Calculating a drone camera's image footprint OR How I learned to love tan again.

When we use drones for mapping purposes, we usually program them to fly autonomously along a pre-programmed flight path, collecting images with a specified front- and side- overlap. Once we have enough images covering the area to be mapped, we stitch them all together to create the final map. This really doesn’t require us to think a lot about the length and width of each image; the only two limiting factors we usually need to consider are the final map’s (or technically, orthorectified mosaic’s) spatial resolution and the drone’s maximum legal height.

However, one of the projects we’re currently working on is not so much a mapping project as it is ecological research, and to cut a long story short, ensuring that we can correctly apply a set of ecological statistical tools that account for double-counting and observer error requires us to be able to ascertain the length and width of each image at different drone altitudes. Also, as we work with a number of different drones (and thus drone cameras), I wanted to have a set of equations in place that we could use for a variety of situations. All of this required some high-school level trigonometry to work out; I was never a fan of trigonometry as a teenager, but using it to understand both the Triangular Greenness Index (as detailed in a previous post on vegetation indices) and the current problem was actually a lot of fun.

To break the problem down, the two fixed variables are a camera’s field of view (FoV), which is described as the angle which it can ‘see’ at any given instant, and its aspect ratio, which is the ratio between the length and width of its images. For example, from the technical descriptions, a Phantom 3 Advanced camera has a FoV of 94° and user-selectable aspect ratios of 4:3 and 16:9, while the Phantom 4 Pro 2.0 has an FoV of 84° and user-selectable aspect ratios of 3:2, 4:3 and 16:9. In combination with the height of the drone, these two camera-parameters determine the final image footprint. For more on aspect ratios, see this post which recommends using the native aspect ratio for any given camera.


! These equations assume the camera to be perpendicular to the ground and don’t account for lens distortion. For a far more complex solution (which I have to admit I barely understand) look up this post (StackExchange) where mountainunicycler (Github user) describes nesting a Python script within Latex (what) which then calculates the FoV of a drone-mounted camera and outputs a PDF with graphics ( I can’t even.) !

x = Drone Height
θ = Field of View
r = Aspect Ratio

If the diagonal of the image is D, D/2 is the length of the base 
of the right-angled triangle with the two included angles as θ°/2
and (180°-θ°)/2 (as becomes clear when the FoV angle is bisected
to create two identical right-angled triangles).

D = 2 * x * tan(θ°/2)   --- (1)

If (A) and (B) are the sides of the image, then the aspect ratio 
(r) is equal to either A/B or B/A. We assume (A) to be the 
independent variable and (B) to be the dependent variable.

r = A / B
B = r * A   --- (2)

Using the equation of a right-angled triangle again,

D^2 = A^2 + B^2
D^2 = A^2 + (r * A)^2   - substituting the value of B from (2)
D^2 = A^2 * (1 + r^2)
A^2 = D^2 / (1 + r^2)       - flipping the terms of the equation

A = D / sqrt(1 + r^2)       --- (3)
B = r * D / sqrt(1 + r^2)   --- (4)

To express (A) and (B) only in terms of x, θ and r, we now 
combine equations (1) and (3), and (1) and (4).

A = (2 * x * tan(θ°/2)) / sqrt(1 + r^2) --- (5)


B = r * (2 * x * tan(θ°/2)) / sqrt(1 + r^2) - from (1) and (4)
B = 2 * r * x * tan(θ°/2) / (sqrt(1 + r^2) --- (6)

So, if we know the field of view (θ°), the aspect ratio (r) and the height of the drone (x), equations (5) and (6) allow us to determine the image footprint. To calculate the area of the image, the math is simply (A) * (B), which is the formula for the area of a triangle.

To flip this around, the other equation we needed for this project was to determine the height we needed to fly the drone, given a specific image footprint i.e. given (A), calculate (x). This is straightforward, since it means we just need to make (x) the subject, as opposed to (A), in equation (5).

A = (2 * x * tan(θ/2)) / sqrt(1 + r^2) --- (5)

2 * x * tan(θ/2)) = A * sqrt(1 + r^2)

x = A * sqrt(1 + r^2) / (2 * tan(θ/2)) --- (7)

In summary, equations (5) and (6) allow us to use the FoV, aspect ratio and height of any given camera to determine the image footprint, while equation (7) allows us to determine what height we should fly a drone at, given a desired horizontal image dimension.

