Tatsuyuki Sagawa

Using bottom surface reflectance to map coastal marine areas: a new application method for Lyzenga's model

Remote sensing is widely used in coastal management. Lyzengas model has been traditionally used to explain the relationship between bottom surface reflectance and the radiance level measured by satellite. Due to its central assumption, this model lacks accuracy compared with the other radiative transfer models. Nonetheless, it enables, with a single and simple equation, representation of the multiple optical processes taking place in coastal areas. Mapping processes associated with this model may include radiometric correction, a technique previously pointed out as a major driver of mapping accuracy. Radiometric correction is generally based on a depth-invariant index, efficient for clear waters (Jerlov water type I to II) but largely unsuitable when transparency decreases (Jerlov water type II to III). In order to overcome this problem, we developed a new index for radiometric correction, which combines bathymetry data with attenuation coefficients. The improved efficiency of our model with regard to the traditional depth invariant index was demonstrated through two case studies: Funakoshi Bay (Japan; Jerlov water type II) and the Gabes Gulf part located off Mahares (Tunisia; Jerlov water type II to III).

Technical Note. Mapping seagrass beds using IKONOS satellite image and side scan sonar measurements: a Japanese case study

In this study, we mapped seagrass beds in Japanese coastal areas by using a newly developed combination method of satellite and side‐scan sonar images. Traditionally, sea‐truth data used for satellite‐image analysis are collected through direct observations requiring scuba divers or by aquatic video‐camera observation. The method proposed here used side‐scan sonar measurements for collecting accurate sea‐truth data and succeeded in efficiently obtaining precise information about seagrass distribution. Side‐scan sonar images also enabled us to assess the reliability of satellite‐image results from an area perspective and not only from a traditional point data one. The IKONOS image was analysed in two different ways. First, mapping was realized without any reference to bottom depth. Second, mapping was processed with consideration for depth gradients. Results were compared using error matrices and showed that the method proposed here is suitable to map several specific areas efficiently.

Mapping seaweed forests with IKONOS image based on bottom surface reflectance

Seaweed forests are important habitats for many fishery species. However, decrease in seaweed forests is reported in all over Japan. Mapping and monitoring seaweed forest distribution is necessary for understanding their present status and taking measures for their conservation. Since traditional diving visual observation is not efficient for large scale mapping, alternative method is required. Although satellite remote sensing is one of the noteworthy methods, only a few studies have been conducted probably due to two main problems about mapping seaweed forests by remote sensing. The first one is a difficulty to collect field truth data. The second one is a light attenuation effect in water column which makes analysis more difficult. We applied an efficient method to overcome these two problems. We selected the seaweed beds off Shimoda in Izu Peninsula, Japan, as a study area. An IKONOS satellite image was used for analysis because its high spatial and radiometric resolutions are practical for seaweed mapping. We measured spectral reflectance profiles of seaweed and substrates in the study area. The result revealed effective wavelength bands for distinguishing seaweeds from other substrates. Truth data for satellite image analysis and evaluation were collected in the field using the boat and an aquatic video camera. This method allowed us to collect many truth data in short time. Satellite image analysis was conducted using radiometric correction for water column and maximum likelihood classification. The overall accuracy using error matrix reached 97.9%. The results indicate usefulness of the method for seaweed forest mapping.

Mapping is a Key for Sustainable Development of Coastal Waters: Examples of Seagrass Beds and Aquaculture Facilities in Japan with Use of ALOS Images

Sound coastal ecosystems provide important ecological services such as food supply, nutrient cycling, and stabilizing effects of environments (Costanza et al., 1997). They are indispensable for sustainable development of coastal areas. However, human impacts such as fisheries or reclamation destroy coastal environments and ecosystems (e.g. Huitric et al., 2002). To conserve or restore sound coastal ecosystems, it is necessary to know present situation of coastal areas including coastal ecosystems such as seagrass beds, seaweed beds, coral reefs and tidal flats, and also human activities such as fisheries related facilities and reclamation. Diving and observation from the boat are usually used for checking bottom habitats. However, these methods are laborious and time consuming (Komatsu et al., 2002a). If a boat is employed to survey a broad area with many aquaculture facilities, it requires a long time to survey it due to obstruction of rafts against navigation. The survey with the boat is unsuccessful to localize many aquaculture facilities with GPS in the cases that the boat can’t easily approach to the aquaculture facilities due to ropes and rafts. Thus, it is desired to develop efficient mapping and monitoring systems of coastal areas.

