Research Question

How is the spatial presence of postfire woodpecker nests related to the spatial presence of salvage-logged forest stands?

Dataset

This project will use datasets targeting the 2015 Canyon Creek fire complex on the Malheur National Forest in eastern Oregon. A salvage logging operation occurred in the burn area in July 2016. My research is in cooperation with a Rocky Mountain Research Station study examining salvage logging effects on three woodpecker species in the Canyon Creek complex. In 2016 and 2017 I led crews on this project collecting extensive postfire woodpecker occupancy and nest productivity datasets for black-backed, white-headed, and Lewis’s woodpecker populations. This resulted in a 148-nest dataset for 2016 and 2017, representing woodpecker populations before and after salvage logging. A polygon shapefile outlining ten RMRS woodpecker point count survey units serves as the area of interest (6 treatment, 4 control). Within the 6 treatment units, another polygon shapefile outlining 34 salvage harvest units indicates treatment areas. Three silvicultural prescriptions replicating optimal habitat types for each woodpecker species designate salvage variables like post-harvest stand density and diameter. Each salvage unit adheres to one of these three harvest prescriptions. 2016 pre-salvage and 2017 post-salvage lidar datasets are in processing for eventual correlation between 3D forest structure variables and woodpecker nest site selection before and after harvest. Supplementary geospatial data includes a 2015 WorldView-3 1 m raster and ArcGIS basemaps.

Above: The 2015 Canyon Creek Fire burning near John Day, OR.

Above: The Canyon Creek fire complex as a false color WorldView-3 1 m raster. The area of interest includes 10 study units in blue, labeled with yellow text (6 treatment, 4 control). This visual orients the survey units to an area in eastern Oregon southeast of John Day and Canyon City. The false color image displays healthy vegetation as red, with the darkest areas displaying high burn severity. The survey units are found within some of the highest burn severity areas in the fire complex.

Above: A close-up of the 34 salvage treatment polygons outlined in red and labeled with white text. Control units lack red salvage polygons. This image does not include Overholt Creek.

Above: A subset of the 2016 and 2017 nest points featuring survey and salvage unit polygons.

Hypotheses

I expect to see dispersed nests in 2016 with possible trends indicating species habitat preferences. Previous research indicates species-specific preferences for certain forest habitat variables. Black-backed woodpeckers prefer dense, small-diameter stands for foraging and nest excavation. White-headed woodpeckers prefer a mosaic of live and dead variable density and diameter stands for pine cone foraging. Lewis’s woodpeckers prefer tall medium to large-diameter stands for aerial foraging maneuvers. I expect to see nest sites in both years clustered in areas with these forest structures. In 2017 I also expect to see nest sites clustered near salvage treatments implemented for each species. Overall I expect the control units to exhibit nest dispersal and high woodpecker activity.

Black-backed woodpecker (Picodies arcticus)     White-headed woodpecker (Picoides albolarvatus)                       Lewis’s woodpecker (Melanerpes lewis)

A graphic depicting 3 salvage harvest treatment types and a control designed to benefit each of the target woodpecker species (Dave Halemeier 2016).

Approaches

Analyses will consider pre- and post-salvage variables to determine changes in forest structure alongside woodpecker population dynamics. I would like to learn about spatial autocorrelation analyses such as Moran’s I. It is likely I will find patterns of dependent observations based on localized conditions. Woodpecker species presence and nest locations may be affected by burn severity, since highly weakened trees will host their primary food source, bark beetle larvae. In 2017 woodpecker species presence may be grouped according to salvage treatments targeting each species, or control areas. Regression analyses showing the relationship strength between nest distance from salvage units and salvage treatment types could indicate certain forest variables affecting postfire woodpecker colonization.

Expected Outcome

Regression coefficients describing the relationship between woodpecker presence and salvage treatment location and type will help develop inferences towards postfire management effects. I will create interpretive maps of nest locations showing survey unit and salvage unit polygons. These maps could include statistical and geospatial relationships represented with different colors and symbols. Eventually, I will geovisualize the lidar data with these maps and statistical relationships for a comprehensive and communicative representation of woodpecker population and forest structure change dynamics.

Significance

I am processing two lidar datasets of the study area from 2016 and 2017. These datasets were acquired before and after the salvage logging treatments occurred. I will produce forest metrics such as stand density, diameter class, and height in salvage and survey units. I will then correlate geospatial and statistical properties of the nest dataset to quantified forest variables affecting woodpecker nest site selection. I will examine trends between 2016 and 2017 nest selection to understand the effects of harvest treatments on woodpecker populations. At least two more years of woodpecker data will exist for 2018 and 2019, so future research will add these datasets to the analyses. I would like to see a machine learning algorithm developed from this study that could predict areas of optimal habitat suitability for snag-dependent wildlife species. Postfire wildlife habitat prediction will be crucial to resource managers as wildfires increase in the coming decades alongside accelerated landscape restoration.

This spatial problem is important to science and resource managers as climate change amplifies wildfire effects. Using 3D remote sensing datasets for resource management is the future trend across all disciplines. Increased wildfire activity around the world necessitates cutting-edge methods for active fire and postfire ecosystem quantification. In the Blue Mountains ecoregion in eastern Oregon, Rocky Mountain Research Station, Malheur National Forest, and Blue Mountains Forest Partners rely on this project’s lidar quantification for their research and management decisions. Determining acceptable levels of salvage harvest for wildlife habitat affects whether government agencies and rural economies in this region will allow small businesses to profit from sustainable harvest operations. Science overall will benefit from the continued investigation of wildlife response to anthropogenic disturbance, specifically postfire forest management decisions and the controversial practice of salvage logging.

Above: A salvage treatment in the Crazy Creek woodpecker survey unit.

