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Survey Drone Sensor Gimbal Inforgraphic


QuestUAV Sensor Gimbals Improve Flight Efficiency By More Than 15 Percent


Demands on fixed wing drones are growing continually. Other than copter drones, fixed wing platforms are generally used to cover large areas (hundreds of hectares) in a short amount of time. Standards on flight endurance and efficient area coverage are growing throughout different industries such as surveying, agriculture, mining or surveillance. To create a high-quality 3D model of a survey area sensor performance and image overlap is essential. Sensor and drone platform have to compensate for the effects of wind and turbulences causing blurred images and low image overlap. From day one QuestUAV has been developing gimballed systems and fine tuning platform stability in order to gain maximum quality and performance from a flight mission. The following sections outline the importance of image quality and overlap and show how a gimballed system can increase efficiency by more than 15 percent


QuestUAV Gimballed Drones


Image overlap is crucial...

When mapping an area with a drone or Unmanned Aerial Vehicle (UAV), the UAV will have to fly and photograph the survey area in a grid-like pattern ensuring that every feature on the ground (e.g. a tree or a building) is “seen” in multiple photographs. For the generation of 3D models, these photographs have to have sufficient overlap in flight direction and between grid lines (side overlap). Photogrammetry software providers like Pix4D or Agisoft Photoscan generally recommend an overlap of 75% frontal and 60% side overlap.

Flight Image Overlap Diagram

Sensor choice and gimbal influence data quality...

Besides image overlap, GSD (Ground Sampling Distance) is crucial for modelling an object in high detail. Hence, a good sensor and a UAV system which enables a stable flight and continuous overlap are essential for the generation of high-quality maps and 3D models, especially in windy and turbulent conditions. Image sharpness and overlap can significantly deteriorate when the UAV is pushed around in moving air. Therefore, a sensor gimbal might become crucial for data quality, spatial accuracies and hence for mission success. Various QuestUAV missions have proven that a gimballed system compensates for effects like blurred or oblique images and lack of overlap.

Stereo Photogrammetry Diagram

Stereo-photogrammetry to extract 3D positions...

Once a feature is photographed from different angles stereo-photogrammetry can be applied after a flight during the post-processing phase. Common points are identified in each image and a line of sight (or ray) can be constructed from the camera location to the point on the object. The intersection of these rays determines the three-dimensional location of the point and in combination a 3D model of the surveyed area.



The sensor is the heart of a UAV and depending on which sensor is flown it will determine what data a UAV is capable of collecting. Ground Sampling Distance (GSD), image sharpness and noise level are all dependent on the sensor chosen for a flight mission. As an example, the QuestUAV 200 Surveyor carries a Sony A6000 camera which captures very high detail with a 24.3 effective megapixel APS-C sensor allowing to acquire data down to 2.9cm GSD at 400ft. The Exmor APS HD CMOS sensor ensures an extremely fast performance, sharp image quality and low noise images, even in low-light conditions.


The major advantage of a gimbal is simply to allow the sensor to continuously point directly towards the ground (nadir view), while the aircraft itself is manoeuvring around in yaw, pitch and roll. Especially in high winds, a compensation for the movement is essential to keep the image overlap required for photogrammetry processing. If there is no gimbal the general solution is to increase side overlap. However, increasing the side overlap causes the aircraft to fly more grid lines and turns and hence reduces area coverage and flight efficiency. Overlap recommendations by photogrammetry software providers are generally around 75-80 percent frontal and 60-65 percent side overlap.


QuestUAV Gimbals can reduce the image overlap to 40% and still guarantee the data quality.

Various studies with a QuestUAV 200 Surveyor and QuestUAV 100 DATAhawk have proven that a gimballed system allows reducing the image overlap from 65 to 40 percent and still guaranteeing enough overlap for photogrammetric processing and data quality even in high winds. By reducing the amount of grid lines and aircraft turns the already impressive ground coverage of a QuestUAV system is further increased. As shown in the figure below the amount of grid lines is reduced from 13 to 10 and the total path length from 10.1 km to 8.3km - a decrease of 18 percent!


