Enhanced Wetness Modelling in SAGA GIS - Revision history

Smitch: /* Potentially Interesting and Useful Unexplored Tools */

2015-12-31T18:42:35Z

Potentially Interesting and Useful Unexplored Tools

Smitch: /* Conclusion */

2015-12-31T18:41:03Z

Conclusion

Smitch: /* Slope Derivation */

2015-12-31T18:37:49Z

Slope Derivation

Smitch: /* Slope Derivation */

2015-12-31T18:36:48Z

Slope Derivation

Smitch: /* Data & Study Site */

2015-12-31T18:35:03Z

Data & Study Site

Smitch: /* Preamble */

2015-12-31T18:33:08Z

Preamble

Smitch: /* Purpose */

2015-12-31T18:31:56Z

Purpose

Ryder Burt: /* Potentially Interesting and Useful Unexplored Tools */

2015-12-22T00:23:51Z

Potentially Interesting and Useful Unexplored Tools

Ryder Burt: /* Slope Derivation */

2015-12-22T00:21:30Z

Slope Derivation

Ryder Burt: /* Data Modelling */

2015-12-22T00:20:42Z

Data Modelling

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 =Potentially Interesting and Useful Unexplored Tools=
-As demonstrated by this workflow so far, SAGA proves to be highly capable of manipulating spatial data. This however is not merely the tip of the ice berg.
+As demonstrated by this workflow so far, SAGA proves to be highly capable of manipulating spatial data. This however is merely the tip of the iceberg.
-Some other very interesting tools that would've added an extra layer of complexity to this tutorial would've been:
+Some other very interesting tools that would have added an extra layer of complexity to this tutorial include:
 *'''Spatial and Geostatistics''' toolset:
-Broken down into 4 beautifully geeky libraries (Grids, Kriging, Points, and   Regression) which include Principle Component Analysis, Variogram, Moran's I, Geographically weighted regression, Directional trend analysis and much more!
+Broken down into 4 beautifully geeky libraries (Grids, Kriging, Points, and   Regression) which include Principal Component Analysis, Variogram, Moran's I, Geographically weighted regression, Directional trend analysis and much more!
 *'''Show 3D-view tool''':
@@ Line 211: / Line 211: @@
 *'''Simulation'''
-An entire simulation toolset exists with many different simulations that would assist different kinds of research. For this exercise, had their been available precipitation and evapotranspiration weather records for Gatineau park, they would've been couple with the generated TWI and fed into the TOPMODEL simulation which would've returned a detailed model about elements of the hydrological cycle in the basin.
+An entire simulation toolset exists with many different simulations that would assist different kinds of research. For this exercise, had there been available precipitation and evapotranspiration weather records for Gatineau park, they would have been coupled with the generated TWI and fed into the TOPMODEL simulation which would have returned a detailed model about elements of the hydrological cycle in the basin.
 =Conclusion=

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 =Conclusion=
-Compared to other GIS software, SAGA GIS proved to be significantly capable of hydrological modelling. The software is not based of a plug and play logic but rather allows the user to understand what they really want by offering them different options for modelling. That is not to say that the software did not crash many different times; that is why save individual layers every time you run one because SAGA does not autosave! This workflow could've been improved for wetland prediction if the Imagery capabilities of SAGA were explored. A vegetation index could've been very useful in identifying wetlands that are open, and RADAR data for identifying forested, below canopy wetlands. Moreover, a further enhancement of the method would be through separating the first return of the point cloud from the last; that way a highly precise bare earth terrain model could yield higher accuracy in wetness modelling and prediction.
+Compared to other GIS software, SAGA GIS proved to be very capable of hydrological modelling. The software is not based on a plug and play logic but rather allows the user to understand what they really want by offering them different options for modelling. That is not to say that the software did not crash many different times; that is why save individual layers every time you run one because SAGA does not autosave! This workflow could have been improved for wetland prediction if the Imagery capabilities of SAGA were explored. A vegetation index could have been very useful in identifying wetlands that are open, and RADAR data for identifying forested, below canopy wetlands. Moreover, a further enhancement of the method would be through separating the first return of the point cloud from the last; that way a highly precise bare earth terrain model could yield higher accuracy in wetness modelling and prediction.
 =References=

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 *Make sure that the selected method is 9 parameter 2nd order polynomial (Zevenbergen & Thorne 1987).
-*The module automatically generates an '''Aspect''' map. This is the output, however it won;t directly be used in this tutorial.
+*The module automatically generates an '''Aspect''' map. This is the output, however it won't directly be used in this tutorial.
 [[File:aspectresfinal.png]]

@@ Line 114: / Line 114: @@
 **10 parameter 3rd order polynomial (Haralick 1983)
-*The used method is the one proposed by Zevenbergen & Thorne (1987) which is the 9 parameter 2nd order polynomial where slope is the first derivative of elevation.
+*The method used here is the one proposed by Zevenbergen & Thorne (1987) which is the 9 parameter 2nd order polynomial where slope is the first derivative of elevation.
 **It uses a rectangular matrix of evenly spaced elevations that covers the entire area of the input layer where elevation points are used to determine the parameters in the quadratic equation. In other words, equation fitting is used to map out the surface and derive different indices (slope, aspect, curvature, etc.)
 **This method uses an equation with 9 parameters to resolve for indices. It is more general than the other methods using 6 and more flexible (if a surface is of lower order than the equation, the corresponding coefficients will equal zero and wont influence the equation).
@@ Line 121: / Line 121: @@
 **The first three methods are focused on flow routing logic, operate on a local scale basis and have a particular restriction because they calculate slope in a uni-directional way from one cell to the other and thus would be fine for a simplistic wetness model but not this one.
-In order to compute for slope go to:
+In order to compute slope go to:
 '''Geoprocessing | Terrain Analysis | Morphometry | Slope, Aspect, Curvature'''
 *Set the '''Grid''' to the one we’ve been using throughout the workflow, and the '''inputted''' DEM.

