A Low-cost Unmanned Aerial System for Remote Sensing of Forested Landscapes

International Journal of Remote Sensing Applications Volume 4 Issue 3, September 2014 doi: 10.14355/ijrsa.2014.0403.01

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A Low-cost Unmanned Aerial System for Remote Sensing of Forested Landscapes M.G. Wing*1, J. Burnett1, S. Johnson2, A.E. Akay3, J. Sessions1 Oregon State University, U.S.

1

VDOS Global, LLC, U.S.

2

KahramanmaraşSütçü İmam University, Turkey

3

Michael.Wing@oregonstate.edu; Jonathan.Burnett@oregonstate.edu; Seth.Johnson@vdosglobal.com; akay@ksu.edu.tr; John.Sessions@oregonstate.edu *

Abstract We developed a low-cost unmanned aerial system (UAS) for less than $1400 that is capable of capturing high-resolution imagery of landscapes. We accomplished this feat by making use of advances in open-source technology that are propelling small UAS applications for remote sensing.

We describe the low-cost UAS that we developed, the process for assembling the aircraft, and the various components that are necessary for flight. We also describe software and piloting options that can support automated flight. In addition, we provide examples of high-resolution imagery that were captured of forest stands, buildings, and other features during a flight over a university campus in Turkey.

We hope the descriptions and examples we provide will encourage researchers to develop and advance their own UAS for remote sensing applications. We anticipate that future years will bring more technological advancements that reduce the weight and size of high-resolution sensors and autopilot systems while increasing the reliability of UAS. It is also likely that the future will see small UAS as the primary means of collecting remotely sensed data for a variety of scientific, engineering, and resource management applications.

Keywords UAS Technology; Affordable; Imagery

Introduction Unmanned aerial systems (UASs) include aircraft that do not have a pilot on board. UAS aircraft platforms can be controlled either through remote communication or through an autopilot. The autopilot can include a pre-planned flight path or other guidance, such as automatically reacting to other features that are within a certain radius. UAS technology is in a state of rapid advancement. Current UAS technology allows operators to mount color and

infrared digital cameras to take high-resolution imagery of forested and other landscapes. The imagery is immediately available following a flight, and subsequent flights can be rapidly accommodated.

We describe the development and application of a low-cost (<$1400) UAS for remote sensing of landscapes. This UAS platform requires some effort in building and other preparations, but presents a potentially significant instrument for foresters and others that can benefit from high-resolution imagery related to land management or other activities.

We flew our platform over the Kahramanmaraş Sütçü İmam University (KSU) campus, located in the eastern Mediterranean city of Kahramanmaraş, Turkey. While the Federal Aviation Administration (FAA) tightly controls UAS flights in the U.S., many other countries do not restrict flights. Our objectives were to capture high resolution digital color imagery of the campus in general, and of tree stands within the campus. We describe UAS applications, the construction and componentry of our low-cost UAS, and the imagery that we captured of the KSU campus and surrounding lands.

Background Remote sensing and GIS technologies have proven beneficial to natural resource managers for decades (Wing and Bettinger 2003). Remote sensing vehicles can include satellite, airborne, and ground-based platforms. One of the great advantages of remotes sensing is the potential to cover large areas quickly but this sometimes involves costs that are prohibitive to some organizations (USFS 2004).

In the case of remote sensing across broad landscapes, one of the prohibitive costs has been the expense involved in supporting aerial platforms that enable

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high spatial resolution imagery. Manned aerial platforms (airplanes or helicopters) that support high resolution imagery require, at a minimum, one pilot and an aircraft capable of supporting significant payloads. There is always pilot risk in manned flights.

Pilot risks constrain the ability of platforms to fly close to the ground, which in turn limits the potential resolution of imagery. In addition, cloud cover and weather patterns can obscure sensors from being able to see ground cover as most aircraft must fly at significant heights (Hunt et al. 2008). The Federal Aviation Administration (FAA) governs airspace within the U.S. and requires that manned aircraft operate at least 305 m (1000 ft) above the highest ground obstacle in populated areas and 153 m (500 ft) elsewhere (Federal Aviation Regulations 2010).

