Then you could move this to below the research sites sections and rewrite this as the short introductory paragraph to your main methods called "Data-processing and analysis" or something similar. Essentially describe your general workflow and then go into detail in the following sections. You could take a similar approach to this paper, although there may be other examples: https://www.mdpi.com/1999-4907/15/6/1043

It would end with your existing sentences

A slightly reworded version of this may be better at the end of your intro as part of an aims/objectives and methodological approach paragraph (without the figure reference at this stage).

Add how you tagged each tree. E.g., did you tag in the centre of each crown, at the base of the trunk, within a set distance from the trunk?

I would probably add a statement about the final accuracy if you have that data, especially if it was much better than 30cm. E.g., "..confirmed accuracy of <30 cm with the final mean location accuracy being XX ± XX cm (SD).

I assume these numbers are just place holders

Maybe add env./weather conditions here and remove the conservation groups unless you need to refer to this later. You can add them all to your acknowledgements. Also maybe add the size of each site.

I think it is important to give an indication/description here of the type of bush you are working in (plant types, size of areas, what the surrounding areas are).

For example, mixed native and give some examples of plants. You could even include an example image/snip of the ortho for each to give people some perspective on the sites. They key message being that it is a complex matrix of different but similar trees, the bush is dense but they relatively small sites nestled in urban areas close to houses. All things you will come back to in your discussion.

For auckland sites, you could use the codes found in the "ecosystem extent" layer on the council GIS and described here: https://knowledgeauckland.org.nz/media/1399/indigenous-terrestrial-and-wetland-ecosystems-of-auckland-web-print-mar-2017.pdf

You would have to look into if there is something equivalent for Hamilton

add: "H1 is unmanaged and the management status of H2 is unknown". If your observations suggest it is also not managed you could add "but assumed to be unmanaged based on field observations."

I would just include this information in the text. If anything, a summary table of the flight parameters etc. per site may be more useful. If this was for a paper, I would probably put the site-based parameters table in an appendix though, so could go either way for the traditional thesis-style

I would split this into two sub-headings "UAV-MSI data capture" and "UAV-LiDAR data capture" or similar names,

Key information to get across: - Drone used - Sensor used (incl. band wavelengths, and maybe the accuracy levels for the LiDAR vertical and horizontal measurements) - Flight parameters for multispec (resolution (height + GSD), overlap, flight speed) - Flight parameters for LiDAR (no. of returns, flight height, speed, overlap), maybe also resolution, Graham might have more thoughts on the LiDAR parameters. LiDAR/MSI pre-processing - You could take the approach of this paper and include the pre-processing steps in these sections too since these steps are not the point of your work - https://www.mdpi.com/1999-4907/15/6/1043

But essentially, you should include anything someone would need to repeat your flights in another location. So maybe also state that all other camera settings were set to default settings. You can always include a summary table in the appendix too if there is too much going on.

RR:

Something else you could consider doing in the 'traditional thesis' style, is a second intro or background section for your methods. Essentially a "methodological approach" section or similar where you can discuss the DL methods in more detail, include some more literature review around this and justification for your approach.

If you change the framing as I suggested though you could also flip this and instead include a short chapter or section in your methods that describes the case study (e.g., describe the species, MR etc).

RR:

Ok, so having had a general read through of your draft thesis, I have some thoughts on a possible framing change that may fit your findings a bit more cohesively. This is just an option but I think it may help with story throughout a bit more.

Rather than focussing on swamp maire and myrtle rust, it could be better to use this more as a case study to talk about the application of 'low-cost' ag drones and DL to detect and classify rare and morphologically similar species in the New Zealand bush (or just dense species diverse bush in general) and detect changes in plant health in response to disease or env. changes.

This would not necessarily mean rewriting what you have done but more reordering

Here is a bit of a summary of what I see as some of the key takeaways from your current findings and how that story could work. Some specifics might not be exactly correct to the literature, so don't take it all word for word.

"Low-cost drones and DL workflows are increasingly being suggested as solution to detect and classify plant spp. in densely forest areas. Esp. when they are large areas, inaccessible or when human-mediated disease spread is a concern

However, drones and DL are still not widely applied (and maybe mostly with more expensive sensors?, only single sites and with more morphologically unqiue and common species? - would need to check the literature on this)

LiDAR is one potential solution to detecting more difference is morphologically similar spp. However, this is still pretty novel and untested, esp. in dense bush and urban areas where regs limit flight optimisation.

MSI is one potential solution to the morphologically similar spp part. However, MSI differences are often theoretical and the sensitivity of lower cost ag drones is known to have significant limitations.

In this study, you use SMaire and MR as a case study to investigate the efficacy and limitations of drone-based LiDAR and MS for detecting rare and morphologically similar tree species.