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Ms. Siti Saringatin
Gadjah Mada University

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0 Remote Sensing
0 Remote Sensing Applications
0 remote sensing data processing and applicationEnvironmental remote sensing
0 Remote sensing data processing
0 Remote sensing ecological index

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Short Biography

Student of Geography Faculty, Gadjah Mada University

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Journal article
Published: 22 January 2021 in Land
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Coastal regions are one of the most vulnerable areas to the effects of global warming, which is accompanied by an increase in mean sea level and changing shoreline configurations. In Indonesia, the socioeconomic importance of coastal regions where the most populated cities are located is high. However, shoreline changes in Indonesia are relatively understudied. In particular, detailed monitoring with remote sensing data is lacking despite the abundance of datasets and the availability of easily accessible cloud computing platforms such as the Google Earth Engine that are able to perform multi-temporal and multi-sensor mapping. Our study aimed to assess shoreline changes in East Java Province Indonesia from 2000 to 2019 using variables derived from a multi-sensor combination of optical remote sensing data (Landsat-7 ETM and Landsat-8 OLI) and radar data (ALOS Palsar and Sentinel-1 data). Random forest and GMO maximum entropy (GMO-Maxent) accuracy was assessed for the classification of land and water, and the land polygons from the best algorithm were used for deriving shorelines. In addition, shoreline changes were quantified using Digital Shoreline Analysis System (DSAS). Our results showed that coastal accretion is more profound than coastal erosion in East Java Province with average rates of change of +4.12 (end point rate, EPR) and +4.26 m/year (weighted linear rate, WLR) from 2000 to 2019. In addition, some parts of the shorelines in the study area experienced massive changes, especially in the deltas of the Bengawan Solo and Brantas/Porong river with rates of change (EPR) between −87.44 to +89.65 and −18.98 to +111.75 m/year, respectively. In the study areas, coastal erosion happened mostly in the mangrove and aquaculture areas, while the accreted areas were used mostly as aquaculture and mangrove areas. The massive shoreline changes in this area require better monitoring to mitigate the potential risks of coastal erosion and to better manage coastal sedimentation.

ACS Style

Sanjiwana Arjasakusuma; Sandiaga Swahyu Kusuma; Siti Saringatin; Pramaditya Wicaksono; Bachtiar Wahyu Mutaqin; Raihan Rafif. Shoreline Dynamics in East Java Province, Indonesia, from 2000 to 2019 Using Multi-Sensor Remote Sensing Data. Land 2021, 10, 100 .

AMA Style

Sanjiwana Arjasakusuma, Sandiaga Swahyu Kusuma, Siti Saringatin, Pramaditya Wicaksono, Bachtiar Wahyu Mutaqin, Raihan Rafif. Shoreline Dynamics in East Java Province, Indonesia, from 2000 to 2019 Using Multi-Sensor Remote Sensing Data. Land. 2021; 10 (2):100.

Chicago/Turabian Style

Sanjiwana Arjasakusuma; Sandiaga Swahyu Kusuma; Siti Saringatin; Pramaditya Wicaksono; Bachtiar Wahyu Mutaqin; Raihan Rafif. 2021. "Shoreline Dynamics in East Java Province, Indonesia, from 2000 to 2019 Using Multi-Sensor Remote Sensing Data." Land 10, no. 2: 100.

Journal article
Published: 04 November 2020 in ISPRS International Journal of Geo-Information
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The rise of Google Earth Engine, a cloud computing platform for spatial data, has unlocked seamless integration for multi-sensor and multi-temporal analysis, which is useful for the identification of land-cover classes based on their temporal characteristics. Our study aims to employ temporal patterns from monthly-median Sentinel-1 (S1) C-band synthetic aperture radar data and cloud-filled monthly spectral indices, i.e., Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Built-up Index (NDBI), from Landsat 8 (L8) OLI for mapping rice cropland areas in the northern part of Central Java Province, Indonesia. The harmonic function was used to fill the cloud and cloud-masked values in the spectral indices from Landsat 8 data, and smile Random Forests (RF) and Classification And Regression Trees (CART) algorithms were used to map rice cropland areas using a combination of monthly S1 and monthly harmonic L8 spectral indices. An additional terrain variable, Terrain Roughness Index (TRI) from the SRTM dataset, was also included in the analysis. Our results demonstrated that RF models with 50 (RF50) and 80 (RF80) trees yielded better accuracy for mapping the extent of paddy fields, with user accuracies of 85.65% (RF50) and 85.75% (RF80), and producer accuracies of 91.63% (RF80) and 93.48% (RF50) (overall accuracies of 92.10% (RF80) and 92.47% (RF50)), respectively, while CART yielded a user accuracy of only 84.83% and a producer accuracy of 80.86%. The model variable importance in both RF50 and RF80 models showed that vertical transmit and horizontal receive (VH) polarization and harmonic-fitted NDVI were identified as the top five important variables, and the variables representing February, April, June, and December contributed more to the RF model. The detection of VH and NDVI as the top variables which contributed up to 51% of the Random Forest model indicated the importance of the multi-sensor combination for the identification of paddy fields.

ACS Style

Sanjiwana Arjasakusuma; Sandiaga Swahyu Kusuma; Raihan Rafif; Siti Saringatin; Pramaditya Wicaksono. Combination of Landsat 8 OLI and Sentinel-1 SAR Time-Series Data for Mapping Paddy Fields in Parts of West and Central Java Provinces, Indonesia. ISPRS International Journal of Geo-Information 2020, 9, 663 .

AMA Style

Sanjiwana Arjasakusuma, Sandiaga Swahyu Kusuma, Raihan Rafif, Siti Saringatin, Pramaditya Wicaksono. Combination of Landsat 8 OLI and Sentinel-1 SAR Time-Series Data for Mapping Paddy Fields in Parts of West and Central Java Provinces, Indonesia. ISPRS International Journal of Geo-Information. 2020; 9 (11):663.

Chicago/Turabian Style

Sanjiwana Arjasakusuma; Sandiaga Swahyu Kusuma; Raihan Rafif; Siti Saringatin; Pramaditya Wicaksono. 2020. "Combination of Landsat 8 OLI and Sentinel-1 SAR Time-Series Data for Mapping Paddy Fields in Parts of West and Central Java Provinces, Indonesia." ISPRS International Journal of Geo-Information 9, no. 11: 663.