Remote Monitoring of Expansion of Aquaculture Ponds Along Coastal Region of the Yellow River Delta from 1983 to 2015

Abstract

Aquaculture ponds are one of the fastest-growing land use types in valuable and fertile coastal areas and have caused serious environmental problems. Quantitative assessment of the extent, spatial distribution, and dynamics of aquaculture ponds is of utmost importance for sustainable economic development and scientific management of land and water resources in the coastal area. An object-oriented classification approach was applied to Landsat images acquired over three decades to investigate the long-term change of aquaculture ponds in the coastal region of the Yellow River Delta. The results indicated that the aquaculture ponds in the study area undergone a sharp expansion from 40.38 km 2 in 1983 to 1406.89 km 2 in 2015, and the fast expansion occurred during the period of 2010-2015 and 1990-2000. Natural wetlands, especially mudflat, and cropland were main land use types contributing to the increase of aquaculture ponds. The patches of aquaculture ponds were consequently prevalence in the north of the Yellow River Estuary and landscape metrics indicated an increase of the aquaculture ponds of the study area in the quantity and complexity. The expansion of aquaculture ponds inevitably had negative effects on the coastal environment, including loss of natural wetlands, water pollution and land subsidence, etc. The results from this study provide baseline data and valuable information for efficiently planning and managing aquaculture practices and for effectively implementing adequate regulations and protection measures.

Tracking three decades of land use and land cover transformation trajectories in China's large river deltas

“…Although the overall accuracy of LULC classification for river deltas has been estimated to be over 85% (Chen et al, ; Ren et al, ), accurately mapping tidal flats is very challenging because tidal flats are fully exposed for only a brief period at low tide, and it is quite difficult for Landsat images to capture remotely sensed data in a timely and precise manner (Murray, Phinn, Clemens, Roelfsema, & Fuller, ). These challenges might bias assessment of the magnitudes of accretion and erosion processes occurring in tidal flats based on multitemporal LULC transformation analysis and deviate assessments of their spatiotemporal interactions with other natural and anthropogenic processes.…”

“…Time series (1980, 1990, 2000, and 2010) LULC data for the LRD, YRD, YtRD, and PRD were collected from the Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences. The classifications of LULC types within the river deltas were generated by object‐oriented image classification using the nearest neighbor classifier with manually selected training samples of cloud‐free Landsat images acquired by the Multispectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper (ETM) in the 1980, 1990, 2000, and 2010 (Chen et al, ; Ren et al, ). All remotely sensed data were downloaded from the United States Geological Survey Center for Earth Resources Observation and Science (http://glovis.usgs.gov/).…”

“…To ensure consistency with the resolutions of images from other years, the MSS images from the 1980 were resampled to a pixel size of 30 m × 30 m by nearest neighbor resampling in ENVI software (ENVI, 2010), which is commonly used to resample remote sensing data (Hansen et al, ). The overall accuracy and kappa coefficient of the confusion matrix were used to assess the accuracy of LULC type classification in all maps between the classification results and validation sample results from historic investigation points, topographic maps, resource maps, and images from Google Earth (Chen et al, ; Ren et al, ). The overall accuracies for all the years were estimated to be over 85%, and the kappa coefficients were calculated to be above 0.80, indicating sufficient accuracy of the classification results.…”

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

“…Ponds have been rapidly expanding during the last decades within fertile coastal zones. SRS can be used to quantitively assess the expansion of the pond's land use and its impact on the coastal environment using satellite images, such as Landsat for long time series analysis [100] and/or Sentinel-1 [146].…”

Sea Surface-Visible Aquaculture Spatial-Temporal Distribution Remote Sensing: A Case Study in Liaoning Province, China from 2000 to 2018

“…Many studies have been conducted on remote sensing of land pond aquaculture. For example, Ren et al [20] studied the dynamic changes of pond aquaculture in the coastal areas of the Yellow River Delta from 1983 to 2015 using Landsat remote sensing satellite images. The results provide benchmark data and valuable information for effective planning and management of aquaculture.…”