Integrating drone imagery with existing rangeland monitoring programs

Gillan, Jeffrey K.; Karl, Jason W.; Leeuwen, Willem J. D. van

doi:10.1007/s10661-020-8216-3

Cited by 37 publications

(44 citation statements)

References 55 publications

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“…We identified six species with different plant architectures. While most of our measured transects had 1-2 species (Table 1), the different plant architectures could affect image capture due to poor modelling of plant extremities which created a higher level of heterogeneity than monocultures and could explain our relatively lower r 2 values [26]. Therefore, we chose to work with forage mass volume derived from entire quadrats rather than individual plant heights within quadrats.…”

Section: Discussionmentioning

confidence: 99%

“…At the time of our flights (August 2018), Cunliffe et al [25] reported flight altitudes of 20 m AGL. More recently, Gillan et al [6,26] reported flights for biomass estimation of 20 m and 40 m AGL. For our study we set our flight settings to a double grid style flight pattern, at a speed of 3-5 km/h, 70 • camera angle, with 80% image overlap using the Pix4D capture application for Android.…”

Section: Image Acquisitionmentioning

confidence: 99%

“…Landsat 8 (multispectral resolution = 30 m) and SPOT-7 (Satellite Pour l'Observation de la Terre, multispectral resolution = 6 m) are used from local to regional scales [20,21]; Quickbird (multispectral resolution = 2.4 m), Rapideye (multispectral resolution = 5 m), and Worldview 2 (multispectral resolution < 1 m) are common sensors used to estimate forage mass at local scales [19,22,23]. The use of unmanned aerial vehicles (UAVs) offers new opportunities to estimate forage mass in rangelands using 3D information at very high spatial resolutions (<5 cm resolution) with practical management applications [6,[24][25][26].…”

Section: Introductionmentioning

confidence: 99%

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A Pilot Study to Estimate Forage Mass from Unmanned Aerial Vehicles in a Semi-Arid Rangeland

et al. 2020

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The application of unmanned aerial vehicles (UAVs) in the monitoring and management of rangelands has exponentially increased in recent years due to the miniaturization of sensors, ability to capture imagery with high spatial resolution, lower altitude platforms, and the ease of flying UAVs in remote environments. The aim of this research was to develop a method to estimate forage mass in rangelands using high-resolution imagery derived from the UAV using a South Texas pasture as a pilot site. The specific objectives of this research were to (1) evaluate the feasibility of quantifying forage mass in semi-arid rangelands using a double sampling technique with high-resolution imagery and (2) to compare the effect of altitude on forage mass estimation. Orthoimagery and digital surface models (DSM) with a resolution <1.5 cm were acquired with an UAV at altitudes of 30, 40, and 50 m above ground level (AGL) in Duval County, Texas. Field forage mass data were regressed on volumes obtained from a DSM. Our results show that volumes estimated with UAV data and forage mass as measured in the field have a significant relationship at all flight altitudes with best results at 30-m AGL (r2 = 0.65) and 50-m AGL (r2 = 0.63). Furthermore, the use of UAVs would allow one to collect a large number of samples using a non-destructive method to estimate available forage for grazing animals.

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Section: Discussionmentioning

confidence: 99%

Section: Image Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Pilot Study to Estimate Forage Mass from Unmanned Aerial Vehicles in a Semi-Arid Rangeland

et al. 2020

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“…Uncalibrated spectral parameters cannot be applicable to another study site or even to a different time a. b. period in the same study site and were therefore omitted. In the sagebrush steppe, annual grasses are often mixed with perennial grasses and forbs and can be difficult to discriminate using spectral data alone, even with high spatial resolution UAS images (Gillan et al, 2020). With additional SfM field plots we could investigate models with individual PFT's to validate our assumption that a mixed PFT model will best represent the ecosystem.…”

Section: Discussionmentioning

confidence: 99%

“…Field work for capturing extremely close-range SfM imagery requires less training for field crews compared to TLS and UAS data collection and eliminates the need for specialized equipment. Developing an allometric relationship at a plot scale (cm) will inform future SfM studies that utilize UAS imagery (Gillan et al, 2020). The protocol for the SfM data collection was designed to be efficient, minimize cost, and to coincide with rangeland data collection.…”

Section: Future Workmentioning

confidence: 99%

A Sense of Scale

Vermillion¹

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The native vegetation communities in the sagebrush steppe, a semi-arid ecosystem type, are under threat from exotic annual grasses. Exotic annual grasses increase fire severity and frequency, decrease biodiversity, and reduce soil carbon storage amongst other ecosystem services. The invasion of exotic annual grasses is causing detrimental impacts to land use by eliminating forage for livestock and creating a huge economic cost from fire control and post-fire restoration. To combat invasion, land managers need to know what exotic annual grasses are present, where they are invading, and estimates of their biomass. Mapping exotic annual grasses is challenging because many areas in the sagebrush steppe are difficult to access; yet field measurements are the main method to identify and quantify their existence. In this study, we address this challenge by exploring the use of both landscape-scale and plot-scale observations with remote sensing. First, we use satellite imagery to map where exotic annual grasses are invading and identify the native species which are being encroached upon. Second, we investigate the use of fine-scale imagery for non-destructive measurements of biomass of exotic annual grasses. Understanding the location of exotic annual grasses is important for restoration efforts, e.g. large swath (~100m) herbicide spraying. Restoration efforts are expensive and often ineffective in areas already dominated by exotic annual grasses. Early detection of exotic annual grasses in sagebrush and native grasses communities will increase the chances of effective ecosystem restoration. We used Sentinel-2 satellite imagery in Google Earth Engine, a cloud computing platform, to train a random forest (RF) machine learning algorithm to map vegetation in ~150,000 acres in the sagebrush steppe in southeast Idaho. The result is a classification map of vegetation (overall accuracy of 72%) and a map of percent cover of annual grass (R2 = 0.58). The combination of these two maps will allow land managers to target areas of restoration and make informed decisions about where to allow grazing. In addition to knowing what exotic annual grasses exist and their percent cover, detailed information about their biomass is important for understanding fuel loads and forage quality. Structure from Motion (SfM) is a photogrammetry technique that uses digital images to develop 3-dimensional point clouds that can be transformed into volumetric measurements of biomass. The SfM technique has the potential to quantify biomass estimates across multiple plots while minimizing field work. We developed allometric equations relating SfM-derived volume (m3) to biomass (g/m2) for a study area in southeast Oregon. The resulting equation showed a positive relationship (R2 = 0.51) between the log transformed SfM-derived volume and log transformed biomass when litter was removed. This relationship shows promise in being upscaled to larger surveys using aerial platforms. This method can reduce the need for destructively harvesting biomass, and thus allow field work to cover a greater spatial extent. Ultimately, increasing spatial coverage for biomass will improve accuracy in quantifying fuel loads and carbon storage, providing insights to how these exotic plants are altering ecosystem services.

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