Changes in the environment occur in cities due to increased urbanization and population growth. Sustainable Development Goal (SDG) 11 is intrinsically linked to the environment, one facet of which is the need for universal access to secure, inclusive, and accessible green and public places. As urban heat islands (UHI) have the potential to negatively influence cities and their residents, existing resources and data must be used to identify and quantify these effects. To address this, we present the use of satellite-derived (2013–2022) and meteorological data (2014–2020) to assess intra-urban heat islands in Manila City, Philippines. The assessment includes (a) understanding the temporal variability of air temperature measurements and outdoor thermal comfort based on meteorological data, (b) comparative and correlative analysis between common Land-Use Land Cover indicators (Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Built-up Index (NDBI)) and Land Surface Temperature (LST), (c) spatial and temporal analysis of LST using spatial statistics techniques, and (d) generation of an intra-urban heat island (IUHI) map with a recommended class of action using a suitability analysis model. Finally, the areas that need intervention are compared to the affected population, and suggestions to enhance the thermal characteristics of the city and mitigate the effects of UHI are established.

Many regions in developing countries do not have any access to communication networks even though the number of devices connected through the Internet of Things (IoT) is increasing significantly. A small satellite platform could provide global network coverage in low Earth orbit to these remote locations at a low cost. This paper describes the overall mission architecture and the implementation of remote IoT using a 1U volume in 6U CubeSat platform named KITSUNE. In KITSUNE, one of the missions is to leverage IoT for building a network of remote ground sensor terminals (GST) in 11 mostly developing countries. This paper evaluates the capacity and coverage of a satellite-based IoT network for providing remote data-collection services to these countries. The amount of data that could be collected from the GSTs and forwarded accurately to the users determines the actual capacity of the Store and Forward (S&F) mission. Therefore, there are several proposed parameters to estimate this capacity in this study. In addition, these parameters are retrieved from the simulations, ground test results, and on-orbit observations with the KITSUNE satellite. The proposed IoT system, which is composed of the GSTs and IoT subsystem onboard KITSUNE satellite, is determined to be capable of providing valuable information from remote locations. In addition, the collected data are achieved and analyzed to monitor sensory data specific to each country, and it could help to generate prediction profiles as well.

Remote Sensing is proven to be helpful in various ways, and for an agricultural country like the Philippines, mapping in farmlands is not that common. Using the Sentinel 1 Synthetic Aperture Radar (SAR) data, the study reveals rice paddy classification brought by supervised machine learning on the municipality of Guimba, in Nueva Ecija - the Rice Bowl Capital of the country. The study in Guimba, Nueva Ecija, addresses the absence of comprehensive rice classification studies, focusing on creating a classified rice paddy map and assessing spectral indices (NDMI, NDVI, NDWI) using synthetic aperture radar and optical imager. Results show successful differentiation between rice and non-rice paddies. Temporal analysis emphasizes monitoring water availability, soil moisture, and vegetation health. Despite signs of overprediction in the CART model, its effectiveness in mapping rice paddies is notable. The spatial distribution maps contribute to targeted monitoring, enabling efficient interventions and improved agricultural practices. This research highlights the inherent use of remote sensing in rice crop management, offering valuable insights for farmers and country's food security.

CubeSats are giving the opportunity for educational institutes to participate in the space industry, develop new technologies and test out new ideas in outer space. CubeSat missions are developed to perform scientific research and demonstrate new space technologies with relatively cheap cost and limited resources. This category of satellites has many limitations such as the short development time, the power consumption and the limited time and capability of data downlink. Earth Observation from a Low Earth Orbit is one of the most appealing m applications of CubeSats developed by students or non-space faring countries. Investigating new technologies to improve image quality and studying ways to increase acquisition adequacy is very promising. This paper aims to introduce a mission hardware design and machine learning-based algorithm used within an Earth Observation (EO) CubeSat. The case study of this paper is Alainsat-1 project which is a 3U CubeSat developed with the support of IEEE Geo-science and Remote Sensing Society (GRSS) at the National Space Science and Technology Center, UAE. The satellite is planned to be launched by 2022. A low-resolution Commercial off-the-shelf (COTS) camera for EO is developed as a primary mission in this CubeSat. The compatible hardware design and software algorithm proposed is responsible for classifying the images captured by the camera into different categories based on cloud intensity detected in these images before downloading them to the ground station. A microcontroller-based architecture is developed for controlling the mission board; it is responsible for accessing the memory, reading the images, and running the cloud detection algorithm. The cloud detection algorithm is based on a U-net architecture while the algorithm is developed using a Tensor-flow library. This model is trained using a dataset of images taken from the Landsat 8 satellite project. Moreover, the SPARCS cloud assessment dataset is used to evaluate the developed model on a new set of images. The overall accuracy achieved by the model is around 85% in addition to the acceptable performance of the model observed on a set of low-resolution images. The plan is to make the design modular and optimize its performance to be used on-board CubeSats fulfilling the size constraint and overall power consumption limitation of an add-on module to a camera mission.

We developed two distinct automatic packet reporting system (APRS) payload designs to establish a reliable backup communication system for disaster risk reduction and management. The payloads are designed to perform a significant key operation, primarily APRS digital repeating (DP), enabling continuous communication access even in areas where conventional ground-based infrastructure is damaged by natural disasters through the relay of APRS packets to extend communication coverage. A detailed framework is designed using the standard amateur packet radio (AX.25 protocol). It specifies the structure of APRS data frames and packets, which are used to transmit alerts, emergency status updates, and text messages. This structure ensures that important information is transmitted reliably and effectively during an emergency. The designs for the APRS payloads share a common overall operating system architecture but differ in their very high frequency transceiver modules used for the amateur radio (Radiometrix BiM1H very high frequency (VHF) Narrowband Transceiver and Dorji DRA818V VHF Band Voice Transceiver Module).