Image Overlap Comparison

When compared to an orthomsaic based on 65 percent overlap the 40 percent overlap orthomosaic is equally good in terms of image matches and data quality. The number of overlapping images was in both cases continuously higher than five for each pixel of the orthomosaic resulting in an excellent 3D model of the surveyed area.


Orthomosaic Overlap Comparison


QuestUAV has proven that a sensor gimbal significantly improves the already outstanding ground coverage of a QuestUAV drone. The QuestUAV sensor gimbal compensates for the effects of wind and turbulences causing blurred images and low image overlap. By using a gimbal an area can be flown with only 40 percent side overlap without a reduction in data quality. Hence, mission efficiency is increased by more than 15 percent.

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Demonstration of High Geospatial Accuracy Achieved by a Fixed Wing UAV | QuestUAV News

Demonstration of High Geospatial Accuracy Achieved by a Fixed Wing UAV

1 (3cm) pixel accuracy across 2 Km grid using a QuestUAV Surveyor Pro UAV

1 Survey Objectives and Deliverable Items

The purpose of this survey was to achieve a high accuracy topographic map and Digital Elevation Model (DEM) of a study area Post Processing Mapin a foreign country that:

(a) was managed by an independent and competent third party, totally independent to QuestUAV.
(b) provided the opportunity to compare QuestUAV performance against other UAV vendors such as EBEE, Dronematrix, Trimble, UAVER

The survey was conducted in August 2015 in South Korea, under the auspices of LX, the South Korea governmental survey agency.

The agency laid out a set of twelve Ground Control Points (GCP) spread across the 1200m x 1200m survey area.

To check consistency the agency also laid out six Control Points (CP’s) across the survey area with the intention that these be used to independently confirm the accuracy of the survey after it had been completed. The survey has returned excellent results with an average accuracy of 3cm (1 pixel) across the survey area, using the control points for validation.
For this survey we have focussed simply on land mapping and elevation analysis. It is worth noting that in addition to this a survey can return a vast array of data in terms Copyright © QuestUAV 2015 All Rights Reserved of land mapping, infrastructure, elevation change, agriculture, social change, road usage, boundaries, forestry, floodplain and drainage, erosion, crop volume and many more subject analysis.
In accordance with our agreement with the agency, QuestUAV has the following deliverables associated with this survey. (All deliverables are available through our Geotech department, identified at the end of this document.)
  • Survey report (this document), including a survey description, a summary of the results and a basic image analysis.
  • A3 map of the survey area (PDF format).
  • Image processing report (Agisoft Photoscan).
  • Agisoft Photoscan project file (PSZ format).
  • Natural Colour Image with 3cm spatial resolution (GeoTiff, KML and ECW format).
  • Digital Elevation Model with 6cm spatial resolution (GeoTiff, KML and ECW format).

South Korea

Ground Image of the Northern sector of the Survey Area

2 About the Survey, and the Equipment Used

The QuestUAV survey was undertaken on 26 August 2015 with a standard QuestUAV Surveyor Pro (see front page for the UAV) in a built up area close to Jeonjo, Southern Korea. The UAV was equipped with a gimballed Sony A6000 camera operating on a 2 second trigger. 1,350 images were acquired in total with an overlap of 80% in-flight and 60% side lap. The figure below shows the flight path and indicates the image overlap. (The irregular area to the top is the result of high ground reducing the overlap.)

The area of survey has 13 GCP’s and 6 control points for accuracy assessment and is contained within approximately a 1 square kilometre grid.

The UAV took off from the school grounds in the centre of the survey area and flew the area once EAST-WEST and then routed to a NORTH-SOUTH grid, all within a single fight. The UAV returned to the launch area for a parachute landing. The flight took approximately 45 minutes.

A crew of two was used for the survey; a pilot (N King) and a laptop commander (R Moore). The UAV was visible throughout the flight. Flight was autonomous from take off until the decision for parachute landing preparation.

