Land Use, Climate Change and Biodiversity Modeling: Perspectives and Applications, 2011, 512 pages

Geographic Information Systems (GIS) is one of the fastest growing technologies. It is hard to resemble definitions of GIS, because it has such a broad application. Principally, it is combining technology and processing capabilities of map and its attributes. One of general definition is a system used to store, manipulate, analyze and display spatial data that has reference to the earth. In the early of its development, GIS is only used for digital mapping for the purposes of resource inventories, cadastral, planning, transportation and census, but by this time GIS has been widely used for modeling and decision making. Along with advances in GIS, remote sensing technology is also increasingly available with different levels of temporal and spatial resolution. Remote sensing data can be used as inputs and spatial modeling validation using GIS. DOI: 10.7226/jtfm.19.3.21

Land Use, Climate Change and Biodiversity Modeling: Perspectives and Applications, 2011, 512 pages

Geographic Information Systems (GIS) is one of the fastest growing technologies. It is hard to resemble definitions of GIS, because it has such a broad application. Principally, it is combining technology and processing capabilities of map and its attributes. One of general definition is a system used to store, manipulate, analyze and display spatial data that has reference to the earth. In the early of its development, GIS is only used for digital mapping for the purposes of resource inventories, cadastral, planning, transportation and census, but by this time GIS has been widely used for modeling and decision making. Along with advances in GIS, remote sensing technology is also increasingly available with different levels of temporal and spatial resolution. Remote sensing data can be used as inputs and spatial modeling validation using GIS. DOI: 10.7226/jtfm.19.3.21

EFFECTS OF HUMAN FACTORS IN THE EXISTENCE OF BALI STARLING (Leucopsar rothschildi) THROUGH GEOGRAPHIC INFOMATION SYSTEM APPROACH IN WEST BALI NATIONAL PARK AND NUSA PENIDA BALI

Bali starling (Leucopsar rothschildi) is one of the animals that getting more attention because is categorized as an endengered species on the IUCN red list, Appendix 1 of CITES, and protected animals by goverment of Indonesia. The conservation for recovery of species was carried out by West Bali National Park (WBNP) through release activity and collaboration with conservation organization for release in different place from their natural habitat. The population of bali starling on both locations is tend to decrease, the study aimed to analized the impact of human factor with the existence of bali starling based on geographic information system. The farthest point of bali starling existence form road distance is 1 359 meters on WBNP and 660 meters on Nusa Penida Island, while the closest point on both locations is 0 meter from road distance. The second human factor is village distance with the farthest point of bali starling is 7 296 meters on WBNP and 295 meters on Nusa Penida Island, while the closest point of bali starling is 543 meters on WBNP and 0 meter on Nusa Penida Island. The third human factor is community’s garden distance with the farthest point of bali starling is 5 696 meters on WBNP and 67 meters on Nusa Penida Island, while the closest point of bali starling is 408 meters on WBNP and 0 meter on Nusa Penida Island. The existence point of bali starling that are close to human activites have a negative impact. Bali starling will depend on the resources provided by the community on Nusa Penida Island and part of WBNP and also make it difficult for the bali starling to restore the wild nature for adaptation in natural habitat.  Key words: bali starling, geographic information system, human factor, Nusa Penida, West Bali National Par

Spatial Model of Deforestation in Kalimantan from 2000 to 2013

Forestry sector is the biggest carbon emission contributor in Indonesia which is mainly caused by deforestation.  In Kalimantan island one of the largest island in Indonesia has a significant area of forest cover still can be found although an alarming rates deforestation is also exist. This study was purposed to established spatial model of deforestation in Kalimantan island. This information is expected to provide options to develop sustainable forest management in Kalimantan trought optimizing environment and socio-economic purposes. This study used time-series land cover data from the Ministry of Environment and Forestry (2000 – 2013) and is validated by SPOT 5/6 images in 2013. The spatial model of deforestation were developed using binary logistic. The results of logistic regression analysis obtained spatial model of deforestation in Kalimantan = 1.1480714 – (0.033262*slope) – (0.002242*elevation) – (0.000413*distance from forest edge) + (0.000045*Gross Regional Domestic Product). Validation test showed overall accuracy about 79.64% and 77.01% for models of deforestation in 2000–2006 and 2006–2013 respectively.

