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DETEKSI AREA BEKAS KEBAKARAN HUTAN DAN LAHAN MENGGUNAKAN DATA CITRA RESOLUSI MENENGAH MODIS DENGAN PENDEKATAN INDEKS KEBAKARAN Mirzha Hanifah; Lailan Syaufina; Indah Prasasti
Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Vol. 6 No. 1 (2016): Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (JPSL)
Publisher : Graduate School Bogor Agricultural University (SPs IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jpsl.6.1.77

This research examined the use of fire index algorithms to detect and recognize the burnt area in West Kalimantan by applying the pre-fire and post-fire image comparison technique. The main data used were derived from remotely sensed data MODIS acquired from Januari to April 2014. The examined algorithms utilized the near-infrared (NIR) and short-infrared (SWIR) wavelength spectrums. in the case of forest and land fires, occured the value of NIR decreases as the amount of chlorophyll decrease, while the pixel values and the inceasing value of SWIR will increase due to the rising temperature. The research objective was to the capability of the algorithms in detecting burnt forest and land areas in several selected areas in West Kalimantan, using few indices generated from MODIS data. The examined indices were NDFI (Normalized Difference Fire Index) and MNDFI (Modified Normalized Difference Fire Index), which utilize the reflectance values of band 2 (NIR) and band 7 (SWIR) from MODIS. The study results show that both the NDFI and MNDFI were applicable in detecting burnt area having good performance with the Normalize Distance (D) values larger than 1. Based on D-Value and accuracy assessment, MNDFI algorithm gave better index than the NDFI in detecting both forest and land areas.

SPECTRAL ANALYSIS OF THE HIMAWARI-8 DATA FOR HOTSPOT DETECTION FROM LAND/FOREST FIRES IN SUMATRA Hana Listi Fitriana; Sayidah Sulma; nFN suwarsono; Any Zubaidah; Indah Prasasti
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 15, No 1 (2018)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Himawari-8 is the last generation of the low spatial resolution satellite imagery that has capability to detect the thermal variation on the earth of every 10 minute. This must be very potential to be used for detecting land/forest fire. This paper has explored the spectral prospective of the Himawari-8 for detecting land/forest fire hotspot. The main objective for this study is to identify the potential use of Himawari-8 for detecting of land forest fire hotspot. The study area was performed in Ogan Komering Ilir, South of Sumatra, which on 2015 occur great forest/land fire event. The main process included in this study are image projection, training sample collection and spectral statistical analysis measured by calculate statistic, they are average values, standard deviation values from reflectance visible band value and brightness temperature value, beside that validation of data obtained from medium resolution data of Landsat 8 with the similar acquisition time. The study found that the Himawari-8 has good capacity to identify land/forest fire hotspot as expressed for high accuracy assessment using band 3 and band 7.

THE EFFECT OF ENVIRONMENTAL CONDITION CHANGES ON DISTRIBUTION OF URBAN HEAT ISLAND IN JAKARTA BASED ON REMOTE SENSING DATA Indah Prasasti; . Suwarsono; Nurwita Mustika Sari
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 12, No 1 (2015)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Anthropogenic activities of urban growth and development in the area of Jakarta has caused increasingly uncomfortable climatic conditions and tended to be warmer and potentially cause the urban heat island (UHI). This phenomenon can be monitored by observing the air temperature measured by climatological station, but the scope is relatively limited. Therefore, the utilization of remote sensing data is very important in monitoring the UHI with wider coverage and effective. In addition, the remote sensing data can also be used to map the pattern of changes in environmental conditions (microclimate). This study aimed to analyze the effect of changes in environmental conditions (land use/cover, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Build-up Index (NDBI)) toward the spread of the urban heat island (UHI). In this case, the UHI was identified from pattern changes of Land Surface Temperature (LST) in Jakarta based on data from remote sensing. The data used was Landsat 7 in 2007 and Landsat 8 in 2013 for parameter extraction environmental conditions, namely: land use cover, NDVI, NDBI, and LST. The analysis showed that during the period 2007 to 2013, there has been a change in the condition of the land use/cover, impairment NDVI, and expansion NDBI that trigger an increase in LST and the formation of heat islands in Jakarta, especially in the area of business centers, main street and surrounding area, as well as in residential areas.

