Remote sensing mapping of macroalgal farms by modifying thresholds in the classification tree

Abstract

Z, et al. (2018) Remote sensing mapping of macroalgal farms by modifying thresholds in the classification tree. Geocarto International: 1-18. Available: http://dx. AbstractRemote sensing is the main approach used to classify and map aquatic vegetation, and classification tree (CT) analysis 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 with the same sensor , and two features were then employed to extract macroalgae from aquaculture farms from the seawater background. Besides, object-based classification and other image analysis methods were adopted to improve the classification accuracy in this study. Results show that the overall accuracies of traditional CTs for three images are 92.0%, 94.2% and 93.9%, respectively, whereas the overall accuracies of the two corresponding modified CTs for images obtained on January 21, 2015 and November 5, 2014 are 93.1% and 89.5%, respectively. This indicates modified CTs can help map macroalgae with multi-date imagery and monitor the spatiotemporal distribution of macroalgae in coastal environments.

Seaweed Habitats on the Shore: Characterization through Hyperspectral UAV Imagery and Field Sampling

“…So, the use of common algorithms could be perfectible, but it does answer the problem of the study by classifying correctly macroalgal communities. Other algorithms such as random forests or support vector machines might be considered to estimate entire shores, as for coastal/terrestrial objects [93][94][95][96][97].…”

Mapping Long-Term Spatiotemporal Dynamics of Pen Aquaculture in a Shallow Lake: Less Aquaculture Coming along Better Water Quality

“…Satellite remote sensing has been proved to be an effective tool to map aquaculture distribution at a large scale [19,26,28,29,31,49]. Sensitive image features of the aquaculture facilities (e.g., cages and pens) and classification methods are key in mapping an aquaculture area.…”

“…Satellite remote sensing is an effective tool to track the spatial distribution and historical change of many water properties, such as aquatic vegetation cover and chlorophyll a, with a relative low cost [18,[21][22][23][24][25]. With an increasing concern about aquaculture's effect on the environment, different moderate-and high-resolution satellite data, such as IKONOS, QuickBird, Worldview, China's satellite ZiYuan-3 and GaoFen-1 and Landsat-5/8 [19,[26][27][28][29], as well as SAR data, such as RADARSAT SAR and Sentinel-1, have been utilized for mapping aquaculture facilities, including cages, raft pens and ponds in different coastal areas, estuaries and inland lakes [30][31][32]. Various classification methods have been developed for mapping the aquaculture facilities based on different satellite data, including visual interpretation of the enhanced images, object-oriented classification and decision-tree classification [20,[33][34][35][36][37].…”

Human Activities Increase the Nitrogen in Surface Water on the Eastern Loess Plateau

“…Changing a paddy field to dry land or construction land will increase water yield while changing a water area to a paddy field or dry land will reduce water yield. The transfer of land use to the surface with a high evaporation rate will reduce runoff [27]. Agricultural coverage explained the 69% variability of mean nitrate concentrations in the Mediterranean river basin during the 25 years (1981-2005) [28].…”