                  Q-200 Surveyor Pro Launch Aerial Image

Launching the QuestUAV Surveyor 200 (L) Natural colour image of the study area showing distribution of ground control points (R)

3 Image Processing

Pix4D ScreenshotAn A4 sheet was laid on the ground with an identifying mark in the centre of the sheet. The centre was referenced using high accuracy DGPS survey instruments returning mm accuracy.The image processing was
completed in Agisoft Photoscan. The computer used took approximately 18 hours to complete the dense point cloud creation – the longest part of the processing.

Input for the image processing were the following 3 datasets:

  • 1,350 UAV raw images
  • QuestUAV log file (image name, latitude, longitude altitude, yaw, pitch, roll)
  • 19 ground control points for geo-rectification

A total area of 2.3 square kilometres has been processed. Details on the image processing can be taken from the Agisoft Photoscan Processing Report, which is part of our deliverables.

4 Image Accuracy

The outcome of the image processing was a high resolution Natural Colour mosaic with a spatial resolution of 3cm and a Digital Surface Model (DSM) with a spatial resolution of 6cm.

The accuracy error, calculated through the CP’s, throughout the mosaic was on average one pixel (ie the same as the spatial resolution).

The processed Natural Colour Image shows:

  • Land usage: Spread of buildings. Building and land boundaries can be clearly identified. Heights of buildings can be assessed.
  • Power lines routing and condition.
  • Roads and highways: Sizes, dimensions, road surfaces, barriers, road marking, dangers from signals and signage
  • Agricultural information: Field boundaries and field roads can be identified. Crop types, crop status and crop health can be assessed. Presence of illegal crops or illegal usage of land can be detected.
  • River boundaries and conditions: Conditions of river structures, flow of water and water colour.

                                                                                                             Detail from the Natural Colour Image

                                Drone Aerial Image Crops Drone Aerial Image

                                                       Different crop types of agricultural areas can be easily identified.(L) Markings on a road intersection. (R)

4.1 Digital Elevation Model

Elevation ModelA Digital Elevation Model (DEM) is a digital representation of the elevation of a terrain. Each pixel of a DEM contains an elevation value. Our
DEM of the study area shows a minimum elevation of 45 meters and a maximum elevation of 105 meters above sea level. The terrain rises from the centre line of the study area in both directions, Northwest and Southeast.

Digital elevation models are the basis for in-depth terrain analysis and hydrologic calculations, like for example:

  • Determining the slope of roads
  • Calculation of height profiles along roads
  • Derivation of contour lines
  • Calculation of hill slopes and determination of aspects
  • Determining watersheds and stream networks
  • Modelling flow accumulation and runoff volumes
Flight Data

Please note there is no universal usage of the terms Digital Elevation Model (DEM), Digital Terrain Model (DTM) and Digital Surface Model (DSM) in scientific literature. In most cases the term digital surface model represents the earth's surface and includes all objects on it. In contrast to a DSM, the digital terrain model represents the bare ground surface without any objects like plants and buildings

In our case we have produced a Digital Surface Model (DSM), showing the elevation of all objects on the ground. A DSM can be used as basis to derive a Digital Terrain Model (DTM).

The accuracy of the DEM was assessed by comparing the elevation values of the ground measurements (GCPs and CPs) with the DEM values at the ground control locations. The table below shows how good the elevation of GCP and DEM match. The average difference is between 0 and 3 cm.


5. Land Mapping and Elevation Analysis

The survey can return a vast array of data in terms of land mapping, infrastructure, elevation change, agriculture, social change, road usage, and many more subjects. The following sub-chapters show examples of an in-depth data analysis.

5.1 Mapping the Location and Size of Buildings
Mission PlanningThe location and size of buildings or other objects on the ground (field boundaries, ponds, parking areas, etc.) can be precisely determined on the basis of the Natural Colour Image.

Each pixel of the Natural Colour Image has a unique geo-coordinate and represents an area of 3 cm x 3 cm. With such a high spatial resolution, roofs can be easily identified and digitized inside a Geo-Information System (GIS). A GIS allows to automatically calculate the roof area.

Example of mapping greenhouses and determination of roof sizes.


