The potential of remote sensing in ecological status assessment of coloured lakes using aquatic plants

Field-based survey methods for aquatic vegetation have been identified as resource-demanding. Recent advances in remote sensing (RS) with sub-decimetre resolution allow for surveying aquatic vegetation at the species level. Coloured lakes, mainly due to high concentrations of humic substances, are globally widespread. However, high colour impedes the identification of submerged vegetation via remote sensing. Here, we evaluate the potential of using only emergent, floating and floating-leaved taxa that are detectable by high-resolution RS (RS-taxa) to assess the ecological status of lakes. In a dataset covering 72 Swedish low alkaline coloured lakes, we identified 31 RS-taxa. The power of RS-taxa assemblages to predict non-RS assemblages was analysed by a combination of ordination (Detrended Correspondence Analysis, DCA, and Redundancy Analysis) and multiple regression analysis. We compared the performance of a trophic metric score based on RS-taxa with that based on field data from all taxa along different environmental gradients. Forty percent of the variability of the first non-RS taxa DCA-axis was predicted by the DCA-results based on RS-taxa. Correlations of the trophic metric score and total nitrogen concentrations were equally strong for the dataset based on RS-taxa compared to the dataset based on all taxa. For total phosphorous concentrations, the correlation was stronger for the dataset based on all taxa, but for a complex water quality gradient (including sulphate, N-species, chlorophyll and percent cover of wetlands in the riparian buffer) the correlation was higher for the RS-taxa dataset. The significant linkage between the two community fractions (remotely-sensible and non-sensible) revealed considerable assemblage concordance, suggesting a notable potential of the use of remote sensing in lake macrophyte monitoring. The established trophic metric score seems most qualified for surveillance monitoring that, in combination with the eased efforts of data acquisition, detects long-term changes of the aquatic environment caused by shifts in climate, land use and (related) eutrophication.

[1]  Christian K. Feld,et al.  Cook book for the development of a Multimetric Index for biological condition of aquatic ecosystems: experiences from the European AQEM and STAR projects and related initiatives , 2006 .

[2]  N. Willby,et al.  The applicability of macrophyte compositional metrics for assessing eutrophication in European lakes , 2014 .

[3]  R. Lunetta,et al.  Remote Sensing Change Detection: Environmental Monitoring Methods and Applications , 1999 .

[4]  Wolf M Mooij,et al.  The Contribution of Marsh Zones to Water Quality in Dutch Shallow Lakes: A Modeling Study , 2008, Environmental management.

[5]  O. Hagner,et al.  Unmanned aircraft systems help to map aquatic vegetation , 2014 .

[6]  Norbert Pfeifer,et al.  Categorizing Wetland Vegetation by Airborne Laser Scanning on Lake Balaton and Kis-Balaton, Hungary , 2012, Remote. Sens..

[7]  S. Carpenter,et al.  Effects of vascular and nonvascular macrophytes on sediment redox and solute dynamics , 1986 .

[8]  Barry T. Hart,et al.  Australian water quality guidelines: a new approach for protecting ecosystem health , 1993 .

[9]  Kirsi Valta-Hulkkonen,et al.  Assessment of aerial photography as a method for monitoring aquatic vegetation in lakes of varying trophic status , 2005 .

[10]  P. Pellikka,et al.  Digital false colour aerial photographs for discrimination of aquatic macrophyte species , 2003 .

[11]  Yaakov Anker,et al.  APPLICATION OF AERIAL DIGITAL PHOTOGRAPHY FOR MACROPHYTE COVER AND COMPOSITION SURVEY IN SMALL RURAL STREAMS , 2014 .

[12]  L. F. Curtis,et al.  Introduction to Environmental Remote Sensing. , 1978 .

[13]  Ruprecht Düll,et al.  Zeigerwerte von Pflanzen in Mitteleuropa , 1992 .

[14]  J. Smol,et al.  19th century eutrophication of a remote boreal lake: a consequence of climate warming? , 2002 .

[15]  M. Neteler,et al.  Benefits of hyperspectral remote sensing for tracking plant invasions , 2011 .

[16]  G. Bornette,et al.  Eutrophication and drought disturbance shape functional diversity and life-history traits of aquatic plants in shallow lakes , 2011, Aquatic Sciences.

[17]  G. Phillips,et al.  Maximum growing depth of submerged macrophytes in European lakes , 2012, Hydrobiologia.

[18]  John M. Melack,et al.  Remote sensing of aquatic vegetation: theory and applications , 2008, Environmental monitoring and assessment.

[19]  E. M. Thurman,et al.  Organic Geochemistry of Natural Waters , 1985, Developments in Biogeochemistry.

[20]  D. Angeler.  Revealing a conservation challenge through partitioned long‐term beta diversity: increasing turnover and decreasing nestedness of boreal lake metacommunities , 2013 .

