The Storm That Started It All

In May 2026, the region I live in was hit by tremendous storms. First came the rain; 216 mm over 48 hours. This was particularly damaging in itself, as this level of rain broke a 12-month drought period. Erosion and flooding was rife. Then, to add salt to the wound, winds hitting up to 160 kph (100 mph) hit just 2 days later, tearing roofs off buildings and blowing down trees all over the town and nearby city. The ground was so saturated that the high winds had an easy time of it, felling trees over roads and crushing buildings and vehicles left, right and centre.

Months later, the community is still recovering. It was a hell of a thing to experience. If you have ever lived through one of Mother Nature’s “reminders” that she runs the show, you will know what I mean. If you haven’t, well, let me put it this way; it’s like being slapped in the face by Goliath…and then drop-kicked through a tempered glass door by Sekhmet herself. You feel completely and utterly tiny, infinitesimally insignificant, a biological micro bug on Mama Gaia’s abiotic jack boot. Nature doesn’t give a flying saucer about you, your political beliefs, your financial position, your race, your religion, your creed, what material possessions you own, what ideals you hold, nothing. We all end up in the recycling bin one day and life continues.

From Garden Disaster to Carport

Our garden looked pretty beaten up after the storm, trees lying over other trees, large broken limbs hanging off the remaining skeleton of a stunning old fig tree that narrowly missed the house and debris everywhere. We were very lucky though; many people had trees destroy their houses and cars, so we came out of it with a messy hairdo by comparison rather than a broken face. Born from the destruction, however, we got the opportunity to do some much-needed landscaping. This, in turn, brought about the opportunity to build a proper house for our precious transport.

After the storm, we realised that the fig tree had come apart from a beetle infestation, so we couldn’t just take the damaged limbs off and leave it be. Sadly, the whole thing had to come down to mitigate any potential damage to the house in future. It was a glorious tree, full of figs in the summer, which attracted a plethora of wildlife in the form of herds of epilated fruit bats, troops of birds and huge pods of invertebrates day and night. You will be missed, Figgy.

This did, however, open up the space needed to build the carport. And what better way to plan a DIY build than to use the very tech I use on a daily basis for much larger-scale operations; drones, photogrammetry and GIS. T learn more about these amazing tools follow the link here: https://geowingacademy.com/courses/free-mini-course/

Image 1: High resolution drone ortho map with the fence (blue) and garden tier (green) illustrated by the lines in GIS.

Using a Drone to Map the Space

So a high-res, up-to-date map and 3D model of the space, generated from drone photos in photogrammetry software, helped us design the perfect carport/future garage in geographic information systems software. We were able to position different designs, starting off with a couple of square designs that simply would not have worked. As I said before, the space was awkward. Perpendicular to the gate is a beautiful tier full of flowering plants to the East. This, along with a fence to the West, bottlenecks the space towards the gate itself and therefore makes turning or reversing a vehicle in the space awkward. We were able to test how the car would move through the space by using its actual dimensions and the digital surface model (DSM) and 3D model (the car was deliberately captured in the drone scan for this exact reason). So a square wouldn’t work; the best option, and possibly the only option, was to make the carport/future garage rhomboid. FUN! This would make it a pretty challenging build

Image 4: Car, carport rhombus design, roof frame and 7 poles to scale with measurements in metres overlayed to the DSM. The DSM shows high (red-tree tops), middle (blue/green-bushes fence) and low (purple-ground) areas within the map using 3D data.

Image 5: The drone was used to scan the area once again after the roof frame had been built. Note the two reinforcing braces added to the super structure for strentgh after it became aparrent that the free standing perlins were not stiff enough. 

And finally, here is the real thing! All in all, it worked really well, and now we have a home for our car, bikes and stuff that doesn’t fit in our tiny house that we really need to get rid of!

This may sound provocative, but hear me out

Earlier in the year I attended and spoke at a conservation forum. There were many excellent speakers and the topics were of conservation importance. Most of the attendees were from the conservation world, be it nature reserve managers, conservation committees, environmental companies, environmental and conservation government institutions and university students and lecturers. You know, the usual nature-loving science fundies that make up this sector of society.