5.2 Determining the Slope of a Road

Digital Elevation ModelThe slope is a measure of the steepness of a road and can be determined on the basis of a Digital Elevation Model (DEM). Slopes are calculated by determining the change in elevation along two points. Height profiles allow us to understand the elevation changes and show the ups and downs along a track. The figure below shows the height profile along a road section in the north-eastern corner of the study area.

The elevation changes in south-north direction from 49.2m to 53.8m, along a length of 346m. According to the common slope formula (slope = rise/run x 100), the slope of the road section is 1.3 %.

Please visit our dataset page for more examples - Datasets

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QuestUAV Provide Own PPK Solution For Q-200

QuestUAV Provides Own PPK Solution For Q-200 Surveyor UAV


QuestUAV Own PPK

PPK (Post-Processing Kinematic) provides much higher accuracy in GPS location when stored against images taken in a UAV. Standard GPS signals are accurate to 10's of metres - PPK increases that accuracy to cm-levels. On board the Q-200 UAV, PPK eliminates the need for physical Ground Control Points (GCP) that are often used to gain high accuracy in surveys. This saves hours of mission planning and setup time, physically measuring location points and walking the survey site for placement.


GCPs – The underestimated part of a UAV survey

Surveys involving GCP generally run like this:

  • Initial site is viewed to establish useful locations for Ground Control targets.
  • Each location is visited with a GCP and a Differential GPS receiver to accurately place the target.
  • Targets may need revisiting before survey takes place.
  • Locations are stored for post processing reference.

In most cases - up to half of the mission time is taken up with GCP placement. GCP targets may shift or collapse with changing weather conditions – requiring the original placement to be repeated (often wasting up to an hour of survey setup time); coastal surveys can suffer from tidal changes and cliffs make it difficult to place GCPs across the survey area; general survey ground conditions can make it difficult to secure GCPs - quarries are a good example of difficult, variable ground surfaces.


The advantage of PPK - Overcoming GCPs

The PPK solution offered by QuestUAV uses a higher performance, highly-accurate receiver placed within the aircraft - following more than 10 GPS satellites at any given time and storing location information against the triggered images taken. Combined with differential signal information collected by the fixed position Ground Station (which stores signal drift and signal error values), the image locations are recalculated to a much higher accuracy – down to centimetre level in x, y and z direction.

QuestUAV Own PPK

Compared to RTK (Real Time Kinematic), PPK also eliminates the need for a real-time data link with a fixed reference station during the flight, whilst guaranteeing RTK cm-level position accuracy of the images once post-processing has taken place, after the UAV lands. This simplifies the UAV set-up, reduces the requirements and power drain on-board and eliminates any loss of accuracy in data due to potentially unreliable radio links - which often plague RTK UAV operations.

The Q-200 Surveyor Pro is available with PPK at purchase or as an upgrade to an existing aircraft with the provision of just the PPK QPod.

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Flight Team in Truck

Long Range MicaSense Agronomy Surveys

Long Range MicaSense RedEdge Agronomy Surveys

Q-200 and Q-100 Aircraft Are Put Through Their Paces In Agricultural Survey

Long Range Flight Op

This long range agronomy survey was part of a mission in the English countryside. We regularly fly test and demonstration missions across the UK. A multiple airframe survey demonstration flown for Hummingbird Technologies was no exception.

The survey took place over 2 days, with both a Q-200 AGRI MicaSense UAV and a Q-100 DATAhawk MicaSense UAV. Each airframe is equipped with the MicaSense RedEdge multispectral sensor suite, capable of detailed multiband imaging across 5 discrete spectral bands.


Aerial Field

The survey site consisted of agricultural research samples and was imaged using both airframes using a 3-person flight team - 1 pilot, 1 commander and 1 spotter/driver for mobile operations.

On day 1 the Q-100 DATAhawk missions were repeated at different altitudes to demonstrate flight performance and MicaSense image quality. The DATAhawk covered 325Ha during a 42 minute flight at 400ft with a 70% overlap.



Day 2

Day 2 missions were designed to showcase the endurance of both the Q-100 DATAhawk and the similarly RedEdge-equipped Q-200 UAV. The Q-200 required only two flights to cover 730Ha at 400ft with 70% overlap. Parachute landings were performed in each case with a 46 minute flight time (per flight). Mobile ops were used to ensure Line of Sight requirements were adhered to.

