Use of Unmanned Aerial Systems for multispectral survey and tree classification: a test in a park area of northern Italy

Gini, Rossana; Passoni, D.; Pinto, Livio; Sona, Giovanna

doi:10.5721/eujrs20144716

Cited by 78 publications

(56 citation statements)

References 21 publications

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“…Another step would be to assess the potential of 3D mapping of the canopy surface for nest detection. 3D models can now be created using point clouds from drone imagery [74] providing better perspectives for visual interpretation of the data. Another complimentary approach would be to use light detection and ranging (LiDAR) technology.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Chimpanzee Nest Detectability in Drone-Acquired Images

Bonnin

Andel²,

Kerby

et al. 2018

Drones

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Abstract:As with other species of great apes, chimpanzee numbers have declined over the past decades. Proper conservation of the remaining chimpanzees requires accurate and frequent data on their distribution and density. In Tanzania, 75% of the chimpanzees live at low densities on land outside national parks and little is known about their distribution, density, behavior or ecology. Given the sheer scale of chimpanzee distribution across western Tanzania (>20,000 km 2 ), we need new methods that are time and cost efficient while providing precise and accurate data across broad spatial scales. Scientists have recently demonstrated the usefulness of drones for detecting wildlife, including apes. Whilst direct observation of chimpanzees is unlikely given their elusiveness, we investigated the potential of drones to detect chimpanzee nests in the Issa valley, western Tanzania. Between 2015 and 2016, we tested and compared the capabilities of two fixed-wing drones. We surveyed twenty-two plots (50 × 500 m) in gallery forests and miombo woodlands to compare nest observations from the ground with those from the air. We performed mixed-effects logistic regression models to evaluate the impact of image resolution, seasonality, vegetation type, nest height and color on nest detectability. An average of 10% of the nests spotted from the ground were detected from the air. From the factors tested, only image resolution significantly influenced nest detectability in drone-acquired images. We discuss the potential, but also the limitations, of this technology for determining chimpanzee distribution and density and to provide guidance for future investigations on the use of drones for ape population surveys. Combining traditional and novel technological methods of surveying allows more accurate collection of data on animal distribution and habitat connectivity that has important implications for ape conservation in an increasingly anthropogenically-disturbed landscape.

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Section: Discussionmentioning

confidence: 99%

Assessment of Chimpanzee Nest Detectability in Drone-Acquired Images

Bonnin

Andel²,

Kerby

et al. 2018

Drones

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“…Because of its very high spatial resolution (<0.1-m ground sampling distance (GSD)), UAS imagery is regularly characterized as hyperspatial imagery (Carbonneau and Piégay 2012;Greenberg et al 2005;Laliberte et al 2007;Strecha et al 2012). Many studies have taken advantage of these two characteristics, for a broad range of environmental applications, such as landslide mapping (Lucieer et al 2014), forest fire mapping (de Dios et al 2011;Merino et al 2012;Urbahs et al 2013), precision farming (Bendig et al 2013), wildlife census (Lisein et al 2013a;Vermeulen et al 2013), tree and forest characterization (Lisein et al 2013b;Zarco-Tejada et al 2014), forest biodiversity assessment (Getzin et al 2012), and forest species composition (Dunford et al 2009;Gini et al 2014). Recent studies have demonstrated the potential of UAS imagery to finely describe the forest canopy Ellis 2010, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…When a good-quality digital terrain model is available, UAS-derived photogrammetric point clouds (>10 points/m 2 ) can provide a canopy height model (CHM) with a quality comparable to light detection and ranging (LiDAR) CHM but with significant cost differences (Lisein et al 2013b). References on classification of forest species by use of UAS imagery are still rare in the literature and include only the single-date approach (Dunford et al 2009;Gini et al 2014). However, useful pioneering studies are available on vegetation mapping projects based on UAS imagery (Knoth et al 2013;Zaman et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system