I have attended and spoken at many of these types of forums over my career and there are a number of things that always stand out for me at these 3 – 4 day events and unfortunately they are not necessarily beneficial for the environment and conservation, the very thing we are all there to look after.

Firstly, we are preaching to the choir. A room full of conservation scientists, managers, academics and enthusiasts talking about topics they understand well does not help change the deep challenges we are facing regarding the environment and its well-being. It’s a bit like your personal internet space, an echo chamber. We talk about all the problems and the amazing findings that have been made but that’s where it ends, in the room after the last cup of coffee has been drunk on the last day of the conference. The rest of the world and its people at large never hear about the problems or the solutions that have been discovered.

Secondly, it’s often too complicated. Academics speak endlessly about their ground breaking new discoveries (which I am sad to say many of which are not new or ground-breaking – if you took 5 minutes during one of the very complicated statistics monologues and Googled similar projects around the world, someone in Bangladesh, Cambodia or Australia’s Gold Coast has already done this study eons ago, probably in 2015 or earlier). Most bright minds in the audience love this sort of stuff and so do I. But it is often a very complicated word salad that defeats the purpose of sharing knowledge. The rest of the world and its people at large will never hear about the project or paper and even if they did, they wouldn’t read it or understand it because the important info is locked up in fancy pants scientific papers and unnecessarily complicated word waffle in some Nature journal. I get it, academia wants lots of papers pumped out for funding reasons and therefore people are also very cagey about sharing too much of their precious research openly. A bit like Gollum obsessing over The Ring in some shitty cave somewhere beneath the Misty Mountains. “A bit harsh there mate?” you might say. No, I don’t think so, the world at large needs this important info, share it far and wide and simply not for the love of money or prestige amongst a select group.

Thirdly, when it comes to landscape management topics, the data and references are often old, often 6 or 7 years old and sometimes 20 years or more! A lot happens in landscapes over just a few months and most of those changes go unnoticed because landscapes are large and the changes are small so it is hard to keep up to date with it all. Until now we have only been able to notice changes in landscape when it has become drastic enough to notice. So naturally it is slow going, but we do have solutions for this now (you know what I am going to say here of course). The rest of the world and its people at large never notice, hear about or get the opportunity to fully grasp and understand what to look for, the subtle signs that changes are happening for good or bad in their environment

As fun and interesting as these functions are for me and other conservationists, in my opinion they are ultimately a waste of time as they don’t serve the greater purpose of caring for life on Earth in all its glorious complexity. Why? Because the rest of the world simply isn’t exposed to this knowledge in a way that truly connects. We need a mass mindset shift.

I’m not talking about starting some over-the-top Greenpeace movement or encouraging Greta Thunberg-style emotional rampaging and virtue-signalling on social media. That achieves very little. In many cases, it makes things worse by dividing people through emotion and opinion. And opinions are like arseholes—everyone has one. They don’t unite people around the world behind a common understanding of the problem or inspire practical action.

Instead, the spotlight shifts away from the issue itself and onto personalities, identity politics, and rage-bait designed to garner clicks. The conversation becomes about the people arguing instead of the problem they’re supposedly trying to solve.

I’ll prove it right now. The moment I mentioned Greta’s name, your mind probably jumped to an angry teenager shouting, “How dare you!”, surrounded by a wall of camera flashes. It probably didn’t jump to the environmental cost of mining and processing lithium for your solar panels or electric vehicle. You probably didn’t think about removing alien invasive plants from your local neighbourhood. You didn’t think about how buying an Australian Sugar Glider for your 10-year-old in Norway because it’s cute could have serious consequences for both the species itself and an ecosystem on the other side of the world. Some of you are probably already composing an angry reply because I mentioned Greta Thunberg. That’s exactly my point. Your attention was immediately drawn to the people, not the message. Imagine if conservation science could capture attention that easily?

Real change starts when enough people have enough information. Our purpose as conservation scientists, ie researchers studying the only known biological system in the universe, is to get that knowledge out there. It’s a challenge. A really big one.

I propose a new tactic: create articles that everyone can understand and enjoy. Throw some humour in there. Make it a story, like some wild Tolkien adventure that captures people’s attention. Make it fun, make it alarming. People love a good drama with a bit of shock and horror thrown in.