DETECTING THE AREA DAMAGE DUE TO COAL MINING ACTIVITIES USING LANDSAT MULTITEMPORAL (Case Study: Kutai Kartanegara, East Kalimantan) nFn suwarsono; Nanik Suryo Haryani; Indah Prasasti; Hana Listi Fitriana; M. Priyatna; M. Rokhis Khomarudin
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 14, No 2 (2017)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Coal is one of the most mining commodities to date, especially to supply both national and international energy needs. Coal mining activities that are not well managed will have an impact on the occurrence of environmental damage. This research tried to utilize the multitemporal Landsat data to analyze the land damage caused by coal mining activities. The research took place at several coal mine sites in East Kalimantan Province. The method developed in this research is the method of change detection. The study tried to know the land damage caused by mining activities using NDVI (Normalized Difference Vegetation Index), NDSI (Normalized Difference Soil Index), NDWI (Normalized Difference Water Index) and GEMI (Global Environment Monitoring Index) parameter based change detection method. The results showed that coal mine area along with the damage that occurred in it can be detected from multitemporal Landsat data using NDSI value-based change detection method. The area damage due to coal mining activities can be classified into high, moderate, and low classes based on the mean and standard deviation of NDSI changes (ΔNDSI). The results of this study are expected to be used to support government efforts and mining managers in post-mining land reclamation activities.

APPLICATION OF CMORPH DATA FOR FOREST/LAND FIRE RISK PREDICTION MODEL IN CENTRAL KALIMANTAN Indah Prasasti; Rizaldi Boer; Lailan Syaufina
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 11, No 1 (2014)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Central Kalimantan Province is a region with high level of forest/land fire, especially during dry season. Forest/land fire is a dangerous ecosystem destroyer factor, so it needs to be anticipated and prevented as early as possible. CMORPH rainfall data have good potential to overcome the limitations of rainfall data observation. This research is aimed to obtain relationship model between burned acreage and several variables of rainfall condition, as well as to develop risk prediction model of fire occurrence and burned acreage by using rainfall data. This research utilizes information on burned acreage (Ha) and CMORPH rainfall data. The method applied in this research is statistical analysis (finding correlation and regression of two phases), while risk prediction model is generated from the resulting empirical model from relationship of rainfall variables using Monte Carlo simulation based on stochastic spreadsheet. The result of this study shows that precipitation accumulation for two months prior to fire occurrence (CH2Bl) has correlation with burned acreage, and can be estimated by using following formula (if rainfall ≤ 93 mm): Burnt Acreage (Ha) = 5.13 – 21.7 (CH2bl – 93) (R2 = 67.2%). Forest fire forecasts can be determined by using a precipitation accumulation for two months prior to fire occurrence and Monte Carlo simulation. Efforts to anticipate and address fire risk should be carried out as early as possible, i.e. two months in advance if the probability of fire risk had exceeded the value of 40%.

Analisis Pemanfaatan Data CMORPH-IRI untuk Estimasi Curah Hujan Wilayah di Palangka Raya, Kalimantan Tengah dan Pekanbaru, Riau Indah Prasasti; Suciantini Suciantini
Jurnal Tanah dan Iklim (Indonesian Soil and Climate Journal) Vol 37, No 1 (2013)
Publisher : Balai Besar Penelitian dan Pengembangan Sumberdaya Lahan Pertanian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21082/jti.v37n1.2013.1-8

EFFECT OF ATMOSPHERIC CORRECTION ALGORITHM ON LANDSAT-8 AND SENTINEL-2 CLASSIFICATION ACCURACY IN PADDY FIELD AREA Fadila Muchsin; Kuncoro Adi Pradono; Indah Prasasti; Dianovita; Kurnia Ulfa; Kiki Winda Veronica; Dandy Aditya Novresiandi; Andi Ibrahim
International Journal of Remote Sensing and Earth Sciences Vol. 20 No. 1 (2023)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3845

Landsat-8 and Sentinel-2 satellite imageries are widely used for various remote sensing applications because they are easy to access and free to download. A precise atmospheric correction is necessary to be applied to the optical satellite imageries so that the derived information becomes more accurate and reliable. In this study, the performance of atmospheric correction algorithms (i.e., 6S, FLAASH, DOS, LaSRC, and Sen2Cor) was evaluated by comparing the object's spectral response, vegetation index, and classification accuracy in the paddy field area before and after the implementation of atmospheric correction. Overall, the results show that each algorithm has varying accuracy. Nevertheless, all atmospheric correction algorithms can improve the classification accuracy, whereby those derived by the 6S and FLAASH yielded the highest accuracy.

