bagus Wiksuana Iin Arianti Imas Sukaesih Sitanggang Irdika Mansur Ismail HJ Hashim Israr Albar Ita Carolita Iwan Gunawan Jarunton Boonyanuphap Kartodihardjo, Hariadi Kukuh Murtilakono Kukuh Murtilaksono Kukuh Murtilaksono Kusnadi Lailan Syaufina LILIK BUDIPRASETYO Liu Qian Liu Qian Lukman Hakim Lukman Mulyanto M. Bismark Makin Basuki Marlina, Etty Moch. Anwar Muhammad Ardiansyah Muhammad Buce Saleh Muhammad Ikhwan Mulyaningrum Mulyaningrum Muzailin Affan Muzailin Affan N Nurhendra Naik Sinukaban Naik Sinukaban Nanin Anggraini Nining Puspaningsih Nitya Ade Santi Nitya Ade Santi Nitya Ade Santi Nitya Ade Santi Nobuyuki Abe Nurdin Sulistiyono Omo Rusdiana Oteng Haridjaja Oteng Haridjaja Patrich Phill Edrich Papilaya Pratiwi Pratiwi Pratiwi Pratiwi Purnama, Edwin Setia R Assyfa El Lestari Rahimahyuni Fatmi Noor'an Robert Parulian Silalahi Rudi Ichsan Ismail Samsuri Samsuri Samsuri Samsuri Samsuri Samsuri Samsuri, Samsuri Sendi Yusandi Sigit Nugroho Soedari Hardjoprajitno Sri Wahyuni Suria Darma Tarigan Susilawati Suyadi Suyadi Suyadi Suyadi Syamsu Rijal Tatang Tiryana Teddy Rusolono Tien Lastini Tien Lastini Tirta Negara Tirta Negara Tomi Yuwono Tomi Yuwono, Tomi Unik, Mitra Uus Saepul Mukarom Wang Xuenjun Wang Xuenjun Wibisono, Haryo Tabah Wibisono, Haryo Tabah Wibisono, Haryo Tabah Widi Atmaka Widyananto Basuki Aryono Wijanarto, Antonius B. Wijanarto, Antonius B. Yadi Setiadi YANTO SANTOSA Zhang Yuxing

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