Michez

Piégay

Lisein

et al. 2016

Environ Monit Assess

204

179

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Riparian forests are critically endangered many anthropogenic pressures and natural hazards. The importance of riparian zones has been acknowledged by European Directives, involving multi-scale monitoring. The use of this very-high-resolution and hyperspatial imagery in a multi-temporal approach is an emerging topic. The trend is reinforced by the recent and rapid growth of the use of the unmanned aerial system (UAS), which has prompted the development of innovative methodology. Our study proposes a methodological framework to explore how a set of multi-temporal images acquired during a vegetative period can differentiate some of the deciduous riparian forest species and their health conditions. More specifically, the developed approach intends to identify, through a process of variable selection, which variables derived from UAS imagery and which scale of image analysis are the most relevant to our objectives.The methodological framework is applied to two study sites to describe the riparian forest through two fundamental characteristics: the species composition and the health condition. These characteristics were selected not only because of their use as proxies for the riparian zone ecological integrity but also because of their use for river management.The comparison of various scales of image analysis identified the smallest object-based image analysis (OBIA) objects (ca. 1 m 2 ) as the most relevant scale. Variables derived from spectral information (bands ratios) were identified as the most appropriate, followed by variables related to the vertical structure of the forest. Classification results show good overall accuracies for the species composition of the riparian forest (five classes, 79.5 and 84.1 % for site 1 and site 2). The classification scenario regarding the health condition of the black alders of the site 1 performed the best (90.6 %).The quality of the classification models developed with a UAS-based, cost-effective, and semi-automatic approach competes successfully with those developed using more expensive imagery, such as multi-spectral and hyperspectral airborne imagery. The high overall accuracy results obtained by the classification of the diseased alders open the door to applications dedicated to monitoring of the health conditions of riparian forest. Our methodological framework will allow UAS users to manage large imagery metric datasets derived from those dense time series.

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“…These systems are commonly used for high-resolution surveys [29][30][31][32][33][34][35][36][37][38] as they allow flights to be performed in an automatic way [39,40]. The miniaturization of imaging and positioning sensors also reduces the payload and thus enables flights up to about one hour long [41].…”

Section: Introductionmentioning

confidence: 99%

Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation

et al. 2018

Self Cite

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Performing two independent surveys in 2016 and 2017 over a flat sample plot (6700 m 2 ), we compare snow-depth measurements from Unmanned-Aerial-System (UAS) photogrammetry and from a new high-resolution laser-scanning device (MultiStation) with manual probing, the standard technique used by operational services around the world. While previous comparisons already used laser scanners, we tested for the first time a MultiStation, which has a different measurement principle and is thus capable of millimetric accuracy. Both remote-sensing techniques measured point clouds with centimetric resolution, while we manually collected a relatively dense amount of manual data (135 pt in 2016 and 115 pt in 2017). UAS photogrammetry and the MultiStation showed repeatable, centimetric agreement in measuring the spatial distribution of seasonal, dense snowpack under optimal illumination and topographic conditions (maximum RMSE of 0.036 m between point clouds on snow). A large fraction of this difference could be due to simultaneous snowmelt, as the RMSE between UAS photogrammetry and the MultiStation on bare soil is equal to 0.02 m. The RMSE between UAS data and manual probing is in the order of 0.20-0.30 m, but decreases to 0.06-0.17 m when areas of potential outliers like vegetation or river beds are excluded. Compact and portable remote-sensing devices like UASs or a MultiStation can thus be successfully deployed during operational manual snow courses to capture spatial snapshots of snow-depth distribution with a repeatable, vertical centimetric accuracy.

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Use of Unmanned Aerial Systems for multispectral survey and tree classification: a test in a park area of northern Italy

Cited by 78 publications

References 21 publications

Assessment of Chimpanzee Nest Detectability in Drone-Acquired Images

Assessment of Chimpanzee Nest Detectability in Drone-Acquired Images

Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system

Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation

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