International Journal of Remote Sensing and Earth Sciences (IJReSES)
Not a member yet
334 research outputs found
Sort by
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.
COMBINATION OF SPECKLE DIVERGENCE AND NEIGHBORHOOD ANALYSIS TO CLASSIFY SETTLEMENT FROM TERASAR-X DATA
No full textAbstract. The objectives of this research were to develop and improve methods for determination of settlements area with focus on synthetic aperture radar (SAR) data. Remote sensing settlement classification has made great progress, both for optical and radar data as well for their fusion. Yet, in radar imagery, settlement classification still contains some problems. Several studies on application of radar imagery have been conducted using techniques such as textural analysis, multi-temporal analysis, statistical model, spatial indexes, and object-based classification. Most of the development methods have several problems in the specific area especially in the tropical country. Several studies also showed that settlement classification accuracies were just below 60%. This was not sufficient enough to classify settlement areas using SAR imagery. Therefore, in this research, we proposed a new method i.e., the combination of the speckle divergence and the neighborhood analysis. The proposed method was applied to classify settlement area in Cilacap and Padang Districts of Indonesia. The results showed that the proposed method produced a good accuracy i.e., 85.5% for Cilacap Districts and 78.1% for Padang Districts.
ESTIMATION OF GROSS PRIMARY PRODUCTION USING SATELLITE DATA AND GIS IN URBAN AREA, DENPASAR
Get PDFRemote sensing data with high spatial resolution is very useful to provideinformation about Gross Primary Production (GPP) especially over spatial coverage in theurban area. Most models of ecosystem carbon exchange based on remote sensing data usedlight use efficiency (LUE) model. The aim of this research was to analyze the distributionof annual GPP urban area of Denpasar. Two main satellite data used in this study wereALOS/AVNIR-2 and Aster satellite data. Result showed that annual value of GPP usingALOS/AVNIR-2 varied from 0.130 gC m-2 yr-1 to 2586.181 gC m-2 yr-1. Meanwhile, usingAster the value varied from 0.144 gC m-2 yr-1 to 2595.264 gC m-2 yr-1. The annual value ofGPP ALOS was lower than the value of Aster, because ALOS have high spatial resolutionand smaller interval of spectral resolution compared to Aster. Different land use couldeffect the value of GPP, because the different land use has different vegetation type,distribution, and different photosynthetic pathway type. The high spatial resolution of theremote sensing data is crucial to discriminate different land cover types in urban region.With heterogeneous land cover surface, maximum value of GPP using ALOS/AVNIR-2was smaller than that of Aster, however, the annual mean of GPP value usingALOS/AVNIR-2 was higher than that of Aster
ESTIMATION OF DATA MEMORY CAPACITY FOR CIRCULARLY POLARIZED SYNTHETIC APERTURE RADAR ONBOARD UNMANNED AERIAL VEHICLE PLATFORM (CP-SAR UAV)
Get PDFPreviously only linear polarization is widely used in the Synthetic Aperture Radar(SAR) system onboard spaceborne and airborne platforms. In such linearly polarized SAR(LP-SAR) systems, Faraday rotation in the ionosphere and platform posture will contribute tothe system noise. Therefore to improve this situation, currently a novel Circularly PolarizedSynthetic Aperture Radar (CP-SAR) sensor is developed in Microwave Remote SensingLaboratory, Chiba University. Moreover, from this research, a new backscattering data basedon circularly polarized wave in the remote sensing field can be obtained. As an early stage ofthe development of this CP-SAR sensor, we built an Unmanned Aerial Vehicle (UAV)platform for testing CP-SAR sensor capabilities. In this paper, we describe the novel CP-SARsensor and the method to design CP-SAR UAV especially in estimating the requirement ofdata memory capacity. Also a smaller antenna is possible to be implemented since the 3-dBaxial ratio on antenna characteristic becomes the main parameter in this new CP-SARtechnique. Hence, a compact CP-SAR sensor onboard a small and low cost spaceborneplatform yielding a high accuracy SAR image data can be realized in the near future
MODIFICATION OF INPUT IMAGES FOR IMPROVING THE ACCURACY OF RICE FIELD CLASSIFICATION USING MODIS DATA
Get PDFThe standard image classification method typically uses multispectral imageryon one acquisition date as an input for classification. Rice fields exhibit high variability inland cover states, which influences their reflectance. Using the existing standard method forrice field classification may increase errors of commission and omission, thereby reducingclassification accuracy. This study utilised temporal variance in a vegetation index as amodified input image for rice field classification. The results showed that classification ofrice fields using modified input images provided a better result. Using the modifiedclassification input improved the correspondence between rice field area obtained from theclassification result and reference data (R2 increased from 0.2557 to 0.9656 for regencylevelcomparisons and from 0.5045 to 0.8698 for district-level comparisons). Theclassification accuracy and the estimated Kappa value also increased when using themodified classification input compared to the standard method, from 66.33 to 83.73 andfrom 0.49 to 0.77, respectively. The commission error, omission error, and Kappa variancedecreased from 68.11 to 42.36, 28.48 to 27.97, and 0.00159 to 0.00039, respectively, whenusing modified input images compared to the standard method. The Kappa analysisconcluded that there are significant differences between the procedure developed in thisstudy and the standard method for rice field classification. Consequently, the modifiedclassification method developed here is significant improvement over the standardprocedure
ESTIMATION OF TIDAL ENERGY DISSIPATION AND DIAPYCNAL DIFFUSIVITY IN THE INDONESIAN SEAS
Get PDFThe Indonesian Seas separating the Indian Ocean from the West Pacific Oceanare representative regions of strong tidal mixing in the world oceans. In the present study,we first carry out numerical simulation of the barotropic tidal elevation field in theIndonesian Seas using horizontally two-dimensional primitive equation model. It is foundthat, to reproduce realistic tidal elevations in the Indonesian Seas, the energy lost by theincoming barotropic tides to internal waves within the Indonesian seas should be taken intoaccount. The numerical experiments show that the model predicted tidal elevations in theIndonesian Seas best fit the observed data when we take into account the baroclinic energyconversion in the Indonesian Seas ~86.1 GW for the M2 tidal constituent and ~134.6 GWfor the major four tidal constituents (M2, S2, K1, O1). For this baroclinic energy conversion,the value of Kñ averaged within the eastern area (Halmahera, Seram, Banda and MalukuSeas), the western area (Makassar and Flores Seas), and the southern area (Lombok Straitand Timor Passage) are estimated to be ~23 × 10-4 m2s-1, ~5 × 10-4 m2s-1, and ~10× 10-4m2s-1, respectively. This value is about 1 order of magnitude more than assumed for theIndonesian Seas in previous ocean general circulation models. We offer this study as awarning against using diapycnal diffusivity just as a tuning parameter to reproduce largescalephenomena