1. Question Asked: How does the density of juniper vary based on characteristics associated with soil moisture? Additionally, does this pattern vary with juniper density (as an indicator of potential seed source) surrounding each transect?
The study site for this analysis is the Camp Creek Paired Watershed Study (CCPWS) in central Oregon. The majority of juniper was removed from one watershed (“treated WS”) while one watershed is dominated by mature juniper (“untreated WS”). This analysis seeks to examine the density of juniper sapling reestablishment in the treated WS and mature juniper density in the untreated WS as it relates to indicators of soil moisture (slope, aspect, and a combination of both) and seed sources (surrounding juniper density).
2. Tools and steps used in analysis: All steps were completed in ArcGIS 10.6 or ArcGIS Pro.
Surrounding juniper density: Thirty-three belt transects were conducted in the summer of 2018 in the treated WS. The number of juniper for each transect (30m long by 3m wide) was recorded. For the purposes of this analysis, the transects were represented by a point. Supervised classification (support vector machine) was applied to National Agriculture Imagery Program (NAIP) imagery for this area and pixel-based analysis was used to estimate the density of juniper. General steps are as follows:
- Buffers were created surrounding each transect point at 50m, 100m, and 250m using the buffer tool.
- The zonal histogram was used to extract the pixel values within each buffer. To ensure proper counts, each buffer must be iterated separately if there is overlap between buffers.
- A model was created in ModelBuilder to extract values from the buffers and combine values in a table. In-line variables were used to separate the data for each transect into separate tables prior to being merged.The submodel (first image) and full model (second image) are displayed below:
- The percentage of juniper pixels within each buffer (number of juniper pixels/total number of pixels) was calculated using excel. This percentage was used as an estimate of surrounding juniper canopy/density within the surrounding areas.
- Point plots were created indicating the number of juniper per transect and the corresponding juniper density.
- The transects were divided in to two groups representing “low” counts (transects where one or fewer juniper saplings were found) and “high” counts (transects where five or greater juniper were found).
Slope and Aspect: Slope and aspect were considered to be indicators of soil moisture within the watershed. Separate approaches were used to assess if juniper density was found to vary with these characteristics: regression (using ordinary least squares and global weighted regression) and a rating model based on slope and aspect categories. General steps are as follows:
- Using a 30m DEM, the slope and aspect values were extracted using the slope and aspect tools within ArcMap.
- Using the calculate field function, aspect and slope were each ranked from 1-9, with areas were greater soil moisture is expected (flat and shallow slopes and northern slopes were weighted heaviest). The following parser was used (aspect values are in degrees and slope is in percent):
def Reclass(Aspect_category):
if (Aspect_category<0 and Aspect_category>=-1):
return 9
if (Aspect_category>=337.5 and Aspect_category<360):
return 8
if (Aspect_category>=0 and Aspect_category<22.5):
return 8
if (Aspect_category>=22.5 and Aspect_category<67.5):
return 7
if (Aspect_category>=292.5 and Aspect_category<337.5):
return 6
if (Aspect_category>=67.5 and Aspect_category<112.5):
return 5
if (Aspect_category>=247.5 and Aspect_category<292.5):
return 4
if (Aspect_category>=112.5 and Aspect_category<157.5):
return 3
if (Aspect_category>=202.5 and Aspect_category<247.5):
return 2
if (Aspect_category>=157.5 and Aspect_category<202.5):
return 1
def Reclass(Slope_category):
if(Slope_category>=0 and Slope_category<2):
return 9
if(Slope_category>=2 and Slope_category<4):
return 8
if(Slope_category>=4 and Slope_category<6):
return 7
if(Slope_category>=6 and Slope_category<8):
return 6
if(Slope_category>=8 and Slope_category<10):
return 5
if(Slope_category>=10 and Slope_category<12):
return 4
if(Slope_category>=12 and Slope_category<14):
return 3
if(Slope_category>=14 and Slope_category<16):
return 2
if(Slope_category>=16):
return 1
- Based on the categorized values for aspect and slope, a rating model was calculated using the raster calculator to determine if areas of expected greatest soil moisture corresponded to juniper density. The formula for this rating model was: (slope category +aspect category)/2.
- As the rating model had a lower spatial resolution and extent than the classified NAIP raster, the classified raster was resampled to be the same resolution as the rating model. The NAIP raster was masked using the extent of the rating model raster. These rasters will be used for further analysis in exercise 3. For the purposes of exercise 2, two maps were created for visual analysis. For future analysis, both rasters were projected to NAD 1983 UTM Zone 10N.
