39 research outputs found
VERIFICATION OF LAND MOISTURE ESTIMATION MODEL BASED ON MODIS REFLECTANCES IN AGRICULTURAL LAND
Get PDFFrom this research, it is found that reflectances in the first, second, and sixth channels (R1, R2, R6) of MODIS have high correlations with surface soil moisture (percent weight) at 0-20 cm depth. An index called Land Moisture INdex (LMI) was created from the linier combination of R1 (percent), R2(percent), and R6 (percent). The MODIS reflectances and field soil moisture in paddy field taken from the Central and East Java during Juli-September 2005 are applied into the previous model which have been generated from data during July-September 2004. The result showed that there was a high correlation between Land/Soil Moisture (SM) which was measured from field survey, and LMI which was generated from the MODIS refectances. The best model equation between SM and LMI is the power regression model, which has the coeficient of determination of 88 percent. It is implied that soil moisture condition can be obtained from the MODIS data using LAnd Moisture Index. Therefore, the spatial information of drouht condition analysed throught the soil moisture in the agricultural land can be provided from the MODIS data. Keywords: Land Moisture Index, Soil Moisture Estimation, Spatial information, drought
CROP WATER STRESS INDEX (CWSI) ESTIMATION USING MODIS DATA
Get PDFCrop Water Stress Index (CWSI) is an index which is used to explain the amount of crop water defisiency based on canopy surface temperature. Many researches of CWSI have been done for arranging irigation water system in several crops at different areas. Beside its application in irigation system, CWSI is also known as one of parameters that can influence crop productivity. Regarding the above explanation, it is implied that CWSI is important for monitoring crop drought, arranging irigation water, and estimating crop productivity. This research is proposed to estimate CWSI using MODIS (Moderate Resolution Imaging Spectroradiometer) data which is related to Normalized Difference Vegetation Index (NDVI) and Soil Moisture Storage (ST) in paddy field. The interest area is in East Java wich is the driest area in Java Island. MODIS land surface temperature is used to estimate CWSI, while MODIS reflectance 500 m is used to estimate NDVI. They were downloaded from NASA website. Data period was from June 15th to June 30 th, 2004. Based on the correlation between NDVI and CWSI, we can estimate NDVI value when paddy water stress occured. The result showed that the largest paddy area in East Java which has high water stress is located in Bojonegoro District. The water stress areain Bojonegoro Distric increase from June 15th to June 30th, 2004. The high to medium water stress level in East Java were predicted as bare land. The CWSI has negative correlation with NDVI and ST. The CWSI 0.6 are obtained in NDVI 0.5 with ST less than 50 percent. This showed that the paddy water stress began at NDVI 0.5 and ST 50 percent. Coefficient of correlation between CWSI and NDVI is 0.58, while CWSI and ST is 0.71. The correlation model between CWSI, NDVI and ST is statistically significant. Keywords: CWSI,NDVI, ST, MODIS Land Surface Temperature, Water Stress
RELATIVE HUMADITY ESTIMATION BASED ON MODIS PRECIPITABLE WATER FOR SUPPORTING SPATIAL INFORMATION OVER JAVA ISLAND
Get PDFThis research is performed to derive weather property, i.e. relative humidity, based on precipitable water from MODIS (Moderate Resolution Imaging Spectroradiometer) data which on board of TERRA/AQUA satellites. As one of dynamic atmospheric parameters, the precipitable water has ability to indicate the dryness or wetness of a certain area. It can be derived by MODIS at 0.865, 1.24, 0.905, 0.936 and 0.940 um of its wavelength ranges. Verification of MODIS precipitatble water is made using radiosonde data at 2 climatological stations in Java island (Jakarta and Surabaya). The result shows that the standard deviation between precipitable water which is derived by MODIS and radiosonde data (August-October 2004), is 1.6 cm, Meanwhile, through the statistical analysis, they have significant correlation of about 0.82. In adition, the relationship between the MODIS precipitable water and the altitude has a negative correlation (r= -0.98). It means that the precipitable water tends to decrease along with the increase of altitude, According to the climate condition in West Java which is mostly wetter rather than of East Java, we knew that the precipitable water in West Java is higher than East Java. Related to related to relative humidity, the mODIS precipitable water can be used to estimate relative humidity, based on topography area, the correlation coeficient between 0.84-0.92. Keywords: MODIS Precipitable water, Radiosonde, Relative humidity, Verification
COMPARISON OF THE VEGETATION INDICES TO DETECT THE TROPICAL RAIN FOREST CHANGES USING BREAKS FOR ADDITIVE SEASONAL AND TREND (BFAST) MODEL
No full textRemotely sensed vegetation indices (VI) such as the Normalized Difference Vegetation Index (NDVI) are increasingly used as a proxy indicator of the state and condition of the land cover/vegetation, including forest. However, the Enhanced Vegetation Index (EVI) on the outcome of forest change detection has not been widely investigated. We compared the influence of using EVI and NDVI on the number and time of detected changes by applying Breaks for Additive Seasonal and Trend (BFAST), a change detection algorithm. We used MODIS 16-day NDVI and EVI composite images (April 2000-April 2012) of three pixels (pixels 352, 378, and 380) in the tropical peat swamp forest area around the flux tower of Palangka Raya, Central Kalimantan. The results of BFAST method were compared to the Normalized Difference Fraction Index (NDFI) maps and the maps were validated by the hotspot of the Infrastructure and Operational MODIS-Based Near Real-Time Fire(INDOFIRE). Overall, the number and time of changes detected in the three pixels differed with both time series data because of the data quality due to the cloud cover. Nonetheless, we found that EVI is more sensitive than NDVI for detecting abrupt changes such as the forest fires of August 2009-October 2009 that occurred in our study area and it was verified by the NDFI and the hotspot data. Our results demonstrated that the EVI for forest monitoring in the tropical peat swamp forest area which is covered by intense cloud cover is better than that NDVI. Nonetheless, further research with improving spatial resolution of satellite images for application of NDFI is highly recommended.
