Lichen Biomonitoring using GIS @kombilivasia - Tumblr Blog

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Hutten, M.A. 2015. Yosemite Region nitrogen deposition patterns in the composition of lichen communities. Ph.D. Dissertation, Oregon State University, Department of Botany and Plant Pathology.

Over 300 epiphytic lichen tissue samples were collected throughout Yosemite National Park in the Sierra Nevada mountains of California. The lichen samples were tested for nitrogen concentrations and the vast majority of samples were found to exceed the critical load for nitrogen content for this region. Using GIS the researchers were able to show spatially that this region exceeded critical loads (thresholds at which the element in question starts to effect ecological change) in 68% of sampled sites. Lichen tissue concentration of nitrogen has been calibrated to throughfall nitrogen deposition measurements in past studies and has been shown to correspond to kilogram/hectare/year throughfall deposition rate of nitrogen. This was done using standard linear regression with three different lichen species. From there they used nonparametric multiplicative regression to extrapolate for the landscape level. This model was then overlayed in ArcGIS with a California-wide nitrogen deposition model, to see the differences. This much higher resolution model (10x10 meters) highlighted areas where the California-wide model (4000 x 4000 meters) was underestimating critical load exceedances, primarily at higher elevations. This study found differences between sites due to elevation and topography, the researchers cautioned against using traditional krigging, which assumes equal variance in all directions. This thesis work is very applicable to mine because it deals with the same species of lichens, same pollutants of interest (nitrogen), and Yosemite is just north of my study area.

#platismatiastenophylla

Mavroulidou, Maria, Susan J Hughes, and Emma E Hellawell. "A Qualitative Tool Combining an Interaction Matrix and a GIS to Map Vulnerability to Traffic Induced Air Pollution." Journal of Environmental Management 70.4 (2004): 283-89. Web.

In Guildford, United Kingdom researchers combined two techniques, an interaction matrix and mapping overlay, to create a new tool for mapping air pollution vulnerability. The interaction matrix is made up of multiple parameters such as wind, traffic, roughness, topography, buildings, and traffic. The response variable was air quality, in particular carbon monoxide (CO). Air pollution is inherently a geographical problem so having a mapping overlay is critical to visualizing and interpreting results. The datasets for all the parameters used in the matrix were stored in the GIS. This tool was tested in an urban area with a high amount of traffic congestion causing air pollution. Each parameter was given a weighting factor for use in the overlay. Datasets had to be created for roughness and buildings, from Ordnance Survey Raster map for Guildford. The weighting factors were multiplied by the spatial datasets. The resulting rasters were added together and the result created a map of areas vulnerable to CO emissions in Guildford. These results were validated from a predictive numerical model for the same area and were shown to be comparable, but at a much lower resolution than the numerical model. Overall using the matrix weighting method proved to be feasible and useful. This tool could be helpful in preliminary air quality assessments as well as predictive scenarios. This research is of interest to me because I am curious about incorporating tools like these to create air quality assessments, as well as predictions, using lichen biomonitoring data.

#platismatiaglauca

Gulliver, John, and David Briggs. "STEMS-Air: A Simple GIS-based Air Pollution Dispersion Model for City-wide Exposure Assessment." Science of the Total Environment 409.12 (2011): 2419-429. Web.

Dispersion models are well suited to short term exposures such as air pollution from car traffic, but can also be expanded to assess long-term exposures. Using ArcGIS, GIS-based exposure assessments can be done using these models. This study used a STEMS-Air model with Gaussian dispersion model and grid-based FOCALSUM routines. Their pollutant of interest was particulate matter of 10 micrometers (PM10) from vehicle exhaust in the study area of London, UK. GIS is used in the dispersion model component of STEM-Airs, as well as the exposure assessment tool. The model accounted for all variables associated with vehicle traffic, such as speed, volume, etc, and was validated using available monitoring data. Forty-five degree angle wedges are created for prediction to break up the assessment area and from here they did short term and long term modeling. They also compared their model to the ADMS-Urban dispersion model using the same validated data. The results were significant that STEM-Airs could produce short and long-term predictions of PM10, and there is much promise that this could be a useful screening and mapping tool. Understanding air pollution is key to better interpreting the lichen data in my study area, so it was important to me to see what kind of models are created for air pollution using GIS and direct measurements of different kinds of pollutants. Car exhaust is one of the sources of pollution in my study area as well.

