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International Journal of Remote Sensing and Earth Sciences (IJReSES)

ijreses
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Published by Lembaga Penerbangan dan Antariksa Nasional

ISSN : 02166739 EISSN : 2549516X DOI : -

Core Subject : Science,

Earth & Planetary Sciences

International Journal of Remote Sensing and Earth Sciences (IJReSES) is expected to enrich the serial publications on earth sciences, in general, and remote sensing in particular, not only in Indonesia and Asian countries, but also worldwide. This journal is intended, among others, to complement information on Remote Sensing and Earth Sciences, and also encourage young scientists in Indonesia and Asian countries to contribute their research results. This journal published by LAPAN.

Arjuna Subject : -

Articles 320 Documents

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BIOMASS ESTIMATION MODEL FOR MANGROVE FOREST USING MEDIUM-RESOLUTION IMAGERIES IN BSN CO LTD CONCESSION AREA, WEST KALIMANTAN Sendi Yusandi; I Nengah Surati Jaya; Fairus Mulia
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 15, No 1 (2018)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Mangrove forest is one of the forest ecosystem types that have the highest carbon stock in the tropics. Mangrove forests have a good assimilation capability with their environmental elements as well as on carbon sequestration. However, the availability of data and information on carbon storage, especially on tree biomass content of mangrove is still limited. Conventionally, an accurate estimation of biomass could be obtained from terrestrial measurements, but those methods are very costly and time-consuming. This study offered an alternative solution to overcome these limitations by using remote sensing technology, i.e. by using Landsat 8 and SPOT 5. The objective of this study is to formulate the biomass estimation model using medium resolution satellite imagery, as well as to develop a biomass distribution map based on the selected model. The study found that the NDVI of Landsat 8 and SPOT 5 have considerably high correlation coefficients with the standing biomass with a value of higher than 0.7071. On the basis of the values of aggregation deviation, mean deviation, bias, RMSE, χ², R², and s, the best model for estimating the mangrove stand biomass for Landsat 8 is B=0.00023404 e(20 NDVI) with the R² value of 77.1% and B=0.36+25.5 NDVI² with the R² value of 49.9% for SPOT 5. In general, the concession area of Bina Silva Nusa (BSN) Group (PT Kandelia Alam and PT Bina Ovivipari Semesta) have the potential of biomass ranging from 45 to 100 ton per ha.

HAZE REMOVAL IN THE VISIBLE BANDS OF LANDSAT 8 OLI OVER SHALLOW WATER AREA . Kustiyo; Anis Kamilah Hayati
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 13, No 2 (2016)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Haze is one of radiometric quality parameters in remote sensing imagery. With certain atmospheric correction, haze is possible to be removed. Nevertheless, an efficient method for haze removal is still a challenge. Many methods have been developed to remove or to minimize the haze disruption. While most of the developed methods deal with removing haze over land areas, this paper tried to focus to remove haze from shallow water areas. The method presented in this paper is a simple subtraction algorithm between a band that reflected by water and a band that absorbed by water. This paper used data from Landsat 8 with visible bands as a band that reflected by water while the band that absorbed by water represented by NIR, SWIR-1, and SWIR-2 bands. To validate the method, a reference data which relatively clear of cloud and haze contamination is selected. The pixel numbers from certain points are selected and collected from data scene, results scene and reference scene. Those pixel numbers, then being compared each other to get a correlation number between data scene to reference scene and between result scene and reference scene. The comparison shows that the method using NIR, SWIR-1, and SWIR-2 all significantly improved correlations numbers between result scene with reference scene to higher than 0.9. The comparison also indicates that haze removal result using NIR band had the highest correlation with reference data..

CROP WATER STRESS INDEX (CWSI) ESTIMATION USING MODIS DATA M.Rokhis Khomarudin; Parwati Sofan
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 3,(2006)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Crop 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.

WATER CLARITY MAPPING IN KERINCI AND TONDANO LAKE WATERS USING LANDSAT 8 DATA Bambang Trisakti; Nana Suwargana; I Made Parsa
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 12, No 2 (2015)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Land conversion occurred in the lake catchment area caused the decreasing of water quality in many lakes of Indonesia. According to Lake Ecosystem Management Guidelines from Ministry of Environment, tropic state of lake water is one of parameters for assessing the lake ecosystem status. Tropic state can be indicated by the quantity of nitrogen, phosphorus, chlorophyll, and water clarity. The objective of this research is to develop the water quality algorithm and map the water clarity of lake water using Landsat 8 data. The data were standardized for sun geometry correction and atmospheric correction using Dark Object Subtraction method. In the first step, Total Suspended Solid (TSS) distributions in the lake were calculated using a semi empirical algorithm (Doxaran et al., 2002), which can be applied to a wide range of TSS values. Secchi Disk Transparency (SDT) distributions were calculated using our water clarity algorithm that was obtained from the relationship between TSS and SDT measured directly in the lake waters. The result shows that the water clarity algorithm developed in this research has the determination coefficient that reaches to 0,834. Implementation of the algorithm for Landsat 8 data in 2013 and 2014 showed that the water clarity in Kerinci Lake waters was around 2 m or less, but the water clarity in Tondano Lake waters was around 2 – 3 m. It means that Kerinci Lake waters had lower water clarity than Tondano Lake waters which is consistent with the field measurement results.

