Lesson 8 is an introduction to raster modeling in ArcGIS. Natural resource professionals must make decisions on a regular basis in which he or she uses formal or informal models. Where is a good place to survey for Lewis’s Woodpecker? Where is a suitable location for a new campground? What is the risk of rapid fire spread near a residential area? These questions can be answered based on experience or best judgment, however GIS models can be a useful guide in decision-making.

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GIS models are spatially explicit, meaning that the results are expressed in a form of a map. A model is an abstraction (simplification) of reality, and indeed all maps are spatial models. A map, as well as a model, is meant to serve as a guide for decision making, but is hardly the almighty construction that gives you only correct answers. The accuracy of a model in predicting future conditions is highly dependent on the construction of the model, the model assumption and the input parameters.GIS models can aid in identifying locations that meet specified criteria or they can help synthesize information from many data layers to visualize a specific aspect of the landscape. For example, we know that many factors affect the risk of soil erosion in a landscape; soil type, precipitation, landuse, and topography among others. These factors can be visualized in a GIS, and a GIS model can identify areas where these factors are compounding and point out areas of extreme erosion risk.The map in the slide illustrates results from a model that predicts Mountain Bluebird habitat in Idaho (created by Idaho GAP). Input data layers to this model are: vegetation type, elevation, and a range map.

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All models are simplifications of reality and they can provide useful guidance and increased understanding of a situation or system. Models can be used to predict future conditions or to apply current knowledge for one geographic area to another. In many instances models are our only tool in estimating future condition. In natural resources models are commonly use to evaluate the outcome of different management actions. g

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Models can be used to predict future conditions based on current knowledge. For example, if the growth rate in a forest ecosystem is known along with the probability of fire occurrence, the distribution of forest in different growth stages could be modeled into the future. If the fire probability is very low it is likely that this forest would end up in an old-growth stage while if the fire probability is very high the forest would burn constantly and create a shrub-field. Different management scenarios could be modeled to estimate a fire

i h ld b fi h bj i f hi fregime that would best fit the management objectives for this forest.GIS models are commonly used for site selection, landuse planning and habitat modeling. These applications are all based on multi-layer selection in GIS.

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The raster data structure lends itself well to GIS modeling. Spatial modeling using vector data involves overlay analysis of two or more vector datasets, a procedure that in most cases creates output data with numerous ‘slivers’. Slivers are caused by the fact that the input data layers rarely overlay perfectly, i.e. the boundaries do not match up precisely. This ‘mis-match’ of arcs yields numerous small polygons (slivers) when the vector layers are intersected during the modeling procedure. The sliver phenomenon is much less

bl i h ki i h d i h i l i d lproblematic when working with raster data since the pixels in two data layers can be lined up precisely.In a raster model involving multiple data-layers it is important to use the same cell size for all data layers. Mixing and matching spatial scales will likely lead to results that are difficult to interpret. It is important to select data layers with a fine enough pixel size that the modeled objects will be represented. In a modeling project of ponds in Latah county (pond size ~ 30x30 m) it is obviously impossible to use an input data layer with a pixel size of 1000x1000 meters. On such a broad scale data layer you will not find such small ponds.

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Binary models is the most simple form of raster models. In a binary model each pixel is represented by 0 or 1. In the map in this slide the slope on Craig Mountain is displayed as a 0 and 1 where 0 is slopes less than 20 degrees (green) and 1 is areas where the slope is steeper than 20 degrees (yellow). Several binary models of 0 and 1 can be combined into a more complex model.

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Let’s pretend that you are creating a spatial model displaying potential locations where you may find a rare plant. This model will be used for the field crew to narrow the search to areas where the plant is more likely to be found. Instead of creating a binary model it may be advantageous to create a model ranging from 0-10, where 10 are areas where the plant is most likely to be found and 0 is less likely areas. This plant grows at high elevations on steep slopes. To create an index model, p g g p pelevation and slope could be classified into five classes each where 0 is low elevation (slope) and 5 is high elevation (slope). Then index may be as simple as I = elevation class + slope classWhen both the elevation class and the slope class is 5 the index (I) will assume the value 10 In this simple example elevation and slope were weightedthe value 10. In this simple example elevation and slope were weighted equally important, however, in an index model it is possible to weight the variables differently. I the case that the elevation criteria is more important than the slope criteria a higher weight could be given to the elevation variable. The weighted index may look like:I = 0.7 x elevation class + 0.3 x slope class