- The hot spot analysis from exercise one was overlaid on the rating model to visually assess the patterns over a small scale.
- An ordinary least squares (OLS) analysis was performed using the number of juniper counted for each transect as the dependent variable and the slope category, aspect category, and combination of both categories as the explanatory variables.
- The standard residual for each OLS was used to calculate Global Moran’s I.
- Geographically Weighted Regression (GWR) was used with the same inputs described for the OLS.
3. Overview of results.
Buffer. No clear trend was found between juniper density and the number of juniper sapling found in each transect. However, for all transects the juniper density was lower with increasing buffer distance. Results are displayed in the chart below, points are labeled by distance and “high” (corresponding to transects with 5 or more juniper) and “low” (corresponding to transects with 1 or fewer juniper:
Comparison of Rating Model and Classified Raster. The classified NAIP (prior to resampling and reprojection) and rating model raster are displayed below. Red pixels within the classified raster are pixels classified as juniper. For the rating model, lighter shaded areas correspond to those areas where higher soil moisture is expected based on the slope and aspect characteristics. The hot spot analysis provided limited use (as only one hot spot and one cold spot were indicated) but could be useful for additional analysis. However, the cold spot aligned with areas of expected lower soil moisture and the hot spot corresponded to areas with expected higher soil moisture. Further analysis of these two datasets will continue with exercise 3.
Regression analysis. The R-squared values for OLS for each explanatory variable(s)(slope category only, aspect category only, and a combination of slope and aspect) ranged from -0.025 to -0.060, suggesting that these characteristics were not a good predictor of juniper density (at least for the data set used in this case). The coefficient for each explanatory variable(s) ranged from -0.087 to 0.024, also indicating that this model was not a good fit. The Koenker (BP) statistic was insignificant for all cases. The p-values (p>0.86 for all cases) for Global Moran’s I for each combination of explanatory variables indicated random spatial patterns. As expected, results were similar for the GWR analysis with R-squared values ranging from -0.011 to -0.037. The GWR residual squares ranged from 180.8 to 182.3.
4. Discussion/critique of methods. Based on the buffer analysis, no clear patterns were indicated for repulsion/attraction based on the transect numbers and the density analysis. However, the juniper density (both in range and overall percentage) decreased for the 250m buffer regardless of the number of juniper counted per transect. These patterns are most likely related to the fact that juniper was removed from one watershed fifteen year ago and that the adjacent watershed was left untreated. This coupled with the small sample size may not be sufficient to indicate if spatial patterns between juniper density and saplings found for each transect exist. The OLS and GWR analysis indicated that slope and aspect (at least based on the data used here) were not good predictors of juniper density. For exercise 3, regression analysis of the classified raster and the rating model will take place.
The methods used here were relatively easy to apply, and can be accomplished using tools available in ArcGIS Pro. The ModelBuilder process required time to develop and some issues, particularly with the merge function, required that the tables be manually manipulated. However, ModelBuilder still was more efficient than executing the buffer and zonal histogram for each transect separately. Also, the supervised classification can easily be accomplished within ArcGIS. However, the spatial resolution of the NAIP imagery (1m) likely led to reduced accuracy of juniper sapling identification compared to images with higher spatial resolution. This was the highest resolution imagery available for the entirety of the two watersheds but this methodology could easily be applied to other data.
Overall, the methods used here may inform future analysis to be applied to other datasets. For the purpose of these exercises, very few spatial trends are evident but that may be the result of a)the sample size, b)the effects of juniper removal, and c)characteristics of the dataset (e.g., spatial resolution of the raster). In particular, I would be interested to see how these approaches would work in areas where larger-scale removal has taken place. Additionally, the use of rasters with higher spatial resolution (such as UAV-based imagery) would likely improve the classification and increase accuracy of juniper sapling detection.
Nicole, this is promising work. The soil moisture analysis seems to have produced some useful results. Because you then ran a regression model, I would suggest that you try geographically weighted regression, and create a map of the slope terms (beta values) in the model, to see whether there is geographic variation in relationships. If you have time to do this and include it in your final project that would be nice (not required). Also in your final project, please state some hypotheses about what factors might influence juniper density in space, how long it might take for juniper to respond to those factors, what spatial patterns you might expect to see as a result, and how your analyses supported (or did not support) these hypotheses.