As the largest freshwater lake in China, Poyang Lake is suffering from declining water quality related to the excessive dredging of sand. Field supervision is difficult due to the size of the lake (>3000 km2, wet season) and limited human resources. In this study, an approach is proposed to monitor sand-dredging activities using medium-resolution optical remote-sensing imagery, including 45 Landsat TM/ETM+ images from 2002 to 2012 and 140 HJ1A/B CCD images from 2009 to 2012. The procedure for detecting dredging vessels involves three steps. (1) The entire image is segmented into different homogeneous partitions to overcome water body heterogeneity, and ships in each partition with different levels of water clarity are detected using three types of contrast box architecture. (2) Dredging vessels are then identified based on a spatial overlay analysis of ships and dredging plumes, which are extracted from remote-sensing imagery. (3) False alarms (FAs) of dredging vessels are screened according to the distribution of the sandy lake bed. The results showed significant spatio-temporal variation in dredging activities; sand-dredging activities were concentrated at the northern part of Poyang Lake prior to 2008 and have expanded southwards since 2009. The northern part of the lake experienced persistent dredging operations throughout the year, whereas dredging was observed only during the wet seasons in the southern portion of the lake. A high intensity of illegal dredging was discovered based on two lines of evidence: dredging vessels were detected during the sand-dredging ban, and the estimated quantities of sand dredging were much higher than those planned by the authority. The sediment balance in Poyang Lake has continued to be disrupted, and the lake has become a sediment-exporting system. This study provides an effective solution for monitoring sand-dredging dynamics as well as useful information for managing sand dredging in fresh water environments and assessing its potential impacts on aquatic ecosystems.
Abstract A cyclone is an intensive synoptic activity that occurs frequently over Baffin Bay. By modifying the large‐scale distribution pattern of sea level pressure, a passing cyclone can serve as an important regulator of sea ice outflow via the Davis Strait. We obtain a nearly 40‐year‐long record (1979/1980–2017/2018) of the sea ice area flux (SIAF) through the Davis Strait and Arctic cyclone activities in winter. A case study and statistical results indicate that the sea ice concentration and motion fields can be greatly altered by the occurrence of cyclones, thereby contributing to changes in sea ice export. Moreover, the effects of cyclones on sea ice export in Baffin Bay are dependent on the spatial distribution pattern of the storms. In terms of the cyclone center count and intensity, the key regions with significant impacts on sea ice export out of Baffin Bay are identified, one around Baffin Island (80°W–60°W, 60°N–70°N) and the other over the southern Labrador Peninsula (70°W–50°W, 40°N–60°N). A robust correlation exists between the winter‐accumulated SIAF via the Davis Strait and the average winter cyclone intensity (center count) in the critical regions with R = −0.57 (+0.49), affirming the vital role of cyclone activity in modulating the interannual variability of sea ice export in Baffin Bay.
Wu, M.; Zhao, Y.F.; Sun, L.E.; Huang, J.; Wang, X.H., and Ma, Y., 2020. Remote sensing of spatial-temporal variation of chlorophyll-a in the Jiaozhou Bay using 32 years Landsat data. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T.W. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 271-279. Coconut Creek (Florida), ISSN 0749-0208.The chlorophyll-a concentration (Chla, µg/L) is a vital indicator of water quality and eutrophication, yet optical complexity and significant variability of coastal waters make the accurate estimation of Chla challenging. Monitoring spatial-temporal distribution and variation of Chla and comprehending the correlation between Chla and environmental factors are necessary for long-term water quality assessment. This study calibrated and validated the Chla estimation model with satisfactory performance (R2, RMSE, and MRE values are 0.77, 0.64 µg/L, and 32.5 %) and further characterized the spatial-temporal variation of Chla in Jiaozhou Bay (JZB) based on 381 cloud-free Landsat images of 32 years (1986-2017). The annual mean Chla in JZB reached the highest value in 1997 and decreased gradually in the following two decades. The seasonal variation of Chla is obvious: the highest value of Chla appeared in summer, followed by spring, autumn and winter. Accordingly, the monthly averaged Chla peaked in July, while the minimum occurred in January. The spatial distribution of Chla on different time scales shared a similar pattern. High Chla appeared in the northwestern part of JZB and decreased gradually to the southwest, resulting in the lowest Chla near the water channel connected to the open sea. The spatial heterogeneity mainly arose from river discharge, while the temporal heterogeneity may be caused by seasonal variations in precipitation, temperature, and river discharge. This study indicated that the empirical models for the Landsat data could effectively monitor the long-term Chla variation in JZB.
The in situ data and forward radiative transfer model were applied to simulate the nonuniform vertical profiles of suspended particles in turbid Poyang Lake. The sensitivity of remote sensing reflectance (R rs ) associated with nonuniform water column showed correlation with suspended particulate matter (SPM), wavelength and water depth. Different nonuniform vertical profiles could cause more than 108% overestimation or 60% underestimation of R rs at most. The uncertainties in R rs decreased with the increase of water depth. The sensitive wavelength moved to longer wavelength and the maximum influence water depth let up, along with the increase in concentration of SPM in surface water. A dimensionless parameter made up of beam attenuation coefficient of surface water, water depth and SPM vertical distribution, was established to quantitatively describe the effects of vertically nonuniform water column on R rs .
The six largest Arctic rivers (Yenisey, Lena, Ob’, Kolyma, Yukon, and Mackenzie) drain the organic-rich Arctic watersheds and serve as important pools in the global carbon cycle. Satellite remote sensing data are considered to be a necessary supplement to the ground-based monitoring of riverine organic matter circulation, especially for the ice-free periods in high-latitudes. In this study, we propose a remote sensing retrieval algorithm to obtain the chromophoric dissolved organic matter (CDOM) levels of the six largest Arctic rivers using Sentinel-2 images from 2016 to 2018. These CDOM results are converted to dissolved organic carbon (DOC) concentrations using the strong relationship (R2 = 0.89) between the field measurements of these two water constituents. The temporal-spatial distributions of the DOC in the six largest Arctic rivers during ice-free conditions are depicted. The performance of the retrieval algorithm verifies the capacity of using Sentinel-2 data to monitor riverine DOC variations due to its improved spatial resolution, better band placement, and increased observation frequency. River discharge, watershed slopes, human activities, and land use/land cover change drove much of the variation in the satellite-derived DOC. The seasonality, geography, and scale would affect the correlation between DOC concentration and these influence factors. Our results could improve the ability to monitor DOC fluxes in Arctic rivers and advance our understanding of the Earth’s carbon cycle.
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