The use of remote sensing and GIS for modelling aquaculture site suitability in relation to changing climate

Abstract

Globally fish production has continued to increase during recent years at a rate exceeding that of human population growth. However the contribution from capture fisheries has remained largely static since the late 1980s with the increase in production being accounted for by dramatic growth in the aquaculture sector. As of 2012 aquaculture accounted for approximately 42% of total fisheries production and 78% of inland fish production. In view of these figures it is unsurprising that for a number of regions aquaculture represents an important source of both food security and income. The use of Geographical Information Systems (GIS) and spatial data have seen substantial developments in recent years with the help of increasingly affordable computing capacity. From an aquaculture perspective the use of GIS has shown significant potential as a means of combining varied data sources, including those acquired via remote sensing, into models to provide decision support in relation to site selection. A common theme amongst site suitability assessments is the incorporation of climate variables relating to temperature and water availability. These factors in turn can have a significant influence on aquaculture in terms of water availability and quality, and temperature modulated growth performance. There is now a strong consensus that during the 20th century, and especially during recent decades, the earth has experienced a significant warming trend. There is also strong agreement that this warming trend is at least partially a consequence of anthropogenic greenhouse gas emissions and that some degree of further warming is inevitable. While global warming is typically discussed in terms of degrees centigrade of average global temperature increase the full effects in terms of climate changes will be varied both in terms of location and season. The current project focuses on site suitability for aquaculture in relation to changing climate conditions. Significant use is made of GIS and a range of spatial data including remotely sensed data and output from a series of climate models. The project consists of a number of key components: 1. Vulnerability of aquaculture related livelihoods to climate change was assessed at the global scale based on the concept of vulnerability to climate related impacts as a function of sensitivity to climate change, exposure to climate change, and adaptive capacity. Use was made of national level statistics along with gridded climate and population data. Climate change scenarios were supplied using the MAGICC/SCENGEN climate modelling tools. Analysis was conducted for aquaculture in freshwater, brackish, and marine environments with outputs represented as a series of raster images. A number of Asian countries (Vietnam, Bangladesh, Laos, and China) were indicated as most vulnerable to impacts on freshwater production. Vietnam, Thailand, Egypt and Ecuador stood out in terms of brackish water production. Norway and Chile were considered most vulnerable to impacts on Marine production while a number of Asian countries (China, Vietnam, and the Philippines) also ranked highly. 2. Site suitability for pond-based aquaculture was modelled at the global scale using a 10 arcsecond grid. Data from an ensemble of 13 climate models was used to model pond temperature and water availability for rain fed ponds under late 20th century conditions and for a 2°C global warming scenario. Two methods are demonstrated for combining data with a focus on the culture of warm water species. Results suggest both positive and negative impacts in relation to the 2°C warming scenario depending on location and season. Some areas are projected to see negative effects from maximum temperatures during the warmest parts of the year while for many regions there are likely to be potential increases in growth performance during colder months with possible expansion into previously unsuitable areas. 3. Methods for detecting surface water using remotely sensed data were investigated for Bangladesh. Use was made of data from the Moderate-resolution Imaging Spectroradiometer (MODIS) and Landsat ETM+ instruments with accuracy assessed against ground truth data collected in the field. A time series was constructed using all available MODIS data (approximately 13 years with an 8 day temporal resolution) to show areas of: surface water, land, and mixed land and water. The time series was then analysed to produce a layer showing the percentage of the total time series where surface water is indicated thus providing a spatial representation of flood prevalence. 4. A land cover data set was produced using 9 Landsat ETM+ scenes to cover the majority of Bangladesh. 10 different classification routines were evaluated including a decision tree approach unique to the current study. Classification results were assessed against two sets of ground control points produced: one based on field collected ground truth data and the other using a stratified random sampling procedure in association with visual analysis of high resolution true colour satellite images and ETM+ composites. The most accurate classifications were provided by the decision tree method developed for the current study and a Multi-Layer Perceptron (MLP) neural network based classifier. 5. Site suitability for pond-based aquaculture within Bangladesh was assessed using a GIS in combination with the ETM+ based land cover data, the MODIS based surface water time series, and components of the global site suitability assessment including modelled pond temperature data. Assessments were made based on late 20th century conditions and a 2°C global warming scenario. The MODIS surface water time series was also used to show the effects of storm surge flooding in relation to cyclone Aila that struck Bangladesh on 25th May 2009. The south and east of the country were considered most suitable for aquaculture due to more favourable cold season temperatures and higher water balance values. The north west of the country was considered least favourable due to higher maximum modelled pond temperatures and lower water balance values. The effect of the 2°C warming scenario was to enhance these trends.

To date the potential spatial implications of changing climate for aquaculture has been significantly under researched. In this respect the current study provides a highly useful indication of where aquaculture related livelihoods may be vulnerable. In addition valuable and unique insights are provided into the distribution of areas of both potential increased, as well as decreased, suitability for existing aquaculture and further aquaculture development.