Understanding variation in trophic status of lakes on the Boreal Plain: A 20 year retrospective using Landsat TM imagery (original) (raw)
Related papers
Water Resources Research, 2008
1] We explored relations between climate and trophic status of shallow lakes (lake area > 5 ha, mean depth < 3.2 m) located on the subhumid western Boreal Plain of Canada. Correlation and regression analyses were used to assess the association between indicators of climate and satellite-based estimates of trophic status (chlorophyll a (Chl a)). Chl a was derived using red band reflectance of Landsat satellite images for 76 lakes, which were then averaged for each year to produce a landscape median for summer (August) over a 20-year period from 1984 and 2003. Our results showed that climate was related to interannual changes in trophic status. Average May temperature was positively correlated to Chl a, suggesting the importance of conditions in the early part of the growing season. Growing season effective precipitation (P -PET) was negatively correlated to Chl a such that wetter conditions seemed to lead to a dilution of Chl a. Very wet years resulted in a larger Chl a drop than one expected by a linear model, suggesting greater water contribution from the landscape. P -PET explained 64% of the variance in Chl a using a nonlinear regression tree. Our study offers clues as to how shallow lake systems may behave on the subhumid Boreal Plain as a function of future climate change.
Trophic status of inland lakes from LANDSAT
A first-cut assessment of the trophic status of inland lakes in Wisconsin was obtained from LANDSAT data. To satisfy the criteria of the project, a large and versatile computer program was developed to gain access to LANDSAT data. This analysis technique has proven to be a cost-effective method of classifying inland lakes in Wisconsin.
Water Resources Research, 2008
1] We used metrics of surface water and groundwater connectivity as explanatory variables in nonparametric regression models to explain the spatial heterogeneity in trophic status of shallow lakes. The concentration of chlorophyll a (Chl a) was used as an indicator of trophic status and was estimated from 17 Landsat images acquired during the end of summer (August) over a 20-year period from 1984 to 2003. A long-term median of Chl a was computed for each of 40 lakes on the basis of the 17-year data set. Hydrologic metrics explained 72% of the spatial variation in Chl a. The regression tree showed that lakes with a higher concentration of calcium plus magnesium (Ca + Mg) exhibited higher Chl a than lakes with a lower Ca + Mg. We hypothesized that this trend was a result of either higher internal nutrient loading in high Ca + Mg lakes due to groundwater discharge or differences in surficial geology. Among high Ca + Mg lakes, lakes with no inflowing streams had lower Chl a, while connected lakes had higher Chl a, possibly reflecting enhanced nutrient delivery to lakes connected to the stream network. Among low Ca + Mg lakes, lakes with larger wetland cover in their drainage basins had lower Chl a, while lakes with smaller wetland cover in their drainage basins had higher Chl a, possibly reflecting differential water loading rates between small and large wetlands that lead to either Chl a concentration or dilution. These results suggest that relatively simple hydrologic metrics can be used to predict the trophic status of lakes in this area of the boreal forest.
2017
Lakes are a ubiquitous landscape feature in northern permafrost regions. They have a strong impact on carbon, energy and water fluxes and can be quite responsive to climate change. The monitoring of lake change in northern high latitudes, at a sufficiently accurate spatial and temporal resolution, is crucial for understanding the underlying processes driving lake change. To date, lake change studies in permafrost regions were based on a variety of different sources, image acquisition periods and single snapshots, and localized analysis, which hinders the comparison of different regions. Here, we present a methodology based on machine-learning based classification of robust trends of multi-spectral indices of Landsat data (TM, ETM+, OLI) and object-based lake detection, to analyze and compare the individual, local and regional lake dynamics of four different study sites (Alaska North Slope, Western Alaska, Central Yakutia, Kolyma Lowland) in the northern permafrost zone from 1999 to 2014. Regional patterns of lake area change on the Alaska North Slope (−0.69%), Western Alaska (−2.82%), and Kolyma Lowland (−0.51%) largely include increases due to thermokarst lake expansion, but more dominant lake area losses due to catastrophic lake drainage events. In contrast, Central Yakutia showed a remarkable increase in lake area of 48.48%, likely resulting from warmer and wetter climate conditions over the latter half of the study period. Within all study regions, variability in lake dynamics was associated with differences in permafrost characteristics, landscape position (i.e., upland vs. lowland), and surface geology. With the global availability of Landsat data and a consistent methodology for processing the input data derived from robust trends of multi-spectral indices, we demonstrate a transferability, scalability and consistency of lake change analysis within the northern permafrost region.
Environmental Monitoring and Assessment, 2015
The concentration of chlorophyll a (chl a; as a proxy for phytoplankton biomass) provides an indication of the water quality and ecosystem health of lakes. An automated image processing method for Landsat images was used to derive chl a concentrations in 12 Rotorua lakes of North Island, New Zealand, with widely varying trophic status. Semi-analytical and empirical models were used to process 137 Landsat 7 Enhanced Thematic Mapper (ETM+) images using records from 1999 to 2013. Atmospheric correction used radiative transfer modelling, with atmospheric conditions prescribed with Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and AIRS data. The best-performing semi-analytical and empirical equations resulted in similar levels of variation explained (r 2 =0.68 for both equations) and root-mean-square error (RMSE=10.69 and 10.43 μg L −1 , respectively) between observed and estimated chl a. However, the symbolic regression algorithm performed better for chl a concentrations <5 μg L −1 . Our Landsat-based algorithms provide a valuable method for synoptic assessments of chl a across the 12 lakes in this region. They also provide a basis for assessing changes in chl a individual lakes through time. Our methods provide a basis for cost-effective hindcasting of lake trophic status at a regional scale, informing on spatial variability of chl a within and between lakes.
