Planning Photo and Video Missions for UAVs

When wanting to conduct a drone flight or mapping mission over a precise area of interest, one needs to plan the flight and path beforehand. This ensures that the entire area is covered completely and removes any visual errors or barriers that might occur during field work. Further, prefixing flight settings allows for more consistent data collection as compared to manual flights on the go.

We use a combination of two softwares for planning flights.The first among them is FlyLitchi Mission Hub (FMH) and the second is QGIS. The two can also be used in conjunction with one another. The process using both these softwares is described in this blog.

Using FlyLitchi:

FMH is a web app that enables us to plan drone flights from the desktop and later execute them using the android based Litchi app - Litchi for DJI drones which is available for a one time purchase in Google Play Store. 

The FlyLitchi mission hub interface has many flights that are publicly available for viewing on the desktop app as shown in Fig 1. We can also share our flight paths and drone videos to this application.

Fig. 01: FlyLitchi mission hub interface.

Login and Register: 

FMH is a free desktop application. One needs to register in order to create, upload and save the flight missions. To do this, click the ‘Log in’ button on the top right of the interface and create your account.

Set your area of interest:

a. Search for your Area Of Interest (AOI) in the search bar which is at the top left of the interface.

b. Click around the AOI to create way points to set the route of the mission.

Fig. 02: Way Point settings

c. Click around the AOI to create way points to set the route of the mission.

d. This mission can be stored by clicking on the MISSIONS option - bottom left and then clicking Save.

Way Point Settings:

I. Lat-Long: The location of the point is automatically logged as soon as you create a point.

II. Altitude (m): One needs to set the altitude of the drone above ground for all the points. In India, the legal limit as per the Directorate General of Civil Aviation (DGCA) is 120m. 

There is an ‘Above Current’ option under ‘Altitude settings’ available when you are batch editing the way point attributes. The “Above Current” option in FMH allows you to add a specific altitude to the current altitude of selected waypoints. It’s a convenient way to elevate all waypoints by a set distance without adjusting each one separately.

III. Speed (m/s): One can set the speed depending on the purpose of the mission. Max speed is 15m/s.

In a video mission like this, the speed can be set between 10m/s to 15m/s.

Pro tip: If you’re designing a mission to create an orthomosaic, keep the maximum speed below 8m/s to avoid distortion.

IV. Curve (m): If the curve is 0m then the drone will exactly fly to each point and then head to the next. If the purpose of the mission does not necessitate going to the exact location (esp. Turning Points) of the points, the drone will follow a curved path (blue line in Fig: 2) near the point (curve > 0m).

Fig. 03: Curve at the turns 

V. Heading: This is a circular (turn right - turn left) movement of the drone at a point location. It ranges from 0 to 360°. This controls which direction the drone is pointing. It is marked by a triangle-like marker on each way point. 

VI. POI: One can set one or multiple Points of Interest (POI) in FMH. The drone automatically points to the POI if set to do so. Here you can select which POI the drone should focus on if there are multiple POIs.

VII. Gimbal Pitch: This is like the angle defining the up-down movement of the camera. One can choose between 30 to -90°, where -90° points straight down. 

If you have chosen to Focus on a POI, then the drone automatically recalibrates its gimbal pitch to capture the POI throughout its mission. Alternatively, by picking the ‘Interpolate’ option, you can set a constant pitch angle which will be consistently maintained through the mission.

VIII. Intervals and actions: One can add some actions at each waypoint like Start/ Stop recording, Click a picture, Stay/ Hover etc. This option allows to set an action and the duration of the action if applicable.

Fig. 04: Way Point settings and Mission settings.

Mission settings: 

I. Units: One can choose the unit of measurement here.

II. Map Type: One can choose the base map upon which the flight missions appear. 

III. Heading Mode: The direction that the drone faces is set by this.

A. Auto: The drone faces the next way point.

B. Initial: The heading of the first way point is fixed for all.

C. Manual: Heading can be fixed during the flight. 

D. Custom: You can set the heading for each way point in FlyLitchi.

IV. Finish Action: At the end of the automated flight, the drone follows this action. 

A. RTH: Return to home - flies back to the drone launch point.

B. Land: It will land at the end of the mission.

C. Back to Start: The drone returns to the start of the mission.  

D. Reverse: It comes back the same path as the mission. 

V. Path Mode: The curve setting in way point settings is overridden by this path mode. If path mode is set to ‘Straight line’ then the flight path cannot have curved turns. If ‘Curved path’ is selected, one can set the curves at the points. 