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Let’s pretend that you are creating a spatial model displaying potential locations where you may find a rare plant. This model will be used for the field crew to narrow the search to areas where the plant is more likely to be found. Instead of creating a binary model it may be advantageous to create a model ranging from 0-10, where 10 are areas where the plant is most likely to be found and 0 is less likely areas. This plant grows at high elevations on steep slopes. To create an index model, p g g p pelevation and slope could be classified into five classes each where 0 is low elevation (slope) and 5 is high elevation (slope). Then index may be as simple as I = elevation class + slope classWhen both the elevation class and the slope class is 5 the index (I) will assume the value 10 In this simple example elevation and slope were weightedthe value 10. In this simple example elevation and slope were weighted equally important, however, in an index model it is possible to weight the variables differently. I the case that the elevation criteria is more important than the slope criteria a higher weight could be given to the elevation variable. The weighted index may look like:I = 0.7 x elevation class + 0.3 x slope class

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In a regression model the regression equation is applied to each pixel. Each independent variable in the regression equation must be represented by a raster layer. In the example above TB (Total Biomass) is calculated from CH (Canopy Height). In order to create the TB-grid, a CH-grid must exist. The mechanics of creating the TB grid in the Raster Calculator is very simple, you

simply type the equation [5.5 + 0.0385*(CH)5.5 + 0.0385*(CH)2 2 ] where ] where CH i th CHCH i th CH id t i th A Mid t i th A MCH is the CHCH is the CH--grid present in the ArcMap grid present in the ArcMap project.project.

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Before starting a modeling project it is important to think through what the objectives and the purpose of the model is. What scale data is desirable, is the data available, is there a way to validate the model…..It is important to be state assumptions and be aware of what effects these assumptions may have on the model – will the model overpredict or underpredict? In some cases it is more advantageous to make a more general model and in other cases a more specific (limited) model is desired.p ( )Model variables must be identified and GIS data layers representing the model variables must be located. Finally the model is implemented and evaluated.

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As and example we will go through the procedure of creating a spatial wildlife habitat model for the Coeur d’Alene Salamander. This data is an example taken from the Idaho GAP project (http://www.wildlife.uidaho.edu/idgap/index.htm). 1. The objectives for this modeling project is to create a model for potential habitat areas for the Coeur d’Alene Salamander. This model will later be combined with other wildlife habitat models to evaluate the potential species p prichness and identifying species rich ‘hot spots’ .2. The model will be created at a 30 m scale (pixel size) for the state of Idaho. It is assumed that habitat features important to this salamander can be detected and mapped using 30 m data. It is also assumed that specific habitat information from adjacent states also apply to Idaho. 3 The model variables were identified based on literature information county3. The model variables were identified based on literature information, county records in Idaho and adjacent states and expert opinion. Elevation, vegetation type and distance to water were determined to be important variables, and they are also available in GIS format. The fourth variable is a rangemap, which will limit the spatial model (map) to the area where the salamander has actually been found.

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The model layer and selection criteria are listed in the slide.

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The final model is a binary map where areas are identified as either habitat or non-habitat. A more complex model could identify primary and secondary habitats for example, or estimate the probability of finding the Coeur d’Alene salamander on a scale from 0-100%.

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Finally the model can be evaluate using ground control GPS points of location where the Cd’A salamander has been detected. Notice that for wildlife habitat models it is most feasible to evaluate correct presence and omission, i.e areas where the salamander is actually found. It is more difficult to assess commission and correct absence. Maybe the animal was sleeping or hiding during the survey, or maybe it only uses the habitat in a certain season or time of day. To show that the salamander is absent in an area would require long

b iterm observations.

Omission represents areas where the animal was found although the model predicts the species to be absent.Commission represents areas there the animal was not found but the model predicts its presence It is difficult to show that the animal never uses an areapredicts its presence. It is difficult to show that the animal never uses an area.

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The Raster Calculator in Spatial Analyst is a tool for rater query. Notice that the selection tools that we used for vector query are not applicable to raster data. In the raster calculator you can query one or many grids simultaneously. The example selects areas that are above 1500 m in elevation and where the slope is less than 15 degrees. The output from such a query is a binary grid with values 0 and 1. The pixels that fulfill the query criteria (elevation > 1500 and slope < 15 degrees) will be given the value 1 and the remaining area will h h l 0have the value 0.

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