@@ Line 26: / Line 26: @@
 =Data & Study Site=
 This exercise is done using data from Gatineau Park, Quebec (Canada).
-The used data:
+The data used are:
 *Digital Elevation Model (DEM): LiDAR-derived, 5 meter resolution
 *Gatineau Park boundary: polygon layer
-The data used is acquired from a non-public source, however, any DEM can be used to execute the same workflow demonstrated by this tutorial. Many DEM's are found online for the public, GRASS GIS for example has a [https://grass.osgeo.org/download/sample-data/ sample data download] page that has LiDAR data available in 3 zip files ("complete NC location" ,"smaller subset NC location", "extra time series of LiDAR datasets")
+The data used are acquired from a non-public source, however, any DEM can be used to execute the same workflow demonstrated by this tutorial. Many DEM's are found online for the public, for example the GRASS GIS project has a [https://grass.osgeo.org/download/sample-data/ sample data download] page that has LiDAR data available in 3 zip files ("complete NC location" ,"smaller subset NC location", "extra time series of LiDAR datasets"), and this could also be used with SAGA.
 =Setting Environment=

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 =Preamble=
-This is a workflow that offers an enhanced method for modelling wetness of a terrain. A terrain’s wetness index is a secondary terrain attribute derived from a digital elevation model through different transformations to the raster grid. The method presented in this tutorial is rather more detailed and precise then others because the two defining parameters involved in calculating a wetness index (up slope area and slope) are calculated in particular ways that do not use the assigned defaults or the one step algorithms.
+This is a workflow that offers an enhanced method for modelling wetness of a terrain. A terrain’s wetness index is a secondary terrain attribute derived from a digital elevation model through different transformations to the raster grid. The method presented in this tutorial is more detailed and precise than others because the two defining parameters involved in calculating a wetness index (up slope area and slope) are calculated in particular ways that do not use the assigned defaults or the one step algorithms.
 Attention: this workflow has been generated using SAGA GIS 2.2.2 thus all the headings and toolset names are specific to this version. Slight differences in module names are introduced between this version and the one before. SAGA offers [http://www.saga-gis.org/saga_module_doc/ Module Library Documentations] for the last 5 version. Note that some instructions that yield the same result might have different names/options in different versions (e.g. Catchment Area tool in 2.2.2 was called Flow Accumulation in 2.1.2). Another helpful resource would be the [http://sourceforge.net/p/saga-gis/discussion/?source=navbar SAGA Forum] where a lot of resources and exchanges between GIS users are available.

@@ Line 1: / Line 1: @@
 =Purpose=
-The purpose of this tutorial is not only to present an efficient workflow for modelling wetness, but also to explain the different options offered for module inputs along the way, as well as why the chosen ones were favoured. The SAGA documentations show that different options are available, yet only includes a reference to a paper that explains where the method was derived from with minimal or no description. This tutorial will explain how the used methods work and what differentiates them from others. Other methods that were options but not used will be briefly explained as well. This tutorial is part of an assignment for GEOM 4008 at Carleton University, Ottawa.
+The purpose of this tutorial is not only to present an efficient workflow for modelling wetness, but also to explain the different options offered for module inputs along the way, as well as why the chosen ones were favoured. The SAGA documentations shows that different options are available, yet only includes a reference to a paper that explains where the method was derived from, with minimal or no description. This tutorial will explain how the used methods work and what differentiates them from others. Other methods that were options but not used will be briefly explained as well. This tutorial is part of an assignment for GEOM 4008 at Carleton University, Ottawa.
 =Preamble=

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 *'''Show 3D-view tool''':
-allows for a completely different perspective on how different phenomena would vary with elevation
+Allows for a completely different perspective on how different phenomena would vary with elevation
 *'''Simulation'''

@@ Line 127: / Line 127: @@
 [[File:slopeZT.png]]   [[File:slope-res.png]]
-*Make sure that the selected method is 9 parameter 2nd order polynom (Zevenbergen & Thorne 1987).
+*Make sure that the selected method is 9 parameter 2nd order polynomial (Zevenbergen & Thorne 1987).
 *The module automatically generates an '''Aspect''' map. This is the output, however it won;t directly be used in this tutorial.

@@ Line 156: / Line 156: @@
 ===Sink Visualization===
-As mentioned above, the DEM with the removed sinks will look almost identical to the original one. In order to effectively demonstrate the filled sinks, we can use Grid Calculus to subtract the Preprocessed DEM from the original DEM. This will highlight areas where change in elevation is detected (which in turn means where filled). The output won't be used for the modelling of wetness, yet is a huge asset for dicussion pertaining to the influence on sinks on wetness modelling, the efficiency of sink fill method used, and the effect of DEM resolution of wetness modelling.
+As mentioned above, the DEM with the removed sinks will look almost identical to the original one. In order to effectively demonstrate the filled sinks, we can use Grid Calculus to subtract the Preprocessed DEM from the original DEM. This will highlight areas where change in elevation is detected (which in turn means where filled). The output won't be used for the modelling of wetness, yet is a huge asset for discussion pertaining to the influence on sinks on wetness modelling, the efficiency of sink fill method used, and the effect of DEM resolution of wetness modelling.
 To do such subtraction,