I’ve used these equations to create a calculator in Excel; note that Excel uses radians as the default unit for angles. Using this calculator, I can determine that with a FoV of 94°, an aspect ratio of 4:3 and a drone height of 60m, the image footprint would be 102.9m * 77.2m, while with an FoV of 77°, an aspect ratio of 16:9 and the same drone height, the image footprint would be 83.1m * 46.8m. Similarly, if I wanted an image length of 50m with the first camera (FoV = 94° and aspect ration = 4:3), I would need to fly the drone at a height of 41.25m.

Let us know if you have any comments or find any errors in the math! We’re on Twitter at @techforwildlife, and you can mail us at As usual, comments on this post will be open for a few days.

Analysing Drone and Satellite Imagery using Vegetation Indices

A majority of our ecosystem monitoring work involves acquiring, analysing and visualising satellite and aerial imagery. Creating true-colour composites, using the Red, Green and Blue (RGB) bands, allows us to actually view the land areas we’re studying. However, this is only a first step; creating detailed reports on deforestation, habitat destruction or urban heat islands requires us to extract more detailed information, which we do by conducting mathematical operations on the spectral bands available from any given sensor. For example, we can extract surface temperature from Landsat 8 satellite data, as detailed in a previous blogpost.

A true-colour composite image created using data from Landsat 8 bands 2, 3 and 4.

As you may imagine, understanding how much vegetation is available in any given pixel is essential to many of our projects, and for this purpose, we make use of Vegetation Indices. In remote sensing terms, a Vegetation Index is a single number that quantifies vegetation within a pixel. It is extracted by mathematically combining a number of spectral bands based on the physical parameters of vegetation, primarily the fact that it absorbs more more light in the red (R) than in the near-infrared (NIR) region of the spectrum.  These indices can be used to ascertain information such as vegetation presence, photosynthetic activity and plant health, which in turn can be used to look at climate trends, soil quality, drought monitoring and changes in forest cover. In this blogpost, we’re going to provide a technical overview of some of the vegetation indices available for analysing both aerial and satellite imagery. We’ve included the basic formulae used to calculate the indices, using a bracketing system that allows for the formulae to be copy-pasted directly into the Raster Algebra (ArcMap) and Raster Calculator (QGIS) tools; don’t forget to replace the Bx terms with the relevant band filenames when doing the calculations! We’ve also noted down the relevant band combinations for data from Landsat 8’s Operational Land Imager and both the Sentinel-2’s MultiSpectral Instruments.

We’ve created maps for most of the vegetation indices described below, using data from Landsat 8 acquired over Goa, India on the 28th of December 2018. Each band was clipped to the area of interest and the Digital Numbers were rescaled to calculate Top-of-Atmosphere radiance values. All the index calculations were then executed on these clipped and corrected bands. We used a single min-max stretched red-to-green colour gradient to visualise each index. For actual projects, we’d then classify each image to provide our partners with meaningful information.

The Basic Vegetation Indices

Ratio Vegetation Index

One of the first Vegetation Indices developed was the Ratio Vegetation Index (RVI) (Jordan 1969) which can be used to estimate and monitor above-ground biomass. While the RVI is very effective for the estimation of biomass, especially in densely-vegetated areas, it is sensitive to atmospheric effects when the vegetation cover is less than 50%, (Xue et al. 2017).


Sentinel 2: B4 / B8

Landsat 8: B4 / B5


Difference Vegetation Index

The Difference Vegetation Index (DVI) (Richardson et al. 1977) was developed to distinguish between soil and vegetation, and as the name suggests, is a simple difference equation between the red and near-infrared bands.


Sentinel 2: B8 - B4

Landsat 8: B5 - B4

Normalised Difference Vegetation Index

The Normalised Difference Vegetation Index (NDVI) (Rouse Jr. et al. 1974) was developed as an index of plant “greenness” and attempts to track photosynthetic activity. It has since become one of the most widely applied indices. Like the RVI and the DVI, it is also based on the principle that well-nourished, living plants absorb red light and reflect near-infrared light. However, it also takes into account the fact that stressed or dead vegetation absorbs comparatively less red light than healthy vegetation, bare soil reflects both red and near-infrared light about equally, and open water absorbs more infrared than red light. The NDVI is a relative value and cannot be used to compare between images taken at different times or from different sensors. NDVI values range from -1 to +1, where higher positive values indicate the presence of greener and healthier plants. The NDVI is widely used due to its simplicity, and several indices have been developed to replicate or improve upon it.

