Journal of Geospatial Science and Technology

The Impact of Tree Density on Automated Oil Palm Tree Counting Accuracy

2024-08-13T14:55:09+07:00

The rapid development of oil palm plantations in Kualuh Leidong Subdistrict, Labuhanbatu Utara District, North Sumatra Province has led to an increased need for effective and efficient monitoring and supervision of oil palm trees. One method that supports such monitoring and supervision is the use of an automatic counting method using orthophoto data. This orthophoto data was used for automatic tree counting using a deep learning method with the Faster R-CNN algorithm. The study considered two planting patterns: regular planting patterns with spacing of 4 to 9 meters, and random planting patterns with varying spacing. Data processing involved an epoch value of 80 and a batch size value of 4. The accuracy of the automatic oil palm tree counting was evaluated based on the density of the spacing between trees with reference to the ground truth. The findings indicated that the deep learning Faster R-CNN algorithm achieved higher accuracy in automatic calculations for regular planting patterns.

Pemanfaatan Data Pengukuran Mobile Laser Scanner untuk Analisis Perubahan Elevasi Ruas Tol

2024-07-30T14:50:55+07:00

Pembangunan jalan tol di Indonesia yang semakin pesat di Indonesia perlu diimbangi monitoring yang baik. Salah satu metode yang dapat dimanfaatkan untuk monitoring jalan tol adalah metode Mobile Laser Scanner (MLS) yang cukup efisien, salah satunya untuk monitoring elevasi jalan tol. Penelitian ini menggunakan data MLS Ruas Tol Terbanggi Besar Pematang Panggang Kayu Agung (TBPPKA) STA 27+500 s.d. STA 30+212 yang diambil pada tahun 2020 dan 2021 yang diolah menggunakan perangkat lunak Global Mapper dengan metode subtract surface untuk mengetahui nilai perubahan elevasinya. Hasil dari penelitian ini ditemukan adanya perubahan elevasi ruas tol TBPPKA dari tahun 2020 ke 2021 di Track A sebesar -0,017 m s.d. 0,022 m dan di Track B sebesar -0,025 m s.d. 0,019 m.

The rapid development of toll roads in Indonesia must be balanced with good monitoring. One method that can be used for toll road monitoring is the Mobile Laser Scanner (MLS) method, which is quite efficient for monitoring toll road elevation. This study uses MLS data for the Terbanggi Besar Pematang Panggang Kayu Agung Toll Road (TBPPKA) STA 27+500 to STA 30+212 taken in 2020 and 2021, which is processed using Global Mapper software with the subtract surface method to determine the value of elevation changes. The results of this study found that there was a change in the elevation of the TBPPKA toll road from 2020 to 2021 on Track A of -0.017 m to 0.022 m and on Track B of -0.025 m to 0.019 m.

A Development of A Flood Vulnerability Level Map Using Scoring and Weighting Methods in Bogowonto Sub Watershed

2024-07-30T14:50:47+07:00

Daerah Aliran Sungai (DAS) Bogowonto termasuk salah satu DAS di Kabupaten Purworejo yang rutin terjadi bencana banjir. Berdasarkan data rekap bencana banjir tahun 2019, tercatat 23 bencana banjir terjadi di Kecamatan Purwodadi, Kabupaten Purworejo. Dalam rangka membantu Badan Nasional Penanggulangan Bencana (BNPB) dan pemerintah daerah Kabupaten Purworejo untuk melakukan tindakan mitigasi bencana, langkah awalnya adalah melakukan kajian risiko bencana mengenai potensi rawan terdampak banjir di Sub DAS Bogowonto. Untuk keperluan tersebut, belum tersedia peta tingkat kerawanan banjir di Sub DAS Bogowonto yang mengacu pada SNI 8197 tahun 2015 dan Perka BNPB Nomor 2 tahun 2012. Berdasarkan hal itu, perlu dilakukan pembuatan peta tingkat kerawanan bencana banjir di Sub DAS Bogowonto tepatnya Kecamatan Purwodadi, Kabupaten Purworejo. Pembuatan peta tingkat kerawanan banjir di Sub DAS Bogowonto mengacu pada SNI 8197 tahun 2015 dan Perka BNPB Nomor 2 tahun 2012 menggunakan beberapa parameter, antara lain yaitu penggunaan lahan, curah hujan, kelerengan, elevasi, dan jenis tanah. Berdasarkan hasil pengolahan data, diperoleh peta dengan tiga kelas tingkat rawan banjir yaitu kelas rendah, sedang, dan tinggi. Pada kelas rendah nilai rentangnya sebesar 0,1 s.d. 0,817, kelas sedang sebesar 0,817 s.d. 1,534, dan untuk kelas rawan banjir tinggi sebesar 1,534 s.d. 2,25. Pada kelas rendah, nilai luasan area rawan banjir sebesar 164,019 ha, kelas sedang sebesar 119,107 ha, dan untuk kelas tinggi sebesar 3742,205 ha. Evaluasi daerah rawan banjir menunjukkan bahwa dari total 30 desa, 26 desa (86,67%) yang termasuk klasifikasi daerah rawan banjir terbukti mengalami bencana banjir berdasarkan catatan kejadian banjir tahun 2019 dan 2020 dari Balai Besar Wilayah Sungai Serayu Opak.