COMPARISON OF THE RADIOMETRIC CORRECTION LANDSAT-8 IMAGE BASED ON OBJECT SPECTRAL RESPONSE AND VEGETATION INDEX Fadila Muchsin; Supriatna; Adhi Harmoko; Indah Prasasti; Mulia Inda Rahayu; Liana Fibriawati; Kuncoro Adi Pradhono
International Journal of Remote Sensing and Earth Sciences Vol. 18 No. 2 (2021)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2021.v18.a3632

Landsat-8 standard level (level 1T) data received by users still in digital form can be used directly for land cover/land use mapping. These data have low radiometric accuracy when used to produce information such as vegetation indices, biomass, and land cover/land use classification. In this study, radiometric/atmospheric correction was conducted using FLAASH, 6S, DOS, TOA+BRDF and TOA method to eliminate atmospheric disturbances and compare the results with field measurements based on object spectral response and NDVI values. The results of the spectral measurements of objects in paddy fields at harvest time in the Cirebon Regency, West Java, Indonesia show that the FLAASH and 6S method have spectral responses that are close to those of objects in the field compared to the DOS, TOA and TOA+BRDF methods. For the NDVI value, the 6S method has the same tendency as the object's NDVI value in the field.

HOTSPOT VALIDATION OF THE HIMAWARI-8 SATELLITE BASED ON MULTISOURCE DATA FOR CENTRAL KALIMANTAN Khalifah Insan Nur Rahmi; Sayidah Sulma; Indah Prasasti
International Journal of Remote Sensing and Earth Sciences Vol. 16 No. 2 (2019)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2019.v16.a3293

The Advanced Himawari Imager (AHI) is the sensor aboard the remote-sensing satellite Himawari-8 which records the Earthâ€™s weather and land conditions every 10 minutes from a geostationary orbit. The imagery produced known as Himawari-8 has 16 bands which cover visible, near infrared, middle infrared and thermal infrared wavelength potentials to monitor forestry phenomena. One of these is forest/land fires, which frequently occur in Indonesia in the dry season. Himawari-8 can detect hotspots in thermal bands 5 and band 7 using absolute fire pixel (AFP) and possible fire pixel (PFP) algorithms. However, validation has not yet been conducted to assess the accuracy of this information. This study aims to validate hotspots identified from Himawari images based on information from Landsat 8 images, field surveys and burnout data. The methodology used to validate hotspots comprises AFP and PFP extraction, determining firespots from Landsat 8, buffering at 2 km from firespots, field surveys, burnout data, and calculation of accuracy. AFP and PFP hotspot validation of firespots from Landsat-8 is found to have higher accuracy than the other options. In using Himawari-8 hotspots to detect land/forest fires in Central Kalimantan, the AFP algorithm with 2km radius has accuracy of 51.33% while the PFP algorithm has accuracy of 27.62%.

SPECTRAL ANALYSIS OF THE HIMAWARI-8 DATA FOR HOTSPOT DETECTION FROM LAND/FOREST FIRES IN SUMATRA Hana Listi Fitriana; Sayidah Sulma; Any Zubaidah; Suwarsono; Indah Prasasti
International Journal of Remote Sensing and Earth Sciences Vol. 15 No. 1 (2018)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2018.v15.a2836

Himawari-8 is the last generation of the low spatial resolution satellite imagery that has capability to detect the thermal variation on the earth of every 10 minute. This must be very potential to be used for detecting land/forest fire. This paper has explored the spectral prospective of the Himawari-8 for detecting land/forest fire hotspot. The main objective for this study is to identify the potential use of Himawari-8 for detecting of land forest fire hotspot. The study area was performed in Ogan Komering Ilir, South of Sumatra, which on 2015 occur great forest/land fire event. The main process included in this study are image projection, training sample collection and spectral statistical analysis measured by calculate statistic, they are average values, standard deviation values from reflectance visible band value and brightness temperature value, beside that validation of data obtained from medium resolution data of Landsat 8 with the similar acquisition time. The study found that the Himawari-8 has good capacity to identify land/forest fire hotspot as expressed for high accuracy assessment using band 3 and band 7.

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