NDVI = NIR - R / NIR + R

Sentinel 2: B8 - B4 / B8 + B4

Landsat 8: B5 - B4 / B5 + B4


Synthetic NDVI

Synthetic NDVI

The Synthetic NDVI is an index that attempts to predict NDVI values using only Red and Green bands. Hence it can be applied to imagery collected from any RGB sensor., including those used on consumer-level drones. Like the NDVI, its values also range from -1 to +1, with higher values suggesting the presence of healthier plants. However, it is not as accurate as the NDVI and needs to be calibrated using ground information to be truly useful. It is also known as the Green Red Vegetation Index (GRVI) (Motohka et al. 2010).

Synthetic NDVI = ( G - R ) / ( G + R )

Sentinel 2: ( B3 - B4 ) / ( B3 + B4 )

Landsat 8: ( B3 - B4) / ( B3 + B4 )


Visible Difference Vegetation Index

Similarly, the Visible Difference Vegetation Index (VDVI) (Wang et al. 2015) can also be calculated using information from only the visible portion of the electromagnetic spectrum. Some studies indicate that VDVI is better at extracting vegetation information and predicting NDVI than other RGB-only indices,.

VDVI = ( (2*G) - R - B ) / ( (2 * G) + R + B )

Sentinel 2:  ( ( 2 * B3 ) - B4 - B2 ) / ( (2 * B3 ) + B4 + B2 )

Landsat 8: ( ( 2 * B3 ) - B4 - B2 ) / ( ( 2 * B3 ) + B4 + B2 ) 


Excess Green Index

The Excess Green Index (ExGI) contrasts the green portion of the spectrum against red and blue to distinguish vegetation from soil, and can also be used to predict NDVI values. It has been shown to outperform other indices (Larrinaga et al. 2019) that work with the visible spectrum to distinguish vegetation.

ExGI = ( 2 * G ) - ( R + B )

Sentinel 2: ( 2 * B3) - ( B4 + B2 )

Landsat 8: ( 2 * B3 ) - ( B4 + B2 )

Green Chromatic Coordinate

The Green Chromatic Coordinate (GCC) is also an RGB index (Sonnentag et al. 2012) which has been used to examine plant phenology in forests.

GCC = G / ( R + G + B )

Sentinel 2: B3 / ( B4 + B3 + B2 )

Landsat 8: B3 / ( B4 + B3 + B2 )

One of the primary shortcomings of the NDVI is that it is sensitive to atmospheric interference, soil reflectance and cloud- and canopy- shadows. Indices have thus been developed that help address some of these shortcomings.

Indices that address Atmospheric (and other) Effects

Enhanced Vegetation Index

The Enhanced Vegetation Index (EVI) was devised as an improvement over the NDVI (Heute et al. 2002) to be more effective in areas of high biomass, where it is possible for NDVI values to become saturated. The EVI attempts to reduce atmospheric influences, including aerosol scattering, and correct for canopy background signals. In remote sensing terms, a saturated index implies a failure to capture variation due to the maximum values being registered for some pixels. 

EVI = 2.5 * ( ( NIR - R ) / ( NIR + (6 * R) - ( 7.5 * B ) + 1 ) )

Sentinel 2: 2.5 * ( ( B8 - B4) / ( B8 + ( 6 * B4) - ( 7.5 * B2 ) + 1) )

Landsat 8: 2.5 * ( ( B5 - B4) / ( B5 + ( 6 * B4) - ( 7.5 * B2 ) + 1 ) )


Atmospheric Reflection Vegetation Index

The Atmospheric Reflection Vegetation Index (ARVI) was developed specifically to eliminate atmospheric disturbances (Kaufman et al. 1992).  However, for a complete elimination of aerosols and the ozone effect, the atmospheric transport model has to be implemented, which is complicated to calculate and for which the data is not always easily available.  Without integrating this model into the calculation, the ARVI is not expected to outperform the NDVI in terms of accounting for atmospheric effects, but can still be useful as an alternative to it.