The Bogowonto Watershed (DAS) is one of the watersheds in Purworejo Regency which regularly experiences floods. Based on data from the 2019 flood disaster recap, 23 flood disasters occurred in Purwodadi District, Purworejo Regency. To assist the National Disaster Management Agency (BNPB) and the local government of Purworejo Regency to carry out disaster mitigation actions, the first step is to conduct a disaster risk study regarding the potential for flood-prone areas in the Bogowonto sub-watershed. For this purpose, there is no map of the level of flood vulnerability in the Bogowonto sub-watershed which refers to SNI 8197 of 2015 and Perka BNPB No. 2 of 2012. Based on this, it is necessary to development a map of the level of flood hazard in the Bogowonto sub-watershed, precisely in Purwodadi District, Kabupaten Bogowonto. Purworejo. Development a map of the level of flood vulnerability in the Bogowonto sub-watershed refers to SNI 8197 of 2015 and Perka BNPB No. 2 of 2012 using several parameters, including land use, rainfall, slope, elevation, and soil type. Based on the results of data processing, a map was obtained with three classes of flood vulnerable levels, namely low, medium, and high classes. In the low class, the range value is 0.1 to d. 0.817, medium class of 0.817 s.d. 1.534, and for the high flood vulnerable class of 1.534 s.d. 2.25. In the low class, the value of the flood vulnerable area is 164,019 ha, the medium class is 119,107 ha, and for the high class, it is 3742.205 ha. The evaluation of flood vulnerable areas showing that out of a total of 30 villages, 26 villages (86.67%) which are classified as flood vulnerable areas, have been proven to have experienced floods based on flood event records in 2019 and 2020 from the Serayu Opak River Region Center.

The Utilization of Sentinel-2 Image Data for Bathymetric Mapping with Satellite Derived Bathymetry Approach

2024-07-30T14:50:22+07:00

Pembaharuan informasi mengenai kedalaman perairan semakin diperlukan untuk berbagai keperluan seperti monitoring, penelitian, manajemen, dan pemetaan area perairan. Adanya perkembangan teknologi pengindraan jauh dan pemodelan Satellite-Derived Bathymetry (SDB) memungkinkan digunakan untuk memperoleh nilai kedalaman perairan. enelitian ini bertujuan untuk memperoleh nilai kedalaman dengan pendekatan Satellite Derived Bathymetry (SDB) dan pemodelan ekstraksi Stumpf (2003) serta melakukan evaluasi terhadap hasil yang diperoleh. Data yang digunakan dalam penelitian ini adalah data navionics dan data pengukuran SBES. Pada penelitian ini nilai korelasi terbaik dihasilkan dari komposit band hijau (B3) dan merah (B4) sebesar 0,642. RMSE terbaik dengan pemodelan Stumpf (2003) diperoleh 2,46 meter dengan rentang kedalaman 0 s.d 5 meter dengan perbandingan kedalaman Navionics Nauticalchart. Hasil evaluasi ketelitian dari pemodelan Stumpf (2003) sudah memenuhi syarat dari SNI 8202 Tahun 2015 tentang Ketelitian Peta Dasar untuk pembuatan LPI dan LLN skala 1 : 25.000 dengan interval kontur 10 meter.

The updating of information on water depth is increasingly essential for various purposes such as monitoring, research, management, and mapping of water areas. The advancement in remote sensing technology and Satellite Derived Bathymetry (SDB) modeling allows obtaining water depth values. This research aims to acquire depth values using the Satellite Derived Bathymetry (SDB) approach and Stumpf (2003) extraction modeling, followed by an evaluation of the obtained results. The data utilized in this study consist of Navionics data and SBES measurement data. The best correlation in this research was achieved from the composite of green (B3) and red (B4) bands at 0.642. The best Root Mean Square Error (RMSE) with the Stumpf (2003) modeling was 2.46 meters within the depth range of 0 to 5 meters compared to Navionics Nauticalchart depths. The accuracy evaluation of the Stumpf (2003) modeling met the requirements of SNI 8202 Year 2015 concerning the Accuracy of Basic Maps for the creation of LPI and LLN at a 1:25,000 scale with a 10-meter contour interval.