Preparation

I took an ArcInfo class (ARC Macro Language) during my undergraduate program. I am currently taking a Python class for geospatial programming. I have experience in image processing through an undergraduate degree in land use and GIS, multiple years of professional experience in remote sensing and GIS, and my current MS program in Forest Geomatics. I have academic and professional experience with R, C++, ArcGIS, and multiple types of remote sensing software for 2D and 3D data analysis.

Research Question

How is the spatial presence of aquatic organisms in the lower South Fork McKenzie River related to the spatial presence of hydromorphological changes induced by Stage 0 stream restoration?

Global hydrologic systems historically contained anastomosing (braided and connected) channels and active floodplains. Anthropogenic disturbances of the late 19^th and early 20^th centuries and prevailing natural disturbances are responsible for the channelization and incision of stream systems worldwide. Stage 0 riparian restoration restores degraded streams to historic conditions through large woody debris placement and filling in channels to increase aquatic habitat quality and availability. In this study, I will explore Stage 0 restoration effects on a 2 mile reach of the lower South Fork McKenzie River 45 miles east of Eugene, OR. I will test these effects by spatially relating early post-restoration species colonization to restoration-induced hydromorphological changes. These morphological changes include habitat features created by the reduction and redirection of water flow energy, such as riffles, pools, side channels, slack water, and sediment and organic material deposition. Aquatic organisms depend on these features for critical habitat. Therefore, sampling for aquatic species presence before and after restoration will indicate how successful Stage 0 restoration was in creating habitat features for these organisms.

Datasets

I will analyze two biological datasets to detect species presence and multiple remote sensing datasets to detect hydromorphological features. The first biological dataset includes pre- and post-restoration lentic (still freshwater) and lotic (rapid freshwater) aquatic macroinvertebrate samples taken at established transects and randomly throughout the study area. The second biological dataset includes eDNA samples taken at these same transects, intended to capture up to 48 species in a single sample (macroinvertebrates, fish, amphibians, crayfish). The remote sensing datasets include pre- and post-restoration aerial lidar, bathymetric lidar, Structure from Motion, RGB, multispectral, and thermal infrared products acquired with an Unmanned Aerial System for the 2 mile reach. The temporal resolution for all datasets are approximately 1 year (summer 2018 – summer 2019). The datasets are collected pre- and post-implementation (summer 2018) in the early summer during high flow conditions and early fall for low flow conditions. The spatial resolution of the UAS datasets is fine scale (sub-meter to cm).

 Hypotheses

In the biological datasets, I expect to see macroinvertebrate presence clustered around features characterized by low velocity, shallow depth, and high organic material. These features allow macroinvertebrates foraging ease and shelter from predators. I expect species type to be dominated by post-disturbance colonizers throughout the system. I expect the eDNA data to return descriptions of upstream-downstream species presence. I expect non-macroinvertebrate species to be present in areas with more pools, side channels, and high wetted area, which provide habitat for resting, feeding, and nurseries. I expect the macroinvertebrate eDNA data to reflect the physical sampling data. In the remote sensing datasets, I expect habitat features to be present downstream of and adjacent to large woody debris, sediment deposits, and organic material, since these factors reduce and redirect flow energy for establishment of lentic and lotic features (e.g. pools, riffles, slack water).

 Approaches

All analyses will consider pre- and post-restoration variables to determine rate and degree of change. I would like to learn about spatial autocorrelation analyses such as Moran’s I. It is likely that in these datasets I will find patterns of dependent observations based on localized conditions. For example, aquatic species presence may be grouped according to centralized nursery or hatch locations, so they are not truly independent samples. Fish species presence may be grouped based on macroinvertebrate presence as a food source, and certain species may be grouped by areas of similar substrate sizes. Regression analyses showing relationship strengths between different combinations of species and morphology variables (such as PCA) could indicate the likelihood of certain features affecting species colonization.

Expected Outcome

I will produce site maps showing point and polygon locations of certain hydrologic features alongside point locations of specific aquatic species presences and types. For the eDNA datasets, I will produce graphs indicating the presence and type of species detected. Ideally, I will be able to determine correlation coefficients for the relationships between specific hydrologic features created and the presence and location of aquatic species types, likely presenting them in a correlation matrix.

 Significance

 This research presents a novel opportunity to study Stage 0 restoration. Powers et al. (2018) present one of the only formal studies investigating Stage 0 restoration outcomes. This study will add to the limited knowledge surrounding this relatively unexamined strategy. To my knowledge, this proposal will be the first study testing Stage 0 UAS monitoring and one of the few existing studies linking aquatic organism sampling to UAS hydromorphology datasets. On a regional scale, U.S. Forest Service fisheries and hydrology divisions in the Pacific Northwest and across the United States will design restoration effectiveness monitoring objectives using results from this study. The McKenzie Watershed Council will determine their implementation techniques and success rates on future projects with results from this study. The study results will provide a viable Stage 0 restoration monitoring methodology for agencies and landowners on a global scale. The South Fork McKenzie River also sustains fish species listed as Endangered and Threatened under the Endangered Species Act, specifically the Chinook salmon (Onchoryncus tshawytscha) and bull trout (Salvelinus confluentus), respectively (USFWS 2019). These species use the South Fork McKenzie River for annual spawning and rearing habitat and feed on resident macroinvertebrates (Meyer et al. 2016). The Chinook salmon and bull trout are among the western U.S.’s most controversial game fish due to the environmental policies surrounding their protection. Conflicts among agencies, companies, and the public frequently arise concerning these species’ conservation. Researching restoration effects on Chinook salmon and bull trout habitat and food sources will provide scientific basis for management decisions regarding these fish.

Level of Preparation

I took an Arc-Info (ARC Macro Language) class during my undergraduate program. I am currently taking a Python class for geospatial programming, which is my only experience with this language. I have experience in image processing through an undergraduate degree in land use and GIS, multiple years of professional experience in remote sensing and GIS, and my current MS program in Forest Geomatics. I have academic and professional experience with R, C++, ArcGIS, and multiple types of remote sensing software for 2D and 3D analysis.