ARVI (w/o atmospheric transport model) = ( NIR – ( R * B ) ) / ( NIR + (R * B) )

Sentinel 2: ( B8 - ( B4 * B2 ) ) / ( B8 + ( B4 * B2 ) )

Landsat 8: ( B5 - ( B4 * B2) ) / ( B5 + (B4 * B2 ) )


Green Atmospherically Resistant Index

The Green Atmospherically Resistant Index (GARI) was also developed to counter the effects of atmospheric interference in satellite imagery. It shows much higher sensitivity to chlorophyll content (Gitelson et al. 1996) and lower sensitivity to atmospheric interference.

GARI = ( NIR – ( G – ( γ * ( B – R ) ) ) ) / ( NIR + ( G – ( γ * ( B – R ) ) ) )

Sentinel 2: ( B8 – ( B3 – ( γ * ( B2 – B4 ) ) ) ) / ( B8 + ( B3 – ( γ * (B2-B4) ) ) )

  Landsat 8: ( B5 – ( B3 – ( γ * ( B2 – B4 ) ) ) ) / ( B5 + [ B3 – ( γ * ( B2 – B4) ) ) )

In the formula above, γ is a constant weighting function that the authors suggested be set at 1.7 (Gitelson et al. 1996, p 296) but may have to be recalibrated in areas of complete canopy coverage. For this image, we used a γ value of 1.


Visible Atmospherically Resistant Index

The Visible Atmospherically Resistant Index (VARI) can be used to account for atmospheric effects in RGB imagery.

VARI = ( G - R) / ( G + R - B )

Sentinel 2: ( B3 - B4 ) / ( B3 + B4 - B2 )

Landsat 8: ( B3 - B4 ) / ( B3 + B4 - B2 )

Addressing Soil Reflectance

As in the case of atmospheric effects, indices were also developed to address the effects of varying soil reflectance.

Soil Adjusted Vegetation Index

The Soil Adjusted Vegetation Index is a modified version of the NDVI designed specifically for areas with very little vegetative cover, usually less than 40% by area. Depending on the type and water content, soils reflect varying amounts of red and infrared light. The SAVI accounts for this by suppressing bare soil pixels.

SAVI = [ ( NIR – R ) / ( NIR + R + L ) ] * (1 + L)

Sentinel 2: [ ( B8 – B4 ) / ( B8 + B4 + L ) ] * (1 + L)

Landsat 8: [ ( B5 – B4 ) / (B5 + B4 + L ) ] * (1 + L) 

In the above equations, L is a function of vegetation density; calculating L requires a priori information about vegetation presence in the study area. It ranges from 0-1 (Xue et al. 2017) with higher vegetation coverages resulting values approaching 1.  


The Modified Chlorophyll Absorption in Reflectance Index (MCARI) was developed as a vegetation status index. The Chlorophyll Absorption in Reflective Index (Kim 1994) was initially designed to distinguish non-photosynthetic material from photosynthetically active vegetation. The MCARI is a modification of this index and is defined as the depth of chlorophyll absorption (Daughtry et al. 2000) in the Red region of the spectrum relative to the reflectance in the Green and Red-Edge regions.  

MCARI = (Red-Edge - R ) - 0.2 * ( Red-Edge - G) * ( Red-Edge / Red )

Sentinel 2: ( B5 - B4) - 0.2 * ( B5 - B3) * ( B5 / B4)

 Landsat 8: No true equivalent

The Structure Insensitive Pigment Index (SIPI) is also a vegetation status index, with reduced sensitivity to canopy structure and increased sensitivity to pigmentation. Higher SIPI values are strongly correlated with an increase in carotenoid pigments, which in turn indicate vegetation stress. This index is thus very useful in the monitoring of vegetation health.

SIPI = (800nm - 445nm) / (800nm - 680nm)

Sentinel 2: (B8 - B1) / (B8 - B4)

Landsat 8: (B5 - B1 ) /( B5 - B4)

Agricultural Indices

Some indices that were initially designed for agricultural purposes can also be used for the ecological monitoring of vegetation.

Triangular Greenness Index

The Triangular Greenness Index (TGI) was developed to monitor chlorophyll and indirectly, the nitrogen content of leaves (Hunt et al. 2013) to determine fertilizer application regimes for agricultural fields. It can be calculated using RGB imagery and serves as a proxy for chlorophyll content in areas of high leaf cover.