#peltigeracollina

Wehrden, Henrik, Heike Zimmermann, Jan Hanspach, Katrin Ronnenberg, and Karsten Wesche. "Predictive Mapping of Plant Species and Communities Using GIS and Landsat Data in a Southern Mongolian Mountain Range." Folia Geobotanica 44.3 (2009): 211-25. Web.

The researchers attempted to assess the validity of combining logistic regression models and remote sensing data to predict plant species and community distributions in the arid mountains of Mongolia. Many kinds of vegetation and environmental data were collected at randomly chosen sites that were selected using ArcMap 8.2 and the help of DEM layers. Predictor variables were derived from Landsat imagery data with a somewhat coarse resolution of 30 x 30 meters. Variables such as slope, aspect, vegetation indices, Normalized Difference Vegetation Index (NDVI), and soil composition. Regression models were calculated for all abundant species using the software R. Elevation and northern exposure proved to be the most predictive variables for abundant plant species. The interpolated maps that were created from these models helped to visualize vegetation patterns. This study had nothing to do with lichens, but similar practices still apply to predicting lichen communities. My study area is also somewhat arid, and this is helpful because many of these remote sensing modeling studies pertain to quite wet ecosystems.

#parmeliasulcata

Bowker, Matthew A, Jayne Belnap, and Mark E Miller. "Spatial Modeling of Biological Soil Crusts to Support Rangeland Assessment and Monitoring." Rangeland Ecology & Management 59.5 (2006): 519-29. Web.

In this study the researchers used GIS techniques to attempt to predict biological soil crust cover in Grand Staircase Escalante National Monument in Colorado, USA. To model soil crust habitat environmental data was collected such as various data on soils where the crusts are found, and climate data, such as precipitation. Using this environmental data, models were validated by survey data collected at 114 sites. Another variable used was an ordinal ranking of grazing disturbance, a practice which is very destructive for soil crusts. The model results were turned into spatial layers using ArcMap 9.0. Models that predicted moss, lichen and cyanobacterial crusts performed the best and were enhanced by GIS. Interpolated maps of mean cover of soil crusts were created. This research maintains that biological soil crusts are ecological indicators of good soil and ecosystem health. These maps and layers will help land managers to select areas to prioritize for grazing and which areas to protect from grazing. This is valuable to my research because it is another model for how to quantify and predict habitat for ecological indicator groups and species of lichens and bryophytes.

#parmotremaperlatum

Hinchcliffe, Graham, Barbara Bollard-Breen, Don A. Cowan, Ashray Doshi, Len N. Gillman, Gillian Maggs-Kolling, Asunción De Los Rios, and Stephen B. Pointing. "Advanced Photogrammetry to Assess Lichen Colonization in the Hyper-Arid Namib Desert." Frontiers in Microbiology 8 (2017): 2083. Web.

The study area was the Namib Desert in southern Africa and in particular measuring crustose lichen communities that grow on the exposed quartz rock there. Field measurements are difficult because these communities are very sensitive to disturbance and very remotely located. Photographs were taken in the field as well as hyperspectral measurements. They used Structure from Motion (SfM) to get 3-D information from these 2-D images, and this is photogrammetry! Point cloud visualization software CloudCompare was used to group lichen species by color and spectral properties. This data was exported in classification raster and Digital Surface Model (DSM) raster forms. It was then analyzed for slope and aspect. Because of stark moisture gradients in deserts, it is useful to see how lichens colonize these rocky surfaces to take advantage of what little available moisture there is, in this case, coastal fog events. The authors also believe these techniques could tell us more about life on Mars! This study is particularly applicable to my research because my study area is also very arid and deals with topographic position when it comes to where lichens grow on rock. It would be a very cool addition to my thesis work!

#lobariapulmonaria

Bajpai, Rajesh, Vertika Shukla, D. Upreti, and K. Semwal. "Selection of Suitable Lichen Bioindicator Species for Monitoring Climatic Variability in the Himalaya." Environmental Science and Pollution Research 21.19 (2014): 11380-1394. Web.