References

Meyer, K., Hammons, B., Hogervorst, J., Powers, P., Weybright, J., Bair, B., Robertson, G., Mazullo, C. (2016). Lower South Fork McKenzie River Floodplain Enhancement Project 80% Design Report. Prepared by the Willammette National Forest and McKenzie Watershed Council, March 4, 2016.

Meyer K. (2018). Deer Creek: Stage 0 alluvial valley restoration in the western Cascades of Oregon. In StreamNotes: The Technical Newsletter of the National Stream and Aquatic Ecology Center, David Levinson (editor). US FOrest Service, Fort Collins, CO, May 2018. https://www.fs.fed.us/biology/nsaec/assets/streamnotes2018‐05.pdf

Newson, M.D., Newson, C.L. (2000). Geomorphology, ecology and river channel habitat: mesoscale approaches to basin-scale challenges. Progress in Physical Geography 24(2), 195 – 217.

Powers, P. D., Helstab, M., & Niezgoda, S. L. (2019). A process-based approach to restoring depositional river valleys to Stage 0, an anastomosing channel network. River Research and Applications, 35(1), 3–13. https://doi.org/10.1002/rra.3378

U.S. Fish and Wildlife Service. (2019). Environmental Conservation Online System. https://www.fws.gov/endangered/

My research explores the variability in streamflow patterns in rain-dominated systems of the Pacific Northwest. Rain-dominated climate regimes occur primarily in the coastal portion of the PNW. Because precipitation only occurs as rain and does not occur in significant quantities during the summer season, underground storage is a crucial component of both the water cycle and streamflow stability in these systems. The objectives of my research are: first) to describe variations in stream hydrograph stability across multiple catchments and multiple catchment scales, and second) to use estimations of catchment storage processes to help explain potential variations in streamflow patterns.

I will be analyzing multiple datasets in order to meet my objectives. Those include: geophysical data, land-use/management data, and streamflow data. All landscape data will be analyzed at the finest spatial resolution available for the dataset. Hourly streamflow data will be analyzed for the available period of record, which varies by site. Second-fourth order streams in the Siletz and Smith river basins have hourly discharge data for the recent 5-8 years. USGS hydrological stations have similar data for 10-30 years.

I expect to see that streams in different hydrogeologic setting demonstrate different streamflow patterns over time. Streams located in more permeable, thicker lithosphere, may demonstrate more stable stream flows. In the winter, that may appear as muted storm peaks, while in the summer, that may appear as more sustained baseflows through the non-rainy season. Land cover may play an important role in streamflow regimes as well. The amount of water taken up and stored by vegetation may depend on the density, age, structure, and species composition of the forest. Watersheds with a large areas managed under industrial timber production may confound streamflow behavior.

I envision approaching my objectives by first developing some descriptive statistics for the hydrographs at each site. Such descriptors may include: recession analysis, dynamic storage, 7q10, arc peak, and various other streamflow analysis metrics with which I am not yet familiar. All sites are of varying contributing areas, so that will have to be taken into consideration in the analysis. Once descriptive statistics are developed across each site, I would like to integrate an analysis of the landscape attributes as potential explanatory variables for any variations observed in hydrograph statistics across space to see how much explanatory power they have, and how much variation there is.

The product of this project is expected to include both maps and statistical relationships. For maps, I would like to produce: 1) a depiction of the hydrogeologic settings across the study area, and 2) a depiction of expected streamflow patterns given the hydrogeologic settings. I would like to understand the statistical relationship between various streamflow metrics and the hydrogeologic setting of the given stream.

The results of this analysis may be important to resource managers and the scientific community as it will contribute information about hydrological processes, with a focus on the critical zone, in the PNW coastal landscapes. The persistence of streams and rivers in this region is crucial to several species of native salmonids, as well as to the economic well-being of the local communities. With potential variations in the climate, understanding underlying hydrological processes and the drivers of such processes may contribute to more proactive management approaches. Furthermore, an analysis that is relevant to the fine-scale processes that exist in the study area may contribute more accurate classifications of hydrologic regimes and analyses of streamflow vulnerability to long-term changes in the hydrological cycle.

As far as preparation for this project: I am well-versed in ArcGIS software. I have used Model Builder and Python some, though could use more practice. I am most comfortable with spatial and statistical analyses through R software. I have only used imagery analyses in a class, and I’d like to use it for my analysis so I am looking forward to learning more through this class.

1. My spatial problem

• A description of the research question that you are exploring.

Research question: How is the spatial pattern of juniper density (A) related to the spatial pattern of slope, aspect, and a combination of the two (B) via soil moisture and solar radiation?

The expansion of western juniper has become a concern in many rangeland areas, and is associated with a number of ecological and hydrological impacts [1,2]. The study site is located in a semiarid watershed in central OR, and was established to assess ecohydrological characteristics associated with juniper expansion and removal. The majority of western juniper was removed from this watershed in 2005 -2006 and juniper saplings have become re-established in this area. The objectives of this project are 1) to determine the relationship between the density of western juniper sapling re-establishment and slope and aspect in this watershed and 2) assess density changes in vegetation cover at this watershed since juniper removal.

The intent of this project is also to establish a methodology that will be expanded to include patterns of soil moisture, soil surface temperature, and soil type (but additional data needs to be collected). While juniper density and vegetation cover for the purposes of this project are the dependent variables, in the future I am exploring how certain ecohydrological characteristics vary with juniper density.