Spatial Model of Deforestation in Kalimantan from 2000 to 2013

Forestry sector is the biggest carbon emission contributor in Indonesia which is mainly caused by deforestation.  In Kalimantan island one of the largest island in Indonesia has a significant area of forest cover still can be found although an alarming rates deforestation is also exist. This study was purposed to established spatial model of deforestation in Kalimantan island. This information is expected to provide options to develop sustainable forest management in Kalimantan trought optimizing environment and socio-economic purposes. This study used time-series land cover data from the Ministry of Environment and Forestry (2000 – 2013) and is validated by SPOT 5/6 images in 2013. The spatial model of deforestation were developed using binary logistic. The results of logistic regression analysis obtained spatial model of deforestation in Kalimantan = 1.1480714 – (0.033262*slope) – (0.002242*elevation) – (0.000413*distance from forest edge) + (0.000045*Gross Regional Domestic Product). Validation test showed overall accuracy about 79.64% and 77.01% for models of deforestation in 2000–2006 and 2006–2013 respectively.

Habitat Suitability of Javan Gibbon in Gunung Salak, West Java (Kesesuaian Habitat Owa Jawa di Gunung Salak, Jawa Barat)

Objective of this study was to provide spatial information of Javan gibbon habitat suitability and distribution in Gunung (Mt.) Salak area for Management Authority of Mt. Halimun-Salak National Park. Informations on Javan gibbon distribution was collected through a number of survey during December 2005–June 2006 in Kawah Ratu (Parakan Salak, Sukabumi), Pondok Wisata Cangkuang (Cidahu, Sukabumi), and Bobojong Village (Bogor). Twenty two groups were identified using direct count and triangle count method from over 47 identified positions. Habitat suitability was formulated based on10 ecogeographical variables (criteria), consisting of forest type (primary forest, secondary forest, low-land forest, and submontane forest), slope (0–15%, 15–45%, >45%), and distance to non-forested land, river/water body, and road/tracks. The result showed that Mt. Salak consisted of 13.20% (17.53 km2), 26.25% (34.86 km2), 19.40% (25.77 km2), 4.16% (5.53 km2), and 20.17% (26.78 km2) of high-suitable, suitable, moderate suitable, less and low suitable level subsequently, and 12.69 km2 or 9.56% was not suitable for Javan gibbon habitat. It was also revealed that that 3 and 9 groups were living in high suitable and suitable habitat respectively; 13 groups in moderate suitable, while for each less and low suitable habitat, 2 Javan gibbon groups lived in

Habitat Suitability of Javan Gibbon in Gunung Salak, West Java (Kesesuaian Habitat Owa Jawa di Gunung Salak, Jawa Barat)

Objective of this study was to provide spatial information of Javan gibbon habitat suitability and distribution in Gunung (Mt.) Salak area for Management Authority of Mt. Halimun-Salak National Park. Informations on Javan gibbon distribution was collected through a number of survey during December 2005–June 2006 in Kawah Ratu (Parakan Salak, Sukabumi), Pondok Wisata Cangkuang (Cidahu, Sukabumi), and Bobojong Village (Bogor). Twenty two groups were identified using direct count and triangle count method from over 47 identified positions. Habitat suitability was formulated based on10 ecogeographical variables (criteria), consisting of forest type (primary forest, secondary forest, low-land forest, and submontane forest), slope (0–15%, 15–45%, >45%), and distance to non-forested land, river/water body, and road/tracks. The result showed that Mt. Salak consisted of 13.20% (17.53 km2), 26.25% (34.86 km2), 19.40% (25.77 km2), 4.16% (5.53 km2), and 20.17% (26.78 km2) of high-suitable, suitable, moderate suitable, less and low suitable level subsequently, and 12.69 km2 or 9.56% was not suitable for Javan gibbon habitat. It was also revealed that that 3 and 9 groups were living in high suitable and suitable habitat respectively; 13 groups in moderate suitable, while for each less and low suitable habitat, 2 Javan gibbon groups lived in

Vegetation composition on peatlands with different fire frequency in Musi Banyuasin, South Sumatra