This study taking place in the Govind Wildlife Sanctuary in the Himalayan mountains of India looked at three different epiphytic macrolichens and compared their potential as indicator species for certain kinds of air pollution using environmental variables and comparison to a GIS created map of pollution profiles. Samples were analyzed for metals and polycyclic aromatic hydrocarbons (PAH). They mapped the different elemental concentrations of the three species separately using geostatistical analysis techniques. Rasters were created for causational air quality factors using interpolated distance weighting (IDW) that were then normally co-krigged, all in ArcGIS. The different lichen species accumulated metals and PAHs significantly differently and that long-range transport of pollutants is affecting the remote Himalayan mountains at high elevations. The IDW map helped show the spatial distribution of contaminants based on the lichen data for areas where no data had been collected. This is applicable to my research because my study area has quite a bit of high elevation land in it, and learning more about pollution transport and lichens in these kinds of systems is very useful. I would love to be able to make maps like these for my elemental analysis data.

#lethariavulpina

Schröder, Winfried, and Stefan Nickel. "Mapping Percentile Statistics of Element Concentrations in Moss Specimens Collected from 1990 to 2015 in Forests throughout Germany." Atmospheric Environment 190 (2018): 161-68. Web.

The European Moss Survey (EMS) program has been chemically analyzing concentrations of heavy metals in mosses in 7,300 sampling sites throughout 36 European countries every five years since 1990. This study looked at the subset of samples in Germany and the most important pollutants in that country, such as lead, cadmium, and mercury. This has allowed them to create spatial maps of bioaccumulation and derive deposition estimates via regression modelling. Ten percentile classes were created in order to calculate statistics for spatial point data to allow detection of hotspots of selected elements. They were able to compare the temporal changes in the maps to see which elements were increasing or decreasing. There has been a statistically significant decrease in the previously mentioned metals overall in Germany since 1990, as well as a visual one look at the interpolated maps created using GIS kriging techniques. The EMS is very interesting to me because of it’s large scale and length of existence, and I wonder about the feasibility of having a program like this for the United States. The US Forest Service has managed something similar to this for the US, but the funding ran out roughly ten years ago. Maybe the incorporation of more recent GIS technologies, such as krigged maps, could help with starting this program back up again.

#lethariacolumbiana

Holt, E.A., McCune, B., and Neitlich, P. "Spatial Scale of GIS-derived Categorical Variables Affects Their Ability to Separate Sites by Community Composition." Applied Vegetation Science 11 (2008): 421-30. Web.

These researchers used field measurements of lichen community data from 77 plots in northwestern Alaska and combined them with 17 categorical environmental variables, such as substrate pH and land cover, derived from GIS maps. Landsat data was used to determine land cover. The spatial characteristics of interest were patch size, number of patch types, and spatial correlation. There were two methods used to evaluate the ability of the categorical variables to separate lichen communities: either separation via species abundances or a clustered groupings (20 clusters) of the community data from the 77 plots. The results showed that the GIS created categorical variables were able to explain more variability in the community than would be expected by chance alone, with 13 of the 17 variables being statistically significant. Generally low spatial correlation, which was also strongly related to patch size, was associated with high lichen community separation looking at both methods of community separation. This study helps highlight that GIS can be very useful in ecological survey design. Reading this study made me wonder about my research, which pertains to lichens that grow on rocks and trying to do something like this with my data to see the effects of different variables that pertain to rocks and soil.

#hypogymniaimshaugii

Erich Neurohr Bustamante, Julián Monge-Nájera, and Víctor Hugo Méndez-Estrada. "Use of a Geographic Information System and Lichens to Map Air Pollution in a Tropical City: San José, Costa Rica Uso Del Sistema De Información Geográfica Y Líquenes Para Mapear La Contaminación Del Aire En Una Ciudad Tropical: San José, Costa Rica." Revista De Biología Tropical 61.2 (2013): 557-63. Web.