Ruang Terbuka Hijau Di DKI Jakarta Berdasarkan Analisis Spasial Dan Spektral Data Landsat 8
No full textRuang Terbuka Hijau (RTH) merupakan suatu bentuk pemanfaatan lahan pada satu kawasan yang diperuntukan untuk penghijauan, dimana luasnya minimal 30% dari luas suatu kota. DKI Jakarta yang memiliki luas lahan keseluruhan mencapai 66.233 hektare, saat ini hanya terdapat 10% dari total luasnya yang merupakan RTH. Dalam mendukung program RTH, maka diperlukan perhitungan luas lahan RTH secara tepat, sehingga data penginderaan jauh kini semakin banyak dimanfaatkan untuk pengukuran RTH. Telah banyak juga metode yang digunakan untuk mengidentifikasi luas lahan ini namun dirasa masih kurang praktis dalam pembuatannya. Pada penelitian ini analisis RTH di DKI Jakarta menggunakan data Landsat 7 tahun 2007, dan Landsat 8 tahun 2013. Metode yang digunakan adalah integrasi antara klasifikasi penutup lahan menggunakan metode Maksimum Likelihood, dan indeks vegetasi yaitu Normalized Difference Vegetation Index (NDVI). Dari penelitian ini diketahui bahwa data satelit resolusi menengah sudah dapat digunakan dalam mengidentifikasi lokasi dan luas RTH. Dalam perhitungan RTH lebih tepat bila menggunakan metode indeks vegetasi, hal ini untuk menghindari kesalahan identifikasi tutupan vegetasi menjadi jenis penggunaan lahan lainnya. Dari perhitungan RTH dengan menggunakan NDVI diperoleh informasi bahwa pada 2007 DKI Jakarta memiliki RTH mencapai 29% namun di tahun 2013 hanya tersisa 9% saja dari luas seluruh daerah.Hlm. 498-50
Verification of Land Moisture Estimation Model Based on Modis Reflectances in Agricultural Land
No full textFrom this research, it is found that reflectances in the first, second, and sixth channels (R1, R2, R6) of MODIS have high correlations with surface soil moisture (percent weight) at 0-20 cm depth. An index called Land Moisture INdex (LMI) was created from the linier combination of R1 (percent), R2(percent), and R6 (percent). The MODIS reflectances and field soil moisture in paddy field taken from the Central and East Java during Juli-September 2005 are applied into the previous model which have been generated from data during July-September 2004. The result showed that there was a high correlation between Land/Soil Moisture (SM) which was measured from field survey, and LMI which was generated from the MODIS refectances. The best model equation between SM and LMI is the power regression model, which has the coeficient of determination of 88 percent. It is implied that soil moisture condition can be obtained from the MODIS data using LAnd Moisture Index. Therefore, the spatial information of drouht condition analysed throught the soil moisture in the agricultural land can be provided from the MODIS data.p.46-54 : ilus. ; 30 c
Crop Water Stress Index (CWSI) Estimation Using Modis Data
No full textCrop Water Stress Index (CWSI) is an index which is used to explain the amount of crop water defisiency based on canopy surface temperature. Many researches of CWSI have been done for arranging irigation water system in several crops at different areas. Beside its application in irigation system, CWSI is also known as one of parameters that can influence crop productivity. Regarding the above explanation, it is implied that CWSI is important for monitoring crop drought, arranging irigation water, and estimating crop productivity. This research is proposed to estimate CWSI using MODIS (Moderate Resolution Imaging Spectroradiometer) data which is related to Normalized Difference Vegetation Index (NDVI) and Soil Moisture Storage (ST) in paddy field. The interest area is in East Java wich is the driest area in Java Island. MODIS land surface temperature is used to estimate CWSI, while MODIS reflectance 500 m is used to estimate NDVI. They were downloaded from NASA website. Data period was from June 15th to June 30 th, 2004. Based on the correlation between NDVI and CWSI, we can estimate NDVI value when paddy water stress occured. The result showed that the largest paddy area in East Java which has high water stress is located in Bojonegoro District. The water stress areain Bojonegoro Distric increase from June 15th to June 30th, 2004. The high to medium water stress level in East Java were predicted as bare land. The CWSI has negative correlation with NDVI and ST. The CWSI 0.6 are obtained in NDVI 0.5 with ST less than 50 percent. This showed that the paddy water stress began at NDVI 0.5 and ST 50 percent. Coefficient of correlation between CWSI and NDVI is 0.58, while CWSI and ST is 0.71. The correlation model between CWSI, NDVI and ST is statistically significant.p.80-84 : ilus. ; 30 c
