Judul: Estimating Above Ground Trees Biomass Of Forest Cover Using Field Measurement And QuickBird Image In Lore Lindu National Park-Central Sulawesi
Abstrak: Forests play an important role in global carbon cycling, since they hold a large pool of carbon as well as potential carbon sinks and sources to the atmosphere. Accurate estimation of forest biomass is required for greenhouse gas inventories and terrestrial carbon accounting (Muukkonen and Heiskanen, 2006). The biomass of forest provides estimates of the carbon pools in forest vegetation because about 50% of it is carbon. Direct measurement of biomass on the ground is time consuming (expensive), and repeated measurements, if they occur at all, are generally limited to 10 year interval. The possibility that above ground forest biomass can be determined from space is a promising alternative to ground-based methods. Remote sensing has opened an effective way to estimate forest biomass and carbon. By the combination of data field measurement and allometric equation, the above ground trees biomass is possible to be estimated over the large area. The objectives of this research are: (1) To estimate the above ground tree biomass and carbon stock of forest cover in Lore Lindu National Park by combination of field data observation, allometric equation and multispectral satellite image; (2) to find the equation model between parameter that determines the biomass estimation. The method of this research use an approach of estimating the biomass that combines field studies (forest inventory data), analysis of multispectral satellite imagery, allometric equation and statistical analysis. Forest cover type classification was utilized for analyzing the biomass per pixel in each different cover type. The classifications for each cover type by using the region based different spectral value in each observation plot refer to QuickBird Image. The field data observation and satellite image classification influencing much on the accuracy of trees biomass and carbon stock estimation. The correlation analysis resulted the main regression equation in estimating the tree biomass of tropical forest, biomass=6E-05DBH2.6705 and biomass= 0.0065e 13.615 NDVI. There are 4 forest cover types observed in this research. Forest cover type A is natural forest without timber extraction (closed canopy). Forest cover type B is natural forest with minor extraction. Forest cover type C is natural forest with major timber extraction. Forest cover type D is agroforestry system. Forest cover type A and B has the higher biomass than C and D, it is about 596.41-618.66 ton/ha and 583.94-622.19 ton/ha. Forest cover type C is 446.65-468.50 ton/ha. Forest cover type D has the lowest biomass is about 193.31-214.34 ton/ha. Natural forest has high biomass, because of the tropical vegetation trees v heterogeneity with minimum disturbance. Forest cover D has the lowest trees biomass because its vegetation component as secondary forest with the homogeneity of cacao plantation. The forest biomass for each cover type will be useful for the further equation analysis when using the remote sensing technology for estimating the total biomass.
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Judul: Model Penduga Biomassa Hutan Alam Lahan Kering Menggunakan Citra ALOS PALSAR Resolusi 50 M di Areal Kerja PT. Trisetia Intiga
Abstrak: The increase of Green House Gases (GHG), particularly carbon dioxide in the atmospheric is one factor that affect the global warming. One cause of the increase was due to emission of carbon that came from deforestation and land degradation. In the mitigation process for reducing the impact of global warming, one crusial activity is estimation of carbon sequestered in the vegetation. Commonly, carbon content sequestered at vegetation is derived from biomass estimation either from above ground biomass or below ground biomass. In this study, the focus was on estimation of the above ground biomass using remote sensing technology. Nowdays, the development of remote sensing techniques had been widely used for providing various information related to natural resources. Indonesia which is located in the tropical region, have several handicaps in using optical remote sensing due to cloud covers during wet season and smoke and haze during dry season. For the above reason, this study emphasizes on the use of radar image technology for estimating the above ground biomass. In this study the used radar image is ALOS PALSAR (Advance Land Observing Sattelite – Phase Array L-band Synthetic Aperture Radar), that had been launched in Januari 2006 by the Japanese government. This sensor is an active microwave sensor which has capability to penetrate thick cloud layer. An active microwave sensor be accepted dan reflectanced by several diresction that usually called polarization. Polarization is the vector direction from electromagnetic microwave on vertical line (V) and horizontal line (H). The main objective of this study is to develop the biomass estimation model using 50 meter resolution of ALOS PALSAR image in concession of PT. Trisetia Intiga, Central Kalimantan. Biomass model was developed by analyzing the relationship between backscatter value and field biomass. Backscatter value from tree polarization images, namely HH, HV, and HH/HV were analyzed simultaneously with field biomassa that derived using Ketterings allometric equation. In this study the obtained stand structure equation, Y=2482 ℮-0,04X with negative exponential or J-reverse form having realtively steep slope. From the stand structure equation, then it can be concluded that stand condition within the study area is belonged to logged over area secondary forest, where tree density with diameter less or equal to 60 cm is relatively low in comparison with the smaller tree size. From 30 sample plots measured, the above ground biomass is ranged from 68,03 ton/ha to 599,43 ton/ha with coefficient of variation (CV) equal to 50,29%. Proportionaly, the highest biomass is come from tree biomass (64,97%), then followed by biomass of necromass (20,13%), pole biomass (12,16%), sapling biomass (2,75%), litter biomass (3,3x10-5), and undergrowth biomass (5x10-5). The correlation and regretion analyses between backscatter value and field biomass show that there are 38 statistically significant equations having R² ≥ 50%. However, the verification analysis found that only two models identified as the best models having no-significant X²-test, low RMSE (Root Mean Square Error), low bias, and mean deviation < 10%. The best models are: 1) AGB = 3880.40613930209exp (0.250184794335192 HV) with coefficient determination (R²) of 56% and RMSE of 0.47%. 2) AGB = 1022.27050600692 exp (-0.0114464608949151 HV²)
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Judul: Infeksi Virus Parainfluenza Pada Anjing
Abstrak: Virus parainfluenza anjing termasuk famili Paramyxoviridae, berbentuk pleomorf dengan ukuran bervariasi (bergaris tengah antara 100-500 nm), berpembungkus peka ether, berisi genom RNA dan secara serologis mempunyai kesamaan dengan virus SV-5 kera; dikenak sebagai salah satu kausa penting dari sindrom "kennel cough". Infeksinya dapat mengenai anjing semua umur yang belum pernah terinfeksi atau mendapatkan vaksinasi mengakibatkan gejala klinis ringan yang terbatas pada traktus respirasi atu infeksi yang secara klinis tidak memperlihatkan gejala nyata. Gejala klinis yang nampak setelah masa inkubasi (1-5 hari) terlewati berupa batuk yang tidak berat selama beberapa hari, berbagai tingkat tonsilitis dan pharyngitis dan kadang-kadang ditemukan kenaikan temperatur rektal antara 1-2 derajat C dari temperatur normal rat-rata. Anjing terinfeksi mengandung virus pada traktus respirasi bagian atas dan bawah serta limfonoduli sampai hari ke 9 setelah infeksi. Penyebaran virus antar anjing terjadi secara aerogen.
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