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

A combination of National Agriculture Imagery Program (NAIP), unmanned aerial vehicle (UAV) imagery, and ground-based data will be used. Forty-one belt transects (3m by 30m) representing areas of varying aspect and slope were conducted in summer of 2018 to assess juniper density across the entire watershed.  UAV-based multispectral imagery (red, green, blue, red-edge, and near-infrared bands) was collected at a study plot in the watershed in October 2018, but needs to be expanded to represent topography of the watershed. Resolution of the UAV imagery is approximately 2.5cm/pixel. NAIP imagery (1 m resolution) will be used to assess density over time. A 10 m digital elevation model (DEM) will be used to model the topography of the watershed. Alternatively, a higher-resolution DEM created from UAV imagery may be used if it is available. Prior to analysis, support vector machine (SVM) supervised classification will be used to identify juniper in the UAV and NAIP imagery and to assess overall vegetation cover in NAIP imagery. It should be noted that while I have used SVM classification successfully with multispectral UAV imagery to assess juniper density (shown in the figure below), the accuracy of this process will need to be assessed with the NAIP data as there is a large difference in spatial resolution (2.5 cm compared to 1 m). If I am unable to identify juniper successfully in NAIP imagery, then only UAV and ground-based data will be used to assess juniper density.

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

I expect that juniper density will be positively related to north aspects and negatively related to slope angle as these characteristics promote higher levels of soil moisture. However, these patterns may vary with both soil type and amount of non-juniper vegetation cover.  Similar to past studies [2], we may anticipate that overall vegetation cover may change at this watershed in response to the removal of juniper. Changes in vegetation cover associated with juniper density may be related to juniper transpiration, soil moisture, and canopy interception.

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

Initially, I will assess the spatial pattern of slope, aspect, and a combination of the two in the watershed. Further, the relationship between observed juniper density and the slope angle and aspect characteristics associated with highest soil moisture will be assessed. I would like to integrate the NAIP imagery, DEM, transect data, and UAV imagery for analysis of juniper density. During this term, this model will focus on slope and aspect but I would like to establish a model that I can expand to use to assess other characteristics as well (such as soil moisture, soil temperature, and vegetation cover).  Using the NAIP imagery, I would like to see assess temporal changes in vegetation cover. In particular, I am interested in understanding how to best use data with different resolutions and how to expand this methodology for more complicated analysis in future research.

• Expected outcome: what do you want to produce — maps? statistical relationships?

I would like to create maps characterizing juniper density within the watershed and determine if there is a statistical relationship between juniper density and slope and aspect (that can be expanded as more variables are addressed). Additionally, I would like to assess changes in total vegetation cover over time in this watershed.

• How is your spatial problem important to science? to resource managers?

The expansion of western juniper is associated with a number of ecohydrological changes, to include reduced undercanopy soil moisture [3], reduced productivity[4] and increased erosion[5]. The removal of western juniper is labor intensive and expensive, and can be difficult to implement. An improved understanding of the spatial patterns associated with juniper re-establishment after removal can be used to target treatment when appropriate but also to improve our understanding of the ecohydrologic impacts of juniper expansion within these communities. This analysis can also provide information about which areas are at greatest risk based on topography. Additionally, understanding changing in vegetation cover helps to determine the timing and impacts of removal. While beyond the scope of this project, this information may be used to determine how the rate of juniper expansion relates to other ecohydrological characteristics over time.

2. 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

• I have used ArcMap/Pro a moderate amount in a classroom setting (GIS 1&2, GIS in water resources), for independent research, and in the workplace. Outside of a classroom setting, I have largely used ArcInfo for map creation and geospatial analysis (primarily unsupervised and supervised classification).
• I have used Modelbuilder a limited amount, and it has largely been limited to class assignments in Geog 560/561. I do not have experience with GIS programming in Python.
• I have some experience using R, primarily for basic statistical analysis. I have no experience using R for spatial statistics.
• I have limited experience using ENVI, but I would need to refresh the basic procedures. I do not have experience with MATLAB.
• I have some experience with Google Earth Engine, although I would need to review the basics to be effective.

References

1. Coultrap, D.E.; Fulgham, K.O.; Lancaster, D.L.; Gustafson, J.; Lile, D.F.; George, M.R. Relationships between western juniper (Juniperus occidentalis) and understory vegetation. Invasive Plant Sci. Manag. 2008, 1, 3–11.
2. Dittel, J.W.; Sanchez, D.; Ellsworth, L.M.; Morozumi, C.N.; Mata-Gonzalez, R. Vegetation response to juniper reduction and grazing exclusion in sagebrush-steppe habitat in eastern Oregon. Rangel. Ecol. Manag. 2018, 71, 213–219.
3. Lebron, I.; Madsen, M.D.; Chandler, D.G.; Robinson, D.A.; Wendroth, O.; Belnap, J. Ecohydrological controls on soil moisture and hydraulic conductivity within a pinyon-juniper woodland. Water Resour. Res. 2007, 43, W08422.
4. Miller, R.F.; Svejcar, T.J.; Rose, J.A. Impacts of western juniper on plant community composition and structure. J. Range Manag. 2000, 53, 574–585.
5. Reid, K.; Wilcox, B.; Breshears, D.; MacDonald, L. Runoff and erosion in a pinon-juniper woodland: Influence of vegetation patches. Soil Sci. Soc. Am. J. 1999, 63, 1869–1879.

Introduction

Of the many pathogens disrupting healthy growth of brassica species in the valley is the pathogen commonly referred to as Blackleg. This fungal pathogen has been reported to nearly wipe out canola production in Europe, Australia, Canada and in more recent years has devastated the United States (West et al., 2001). In 2013 the pathogen was reported for the first time in the pacific northwest since the 1970’s (Agostini et al., 2013) and has since been reported in the Willamette Valley (Claassen, 2016). There are two known species of Blackleg, Leptosphaeria maculuns and Leptosphaeria biglobosa. These are not to be mistaken with the potato bacterial pathogen Pectobacterium atrosepticum, which is also referred to as Blackleg. While much of the crop failure in canola has been associated with Leptosphaeria maculuns, both species are found in the valley and seem to be of similar consequence to turnip.

