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International Journal of Remote Sensing and Earth Sciences (IJReSES)
ISSN : 02166739     EISSN : 2549516X     DOI : -
Core Subject : Science,
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.
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Articles 8 Documents
Search results for , issue " Vol 10, No 1 (2013)" : 8 Documents clear
GROWTH RATE AND PRODUCTIVITY DYNAMICS OF ENHALUS ACOROIDES LEAVES AT THE SEAGRASS ECOSYSTEM IN PARI ISLANDS BASED ON IN SITU AND ALOS SATELLITE DATA Rustam, Agustin; Bengen, Dietriech Geoffrey; Arifin, Zainal; Gaol, Jonson Lumban; Arhatin, Risti Endriani
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 10, No 1 (2013)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Original Source | Check in Google Scholar | Full PDF (413.871 KB) | DOI: 10.30536/j.ijreses.2013.v10.a1847

Abstract

Enhalus acoroides is the largest population of seagrasses in Indonesia. However, growth rate  and  productivity  analyses  of Enhalus  acoroides and  the use  of  satellite data to estimate its the productivity are still rare. The goal of the research was to analyze the growth rate, productivity rate,seasonal productivity of Enhalus acoroides in Pari island and its surroundings. The study was divided into two phases i.e., in situ measurments and satellite image processing. The field study was conducted to obtain the coverage percentage, density, growth rate, and productivity rate, while the satellite image processing was used to estimate the extent of seagrass. The study was conducted in August 2011 toJuly  2012  to  accommodate  all  four  seasons. Results  showed  that  the highest  growth  rate  andproductivity occurred during the transitional season from west Monsoon to the east Monsoon of 5.6cm/day  and  15.75  mgC/day, respectively.   While, the  lowest growth rate  and productivity occurred during  the  transition  from east  Monsoon  to  the  west  Monsoon of 3.93  cm/day  and  11.4  mgC/day, respectively. Enhalus  acoroides productivity reached its maximum during  the  west  Monsoon  at 1081.71 mgC/day/m2 and minimum during east Monsoon with 774.85 mgC/day/m2 . Based on ALOS data in 2008 and 2009, total production of Enhalus acoroides in the proximity of Pari islands reached its maximum occur during the west Monsoon (48.73 – 49.59 Ton C) and minimum during transitional season (16.4-16.69 Ton C). Potential atmospheric CO2 absorption by Enhalus acoroides in Pari island was estimated at the number 60.14 – 181.82 Ton C.
DETERMINATION OF STRATIFICATION BOUNDARY FOR FOREST AND NON FOREST MULTITEMPORAL CLASSIFICATION TO SUPPORT REDD+ IN SUMATERA ISLAN Kartika, Tatik; Sari, Inggit Lolita; Trisakti, Bambang
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 10, No 1 (2013)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Original Source | Check in Google Scholar | Full PDF (343.065 KB) | DOI: 10.30536/j.ijreses.2013.v10.a1843

Abstract

Multi-temporal classification is a method to determine forest and non-forest by considering a missing data, such as cloud cover using correlations value from the other data. This circumstances is frequently occured in a tropical area such as in Indonesia. To gain an optimum result of forest and non-forest classification, it is needed a stratification zone that describes the difference of vegetation condition due to different of vegetation type, soil type, climate, and land use/cover associations. This stratification zone will be useful to indicate the different biomass volume relating to carbon content for supporting the REDD+ project. The objective of this study was to determine stratification boundary by performing multi temporal  classification in Sumatera Island  using  Landsat  imagery  in  25 meter resolution and Quick Bird imagery in 0.6 meter. Rough stratification was made by considering land use/cover, DEM and landform, using visual interpretation of moderate spatial resolution of satellitedata. High spatial resolution data was also provided in some areas to increase the accuracy level of stratification zone. The stratification boundary was evaluated using forest classification indices, and it was  redetermined  to  obtain  the  final  stratification  zone. The  indices was generated  by CanonicalVariate Analysis (CVA) method, which was depend on training samples of forest and non-forest in each previous stratification zone. The amount of indices used in each zone were two or three indices depending on the separability of the forest and non-forest classification. The suitable indices used in each  zone  described forest  as  100, non-forest  as  0, and  uncertain  forest between  50-99. The  result showed 20 stratification zones in Sumatera spreading out in coastal, mountain, flat area, and group of small islands. The stratification zone will improve the accuracy of forest and non-forest classification result and their change based on multi temporal classification.
DEVELOPMENT OF LAND MOISTURE ESTIMATION MODEL USING MODIS INFRARED, THERMAL, AND EVI TO DETECT DROUGHT AT PADDY FIELD Domiri, Dede Dirgahayu
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 10, No 1 (2013)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Original Source | Check in Google Scholar | Full PDF (448.216 KB) | DOI: 10.30536/j.ijreses.2013.v10.a1842