SEMI-AUTOMATIC SHIP DETECTION USING PI-SAR-L2 DATA BASED ON RAPID FEATURE DETECTION APPROACH Katmoko Ari Sambodo
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 9, No 2 (2012)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Synthetic Aperture Radar (SAR) satellite an active sensor offering unique high spatial resolution regardless of weather conditions can operate both day and night time with wide area coverage. Therefore, SAR satellite can be used for monitoring ship on sea surface. This study showed on an alternative method for ship detection of SAR data using Pi-SAR-L2 (L-band, JAXA-Airborne SAR) data. The ship detection method is this study was consisted of eight main stages. After the Pi-SAR data was registered and speckle was filtered, then the land was masked using SRTM-DEM (Shuttle Radar Topography Mission-Digital Elevation Model) data since most ship detectors produced false detections when it applied to land areas. A ship sample image was then selected (cropped). The next step was to detect some unique keypoints of ship sample image using Speeded Up Robust Features (SURF) detector. The maximum distance (‘MaxDist’) of keypoints was also calculated. The same detector was then applied to whole Pi-SAR imagery to detect all possible keypoints. Then, for each detected keypoint, we calculated distance to other keypoint (‘Dist’). If ‘Dist’ was smaller than ‘MaxDist’, then we marked these two (or more) keypoints as neighboring keypoints. If the number of neighbor keypoints was equal or greater than two, finally we marked these keypoints as ‘Detected Ship’ (draw rectangle and show its geographic position). Results showed that our method can detect successfully 32 ‘possible ships’ from Pi-SAR-L2 data acquired on the area of North Sulawesi, Indonesia (August 8, 2012).

ANALYSIS OF CRITICAL LAND IN THE MUSI WATERSHED USING GEOGRAPHIC INFORMATION SYSTEMS Danang Surya Candra
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 8, (2011)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Critical land is a land that is no longer functioning as a regulator of water, agricultural production elements and environmental protection elements. Owing to the fact that the analysis of critical land is usually carried out manually, the probability of errors in processing (human error) is very high. This research utilizes the Geographic Information System (GIS) technology to analyze critical area in protected forest area of Musi Watershed. The application of GIS technology, enables the analysis of critical land according to standard of critical land criteria. The results show that the very critical level area in protected forest area of Musi Watershed is 1.7%. The dominant level is in critical potential area (53.34%). Keywords: Critical Land, Watershed, Remote Sensing, GIS, Weighting Method, SPO-4.

THE UTILIZATION OF LANDSAT 8 FOR MAPPING THE SURFACE WATERS TEMPERATURE OF GRUPUK BAY - WEST NUSA TENGGARA: WITH IMPLICATIONS FOR SEAWEEDS CULTIVATION Bidawi Hasyim; Syarif Budiman; Arlina Ratnasari; . Emiyati; Anneke K. S. Manoppo
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 12, No 1 (2015)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Locating a suitable site is the key to success in cultivating seaweed, as it is becomes one of the coastal and marine prospects for improving the national economy. Numerous factors such as water movement, substratum, depth, salinity, light intensity, surface water temperature, influence the growth of this aquatic plant, and should be considered while choosing a farming area. One of key parameters on studying sea water conditions is surface temperature distribution, as changes on temperature effecting physical, chemical, and biological condition of the sea water. Surface waters temperature is affected by radiation, and sun position, geographic, seasons, overcast, interaction process between air and waters, evaporation level, and wind blowing. It's rarely easy job to measure surface waters temperature, because often, researcher has to deal with strong winds and high waves. The objectives of this research is to do surface waters temperature mapping of Grupuk Bay – West Nusa Tenggara, using thermal infrared channel of Landsat8 data, which is supported by field observation data. Surface temperature measurement is conducted through field survey in conjunction with Landsat 8 orbit. Surface temperature calculation is carried out by using certain method issued by United States Geological Survey (USGS, 2013). Calculation result on Grupuk Bay's water surface temperature shows that it ranges from 28.00 to 30.00oC, while field survey result shows that it ranges from 28.27 to 29.69oC. This research shows that sea surface temperature measurement result based on Landsat8 data has nearly identical range with field survey result.

A PARTIAL ACQUISITION TECHNIQUE OF SAR SYSTEM USING COMPRESSIVE SAMPLING METHOD Rahmat Arief
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 14, No 1 (2017)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

In line with the development of Synthetic Aperture Radar (SAR) technology, there is a serious problem when the SAR signal is acquired using high rate analog digital converter (ADC), that require large volumes data storage. The other problem on compressive sensing method,which frequently occurs, is a large measurement matrix that may cause intensive calculation. In this paper, a new approach was proposed, particularly on the partial acquisition technique of SAR system using compressive sampling method in both the azimuth and range direction. The main objectives of the study are to reduce the radar raw data by decreasing the sampling rate of ADC and to reduce the computational load by decreasing the dimension of the measurement matrix. The simulation results found that the reconstruction of SAR image using partial acquisition model has better resolution compared to the conventional method (Range Doppler Algorithm/RDA). On a target of a ship, that represents a low-level sparsity, a good reconstruction image could be achieved from a fewer number measurement. The study concludes that the method may speed up the computation time by a factor 4.49 times faster than with a full acquisition matrix.

COMPARISON OF THE VEGETATION INDICES TO DETECT THE TROPICAL RAIN FOREST CHANGES USING BREAKS FOR ADDITIVE SEASONAL AND TREND (BFAST) MODEL Yahya Darmawan; Parwati Sofan
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 9, No 1 (2012)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Remotely 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.

Back Pages IJReSES Vol. 13, No. 1(2016) Editorial Journal
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 13, No 1 (2016)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

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Back Pages IJReSES Vol. 13, No. 1(2016)

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