[21]  L. Arvola.  Vertical distribution of primary production and phytoplankton in two small lakes with different humus concentration in southern Finland , 1984 .

[22]  Vytautas Klemas,et al.  Quantification of biomass of the marsh grass Spartina alterniflora Loisel using Landsat Thematic Mapper imagery , 1987 .

[23]  John Van Sickle,et al.  ANALYZING CORRELATIONS BETWEEN STREAM AND WATERSHED ATTRIBUTES 1 , 2003 .

[24]  Geoff Phillips,et al.  Classifying aquatic macrophytes as indicators of eutrophication in European lakes , 2008, Aquatic Ecology.

[25]  V. Vallinkoski,et al.  A comparison of two methods for surveying aquatic macrophyte communities in boreal lakes: Implications for bioassessment , 2013 .

[26]  T. Kleinebecker,et al.  Unmanned aerial vehicles as innovative remote sensing platforms for high‐resolution infrared imagery to support restoration monitoring in cut‐over bogs , 2013 .

[27]  Sebastian Birk,et al.  Biological assessment of European lakes: ecological rationale and human impacts , 2013 .

[28]  R. Carignan,et al.  An evaluation of approaches used to determine the distribution and biomass of emergent and submerged aquatic macrophytes over large spatial scales , 2003 .

[29]  P. Döll,et al.  Development and validation of a global database of lakes, reservoirs and wetlands , 2004 .

[30]  M. Scheffer,et al.  Alternative equilibria in shallow lakes. , 1993, Trends in ecology & evolution.

[31]  Anne Courrat,et al.  Three hundred ways to assess Europe's surface waters: An almost complete overview of biological methods to implement the Water Framework Directive , 2012 .

[32]  George W. Kling,et al.  Comparative transparency, depth of mixing, and stability of stratification in lakes of Cameroon, West Africa1 , 1988 .

[33]  M. Hammershøj,et al.  Does the impact of nutrients on the biological structure and function of brackish and freshwater lakes differ? , 1994, Hydrobiologia.

[34]  J. Verhoeven,et al.  Interacting effects of sulphate pollution, sulphide toxicity and eutrophication on vegetation development in fens: a mesocosm experiment. , 2009, Environmental pollution.

[35]  C. Hartog,et al.  Ammonium and nitrate uptake by aquatic plants from poorly buffered and acidified waters , 1986 .

[36]  Antti Kanninen,et al.  Response of macrophyte communities and status metrics to natural gradients and land use in boreal lakes , 2012 .

[37]  L. Bengtsson,et al.  Encyclopedia of Lakes and Reservoirs , 2012 .

[38]  B. Cumming,et al.  Climate-related eutrophication of a small boreal lake in northwestern Ontario: a palaeolimnological perspective , 2009 .

[39]  P. Mumby,et al.  A review of remote sensing for the assessment and management of tropical coastal resources , 1996 .

[40]  Bethany A. Bradley,et al.  Can remote sensing of land cover improve species distribution modelling? , 2008 .

[41]  M. Ritchie,et al.  [Letters to nature] , 1975, Nature.

[42]  Christian K. Feld,et al.  Ecological status assessment of European lakes: a comparison of metrics for phytoplankton, macrophytes, benthic invertebrates and fish , 2013, Hydrobiologia.

[43]  Jonathan J. Cole,et al.  Patterns and regulation of dissolved organic carbon: An analysis of 7,500 widely distributed lakes , 2007 .

[44]  Toon Spanhove,et al.  Can remote sensing estimate fine-scale quality indicators of natural habitats? , 2012 .

[45]  Per Ask,et al.  Light limitation of nutrient-poor lake ecosystems , 2009, Nature.

[46]  G. Nürnberg,et al.  Productivity of clear and humic lakes: nutrients, phytoplankton, bacteria , 1998, Hydrobiologia.

[47]  A. Rango,et al.  Acquisition, orthorectification, and object-based classification of unmanned aerial vehicle (UAV) imagery for rangeland monitoring. , 2010 .

[48]  M. Hill,et al.  Detrended correspondence analysis: an improved ordination technique , 1980 .

[49]  J. Downing,et al.  The global abundance and size distribution of lakes, ponds, and impoundments , 2006 .

[50]  R. Wetzel.  Limnology: Lake and River Ecosystems , 1975 .

[51]  C. Curtis,et al.  NORTHERN EUROPEAN LAKE SURVEY, 1995 , 1998 .

[52]  J. Stoddard,et al.  Dissolved organic carbon trends resulting from changes in atmospheric deposition chemistry , 2007, Nature.

[53]  Frauke Ecke,et al.  Drainage ditching at the catchment scale affects water quality and macrophyte occurrence in Swedish lakes , 2009 .