Detection of Seagrass Distribution Changes from 1991 to 2006 in Xincun Bay, Hainan, with Satellite Remote Sensing

Seagrass distribution is a very important index for costal management and protection. Seagrass distribution changes can be used as indexes to analyze the reasons for the changes. In this paper, in situ hyperspectral observation and satellite images of QuickBird, CBERS (China Brazil Earth Resources Satellite data) and Landsat data were used to retrieve bio-optical models and seagrass (Enhalus acoroides, Thalassia hemperichii) distribution in Xincun Bay, Hainan province, and seagrass distribution changes from 1991 to 2006 were analyzed. Hyperspectral results showed that the spectral bands at 555, 635, 650 and 675 nm are sensitive to leaf area index (LAI). Seagrass detection with QuickBird was more accurate than that with Landsat TM and CBERS; five classes could be classified clearly and used as correction for seagrass remote sensing data from Landsat TM and CBERS. In order to better describe seagrass distribution changes, the seagrass distribution area was divided as three regions: region A connected with region B in 1991, however it separated in 1999 and was wholly separated in 2001; seagrass in region C shrank gradually and could not be detected in 2006. Analysis of the reasons for seagrass reduction indicated it was mainly affected by aquaculture and typhoons and in recent years, by land use changes

Seagrass distribution changes in Swan Lake of Shandong Peninsula from 1979 to 2009 inferred from satellite remote sensing data

Seagrass and associated bio-resources are very important for swan’s overwintering in Swan Lake in Rongcheng of Shandong Peninsula of China. The seagrass distribution changes, which are usually affected by the regional human activities, can indirectly affect swan’s habitat. In this study the satellite remote sensing data in years 1979–2009 together with in-situ observations in recent years were used to examine the seagrass distribution changes in Swan Lake. The band ratio of band 1 to band 2, Lyzenga’s methods and band synthesize of band 1, band 2 and band 3 were used for seagrass retrieval. The band ratio of band 1 to band 2 with ranges greater than 4.5 was used for estimating the seagrass coverage greater than 50%. Results showed that in years 1979–1990 seagrass coverage greater than 50% occupied more than half of the surface area of Swan Lake. In years 2000–2005, the total area with seagrass distributions reduced greatly, only about one sixth to one fourth of Swan Lake’s surface area. After 2005, the seagrass area in Swan Lake increased gradually and occasionally was greater than one third of the total surface area of the Lake. It was shown that human activities such as the dam and fish pond establishment and the awareness of seagrass importance and protected actively result in the seagrass distributions changes in Swan Lake which decreased first and then increased afterwards

Using Landsat-8 Imagery Data on Mapping of the Seagrass Distribution in Matahari Island, Pulau Banyak District, Aceh Province, Indonesia

The objective of the present study was to gathers information about the seagrass distribution area using the remote sensing that retrieved from Landsat-8. Analysis of Landsat-8 image is classified into 6 classes; seagrass, coral reefs, sand, sea, and cloud. The agreement between processed image of seagrass bed and ground truth reference was 70%. The seagrass ecosystem is well distributed along the Matahari Island, with the total area approximately 44.0123 Ha

Satellite image analysis reveals changes in seagrass beds at Van Phong Bay, Vietnam during the last 30 years

Seagrass meadows are fragile ecosystems in the coastal zone. Natural disasters, land reclamation and various human activities seem to exert negative impacts on the distribution and biological performance of seagrass beds in Vietnam. In this present study, satellite Landsat TM/OLI image analysis was applied to determine changes in seagrass distribution at Van Phong Bay, Vietnam in the last 30 years. The maximum likelihood decision rule was used to extract seagrass bed distribution data. The error matrix using the in situ reference data for HLM image classification was 81–95% accurate, and Kappa coefficients were between 0.72 and 0.91. The results indicated that 186.2 ha (or 35.8%) of the original seagrass beds were lost in the last three decades at Van Phong Bay, and decline in each specific site may have been due to different causes. Typhoons may have caused the loss of seagrass beds at open-sea sites whereas aquaculture activities, excavation and terrigenous obliteration may have caused such losses in protected sites

Urban detection, delimitation and morphology: comparative analysis of selective "megacities"

Postprint (published version

Quantifying Seagrass Distribution in Coastal Water With Deep Learning Models

Coastal ecosystems are critically affected by seagrass, both economically and ecologically. However, reliable seagrass distribution information is lacking in nearly all parts of the world because of the excessive costs associated with its assessment. In this paper, we develop two deep learning models for automatic seagrass distribution quantification based on 8-band satellite imagery. Specifically, we implemented a deep capsule network (DCN) and a deep convolutional neural network (CNN) to assess seagrass distribution through regression. The DCN model first determines whether seagrass is presented in the image through classification. Second, if seagrass is presented in the image, it quantifies the seagrass through regression. During training, the regression and classification modules are jointly optimized to achieve end-to-end learning. The CNN model is strictly trained for regression in seagrass and non-seagrass patches. In addition, we propose a transfer learning approach to transfer knowledge in the trained deep models at one location to perform seagrass quantification at a different location. We evaluate the proposed methods in three WorldView-2 satellite images taken from the coastal area in Florida. Experimental results show that the proposed deep DCN and CNN models performed similarly and achieved much better results than a linear regression model and a support vector machine. We also demonstrate that using transfer learning techniques for the quantification of seagrass significantly improved the results as compared to directly applying the deep models to new locations