A Photogrammetric Workflow for the Creation of a Forest Canopy Height Model from Small Unmanned Aerial System Imagery

Lisein, Jonathan; Pierrot-Deseilligny, Marc; Bonnet, Stéphanie; Lejeune, Philippe

doi:10.3390/f4040922

Cited by 344 publications

(298 citation statements)

References 54 publications

Supporting

Mentioning

274

Contrasting

Unclassified

Order By: Relevance

“…High spatial and temporal resolutions represent some of the most important characteristics of data acquired by UASs [1][2][3][4]. These aspects, in addition to considerable operational flexibility [5], project customizability, rapidity of data delivery, low cost of imagery acquisition, cloud insensitivity, and shallow learning curve of UAS usage [6], are likely to have a large impact on future studies of forests at fine scales [4] for both commercial and research purposes [5].…”

Section: Introductionmentioning

confidence: 99%

Inventory of Small Forest Areas Using an Unmanned Aerial System

et al. 2015

View full text Add to dashboard Cite

Acquiring high spatial and temporal resolution imagery from small unmanned aerial systems (sUAS) provides new opportunities for inventorying forests at small scales. Only a few studies have investigated the use of UASs in forest inventories, and the results are inconsistent and incomplete. The present study used three-dimensional (3D) variables derived from UAS imagery in combination with ground reference data to fit linear models for Lorey's mean height (hL), dominant height (hdom), stem number (N), basal area (G), and stem volume (V The results revealed that the use of UAS imagery can provide relatively accurate and timely forest inventory information at a local scale. In addition, the present study highlights the practical advantages of UAS-assisted forest inventories, including adaptive planning, high project customization, and rapid implementation, even under challenging weather conditions.

show abstract

Section: Introductionmentioning

confidence: 99%

Inventory of Small Forest Areas Using an Unmanned Aerial System

et al. 2015

View full text Add to dashboard Cite

show abstract

“…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%

“…Recent studies have demonstrated the potential of UAS imagery to finely describe the forest canopy Ellis 2010, 2013). 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).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Self Cite

204

179

View full text Add to dashboard Cite

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.

show abstract

“…St-Onge et al (2015) compares airborne LiDAR and UAV photogrammetric clouds. Lisein et al (2013) compared a hybrid photo/ LiDAR (canopy height model) CHM construction to LiDAR and forest inventory data. They claimed that UAV flights with short time intervals can refresh LiDAR CHMs in order to create canopy height series.…”

Section: Introductionmentioning

confidence: 99%

Estimating tree heights with images from an unmanned aerial vehicle

Birdal

Avdan

Türk

2017

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAV) have been used in a variety of fields in the last decade. In forestry, they have been used to estimate tree heights and crowns with different sensors. This approach, with a consumer-grade onboard system camera, is becoming popular because it is cheaper and faster than traditional photogrammetric methods and UAV-light detecting and ranging systems (UAV-LiDAR). In this study, UAV-based imagery reconstruction, processing, and local maximum filter methods are used to obtain individual tree heights from a coniferous urban forest. A low-cost onboard camera and a UAV with a 96-cm wingspan made it possible to acquire high resolution aerial images (6.41 cm average ground sampling distance), ortho-images, digital elevation models, and point clouds in one flight. Canopy height model, obtained by extracting the digital surface model from the digital terrain model, was filtered locally based on the pixel-based window size using the provided algorithm. For accuracy assessment, ground-based tree height measurements were made. There was a high 94% correlation and a root-mean-square error of 28 cm. This study highlights the accuracy of the method and compares favourably to more expensive methods.

show abstract

A Photogrammetric Workflow for the Creation of a Forest Canopy Height Model from Small Unmanned Aerial System Imagery

Cited by 344 publications

References 54 publications

Inventory of Small Forest Areas Using an Unmanned Aerial System

Inventory of Small Forest Areas Using an Unmanned Aerial System

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

Estimating tree heights with images from an unmanned aerial vehicle

Contact Info

Product

Resources

About