Classification of cercospera leaf spot for instance has been accomplished applying a support vector machine model but utilized a hyperspectral camera with high spectral and spatial resolution (Rumpf et al., 2010). Because plant diseases can oftentimes be difficult to see even with the naked eye, researchers have struggled to successfully detect specific plant diseases as spatial resolution decreased. While this analysis focuses solely on detection at 1.5 meters, it is possible for detection of blackleg despite lowered spatial resolution as result of increased flying elevations.

Here we consider how spatial patterns from diseased leaves is related to ground truth disease ratings of turnip leaves based on spectral signatures. With the application of a support vector machine model, classification of diseased versus non-diseased tissue is expected to generate a predictive model. This model will be used to determine if single turnip leaves which are diseased and non-diseased are accurately categorized based on the ground truth.

Data

This project will be using a data set derived from roughly 500 turnip leaves, harvested here in the valley during the months of February to April of 2019. Roughly 200 of these leaves will be used in training the data model. The remaining leaves will be used for the data analysis in determining accuracy of the model versus ground truth. The spatial resolution is less than 1 mm and images are in raster format of single turnip leaves with five bands (blue, green, red, far-red, NIR). I do not anticipate using every band for the analysis and will likely apply some VI as a variable. The camera being used is a mica sense Red-edge which is 12-bit radiometric resolution.

Hypothesis

I hypothesize that spatial patterns of pixels based on image classification are related to manually classified spatial patterns of observed disease on turnip leaves because disease has a characteristic spectral signature of infection on the leaves.

Approaches

In order to accomplish this analysis, I will be using ArcGIS Pro where I have quite a bit of experience, but not particularly on this subject or type of analysis. The workflow for analysis will begin with image processing where I have little experience but don’t require expertise in this area. I hope to conduct the image processing in Pix4D where I will begin with image calibration based on the reflectance panel in each image. Followed by cropping down to simply the leaf under assessment. From here there may be some smoothing and enhancing the contrast of the image but is still undetermined.

Images will then be brought into ArcGIS Pro for conducting a spatial analysis. I intend to use spatial pattern analysis of manually classified disease versus unsupervised segmentation of the leaves for exercise 1. Next I plan on then using this information in spatial regression to improve image-based classification for exercise 2. For exercise 3 I intend to use the support vector model wizard, which will be used for training a model. This involves highlighting regions of diseased tissue and regions of non-diseased tissue to obtain a trained model when a sufficient number of pixels to create support vectors is reached. The x and y-axis for the model are yet to be determined but will likely be NIR and red-edge digital number values. Some alternatives are using different VI’s such as NDVI as explanatory variable. Turnip images which were never used for training the model will be used for analysis of the support vector machine’s ability to classify diseased or non-diseased regions and then leaves entirely. I anticipate every leaf to have at least a few pixels which will classify as diseased and will therefore set a threshold for a maximum number of diseased pixels in the image, while yet classifying it as non-diseased. I also might require a certain number of pixels to be bordering one another qualify as diseased region. The methodology may require some troubleshooting, but the expectations are clear and the methods to reach that outcome are mostly drawn out.

Expected Outcome

I expect the model to have very high accuracy after the model is fine tuned for accuracy based on contrast in spectral signatures I expect to see between diseased versus non-diseased leaves. Below I have outlined the three outcomes I would like to ultimately achieve. Due to time restrictions, the scope of my research is limited to outcomes 1 & 2 below.

1. Train a support vector machine model for classification of pixels in turnip leaves as either diseased or non-diseased.
2. Accurately apply the SVM model on turnip leaves from many geographical locations in the valley with different levels of diseases severity and different times in the year.
3. Scale up from 1.5 meters and test the ability of the model to maintain accurate classification of blackleg on turnip.

Significance

I intend to publish and further the collective knowledge in aerial remote sensing. This more specifically applies to those in the area of agronomy or plant pathology. This is very applied science and is a resource for those in the industry of agriculture.

Traditionally detection for this pathogen has depended on a reliable field scout who may need to cover fifty acres or more looking for signs or symptoms of this disease. Nowadays, precision agriculture has introduced the use of drones to perform unbiased field scouting for the grower. This saves time and can be very reliable if done properly. An important aspect of disease control relies on early detection. If early detection can be accomplished, growers have time to respond accordingly. This may allow for early sprays with lighter applications rates or less controlled substances, cultural control of nearby fields, etc. in order to stop the spread of disease.

Works cited

Agostini, A., Johnson, D. A., Hulbert, S., Demoz, B., Fernando, W. G. D., & Paulitz, T. (2013). First report of blackleg caused by Leptosphaeria maculans on canola in Idaho. Plant disease, 97(6), 842-842.

Claassen, B. J. (2016). Investigations of Black Leg and Light Leaf Spot on Brassicaceae Hosts in Oregon.

Rumpf, T., Mahlein, A. K., Steiner, U., Oerke, E. C., Dehne, H. W., & Plümer, L. (2010). Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance. Computers and Electronics in Agriculture, 74(1), 91-99.

West, J. S., Kharbanda, P. D., Barbetti, M. J., & Fitt, B. D. (2001). Epidemiology and management of Leptosphaeria maculans (phoma stem canker) on oilseed rape in Australia, Canada and Europe. Plant pathology, 50(1), 10-27.

