Tag Archives: lidar

Using LiDAR data to assess forest aesthetics in McDonald-Dunn Forest

Bryan Begay

GEOG566

05/31/2019

  1. Question asked?

How does forest aesthetics vary depending on forest structure as a result of active management that retains vegetation and landforms?

In order to answer my question I would look at two stands in the McDonald-Dunn forest to do some analysis on how their forest structure is related to forest aesthetics. The first stand is Saddleback which had been logged in 1999 and shows signs of management. The second stand was identified near Baker Creek, and is 1 mile west of Saddleback. Baker Creek was chose for its riparian characteristics, as well as having no signs of management activates.

  1. A description of the data set.

The LiDAR data that I used with my initial analysis was 2008 DOGAMI LiDAR flown over the McDonald-Dunn forest. The RMSE for this data set was 1.5cm. The DEM data set I used was from 2009 has a RMSE of 0.3 m. A Canopy Height Model (CHM) was made in RStudio lidR package that used a digital surface model with a 1 meter resolution. The CHM was used to create an individual tree segmentation, where segmented trees were then converted to point data.

https://gimbalmonkey.github.io/SaddlebackPC/index.html

A link to a visualization of the raw point cloud that is georeferenced to its terrain.

  1. Hypotheses: predictions of patterns and processes you looked for.

I suspected that initially the Baker Creek stand would have higher forest aesthetic that would reflect in the stand’s unmanaged vegetation structure.

Rational

Since the Saddleback had been managed and cut I figured the more natural structure of the riparian stand would have generally higher forest aesthetics than a stand that has been altered by anthropogenic factors. Some processes that I hypothesized that relates to forest aesthetics to these stands was the spatial point pattern of trees could be related to forest aesthetics. Insert forest aesthetic link:

  1. Approaches: analysis approaches you used.

Exercise 1: Ripley’s K Analysis point pattern analysis

The steps taken to create a point pattern analysis was to identify individual trees and convert the trees into point data. The RStudio lidR package was used to create a Canopy Height Model and then an Individual tree segmentation. Rasters and shapefiles were create to export the data so I could then use the tree polygons to identify tree points. The spatstat  package was used in RStudio as well to perform a Ripley’s K analysis on the point data.

Figure 1. Individual Tree Segmentation using watershed algorithm on Saddleback stand.

Exercise 2: Geographically weighted Regression

The steps taken to do the geographically weighted regression included using the polyongs created from the individual tree segmentation to delineate tree centers. When tree points were created from the centroids of the polygons, which would be inputs for the GWR in ArcMap. A density raster and CHM raster had their data extracted to the point data so that density and tree height could be the variables used in the regression. Tree height was the explanatory variable and density was the independent variable.

Figure 2. Polygon output from Individual tree segmentation using the lidR package. The Watershed algorithm was the means of segmentation, and points were created from polygon centroids in ArcMap.

Exercise 3: Supervised Classification

This analysis involved creating a supervised classification by using training data from NAIP imagery and a maximum likelihood classification algorithm. It involved using the NIR band and creating a false color image that would show the difference spectral reflectance values from conifers and deciduous trees. I used a histogram stretch to visualize the imagery better and spent time gathering quality training data. I then created a confusion matrix by using accuracy points on the training data. I then clipped the thematic map outputs with my individual tree segmentation polygons to show how each tree had their pixels assigned.

  1. Results

The Ripley’s K analysis in ArcMap showed me that Saddleback stand’s trees are dispersed, and the Baker Creek stand’s trees were spatially clustered. GWR outputs told me that the model in the Saddle back stand showed me a map output where tree heights and density were positively related. The adjusted R2 was 0.69 and gave me a good output that showed me the tallest and densest trees were on the edges of Saddleback stand. The Baker Creek stand’s model performed poorly on the point data with an adjusted R2 of 0.5. The outputs only showed relationships could only be modeled on the upper left of the stand. The classified image worked well on Saddleback stand due to less distortion in the NAIP imagery on that stand, and the Baker Creek stand’s classification was not useful since it had significant distortion in the NAIP imagery.

Exercise 1:

Figure 3. ArcMap Ripley’s K function output for Saddleback stand assessing tree points.

Exercise 2:

Figure 4. Geographically weighted regression of Baker Creek and Saddleback stand. The Hotter colors indicate positive relationships between tree density and tree height.

Exercise 3.

Figure 5. Supervised image classification using a maximum likelihood algorithm on Saddleback stand.