Peatlands that experience fire have the ability to restore their own environment through a process of vegetation succession. Areas with different frequency of fires have different vegetation dominance. Observations were made on areas that experienced a frequency of one, two, three and four times burned. The purpose of this study was to explain the differences in vegetation that make up areas with different fire frequencies. Vegetation growth rates of saplings and understorey were found in all burnt frequency areas, seedling growth rates were found in areas one and three times burned, pole growth rates were found in one burnt area and tree growth rates were found in areas one and two burns. The growth rate of saplings in the one-time burns frequency area was dominated by Sepongol vegetation, the two-burn frequency area was dominated by Bangun -anguns and the three and four-time burnt frequency areas were dominated by Melastoma malabatrihcum. Lower plants in areas with a frequency of one, two and three times burned were dominated by Asplenium longissimum and in areas with four times the frequency of burns was dominated by Athyrium esculentum.Lahan gambut memiliki kemampuan untuk memulihkan lingkungannya sendiri melalui proses suksesi vegetasi ketika mengalami kebakaran. Area dengan frekuensi kebakaran yang berberda memiliki dominasi vegetasi yang berbeda. Pengamatan dilakukan pada area yang mengalami frekuensi satu, dua, tiga dan empat kali terbakar. Tujuan penelitian ini untuk menjelaskan perbedaan vegetasi yang menyusun area dengan frekuensi kebakaran berbeda. Vegeteasi tingkat pertumbuhan pancang dan tumbuhan bawah ditemukan pada semua area frekuensi terbakar, tingkat pertumbuhan semai ditemukan pada area satu dan tiga kali terbakar, tingkat pertumbuhan tiang ditemukan pada area satu kali terbakar dan tingkat pertumbuhan pohon ditemukan pada area satu dan dua kali terbakar. Tingkat pertumbuhan pancang pada area frekuensi satu kali terbakar didominasi oleh vegetasi Sepongol, area frekuensi dua kali terbakar didominasi oleh Bangun-bangun dan area frekuensi tiga dan empat kali terbakar didominasi oleh Melastoma malabatrihcum. Tumbuhan bawah pada area dengan frekuensi satu, dua dan tiga kali terbakar didominasi oleh Asplenium longissimum dan pada area dengan frekuensi empat kali terbakar didominasi oleh Athyrium esculentu

Predicting the Spread of Acacia Nilotica Using Maximum Entropy Modeling

Acacia nilotica planted in Baluran National Park aims to prevent the spread of fire from savanna to teak forest became developed into invasive and led to a decrease in the quality and quantity of savannas. Therefore, it is required to predict the spread of A. nilotica to minimize the impacts of invasion on savanna area. The study aims to identify environmental factors which affect spread of A. nilotica. Furthermore, the spread of A. nilotica is predicted using Maximum Entropy. Maximum Entropy is efficient model since it uses presence-only data while the most of other models use presence and absence data. The experimental results reveal six environmental factors, including elevation, slope, NDMI, NDVI, distance from the river, and temperature were identified affecting the spread of A. nilotica. The most dominant environmental factors were elevation and temperature with 40% and 39.6% contributions. Maximum Entropy performed well in predicting the spread of A. nilotica, it was indicated by AUC value of 0.938

PENDUGAAN PERUBAHAN CADANGAN KARBON DI TAMBLING WILDLIFE NATURE CONSERVATION TAMAN NASIONAL BUKIT BARISAN SELATAN

Global warming effect can be mitigated in two ways, namely carbon loss reduction or emission and increasing carbon storage within vegetation. Forest can absorb CO2 trough photosynthesis process and sink them in biomass. Tambling Wildlife Nature Conservation (TWNC) as a part of Bukit Barisan Selatan National Park (BBSNP) have been facing land cover change due to encroachment. The study aimed to measure carbon stocks in various land cover and to compare carbon stocks for the whole are of TWNC TNBBS in 2000-2009. Carbon stocks measurement was conducted in TWNC TNBBS during August 8th to October 8th 2009, 50 plots were sampled including nature forest, secondary forest, agroforestry, shrub, Imperata cylindrica , and grassland by purposive sampling method. Soil carbon was not measured in this study. Nature forest has the highest carbon stocks by 178,44 MgC.ha-1, and grassland be a poorest carbon stocks (1,47 MgC.ha-1). During the time between 2000 to 2009,  primary forest carbon stock  decrease in amount of 457,792.52 Mg  along with the decrease in land cover of this forest type. As many as 24.4% of natural forests in 2000 turned into the others type of land use such as a secondary forest of  21.63%, for shrubs 1.61% and  0.06% for agroforestry in 2009. Totally, TWNC TNBBS has loss its carbon stocks as many as 279422 Mg, it’s mean the annual average carbon stocks contained in the TWNC TNBBS area decreased by around 27,942.2 Mg (0.72%) per year. Its mean, 1,024,547 Mg CO2 or 102,454.7 Mg CO­2 every year was lose from TWNC TNBBS area. Keywords: biomass, carbon stock, emission, fores