Analisis resiko gunung api merapi berdasarkan data penginderaan jauh dan sistem informasi geografis
No full textGunung Merapi merupakan gunung api yang paling aktif di Indonesia. Kejadian letusan gunung Merapi pada akhir tahun 2010 membawa banyak korban baik jiwa maupun material yang sangat besar. Bahaya primer dan sekunder Gunung Merapi adalah berupa aliran pyroclastic dan aliran lahar dingin. Bahaya-bahaya ini memiliki resiko yang sangat tinggi, jika terjadi di wilayah yang padat penduduk dan banyak infrastruktur yang penting di daerah tersebut. Oleh karena itu, pada penelitian ini dilakukan integrasi antara model bahaya dan model kerentanan untuk analisis resiko gunung api. Zonasi bahaya Gunung Merapi dilakukan berdasarkan data Digital Elevation Model - Shuttle Radar Thematic Mapper (DEM SRTM) 30 m dengan metode analisis probabilistik aliran erupsi dan metode energy cone untuk zonasi bahaya pyroclastic. Sementara untuk analisis kerentanan dilakukan pendetailan sebaran penduduk berdasarkan jenis penggunaan lahan dari data satelit SPOT-4 tahun 2009. Guna mengantisipasi datangnya bencana yang tidak mengenal waktu siang ataupun malam, maka analisis resiko dibagi menjadi 2 waktu yaitu resiko slang hari dan malam hari berdasarkan kecenderungan penduduk berada di suatu penggunaan lahan dari jenis pekerjaannya. Resiko tinggi Gunung Merapi pada siang hari terdapat pada wilayah selatan Merapi yaitu di Kecamatan Cangkringan (Desa Wukirsari, Desa Argomulyo, Desa Umbulharjo), Kecamantan Pakem (Desa Hargobinangun), dan Kecamantan Turi (Desa Wonokerto). Zona resiko tinggi Merapi pada malam hari terdapat di Kecamantan Cangkringan (Desa Umbulharjo dan Wukirsari), Kecamantan Pakem (Desa Hargobinangun). Selanjutnya, jalur evakuasi telah disusun berdasarkan zonasi resiko Merapi dan infrastruktur penting di daerah setempat.Hal.62-74 : ilus. ; 24 c
Comparison of the Vegetation Indices to Detect the Tripocal Rain Forest Changes using Breaks for Additive Seasonal and Trend (BFAST) Model
No full textRemotely sensed vegetation indices (VI) such as the Normalized Difference Vegetation Index (NDVI) are increasingly used as a proxy indicator of the state and condition of the land cover/vegetation, including forest. However, the Enhanced Vegetation Index (EVI) on the outcome of forest change detection has not been widely investigated. We compared the influence of using EVI and NDVI on the number and time of detected changes by applying Breaks for Additive Seasonal and Trend (BFAST), a change detection algorithm. We used MODIS 16-day NDVI and EVI composite images (April 2000-April 2012) of three pixels (pixels 352, 378, and 380) in the tropical peat swamp forest area around the flux tower of Palangka Raya, Central Kalimantan. The results of BFAST method were compared to the Normalized Difference Fraction Index (NDFI) maps and the maps were validated by the hotspot of the Infrastructure and Operational MODIS-Based Near Real-Time Fire(INDOFIRE). Overall, the number and time of changes detected in the three pixels differed with both time series data because of the data quality due to the cloud cover. Nonetheless, we found that EVI is more sensitive than NDVI for detecting abrupt changes such as the forest fires of August 2009-October 2009 that occurred in our study area and it was verified by the NDFI and the hotspot data. Our results demonstrated that the EVI for forest monitoring in the tropical peat swamp forest area which is covered by intense cloud cover is better than that NDVI. Nonetheless, further research with improving spatial resolution of satellite images for application of NDFI is highly recommended.p.21-34 : ilus. ; 30 c