And most importantly, instead of complex stats, use pictures A LOT! As the cliché goes, they speak ten million words. Drone maps present visual information that people can understand at a glance with nothing more than a short, simple caption. The colours and imagery do the rest. “Here, this is the change we see over 2 months and why it’s happening”.

Any car salesman will tell you that most customers buy with their eyes and their emotions, not with spreadsheets, tables, or peak torque graphs buried in some PDF they were emailed a week before they walked into the dealership. We need to do the same. Tell the story. Paint the picture. Start the movement.

A few weeks ago I found myself doing something that probably sounds a little odd. I was calculating the volume of water in an old borrow pit. Not exactly headline-grabbing stuff, I mean who cares, we do that on mines all the time and why monitor a road side borrow pit anyway right?

The borrow pit sits out of sight in the bush, alongside a road in a patch of Western Cape fynbos. More than 20 years ago material was dug out of that spot to build the road. Today, after a good rainfall event, it fills with water as it has surely done since it was carved into the bush.

On a recent drone survey I decided to see if I could estimate how much water it was holding by comparing the drought data to the post flood data. Using drone photogrammetry, CloudCompare and GIS, I clipped out the basin, fitted the water surface and calculated the volume. The answer was about 228 cubic metres of water at the time of the post flood scan.

An interesting metric sure, the value to conservation on its own, not much. But then I started thinking. What does 228 cubic metres actually tell us? The pan was surveyed shortly after a rainfall event of roughly 216 mm in 24 hours. The pan went from empty to full. The water surface area at the time of the survey was about 214 m².

If we assume every drop of rain that fell directly onto the water surface stayed in the pan, the maths is fairly straightforward:

214 m² × 0.216 m = 46.2 m³

Yet the estimated volume of water in the pan was around 228 m³. In other words, direct rainfall could only account for about 46 m³ (20%) of the water present. The remaining 182 m³ (80%) must have arrived as runoff from the surrounding landscape.

That immediately changes how you think about the system. The pan isn’t simply collecting rain that falls into it, it is connected to the surrounding catchment directly. The condition of the surrounding fynbos, the soils, the slopes and even the nearby road all potentially influence how much water eventually ends up in the wetland. The funny thing is, this borrow pit is on a ridge line, not in a valley. So this immediately opens the door for more questions.

That switches the conversation from “How much water is in the pan?” to; “Holy shit, where did that water come from, and what controls how much arrives here after a storm?”

Then I thought about what I’d seen around the pan over the years; Cape blue water lilies (Nymphaea nouchali var. caerulea) growing in the water, frogs and toads calling from the banks and bushes around the pit, bushbuck tracks, bush pig tracks, porcupine tracks, grey mongoose, water mongoose and even the occasional caracal track. So clearly this artificial pan has become an important part of the ecosystem.

For years we’ve used drones to create maps orthophotos, elevation models 3D models all useful. But lately I’ve been finding myself less interested in the maps and more interested in what the maps allow us to measure.

Take the frogs for example; if somebody asked whether frogs had a good breeding season, most of us would probably look at rainfall records which makes sense right? More rain, more water, more frogs.

Except that’s not necessarily what the frogs experience. The froggos don’t care how much rain fell; they care more about how long the water sticks around for in their area after the rain so their offspring will make it to froghood. A wetland that holds water for six months tells a very different story from one that dries out after six weeks.

That’s something a drone data and GIS can help us measure over time. Survey the pan after the rain, survey it again a month later then two months later and so forth. Now we’re tracking how quickly the system loses water, how long aquatic habitat remains available. From that information we will then be able to say…”With this amount of rain, in these conditions, the borrow pit should accumulate X amount of water and retain it for X number of weeks therefore froggo X should have a glorious breeding season relative to water availability in the borrow pit”. That’s a completely different type of information.

The same applies to the water lilies. The lilies growing in this borrow pit are telling us something. They suggest the wetland is stable enough for them to survive year after year. They suggest the basin is retaining water for long enough to support wetland plants. If drone surveys show lily cover expanding or shrinking through time, which starts telling us something about the health of the wetland itself. That means every water lily in the pan is quietly recording information about the wetland itself.