The potential applications of UAS for wildfire fighting and tracking are significant yet still largely untapped (Ambrosia et al. 2011, Wing et al. in press). Aircraft such as rotary-wing platforms (helicopters) with extended flight times (2-3 hours) could enhance remote sensing of burning landscapes. Fire direction and speed can change rapidly with results that place human lives and structures in peril. A sustained observation of wildfire can help direct firefighting resources where they are most needed. Communities can be warned and evacuated should fire spread warrant. Firefighting crews can also be prompted for movement to avoid danger.

Natural resource research has been the subject of previous UAS applications for remote sensing but many of these studies have involved platforms that were well beyond $5,000 in costs (Wing et al. 2013).

Despite relatively high costs, researchers have found success with the relatively fine resolution imagery and flight flexibility that are typically associated with UAS. Getzin et al. (2012) used high-resolution (~7 cm, 2.8 in) imagery to associate disturbance patterns with forest understory plant diversity. Watts et al. (2010) explored UAS applications for wildlife surveys with imagery as fine as 1 cm (0.4 in). This allowed identification of alligators within swamp land. Vermeulen et al. (2013) found that elephants were clearly discernible in UAScollected imagery but that other small- and mediumsized mammals were not. UAS have also been used for measuring and monitoring rangeland. Rango et al.

(2009) used hyperspatial UAS imagery to measure vegetative cover and structure within a rangeland ecosystem.

One potential application where UAS could make

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strong contributions is in the remote sensing of vegetation disease outbreak and spread. Vegetation moisture content can be measured by sensors that operate within the infrared spectrum. The moisture content of leaves and needles is often reduced by disease and infestation. Many UAS are able to fly at slower speeds and lower elevations than typical manned aircraft. Thus a UAS outfitted with high resolution infrared sensors should be able to more readily detect moisture stress.

Supporting law enforcement activities is another potential application area for UAS. Wing and Tynon (2006, 2008) described the spatial patterns spread of what was once considered urban-centric crime on public recreation lands in the U.S. Recreational lands, such as the National Forests, National Parks, and other areas are experiencing increases in criminal activities.

Activities involve physical altercations, vandalismincluding arson, and drug production. One of the reasons for this increase is that many public land law enforcement officers are responsible for very large geographic areas, and not uncommonly, lands that are remote or difficult to access quickly (Wing and Tynon 2006). Large areas make it extremely difficult for officers to be available when needed. In addition, even in cases where an officer is able to arrive at a location where criminal activity has been reported, there have been deadly consequences as many public lands officers work solo and not in proximity to rapid assistance. Previous research has considered very small UASs which are capable of highly detailed ground observations (Helble and Cameron 2007).

Small UASs with extended flying range could strongly enhance law enforcement officer ability to safely observe large areas and make more informed decisions about response. In particular, UASs that are powered by electrical batteries could observe target areas much more discreetly than other vehicles due to their significantly reduced sound signature compared to gas power. This advantage can be significant in identifying and reacting to illegal operations.

Wildlife habitat research has been the focus of several natural resource and UAS application studies (Koski et al. 2009, Watts et al. 2010). UAS that are coupled with an autopilot have the ability to fly a pre-determined path, and to repeat any flight pattern (Laliberte et al.

2010). This direct flight planning and repeatability can strengthen the statistical strength of habitat sampling results by avoiding convenience sampling.

UAS technology can also be beneficial when high

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spatial resolution is necessary because the requisite flight altitudes result in dangerous conditions for manned aircraft. The U.S. Fish and Wildlife Service (USFWS) with the cooperation of the United States Geological Survey (USGS) conducted a sandhill crane population inventory in 2011 (Hutt 2011, Owen 2011).

Sensors were required that could support individual bird species delineation in low light conditions. The platform also had to operate quietly enough so as not to alarm birds into flight. A ~ 1.8 kg (4 lb) RQ-11A Raven containing a thermal camera was handlaunched and flew a gridded sampling pattern above a nesting sandhill crane flock at ~61 m (200 ft) elevation.

The resulting imagery was used to count individual birds. The count generated from the imagery only differed by less than 5% from that collected by trained ground crew (2,567 compared to 2,692). The flight had notable successes including an accurate inventory count, not flushing the nest population, and the ability to gather high resolution imagery in low-light conditions. The only significant issue reported by the authors was a five month approval process by the FAA in order to fly. The FAA also denied the original request to fly during night-time conditions.

