Eye in the sky: protecting our wetlands

Confronted with sea level rise, a warming climate, more extreme storm events, deforestation and intense population pressures. These seem to be the reoccurring motifs of the current state of our environment. However, we neglect to associate this with a degradation to one of our most valuable resources. Wetlands. Wetlands are immensely important ecological systems, which provide a multitude of ecosystem services on which we all depend. With half of our wetlands already lost due to such stresses, and with this number expected to increase, and with what remains being in a degraded ecological state, efforts have been implemented to protect this precious resource. However, these ecosystems are incredibly complex, diverse and mostly inaccessible, making routine inspection and quantification difficult. However, keep in mind, forecasts predict that at current rates of decline, 30–40% of coastal wetlands (IPCC, 2007; Giri et al., 2011) and 100% of mangroves (Duke et al., 2007; Giri et al., 2011) could be lost within the next 100 years (Giri et al., 2011). Therefore, something needs to be done so that we don’t lose this valuable resource.

Recognising that successful wetland conservation and management requires consistent and timely data. There has been establishment of maps and consistent data retrieval has been achieved through the employment of remotely sensed images as a tool of analysis. This has allowed for the development of baseline conditions from regional to global scales, which provide us with an inventory of our wetland resources (figure 1).

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Figure 1: Classification map of salt marshes to identify invasive species. Source: Sun et al., (2016)

This remotely sensed data has come from a variety of sources, utilising different lengths of the electromagnetic spectrum in order to quantify phenological and abiotic characteristics of these ecosystems. Ideally, high resolution data is required in order to delineate the complex characterisation of these environments. This has been achieved through the use of such medium resolution sensors as as Landsat, SPOT and MODIS, which have been useful for regional studies. Whilst the use of higher resolution sensors, such as IKONOS, Worldview and GeoEYE are more relevant for deciphering species composition as a result of the finer scale (figure 2). In addition to optical sensors the use of Synthetic Aperture Radar (SAR) is found to be incredibly useful when it comes to mapping wetlands. This is as a result of its multiple wavelengths and polarisations, which can operate day and night and are not affected by cloud cover. Recently, the use of Light Detection and Ranging (LiDAR) intensity data has also proven a valuable tool in creating precise Digital Elevation Models (DEMs), which are useful in determining the likely hydrology of wetlands. The information gathered from these sensors is then produced in to reports detailing the extent, health, and changes of these ecosystems, providing a tool for natural resource managers to compare targets and set goals to.

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Figure 2: A complex classification of the Selenga River Delta of Lake Baikal, Russia, source: Lane et al., (2015)

What has been most successful in mapping and classifying these complex and intricate environments is the use of a fusion of these technologies, namely optical and radar imagery. By churning this data through decision tree algorithms or object based image analysis techniques, reliable and accurate classifications of wetlands have been achieved.  Including maps of losses and gains in biomass, changes to inundation and species composition. This has then been used to predict the likely effects of certain changes to inundation, land use or sea level rise on the ecosystem.

So what can we do with this information?

There has been a multitude of studies, which have investigated wetlands, at varying degrees of scale. Some studies have suggested that sediment deposition will be able to keep pace with seal level rise, so there may not be an extensive loss of wetlands as a result of this concern (Ward et al., 2015). Concurrently, there has been a loss of mangroves in some regions, which were previously thought to be in tact (Lucas et al.) and models have been calibrated to predict response of vegetation to inundation events in fresh water wetlands (Thomas et al., 2015). In addition, invasive species encroachment and identification has been achieved at relatively inexpensive prices (Boyden et al., 2013). Invasive species are one of the foremost threats to wetland ecosystems in Kakadu National Park and thus this provides an invaluable tool to decision makers, to protect this world-renowned site.

Problems?

There is constant reference to the superior use of hyperspectral, LiDAR, and high resolution data, but this is also followed by the qualm that these sources of data are relatively expensive to obtain and thus, repeat observations are seldom made. Thus, their use as a tool to natural resource managers is limited. However, with increasing recognition of the immense use of these systems and the resulting increase in demand, this data is becoming more easily accessible.

So, with the data at our fingertips and an ever increasing database of successful implementation, will we utilise our knowledge and prevent the loss of this valuable ecosystem?

References

Boyden J., Joyce K.E., Boggs G., & Wurm P., (2013), Object-based mapping of native vegetation and para grass (Urochloa mutica) on a monsoonal wetland of Kakadu NP using Landsat 5 TM Dry-season time series, Journal of Spatial Science, vol. 58(1), pp. 53-77

Giri C., Ochieng E., Tieszen L.L., Zhu Z., Singh A., Loveland T., Masek J., & Duke N., (2011), Status and distribution of mangrove forests of the world using earth observation satellite date, Global Ecology and Biogeography, vol. 20, pp. 154-159

Lane CR., Anenkhonov O., Lui H., Bradley C., & Chepingoa A.V., (2015), Classification and inventory of freshwater wetlands and aquatic habitats in the Selenga River Delta of Lake Baikal, Russia, using high-resolution satellite imagery, Wetlands Ecology Management, vol. 23(2), pp. 195-214

Lucas R.M., Bunting P., Clewley D., Proisy C., Filho P.W.M.S., Viergever K., Woodhouse I., Ticehurst C., Carreiras J., Rosenqvist A., Accad A., & Armston J., Characterisation and Monitoring of Mangroves Using ALOS PALSAR Data

Sun C., Liu Y., Zhao S., Zhou M., Yang Y., &Li F., (2016), Classification mapping and species identification of salt marshes based on a short-time interval NDVI time-series from HJ-1 optical imager, International Journal of Applied Earth Observation and Geoinformation, vol. 45, pp. 27-41

Thomas R., Kingsford R.T., Lu Y., Cox S.J., Sims N.C., & Hunter S.J., (2015), Mapping inundation in the hetergenous floodplain wetlands of the Macquarie Marshes, using Landsat Thematic Mapper, Journal of Hydrology, vol. 524, pp. 194-213

Ward R.D., Burnside N.G., Joyce C.B., Sepp K., Teasdale P.A., (2015), Improved modelling of the impacts of sea level rise on coastal wetland plant communities, Hydrobiologia, vol. 757

 

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