Journal of Geospatial Science and Technology

Pengaruh Koreksi Ionosfer Terhadap Pergeseran Vertikal Pada Citra Satelit ALOS-PALSAR

2024-02-07T10:41:32+07:00

Interferometric Synthetic Aperture Radar (InSAR) is an effective technique for changing the earth's surface with wide coverage and high accuracy. However, the accuracy of InSAR can be affected by wave propagation activity in the atmosphere. The ionospheric medium in the atmosphere contains free electrons which cause unstable waves to produce bias. Therefore, it is necessary to correct the ionosphere in SAR image processing so that it does not contain bias. This study examines the effect of ionospheric correction on vertical deformation in ALOS-PALSAR satellite imagery. The data used includes satellite image data and GNSS. Image data is processed by considering the presence or absence of ionospheric correction so that the results can be analyzed to determine the effect of the ionosphere on SAR images. GNSS data is processed by a static method to produce coordinates that can be used as a reference for validating the results of SAR image processing. The results of SAR and GNSS image processing are then visualized in the form of a vertical deformation map to facilitate the analysis of the results. The results showed that the direction of the vertical deformation in the ionospheric corrected satellite image was consistent with the direction of the vertical deformation in the GNSS. This indicates that the ionospheric correction affects the vertical deformation in the satellite image. In the process of vertical changes, there are obstacles, namely the vertical point reference has not been used as a validation of the results. In addition, there are no other satellite image references to compare the results of the ALOS-PALSAR satellite image processing.

Pendugaan Cadangan Karbon Dengan Citra Sentinel-2B dan Terrestrial Laser Scanner Di Kawasan Hutan Dengan Tujuan Khusus (KHDTK) Pendidikan dan Pelatihan Kehutanan Fakultas Kehutanan Universitas Mulawarman

2024-02-07T10:30:20+07:00

Measurement of forest carbon on the ground in a large scale is time-consuming and expensive. This research utilized remote sensing with Sentinel-2B image through its Vegetation Index and related those values with those obtained from field measurements and Terrestrial Laser Scanner (TLS). This study specifically compares the forest stand carbon value using TLS and field measurements and link it to the vegetation index. This study transforms the vegetation indices, i.e.: Transformed Vegetation Index (TVI), Normalized Difference Vegetation Index (NDVI) and Simple Ratio (SR) from Sentinel-2B imagery and relates them with field measurements and TLS. The calculation of carbon in forest stands using TLS revealed an average carbon of 151.35 ton/Ha. In contrast, in the field measurements, carbon was measured at 149.81 ton/Ha, and statistically, there was no difference between these two measurements. The relationship between the vegetation index and field measurements showed the best coefficient of correlation of TVI with (r) = 0.784 and (R²) = 0.524. The relationship between the vegetation index and TLS measurements showed the best coefficient of correlation of TVI with (r) = 0.759 and the coefficient of determination (R²) = 0.577.

Perbandingan Suhu Permukaan Sebelum dan Sesudah Pembangunan Sirkuit Mandalika menggunakan Metode Split Window Algorithm (SWA)

2024-02-07T10:41:36+07:00

The Mandalika Circuit has undergone extensive sociological and economic study as one of the sporting venues constructed to boost economic output. The effects of temperature variations brought on by less vegetation have not, however, been thoroughly researched. The effects of development typically lead to a reduction in flora, which raises the surface temperature. The effects of development typically lead to a reduction in flora, which raises surface temperatures. The Split Window Algorithm (SWA) method should be used to compare variations in surface temperature before and after the presence of a circuit mandalika. Images from Landsat 8 OLI-TIRS, Landsat 9 OLI2-TIRS2, and MODIS were used as data in this study. Before circuit construction, temperature changes were visualized using Landsat 8 imagery taken on June 29, 2019, and after construction was complete, temperature variations were visualized using Landsat 9 imagery taken on April 26, 2022. The ENVI 5.3 software is used to obtain the value of surface temperature and vegetation cover. The findings of this study demonstrate a change in open land cover, which has increased by 41.55%. The increase in surface temperature is estimated to be 4.83°C between June 29, 2019, and standard deviation April 26, 2022, with an average surface temperature value of 29.50°C in 2019 and 34.330°C in 2022.

