Judul: Pemanfaatan data READY-ARL NOAA dan CMORPH untuk pengembangan model risiko kebakaran hutan dan Lahan di Kalimantan Tengah
Abstrak: Land and forest fires are one of the many causes of land degradation in Central Kalimantan. The utilization of remote sensing data, particularly READY-ARL NOAA and CMORPH data, is helpful in providing climate observation data. The objectives of this study are: 1) to analyze the relationship between the surface observation data and the READY-ARL NOAA and CMORPH (CPC Morphing) data by using Partial Least Square (PLS) Method to extract climate data from the satellite, 2) to develop the FDRS (Fire Danger Rating System) indices by using READY-ARL NOAA, CMORPH and hotspot data derived from the satellite data, 3) to develop an estimation model for burned area from hotspot, rainfall condition, and FDRS indices, and 4) to develop fire risk prediction model. The result of this study indicates that the READY-ARL NOAA and CMORPH data have the potential to make climate data estimation and are relatively good as FDRS (SPBK) data input. The use of PLS method is much better in generating a model estimation than simple regression. Precipitation accumulation for two months prior to fire occurrence and drought condition have correlation with the burned area. There is a correlation between the total number of fire hotspot and a series of days without rain around one to two months prior to fire occurrence. In addition, this study also found that the burned area in Central Kalimantan will increase if the drought code exceeds 500 point. Burned area can be estimated by using the following formulas: Burned Area (Ha) = 5.13 – 21.7 (CH2bl – 93) (R-sq = 67.2%) and this formula: Burned Area (Ha) = -62.9 + 5.14 (DC – 500) (R-sq = 58%) where CH2bl = precipitation accumulation for two months prior to fire occurrence and DC = drought code. The forecasts of fire occurrence probability can be determined by using a precipitation accumulation for two months prior to fire occurrence and Monte Carlo simulation. Efforts to anticipate and address the fire risk should be carried out as early as possible, i.e. two months in advance if the probability of fire risk has exceeded the value of 40%.
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Judul: Pengembangan Model Prediksi Risiko Kebakaran Hutan dan Lahan di Kalimantan Barat menggunakan Data Satelit
Abstrak: The frequency and intensity of forest and land fires in recent decades tend to increase, despite the prevention efforts have been made. In most cases, the prevention efforts were not effectice since the available early warning systems is on a daily basis or the longest is on weekly basis. The available time for the prevention is very short. This study aimed to develop seasonal fire early warning system, a system that can provide warning information with lead time of at least 1 to 3 month. With this system, it is expected that the preventive actions can be implemented more effectively. The method used was to build a hotspots prediction model based on information of sea surface temperature anomaly in the Pacific Ocean (NIÑO 3.4 region) and monthly and seasonally rainfall. Hotspots are used as an indicator to indicate the level of fire threat. Models that relate hotspot and sea surface temperature anomaly/seasonal rainfall were developed for seven land use/cover categories in two soil types, i.e. peat lands and minerals. The level of fire risk is determined based on the probability of having number of hotspot above threshold values under different condition of the sea surface temperature in the NIÑO 3.4. The threshold value is the same as the monthly average of hotspot number. The fire risk model was developed only for West Kalimantan province using hotspot data from 2001-2013 from Terra and Aqua satellites Modis. The results of the analysis suggested that the highest density of hotspots generally occured around August for non-forest land cover types of peat lands. Variability of hotspot density from July to October in this area was significantly correlated with the variability of SST anomalies in NIÑO 3.4 region with time lag of zero to two months. Based on risk matrix, it was found that the probability of having hotspot above the threshold value will be more than 50% if the the SST anomaly in NINO3.4 region was more than 0.5 oC. The probability to have hotspot number more than threshold value when the SST anomaly at lag 0, 1 and 2 month were 63%, 54% and 50% respectively. For the future research, it is suggested that the the development of hotspot forecasting model should use twophase regression techniques.
Keyword: Bogor Agricultural University (IPB), Risk Matrix, MODIS, Land Cover, Hotspot, Forest Fire

Judul: Trichomonas Gallinae Penyebab Trichomonosis Pada Unggas
Abstrak: Trichomonas gallinae adalah protozoa yang bergerak aktof dengan keempat flagella yang mempunyai layar dan pada salah satu sisinya terdapat membran seperti sirip ikan yang berombak. Spesies dari genus Trichomonas ini mempunyai arti penting pada unggas, karena dapat menyebabkan infeksi pada alat pencernaan bagian atas.
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