VI. Cruising Speed and Max Flight Speed (m/s): The speed can be set here. 

VII. Default Curve Size (%): If path mode = ‘Curved turns’, then the new points that you add to the flight path will have the default curve size % that you specify. Default curve size will not work if the Path mode is set to ‘Straight Lines’.

VIII. Default Gimbal Pitch Mode: Same as way point settings but ‘MISSIONS’ settings override the way point settings.

Note: Mission settings override the way point settings.

Using QGIS to aid flight planning:

The boundary of the video mission can also be created in QGIS: This method is useful when the area of interest may not be obviously distinguishable in a satellite image. For example, if one owns a parcel of land, the boundaries need to be incorporated precisely and cannot be determined accurately through an image.

Here one has two options:

a. Load a boundary file of the AOI that you may already have.

b.  Create a shapefile layer from the Layer menu.

If you already have a shapefile for your area of interest-load it into QGIS and export it as a .kml file. This is because the FMH only processes .kmls and .csvs at present. The process for importing the .kml into FMH is described towards the end of the blog.

If you don’t already have a shapefile, you can create one using the following steps:

a. Go to Layer> create Layer and select New Shapefile Layer

Fig. 05: Creating a shapefile in QGIS.

b. Choose the name, source folder and projection to save your file.

Fig. 06: Creating a line shapefile.

c.  Now right click on the layer and select toggle editing. Then click on the Add line feature. You can now create a feature by digitising this boundary. 

Fig. 07: Digitising a line shapefile.

d. Export this line file to a .kml file. 

e. Import the .kml file to FMH using the ‘import’ option from the MISSIONS tab on the bottom left of the screen. 

Fig. 08: Import kml to FMH

f. Check and edit settings of each way point as mentioned in the previous section. 

Thus FlyLitchi and QGIS can be used to create video missions. The planned flights saved in FMH can then be executed using Litchi for DJI drones android app. The videos from the drone are recorded as .mp4 files.

The next step in our workflow would be processing these videos to sync them with telemetry data. More information on this process can be found on our blog on using Dashware.

Mapping Floods: Exploring Thresholding Methods

As part of our collaboration with Mongabay-India, we have utilised spatial analysis and visualisation to accompany their reporting. In June 2023, they published an explainer by Vinaya Kurtkoti on floodplains and their management in India. Their article discusses the ongoing process of concretisation and development in floodplains, which reduces the carrying capacity of rivers, leading to urban flooding. 

Presence of water bodies in Mahad pre and post-flood event.

We received information from the Mongabay-India team on urban floods in different parts of India. The data spanned periods exceeding ten years as well as recent occurrences. The task was to create maps of the areas before, during and after each flood event. Availability of suitable satellite imagery was key for creating these maps. This was a challenge as cloud cover during monsoon season - when the floods occurred was often 90% or more.  Thus the initial, critical step towards creating these maps was to check if clear imagery existed for the required flood dates. Additionally, for events older than a decade, the issue of low resolution imagery arose. Initially we planned on showing the flood visually using the raw satellite images. Since we found no clear imagery for the flood dates, we had to look for other options that could depict the flood-prone areas. 

Given the lack of clear imagery for the flood dates, I explored alternative approaches to represent flood-prone zones. Three distinct thresholding methods were experimented with, using three different platforms. 

The first method involved utilising Digital Elevation Model (DEM) data in QGIS, an approach that came into play due to QGIS’s simple interface. By loading the area of interest through quick map services and employing the SRTM-downloader plugin, DEM 30m data was directly sourced from NASA Earthdata. I used the DEM data to establish a threshold. This method is a prediction of flood prone areas, given the level of water level rise. I looked for sources like news articles that reported the water level when the areas were flooded. I set that water level as the threshold using the raster calculator. By setting that threshold the algorithm gave the areas based on the elevation that would be inundated, if water level rises to a certain level.

Thresholding flood level from SRTM DEM data.

The second method I tried was using Sentinel-1 Synthetic Aperture Radar (SAR) data, which was available for the exact date when the flooding occurred in this area, using Google Earth Engine (GEE). 