Research Question

The Pacific Coast Feeding Group (PCFG) is a subgroup of the Eastern North Pacific (ENP) population of gray whales (Scordino et al.2018). The ENP, a population of 24,000-26,000 individuals, migrates along the U.S. west coast from breeding grounds in the lagoons of Baja California, Mexico to feeding grounds in the Bering Sea (Omura 1988). The PCFG, currently estimated at 264 individuals (Calambokidis et al.2017), stray from this norm and do not complete the full migration, instead choosing to spend their summer feeding months along the Pacific Northwest coast (Scordino et al.2011). Since gray whales as a species already exhibit specialization (by being the only baleen whale that benthically forages) and since the PCFG display a second tier of specialization by not using the Bering Sea feeding grounds, it seems plausible that individuals within the PCFG might have individual foraging specializations and preferences. Therefore, my research aims to investigate whether individual gray whales in Port Orford exhibit individual foraging specializations. Individual foraging specializations can occur in a number of different ways including habitat type (rocky reef vs sand/soft sediment), distance to kelp, time and distance of foraging bouts, and prey type and density. For this class, my research question is whether prey species drives the amount of time a foraging whale will spend at a specific foraging location.

Data

Prey data

The prey data has been obtained through zooplankton tow net samples from a research kayak in the summers of 2016, 2017 and 2018. Kayak sampling effort varies widely between the three years due to weather creating unsafe conditions for the team to collect samples. These samples have been sorted and enumerated to the zooplankton species level so that for each day when a prey sample was collected, we have known absolute abundances of prey species at each sampling station.

Whale data

The whale data is in the form of theodolite tracklines of gray whales that used the Port Orford study area during the summers of 2016, 2017 and 2018. Since whale tracking occurs at the same sites as prey sampling, we are able to map the prey community present at a particular location that whales forage at. The tracklines occur on a very fine spatial resolution as the study area is approximately 2.5 km in diameter, though some of the tracklines extend out to approximately 8 km offshore. Furthermore, as whales forage in the area, photographs are taken of each individual in order to match the trackline with a particular individual. This way, potential individual specializations may be detected if there are repeat tracklines of an individual.

Hypotheses

There will be differences in time spent by individual gray whales foraging in an areas with different prey communities. However, these differences will likely not be constant/stable over time. Most likely, foraging will be largely driven by availability of prey and therefore individual whales will be rather flexible in their foraging strategies.

Approaches

Individual patterns in time and space use within the Port Orford study area will be assessed through the identification of foraging bouts. Theodolite tracks from 2016-2018 longer than one hour will be included in this analysis. Using ArcGIS, tracklines will be clipped so that only foraging points within the study sites are included in this analysis. Radii will be created around each of the 12 zooplankton sampling locations to identify overlap between whale foraging and known prey communities at sampling stations. Time spent within these radii will be calculated. Statistical analyses (likely GAMs) will be run in order to identify whether individuals spend more and/or less time at a foraging location based on the prey species that are present at that location.

Expected Outcomes

This analysis will likely result in plots of time spent at a foraging locations against abundance of different prey species, which will be based on the results of the statistical analyses. This will allow the comparison of whether individual whales have preferences for different kinds of prey species. Maps might also be very nice to visualize the movements of whales however I am unsure how the time element of foraging bouts could be incorporated into this (perhaps with shading of color?).

Significance

This spatial problem is important to science since genetic evidence suggests that there are significant differences in mtDNA between the ENP and PCFG (Frasier et al.2011; Lang et al.2014), and therefore it has been recommended that the PCFG should be recognized as being demographically independent. In the face of a proposed resumption of the Makah gray whale hunt as well as increased anthropogenic coastal use, there is a strong need to better understand the distribution and foraging ecology of the PCFG. This subgroup has an important economic value to many coastal PNW towns as many tourists are interested in seeing the gray whales. Therefore, understanding what drives their distribution and foraging habits will allow us to properly manage the areas where they prefer to forage.

Proficiencies

I have novice/working knowledge of Arc-Info and Modelbuilder. I have never used Python before. I have working knowledge of R and am proficient in image processing.

Literature Cited

Calambokidis, J. C., Laake, J. L. and A. Pérez. 2017. Updated analysis of abundance and population structure of seasonal gray whales in the Pacific Northwest, 1996-2015. Draft Document for EIS.

Frasier, T. R., Koroscil, S. M., White, B. N. and J. D. Darling. 2011. Assessment of population substructure in relation to summer feeding ground use in the eastern North Pacific gray whale. Endangered Species Research 14:39-48.

Lang, A. R., Calambokidis, J. C., Scordino, J., Pease, V. L., Klimek, A., Burkanov, V. N., Gearin, P., Litovka, D. I., Robertson, K. M., Mate, B. R., Jacobsen, J. K. and B. L. Taylor. 2014. Assessment of genetic structure among eastern North Pacific gray whales on their feeding grounds. Marine Mammal Science 30(4):1473-1493.

Omura, H. 1988. Distribution and migration of the Western Pacific stock of the gray whale. The Scientific Reports of the Whales Research Institute 39:1-9.

Scordino, J., Bickham, J., Brandon, J. and A. Akmajian. 2011. What is the PCFG? A review of available information. Paper SC/63/AWMP1 submitted to the International Whaling Commission Scientific Committee.

Scordino, J., Weller, D., Reeves, R., Burnham, R., Allyn, L., Goddard-Codding, C., Brandon, J., Willoughby, A., Lui, A., Lang, A., Mate, B., Akmajian, A., Szaniszlo, W. and L. Irvine. 2018. Report of gray whale implementation review coordination call on 5 December 2018.

My spatial problem is about land use/land cover (LULC) change associated with the establishment of artisanal, small-scale gold mines (ASGM) in rural Senegal. Put in the vernacular we’ve learned in the course, I’d phrase my question this way:

How is the spatial pattern of LULC change related to the spatial pattern of ASGM establishment via the mechanism of deforestation?

As part of their establishment, ASGM requires clearing the land where the mines will be, as well as additional timber harvesting to build the homes where the miners will live while working and additionally to bolster the mines shafts themselves. As such, I’m curious as to what the exact change is that accompanies ASGM establishment, as this is a sub-set of my graduate thesis which seeks to understand more broadly how ASGM impacts the environment and the lives of the miners themselves, to understand better if a household diversifying into ASGM is better suited towards adapting to future climate change than if they hadn’t.