Landsat-Based Trend Analysis of Lake Dynamics across Northern Permafrost Regions
Remote Sensing
Lakes are a ubiquitous landscape feature in northern permafrost regions. They have a strong impact on carbon, energy and water fluxes and can be quite responsive to climate change. The monitoring of lake change in northern high latitudes, at a sufficiently accurate spatial and temporal resolution, is crucial for understanding the underlying processes driving lake change. To date, lake change studies in permafrost regions were based on a variety of different sources, image acquisition periods and single snapshots, and localized analysis, which hinders the comparison of different regions. Here, we present a methodology based on machine-learning based classification of robust trends of multi-spectral indices of Landsat data (TM, ETM+, OLI) and object-based lake detection, to analyze and compare the individual, local and regional lake dynamics of four different study sites (Alaska North Slope, Western Alaska, Central Yakutia, Kolyma Lowland) in the northern permafrost zone from 1999 to 2014. Regional patterns of lake area change on the Alaska North Slope (−0.69%), Western Alaska (−2.82%), and Kolyma Lowland (−0.51%) largely include increases due to thermokarst lake expansion, but more dominant lake area losses due to catastrophic lake drainage events. In contrast, Central Yakutia showed a remarkable increase in lake area of 48.48%, likely resulting from warmer and wetter climate conditions over the latter half of the study period. Within all study regions, variability in lake dynamics was associated with differences in permafrost characteristics, landscape position (i.e., upland vs. lowland), and surface geology. With the global availability of Landsat data and a consistent methodology for processing the input data derived from robust trends of multi-spectral indices, we demonstrate a transferability, scalability and consistency of lake change analysis within the northern permafrost region.
Monitoring of lake water quality along with trophic gradient using landsat data
International Journal of …, 2011
Effect of differential trophic states on remote sensing-based monitoring and quantification of surface water quality is an important but understudied context. Landsat ETM+ data-based multiple linear regression models were conducted to quantify dynamics of lake surface water quality along oligotrophic-to-eutrophic gradient and to explore the influence of trophic state on the detection of water quality dynamics by the best multiple linear regression models. The best multiple linear regression models of dissolved oxygen, chlorophyll-a, Secchi depth, water temperature, and turbidity had R 2 adj values ranging from 36.2 % in water temperature to 93.1% in dissolved oxygen for eutrophicYenicaga Lake and from 36.1 % in Secchi depth to 99.7 % in water temperature for oligotrophic Abant Lake. The difference in the trophic state between Lakes Abant and Yenicaga , significantly affected the composition of the nine Landsat ETM+ spectral bands included in the multiple linear regression models as well as the predictive power of the multiple linear regression models. Remote sensing-based monitoring of lake water quality variables appears to be promising in terms of devising adaptive management decisions towards sustainability of water resources.
Mapping inland lake water quality across the Lower Peninsula of Michigan using Landsat TM imagery
International Journal of Remote Sensing, 2013
The number, size, and distribution of inland freshwater lakes present a challenge for traditional water-quality assessment due to the time, cost, and logistical constraints of field sampling and laboratory analyses. To overcome this challenge, Landsat imagery has been used as an effective tool to assess basic water-quality indicators, such as Secchi depth (SD), over a large region or to map more advanced lake attributes, such as cyanobacteria, for a single waterbody. The overarching objective of this research application was to evaluate Landsat Thematic Mapper (TM) for mapping nine water-quality metrics over a large region and to identify hot spots of potential risk. The second objective was to evaluate the addition of landscape pattern metrics to test potential improvements in mapping lake attributes and to understand drivers of lake water quality in this region. Field-level in situ water-quality measurements were collected across diverse lakes (n = 42) within the Lower Peninsula of Michigan. A multicriteria statistical approach was executed to map lake water quality that considered variable importance, model complexity, and uncertainty. Overall, band ratio radiance models performed well (R 2 = 0.65-0.81) for mapping SD, chlorophyll-a, green biovolume, total phosphorus (TP), and total nitrogen (TN) with weaker (R 2 = 0.37) ability to map total suspended solids (TSS) and cyanobacteria levels. In this application, Landsat TM and pattern metrics showed poor ability to accurately map non-purgable organic carbon (NPOC) and diatom biovolume, likely due to a combination of gaps in temporal overpass and field sampling and lack of signal sensitivity within broad spectral channels of Landsat TM. The composition and configuration of croplands, urban, and wetland patches across the landscape were found to be moderate predictors of lake water quality that can complement lake remote-sensing data. Of the 4071 lakes, over 4 ha in the Lower Peninsula, approximately two-thirds, were identified as mesotrophic (n = 2715). This application highlights how an operational tool might support lake decision-making or assessment protocols to identify hot spots of potential risk.