Long Range Flight Op

Mobile Ops vehicle and crew


Final Analysis

Once processed, the imagery from both days' missions will form the basis of high quality NDVI and RedEdge Indices, which will be correlated and compared with agricultural information, such as LAI (Leaf Area Index), crop density and nitrogen uptake.

Clients on-site were very pleased with the endurance and performance of both the Q-200 and the Q-100 DATAhawk, especially when considering the high winds that were present during the surveys days.

Thermal Image

MicaSense RedEdge Image Stack

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Archaeological Dataset


QuestUAV Surveys a 4 sqkm Archaeological Site In One Flight With the Q-100 DATAhawk

QuestUAV Team Successfully Flies Paleapaphos in Cyprus - The Birthplace of Aphrodite

In the context of the RSCy2016 Fourth International Conference on Remote Sensing and Geoinformation of Environment in Cyprus, the QuestUAV team went to survey the area of Paleapaphos (The Old Paphos), the mythical birthplace of Aphrodite, Greek goddess of love and beauty. The area is an important archaeological site, about 16 km from the city of Paphos, including remains of temples and several excavation sites. The project area had a size of 3.8 sqkm and was imaged within a 50 minutes flight. The images will be used for detecting changes at the excavation sites and mapping of looting holes.

Mission Details

The QuestUAV team flew the area at 400 ft with a Q-100 DATAhawk, equipped with a Sony QX1 camera. The total mission, from flight planning until landing took us no longer than 4 hours. We acquired more than 850 high resolution images with a GSD of 3.5cm. The weather was sunny with medium wind strength. After 50 minutes flight time, we smoothly landed the DATAhawk with parachute into higher grass.

 Q-100 DATAhawk in car

DATAhawk recovery after parachute landing (L). Q-100 DATAhawk in the car (M). Image analysis in a restaurant (R).

Image Processing and Outlook

We processed the images within 10 hours with Pix4Dmapper Pro and generated a high resolution Orthomosaic and Digital Surface Model (DSM) covering the whole survey area. Orthomosaic and DSM are the basis for further analysis and the generation of archaeological site maps. The QuestUAV team will keep you updated about the outcome of this study.


Paleapaphos (The Old Paphos), the mythical birthplace of Aphrodite

Interactive Orthomosaic

(courtesy of DroneLab)

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GIS Services |  DATAhawk GIS

Multispectral imaging with QuestUAV, Micasense and Pix4Dmapper | QuestUAV News

A Breakthrough in Precision Farming

Multispectral imaging with QuestUAV, Micasense and Pix4D Mapper Pro

Key Achievements

Multispectral crop monitoring has proven to be a vital aspect of successful agricultural operations. QuestUAV’s new industrial grade compact mapper Q-100 DATAhawk, combined with the multi-spectral MicaSense RedEdge sensor, opens up new doors to maximize agricultural productivity. The QuestUAV Q-100 DATAhawk is rugged, reliable and allows easy and safe operation in open and confined environments. In combination with the five-band MicaSense RedEdge sensor, farm maps of unprecedented quality can be generated by means of the intuitive Pix4D Mapper Pro software. The following study reveals that our system provides a turnkey monitoring solution for the agricultural industry.

GIS Services | DATAhawk GIS

Technical Background

QuestUAV Q-100 DATAhawk

The QuestUAV Q-100 DATAhawk Ag is a compact sub 2kg mapping drone with an exceptionally easy hand-launch. The entire UAV is ultra-rugged, can be rapidly deployed and flies fully autonomous. Flight endurance is up to 1 hour at 18m/s with coverage of up to 300 hectares per flight at 400ft altitude. Multiple landing options, automatic and parachute, allow for an easy and safe operation in open and confined environments.

MicaSense RedEdge

The agricultural edition of the Q-100 DATAhawk, the Q-100 DATAhawk Ag, carries the MicaSense RedEdge multispectral unit. This advanced, lightweight camera is optimised for use in UAVs of our type.

