Aquaculture plays an important role in providing food and reducing poverty but it affects environmental change and coastal ecosystems. Remote sensing is a technology that is helpful in the spatial-temporal dynamic monitoring of aquaculture, coastal management, and environmental monitoring. Most research focuses on inland and coastal areas, and little attention is paid to the extensive distribution of marine aquaculture. As an example, we use the freely available Landsat data of the developed marine aquaculture Liaoning Province of China and use the object-oriented automatic extraction method to analyze the spatial and temporal distribution information of marine aquaculture from 2000 to 2018. The accuracy evaluation from the randomly distributed sample points in high-resolution remote sensing images shows that the extraction accuracy for all of the five individual years of aquaculture area was higher than 82%. The results showed that (1) in the past 19 years, the area of marine aquaculture in Liaoning Province showed an increasing trend, which increased from 35.41 km² in 2000 to 201.83 km² in 2018, approximately 5.7 times increase in total area, but the growth rate decreased slightly due to government policy and the environmental quality of the sea area. (2) The centroid of offshore aquaculture in Liaoning Province shows a migration pattern to the northeast, in general, extending from the Dalian Bay sea area to the eastern sea area of the Dalian Chengshantou National Nature Reserve of Coastal Landform in the northeastern direction, and the migration distance reached 48.78 km. Moreover, the migration distance between 2005 and 2010 was the largest of all of the periods, reaching 35.43 km. The new marine aquaculture areas are mainly concentrated in the eastern direction of Xiaoyao Bay, the Changshan Islands, and Guanglu Island in Changhai County. (3) The landscape pattern of marine aquaculture in Liaoning Province is split, large-scale aquaculture and small-scale aquaculture are symbiotic, and landscape ecological activities are active. For local managers, this study can provide valuable supporting data for the assessment of marine aquaculture yield in this region, comprehensive control and management of the marine environment, and stability of the marine ecosystem. For other countries or regions, this work provides a great reference value for monitoring the dynamic spatial distribution of marine aquaculture.

Information, especially spatial distribution data, related to coastal raft aquaculture is critical to the sustainable development of marine resources and environmental protection. Commercial high spatial resolution satellite imagery can accurately locate raft aquaculture. However, this type of analysis using this expensive imagery requires a large number of images. In contrast, medium resolution satellite imagery, such as Landsat 8 images, are available at no cost, cover large areas with less data volume, and provide acceptable results. Therefore, we used Landsat 8 images to extract the presence of coastal raft aquaculture. Because the high chlorophyll concentration of coastal raft aquaculture areas cause the Normalized Difference Vegetation Index (NDVI) and the edge features to be salient for the water background, we integrated these features into the proposed method. Three sites from north to south in Eastern China were used to validate the method and compare it with our former proposed method using only object-based visually salient NDVI (OBVS-NDVI) features. The new proposed method not only maintains the true positive results of OBVS-NDVI, but also eliminates most false negative results of OBVS-NDVI. Thus, the new proposed method has potential for use in rapid monitoring of coastal raft aquaculture on a large scale.

High-intensity human activities have caused dramatic transformations of land function in China’s coastal zone, putting enormous pressure on the region’s ecological environment. It is urgent to fully understand the spatiotemporal evolution of the land-use function in the coastal zone to promote sustainable development. Therefore, based on CNLUCC data for 2000, 2010, and 2020, this study quantitatively explored the spatiotemporal evolution of production–living–ecological land (PLEL) and its eco-environmental response in China’s coastal zone by using multiple land-use analysis methods, gradient analysis, and the eco-environmental quality index. The results showed that over the past 20 years, the production land (PL) continued to decrease, whereas the living land (LL) and blue ecological land (BEL) increased. In the vertical direction, PL and the ecological land (EL) dominated in the northern and the southern coastal zone, respectively. In the horizontal direction, with increasing distance from the coastline, the green ecological land (GEL) increased, whereas it was the opposite for BEL. The transformations of PLEL were high and low frequency in the north and south, respectively. From 2000 to 2020, the eco-environmental quality of China’s coastal zone slightly degraded, with conditions that were “excellent in the south and poor in the north”. The eco-environmental qualities of each sub-coastal zone gradually improved with increasing distance from the coastline. The main transformation types that led to eco-environmental improvement and degradation were from other production lands (OPL) to blue ecological land (BEL) and BEL to OPL, respectively. The findings will guide PLEL planning, eco-environmental protection, and science-based land usage.

