Reaching new heights: can drones replace current methods to study plant population dynamics?

Tay, Jessica Y. L.; Erfmeier, Alexandra; Kalwij, Jesse M.

doi:10.1007/s11258-018-0865-8

Cited by 36 publications

(31 citation statements)

References 44 publications

(45 reference statements)

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“…However, the accuracy of juniper sapling identification varied between seasons. Similar to the results found by Tay et al [39], we found that pixel-based classification applied to UAV images can accurately detect and monitor vegetation. Given the large scale of juniper expansion and time requirements associated with ground surveys, the use of UAV offers the advantage of more efficient data collection compared to using ground-based techniques alone.…”

Section: Study Limitations and Future Researchsupporting

confidence: 90%

“…Another example of an index potentially useful in studying western juniper is the total ratio vegetation index (TRVI) [34], which was developed to address different vegetation characteristics in arid and semiarid ecosystems (e.g., juniper woodlands).Other techniques used in image analysis such as classification have been utilized extensively for the detection and assessment of vegetation [35][36][37]. Pixel-based image analysis has been used for weed detection [38] and vegetation identification in mixed plant communities [39]. The use of classification tools for image analysis has also been shown to decrease time requirements compared to manual analysis of imagery [40], particularly over large areas.…”

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confidence: 99%

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The Use of Low-Altitude UAV Imagery to Assess Western Juniper Density and Canopy Cover in Treated and Untreated Stands

Durfee

Ochoa

Mata-González

2019

Forests

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Monitoring vegetation characteristics and ground cover is crucial to determine appropriate management techniques in western juniper (Juniperus occidentalis Hook.) ecosystems. Remote-sensing techniques have been used to study vegetation cover; yet, few studies have applied these techniques using unmanned aerial vehicles (UAV), specifically in areas of juniper woodlands. We used ground-based data in conjunction with low-altitude UAV imagery to assess vegetation and ground cover characteristics in a paired watershed study located in central Oregon, USA. The study was comprised of a treated watershed (most juniper removed) and an untreated watershed. Research objectives were to: (1) evaluate the density and canopy cover of western juniper in a treated (juniper removed) and an untreated watershed; and, (2) assess the effectiveness of using low altitude UAV-based imagery to measure juniper-sapling population density and canopy cover. Groundbased measurements were used to assess vegetation features in each watershed and as a means to verify analysis from aerial imagery. Visual imagery (red, green, and blue wavelengths) and multispectral imagery (red, green, blue, near-infrared, and red-edge wavelengths) were captured using a quadcopter-style UAV. Canopy cover in the untreated watershed was estimated using two different methods: vegetation indices and support vector machine classification. Supervised classification was used to assess juniper sapling density and vegetation cover in the treated watershed. Results showed that vegetation indices that incorporated near-infrared reflectance values estimated canopy cover within 0.7% to 4.1% of ground-based calculations. Canopy cover estimates at the untreated watershed using supervised classification were within 0.9% to 2.3% of ground-based results. Supervised classification applied to fall imagery using multispectral bands provided the best estimates of juniper sapling density compared to imagery taken in the summer or to using visual imagery. Study results suggest that low-altitude multispectral imagery obtained using small UAV can be effectively used to assess western juniper density and canopy cover. western juniper in particular has arisen in two primary forms: through the encroachment of juniper into areas previously dominated by sagebrush (Artemisia tridentata Nutt.), and through increases in the density of juniper in areas where it was relatively sparse [3]. Historically, juniper was largely found in areas with lower fire risk [4]. Intensive grazing, reduced fire occurrence, and favorable wetter climate conditions have all been cited as reasons for the vast juniper expansion observed in the late 19th and early 20th century [3,5].Juniper expansion is a concern in many rangeland areas as it may lead to reduced water availability for other types of vegetation. Increased juniper canopy cover has been associated with increased bare ground and decreased shrub, forb, and grass cover [6] and reductions in vegetation production and diversity [7]. Several studies [6,[8][9][10][11...