I then analysed pixel values of water by comparing images before and during flooding. Applying a pixel value threshold allowed for identification of sudden changes indicative of flooding. I began by filtering the pre-flood and post-flood dates for the images for Mahad city. So, I had two SAR images: one before the flood and one when it was flooded. I checked the pixel values of the water bodies before the flood from various spots. This pixel value was then set as the threshold. Once I input the threshold and ran the code, GEE highlighted areas with sudden pixel value changes of water bodies in the after image, indicating flood, and those with no change were the existing water bodies. 

Pixel value change of the area marked in red from pre-flood to flood date in the Inspector display box.

The third threshold method I employed was for the Commonwealth Game Village area of Delhi. Initially, we hoped to depict actual visuals of the flooding in one of the areas using satellite imagery. However, demarcating the flood manually for the viewers to clearly differentiate between the pre and post-flood imagery was not possible because clear imagery was not available for the flood dates. When working with older satellite images dating back to 2010, we faced issues stemming from their low spatial resolution. This limitation arose because satellites with enhanced spatial resolutions were launched only after that time, in 2013. In order to show a similar situation, we searched for similar events in recent years and found images from 2022’s flood in Delhi. However, satellite images during the flood were still not useful because of the high cloud cover in them. So I had to look for images just after the flood event when the cloud cover was low but was still indicative of flood, as it takes time for the water to drain away. 

Initially I tried the same method as before, by using SAR data. However, it seemed to detect built up areas like roads instead of water. Therefore I switched to Sentinel-2 L2A data for this region. According to Bhangale et al., 2020 [1] and Lekhak et al., 2023 [2] band 8 (NIR) with band 3 (Green) of Sentinel-2 could be used to identify water bodies. I therefore used the band 8 from both the pre and post-flood images to detect inundation. I checked the pixel values from various spots and noted down an approximate minimum and maximum pixel value of the water body in the image before the flood. This range was then used to differentiate water from non-water areas in post-flood images. After noting the values, I classified this range of values into one category as water and rest as not-water. I similarly applied this step in the post flood image which gave me the change in the water bodies which are the areas that were inundated. 

Checking pixel values of water bodies from pre-flood images.

Setting threshold values to classify water and not-water.

Results after classification.

After applying all the three thresholding methods the question that arises is of their accuracy. While the first method that I applied was a prediction based on elevation and level of water, the other two methods were entirely based on satellite data. 

In the case of Mahad, the first method based on elevation seemed to match the level of inundation to some extent with the SAR output, as it predicted the major areas that were inundated. SAR data, as per existing studies, is widely used and considered appropriate, for detecting floods as it is unaffected by cloud cover. This is because unlike other optical satellite imagery it is able to differentiate land and water contrast easily. However, SAR data [3] can sometimes misclassify shadows of tarmac areas with buildings and roads as water. This issue became evident when I experimented with SAR data in the Delhi case. 

Presence of water bodies in Yamuna floodplain, pre- and post-flood.

On the other hand, Sentinel-2 data gave results similar to the SAR output where built-up areas were misclassified as water. Sentinel-2 data is affected by atmospheric conditions unlike SAR. The process of setting pixel values is more manual, which can be affected by individual judgement, potentially leading to underestimation or overestimation[2].

Sentinel-1 SAR data has been found to have more accuracy in detecting floods than Sentinel 2.  A study by Nhangumbe et al., 2023 [4] suggests combining both the data for attaining higher overall accuracy. 

Overall all three methods provided estimations of the major areas that were inundated or likely to be inundated, fulfilling the purpose of the issue that Mongabay-India wished to convey. Meanwhile, the scope for exploration and improvement remains open!

References

1. Bhangale, U., More, S., Shaikh, T., Patil, S., & More, N. (2020). Analysis of Surface Water Resources Using Sentinel-2 Imagery. Procedia Computer Science, 171, 2645–2654. (https://doi.org/10.1016/j.procs.2020.04.287)

2. Lekhak, K., Rai̇, P., & Budha, P. B. (2023). Extraction of Water Bodies from Sentinel-2 Images in the Foothills of Nepal Himalaya. International Journal of Environment and Geoinformatics, 10(2), 70–81.(https://doi.org/10.30897/ijegeo.1240074)