Economic globalization promotes the economic development of underdeveloped regions but also influences the ecological environments of these regions, such as natural forest degradation. For inland developing regions with underdeveloped traffic routes, are the effects on the ecological environment also as obvious? To reveal the response characteristics of the ecological environment of the inland developing countries to globalization, we took Laos as an example, and used the land use/cover change data and also its exports and imports data to analyze the ecological environment change since the millennium. Land use transfer matrix analysis showed that Laos had encountered a large conversion of 14.43% natural forest to plantation forest since 2000 to 2017, and also a degradation of 5.94% natural forest to shrubland and grassland. Landscape pattern analysis showed that these changes were the main reasons of the fragmentation of ecological patches, which would lead to a reduction in biodiversity. More, topographic analysis further showed that natural forest degradation mainly took place in high-altitude and large slope areas, which could increase the potential of natural hazards such as floods. Coupling analysis with its exports and imports data indicated that economic globalization still had a significant impact on the country’s ecological environment although Laos is an inland developing country. Laos should strengthen the regulation of renewable resources such as forests and water resources, to avoid losing the renewable resources market while still enjoying the dividends of economic globalization. At the same time, it is necessary to accurately evaluate the indirect impacts of development on neighboring countries to ensure sustainable development.

Rapid and accurate updating of urban land areas is of great significance to the study of environmental changes. Although there are many urban land products (ULPs) at present, such as GlobeLand30, Global Urban Footprint (GUF), and Global Human Settlement Layer (GHSL), these products are all static data of a certain year, and are not able to provide high-accuracy updating of urban land areas. In addition, the accuracies of these data and their application value in the update of urban land areas need to be urgently proven. Therefore, we proposed an approach to quickly and accurately update urban land areas in the Kuala Lumpur region of Malaysia, and assessed the accuracies of urban land products in different urban landscape patterns. The approach combined the advantages of multi-source data including existing ULPs, OpenStreetMap (OSM) data, Landsat Operational Land Imager (OLI), and Phased Array type L-band Synthetic Aperture Radar (PALSAR) images. Three main steps make up this approach. First, the urban land training samples were selected in the urban areas consistent with GlobeLand30, GUF, and GHSL, and samples of bare land, vegetation, water bodies, and road auxiliary data were obtained by GlobeLand30 and OSM. Then, the random forest was used to extract urban land areas according to the object’s features in the OLI and PALSAR images. Last, we assessed the accuracies of GlobeLand30, GUF, GHSL, and the results of this study (ULC) by using point and area validation methods. The results showed that the ULC had the highest overall accuracy of 90.18% among the four products and could accurately depict urban land in different urban landscapes. The GHSL was the second most accurate of the four products, and the accuracy in urban areas was much higher than that in rural areas. The GUF had many omission errors in urban land areas and could not delineate a large area of complete spatial information of urban land, but it could effectively extract scattered residential land with small patches. GlobeLand30 had the lowest accuracy and could only express rough, large-scale urban land. The above conclusions provide evidence that ULPs and the approach proposed in this study have a great application potential for high-accuracy updating of urban land areas.

As the largest freshwater Lake in China, Poyang Lake plays an important role in regulating the water level of Yangtze River and maintaining the ecological balance of surrounding areas. Therefore, this study utilizes Google Earth Engine (GEE) geospatial technology to acquire 124 remotely sensed images to explore the spatial and temporal changes of the four seasons of Poyang Lake from 1987 to 2019. The official statistics from relevant cities are used to quantitatively reveal the main driving factors of water body changes in Poyang Lake. Temporally, the water body area of Poyang Lake shows an obvious downward trend in general. Spatially, the water body area of Poyang Lake shows a gradual decrease from the center of the lake to the surroundings. In 1987-1998, the water body area of Poyang Lake showed an increasing trend, with an increase of about 727.90km2. From 1998 to 2006, the water body area of Poyang Lake mainly showed a decreasing trend, with a decrease of 401.58km2. The water body area of Poyang Lake increased slowly during 2006-2017. Anthropogenic factors and natural factors have influenced the changes in water body area of Poyang Lake to a certain extent. This study can provide valuable supporting data for the comprehensive environmental management and flood hazard monitoring of Poyang Lake.