A variety of research endeavors and practical applications necessitate the use of land cover maps. These maps are valuable for tasks such as change detection, forest monitoring, urban expansion monitoring, natural resource mapping, catering to diverse user requirements. While satellite sensors offer essential data for comprehending spatial and temporal variations in land cover, relying on a single satellite system can be limiting, especially considering the potential hindrance of cloud cover in the case of optical sensors. To enhance temporal frequency, it becomes essential to utilize multiple satellite systems, albeit requiring harmonization to ensure consistent outcomes. This study presents a large-scale annual land cover mapping which utilizes harmonized Landsat-8 and Sentinel-2 satellite imagery, in conjunction with supplementary data, and a machine learning algorithm. In addition, the use of powerful computational processing platforms such as Google Earth Engine and Google Colaboratory is now a requirement to manage big geospatial data as well as to run different algorithms for processing and analysis.

In earth observation missions, remote sensing images acquired by an optical payload are widely utilized because of their wide coverage and rich spectral bands. Despite being deployed in airborne platforms, a significant phase of the payload’s development happens on the ground by utilizing terrestrial technologies and testing equipment. While the payload prototype is expected to be close to its final form before testing on a flight platform, the prototype often needs to be tethered to computers and other instruments during earlier stages. To emulate a platform moving at a constant speed, a line-scanning camera under development can be attached to a scanning rail. However, rails are relatively large setups, and the scanning distance will be limited by the available laboratory space and wirings. To overcome these limitations, a proposed scanning approach will be presented in this study by using a rotating apparatus for a line-scanning camera, which will translate real-world images while keeping the camera and optics fixed. This rotating apparatus is an improvement of a previous design developed for the same purpose of optical payload testing and evaluation. The new design has a target minimum rotational speed (ω) of 0.4 ◦/s compared to the previous design with ω = 0.48 ◦/s. These improvements are achieved by changing the types of gear, bearing, and output shaft. The proposed design also incorporates a single-stage worm drive which enables a speed ratio of 1:72. Although the modified design has a smaller gear reduction, it is expected to have better performance as a result of a more manageable backlash due to parts reduction. To efficiently support axial loads and increase capacity of the rotating apparatus, angular contact bearings and a crossed roller bearing will be used. To facilitate low-cost development, a custom software and commercial-off-the-shelf (COTS) components will be implemented to ensure reliable operation under extreme environmental conditions and provide necessary control signals to the motor which controls the rotating mechanism of the proposed design. In this study, the scanning device’s performance will also be demonstrated in terms of versatility, portability, and panoramic shots. The expected outcome is an improved scanning (or rotating) device that advances infrastructure development in terms of earth observation optical payload for remote sensing applications. Leveraging on this proposed scanning approach and understanding its disruptive potential with respect to testing and evaluation of ground-based payloads could be essential to future payload developments for spaceborne remote sensing applications.

Typhoon Rai has recently affected central and southern parts of the Philippines. Based on the valuation of the country’s National Disaster Risk Reduction and Management Council (NDRRMC), it is estimated that the typhoon has damaged 1,700 buildings, 2 million houses, and 10 million hectares of agricultural land in the affected locations. Given the effects of the typhoon, in terms of the extent of the area where it has caused destruction, the tremendous economic losses due to its incurred damages, and to the number of people affected by it, it became necessary to create rapid damage assessment maps that could provide the needed geospatial information to emergency responders so they can prioritize areas of most concern. In this effort, Sentinel-1 synthetic aperture radar (SAR) imagery was used due to the data’s temporal resolution (i.e., pre- and post-disaster images are available), relative independence from atmospheric conditions (i.e., unaffected by cloud cover), and open-access availability (i.e., data can be readily downloaded after the typhoon). Complex coherence correlation from stacks of pre- and post-disaster SAR images were analyzed for change detection in order to create the rapid damage assessment maps. In order to validate the results, ancillary data (i.e., aerial photos, local reports, and UNITAR / UNOSAT damage tags and maps) were used to qualitatively and quantitatively assess the maps. Upon analysis, we found that there is good correspondence between the SAR-derived maps and the aerial photos/UNITAR maps and that the damage tags by UNITAR / UNOSAT would match the rapid damage assessment maps to up to 93% if the correlation threshold is set to 0.5 and if the damage classification is set to just two levels (i.e., “damaged” and “not damaged”). It is deemed that the resulting maps of this research will be useful in the on-going efforts to rehabilitate the affected areas of Typhoo Rai. Future work includes further ground validation efforts and use of other datasets and methods in deriving the rapid damage assessment maps.