Then there are the animals. The drone isn’t going to tell us how many bushbuck visited the pan. We can integrate camera traps to help estimate populations and record visits regarding this information. But it can tell us whether the wetland remained available during a dry summer.

Suddenly that little borrow pit starts becoming a lot more interesting. What happens after a fire? Does the wetland fill faster after fire? Does it retain water longer? Do surrounding plants recover differently near the water (obviously but what is actually happening, how much faster that surrounding areas and what affect does it have on the wildlife etc)? Do certain species persist longer around the wetland margins? Those are all questions that can be explored with repeated surveys.

The funny thing is that none of this started with a grand plan. It started with a volume calculation which on paper is just a nice number. Whoopdy twang, it means nothing on its own. In reality it may be the starting point for understanding how a tiny wetland functions within a much larger ecosystem. And that’s what I find exciting about drones and GIS. Not because they create pretty maps. Because they allow us to measure things that were previously difficult to measure, and ask questions that we may never have thought to ask. Sometimes all it takes is an old borrow pit, some frogs and a bit of curiosity.

Statistics are the mathematical backbone of wildlife conservation and ecology. They allow researchers to estimate population sizes, track migration patterns, understand animal behaviour, and evaluate the impacts of environmental changes or human activity on ecosystems. Here is how key statistical concepts are used in the field, followed by an explanation of the mean and standard deviation.

How Statistics are used in Wildlife

• Population Estimation: Instead of counting every single animal (which is usually impossible), biologist’s use statistical sampling and capture-mark-recapture models to accurately estimate total population sizes.
• Habitat Modelling: Researchers use statistical regression to analyse environmental variables (like temperature, vegetation, and water proximity) to predict where specific species are most likely to live or roam.
• Conservation Monitoring: Statistical tests help determine if a population is growing, shrinking, or staying stable over time, and whether conservation efforts (like anti-poaching patrols) are actually working.

1. Mean

The mean, commonly known as the average, is the central value of a data set.

• How it works: You find the mean by adding all the numbers in your data set together and dividing by the total count of those numbers.
• Wildlife example: If you record the number of eggs laid in 5 different sea turtle nests, let’s say nest 1 = 105 eggs, nest 2 = 110 eggs, nest 3 = 98 eggs, nest 4 = 115 eggs and nest 5 = 102 eggs. We add the number of egg and divide by the number of nests: (105+110+98+115+102)/5 = 106 eggs. The Mean or average number of eggs is 106.

2. Standard Deviation

The standard deviation measures the amount of variation or “spread” in a set of data.

• Low standard deviation: The data points are clustered closely around the mean (most numbers are very similar).
• High standard deviation: The data points are spread out over a wide range of values (the numbers are very different from one another).

Wildlife example: The sample standard deviation for this data is 6.67 eggs (rounded to two decimal places).

In wildlife biology, we use the sample standard deviation because we are looking at a small subset (5 nests) instead of every sea turtle nest on Earth.

Step-by-Step Calculation

Here is exactly how that number is calculated:

1. Find the mean: The mean is 106 eggs, as calculated before.
2. Subtract the mean from each number of eggs in the nest to find the deviation for each nest:
   • 105 – 106 = -1
   • 110 – 106 = 4
   • 98 – 106 = -8
   • 115 – 106 = 9
   • 102 – 106 = -4
3. Square each of those deviations (to remove negative numbers):
   • (-1)² = 1
   • (4) ² = 16
   • (-8) ² = 64
   • (9) ² = 8
   • (-4) ² = 16
4. Sum the squared values: 1 + 16 + 64 + 81 + 16 = 178
5. Divide by n – 1 where n is the number of samples, so 5 – 1 = 4:
   • 178/4 = 44.5*
6. Take the square root of that result (return the number to its original unit of measurement after square of deviations which removed negatives):
   • √44.5 = ±6.67

*Why n -1 and not just n?

Our Standard deviation (spread) either side (±) of the mean (average) is 6.67 eggs. We subtract 1 from the number of samples—a correction known as Bessel’s Correction—to account for the fact that a small sample usually underestimates the true variation of the entire population.

Here is exactly why this mathematical adjustment is necessary.

1. Samples Tend to Underestimate Variation

When you collect a small sample (like 5 turtle nests), the animals or nests you measure are highly likely to come from the main, average part of the population.

