Perbandingan Akurasi Klasifikasi Penutup Lahan Hasil Penggabungan Citra ALOS AVNIR-2 dan ALOS PALSAR pada Polarisasi Berbeda dengan Transformasi Wavelet

Penggabungan citra merupakan salah satu alternatif dari penggunaan citra penginderaan jauh yang konvensional atau secara individu. Penelitian ini mengkaji peningkatan akurasi klasifikasi citra ALOS PALSAR yang digabungkan dengan citra ALOS AVNIR-2.Transformasi wavelet digunakan dalam proses penggabungan citra, mengacu pada kemampuannya mempertahankan kualitas spektral citra. Akurasi total dan indeks kappa pada citra ALOS AVNIR-2 85.26% dan 78.90%, pada citra gabungan I (Citra ALOS AVNIR-2, ALOS PALSAR polarisasi HH) 76.84% dan 67.06%, pada citra gabungan II (Citra ALOS AVNIR-2, ALOS PALSAR polarisasi HV) 71.58% dan 58.64%. Ditinjau dari hasil tersebut citra gabungan mempunyai akurasi yang lebih rendah daripada citra ALOS AVNIR, namun dibanding pada hasil klasifikasi citra ALOS PALSAR polarisasi HH (59.38% dan 45.97%) dan citra ALOS PALSAR polarisasi HV (64.06% dan 49.55%) proses penggabungan citra yang dilakukan menunjukkan peningkatan akurasi

Effect of Terrain Configuration on the Performance of SRTMv3 and ALOS PALSAR DEMs Over the Federal Capital Territory (FCT), Nigeria.

Digital Elevation Model (DEM) and its resulting parameters are essential terrain related information. DEM and the extracted information (slope, aspect, roughness etc.) have been identified as one of the most important and fundamental variables to various streams of engineering and planning designs which are the hall marks of development all over the world. Thus, to delineate the major surface and subsurface structures for evaluating the Planning framework for the Federal Capital City of Nigeria, analyzing the effects of terrain configuration of Shuttle Radar Topographic Mission (SRTMV3) and ALOS PALSAR DEM data is very crucial. Hence this paper aimed at examining the effects of terrain configuration of Shuttle Radar Topographic Mission (SRTMV3) and ALOS PALSAR DEM. The methodology involved data acquisition of ALOS PALSAR, SRTMV3 and Ortho DEMs, after which the ALOS PALSAR and SRTMV3 DEMs were resampled to 10m of the Ortho DEM, image classification and then an assessment of the impact of terrain configuration on DEM performance with horizontal profiles was carried out. The results revealed that SRTMV3 v3 performed better with close resemblance with the Ortho DEM on flat and undulating terrain while it underestimated the rolling terrain and overestimated the hilly and mountainous terrain. ALOS PALSAR DEM when compared against the Ortho DEM grossly overestimated all the terrain configuration in the study area. In all, the overall performance of SRTMV3 v3 had a close resemblance in performance to that of the Ortho DEM, while ALOS PALSAR had a significant difference in performance. It was therefore recommended that SRTMV3 v3 should be used as an alternative DEM source where high-resolution elevation data are not readily available. Keywords: ALOS PALSAR, Digital Elevation Model, SRTM, Terrain modelling. DOI: 10.7176/JEES/10-6-14 Publication date:June 30th 202

PEMANFAATAN CITRA SATELIT ALOS-PALSAR UNTUK PEMETAAN KELEMBABAN TANAH ( STUDI KASUS : WILAYAH KABUPATEN PASER, KALIMANTAN TIMUR )

Kelembaban tanah merupakan rasio kandungan air di bawah permukaan tanah yang merupakan parameter utama di berbagai bidang seperti hidrologi, pertanian dan meteorology. Sejalan dengan perkembangan teknologi, identifikasi kelembaban tanah dapat pula dilakukan melalui teknik penginderaan jauh. Salah satu citra penginderaan jauh yang dapat digunakan untuk estimasi nilai kelembaban tanah adalah citra satelit ALOS-PALSAR. Penelitian ini bertujuan untuk mengetahui nilai kelembaban tanah berdasarkan citra satelit ALOS-PALSAR wilayah Kabupaten Paser. Data yang digunakan adalah data citra satelit ALOS-PALSAR polarisasi HH dan VV. Parameter penentu estimasi nilai kelembaban tanah adalah nilai hambur-balik dan nilai konstanta dielektrik. Nilai kelembaban tanah di estimasi dengan persamaan Dubois dkk.,(1995). Untuk mengetahui akurasi hasil estimasi nilai kelembaban tanah, dilakukan pengukuran sampel nilai kelembaban tanah di wilayah Kabupaten Paser dengan menggunakan soil moisture meter tester. Hasil penelitian ini menunjukan rentang nilai kelembaban tanah di wilayah Kabupaten Paser berdasarkan citra satelit ALOS-PALSAR padabulan Mei tahun 2009 adalah 1 – 25 % Mv dengan klasifikasi berada pada kelas kelembaban tanah kering (Dry). Pada hasil uji akurasi dengan menggunakan 40 sampel pengukuran kelembaban tanah diperoleh nilai overall accuracy sebesar 87,5 %. Kata Kunci : Kelembaban Tanah, Citra ALOS-PALSAR, Nilai Kelembaban Tanah

Karakterisktik Backscatter Citra Alos Palsar Polarisasi Hh dan Hv terhadap Parameter Biofisik Hutan di Sebagian Taman Nasional Kerinci Seblat