Abstract

The drought phenomena often occurs in summer season at paddy field of Java island. The drought phenomena causes decrease in rice production. This research was aimed to develop a model of land  moisture (LM) estimation  at  agricultural field,  especially  for  paddy  field  based  on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data which has seven reflectance and two thermal bands. The method used in this study included data correction, advance processing of MODIS data  (land indices  transformation),  extraction  of  land  indices  value  at  location  of  field  survey,  and regression  analysis  to  make  the  best  model  of  land  moisture  estimation. The  result  showed that reflectance of 2nd channel (NIR) and rasio of Enhanced Vegetation Index (EVI) with Land Surface Temperature (LST) had high correlation with surface soil moisture (% weight) at 0 – 20 cm depth with formula: LM = 15.9*EVI/LST – 0.934*R2 – 16.8 (SE=9.6%; R2 =76.2%). Based on the model, land  moisture  was  derived  spatially at the  agricultural field,  especially at paddy  field to  detect  andmonitor drought events. Information of land moisture can be used as an indicator to detect drought condition and early growing season of paddy crop 
DERIVING INHERENT OPTICAL PROPERTIES FROM MERIS IMAGERY AND IN SITU MEASUREMENT USING QUASI-ANALYTICAL ALGORITHM Ambarwulan, Wiwin; Widiatmaka, -; Budhiman, Syarif
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 10, No 1 (2013)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Original Source | Check in Google Scholar | Full PDF (287.639 KB) | DOI: 10.30536/j.ijreses.2013.v10.a1835

Abstract

The  paper  describes inherent optical properties  (IOP)  of  the  Berau  coastal  waters  derived from in  situ measurements  and Medium  Resolution  Imaging  Spectrometer  (MERIS) satellite  data. Field  measurements  of optical  water,  total  suspended  matter  (TSM), and  chlorophyll-a  (Chl-a) concentrations were carried out during the dry season of 2007. During this periode, only four MERISdata were  coincided with in  situ measurements on 31 August  2007. The MERIS  top-of-atmosphere radiances were atmospherically corrected using the MODTRAN radiative transfer model. The in situ optical  measurement  have  been  processed  into apparent optical properties  (AOP) and sub  surface irradiance. The remote sensing reflectance of in situ measurement as well as MERIS data were inverted into  the  IOP  using quasi-analytical algorithm  (QAA).  The  result  indicated  that coefficient  of determination (R 2) of backscattering coefficients of suspended particles (bbp) increased with increasing wavelength,  however  the  R2 of  absorption  spectra  of  phytoplankton  (aph)  decreased  with  increasing wavelength.
UTILIZATION OF MULTI TEMPORAL SAR DATA FOR FOREST MAPPING MODEL DEVELOPMENT Trisakti, Bambang; Hamzah, Rossi
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 10, No 1 (2013)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Original Source | Check in Google Scholar | Full PDF (1186.627 KB) | DOI: 10.30536/j.ijreses.2013.v10.a1844

Abstract

Utilization  of  optical  satellite  data  in  tropical  region  was  limited to  free  cloud  cover. Therefore, Synthetic  Aperture  Radar  (SAR)  becomes  an  alternative  solution  for  forest  mapping  in Indonesia due to its capability to penetrate cloud. The objective of this research was to develop a forestmapping model based on multi temporal SAR data. Multi temporal ALOS PALSAR data for 2007 and 2008  were  used  for  forest  mapping,  and  one  year  mosaic  LANDSAT  data  in  2008  was  used  as references  data  to  obtain  training  sample  and  to  verify  the  final  forest  classification.  PALSAR processing was done using gamma naught conversion and Lee filtering. Samples were made in forest and  water  area, and  the  statistical  values  of the  each  object  were  calculated.  Some  thresholds  were determined  based  on  the  average  and  standard  deviation,  and  the  best  threshold  was  selected  to classify forest and water in 2008. It was assumed that forest could not change in 1-2 years period. The classification of forest, water, and the change were combined to produce final forest in 2008, and then it was visually verified with mosaic LANDSAT in 2008. The result showed that forest, water, and the change  could  be  well  classified  using  threshold  method.  The  forest  derived  from  PALSAR  was visually  consistent  with  forest  appearance  in  LANDSAT  and  forest  produced  from  INCAS.  It  has better performance than forest derived from INCAS for separating oil palm plantation from the forest.
FISHPOND AQUACULTURE INVENTORY IN MAROS REGENCY OF SOUTH SULAWESI PROVINCE Marini, Yennie; Emiyati, -; Prayogo, Teguh; Hamzah, Rossi; Hasyim, Bidawi
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 10, No 1 (2013)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Original Source | Check in Google Scholar | Full PDF (791.245 KB) | DOI: 10.30536/j.ijreses.2013.v10.a1839

