China is facing intense coastal eutrophication. Large-scale seaweed aquaculture in China is popular, now accounting for over 2/3’s of global production. Here, we estimate the nutrient removal capability of large-scale Chinese seaweed farms to determine its significance in mitigating eutrophication. We combined estimates of yield and nutrient concentration of Chinese seaweed aquaculture to quantify that one hectare of seaweed aquaculture removes the equivalent nutrient inputs entering 17.8 ha for nitrogen and 126.7 ha for phosphorus of Chinese coastal waters, respectively. Chinese seaweed aquaculture annually removes approximately 75,000 t nitrogen and 9,500 t phosphorus. Whereas removal of the total N inputs to Chinese coastal waters requires a seaweed farming area 17 times larger than the extant area, one and a half times more of the seaweed area would be able to remove close to 100% of the P inputs. With the current growth rate of seaweed aquaculture, we project this industry will remove 100% of the current phosphorus inputs to Chinese coastal waters by 2026. Hence, seaweed aquaculture already plays a hitherto unrealized role in mitigating coastal eutrophication, a role that may be greatly expanded with future growth of seaweed aquaculture.

Macroalgae plays an important role in coastal ecosystems. The accurate delineation of macroalgae areas is important for environmental management. This study compared the pixel- and object-based methods using Gaofen satellite no. 2 image to explore an efficient classification approach. Expert system rules and nearest neighbour classifier were adopted for object-based classification, whereas maximum likelihood classifier was implemented in the pixel-based approach. Normalized difference vegetation index, normalized difference water index, mean value of the blue band and geometric characteristics were selected as features to distinguish macroalgae farms by considering the spectral and spatial characteristics. Results show that the object-based method achieved a higher overall accuracy and kappa coefficient than the pixel-based method. Moreover, the object-based approach displayed superiority in identifying Porphyra class. These findings suggest that the object-based method can delineate macroalgae farming areas efficiently and be applied in the future to monitor the macroalgae farms with high spatial resolution imagery.

Mangrove ecosystems play an important role in global carbon budget, however, the quantitative relationships between environmental drivers and productivity in these forests remain poorly understood. This study presented a remote sensing (RS)-based productivity model to estimate the light use efficiency (LUE) and gross primary production (GPP) of mangrove forests in China. Firstly, LUE model considered the effects of tidal inundation and therefore involved sea surface temperature (SST) and salinity as environmental scalars. Secondly, the downscaling effect of photosynthetic active radiation (PAR) on the mangrove LUE was quantified according to different PAR values. Thirdly, the maximum LUE varied with temperature and was therefore determined based on the response of daytime net ecosystem exchange and PAR at different temperatures. Lastly, GPP was estimated by combining the LUE model with the fraction of absorbed photosynthetically active radiation from Sentinel-2 images. The results showed that the LUE model developed for mangrove forests has higher overall accuracy (RMSE = 0.0051, R² = 0.64) than the terrestrial model (RMSE = 0.0220, R² = 0.24). The main environmental stressor for the photosynthesis of mangrove forests in China was PAR. The estimated GPP was, in general, in agreement with the in-situ measurement from the two carbon flux towers. Compared to the MODIS GPP product, the derived GPP had higher accuracy, with RMSE improving from 39.09 to 19.05 g C/m²/8 days in 2012, and from 33.76 to 19.51 g C/m²/8 days in 2015. The spatiotemporal distributions of the mangrove GPP revealed that GPP was most strongly controlled by environmental conditions, especially temperature and PAR, as well as the distribution of mangroves. These results demonstrate the potential of the RS-based productivity model for scaling up GPP in mangrove forests, a key to explore the carbon cycle of mangrove ecosystems at national and global scales.

Mangrove ecosystems are valuable, yet vulnerable, and therefore they have been an important subject of protection and restoration in China. Reliable information on long-term China mangrove dynamics is lacking but vital to analyze the driving forces and evaluate the efforts of mangrove conversation. This study aims to quantify the conversions among mangroves and other land covers with high accuracy. The updated mangrove base map for 2018 was produced by integrating Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar 2 (PALSAR-2) yearly mosaics and Landsat imagery with an overall accuracy of 95.23 ± 6.02%. Then, a novel approach combining map-to-image and image-to-image methods was proposed to detect the changed pixels in mangrove forests from 1985 to 2018. The mangrove base map was adopted to mask the images from other years. To determine the changed pixels, the differencing values in the masked area between two images were calculated and compared with the corresponding thresholds. Based on the changed pixels, the possible driving forces were analyzed and associated with socioeconomic development. The resultant mangrove dynamics demonstrated that mangrove forests in China experienced a tendency of loss first and recovery later during the past 30 years. Most mangrove gains came from aquaculture and mudflat, whilst losses were due to the built-up construction and aquaculture reclamation. These conversions indicated that mangrove deforestations were mainly due to human-induced destruction, while the recoveries were strongly associated with conservation and restoration actions.

Remote sensing is the main approach to map aquatic vegetation, and classification tree (CT) is superior to various classification methods. Based on previous studies, modified CT can be developed from traditional CT by adjusting the thresholds based on the statistical relationship between spectral features to classify different images without ground-truth data. However, no studies have yet employed this method to resolve marine vegetation. In this study, three Gao-Fen 1 satellite images obtained on 30 January 2014, 5 November 2014 and 21 January 2015 were selected, and two features were then employed to extract macroalgae farms. Results show that the overall accuracies of traditional CTs for three images are 92.0, 94.2 and 93.9%, respectively, whereas those of the two corresponding modified CTs for images obtained on 21 January 2015 and 5 November 2014 are 93.1 and 89.5%, respectively. This indicates modified CTs can map macroalgae with multi-date imagery and monitor their spatiotemporal distribution in coastal environments.