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Section: Study Limitations and Future Researchsupporting

confidence: 90%

mentioning

confidence: 99%

The Use of Low-Altitude UAV Imagery to Assess Western Juniper Density and Canopy Cover in Treated and Untreated Stands

Durfee

Ochoa

Mata-González

2019

Forests

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“…There are apparently few studies carried out with the goal of identifying or quantifying native herbaceous species from drone imagery [12,[23][24][25][26][27]. These studies were implemented at a range of spatial and spectral resolutions with varying degrees of sophistication and mixed results.…”

Section: Introductionmentioning

confidence: 99%

Application of UAV-Based Methodology for Census of an Endangered Plant Species in a Fragile Habitat

Rominger

Meyer

2019

Remote Sensing

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Accurate census is essential for endangered plant management, yet lack of resources may make complete on-the-ground census difficult to achieve. Accessibility, especially for species in fragile habitats, is an added constraint. We examined the feasibility of using UAV (unmanned aerial vehicle, drone)-based imagery for census of an endangered plant species, Arctomecon humilis (dwarf bear-poppy), an herbaceous perennial gypsophile endemic of the Mojave Desert, USA. Using UAV technology, we captured imagery at both 50-m altitude (census) and 15-m altitude (validation) at two populations, White Dome (325 ha) and Red Bluffs (166 ha). The imagery was processed into orthomosaics that averaged 2.32 cm ground sampling distance (GSD) for 50-m imagery and 0.73 cm GSD for 15-m imagery. Putative poppy plants were marked in the 50-m imagery according to predefined criteria. We then used the 15-m imagery from each area to verify the identification accuracy of marked plants. Visual evaluation of the 50-m imagery resulted in errors of both commission and omission, mainly caused by failure to accurately identify or detect small poppies (<10 cm diameter). Higher-resolution 30-m altitude imagery (1.19 cm GSD) greatly reduced errors of commission. Habitat classification demonstrated that poppy density variation was closely tied to soil surface color. This study showed that drone imagery can potentially be used to census rare plant species with distinctive morphology in open habitats and understand their spatial distribution.

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“…Typically, higher spatial resolution comes at the expense of lower geographical coverage as it generally depends on flight height. However, such ultra-high-resolution data captured on demand allow researchers to investigate microclimatic differentiation at the scale of single plant individuals within an entire landscape ( Figure 2; Cruzan et al, 2016;Cunliffe, Brazier, & Anderson, 2016;Tang & Shao, 2015;Tay, Erfmeier, & Kalwij, 2018;Wich & Koh, 2018 (Meyer, Weigelt, et al, 2016).…”

Section: Ag G Reg Ati On Of L Arg E Data S E Tsmentioning

confidence: 99%

Macroecology in the age of Big Data – Where to go from here?

Wüest

Zimmermann

Zurell

et al. 2019

Journal of Biogeography

116

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Recent years have seen an exponential increase in the amount of data available in all sciences and application domains. Macroecology is part of this “Big Data” trend, with a strong rise in the volume of data that we are using for our research. Here, we summarize the most recent developments in macroecology in the age of Big Data that were presented at the 2018 annual meeting of the Specialist Group Macroecology of the Ecological Society of Germany, Austria and Switzerland (GfÖ). Supported by computational advances, macroecology has been a rapidly developing field over recent years. Our meeting highlighted important avenues for further progress in terms of standardized data collection, data integration, method development and process integration. In particular, we focus on (a) important data gaps and new initiatives to close them, for example through space‐ and airborne sensors, (b) how various data sources and types can be integrated, (c) how uncertainty can be assessed in data‐driven analyses and (d) how Big Data and machine learning approaches have opened new ways of investigating processes rather than simply describing patterns. We discuss how Big Data opens up new opportunities, but also poses new challenges to macroecological research. In the future, it will be essential to carefully assess data quality, the reproducibility of data compilation and analytical methods, and the communication of uncertainties. Major progress in the field will depend on the definition of data standards and workflows for macroecology, such that scientific quality and integrity are guaranteed, and collaboration in research projects is made easier.

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Reaching new heights: can drones replace current methods to study plant population dynamics?

Cited by 36 publications

References 44 publications

The Use of Low-Altitude UAV Imagery to Assess Western Juniper Density and Canopy Cover in Treated and Untreated Stands

The Use of Low-Altitude UAV Imagery to Assess Western Juniper Density and Canopy Cover in Treated and Untreated Stands

Application of UAV-Based Methodology for Census of an Endangered Plant Species in a Fragile Habitat

Macroecology in the age of Big Data – Where to go from here?

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