The MicaSense captures data in five discrete spectral bands (near-infrared, red-edge, red, green and blue with a Ground Sample Distance of 8.2cm/pixel at 400ft), enabling the creation of crop health indices and orthomosaics. High-grade optical filters deliver precise information specially targeted to agricultural applications.

MicaSense RedEdge Bands

Spectral bands of MicaSense RedEdge

Image Processing (Pix4D Mapper Pro or MicaSense ATLAS)

There are several options to process MicaSense data and to generate orthomosaics and crop health indices. We found the following two options as the most practical solutions for farmers:

  1. MicaSense ATLAS offers a powerful cloud-based data service for storage, processing, analysis, and presentation of multispectral data.
  2. Pix4D Mapper Pro allows to convert multispectral images into accurate index maps and orthomosaics via intuitive software control.

Project Objectives and Scope

Our study took place at a representative precision farm in the Northeast of England in February 2016. The farm has a total size of 172 hectares and was planted with winter wheat, one of the most common crops of the region. The whole farm was covered by a single 32 minute Q-100 DATAhawk flight at 400 ft altitude. During the flight, 945 multispectral MicaSense RedEdge images were taken and processed in Pix4D Mapper Pro and with MicaSense ATLAS.

The key objectives of our study are:

  1. to prove the ease of use and reliability of the QuestUAV Q-100 DATAhawk
  2. to assess the quality of the MicaSense images
  3. to find the best way for a farmer to process and make use of the imagery.

Our study focuses on the technical parameters of the system and excludes farm-related analyses, such as an assessment of plant stresses, characterization vegetative cover or yield estimations. A separate report will cover the farm analysis with the progressing growing season in 2016.

Results and Conclusions

QuestUAV Q-100 DATAhawk – Safe and Reliable

Q-200 Surveyor Pro Launch

Even in high winds (up to 25mph), the Q-100 DATAhawk flew a stable autonomous route and still achieved a comparable data quality to the QuestUAV Q-200.

Only 10 minutes of preparation were required from arriving at the site and getting airborne. Hand-launch, auto-pilot and parachute landing guaranteed a safe and reliable operation at any stage of the flight.

MicaSense RedEdge – Multispectral Imagery for Farm Index Maps

Multispectral ImageryEvery second, MicaSense captures data in five discrete spectral bands. At 400ft flight altitude, a spatial resolution of 8cm is achieved. Fully processed MicaSense products are reflectance-calibrated image mosaics of single bands or combinations of bands. Each layer of the reflectance-calibrated file (GeoTiff) is normalized so that a pixel intensity of 32768 corresponds to 100% reflectance for each band. All GeoTiff-Layers are registered to other layers at the pixel level. Through spectral calibration, images taken at different dates and light conditions become comparable.


Image Processing- Pix4D Mapper Pro and MicaSense ATLAS

Pix4D Mapper SoftwareThe images were processed with Pix4D Mapper Pro and through the MicaSense ATLAS service. Both processing options provided comparable results in terms of image quality and variety of image products (orthomosaic, NDVI, NDRE, digital elevation model). However, the concept of Pix4D and ATLAS to produce farm-relevant image products is different.

MicaSense ATLAS offers a cloud-based processing service. Source images are uploaded to the cloud and are processed by MicaSense. The output is visible on a website (password-protected) or can be downloaded as GeoTiffs. The service requires no knowledge on image processing and is charged per hectare.

Pix4D Mapper Pro is a professional photogrammetry software and runs locally on any Mac or Windows PC. Multispectral source images can be easily converted into accurate farm index maps via intuitive software control. Once a Pix4D licence is purchased, the user can process various datasets without additional costs.


QuestUAV’s new compact mapper Q-100 DATAhawk, with the MicaSense RedEdge sensor on board, is a reliable platform to capture high-quality multi-spectral data for agricultural applications. The Q-100 DATAhawk allows an easy and safe operation in high winds and confined environments. Different processing options, like the professional photogrammetry software Pix4D, allow to easily process the MicaSense data and to create reflectance-calibrated image products. Overall, the tripartite system, Q-100 DATAhawk – MicaSense – Pix4D, has proven to be a reliable turnkey solution for agricultural monitoring.

