Assessing land use-based changes in ecosystem service values (ESVs) is a beneficial approach for land resource planning and ecologically sustainable development. Located in the south of China, Guilin is one of the first Sustainable Development Goals (SDGs) Innovation Demonstration Zones set up by China. It is a typical ecotourism city with an important ecological and economic status. In recent years, the time series, model fit, and spatial scale of ESV assessment in Guilin have needed to be improved in the context of rapid urbanization and natural change. In this study, an improved ESV assessment methodology was utilized to incorporate the effects of biomass, soil conservation, and precipitation and to adjust the equivalence factors based on the ratio of geographic and environmental parameters to the national average to make them heterogeneous in time and space in improving the practical fit of the assessment results. The study analyzed the evolution of land use and its contribution to ESVs in Guilin from 2000 to 2020. County and 3 km × 3 km grid scales were combined to reveal both broad and detailed spatial and temporal characteristics of ESVs in Guilin. The results show that the expansion of building land in Guilin is notable, and the amount of land use transfer continues to increase. ESVs fluctuated in a lateral S-shape, with significant differences in ESV effectiveness between counties, consistently high ESVs near waterbodies and ecological reserves, and low ESVs near commercial and industrial land and cultivated land. Despite the recovery trend in ESVs in the later years, there is still a gap between 2020 and 2000. To a certain extent, it helps Guilin optimize land allocation from different perspectives and promote ecological improvement and resource planning optimization.

As an important transportation infrastructure and a strong support for foreign trades, ports play an indispensable role in economic development. However, the acquisition of operating data has a certain hysteresis, making it difficult to analyse and evaluate in real-time. To solve this problem, in this paper, we proposed a new perspective where the number of vessels was used to replace the traditional operation data, which are not easily available, to reflect the health of the economy. We designed an approximate system for evaluating real-time port operations based on remote sensing images. First, the port’s remote sensing images were acquired and pre-processed in real-time. Then, the vessels in the remote sensing images were identified using object detection technology. Finally, the correlation between the vessels and the port’s operating indicators were established to analyse and predict the port operating conditions. The result demonstrated that the number of ships reflected the change of the port throughput to a certain extent and the predicted accuracy was within 10%. This system effectively solved the problems of operating data hysteresis and the difficulty in acquiring, and it opens large prospects for practical applications.

Advancements in remote sensing technology have enabled the acquisition of high spatial and radiometric resolution imagery, offering abundant and reliable data sources for forest fire monitoring. In order to explore the ability of Sustainable Development Science Satellite 1 (SDGSAT-1) in wildfire monitoring, a systematic and comprehensive study was proposed on smoke detection during the wildfire early warning phase, fire point identification during the fire occurrence, and burned area delineation after the wildfire. The smoke detection effect of SDGSAT-1 was analyzed by machine learning and the discriminating potential of SDGSAT-1 burned area was discussed by Mid-Infrared Burn Index (MIRBI) and Normalized Burn Ratio 2 (NBR2). In addition, compared with Sentinel-2, the fixed-threshold method and the two-channel fixed-threshold plus contextual approach are further used to demonstrate the performance of SDGSAT-1 in fire point identification. The results show that the average accuracy of SDGSAT-1 fire burned area recognition is 90.21%, and a clear fire boundary can be obtained. The average smoke detection precision is 81.72%, while the fire point accuracy is 97.40%, and the minimum identified fire area is 0.0009 km², which implies SDGSAT-1 offers significant advantages in the early detection and identification of small-scale fires, which is significant in fire emergency and disposal. The performance of fire point detection is superior to that of Sentinel-2 and Landsat 8. SDGSAT-1 demonstrates great potential in monitoring the entire process of wildfire occurrence, development, and evolution. With its higher-resolution satellite imagery, it has become an important data source for monitoring in the field of remote sensing.