 TGI = 0.5 * ( ( ( λR - λB ) * ( R - G) ) - ( ( λR - λG ) * ( R - B ) ) )

Sentinel 2A: 0.5 * ( ( ( 664.6 - 492.4 ) * ( B4 - B3 ) ) - ( ( 664.6 - 559.8) * ( B4 - B2 ) ) )

Sentinel 2B: 0.5 * ( ( ( 664.9 - 492.1 ) * ( B4 - B3 ) ) - ( ( 664.9 - 559.0 ) * ( B4 - B2 ) ) )

Landsat 8: 0.5 * ( ( ( 654.59 - 482.04 ) * ( B4 - B3 ) ) - ( ( 654.59 - 561.41 ) * ( B4 - B2 ) ) )

In the above equations, λ represents the center wavelengths of the respective bands; the central wavelengths of Sentinel 2A and Sentinel 2B vary slightly.


Normalised Difference Infrared Index

The Normalised Difference Infrared Index (NDII) uses a normalized difference formulation instead of a simple ratio. It is a reflectance measurement that is sensitive to changes in the water content of plant canopies, and higher values in the index are associated with increasing water content. The NIDI can be used for agricultural crop management, forest canopy monitoring, and the detection of stressed vegetation.

NDII = ( NIR - SWIR ) / (NIR + SWIR )

Sentinel 2 : ( B8 - B11 ) / ( B8 + B11 )

Landsat 8: ( B5 - B6) / ( B5 + B6 )

Green Leaf Index

The Green Leaf Index (GLI) was originally designed for use with a digital RGB camera to measure wheat cover. It can also be applied to aerial and satellite imagery.

GLI = ( ( G - R ) + ( G - B ) ) / ( ( 2 * G ) + ( B + R ) )

Sentinel 2: ( ( B3 - B4 ) + ( B3 - B2 ) ) / [ ( 2 * B3) + ( B2 + B4 ) )

Landsat 8:  ( ( B3 - B4 ) + ( B3 - B2 ) ) / [ ( 2 * B3) + ( B2 + B4 ) )


Task-specific Vegetation Indices

As we can see, one index might be more appropriate than another based on the purpose of your study and the source of the imagery. The following section lists indices developed to meet the needs of specific research requirements.

Transformed Difference Vegetation Index

The Transformed Difference Vegetation Index (TDVI) was developed to detect vegetation in urban settings where NDVI is often saturated.

TDVI = 1.5 * ( NIR - R ) / √( NIR^2 + R + 0.5)]

Sentinel 2: 1.5 * ( B8 - B4 ) / sqrt( B8^2 + B4 + 0.5)

Landsat 8: 1.5 * ( B5 - B4 ) / sqrt( B5^2 + B4 + 0.5)

Calculating square roots in QGIS Raster Calculator and ArcMap’s Raster Algebra have different syntaxes; QGIS uses ‘sqrt’ while ArcMap uses ‘SquareRoot’.

The Leaf Chlorophyll Index (LCI)  was developed to assess chlorophyll content in areas of complete leaf coverage.

LCI= ( NIR − RedEdge) / (NIR + R)

Sentinel 2: ( B8 - B5 ) / ( B8 + B4 )

Landsat 8: No true equivalent

Vegetation Fraction

The Vegetation Fraction is defined as the percentage of vegetation occupying the ground area; since it’s calculated using values generated from a NDVI, it is subject to the same errors. It’s a comprehensive quantitative index in forest management and an important parameter in ecological models, and can also be used to determine the emissivity parameter when calculating Land Surface Temperature.

Vegetation Fraction: [ NDVI - NDVI(min) ] / [ NDVI(max) - NDVI(min) ]

In this blogpost, we’ve listed down and organised the vegetation indices that we’ve found while improving our ecological monitoring techniques. We make extensive use of both satellite and drone imagery, and will be using this blogpost internally as a quick reference guide to vegetation indices.

Find us on Twitter @techforwildlife if you have any questions or comments, or email us at We’ve also opened up the comments for a few days, so please feel free to point out any errors or leave any other feedback!

P.S.: Hat-tip to Harris Geospatial (@GeoByHarris) for a comprehensive list of vegetation indices, which can be found here.

P.P.S.: We’ll be updating this post with Sentinel-2A imagery in the next few days.


·      C. F. Jordan (1969) Derivation of leaf-area index from quality of light on the forest floor. Ecology, vol. 50, no. 4, pp. 663–666, 1969

·      Daughtry, C. S. T., Walthall, C. L., Kim, M. S., De Colstoun, E. B., & McMurtrey Iii, J. E. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74(2), 229-239.