Synthetic Aperture Radar (SAR) is one kind of an active remote sensing system, utilizing microwave to records the earth surface. One of the SAR\u27s satellites is ALOS PALSAR which is capable of penetrating clouds so that the underlying objects can be identified. ALOS PALSAR has a polarization system operating with single beam polarization (HH or HV), dual polarization (HH + HV or VV + VH) and full polarization (HH + HV + VH + VV). This study aims to use the backscatter value of ALOS PALSAR\u27s image for monitoring and mapping the forest. The Utilization of ALOS PALSAR imagery to measure forest biophysical parameters and saturation levels identified based on backscatter sensitivity. Image extraction is done through image calibration where the digital value is converted to sigma naught backscatter value then the result of calibration unit is changed to decibels. Backscatter sensitivity depends on forest structure which  can affect scattering mechanism. The backscatter value is also influenced by humidity and weather dynamics which can also affect the dielectric constant. The result of accurate classification of land use polarization fall in HH accuracy of 62,4%. Whereas for HV polarization accuracy obtained equal to 74,88%. &nbsp

Potential of ALOS2 and NDVI to estimate forest above-ground biomass, and comparison with lidar-derived estimates

Remote sensing supports carbon estimation, allowing the upscaling of field measurements to large extents. Lidar is considered the premier instrument to estimate above ground biomass, but data are expensive and collected on-demand, with limited spatial and temporal coverage. The previous JERS and ALOS SAR satellites data were extensively employed to model forest biomass, with literature suggesting signal saturation at low-moderate biomass values, and an influence of plot size on estimates accuracy. The ALOS2 continuity mission since May 2014 produces data with improved features with respect to the former ALOS, such as increased spatial resolution and reduced revisit time. We used ALOS2 backscatter data, testing also the integration with additional features (SAR textures and NDVI from Landsat 8 data) together with ground truth, to model and map above ground biomass in two mixed forest sites: Tahoe (California) and Asiago (Alps). While texture was useful to improve the model performance, the best model was obtained using joined SAR and NDVI (R2 equal to 0.66). In this model, only a slight saturation was observed, at higher levels than what usually reported in literature for SAR; the trend requires further investigation but the model confirmed the complementarity of optical and SAR datatypes. For comparison purposes, we also generated a biomass map for Asiago using lidar data, and considered a previous lidar-based study for Tahoe; in these areas, the observed R2 were 0.92 for Tahoe and 0.75 for Asiago, respectively. The quantitative comparison of the carbon stocks obtained with the two methods allows discussion of sensor suitability. The range of local variation captured by lidar is higher than those by SAR and NDVI, with the latter showing overestimation. However, this overestimation is very limited for one of the study areas, suggesting that when the purpose is the overall quantification of the stored carbon, especially in areas with high carbon density, satellite data with lower cost and broad coverage can be as effective as lidar

Perbandingan Akurasi Klasifikasi Penutup Lahan Hasil Penggabungan Citra ALOS AVNIR-2 dan ALOS PALSAR pada Polarisasi Berbeda dengan Transformasi Wavelet

Penggabungan citra merupakan salah satu alternatif dari penggunaan citra penginderaan jauh yang konvensional atau secara individu. Penelitian ini mengkaji peningkatan akurasi klasifikasi citra ALOS PALSAR yang digabungkan dengan citra ALOS AVNIR-2.Transformasi wavelet digunakan dalam proses penggabungan citra, mengacu pada kemampuannya mempertahankan kualitas spektral citra. Akurasi total dan indeks kappa pada citra ALOS AVNIR-2 85.26% dan 78.90%, pada citra gabungan I (Citra ALOS AVNIR-2, ALOS PALSAR polarisasi HH) 76.84% �dan 67.06%, pada citra gabungan II (Citra ALOS AVNIR-2, ALOS PALSAR polarisasi HV) 71.58% dan 58.64%. Ditinjau dari hasil tersebut citra gabungan mempunyai akurasi yang lebih rendah daripada citra ALOS AVNIR, namun dibanding pada hasil klasifikasi citra ALOS PALSAR polarisasi HH (59.38% dan 45.97%) dan citra ALOS PALSAR polarisasi HV (64.06% dan 49.55%) proses penggabungan citra yang dilakukan menunjukkan peningkatan akurasi.Kata kunci: Penggabungan citra, Wavelet, Akurasi, Wang bovic, Maximum likelihoo

Mapping Mangrove Extent and Change: A Globally Applicable Approach

This study demonstrates a globally applicable method for monitoring mangrove forest extent at high spatial resolution. A 2010 mangrove baseline was classified for 16 study areas using a combination of ALOS PALSAR and Landsat composite imagery within a random forests classifier. A novel map-to-image change method was used to detect annual and decadal changes in extent using ALOS PALSAR/JERS-1 imagery. The map-to-image method presented makes fewer assumptions of the data than existing methods, is less sensitive to variation between scenes due to environmental factors (e.g., tide or soil moisture) and is able to automatically identify a change threshold. Change maps were derived from the 2010 baseline to 1996 using JERS-1 SAR and to 2007, 2008 and 2009 using ALOS PALSAR. This study demonstrated results for 16 known hotspots of mangrove change distributed globally, with a total mangrove area of 2,529,760 ha. The method was demonstrated to have accuracies consistently in excess of 90% (overall accuracy: 92.293.3%, kappa: 0.86) for mapping baseline extent. The accuracies of the change maps were more variable and were dependent upon the time period between images and number of change features. Total change from 1996 to 2010 was 204,850 ha (127,990 ha gain, 76,860 ha loss), with the highest gains observed in French Guiana (15,570 ha) and the highest losses observed in East Kalimantan, Indonesia (23,003 ha). Changes in mangrove extent were the consequence of both natural and anthropogenic drivers, yielding net increases or decreases in extent dependent upon the study site. These updated maps are of importance to the mangrove research community, particularly as the continual updating of the baseline with currently available and anticipated spaceborne sensors. It is recommended that mangrove baselines are updated on at least a 5-year interval to suit the requirements of policy makers