The study area was a randomly selected sample of 40 parks throughout San José, the capital city in Costa Rica, where extensive foliose lichen cover surveys took place and was analyzed in a previous study. For this paper the researchers sought to create a detailed map of air pollution using the foliose lichen percent cover data and GIS with which to compare to known atmospheric nitrogen oxide (NOx) levels. ArcGIS 9.3 was also used to create an isoline map translating the lichen cover values to pollution categories that were understandable to the general public, ie traffic light colors. This was compared to a map created using an Environmental Protection Agency (EPA) generated dispersion model to map NOx throughout the city. The comparison was done using a binomial test on the two maps and the results were statistically significant that the lichen cover categories matched the air concentration of NOx. The major source of NOx is vehicle traffic and these findings could help better route thoroughfares to avoid housing and schools. This research deals with an area that I am much more familiar with, which is using epiphytic lichens to detect nitrogenous compounds.

#hypogymniaenteromorphaorhypogymniaappinata

Gilichinsky, Michael, Per Sandström, Heather Reese, Sonja Kivinen, Jon Moen, and Mats Nilsson. "Mapping Ground Lichens Using Forest Inventory and Optical Satellite Data." International Journal of Remote Sensing 32.2 (2011): 455-72. Web.

Reindeer in northern Sweden are very important culturally and as a food source to the people that inhabit that region. In the winter when plants are scarce, terrestrial lichens become the main staple of the reindeer’s diet. Using training data of three distinct lichen classes from the Swedish National Forest Inventory (NFI) three different classification methods were tested in this study. The three methods are: maximum likelihood (assigns each image pixel to the class with which it has the highest probability of membership), Mahalanobis distance (distance from each image pixel to the centroid of the training classes), and spectral mixture analysis (training data are class endmembers and results are the ratio of endmembers in each pixel). Classifications were validated in the field. They also used two different kinds of imagery data: Satellite Pour l‘Observation de la Terre (SPOT) and Landsat 7 ETM. The best classification results came from using the SPOT data and the Mahalanobis distance method. The results of this study can contribute to more informed management of reindeer populations when it comes to dealing with winter lichen forage resources. This study is of interest to me because climate change is thought to potentially cause decreases in reindeer lichen cover, which is worrisome for the reindeer and the people that rely on those reindeer.

#everniaprunastri

Nelson, Peter R., Roland, Carl, Macander, Matthew J., and McCune, Bruce. "Detecting Continuous Lichen Abundance for Mapping Winter Caribou Forage at Landscape Spatial Scales." Remote Sensing of Environment 137 (2013): 43. Web.

In Denali National Park and Preserve there are large populations of caribou that eat large quantities of the lichen Cladonia to survive during the winter. These lichens are very light in color and contain usnic acid, which is spectrally distinct. These researchers decided to approach the questions using regressions to analyze spectral data about caribou habitat. These regressions were able to estimate continuous quantities of a target surface within a mixture of target surfaces. This made creation of maps and models of continuous cover possible, which was a specific goal with mapping this habitat. There was a randomized grid of plots that was surveyed to ground-truth the imagery data and plot size was small, much smaller than the area covered by a Landsat pixel. Digital Elevation Model (DEM) data were used to calculate slope and aspect in order to better interpret the Landsat spectral characteristics with topographic effects taken into consideration. Various kinds of environmental data were also extracted from GIS layers for use as predictor variables in the model. This study shows that mapping usnic lichens is a good tool to measure overall terrestrial lichen cover for caribou consumption, as usnic lichens are correlated with overall lichen cover. I first got interested in lichens when I read about heavy metals bioaccumulating in caribou because of how much terrestrial lichen they ate that were close to mining sites and the how this impacts the people that hunt and eat caribou.

#bryoriafremontii

Casanovas, Black, Fretwell, and Convey. "Mapping Lichen Distribution on the Antarctic Peninsula Using Remote Sensing, Lichen Spectra and Photographic Documentation by Citizen Scientists." Polar Research 34.1 (2015): 1-8. Web.