Abstract

Currently, fishpond aquaculture becomes an interesting business for investors because of its profit,  and  a  source  of  livelihood  for  coastal  communities.  Inventory  and  monitoring  of  fishpond aquaculture provide important baseline data to determine the policy of expansion and revitalization of the fishpond. The aim of this research was to conduct an inventory and monitoring of fishpond area inMaros regency of South Sulawesi province using Satellite Pour l’Observation de la Terre (SPOT -4) and Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Apeture Radar (PALSAR). SPOT image classification process was performed using maximum likelihood supervised classification  method and  the  density  slice  method  for ALOS  PALSAR.  Fishpond  area  from  SPOT data was  9693.58  hectares  (ha),  this  results  have  been  through  the  process  of  validation  and verification by the ground truth data. The fishponds area from PALSAR was 7080.5 Ha, less than the result  from  SPOT  data.  This  was  due  to  the  classification  result  of  PALSAR  data  showing someobjects around fishponds (dike, mangrove, and scrub) separately and were not combined in fishponds area  calculation.  Meanwhile, the  result  of  SPOT -4  image  classification  combined object  around fishponds area.
LAND COVER CLASSIFICATION OF ALOS PALSAR DATA USING SUPPORT VECTOR MACHINE Sambodo, Katmoko Ari; Indriasari, Novie
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 10, No 1 (2013)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Original Source | Check in Google Scholar | Full PDF (614.629 KB) | DOI: 10.30536/j.ijreses.2013.v10.a1836

Abstract

Land cover classification is  one  of  the  extensive  used  applications in  the  field  of remote sensing. Recently, Synthetic Aperture Radar (SAR) data has become an increasing popular data source because  its  capability  to  penetrate  through  clouds,  haze,  and  smoke.  This  study  showed  on  an alternative  method  for  land  cover  classification  of  ALOS-PALSAR  data  using  Support  Vector Machine (SVM) classifier. SVM discriminates two classes by fitting an optimal separating hyperplane to the training data in a multidimensional feature space, by using only the closest training samples. In order  to  minimize  the  presence  of  outliers  in  the  training  samples  and  to  increase  inter-class separabilities,  prior  to  classification,  a  training  sample  selection  and  evaluation  technique  by identifying its position in a horizontal vertical–vertical horizontal polarization (HV-HH) feature space was applied. The effectiveness of our method was demonstrated using ALOS PALSAR data (25 m mosaic, dual polarization) acquired in Jambi and South Sumatra, Indonesia. There were nine different classes  discriminated:  forest,  rubber  plantation,  mangrove  &  shrubs  with  trees,  oilpalm  &  coconut, shrubs,  cropland,  bare  soil,  settlement,  and  water.  Overall  accuracy  of  87.79%  was  obtained,  with producer’s accuracies for forest, rubber plantation, mangrove & shrubs with trees, cropland, and water class were greater than 92%.
MULTITEMPORAL LANDSAT DATA TO QUICK MAPPING OF PADDY FIELD BASED ON STATISTICAL PARAMETERS OF VEGETATION INDEX (CASE STUDY: TANGGAMUS, LAMPUNG) Parsa, I Made; Dirgahayu, Dede
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 10, No 1 (2013)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Original Source | Check in Google Scholar | Full PDF (360.578 KB) | DOI: 10.30536/j.ijreses.2013.v10.a1838

Abstract

Paddy  field  has  unique  characteristics  that  distinguish  it  from  other  plants.  Before it planting, paddy field is always flooded so that the appearance is dominated by water (aqueous phase). Within the  growth  of rice, field  conditions  will  be  increasingly  dominated  by  greenish rice  plants.While at the end, the rice plants will turn yellow indicating for harvesting. During flooding stage, the normalized difference vegetation index (NDVI) of pady field is negative. The negative value of NDVI of paddy field will ultimately increase to the maximum value at the maximum vegetative growth. TheNDVI of paddy field will decrease from generative phase until harvest and after harvest. The objective of  this  study  was  to  perform  the vegetation  index  analyses for multitemporal  Landsat  imagery of paddy field. The results showed that the difference of vegetation index values (maximum - minimum)of  paddy  field  were greater than the  difference  of vegetation index  values of  other land  uses.  Such differences values can be used as indicator to map land for rice. The evaluation results with reference data showed that the mapping accuracy (overall accuracy) was of 87.4 percent.

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