·      Gitelson, A., Y. Kaufman, and M. Merzylak. (1996) Use of a Green Channel in Remote Sensing of Global Vegetation from EOS-MODIS. Remote Sensing of Environment 58 (1996): 289-298.

·      Huete, A., et al. (2002) Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices." Remote Sensing of Environment 83 (2002):195–213.

·      Hunt, E. Raymond Jr.; Doraiswamy, Paul C.; McMurtrey, James E.; Daughtry, Craig S.T.; Perry, Eileen M.; and Akhmedov, Bakhyt, (2013) A visible band index for remote sensing leaf chlorophyll content at the canopy scale. Publications from USDA-ARS / UNL Faculty. 1156.

·      J. Richardson and C. Weigand, (1977) Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, p. 43, 1977.

·      Jinru Xue and Baofeng Su. (2017) Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications, Journal of Sensors, vol. 2017, Article ID 1353691, 17 pages, 2017.

·      Kim, M. S. (1994). The Use of Narrow Spectral Bands for Improving Remote Sensing Estimations of Fractionally Absorbed Photosynthetically Active Radiation. (Doctoral dissertation, University of Maryland at College Park).

·      Larrinaga, A., & Brotons, L. (2019). Greenness Indices from a Low-Cost UAV Imagery as Tools for Monitoring Post-Fire Forest Recovery. Drones, 3(1), 6.

·      Motohka, T., Nasahara, K. N., Oguma, H., & Tsuchida, S. (2010). Applicability of green-red vegetation index for remote sensing of vegetation phenology. Remote Sensing, 2(10), 2369-2387.

·      Sonnentag, O.; Hufkens, K.; Teshera-Sterne, C.; Young, A.M.; Friedl, M.; Braswell, B.H.; Milliman, T.; O’Keefe, J.; Richardson, A.D. (2012) Digital repeat photography for phenological research in forest ecosystems. Agric. For. Meteorol. 2012, 152, 159–177

·      X. Wang, M. Wang, S. Wang, and Y. Wu. (2015) Extraction of vegetation information from visible unmanned aerial vehicle images. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, vol. 31, no. 5, pp. 152–159, 2015. 

·      Y. J. Kaufman and D. Tanré. (1992) Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing, vol. 30, no. 2, pp. 261–270, 1992.

My first weeks with Technology for Wildlife

We headed out to the islands in the late morning. A bike ride, ferry and short walk later, we arrived at a beautiful open grassland overlooking the river. An ideal spot for a practice drone flight.

The author enjoyed the open vistas of Goa’s river islands.

The author enjoyed the open vistas of Goa’s river islands.

After a perfect afternoon of minty grass, nice wind and aerial photography, we headed back to the mainland; signing off the work day with big Thalis. That evening we attended a concert that gave us goosebumps and ended the night with pao and honey tea. The next day began at 5.30am for a beach survey in the hope of getting aerial imagery for our project with the PlasticTide ( After a mid-afternoon break to recover from our early start, we headed for a meeting with the head of a Government Department. What we expected to be a 20 minute formal meeting turned into a 2 hour conversation which closed with some exciting prospects for work. We came back to base to discuss the meeting and then ended the day with another amazing performance at an Arts Festival.

I think these two days are a useful sample to describe how my first weeks in Goa with Technology for Wildlife have been. It’s been a wonderful mix of learning, work and making the most of this beautiful state. All 3 aspects have meshed together wonderfully to make it overwhelming in the best way possible.

Learning about Technology for Wildlife’s plans and clientele helped me better understand their impact model. Even in this short period, we were able to meet a variety of people, and I was introduced to a number of diverse and exciting projects. I learned a lot about the technical requirements of the work through both directed and exploratory reading as well as with hands-on practice. For example, understanding how to use a drone for mapping was completely new for me. Over the past few weeks, I’ve learnt how to operate the hardware and how to process the collected data using open source software.

The author preparing a drone for flight

Working with passionate conservationists is an amazing source of learning, ideas and hope. It was wonderful to feel genuinely heard and valued, and this only added to my excitement to create further opportunities and conservation impact with Technology for Wildlife.

