Unmanned aerial vehicles as innovative remote sensing platforms for high‐resolution infrared imagery to support restoration monitoring in cut‐over bogs

Knoth, Christian; Klein, Birte; Prinz, Torsten; Kleinebecker, Till

doi:10.1111/avsc.12024

Cited by 103 publications

(87 citation statements)

References 40 publications

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“…For example, Quilter et al [129] used UAS for monitoring streams and riparian restoration projects in inaccessible areas on Chalk Creek (Utah). Knoth et al [130] applied a UAS-based NIR remote sensing approach to monitor a restored cut-over bog and Ludovisi et al [21] also used TIR data to determine the response of forest to drought in relation to forest tree breeding programs and genetic improvement.…”

Section: Monitoring Of Natural Ecosystemsmentioning

confidence: 99%

On the Use of Unmanned Aerial Systems for Environmental Monitoring

et al. 2018

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Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems; enhancing the understanding of hydrological processes; optimizing the allocation and distribution of water resources; and assessing, forecasting, and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors, and satellite observations. These data are utilized in describing both small-and large-scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal divides that limit current monitoring platforms is key to improving our understanding of environmental systems. In this context, Unmanned Aerial Systems (UAS) have considerable potential to radically improve environmental monitoring. UAS-mounted sensors offer an extraordinary opportunity to bridge the existing gap between field observations and traditional air-and space-borne remote sensing, by providing high spatial detail over relatively large areas in a cost-effective way and an entirely new capacity for enhanced temporal retrieval. As well as showcasing recent advances in the field, there is also a need to identify and understand the potential limitations of UAS technology. For these platforms to reach their monitoring potential, a wide spectrum of unresolved issues and application-specific challenges require focused community attention. Indeed, to leverage the full potential of UAS-based approaches, sensing technologies, measurement protocols, postprocessing techniques, retrieval algorithms, and evaluation techniques need to be harmonized. The aim of this paper is to provide an overview of the existing research and applications of UAS in natural and agricultural ecosystem monitoring in order to identify future directions, applications, developments, and challenges.

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Section: Monitoring Of Natural Ecosystemsmentioning

confidence: 99%

On the Use of Unmanned Aerial Systems for Environmental Monitoring

et al. 2018

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“…drones), which have been used to map individual peatlands at a very high resolution (e.g. Knoth et al, 2013), offer considerable potential for more detailed mapping of domestic peatlands on the national scale.…”

Section: Information Gapsmentioning

confidence: 99%

Derivation of greenhouse gas emission factors for peatlands managed for extraction in the Republic of Ireland and the United Kingdom

Wilson¹,

Dixon

Artz

et al. 2015

Biogeosciences

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Abstract. Drained peatlands are significant hotspots of carbon dioxide (CO 2 ) emissions and may also be more vulnerable to fire with its associated gaseous emissions. Under the United Nations Framework Convention on Climate Change (UNFCCC) and the Kyoto Protocol, greenhouse gas (GHG) emissions from peatlands managed for extraction are reported on an annual basis. However, the Tier 1 (default) emission factors (EFs) provided in the IPCC 2013 Wetlands Supplement for this land use category may not be representative in all cases and countries are encouraged to move to highertier reporting levels with reduced uncertainty levels based on country-or regional-specific data. In this study, we quantified (1) CO 2 -C emissions from nine peat extraction sites in the Republic of Ireland and the United Kingdom, which were initially disaggregated by land use type (industrial versus domestic peat extraction), and (2) a range of GHGs that are released to the atmosphere with the burning of peat. Drainagerelated methane (CH 4 ) and nitrous oxide (N 2 O) emissions as well as CO 2 -C emissions associated with the off-site decomposition of horticultural peat were not included here. Our results show that net CO 2 -C emissions were strongly controlled by soil temperature at the industrial sites (bare peat) and by soil temperature and leaf area index at the vegetated domestic sites. Our derived EFs of 1.70 (±0.47) and 1.64 (±0.44) t CO 2 -C ha −1 yr −1 for the industrial and domestic sites respectively are considerably lower than the Tier 1 EF (2.8 ± 1.7 t CO 2 -C ha −1 yr −1 ) provided in the Wetlands Supplement. We propose that the difference between our derived values and the Wetlands Supplement value is due to differences in peat quality and, consequently, decomposition rates. Emissions from burning of the peat (g kg −1 dry fuel burned) were estimated to be approximately 1346 CO 2 , 8.35 methane (CH 4 ), 218 carbon monoxide (CO), 1.53 ethane (C 2 H 6 ), 1.74 ethylene (C 2 H 4 ), 0.60 methanol (CH 3 OH), 2.21 hydrogen cyanide (HCN) and 0.73 ammonia (NH 3 ), and this emphasises the importance of understanding the full suite of trace gas emissions from biomass burning. Our results highlight the importance of generating reliable Tier 2 values for different regions and land use categories. Furthermore, given that the IPCC Tier 1 EF was only based on 20 sites (all from Canada and Fennoscandia), we suggest that data from another 9 sites significantly expand the global data set, as well as adding a new region.

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“…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

198

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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Unmanned aerial vehicles as innovative remote sensing platforms for high‐resolution infrared imagery to support restoration monitoring in cut‐over bogs

Cited by 103 publications

References 40 publications

On the Use of Unmanned Aerial Systems for Environmental Monitoring

On the Use of Unmanned Aerial Systems for Environmental Monitoring

Derivation of greenhouse gas emission factors for peatlands managed for extraction in the Republic of Ireland and the United Kingdom

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

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