The dataset I have is very high resolution (VHR) satellite imagery (panchromatic and multispectral) courtesy of the Digital Globe Foundation. The panchromatic imagery has a resolution of .3m while the MS imagery is around 1.24m — as part of the preprocessing I’ve pansharpened the images so the overall imagery is stills sub-meter, which is necessary to investigate ASGM as its footprint is too small for detection with Landsat or other sensors. The imagery covers about 16 gold mines I’ve identified, and has imagery from 2018 and 2009/2010.

My present hypothesis is that, while obviously there will be a decrease in LULC at the mine in general, the area around the mine will also be indicative of some change — in my literature review, I’ve found some information that the environment up to 20km around a gold mine can be impacted. I think in this case the impact won’t be as drastic, but it’s what I’m expecting.

Currently I’m not sure how to approach the problem. The first step will be to just map out the mining locations first in ArcPro and from there try to see how the forest cover has changed between present and past. Beyond that I’m not sure how to answer the question.

Ultimately I’d like to produce maps, to demonstrate the environmental impact that ASGM has (or not, potentially!). I’ve also considered producing similar maps showing the relative impact subsistence agriculture has had in non-mining villages as a comparison.

I feel somewhat prepared for this task. I’m comfortable working with ArcGIS and have some knowledge of ArcPy (though I’m a bit rusty). I can use ModelBuilder pretty competently and feel that I have a good grasp of what to do in that arena. My major hurdle right now is not knowing what I don’t know — I need some more exposure to the tools available to me for addressing this problem. However, I feel confident that with enough time and work, this problem is not intractable!

One problem is that, given the rural area and part of the world I’m looking at, Digital Globe does not frequently sample the area and so the imagery I have is not necessarily on the anniversary date. Given the drastic climate differences (rainy season and dry season), the vegetation may look drastically different.

As an example, bellow is Kharakhena in November 2008, with the original village highlighted in red.

Here is the village of Kharakhena in March 2017. The village is highlighted in yellow and the mine in blue.

The difference is very dramatic, but I’m not sure how to approach this analytically.

This is my spatial problem! I believe that this is significant as a part of my thesis work exploring livelihood diversification in rural Kedougou. As 70% of the population in the region lives below the poverty line, and most people engage in subsistence farming as their primary livelihood, I am interested in how ASGM operates as a livelihood diversification option. Specifically, I’m interested in assessing whether it is an “erosive” coping strategy: one which households undertake out of lack of other options and one which may diminish the household’s overall capacity to react to future stresses and shocks. I’m assessing this through an evaluation of the “Three Capitals” (sometimes five) which constitute a household’s assets. The three capitals are Economic, Environmental, and Social. I’m assessing the impact of ASGM on miners’ Social and Economic capital through interviews which I’ve already conducted, and I’m assessing the impact of ASGM on miners’ Environmental capital through remote sensing. Together, I hope to paint a holistic picture of ASGM’s impact on miners’ livelihood capitals in the region in order to better understand if it is indeed an “erosive” coping strategy, which, if it is, needs to be known in order to help miners and farmers find different ways to adapt to future climate change.

1.  A description of the research question that you are exploring.

I am interested in how the spatial pattern of invasion by the recently introduced annual grass, ventenata, is influenced by the spatial pattern of suitable habitat patches (scablands) via the susceptibility of these habitat patches to invasion and ventenata’s invasion potential.  Habitat invisibility is determined by the environmental characteristics, community composition, and spatial arrangement of suitable habitat patches and the invasibility of ventenata is influenced by its dispersal ability and fecundity.

I am also interested in understanding how spatial autocorrelation influences relationships between ventenata, plant community composition, and environmental variables within and across my sample units.

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

I collected spatial data (coordinates and environmental variables) and plant species abundance data (including ventenata) data for 110 plots within and surrounding seven burn perimeters across the Blue Mountain Ecoregion of eastern Oregon (Fig. 1). Target areas were located to capture a range of ventenata cover from 0% ventenata cover to over 90% cover across a range of plant community types and environmental variables including aspect, slope, and canopy cover within and just outside recently burned areas. Once a target area was identified, plot centers were randomly located using a random azimuth and random number of paces between 5 and 100 from the target areas. Sample plots were restricted to public lands within 1600m of the nearest road to aid plot access. Environmental data for sample plots includes canopy cover, soil variables (depth, pH, carbon content, texture, color, and phosphorus content), rock cover, average yearly precipitation, elevation, slope, aspect, litter cover, and percent bare ground cover. I am planning to identify and calculate spatial pattern of habitat patches using Simpson Potential Vegetation Type raster data (Simpson 2013) to 30m resolution in Arc GIS which was developed to identify potential vegetation types across the Blue Mountain Ecoregion.

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

Ventenata appears to readily invade unforested, rocky scabland patches, but is less prominent in surrounding forested areas. These areas may act as “hotspots” of invasion from which ventenata can spread into surrounding, less ideal habitat types. The “biodviersty-invasion hypothesis” (Elton 1958) posits that more biodiverse areas will be less susceptible to invasion, but propagule pressure hypotheses suggests that areas close to areas that are heavily invaded will be more likely to be invaded (Colautti et al. 2005). If environmental factors such as biodiversity influence invasion success, I would expect diverse habitats to have a higher resistance to the ventenata invasion and be less invaded, but if propagule pressure is a stronger driver of the ventenata invasion, diversity may be trumped by proximity to invaded patches which may increase these patches risk of invasion despite species composition.

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

I would like to develop an overall understanding of the spatial pattern of invasion by performing Moran’s I to test for spatial autocorrelation. Additionally, I would like to identify hotspots of invasion using hotspot analysis. From these hotspots, I am interested in predicting spread using kernel density functions that estimate ventenata distribution. Finally I would like to relate the spatial pattern of vententata to environmental characteristics of habitat patches (including species composition) through the use of cross-correlation and geographically weighted regression.