3. Rahman, Md. R., & Thakur, P. K. (2018). Detecting, mapping and analysing of flood water propagation using synthetic aperture radar (SAR) satellite data and GIS: A case study from the Kendrapara District of Orissa State of India. The Egyptian Journal of Remote Sensing and Space Science, 21, S37–S41. (https://doi.org/10.1016/j.ejrs.2017.10.002)

4. Nhangumbe, M., Nascetti, A., & Ban, Y. (2023). Multi-Temporal Sentinel-1 SAR and Sentinel-2 MSI Data for Flood Mapping and Damage Assessment in Mozambique. ISPRS International Journal of Geo-Information, 12(2), 53.(https://doi.org/10.3390/ijgi12020053)

Identifying Forest Fire Scars with GEE

In March 2023, the serene forests of Goa experienced forest fires at an alarming scale. While there are many theories regarding the cause of the fires, mapping is helpful to know the scale of the fires and understand the extent of the damage. The sites identified at this stage are also helpful to monitor recovery and possible spread of invasive species in the aftermath of fires. This can aid in efforts to restore and maintain forest health.

In this blogpost, we look at the use of Google Earth Engine (GEE) to locate burn scars and hence identify areas affected by fires. I’ll be outlining the steps taken and sharing the relevant code for each stage of the analysis.

Importing Satellite Imagery

function maskS2clouds(image) {
  var qa = image.select('QA60');

  // Bits 10 and 11 are clouds and cirrus, respectively.
  var cloudBitMask = 1 << 10;
  var cirrusBitMask = 1 << 11;

  // Both flags should be set to zero, indicating clear conditions.
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0)
      .and(qa.bitwiseAnd(cirrusBitMask).eq(0));

  return image.updateMask(mask).divide(10000);
}

var dataset = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED')
                  .filterDate('2023-02-01', '2023-03-31')
                  // Pre-filter to get less cloudy granules.
                  .filterBounds(goa)
                  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE',1))
                  .map(maskS2clouds);
print(dataset) 

To begin, I implemented the ‘maskS2clouds’ function to filter out cirrus and cloudy pixels. This function uses the QA60 bitmask band of Sentinel imagery to create a mask that identifies cloudless pixels suitable for further processing. This process is applied across the entire image collection dataset.

Creating a burn scar map, necessitates pre-fire and post-fire images, for an estimation of the scarred area. For this purpose, I selected appropriate images of post and pre-fire scenarios from the image collection. I wrote the below code to be able to display all the images date wise in the image collection to the map view.

Display All Images

//Add date attribute to all images to display the images and select the required //
dataset = dataset.map(function(image){
  var str = ee.String(image.get('system:index'))
  var sdate = str.slice(0,8)
  //var sdate = ee.Date(image.get('system:time_start'));
  image = ee.Image(image).set('date',sdate)
  return image;
})

print(dataset, 'with date')
var listOfImages = dataset.toList(dataset.size())
print(listOfImages)

//Add all the images in the dataset to the Map view //////
for(var i = 0; i < 28 ; i++){
  var image = ee.Image(listOfImages.get(i));
  var date = image.get('date')
  Map.addLayer(image, band_viz, ''+date.getInfo())
}

By attributing a date to each image and displaying them by date, I could visually assess the quality of the images in the collection. The first part of the code adds date as an attribute to all the images in the image collections. The second part displays the images using their date attribute. The output of the code is displayed below. This format of display helped me to gauge the quality of all the images in the collection. This further guided my selection of the suitable images for the pre and post-fires scenarios.

Example of all images displayed in the map view.

With all the images on display, I selected the 13th of February 2023 as the pre-fire image and the 10th of March 2023 as the post-fire image. This approach as in the above display also highlighted if the selected images need to be mosaicked. Mosaicking is joining two or more contiguous satellite images for analysis in order to cover the entire area of interest. Accordingly, I proceeded to convert the images to a list, then selected the required images and mosaicked them.

