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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Mangrove conservation effort have been undertaken in China, but there is still a lack of quantitative assessment of its eco-environmental benefits. The ecological footprint (EF) is an important indicator of the sustainability of ecosystems, with carbon footprint (CF) and biocapacity (BC) being key indicators of forest ecosystems. Despite the high carbon sequestration capacity of mangrove ecosystems, they have not yet been included in EF. This study introduces mangrove ecosystems into EF accounting and assesses the eco-environmental benefits of mangrove conservation in China. To achieve this, the net ecosystem exchange (NEE) of mangrove forests was estimated based on the gross primary production and respiration rates. Average forest carbon sequestration (AFCS) and yield factor, key parameters of CF and BC, were calculated from NEE for the first time. The CF and BC of mangrove ecosystems were then refined using newly defined AFCS and yield factor. The proposed framework was applied to the whole mangrove forests in China to assess mangrove conservation efforts. The results showed that the average CF of mangrove forests is about 1000 M ha, which is lower than that of terrestrial forests, while the BC of mangroves is not as high as expected due to the limited area of mangroves. CF and BC vary along the coastline, whereby CF tends to decrease with decreasing latitude while BC remains high at low latitudes owing to the large mangrove area. There is no significant decreasing trend in CF, but BC increases with increasing mangrove area. The newly introduced ecological footprint accounting of mangrove ecosystems suggests that mangrove conservation in China should focus on ecosystem health and expand the area of forestable land. These findings will benefit future management of mangrove ecosystems in China and other countries. • Ecological footprint accounting was built for mangrove ecosystems. • Mangrove conservation contribute to decreasing carbon footprint and increasing bio-capacity. • Due to the limited area, mangrove ecosystems are not fully utilizing their carbon sink role in China. [ABSTRACT FROM AUTHOR]