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Fruit Plantations Image

Maximizing a Fruit Plantations Yield using UAV Imagery | QuestUAV News

Maximizing a Fruit Plantations Yield using UAV Imagery

Plant Counting and Gap Filling Techniques

Determining plant density and identifying canopy gaps is crucial for good plantation management; this helps predict yields and to maximize the yield by refilling planting gaps. Scientific photographs taken from Unmanned Aerial Vehicles (UAVs) allow accurate plant counts and the identification of plant mortality for several hundreds of hectares per day. Studies over different crops show that QuestUAV images are a powerful tool for a plantation management to develop replanting plans and to compare actual vs. target plant density.

                         Q-200 Surveyor Pro in FlightPlant CountingCrop Survey using the Q-200 Surveyor Pro

Healthy Plantation vs High Mortality

Numerous studies have been conducted on the effects of plant density on growth and yield of tropical crops. Plant densities are an important and limiting factor for growth, nutritional status, fruiting and hence for a plantation’s yield. Optimal plant densities depend on different factors, such as cultivars, climate, soil characteristics, land preparation etc.

                                                       Banana Crop Oil Palm with Good Density

QuestUAV images of banana (left) and oil palm with good plant density

Low-quality planting material, wrong plant treatment or climatic anomalies can lead to high mortality. The result can be a huge reduction of yield rates. Further, actual plant densities can differ from target densities for several reasons, such as wrong distances between planting rows or a lack of planting material. Therefore, the refilling of canopy gaps and correction of non-optimal plant densities are of high priority for a good plantation management.

                                                         High Morality Banana Crop High Morality Palm Oil

QuestUAV images of banana and oil palm with high mortality

Case Study: Developing Management Plans on the Basis of UAV Images

Several flights were conducted with the QuestUAV Surveyor-Pro over different tropical crops in Thailand, Indonesia and the Philippines. More than 1,000 ha of pineapple, banana and oil palm were mapped by our crew. All images were processed with Pix4Dmapper Pro and analyzed by plantation management with the open-source software QGIS. The software allows plant counting, density calculations and the generation of mortality maps by visual inspection of the image products. More advanced approaches, like image classification and pattern detection algorithms, allow to map canopy gaps and determine plant densities in an automatic or semi-automatic way.

Identifying and Refilling Canopy Gaps

The figure below shows a study with the objective to map canopy gaps and mortality at a banana plantation in Indonesia. The analysis was done by the plantation management in QGIS. Yellow circles represent missing or dead banana plants, digitally marked by visual inspection of the image products. The map is the basis for the plantation management to: Assess the yield reduction due to plant loss Determine the amount of plants which require replacement Define the replanting locations

Crop Density ProcessingThe total study area had a size of 120ha. The image analysis revealed that over the whole area more than 12,000 banana plants require replanting. In the worst sections of the plantation up to 320 plants per hectare need to be replaced.

Comparing Actual vs. Target Plant density

Instead of mapping gaps and mortality, vital plants can be digitally detected by image inspection and plant densities can be derived. Single plants can be easily identified in an UAV image. Once digitally mapped, they can be automatically counted and plant densities can be calculated.

Pineapple Plants

The fields were flown 2 months after planting. Single plants can be easily identified. Each yellow circle represents a pineapple plant. The average plant density was found to be 28,700 plants/ha. Compared to the target density of 31,000 plants/ha, the actual density is too low.

The plantation management measured the distance between the plants and found that the target plant distance has not been implemented correctly in the field and they were often planted too far away from each other. In effect, the planting crew was either not skilled enough or was not following management instructions: in reality both are prevalent risks in plantation management.


What UAV surveys can give to a plantation management is the full picture, in great detail, of their plantation and control methods that allow intervention and potential for improved profitability, at an early stage of growth. This study shows how both mortality and improved canopy coverage can be detected on a large scale and effective, preventative measures be taken.