You are very unlikely to accidentally pick the absolute extremes—such as the single largest or single smallest turtle nest on the entire beach. Because your sample lacks these rare extremes, the raw spread of your data looks smaller than it actually is in the wild.

2. The Role of the Sample Mean

To calculate standard deviation, you must first calculate the sample mean (106 eggs).

Because that mean is generated from your 5 sample nests, the data points are mathematically closer to their own sample mean than they would be to the true, unknown mean of all millions of turtle nests on Earth.

If you divide by just n, you get a biased result that falsely suggests your data is more tightly clustered than it really is. Dividing by n-1 makes the denominator smaller, which mathematically inflates the standard deviation just enough to correct this bias.

3. Degrees of Freedom

In statistics, this concept is called degrees of freedom.

Imagine you have 5 data points, and you already know their mean is 106.

• The first 4 nests can have any number of eggs imaginable (they are free to vary).
• However, once those first 4 numbers are locked in, the 5th nest has to be a specific number to make the average come out to exactly 106.

Therefore, only n-1 (4 out of 5) of your data points are truly free to vary. The last data point holds no new informational value regarding variation.

Sample vs. Population

Biologists use two different formulas depending on what they are studying:

• Use n-1 (Sample): When you look at a subset of a group (e.g., 5 nests to estimate the whole beach). This is almost always used in wildlife biology.
• Use n (Population): Only when you have counted literally every single individual in existence (e.g., if there are only 5 Kakapo parrots left in a specific sanctuary and you measured all 5).

3. Histograms

A histogram is a graph that shows how frequently different values occur in a dataset.

The x-axis represents the measured values, while the y-axis shows how many observations fall within each range of values.

Histograms are powerful because they reveal the shape of a dataset. While the mean tells us the average and the standard deviation tells us the spread, a histogram shows whether the data are evenly distributed or influenced by a small number of unusually large or small values.

The Significance of Statistics & drone data for vegetation recovery monitoring

Now let’s have a look at what this means with regards to drone data. A site was cleared of sensitive fynbos vegetation for construction. After clearing, the site was scanned using a DJI Phantom 4 Pro V2 and the image data proceeded in WebODM into high resolution maps and 3D point clouds. Construction of the site was put on hold indefinitely and therefore the fynbos had time to recover. 15 months later the site was scanned again in the exact same manner and the data aligned with the first data set and processed in WebODM.

The two 3D point clouds were then analysed using Cloud Compares M3C2 tool to detect the change in vegetation over the site over the 15 month recovery period. The resulting stats showed a mean of 16cm and a standard deviation of 26cm. The standard deviation is larger than the mean itself, indicating substantial variation in vegetation growth across the site. But the resulting histogram shows us why. The overall recovery (vegetation growth) was generally small and uniform, but there were a few species that grew much taller, much faster over that time. This makes a lot of sense as there were a small number of invasive alien plants (Acacia mearnsii) as well as a small number of fast growing pioneer plants (Osteospermum moniliferum) that grew up to 1.5 metres over that time. Most of the site came up with smaller species such as Erica bicolour and the like.

Image 1: The histogram generated from the M3C2 change between data set 1 and 2. Count is the number of points in the point cloud and M3C2 distance is how many metres the points have changed between the two point clouds ove the 15 month period. Note the long tail to the right and the small increase in the number points measuring 1.3 to 1.5 metres. These likely represent the small number of fast-growing plants detected in the survey. There is a small number of points that shift towards the negative, this is most likely due to compaction and break down of sticks and rotting stumps.

Conclusion

A drone survey is far more than a collection of aerial photographs and attractive 3D models. When combined with statistical analysis and change detection tools such as M3C2, those datasets become powerful scientific records. They allow us to quantify ecological recovery, identify unusual patterns of growth, and monitor environmental change with a level of detail that would be difficult to achieve through traditional field methods alone. Understanding the statistics behind the data is what transforms a beautiful map into meaningful ecological insight.

