In this study, we proposed an automatic water extraction index (AWEI) threshold improvement model that can be used to detect lake surface water based on optical remote sensing data. An annual Landsat 8 mosaic was created using the Google Earth Engine (GEE) platform to obtain cloud-free satellite image data. The challenge of this study was to determine the threshold value, which is essential to show the boundary between water and nonwater. The AWEI was selected for the study to address this challenge. The AWEI approach was developed by adding a threshold water value based on the split-based approach (SBA) calculation analysis for Landsat 8 satellite images. The SBA was used to determine local threshold variations in data scenes that were used to classify water and nonwater. The class threshold between water and nonwater in each selected subscene image can be determined based on the calculation of class intervals generated by geostatistical analysis, initially referred to as smart quantiles. It was used to determine the class separation between water and nonwater in the resulting subscene images. The objectives of this study were (a) to increase the accuracy of automatic lake surface water detection by improvising the determination of threshold values based on analysis and calculations using the SBA and (b) to conduct a test case study of AWEI threshold improvement on several lakes’ surface water, which has a variety of different or heterogeneous characteristics. The results show that the threshold value obtained based on the smart quantile calculation from the natural break approach (AWEI ≥ −0.23) gave an overall accuracy of close to 100%. Those results were better than the normal threshold (AWEI ≥ 0.00), with an overall accuracy of 98%. It shows that there has been an increase of 2% in the accuracy based on the confusion matrix calculation. In addition to that, the results obtained when classifying water and nonwater classes for the different national priority lakes in Indonesia vary in overall accuracy from 94% to 100%.

Indonesian National Institute of Aeronautics and Space (LAPAN) started building its experimental microsatellite back in 2007 and finally able to launch its first microsatellite dubbed as LAPAN-A1/LAPAN-Tubsat. With the launch of LAPAN-A3/LAPAN-IPB, Indonesian experimental satellite programme hit its third generation. LAPAN-A3 is carrying multiple payloads including multispectral push-broom imager, digital matrix camera, as well as video camera. This paper aims to highlight the spectral differences between LAPAN-A3 and the well-established Sentinel-2A multispectral to investigate the potential of using LAPAN-A3 data to complement the other well-established medium resolution satellite data. Comparisons between corresponding bands and band transformations were performed over a dataset. Three areas of interest were chosen as the test sites. Linear regression and Pearson correlation coefficient were then calculated between the corresponding bands. The preliminary results showed a moderate correlation between the two sensors with Pearson correlation coefficient ranging from 0.39 to 0.65. Some issues were found regarding the radiometric quality over the whole scene of LAPAN-A3.

The agricultural sector plays a vital role in achieving the second Sustainable Development Goal: “Zero Hunger”. To ensure food security, agriculture must remain resilient and productive. In Indonesia, a major rice-producing country, the conversion of agricultural land for non-agricultural uses poses a serious threat to food availability. Accurate and timely mapping of paddy rice is therefore crucial. This study proposes a phenology-based mapping approach using a Convolutional Neural Network-Random Forest (CNN-RF) Hybrid model with multi-temporal Sentinel-2 and Landsat-8 imagery. Image processing and analysis were conducted using the Google Earth Engine platform. Raw spectral bands and four vegetation indices—NDVI, EVI, LSWI, and RGVI—were extracted as input features for classification. The CNN-RF Hybrid classifier demonstrated strong performance, achieving an overall accuracy of 0.950 and a Cohen’s Kappa coefficient of 0.893. These results confirm the effectiveness of the proposed method for mapping paddy rice in Indramayu Regency, West Java, using medium-resolution optical remote sensing data. The integration of phenological characteristics and deep learning significantly enhances classification accuracy. This research supports efforts to monitor and preserve paddy rice cultivation areas amid increasing land use pressures, contributing to national food security and sustainable agricultural practices.