Evaluasi Perubahan dan Kesesuaian Penggunaan Lahan Tahun 2019 Terhadap Rencana Tata Ruang Wilayah (RTRW) di Kabupaten Bekasi

2024-02-07T10:41:28+07:00

The common pattern of land use in urban areas is agricultural land shrinkage due to conversions to built-up land. Similar things happened in the Bekasi Regency, quoted from LAPAN data (2017), there has been a decline in rice fields by 0.59%whichgoes hand in hand with the increase of industrial land by 0.15% per year. Several conversions in land use that happened will influence the overall spatial structure of the region. If this phenomenon happens continuously, there will be disproportionate changes for each land use classification. This applicative activity aims to find out land use conversions in 2014-2019 and evaluate the land use compatibility in 2019 based on RTRW in the Bekasi Regency. The activity began by ensuring spatial classification by referring to PERDA No.12 of 2011 concerning RTRW, to the Spot 6 image of Bekasi Regency in 2014. Then, on-screen digitization is carried out to the Spot 6 image of Bekasi Regency to obtain2014land use data in shapefile format. Furthermore, an accuracy test is done using a confusion matrix to determine the value of accuracy by comparing the result of Spot 6 classification with reference on google earth’s pro historical image. Other data used are RTRW data and 2019 land use data in the Bekasi Regency area. The data is analyzed using the overlay menu. The largest land conversions occurred in the agricultural to industrial land conversion type, accounting for 78.1 ha. For the land use compatibility in 2019 to the RTRW, Pebayuran District has the largest compatibility inland use classification, accounting for 7196.38 ha

Perhitungan Jumlah Pohon Kelapa Sawit Pada Citra Ortofoto Menggunakan Algoritma Template Matching dan Faster R-CNN

2024-02-07T10:41:40+07:00

Data ortofoto telah banyak digunakan untuk pemantauan kondisi lahan pertanian, khususnya kelapa sawit. Kelapa sawit berperan penting dalam meningkatkan perekonomian Indonesia. Oleh karena itu dibutuhkan perhitungan pohon secara otomatis untuk mempercepat proses monitoring perkebunan secara akurat dan berkala. Penelitian ini bertujuan untuk menghitung jumlah pohon kelapa sawit secara otomatis menggunakan dua algoritma, yaitu Template Matching dan Faster R-CNN. Lokasi penelitian mencakup area perkebunan kelapa sawit yang terletak di Kecamatan Bunga Raya, Kabupaten Siak, Provinsi Riau. Data yang digunakan yaitu data foto udara perkebunan kelapa sawit PT. Teguh Karsa Wana Lestari pada tahun 2017. Data foto udara diolah menjadi data ortofoto. Data ortofoto selanjutnya digunakan untuk perhitungan pohon secara otomatis menggunakan kedua algoritma. Uji akurasi setiap algoritma dibandingkan dengan acuan hitungan manual yang diasumsikan memiliki ground truth. Hasil perhitungan manual (ground truth) sebanyak 4777 pohon, dan hasil perhitungan secara otomatis dengan template matching yaitu 4500 pohon dengan selisih 277 pohon lebih sedikit dibandingkan dengan ground truth. Selain itu, Faster R-CNN menghasilkan 4713 pohon dengan selisih 64 pohon lebih sedikit dibandingkan dengan ground truth. Overall accuracy perhitungan pohon kelapa sawit dengan algoritma Template Matching sebesar 91,58%, Faster R-CNN sebesar 97,98 %. Dengan demikian, algoritma Faster R-CNN secara kuantitatif memberikan hasil yang lebih baik.