India’s Civilian Drone Industry: The Need for Greater Civil Society Engagement with Drone Regulations

This is a longish piece on India’s drone regulations I wrote for the Centre for the Advanced Study of India’s blog, which was also published in the Hindu Business Line on the 4th of December 2018.

On October 7, 2014, India’s aspirations of becoming a global leader in the manufacture and operation of unmanned aerial vehicles (UAVs; commonly known as drones) for civilian use were seemingly crushed overnight. The Directorate General for Civil Aviation (DGCA), India’s civil aviation regulator, issued a short public notice that prohibited any non-governmental entity in India from launching UAVs for any purpose whatsoever due to safety and security issues until regulations were issued. Luckily for India’s nascent drone industry, while the notice ended by demanding strict compliance, it did not articulate the mechanisms or identify the government agencies that would be responsible for enforcing this compliance.

As a result, while the ban was effective in curtailing the widespread use of drones, the regulatory chaos provided just enough space for the creation of a stunted industry. In the past four years, it has been relatively easy to contact and hire individual drone owner-operators for tasks as mundane as mapping farms, conducting event videography and taking photographs for real-estate marketing. These individuals have been able to obtain drones by purchasing them in various urban electronic grey markets, getting friends and family to import them in their personal luggage or by purchasing the required parts and building their own drones. A few businesses that have also managed to navigate the complex set of relationships required to manufacture or operate drones in India, without attracting hostile government attention, provide products and services primarily for the cinematography, agriculture, and infrastructure sectors. However, without regulations in place that guarantee the legality of their products and services, it has been difficult for these businesses to attract investors, limiting their ability to grow. It is not surprising to note that India has no indigenous drone manufacturer capable of competing on the global stage against drone industry giants such as DJI, Parrot, and Yuneec.

In the next few weeks, this may change. On December 1, 2018, the first version of India’s Civil Aviation Requirements for the Operation of Civil Remotely Piloted Aircraft Systems, also referred to as the Drone Regulations 1.0, was implemented. These regulations have emerged from two public consultations and an unknown number of private meetings, and have been vetted by many government agencies before finally seeing the light of day.

This initial version makes it legal for non-governmental agencies, organizations and individuals to use UAVs for specific operations after they obtain permission from a defined set of government agencies. The Drone Regulations 1.0 also include minimum standards for the manufacture of drones, whether made in India or abroad, information on the mandatory training required by drone operators, and various permission forms for specific drone operations. Under this version of the regulations, some activities with the potential for market transformation are not currently permitted. For example, while functional drone-based delivery is considered to be a major growth area for the drone industry and is a focus for research and development—as it will have a significant impact in online retail and healthcare—it is not allowed at this point of time. This is because it requires the operator to conduct beyond visual-line-of sight (BVLOS) operations and for the drone itself to release payloads while in flight, both of which are explicitly prohibited by the Drone Regulations 1.0.

However, subsequent versions of the Drone Regulations are expected to take the industry’s collective experience into account and widen the scope of permissible operations, thus eventually permitting drone-based delivery and other drone applications that are currently prohibited. The DGCA has designated a set of test sites across the country where drone manufacturers and operators can innovate in a safe and secure environment. The question remains as to whether the Drone Regulations will be able to keep up with the pace of growth of the drone industry.

The primary innovation in the Drone Regulations is the introduction of the Digital Sky platform. This is an online platform where a drone operator can obtain all the necessary paperwork required to conduct an operation, including final flight permission immediately before the operation, as part of an enforcement system designated as No Permission No Takeoff (NPNT). This is an ambitious system with a number of complex moving parts, and it remains to be seen how effective this will be in practice.

Aside from technical issues regarding implementation, one societal issue that the regulations as currently framed do not address is that of inclusivity. Drone applications are extremely relevant to large swathes of India’s rural population. For example, farming communities could cooperatively own and operate drones to map vegetation stress, prevent crop-raiding by wild animals, and even conduct precise spraying of fertilizers and pesticides. As currently framed, the processes and fees involved in obtaining permission to fly a drone would render it extremely difficult for them to conduct the drone operations they need most without hiring companies, which again would increase the costs of such operations. The Drone Regulations 1.0 are far more navigable by start-ups and corporations than by India’s non-governmental organizations and rural communities, which is something that must be addressed in future versions of the regulations.

