Question:
Foundational to effectiveness of management of invasive species in agricultural and native landscapes is the question of spatial extent and the ability to quantify the impacts of invasive species on agronomic efforts and native vegetation. Invasive plants represent a threat to both agricultural and native landscapes in the form of reduced ecosystem function, increased resource consumption, and reduced yields from agricultural systems. In spite of significant efforts to control and reduce impacts from invasive species, invasive weeds cause an estimated loss of $2 billion annually in the US. Currently, interspecific competition is one of the major limitations for oilseed and grain production in dryland cropping systems of the Pacific Northwest (PNW). Opportunities for the for the precision monitoring of managed and native ecosystems have become available through the use of low altitude remote sensing systems and high resolution satellite systems. However, methods for resolving species level classification in high resolution multispectral remote sensing systems remain lacking. This is partially due to the relative novelty of these systems, but is also related the lack of suitable reference data at spatial and temporal scales for regionally based models. The broad research question I’m is how does spectral trajectory relate to weed density, and can this information be used to distinguish the spatial extent of weeds in dryland cropping systems? My prediction, is that by increasing the spatial and temporal resolution of these data, crop and non-crop species will be distinguishable based on their relative rate of change in greenness.
The objectives I have for this class are to 1) determine the spatial and temporal resolutions at which weed species are distinguishable from crop species using a spectral trajectory technique, 2) compare these methods with ground reference data in a dryland cropping systems study. The major outcome of this work would be a method for distinguishing weed species from crop species in a dryland environment, and the identification of the minimum temporal resolution for distinguishing species in multispectral imagery.
Dataset:
The data set I have for addressing these questions is a composite of 7 flights of images taken with a multispectral camera in a cropping systems study in Eastern Oregon. Flights were conducted in conjunction with visual estimates of weed density in semi-permanent monitoring frames installed into the cropping systems study. The images are currently at a low level of processing. One of my goals as a part of this project will be to orthomosaic the images such that I can perform a time series analysis across image collection dates. The temporal resolution is from 3-20 days between flights, whiles the spatial resolution is 3 cm. The images cover the entire spatial extent of the experiment.
Hypothesis:
What I plan to do for this experiment is that after generating an orthoraster, I will be able to distinguish between the quantify of weed species and crop species in a frame based on the spectral trajectory of individual pixels in that frame. The question I hope to answer will be how distant in time do sample dates have to be before weed species can be distinguished from crop species based on their spectral trajectory.
Approach:
I plan on using a trial version of Agisoft to orthomosaic my images. It may be possible to conduct this analysis without performing an ortho mosaicing of the images, however, an orthomosaic will have a number of advantages to non-mosaiced images. Without an orthomosaic every individual analysis will have to be hard coded, whereas with an orthomosaic, I can automate much of the processing of these data.
Outcome:
The goal of this analysis will be to identify the minimum time required to discern species, and to describe the statistical relationship between species abundance based on spectral trajectory and ground reference data.
Significance:
While there has been a significant surge in the interest of UAV’s and low altitude remote sensing, the actual number of useful products for land managers to make decisions based on these data is very minimal. This work would identify the minimum temporal resolution a resource manager would need to have before they can identify weed species based on spectral characteristics.
Preparation:
I would say I have minimal experience in Arc-info, model builder and Python. I have moderate to high level experience in R. I would also consider myself to have moderate to high levels of experience in image processing and image analysis.
hi Aron,
As we discussed last week, I’d like you to identify a research question that is about the system, not the question “what is the right scale?”, because this is not a scientific research question. Please explain how your overall problem is related to the familiar problem in remote sensing – the mixed-pixel problem. What mixed-pixel techniques are relevant for your study? What is not known that you can address given your data? Please develop some research questions about weeds and crops, such as “what are the temporal trajectories of greenness in plots of varying scale, crop type, and experimental treatments over the spring of 2015?” “at what dates do the trajectories of greenness of weeds diverge from those of crops?” and/or “how do the dates of divergence between weeds and crops differ among experimental treatments?” This will then help you state a hypothesis such as: “I predict that the greenness trajectory in XX location will diverge from that in YY location on ZZ date because ..” or “I predict that the weed greenness trajectories will be indiscernible from the crop signal at spatial resolutions above XX m, but when data are examined at the XX spatial resolution, weed and crop greenness trajectories can be discriminated.” Please also add a description of your data showing a map of the area where the data were obtained, what is shown in the imagery, and the spatial resolution and temporal frequency and dates of the images.