Researchers have also applied UAS for fish inventory applications. Kudo et al. (2012) flew an unmanned helicopter over the Moheji River in Japan to estimate chum salmon populations. The helicopter has a 5 cc (1.6 cubic inch) gas powered engine equipped with an antivibration device and a Canon SLR camera. The helicopter was flown at an approximately 30 m (100 ft) above the river. Salmon silhouettes were extracted from the imagery. Statistical evidence indicated a positive correlation between the extracted silhouette number and the standardized number of salmon seine-netted between the weir and estuary.

The authors noted several limitations of their study.

One was that a remote pilot was needed with a ground control station on the riverbank in order to control the helicopter. This limited the length of the river that could be surveyed. Another was that the helicopter could not be kept at a consistent height above the river. Due to this, staves had to be placed along the river banks in order to scale and adjust imagery to a consistent dimension. Both of these issues could be addressed with an autopilot that would automatically guide the helicopter to river locations, and maintain a consistent altitude. The authors also pointed out that only a shallow river, free from turbidity, and significant vegetative cover could be surveyed effectively with this approach.

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Search and rescue (SAR) operations are another application where UAS technology can be advantageous. In particular, weather conditions such as extreme cold or landscapes can endanger searchers.

UAS has the potential to reduce risk to searchers, as well as the ability to increase the efficiency and extent of searchers but little published research has been done in this area. One notable study involved the development of a flying wing aircraft for autonomous SAR (Goodrich et al. 2008). The flying wing weighed approximately 2 kg (4.4 lbs) and was equipped with a gimbaled electro optical camera. The wing was programmed to perform a spiral search pattern that was refined through search algorithms designed to detect humans. Trials demonstrated that the UAS was successful at detecting still and mobile humans in a mountainous forest setting. Based on field trials, it was concluded that an ideal search altitude for the UAS was approximately 60 â&#x20AC;&#x201C; 107 m (200-350 ft) above the ground. The study highlighted some of the potential concerns and strengths that UAS can bring to SAR operations. One concern is that there needs to be a preplanned method for providing information that is generated from the UAS to any field-based personnel that are attempting to locate a SAR objective.

However, strengths include being able to operate a UAS closer to the ground than a manned aircraft thus also increasing ground resolution and the probability of object detection. This strength is also enhanced when object detection can be accomplished autonomously.

Methods We used a Zephyr II aircraft platform manufactured

by Ritewing. The Zephyr II is constructed of injection molded, flexible E-por foam, spans 140 cm (55 inches) between wingtips, and weighs approximately 1 kg (2.2 lbs) empty. The Zephyr II sold for $325 and arrived unassembled (Figure 1). The platform required approximately 36 hours of preparation before the initial flight and required that several components be purchased and installed (Table 1).

The initial preparation involved covering the entire platform with flexible and transparent 3 mm laminate.

A hot iron was used to mold the laminate to the aircraft. Although laminate is not necessary, it makes the platform more rigid, increasing flight stability. We then laminated a set of elevons (ailerons + elevator).

The elevons function as control surfaces on the trailing part of the wing and are constructed of balsa wood.

The elevons control the attitude (yaw, pitch, and roll) 115

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of the aircraft when it is powered. The elevons were attached to the end of the wings using strapping tape.

The tape attachment must be done is such a way that the elevons can move up and down with +/- 60 degrees of deflection without resistance. Servos are boxes containing gears to rotate a spinning shaft.

These function as the mechanical means by which the elevons are moved. The RC receiver or autopilot produces a signal that instructs each servo to activate, specifying the speed and degree of rotation. This translates to independent elevon activation in the up or down direction.

FIGURE 1. FROM TOP OF FIGURE: UNASSEMBLED ZEPHYR II PLATFORM, DIGITAL CAMERA, AUTO PILOT, RADIO CONTROLLER, ELEVONS, AND A 31 CM RULER FOR SCALE.

We installed multiple electrical and mechanical components into the platform. Although a cut-out had been already been made into the bottom bay of the platform, we had to expand this bay, and also created several other small cut-outs to house other components.

TABLE 1. COMPONENTS AND COST FOR UAS CONFIGURATION.

Component Cost 2.4 GHz Tx/Rx $360 4500 mAh 11.1 V LiPo $30 Airspeed Sensor $25 ArduPilot APM 2.5 $160 Canon S100 $300 RiteWing Zephyr II $325 TTC Radio $86 uBlox GPS Module $76 Voltage Regulator $15 Total $1,377 We chose to power the aircraft through an 11.1 volt, 4500 mAh lithium-polymer (LiPo) battery that sells for approximately $30.