Can ALOS-3/HISUI detect seaweed beds more precisely than ALOS/AVNIR-2?

Hyperspectral Imager Suite (HISUI) is a Japanese future spaceborne hyperspectral sensor system. It will be launched in 2015 or later as one of mission instruments onboard JAXAs Advanced Land Observation Satellite 3 (ALOS-3). HISUI will consist of a hyperspectral imager and a multispectral imager with 30 m and 5 m spatial resolution and 30 km and 90 km swath, respectively. In order to characterize capability to detect seaweed beds of HISUI multispectral data with 5 m spatial resoution, we comprared classification results between ALOS/AVNIR-2 with spatial resolution of 10 m and simulation data of HISUI produced from WorldView-2. Study site was seletced in Oita Prefecture in Kyushu Island, Japan, where seaweed beds were broadly distributed along the coast. We used AVNIR-2 data taken on 20 February 2007 and simulation data produced from WorldView-2 taken on 18 April 2010. Supervised and unsupervised classifications were applied to these data sets. Miss-classification of analysis using AVNIR-2 was identified in deep waters in offshore waters. Since radiometric resolution of AVNIR-2 is 8 bits, it is considered that low radiometric resolution causes missclassification. Spatial resolution of HISUI multiband data with 5 m permits to detect seaweed beds clearer. Precision of classification using HISUI simulation data was about 90% and 10% higher than that using AVNIR-2. Thus HISUI multiband data are suitable for mapping seaweed beds in coastal waters.

Simulation of seagrass bed mapping by satellite images based on the radiative transfer model

Seagrass and seaweed beds play important roles in coastal marine ecosystems. They are food sources and habitats for many marine organisms, and influence the physical, chemical, and biological environment. They are sensitive to human impacts such as reclamation and pollution. Therefore, their management and preservation are necessary for a healthy coastal environment. Satellite remote sensing is a useful tool for mapping and monitoring seagrass beds. The efficiency of seagrass mapping, seagrass bed classification in particular, has been evaluated by mapping accuracy using an error matrix. However, mapping accuracies are influenced by coastal environments such as seawater transparency, bathymetry, and substrate type. Coastal management requires sufficient accuracy and an understanding of mapping limitations for monitoring coastal habitats including seagrass beds. Previous studies are mainly based on case studies in specific regions and seasons. Extensive data are required to generalise assessments of classification accuracy from case studies, which has proven difficult. This study aims to build a simulator based on a radiative transfer model to produce modelled satellite images and assess the visual detectability of seagrass beds under different transparencies and seagrass coverages, as well as to examine mapping limitations and classification accuracy. Our simulations led to the development of a model of water transparency and the mapping of depth limits and indicated the possibility for seagrass density mapping under certain ideal conditions. The results show that modelling satellite images is useful in evaluating the accuracy of classification and that establishing seagrass bed monitoring by remote sensing is a reliable tool.

Distinguishing resident from transient species along marine artificial reefs

Even if artificial reef studies heavily refer to the distinction between resident and transient species, there is still no widely-shared available method to distinguish objectively these two groups. Such an absence makes any comparison between studies difficult. This study aims to test whether the four objective distinction methods successfully applied to a 21-year-long time-series on fish assemblage in an English estuary may be as successful when applied to marine artificial reefs. For such a purpose, we tested each distinction tool separately with reference to four different artificial reef fish assemblage datasets. Three of them were drawn from the literature. Results indicate that none of these tools, used either individually or collectively, provide an efficient solution to distinguish resident species for the four datasets considered. We suggest that one of the major reasons for this failure may lie in the relative sampling size. Nonetheless, as these four datasets are representative of the datasets generally reported in the literature, tools capable of distinguishing reliably and efficiently resident from transient species along artificial reefs have yet to be developed. However, such a development requires fish residence to be previously and accurately defined by artificial reef scientists and managers.