See YouTube Short Here: https://www.youtube.com/shorts/jDsehHr-Vgw

See Vegetation Recovery Over 15 Months Here: https://www.youtube.com/watch?v=Cb16CN6-WPk&t=20s

I started out my drone journey using the DJI Spark. Bought while working on farms in Australia to film and photograph my adventures in the beautiful Aussie Outback, I never thought this compact little pocket sized bumble bee would be the start of a career shift into all things computers, drones and fancy pants software some two years later. At the time I was a bush baby, a farm lad if you will, lover of open spaces and working with my hands and therefore a “technophobe”. The mere thought of a computer being used for anything other than watching a film or documentary was a total waste of time. I mean, you can’t go wrong with a pen and paper right? Even opening a Word document made me want to regurgitate my breakfast onto the keyboard!

Well that all changed in 2019 didn’t it?! Upon discovering the world of photogrammetry at a drone workshop hosted by a CAA registered drone pilot licensing outfit, my mind was completely blown and my outlook on all things tech-related changed completely! Laptops and computers were no longer these annoying devices that scrambled your Word document when trying to change a heading, or a stupid thing that pinged at you when something on the clipboard didn’t fit the requirements for Excel for no apparent reason. Now they were actual tools, with software I could get behind. I just had to learn it all…from scratch…without any assistance.

The world soon went mad and fell on its arse when 2020 hit and that actually gave me the time I needed to take a deep dive into all things drones, photogrammetry and GIS. I was OBSESSED! The more I researched drone hardware, photogrammetry software and GIS applications the more I thought “well there seems to be no end to what you can apply these tools to. If this is what people use the software for regarding satellite data analysis, then up-to-date high res drone data is a whole other universe that can be tapped into!” So I started looking for drone GIS-related stuff on the internet and the results were very few and far between. There was loads of stuff about photogrammetry software like Pix4D, Drone Deploy and the big players, but nothing about actual geographic information systems software (GIS) analysis of drone data. It was up to me to figure it out the old school way with good old trial and error. Man did that little Spark fly, I mean A LOT!

I learned everything I could about the Spark and tested everything practically; image overlaps, flight altitudes, flight speeds, camera settings, gimbal angles, flight patterns, nadir mapping missions, cross hatch missions, 3D modelling missions and did this for the millions of drone mapping applications that I could think of across different fields (but primarily in conservation as that is my background). I maxed out so many 1-month free trials, using different email addresses for photogrammetry software platforms that allowed them at the time, that I have lost count. That Spark taught me the basics and the more advanced stuff too. I have a soft spot for the wee quadcopter. So much so that it now sits on my desk next to me as I type this, its tired batteries unable to hold a charge for more than 5 minutes, scars incurred from countless small crashes, its long front sensor ever watchful, ever ready for one last flight. A retired veteran of the skies and a reminder of all the lessons learned that got me to this point. What a wonderful piece of technology.

When the world started to open up again and the madness subsided (in 2021, 1.5 years later in my country), the prospect of droning for conservation and other fields was becoming a more serious consideration and Sparky was not only worn out, but was also not the right tool for the job(s). Even if she was in full health regarding batteries etc, her limitations for serious work were already obvious. The flight time limited the area she could cover at any given time, her light weight frame could only withstand moderate winds, her 12MP rolling shutter camera was not quite up to the job and DJI had limited the on-board GPS metadata stamping to the images to only 4 decimal places which meant that the maps rendered in photogrammetry software were so far off the mark that even ground control points (GCPs) could not correct the georeferenced error.

Time for an upgrade then. And there was a clear winner at the time; the DJI Phantom 4. Everything I listened to, read about, watched and digested from hundreds of billions of podcasts, articles and YouTube videos all said that this drone was the best all-rounder for photography, videography, mapping and modelling. Some experts at the time even went as far as to say that the drone would be the workhorse of the decade and maybe longer. Funny, they weren’t wrong!  I was lucky enough to find the Pro V2 which was the latest version of the drone at the time (production had stopped in 2019 due to upgrades to hardware frames, the compact Mavic frame taking the place of the chunkier Phantom range. Steady advances made to hardware and software allowing drones to become more compact).