This battery provided approximately 25 minutes of flight while powering the other electrical components in our platform. We 116

selected a 1200 kv electrical motor that featured a hinged propeller. The hinges allow the propeller blades to be folded during transport. Hinges also allow the prop to fold during landing, preventing blade damage. The motor was fixed to an aluminum mount and glued to the rear of the aircraft using an epoxy.

The heart of the platform is the autopilot (Figure 1).

We selected the ArduPilot APM 2.5 due to its low cost ($160) and strong functionality. The autopilot is an electrical motherboard that communicates movements and conditions to the components that control flight parameters. Movements can either be signaled to the autopilot by radio controller (Figure 1) or be preprogrammed and loaded digitally onto the autopilot.

Both conditions can also occur during a flight. For instance, a pilot might chose to use manual flight during a launch, which would mean that the motor RPM and elevon would be controlled by manipulations of the radio controller levers. Once airborne, auto mode could be enabled and the aircraft would automatically fly to pre-programed locations.

During flight, a pilot can also to have two flight assistance modes. The first is called fly-by-wire and will prevent a pilot from rolling or flipping an aircraft.

Fly-by-wire allows for manual command/direction of the autopilot-the autopilot retains flight control and continues to observe pre-set flight regime limitations.

The second is called stabilize mode. While stabilize mode may not prevent a roll or flip, the aircraft will return to a level flight if the pilot releases the flight controls. After all designated locations had been reached, a manual or assisted (FBW-A mode) landing could be attempted using radio controller levels. It is also possible to program a landing to occur automatically, without pilot interaction.

The autopilot we used can be programmed using open source ArduPilotMissionPlanner software that is freely available (APM Multiplatform Autopilot 2013).

The software will automatically create a flight plan by drawing a line or polygon on an interface that accesses Google Maps (Figure 2). If continuous imagery of an area is desired, a user can specify the spacing between flight paths and the software will create a flight plan that can be loaded onto the autopilot. This capability is significant in that it can support the acquisition of stereo imagery, which can be used to create digital products such as digital terrain models (DTMs), landscape measurements, and point clouds. Point clouds provide visual images that appear as if one is viewing a landscape in a 3-dimensional perspective.

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FIGURE 2. ARDUPILOTMISSIONPLANNER SOFTWARE.

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radio that can be plugged into a laptop. We use a typical Windows laptop as our groundstation. During flight, we are able to view the position of the aircraft with Google-provided imagery in the background.

The positional coordinates and speed of the aircraft are also provided via a wireless telemetry link to the aircraft. The wireless telemetry link is served by a $86 915 MHz telemetry, tracking and commanding radio (Figure 3). This provides the command link by which autopilot parameters can be altered, and airspeed, location, and other flight parameters can be recorded.

The autopilot contains multiple input and output ports. A primary input port is the radio controller receiver. This allows the communication of signals from the controller to the autopilot. The communication frequency is 2.4 GHz. Our radio controller is able to communicate with the autopilot at distances up to 1600 m (1 mile).

Our primary sensor is a Canon Powershot S100 digital camera that sells for approximately $300. The camera captures color imagery at 12.1 megapixel resolution.

This translated into a ground resolution of ~5 cm (2 inches) at a height of 122 m (400 feet) which is the recommended maximum height for "model airplane" enthusiast flights in the U.S. (FAA 1981).

Our camera is powered by the aircraft battery through the use of a voltage regulator. We currently program the camera to begin taking photos at regular intervals (5 seconds). A programming source, the Canon Hack Development Kit (CHDK) for Canon cameras provides detail on how to do this (CHDK Forum 2013). We have also configured our radio controller to transmit a signal at the push of a button that directs when photos are taken. It is also possible to specify specific geolocations for photos to be captured. We have also modified a Canon S100 so that it captures nearinfrared imagery. We use this camera to sense greenness in vegetation, and have used this infrared capability to calculate spectral indices such as the normalized difference vegetation index (NDVI) (Gamon et al. 1995).

The aircraft is also equipped with a $25 airspeed sensor (Figure 3). This is in contrast to ground speed and is an indicator of how much lift is being generated by the aircraft and is used by the autopilot to make speed adjustments to ensure stable flight. A $76 GPS receiver is attached to the autopilot and calculates the x, y and z position of the aircraft. Location and elevation coordinates are transmitted to a transceiver

FIGURE 3. ZEPHYR II WITH COMPONENTS INSTALLED.