  1. What did you learn from your results? How are these results important to science? to resource managers?

I learned that Ripley’s K outputs can differ depending on what packages used. R-studio Ripley’s K outputs told me that both my stands had clustered tree patterning. ArcMap outputs that made more sense told me that my Saddleback stand was actually dispersed. Outputs can be variable if inputs are not explicitly understood or modeled with enough care. I also learned that trying to model a very heterogeneous riparian stand is more difficult because of the variability. This is important for researchers who are interest in riparian areas like Baker Creek since they might need to have more variables to adequately model those stands.

  1. Your learning: what did you learn about software?

I became very familiar with processing and modelling with LiDAR point clouds. I also became familiar with Modelbuilder and learned how to use packages in R like Spatstat. I also found a new method for making a confusion matrix in ArcMap.

  1. What did you learn about statistics or other techniques?

I learned how to do point pattern analysis with Ripley’s K on tree points. This was done in R and in Arc. In Arc using the spatial statistics tool was also something I used and still plan to use. When using GWR I understood what it does, understood the outputs, and learned to properly interpret the results. I also became more concerned with issues of scale and networks that might affect my areas of interest.

Exercise 2: Geographically weighted regression on two forested stands.

Bryan Begay

  1. Initial Spatial Question: How does the spatial arrangement of trees relate to forest aesthetics in my areas of interest?

Context:

To understand forest aesthetics in my stand called Saddleback, I did a Ripley’s K analysis for Saddleback and on a riparian stand called Baker Creek to determine if the stands are clustered or dispersed.  The Baker Creek location is a mile west of the Saddleback stand.

  1. Geographically weighted Regression:

I performed a geographically weighted regression on both the Saddleback and the Baker Creek stands. The dependent variable was a density raster value and the explanatory value was tree height.

  1. Tools and Workflow

Figure 1. The workflow for creating the Geographically Weighted Regression for the Saddleback Stand. The Baker Creek stand followed the same workflow as well.

Results:

 

Figure 2. Geographically Weighted Regression showing the explanatory variable coefficients in the Saddleback and Baker Creek stands near Corvallis Oregon. Yellow color indicates negative relationships and the hotter colors  indicate positive relationships between tree height and density.

Figure 3. Geographically Weighted Regression showing the Local R2 values in the Saddleback and Baker Creek stands near Corvallis Oregon. Yellow color indicates that the local model is performing poorly, while hotter colors indicate better performance locally.

Table 1. Summary table output for the Saddleback stand’s geographically weighted regression.

Table 2. Summary table output for the Back Creek stand’s geographically weighted regression.

4. Interpretation/Discussion:

Having done the Ripley’s K analysis, I wanted to have a connection with this exercise, so I created a point density raster on both my stands (Figure 1). The point density raster calculates a magnitude-per-unit area from my tree points and outputs a density for the neighborhood around each tree point. The raster values would then be a descriptor of the trees neighborhood density. Having the density neighborhood values describes the stands tree spatial arraignment and relates to the Ripley’s K analysis outputs of telling if a stand is spatially clustered or dispersed.

Figure 2. shows that there is a spatial pattern in the Saddleback stand between density and height. There is a positive relationship on the edges of the stand and a decreasing relationship in the middle of stand between the two variables. This makes sense when thinking about how the stand would have denser and higher trees on the edges of the managed stand to screen the forest operations. The coefficient values for the baker creek showed a positive relationship on the north eastern portion of the stand, which would need further investigation to understand the relationship between density and height. Overall the relationship was negative in the Baker creek stand between density and height, but this may be attributed to the low local R2 values that indicate poor modeling (figure 3). Table 2. also shows that the Baker Creek model only accounted for 50% of the variance for the adjusted R2 values, which would indicate that more variables would be needed for the riparian stand. Figure 1. shows the summary table for GWR in the Saddleback stand.

  1. Critiques

The critiques for this exercise is that I only look at height and density. If I had more knowledge of working with LAS data sets I would have liked to have implemented the return values on the LiDAR data as an indicator of density. Another critique would be that I used density as a dependent variable and height as an explanatory variable. Using density as the dependent value allows me to see the spatial patterning of my trees when plotted in ArcMap so I can reference the Ripley’s K outputs for further analysis. Having height as a response variable with density as an explanatory is something that would have been easier for me understand and explain that relationship. Density can affect tree height in a stand but understanding tree height as a factor that affects density is not as intuitive. Looking at how tree height responds to density in my stand would tell something about tree height, but that relationship has already been explored in great depth.

How winter wetland habitat change over time affects songbird communities

A description of the research question that you are exploring.

