Sentinel-2 data provided the opportunity for complementary data to existing missions including Landsat and SPOT. In this study, multitemporal cloud masking (MCM) used to detect cloud and cloud shadow masking for Landsat 8 was improved to detect cloud and cloud shadow for Sentinel-2 data. This improvement takes advantages of the spectral similarity between Landsat 8 and Sentinel-2. To assess the reliability of the new MCM algorithm, several data selected from different environments such as sub-tropical South, tropical, and sub-tropical North were evaluated. Moreover, these data have heterogeneous land cover and variety of cloud types. In visual assessment, the algorithm can detect cloud and cloud shadow accurately. In the statistical assessment, the user’s and producer’s accuracies of sample in sub-tropical environments of cloud masking was 99% and 96%, respectively, and cloud shadow masking was 99% and 98%, respectively. In addition, the user’s and producer’s accuracies of sample in tropical environments of cloud masking was 100% and 95%, respectively, and cloud shadow masking was 100% and 92%, respectively. Compared to Fmask, MCM has higher accuracies in most of the results of cloud and cloud shadow masking in both sub-tropical and tropical environments. The results showed that the improved-MCM algorithm can detect cloud and cloud shadow for Sentinel-2 data accurately in all scenarios and the accuracies are significantly high.

Wildland fire is one of the most causes of deforestation, and it has an important impact on atmospheric emissions, notably CO₂. It occurs almost every year in Indonesia, especially during the dry season. Therefore, it is necessary to identify the burned areas from remote sensing images to establish the zoning map of areas prone to wildland fires. Many methods have been developed for mapping burned areas from low-resolution to medium-resolution satellite images. One of the popular approaches for mapping tasks is a deep learning approach using U-Net architecture. However, it needs a large amount of representative training data to develop the model. In this paper, we present a new dataset of burned areas in Indonesia for training or evaluating the U-Net model. We delineate burned areas manually by visual interpretation on Landsat-8 satellite images. The dataset is collected from some regions in Indonesia, and it consists of 227 images with a size of 512 × 512 pixels. It contains one or more burned scars or only the background and its labeled masks. The dataset can be used to train and evaluate the deep learning model for image detection, segmentation, and classification tasks related to burned area mapping.

Landsat 8 images have been widely used for many applications, but cloud and cloud-shadow cover issues remain. In this study, multitemporal cloud masking (MCM), designed to detect cloud and cloud-shadow for Landsat 8 in tropical environments, was improved for application in sub-tropical environments, with the greatest improvement in cloud masking. We added a haze optimized transformation (HOT) test and thermal band in the previous MCM algorithm to improve the algorithm in the detection of haze, thin-cirrus cloud, and thick cloud. We also improved the previous MCM in the detection of cloud-shadow by adding a blue band. In the visual assessment, the algorithm can detect a thick cloud, haze, thin-cirrus cloud, and cloud-shadow accurately. In the statistical assessment, the average user’s accuracy and producer’s accuracy of cloud masking results across the different land cover in the selected area was 98.03% and 98.98%, respectively. On the other hand, the average user’s accuracy and producer’s accuracy of cloud-shadow masking results was 97.97% and 96.66%, respectively. Compared to the Landsat 8 cloud cover assessment (L8 CCA) algorithm, MCM has better accuracies, especially in cloud-shadow masking. Our preliminary tests showed that the new MCM algorithm can detect cloud and cloud-shadow for Landsat 8 in a variety of environments.

A cloud masking approach based on multi-temporal satellite images is proposed. The basic idea of this approach is to detect cloud and cloud shadow by using the difference reflectance values between clear pixels and cloud and cloud shadow contaminated pixels. Several bands of satellite image which have big difference values are selected for developing Multi-temporal Cloud Masking (MCM) algorithm. Some experimental analyses are conducted by using Landsat-8 images. Band 3 and band 4 are selected because they can distinguish between cloud and non cloud. Afterwards, band 5 and band 6 are used to distinguish between cloud shadow and clear. The results show that the MCM algorithm can detect cloud and cloud shadow appropriately. Moreover, qualitative and quantitative assessments are conducted using visual inspections and confusion matrix, respectively, to evaluate the reliability of this algorithm. Comparison between this algorithm and QA band are conducted to prove the reliability of the approach. The results show that MCM better than QA band and the accuracy of the results are very high.

Cloud and cloud shadow cover on satellite images limit remote sensing and geo-information systems (GIS) applications in all application areas, especially for change detection and time series analyses. A novel method of cloud and cloud shadow removal called Multitemporal Cloud Removal (MCR) is proposed in this paper. The method has main steps: (1) radiometric correction, (2) cloud and cloud shadow detection, and (3) image reconstruction. Top of Atmosphere (TOA) radiometric correction converts digital number values to TOA reflectance for Landsat 8 OLI was first completed. In the second step, Multi-temporal Cloud Masking (MCM) was used to detect cloud and cloud shadow. This method uses a target image which has cloud and cloud shadow contaminated pixels and a reference image which is clear. The aim is to obtain the difference in reflectance values in visible, near-infrared and short wave infrared bands between target and reference images. These values can be used to detect cloud and cloud shadow in Landsat 8 images. The Landsat 8 cirrus band is used to detect thin cirrus cloud in this method. We use target image and reference image from a sequence acquisition dates of Landsat 8 images to avoid the significant land cover change. In the last step, we use multitemporal images to reconstruct pixels which are contaminated by cloud and cloud shadow. Cloud and cloud shadow contaminated pixels on the target image are replaced by pixels from the reference image. Landsat 8 images which have heterogeneous land cover and variety of cloud types are chosen in the experiments to prove that MCR is robust method for removing cloud and cloud shadow and can be used for image that has heterogeneous land cover and variety of cloud types. We use visual and statistical assessments to evaluate the results. As results show, cloud and cloud shadow can be removed by MCR. In visual evaluation, the corrected images are similar to the reference image. In statistical assessments between corrected and references images, the correlation coefficient for each band is quite high (>0.9) for the thick cloud case and equal to 1 for thin cloud case. Within band tandard deviations in the reference image and the corrected image were higher in the corrected image compared to the the reference image and original image. Although not comprehensive, the visual and statistical assessments, provide some indication that th MCR method was robust method for removing cloud and cloud shadow in Landsat 8 images examined in this work. The advantage of this appoach is that original reflectance values can be retained as long as they are not contaminated by cloud and cloud shadow. In addition, as we use a sequence acquisition date of Landsat 8 images, we can produce free cloud and cloud shadow images.

Indonesia a megadiverse country which has the largest area of forest with the richest biodiversity in the world. Unfortunately, the forest-loss in this country is quite big, especially in Kalimantan and Sumatera. Therefore, a robust method for deforestation method has to be developed to monitor deforestation to minimize the forest-loss in this country. In this paper, we proposed a method for deforestation detection called Multitemporal Deforestation Detection (MDD). The basic idea of this method is to utilize the difference of reflectance values on the target image and original image. Band selection was used to select bands in developing the algorithm. To improve the accuracy of the results, NDVI and dNBR were combined to the algorithm. As a result, the MDD can detect deforestation from forest to heterogeneous land cover such as open land, land clearing for plantation, urban, road and small road, and burnt area accurately. The advantage of the MDD is that it does not interfere cloud and cloud shadow on the original image. As it is an automatic algorithm, it can support in detecting deforestation for a big dataset.