The Phantom 4 range, in particular the Phantom 4 Pro V2, is still my work horse for many projects in 2026 and there are a number of good reasons why:

1. It works perfectly well! So why would I get rid of it? Many folks may say, “Well you have to keep up with the times”. I don’t agree in this case; the drone still flies, still works with my mission planner apps and captures exceptional image data and the batteries are still good, even though they are old.
2. The 1-inch CMOS 20MP camera sensor allows for incredible stills image detail and fabulous RGB colour capture which is perfect for not only mapping, but professional photography as well in JPEG and DNG (RAW). Video is up in 4k, 60 frames per second for epic scenic or action shots.
3. The mechanical shutter stops movement in images dead in their tracks. A rolling shutter causes a rolling (warping) effect in the image and this does have an effect on your photogrammetry outputs even though most photogrammetry software nowadays does compensate for rolling shutter. Remember, your subject may be moving slightly in the wind and the drone is moving while mapping as well so stopping everything dead rather than having a warped effect is ALWAYS better (see Foundation Course for more). Most of the newer drones come with rolling shutters and mechanical shutters are only available on very expensive enterprise models.
4. Epic obstacle avoidance. The sensors, although admittedly not as good as their newer counterparts, are still excellent and stop or avoid obstacles very well.
5. Being an older drone it is 3rd party app friendly so when it comes to mission planner apps, you have a wide choice. Many modern drones will only run using the flight planners made for that model and the software comes with an annual subscription fee so even though you have spent billions on the latest drone, you still don’t get free software with it (mostly in the enterprise range of DJI drones including the Agras range).
6. The OcuSync 2.0 C2 link transmission is still very, very good! The picture is clear, the telemetry display is grand and it works very well indeed for my needs even at range.
7. It is solid in the air and flies steady in stronger wind conditions.
8. You can still buy them for a good price second hand and if they have been looked after and the battery maintenance kept up the drone will go a long way! They are affordable.
9. When ground control points (GCPs) are used, georeferenced accuracy is still survey grade; however it does require extra steps compared to modern RTK drones. But even RTK drones benefit from the addition of GCPs.

I could go on but I think I have made my point in terms of the pros. But there are some cons and as with any good reasoning, these have to be considered too:

1. The support services for these drones are, as of 2023 no longer available. That means major software and firmware improvements will no longer be conducted. However minor mandatory compliance updates will continue.
2. Spare parts are no longer produced. However, these drones were incredibly popular so you can still get spares.
3. This might be the big one for the future. Three of my 5 batteries are still good and I can still get about 16 – 20 mins out of them before landing between 20 and 30% (never go below 20% battery use!! It will kill your batteries faster and if you are a commercial pilot, you may have to file an emergency report). So that’s still around the flight time of the battery when the drone came out. The other two still last for about 15 minute a piece which is still very impressive considering the amount of flying the drone has done. Battery maintenance is critical with older drones. Problem is, I won’t be able to get more when these eventually die.
4. It is not as compact as the modern drones which make it more cumbersome to take around in the bush when mapping remote areas.
5. It is loud! Compared to modern airframes the Phantom is a loud drone, this is not ideal when mapping sensitive species or flying in areas where you want to keep noise to a minimum. They really piss off elephants!

Other than these, the notion still stands. The DJI Phantom 4 Pro V2 was and still is a fabulous piece of kit and in my mind still relevant in 2026. And you must admit, whenever you see a sign depicting drone operations, drone sales, “no drone zones” or anything to do with flying robots, the silhouette is usually that unmistakable frame.

In the past I did a number of preliminary dam site survey scans for the local municipality. At the time they were looking to construct new dams for farmers that were expanding due to population increase to the region; more people= more crops = more water. I would generate the usual; contour maps from drone DTMs and did some basic analysis of the site.

I always knew that there was more that could be done for the client with the drone data in GIS but I wasn’t exactly sure what. I then decided to look at the big picture and what questions needed to be answered in the preliminary stages of looking for dam sites.

The client would need to know things like; “how much water would this site hold if the wall was built there, what would the wall dimensions need to be if it is a cement or earth dam, how much material will be needed to construct the wall, what are the depths?” And so fourth.

Open source software allowed me to construct virtual dams extremely quickly with answers to all of those questions. See the short video below (the entire workflow run time shown here runs in real time; 6.97 seconds) and have a look at the sample PDF here. It would be great to have your feedback on this type of workflow.