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Pineapple Plantation

Photogrammetry Dramatically Increases Lifetime of Worlds Largest Pineapple Plantation | QuestUAV News

UAV Image Interpretation Dramatically Increases the Lifetime of the World’s Largest Pineapple Plantation

Soil Erosion Reduced by a Factor of Almost Thirty, Ensuring Fruit Cultivation for the Next 100 Years

Key Achievements

Scientific photographs taken from Unmanned Aerial Vehicles, like the QuestUAV Agri-Pro system, and processed with Pix4D are a powerful tool to fight soil erosion. Our case study shows that image-based farm management can significantly reduce the soil loss on tropical fruit plantations, such as pineapple. In our study a reduction from intolerable 200 t/ha/yr was reduced to tolerable soil loss levels of ; a factor of 27 times improvement. The management at Dole Philippines were both surprised and delighted by the effectiveness of the UAV derived plans.

Soil Erosion

Soil erosion is considered to pose a major threat for pineapple production and environmental preservation in the Philippines. Soil loss rates vary with rainfall, elevation, slope gradient and soil characteristics and can reach up to 250 t/ha/yr. Based on experimental results, those losses can decrease potential yields as much as 30% in one crop cycle. Fighting soil erosion is therefore a major objective to move to a sustainable cultivation of pineapple in the Philippines.

Unmanned Aerial Vehicles (UAVs) combined with know-how in farm management provide new opportunities to significantly reduce soil erosion. Digital Elevation Models (DEMs) gained from UAVs are the basis for designing relief-adapted field layouts (planting rows, drainage channels) and an effective placement of soil conservation structures.


Project Scope

Our study was carried out on the world’s largest pineapple plantation, managed by Dole Philippines Inc. and located at the footslopes of a volcanic cone on the island Mindanao. The area has a total size of 220 sqkm and a strong relief.

 Dole Inc Survey DroneDole Inc SurveyingDole Inc Drone Launch                                                       

Since March 2014 Dole Philippines has been flying their fields on a daily basis with two QuestUAV Agri-Pros. The Quest UAV “Agri-Pro” carries a Twin NDVI sensor providing Dole Philippines with RGB and NIR information at a spatial resolution of 5 cm. The images are the basis for designing and implementing new relief-adapted field layouts and soil conservation structures. The whole implementation workflow was developed by Dole Philippines in close cooperation with the German company ORCA Geo Services (GIS and agricultural consulting) and QuestUAV.

Dole Philippines, as a leading agricultural company in the Philippines, has an excellent environmental protection policy for its agricultural production. Soil erosion is continuously measured and analysed over time, allowing comparisons between soil loss rates of old and new, relief-adapted field layouts.

Results and Conclusions

The graphic below shows how the old field layout has been adapted to the relief on the basis of a Natural Color Image and a Digital Elevation Model (DEM). The total area of the field is 85 hectares. The images were acquired with the QuestUAV Agri-Pro System. The image processing was performed with Pix4Dmapper Pro. QGIS was used to design the new field layout.

Digital Elevation Model Pineapple PlantationThe old field layout shows that planting contours do not follow the contour of the terrain. In some parts of the field, planting blocks are oriented perpendicular to the contour and rain events have a massive erosive effect. Water masses will flow directly downslope transporting huge amounts of soil material. A soil loss rate of 200 t/ha/yr was measured for the steep-slope parts of the field.

Contour lines were calculated on the basis of the DEM. Contour lines indicate the ideal shape and orientation of planting rows. Ideal planting contours would follow curves rather than straight blocks. As curved planting rows are not practical for large field machinery, a compromise was required. The field has been divided into two regions with different block orientations.

After the layout design was implemented in the field, new UAV images were taken. The updated image product shows how the block orientation has been changed according to the design. A direct downslope flow of water is hindered by the pineapple plants. By only changing the block orientation, the soil loss rate was reduced significantly from 200 t/ha/yr to 13 t/ha/yr.

Dole Philippines is planning to install additional soil conservation structures to reduce the soil rate further to . Their conservation programme includes, amongst others, mulching, the protection of receptor and tributary channels, the construction of sediment catching ponds and a special conservation strategy for gullies.

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