This dataverse contains data inputs and products from two complimentary analyses: a spatial analysis and a machine learning approach. Both analyses were geared towards understanding relative importance of ecological factors within the Mission River Floodplain. These datasets are broken down as follows:

Digitized Trees:

    Digitized Downed/Standing Trees in the Mission River Floodplain

Spatial Analysis

    Environmental Variables Used in Spatial Analysis

    Counts of Downed Trees for Levels of Environmental Variables: Results of Spatial Analysis

ML Approach:

    Environmental Variables Used in Maxent Machine Learning Approach

    MaxentVariableselection Inputs/Outputs

    Maxent Models (digitized downed trees with structure):

        Model output with six variables and regularization multiplier of 1.0

        Model output with six variables and regularization multiplier of 2.0

        Model output with six variables and regularization multiplier of 3.0

        Model output with three variables and regularization multiplier of 1.0

        Model output with three variables and regularization multiplier of 3.0

        Model output with three variables and regularization multiplier of 7.0

    Maxent Models (all digitized downed trees):

        Model output with six variables and regularization multiplier of 1.0

        Model output with six variables and regularization multiplier of 2.0

        Model output with six variables and regularization multiplier of 3.0

        Model output with three variables and regularization multiplier of 1.0

        Model output with three variables and regularization multiplier of 3.0

        Model output with three variables and regularization multiplier of 6.0

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171 to 180 of 206 Results
EMST_Fused_Clipped.zip
Jun 8, 2020 - Environmental Variables Used in Spatial Analysis
Shapefile as ZIP Archive - 1.7 MB - MD5: 29036afce9d53b7d3916dfb6d4b51856
Vegetation community and soil type are derived from polygon geometries that represent patches from the Ecological Modeling Systems of Texas (Elliott et.al. 2014). Each patch is assigned a vegetation community and soil type classification are based on the CommonName and EcoName fi...
Inundation_Probability.zip
Jun 8, 2020 - Environmental Variables Used in Spatial Analysis
Shapefile as ZIP Archive - 2.2 MB - MD5: bcc65403593eb55054d70ceae4914972
Consists of three classes that represent levels informed by the inundation probability within the Mission River Floodplain. Inundation probability was based on mean daily discharge record (1939-2016) using inundation extents from Castillo et.al. (under review) as indexes. All ind...
slope_10.zip
Jun 8, 2020 - Environmental Variables Used in Spatial Analysis
Shapefile as ZIP Archive - 1.1 MB - MD5: ded915aa43a5616590528ffc4a92a87c
Polygon shapefile that consists of four classes that represent levels informed by the generalized slope within the Mission River Floodplain. The generalized slope raster map is derived from resampled elevation values at a resolution of 10m using bilinear interpolation. Then, we u...
EDFR.zip
Jun 8, 2020 - Environmental Variables Used in Maxent Machine Learning Approach
ZIP Archive - 413.3 MB - MD5: a7b5e614246f8eb859cc6394cb036597
Euclidean distance (in meters) from the main river channel and major tributaries is calculated with the Euclidean Distance algorithm in ArcGIS Desktop® 10.5. Only those distance values within the model evaluation domain are used.
Elevation.zip
Jun 8, 2020 - Environmental Variables Used in Maxent Machine Learning Approach
ZIP Archive - 314.1 MB - MD5: fae27aed541194dbb3778e8bc6d648f8
Elevation values taken from topography featured in Castillo et.al. (under review). Only those elevation values within the model evaluation domain are used.
Generalized_Slope.zip
Jun 8, 2020 - Environmental Variables Used in Maxent Machine Learning Approach
ZIP Archive - 34.3 MB - MD5: 0e27c4894b9c37b0e0f9e5a34139f11c
Derived from resampled elevation values at a resolution of 20m using bilinear interpolation. Then, we use the Zevenbergen and Thorne (1987) slope approach in QGIS Desktop 3.4 to calculate slope values as degrees with a resolution of 20m. The raster map is then upsampled back to 1...
Inundation_Probability.zip
Jun 8, 2020 - Environmental Variables Used in Maxent Machine Learning Approach
ZIP Archive - 5.9 MB - MD5: c12f5ef9c22b4f20bf912851ae24dbcb
Calculated inundation probability within the Mission River Floodplain. Inundation probability was based on mean daily discharge record (1939-2016) using inundation extents from Castillo et.al. (under review) as indexes. All indices (as raster maps of inundation extent) are sorted...
Soil_Type_Class.zip
Jun 8, 2020 - Environmental Variables Used in Maxent Machine Learning Approach
ZIP Archive - 783.0 KB - MD5: 1200a0ae831ca4b35ff44947f67dcfd7
Raster maps representing soil type is derived from polygon geometries that represent patches from the Ecological Modeling Systems of Texas (Elliott et.al. 2014). Each patch is assigned a soil type classification are based on the EcoName fields within this dataset, respectively. O...
Vegetation_Community_Class.zip
Jun 8, 2020 - Environmental Variables Used in Maxent Machine Learning Approach
ZIP Archive - 1.6 MB - MD5: 1e6f76fdf7012966cc702c301b3e04eb
Raster map representing vegetation community is derived from polygon geometries that represent patches from the Ecological Modeling Systems of Texas (Elliott et.al. 2014). Each patch is assigned a vegetation community classification is based on the CommonName fields within this d...
all_Lidar_trees_wGeometry_0p000.zip
Jun 8, 2020 - Digitized Downed/Standing Trees in the Mission River Floodplain
Shapefile as ZIP Archive - 1.7 MB - MD5: e116143f6548830b7b391267c1d7987f
All digitized downed trees. This data is processed to inform both Environmental Analyses and Hydrodynamic Modelling of Roughness Scenarios
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