Pembangunan infrastruktur seperti bandara akan menyebabkan perubahan penggunaan lahan pada area tersebut dan sekitarnya. Perubahan ini, terutama jika wilayah bandara berada pada wilayah yang dominan dengan lahan pertanian maka akan berpotensi mengubah fungsi lahan pertanian tersebut menjadi lahan non pertanian. Pembangunan infrastruktur bandara termasuk aksesnya akan mendorong pertumbuhan wilayah dan pertumbuhan ekonomi di wilayah tersebut. Tujuan penelitian ini adalah mendeteksi perubahan penggunaan lahan di wilayah Bandara Internasional Jawa Barat (BIJB) Kertajati khususnya alih fungsi lahan pertanian ke non pertanian. Metode yang diusulkan dalam penelitian ini adalah mengidentifikasi perubahan penggunaan lahan pada area kajian menggunakan data SPOT multitemporal dimana dilakukan klasifikasi perpaduan antara segmentasi objek secara digital menggunakan metode multiresolution segmentation dan penentuan kelas penggunaan lahan secara visual. Hasil penelitian menunjukkan bahwa telah terjadi penurunan luasan sejak 2013 hingga 2018 di wilayah kajian yaitu dari 5.822,80 ha pada tahun 2013 menjadi 5.347,30 ha selama kurun waktu 2013-2018. Kata kunci: penginderaan jauh, perubahan penggunaan lahan, pembangunan infrastrukturThe construction of infrastructure such as airport will cause changes in land use in the area and its surroundings. This change, especially if the airport area is in the dominant area with agricultural land or ricefield, will potentially change the function of the ricefield into non-agricultural land. The construction of airport infrastructure, including its access, will drive regional growth and economic growth in the region. The purpose of this research is to detect changes of land use in the West Java International Airport (BIJB) Kertajati region, West Java, especially the conversion of ricefield to non-agricultural land. The method proposed in this study is to identify changes of land use in the study area using multitemporal SPOT data where the hybrid classification is done with digital object segmentation method using multiresolution segmentation and the determination of land use classes with visual interpretation. The result shows that there had been a decrease in the area of ricefield from 5,822.80 hectares in 2013 became 5,347.30 hectares during the period 2013-2018.Keyword: remote sensing, land-use change, infrastructure development

Berdasarkan data Pendapatan Nasional Indonesia 2017, sektor pertambangan dan penggalian mempunyai peran penting bagi Indonesia. Sektor ini menyumbangkan 7,57% pada produk domestik bruto Indonesia di tahun 2017 . Salah satu sektor pertambangan yang potensial di Indonesia adalah pertambangan mineral Timah di Pulau Bangka dan Belitung. Namun kegiatan pertambangan ini banyak menimbulkan dampak negatif dari sisi lingkungan. Salah satu upaya awal untuk menanggulangi dampak negatif terhadap lingkungan adalah melakukan identifikasi kawasan pertambangan timah secara spasial. Teknologi yang dapat membantu untuk hal ini salah satunya adalah teknologi penginderaan jauh radar. Penelitian ini menggunakan data satelit radar sentinel-1 yang diluncurkan oleh European Space Agency (ESA). Tujuan penelitian ini adalah pemanfaatan data radar Sentinel-1 untuk identifikasi kawasan pertambangan menggunakan metode Object-Base Image Analysis (OBIA). Data sentinel-1 disegmentasi menggunakan algorithma multiresolution segmentation kemudian di klasifikasi menggunakan algorithma nearest neighbor. Masukan data yang digunakan untuk proses klasifikasi dibuat menjadi dua variasi, yang pertama adalah data standar deviasi, mean, dan brightness pada masing – masing segmen di tiap band, kemudian variasi kedua adalah penambahan data tekstur berupa nilai grey level coocurance matrix (GLCM). Hasil klasifikasi menunjukan bahwa masukan data yang menggunakan data tekstur GLCM mempunyai akurasi lebih tinggi dibandingkan dengan yang tanpa data tekstur GLCM. Secara statisktik Hasil klasifikasi dengan type satu menunjukan bahwa total akurasi nya adalah sebesar 89,0 %, dengan nilai kappa sebesar 0,48 sedangkan untuk type dua menunjukan bahwa total akurasinya adalah 89,3%, dengan kappa sebesar 0,50. Hasil klasifikasi kawasan pertambangan dapat digunakan sebagai masukan awal dalam rangka identifikasi spasial kerusakan lingkungan akibat aktivitas pertambangan.

LAPAN-A3 is the 3rdgeneration satellite for remote sensing developed by National Institute of Aeronautics and Space (LAPAN). The camera provides imagery with 15 m spatial resolution and able to view a swath 120 km wide. This research analyzes the performance of LAPAN-A3 satellite data to classify land cover in Toba Lake area, North Sumatera. Data processing starts from the selection of region of interest up to the assessment of accuracy. Supervised classification with maximum likelihood approach and confusion matrix method was applied to classify and evaluate the assessment results. The land cover is classified into five classes; water, bare land, agriculture, forest and secondary forest. The result of accuracy test is 93.71%. It proves that LAPAN-A3 data could classify the land cover accurately. The data is expected to complement the need of the satellite data with medium spatial resolution.