Forest and land fires are disasters that greatly impact various sectors. Burned area identification is needed to control forest and land fires. Remote sensing is used as common technology for rapid burned area identification. However, there are not many studies related to the combination of optical and synthetic aperture radar (SAR) remote sensing data for burned area detection. In addition, SAR remote sensing data has the advantage of being a technology that can be used in various weather conditions. This research aims to evaluate the burned area model using a hybrid of convolutional neural network (CNN) as a feature extractor and random forest (CNN-RF) as classifiers on Sentinel-1 and Sentinel-2 data. The experiment uses five test schemes: (1) using optical remote sensing data; (2) using SAR remote sensing data; (3) a combination of optical and SAR data with VH polarization only; (4) a combination of optical and SAR data with VV polarization only; and (5) a combination of optical and SAR data with dual VH and VV polarization. The research was also carried out on the CNN, RF, and neural network (NN) classifiers. On the basis of the overall accuracy on the part of the region of Pulang Pisau Regency and Kapuas Regency, Central Kalimantan, Indonesia, the CNN-RF method provided the best results in the tested schemes, with the highest overall accuracy reaching 97% using Satellite pour l’Observation de la Terre (SPOT) images as reference data. This shows the potential of the CNN-RF method to identify burned areas, mainly in increasing precision value. The estimated result of the burned area at the research site using a hybrid CNN-RF method is 48,824.59 hectares, and the accuracy is 90% compared with MCD64A1 burned area product data.

Rice, cultivated in paddy fields, is one of the staple foods in the world, especially in Asia. In Indonesia, a substantial number of rice paddy fields have been converted into residential or industrial areas, threatening food security. Therefore, it is necessary to monitor the adequacy of rice paddy field areas. Recently, remote sensing has become the most widely used method for mapping rice paddy fields. This research focuses on developing a classification model for rice paddy field mapping using remote sensing with radar and optical data fusion, including input variations in polarization, texture, and optical derivative indices. This study proposes the CNN-RF method, which combines a convolutional neural network (CNN) as a feature extractor and a random forest (RF) as a classifier. The experiment used combinations of input data, including variations of single and multisource data, to achieve optimal results. Research findings in some districts of Indramayu show that the scheme combining Sentinel-1 features with GLCM (gray-level co-occurrence matrix) and Sentinel-2 features with selected bands provides the best results, with an overall accuracy of 98.23% and a Kappa coefficient of 0.96, using the CNN-RF method. CNN-RF outperforms other classifiers owing to the hybrid learning combination, which improves the accuracy through feature extraction by CNN and handles complex relationships between features while reducing overfitting by RF. This study contributes to the development of accurate and efficient rice paddy fields mapping techniques using remote sensing.

Burned area detection plays a critical role in assessing the impact of forest and land fires, particularly in Indonesia, where both peatland and non-peatland areas are increasingly affected. Optical remote sensing has been widely used for this task, but its effectiveness is limited by persistent cloud cover in tropical regions. A Synthetic Aperture Radar (SAR) offers a cloud-independent alternative for burned area mapping. This study investigates the performance of a Stacking Ensemble Neural Network (SENN) model using polarimetric features derived from both C-band (Sentinel 1) and L-band (Advanced Land Observing Satellite—Phased Array L-band Synthetic Aperture Radar (ALOS-2/PALSAR-2)) data. The analysis covers three representative sites in Indonesia: peatland areas in (1) Rokan Hilir, (2) Merauke, and non-peatland areas in (3) Bima and Dompu. Validation is conducted using high-resolution PlanetScope imagery(Planet Labs PBC—San Francisco, California, United States). The results show that the SENN model consistently outperforms conventional artificial neural network (ANN) approaches across most evaluation metrics. L-band SAR data yields a superior performance to the C-band, particularly in peatland areas, with overall accuracy reaching 93–96% and precision between 92 and 100%. The method achieves 76% accuracy and 89% recall in non-peatland regions. Performance is lower in dry, hilly savanna landscapes. These findings demonstrate the effectiveness of the SENN, especially with L-band SAR, in improving burned area detection across diverse land types, supporting more reliable fire monitoring efforts in Indonesia.

Devegetation monitoring is important to maintain the hydrological cycle and ensure the availability of clean water. Rapid monitoring needs to be carried out since land cover can change quickly. Remote sensing can be used as a technology to monitor devegetation since it has multitemporal data and can cover a large area. This study intends to investigate a Synthetic Aperture Radar (SAR) remote sensing data feature extraction method for devegetation detection using a one-dimensional Convolutional Neural Network (CNN) located on Sumba Island. This study examines two schemes: the first one uses sigma (σ⁰) and gamma nought (γ⁰) on VH and VV polarizations with speckle filtering and the other uses Gray Level Co-occurrence Matrix (GLCM) of sigma (σ⁰) and gamma nought (γ⁰) on VH and VV polarizations with no speckle filtering as an input. With the Satellite pour l’Observation de la Terre (SPOT) images as reference data, the result shows that Sentinel-1 SAR multitemporal data detected devegetation with the highest overall accuracy of 81.57%, using 11x11 GLCM texture features on a combination of VH and VV polarization serving as input. Misclassification is mainly found in steep terrain or hilly areas. However, for large-scale monitoring, this rapid method has been promising. This approach can also support the National Research and Innovation Agency in developing a national radar satellite program.