Lichens comprise a large component of the terrestrial ecosystem in Antarctica, which has not been extensively studied botanically speaking. The Normalized Difference Vegetation Index (NDVI) uses Landsat satellite imagery to create a proxy to indicate the presence of chlorophyll spatially. The concern is that NDVI overlooks the presence of lichens because of the difference in reflectance spectra between lichens and vascular plants. In an attempt to remedy this, the researchers measured the spectra for three distinct groups of lichens.They then tried mapping lichen-dominated communities directly using a matched filtering algorithm to match image pixel spectra of mid-resolution satellite imagery with the reference spectra of the lichen groups. They then compared distribution maps created with NDVI to those created with this new matched filtering technique. They analyzed the differences using ArcGIS10 and Exelis Visual Information Solutions (ENVI) version 5.1. This new technique showed a significant improvement over NDVI for mapping of lichen-dominant vegetation, with further study needed to assure that this technique does not overestimate lichen cover. In the polar regions of our planet, terrestrial lichens are the best biomonitors of pollution that we have. I am much more familiar with biomonitoring using epiphytes so it is interesting to see how useful GIS technology can be in these polar biomes.

#Bryoriafremontii

Barre, Julien, P. Deletraz, G. Frayret, Gaëlle Pinaly, Jérôme Donard, and Hervé Amouroux. "Approach to Spatialize Local to Long-range Atmospheric Metal Input (Cd, Cu, Hg, Pb) in Epiphytic Lichens over a Meso-scale Area (Pyrénées-Atlantiques, Southwestern France)." Environmental Science and Pollution Research 22.11 (2015): 8536-548. Web.

In this study the researchers measured elemental concentrations of mercury (Hg), lead (Pb), copper (Cu), and cadmium (Cd) in epiphytic macrolichens across four land use types: urban, industrial, agricultural and forest. GIS was used primarily in this study to determine sampling site locations via categorizing and constraining variables to more effectively interpolate the heavy metal deposition that the lichen elemental analysis data estimated. These interpolation models consisted of multiple linear regressions that the GIS helped determined the indicator variables of industrial density, density of buildings, density of roads, and density of agricultural land. These variables are much easier to measure using GIS as opposed to other methods because these are very spatially obvious. Correlating and spatializing the elemental concentrations of different pollutants detected in the lichens to related this to geographic variables in order to identify local sources and long range transport of pollutants. They spatialized, or interpolated, Cu and Hg lichen tissue concentrations and found them to be globally homogenous, with no difference between rural and urban. Cd and Pb on the other hand, had higher concentrations in urban and industrialized areas. Identifying the source of pollution is often the goal in my lichen biomonitoring research, so it is really encouraging to see a model like this validated!

#Alectoriasarmentosa

Tømmervik, H., M. Johansen, E. Pedersen, and J. Guneriussen. "Integration of Remote Sensed and In-situ Data in an Analysis of the Air Pollution Effects on Terrestrial Ecosystems in the Border Areas between Norway and Russia." Environmental Monitoring and Assessment 49.1 (1998): 51-85. Web.

Sulphur dioxide (SO₂) and heavy metals, such as Nickel (Ni), have been shown to have negative effects on certain kinds of plants. In this study the researchers analyzed transboundary air pollution (SO₂ and Ni) in Norway and Finland coming from Ni smelters on the Kola peninsula of Russia. Satellite remote sensing data was used in combination with ground truthed measurements such as percent cover of vegetation types, barren ground, and terrestrial reindeer lichen (Cladina) cover at randomly chosen sites. High spectral reflectance from Cladina-dominated vegetation, which is sensitive to SO₂ and Ni pollution, was compared to ‘green’ vegetation, for example grass, bilberry, and dwarf shrubs. The images were then analyzed using GIS with SO₂ measurements and elemental analysis results of soil and Cladina tissue samples. The Norwegian Institute of Air Research provided a spatial layer of interpolated SO₂ deposition measurements for the ground layer at a 100 x 100 meter grid. Field testing proved the model to be 82% accurate in classifying remotely sensed land cover types correctly. These results led the researchers to believe that there is a connection between land cover changes and changes in SO₂ emissions concentrations. As SO₂ emissions concentrations increased 5 - 30 km from Ni smelters, dwarf shrub and Cladina dominated land cover types were found significantly to have transformed into bilberry and grass dominated types, which are more tolerant of this kind of pollution. Land cover types within this area deemed contaminated by Ni and SO₂ increased significantly as well. This is valuable to my general interest in lichen biomonitoring, as an example of one of the different sets of tools that falls in this category.

#Ahtianasphaerosphorella

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