Selecting Pre-fire image

dataset = dataset.toList(dataset.size())
print(dataset, 'imageCollection')

//mosaic of 13/02/2023 - pre fire image.
var preFire = ee.ImageCollection([ee.Image(dataset.get(3)),ee.Image(dataset.get(6)),ee.Image(dataset.get(8)),ee.Image(dataset.get(11))])
preFire = (preFire.mosaic()).clip(goa)
print(preFire,'preFire')
Map.addLayer(preFire,band_viz,'preFire')

Selecting Post-fire image

//mosaic of 10/03/2023 - post fire image.
var postFire = ee.ImageCollection([ee.Image(dataset.get(26)),ee.Image(dataset.get(25)),ee.Image(dataset.get(24)),ee.Image(dataset.get(23))])
postFire = (postFire.mosaic()).clip(goa)
print(postFire,'postFire')
Map.addLayer(postFire,band_viz,'postFire')

The next step involved calculating Normalized Burn Ratio (NBR) for both pre and post-fire images. NBR relies on Near Infrared (NIR) and Short Wave Infrared (SWIR2) reflectance data. Burnt areas exhibit high reflectance in the SWIR2 region and very low reflectance in the NIR region of the electromagnetic spectrum. The NBR is designed to exploit this difference in reflectance along the spectrum. NBR is formulated as follows:

NBR = (NIR - SWIR2) / (NIR + SWIR2)

The NBR was calculated for both pre and post-fire images, using the below code.

Normalized Burn Ratio (NBR) Calculation

//Finding Pre and post fire NBR = normalized burn index ratio = NIR-SWR2/NIR+SWIR2
var PreNumer = preFire.select('B8').subtract(preFire.select('B12'))
var PreDenomin = preFire.select('B8').add(preFire.select('B12'))
var PreNBR = PreNumer.divide(PreDenomin)
//Map.addLayer(PreNBR,{},'Pre-NBR')

var PostNumer = postFire.select('B8').subtract(postFire.select('B12'))
var PostDenomin = postFire.select('B8').add(postFire.select('B12'))
var PostNBR = PostNumer.divide(PostDenomin)

The ensuing step involved determining the difference between pre and post-fire NBR images. The resultant output shows us the extent of burnt area in the region.

Difference between Pre and Post Fire NBR

//Subtracting postNBR fron PreNBR for scar map
var scar =PreNBR.subtract(PostNBR)
var palette = ['1a9850','91cf60','d9ef8b','ffffbf','fee08b','fc8d59', 'd73027']
Map.addLayer(scar, ,'ScarMap')

In subsequent stages, I incorporated the application of a threshold to isolate the scarred regions, which were then vectorized using a mask.

Define thresholds

// Define thresholds
var zones = scar.lt(0.09).add(scar.gt(0.09)).add(scar.gte(0.2))
zones = zones.updateMask(zones.neq(0));

I chose the thresholds by closely examining pixel values in the scar map using the Inspector Tool in GEE Code Editor interface. The ‘Inspector’ displays the pixel value at the point chosen on the map, for every displayed layer.

Inspector tool highlighted in the screengrab.

The output of the ‘define threshold’ step here was a raster with two values, 1 and 2. By this means, all values less than 0.2 were classified as 1 and while those greater than 0.2 were assigned the value 2. Using the code below, I converted the classified raster to a vector, yielding a downloadable KML file to create a comprehensive map of the affected area.

Reduce to Vector

// Convert the zones of the thresholded scars to vectors.
var vectors = zones.addBands(scar).reduceToVectors({
  geometry: goa,
  //crs: nl2012.projection(),
  scale: 10,
  geometryType: 'polygon',
  eightConnected: false,
  labelProperty: 'zone',
  reducer: ee.Reducer.mean(),
  bestEffort: true,
  maxPixels: 3784216672400
});


// Display the thresholds.
Map.setCenter(74.13215, 15.46816, 10);
Map.addLayer(zones, {min: 1, max: 3, palette: ['0000FF', '00FF00', 'FF0000']}, 'raster');

// Make a display image for the vectors, add it to the map.
var display = ee.Image(0).updateMask(0).paint(vectors, '000000', 3);
Map.addLayer(display, {palette: '000000'}, 'vectors');

In summary, employing this method was effective to identify forest fire scars in Goa. The inclusion of  ground points of scarred areas were valuable for ground truthing. Thresholding plays a very important role in delineating scarred areas, and hence ground truth points are helpful. If you’re involved in fire scar mapping, this demonstration could be a beneficial reference. Kindly get in touch via our contact form, if you have any further questions.

Link to the code: https://code.earthengine.google.com/c1010c05dd2762d18626b76a90112223

(Note: This code does not include the required shapefiles for the code to work, this is for reference only.)