For my research, I am exploring the relationship between the spatial pattern of the differences between present (2019) and past (1995) wintering songbird community composition metrics (abundance, richness, evenness, and weighted rarity) and the spatial pattern of landscape-level land cover variable changes (listed here: Landscape Variables) in the same timeframe via the mechanisms of change over time and landscape-level variable importance to habitat suitability. I will be looking at data for 20 wetland wintering habitat sites in the Corvallis area.

I am interested in comparing the community composition metrics (abundance, richness, evenness, weighted rarity) of songbirds from 1995 to those found in 2019. I will then look at how the above-mentioned landscape level variables at these sites (within a 100m buffer, 500m buffer, and 1km buffer from the wetland) have changed from 1995 to 2019 at those same sites to determine if and what variable changes influence songbird community composition.

Other spatial factors likely influence songbird community composition metrics but I am only concerned with those that were included in Adamus’s 2002 dissertation study.

A description of the dataset you will be analyzing, including the spatial and temporal resolution and extent.

I have species richness data in the form of a spreadsheet from 1995 including songbird abundance data for my 20 sites recorded via point count surveys from January 4th to March 20th. I have the same information collected by the same methods at those same sites from January 4th 2019 to March 20th 2019. I will use this data to calculate the species metrics (listed above).

I am still in the process of gathering my spatial datasets for this project. As of now, I have open source areal imagery from Google Earth Engine that my advisor used to analyze the sites in 1995 and that same areal imagery from 2019. I hope to locate LIDAR data, NVI data, and more sophisticated ground cover data for my sites in this class. One of the reasons I enrolled in this class was to get ideas and aid with obtaining this data.

Hypotheses: predict the kinds of patterns you expect to see in your data, and the processes that produce or respond to these patterns.

I predict that the greater the change in the landscape level variables at a site from 1995 to 2019 the greater the difference in community composition measurements between those years via the landscape level variables influence on habitat suitability for wintering songbirds. Additionally, I think the changes in the variables that my advisor determined to be most influential to wintering songbird community composition metrics at these sites in 1995 will have the greatest effect on the change in species measurements from 1995 to 2019. For example, he found that wetlands with a higher percentage of open canopy forest cover in the surrounding area had a positive correlation with high abundance (Adamus, 2002) at a site so I hypothesis that those sites that have lost the most open canopy forest will also have the greatest decrease in abundance.

Approaches: describe the kinds of analyses you ideally would like to undertake and learn about this term, using your data.

I would like to learn how to use LIDAR and/or NVI data to determine ground cover in regards to my advisors’ categories (attached). I would also like to explore methods for comparing the amount of change from 1995 to 2019 at my sites that is suitable to my data and my purposes.
Expected outcome: what do you want to produce — maps? statistical relationships? other?
I expect to have a spreadsheet with quantified variable change as well as species richness measurements in order to reevaluate variable importance as well as the statistical relationship between landscape level variable differences and species richness differences. As an intermediate step, I will also produce a map(s) portraying the change at my sites from 1995 to 2019.

Significance. How is your spatial problem important to science? to resource managers?

The quality of wintering habitat is correlated with overall survivability and reproductive success for songbirds the following year (Norris et al. 2004). It is important to know how these habitats have changed as well as the consequences of those changes in regards to songbird community metrics. Therefore, it is extremely important for both science and resource managers. If we want to assure that our environment remains healthy and balanced with stable songbird communities it is this work and work like it is necessary. It is also important to those who wish to manage songbird populations so they know where to allocate resources when it comes to habitat variables to preserve.

Your level of preparation: how much experience do you have with (a) Arc-Info, (b) Modelbuilder and/or GIS programming in Python, (c) R, (d) image processing, (e) other relevant software

a. I am in between proficient and having a working knowledge of the basics of Arc-Info from taking various courses and teaching the basics of Arc-Pro.b. I have a working knowledge with modelbuilder and GIS programming in Python because I took courses on both subjects and now help teach a programming for ArcGIS class.
c. I have a working knowledge of R because I took an R course related to species distributions and I have used R in two statistics courses.
d. I am a novice in image processing but did take a digital terrain modeling course last term.
e. I am a novice in but would like to learn about software that helps me analyze NVI and LIDAR data

References

Adamus P,. 2002. Multiscale Relationships of Wintering Birds with Riparian and Wetland Habitat in the Willamette Valley, Oregon. Oregon State University.

Norris R., Marra P., Kyser K., Sherrt W., & Ratcliffe L. 2004. Tropical Winter Habitat Limits Reproductive Success on the Temperate Breeding Grounds in a Migratory Bird. Biological Sciences (271).