The surface of the Zephyr that faces the ground during flight is very plain in appearance (Figure 4). Some operators preferto paint this surface with a bright color, such as orange, so that the aircraft is more visible during flight operations.

Results The flight over the KSU campus was accomplished in approximately 20 minutes. We launched the aircraft platform using manual flight controls by throwing the aircraft into the air (Figure 5). We conducted our entire flight with manual navigation.

Figure 6 shows a platform-captured image of KSU's courtyard and campus. The sidewalks and other structures are clearly discernible and demonstrate the high detail of the color imagery. Boundary

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measurements and structural foundation measurements would be supported by this image. In addition, one of the young forest stands within the campus is visible.

width. In addition, a definitive planting pattern was evident in the nadir image. The enhanced resolution would also support the derivation of tree heights due to the presence of shadows. With the collection of stereo images, tree heights could be calculated with greater accuracy and precision. This information would be invaluable for harvest and reforestation planning.

A portion of the primary campus is shown in Figure 8.

This image was captured off-nadir which allows for the heights of features such as trees and buildings to be measured. Individual trees and automobiles can be clearly discerned.

FIGURE 5. HAND LAUNCH OF ZEPHYR II.

FIGURE 8. PRIMARY CAMPUS AREA AT KAHRAMANMARAŞSÜTÇÜ İMAM UNIVERSITY. FIGURE 6. A PORTION OF THE CAMPUS AND COURT YARD AT KAHRAMANMARAŞSÜTÇÜ İMAM ÜNIVERSITESI.

Discussion We demonstrated the application of a low-cost (< $1400) UAS that is capable of collecting georeferenced, high-resolution color imagery of structures and forests. This capability transcends the typical process of collecting aerial imagery. The typical process includes the cost of a pilot and aircraft, the cost of image collection, and the prolonged time that is usually required before the imagery becomes available. This process unfolds again, should subsequent imagery be desired.

FIGURE 7. LARGE AREA FOREST STAND AT KAHRAMANMARAŞSÜTÇÜ İMAM UNIVERSITY.

Our collected images demonstrate that high resolution imagery of structures and forests can be quickly captured. Also, the images are almost immediately accessible, and can support a variety of landscape measurements.

Several images of the young forest stands were captured during the flight. One image shows a nadir perspective of a young stand (Figure 7). This image would support estimates of inventory information, such as the number of trees and their associated crown

UAS technology is still in its infancy, but the available technology is still desirable for working foresters and land managers. In particular, the ability to target flight areas, and the ability to schedule subsequent flights quickly is very advantageous for change detection.

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Change detection involves observing the growth patterns of trees, both in crown width and height, as well as basic inventory information such as the number of living trees following time after planting.

With the application of near-infrared, information about the health of trees, based on moisture content in foliage, could also be derived. This information could provide guidance in determining whether tree stress is environmental or pest related. In addition, detection of tree stress could be used to direct mineral amendments or other management responses.

One of the most in-demand sources of spatial data today is that produced by light ranging and detection (LiDAR) sensors. A smaller-format LiDAR sensor has been developed that is less than 2 kg (4.4 lbs) in weight that can be mounted on a UAS. LiDAR provides highly detailed topographic information and also returns infrared spatial reflectances. A threedimensional rendering of a landscape can be created through a LiDAR point cloud, and can be used to measure the dimensions of landscape features.

Researchers have recently begun to use color 3D photogrammetry to create 3D point clouds of similar to density of those created using LIDAR (Ruther et al.

2012). Unlike LIDAR, however, 3D photogrammetric products are limited by the availability of passive light and cannot effectively detect subcanopy structure that is not reflecting light. Despite these limitations, these color imagery point clouds still provide sufficient detail to support reliable feature measurements and the creation of canopy height models In addition color imagery point clouds can be created at much lower costs than LiDAR point clouds.

Although UAS technology is still in a state of rapid development, foresters and land managers can significantly benefit from current offerings at a cost that falls well within the budgets of most organizations. It can only be anticipated that UAS technology capabilities in the near future, will yield increased value for those that need imagery of the resources they manage.

We thankfully acknowledge the OSU Faculty Internationalization Grant program and the U.S. National Institute for Food and Agriculture. for supporting this project.

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