It is clear today that India is ready to begin incorporating drones into its civilian airspace, and drone applications into society. As was evident even four years ago, drones are here to stay. While it is still possible to meet people today who have not yet seen a flying robot in action in India, this is unlikely to be the case even five years in the future. The range of operations that drones will be legally allowed to conduct within the country will expand, and should not be limited to only those with access to capital, as this will exacerbate existing inequalities in Indian society. It is thus imperative that more representatives from outside the drone industry, such as civil society organizations and advocacy groups, become involved in framing subsequent versions of India’s Drone Regulations to ensure that drones are used for the good of the larger population

Drones, spatial analysis and a 3D model: Asola Bhatti WLS

I recently collected some aerial imagery at the Asola Bhatti Wildlife Sanctuary in Delhi in collaboration with the people who run the outreach centre. I've really been enjoying working with the data, and this project has helped me clarify the various processes I use while using drones. So far, I have a three page checklist and am maintaining a mission log-book as well; keeping all the documentation up to date is hard! In this post, I'll be detailing the various applications I'm using to control the UAV and process the aerial imagery+data it generates, and will then describe a couple of the outputs.

TL;DR: Come for the aerial footage and the 3D models; stay for the process walk-through.

I'm using a DJI Phantom 3 Advanced; the P3A can be manually flown using the controller like a regular R/C plane. To tap into its more advanced functions, fly safely and troubleshoot issues though, it  needs to be connected to a smartphone. I use the DJI Go app on a OnePlus3 (Android) for regular flights, but may switch to an iPad soon; DJI-related apps apparently work better on iOS than on Android.

For mapping missions, there are a number of steps involved. The drone must fly a preset pattern autonomously, collecting images at regular intervals. These images can then be processed into a georeferenced mosaic and used to generate a 3D model. Depending on the use case, these can either be used as-is for visualisation, or analysed further to obtain specific outputs.

For mapping, I use DJI Go to configure the camera settings (exposure and shutter speed), and then use DroneDeploy to take-off and fly the drone along the preset mapping pattern. I'm also experimenting with Pix4D Capture; the UI isn't as clean as DroneDeploy's but the app itself is free, and you don't have to buy into the rest of the Pix4D ecosystem. Once the mapping is complete, I disable DroneDeploy and use DJI Go to manually collect more images from different angles and land the drone at the end of the flight. Once back at base, the images are uploaded into PrecisionMapper, where they're processed in the cloud to create:

  1. a RGB orthomosaic depicting reflectance values (.tif)
  2. a digital surface model representing elevation (.dsm)
  3. a 3D model (.ply and .las)
  4. a KML file for visualisation in Google Earth/Maps (.kml)
  5. a design file for visualisation in CAD software (.dxf)

So far, I've worked with all five of these products; there are more advanced ones available in PrecisionMapper, but I prefer to work directly with these products. I use QGIS and ArcGIS for almost all my satellite imagery analysis work, and these products feed directly into that workflow. The primary output I can create are basic maps; I've never had access to such high-resolution imagery before, so just the simple act of putting a scale bar onto one of these maps is exciting.

The images above are true-colour RGB composites, where the red, green and blue layers have been combined to represent the terrain as a human with unimpaired vision would observe it. The thing with composite bands is that they can also be combined to extract information that it's hard for a human observer to see. In a follow-up (more technical) post, I'll discuss the differences between false-NDVI, SAVI, VARI and TGI, which are all indices that use the RGB layers in interesting ways. In this post though, I'm just going to put in two images that depict the Triangular Greenness Index (TGI), which enhances chlorophyll-containing pixels; the greener the pixel, the more likely it is to contain vegetation.

There are various other algorithms that can be applied to the orthomosaic imagery; PrecisionMapper itself offers a couple that can delineate individual trees or count plants in rows. I'm going to be studying up on what else can be done with this imagery, especially with supervised classification and AI-based analysis processes.

And finally, my favourite output: the 3D model! With enough images from multiple perspectives, modern photogrammetry algorithms can generate vertices and meshes that depict an object or a landscape to scale and in three dimensions. I'm excited about these because while it's really cool to see these embedded in a web-page (as above), it's even cooler to see them carved out in wood or 3D-printed in ABS plastic. It's even possible to pull this into a VR system and explore the terrain in person, or make it the basis of an interactive game or... you get the drift; this is exciting stuff!

Get in touch via our contact form if you have any questions or want to discuss a project of your own.