5. Expected outcome: what do you want to produce — maps? statistical relationships? other?

I would like to produce figures that represent multivariate statistical relationships between ventenata, patch size, location, and environmental/ community variables. I am also interested in creating a map depicting areas at highest risk of invasion based on spatial and environmental data if appropriate considering the statistical relationships that result from the analysis.

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

Ventenata is rapidly invading natural and agricultural areas throughout the inland Northwest where associated ecological and economic losses are readily becoming evident. However, possibly the most concerning aspect of the invasion is ventenata’s potential to increase fire intensity and frequency in invaded scabland patches, that prior to invasion supported light fuel loads and acted as natural fire breaks for the surrounding forest.  Such shifts to the fire regime could dramatically alter landscape-scale biodiversity and cause additional socioeconomic losses. Despite these concerns, little is known of the drivers influencing ventenata’s invasion potential and few management options exist. Understanding how the spatial arrangement and size of scabland patch influences susceptibility to invasion by ventenata could help managers target areas at the highest risk for invasion and mitigate losses.

7. 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

I have a working knowledge of programming in R and manipulating spatial data/ map making in ArcGIS. I have taken introductory level classes in R and ArcGIS, and used these tools for work and for my research. I have no experience in modelbuilder, GIS programming in python, and image processing. I am eager to learn how to use these tools and apply them to help answer ecological research questions.

References:

Colautti, R. I., Grigorovich, I. A., & MacIsaac, H. J. (2006). Propagule pressure: a null model for biological invasions. Biological Invasions, 8(5), 1023-1037.

Elton, C.S. (1958). The ecology of invasions by animals andplants. T. Methuen and Co., London.

Simpson, M. 2013. Developer of the forest vegetation zone map. Ecologist, Central Oregon Area Ecology and Forest Health Program. USDA Forest Service, Pacific Northwest Region, Bend, Oregon, USA

Research Question

I am trying to determine potential fluid flow paths based on the spatial distribution of lava flows in the Medicine Lake, Lassen Peak Big Valley Mountain area of Northern California. The final goal of this project is a 3-D subsurface framework of the geology from which we can model fluid and heat flow.

Thus we want to know “How the spatial pattern of the depth of the lava flows attributes of wells is related to the spatial pattern of lava flow depth (B1), which in turn is related to pre-lava-flow topography (B2) and the regional geology (B3), because lava flows follow topography and subside post emplacement at rates that follow basin subsidence rates in the region, and form barriers for/conduits of groundwater (C1).”

Dataset

My data are a series of more than 1500 well logs. Each contain data that pertains to a map coordinate and with information about changes in lithology, or rock type, with respect to depth. Well logs are collected the year the well is made. I have well logs that range from the 1960s to the present. Temporal data is less important for the initial part of my study (Fig 1).

Figure 1: What is a Well Log? A well log is a record of the changes in rock type that occur with changes in depth and a location at the surface of the earth.

I also have data from geologic maps. Geologic maps provide the spatial extent of the surface exposure of a geologic unit, which includes information about its contacts with other rock units in x,y space as well as information about the elevation (Fig 2).

Figure 2: My study area in Northern California, it lies between the Big Valley Mountain, Medicine Lake Volcano and Lassen Peak with the Pit River draining the middle of it. The green dots represent the center of the township and range section that the wells are located in, and the size of the circle indicates the number of wells in that section.

Hypothesis

Figure 3: Cross section of a Basalt Column (Lyle 2000). In Volcanic systems, fluid flow is limited to the Vesicular flow tops, the rubbly bases (P.P.C in this diagram) and sedimentary interbeds that lie between vertically stacked flows. These sections of the rock are the only locations with high enough permeability and porosity to allow the movement of water.

This means that if we know where the lava beds lie, and we know their contacts, then we can outline potential zone of flow. Determining the patterns of how lavas flow and emplacement then allows us to determine the lava flow’s spatial extent, and therefore potential flow paths. I expect lavas to follow the laws of physics. They travel as viscous fluids and fill basins, and so I would expect them to be thickest where paleotopography was lowest, they will likely thin out at the edges, and they will be down slope from the volcano or vent that erupted them. Given the regional geology, I would expect the thickest lava flows to lie in the Pit River basin, near the range bounding Big Valley Mountain Fault (Fig 3).

At its simplest, we expect lava flows to follow the geologic principals of original horizontality and of cross cutting relationships.

Approaches

I would like to learn both how to apply variograms and kriging and how they work; plus any new techniques that I am not yet aware of. We can also make the assumption that all our residuals with follow the rules of stationarity. This means that any irregularities in the data represent unacknowledged geologic features.

Expected outcome

My first goal is to create a 3-D subsurface map of the connectivity and contacts of lava flows in the Medicine Lake, Lassen Peak Big Valley Mountain region.  Ideally I would begin the process with Geog 566. I would like to have a few surface I can test in the field by the end of this term, but understanding different methods with which I can make this framework is my first goal.

Relevance

Understanding the distribution of lava flows in the region ties into the regional geology of my study area. As I stated in Question 3, lavas fill basins. Basin morphology, and the amount of space lavas can fill depends on the slip rates and distribution on faults in the Medicine Lake, Lassen Peak Big Valley mountain triangle. By constraining slip rates in the basin, we both build a better picture of the regional geology, and we can make more rigorous checks on the validity of our statistical outputs.

Another equally important point is the next step in the project. After we make the 3-D framework, the USGS will use it to model fluid and heat flow in the region. Comprehending potential changes in groundwater flow in the region will allow city manages in the region to better manage water in the future.

Preparation

Sources

Lyle, P. “The Eruption Environment of Multi-Tiered Columnar Basalt Lava Flows.” Journal Of The Geological Society, vol. 157, 2000, pp. 715–722.