Mapping and classification of Peatland on the Isle of Lewis using Landsat ETM+

Brown, E.; Aitkenhead, Matt; Wright, Robert; Aalders, Inge

doi:10.1080/14702540701786912

Cited by 18 publications

(12 citation statements)

References 10 publications

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“…However, privately owned oak forest stands pose challenges to conventional remote sensing approaches. These forests show limited spatial dimensions, requiring high-resolution but low-cost multispectral remote sensing data and an "ad hoc" acquisition on demand [3,[18][19][20]. To address these specific challenges, the application of classic multispectral satellite data or conventional aerial imagery is often restricted due to a high cost per unit area of ground coverage or low-resolution image data [21].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Unmanned Aerial System-Based CIR Images in Forestry—A New Perspective to Monitor Pest Infestation Levels

Lehmann

Nieberding

Prinz

et al. 2015

Forests

133

128

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Abstract:The detection of pest infestation is an important aspect of forest management. In the case of the oak splendour beetle (Agrilus biguttatus) infestation, the affected oaks (Quercus sp.) show high levels of defoliation and altered canopy reflection signature. These critical features can be identified in high-resolution colour infrared (CIR) images of the tree crown and branches level captured by Unmanned Aerial Systems (UAS). In this study, we used a small UAS equipped with a compact digital camera which has been calibrated and modified to record not only the visual but also the near infrared reflection (NIR) of possibly infested oaks. The flight campaigns were realized in August 2013, covering two study sites which are located in a rural area in western Germany. Both locations represent small-scale, privately managed commercial forests in which oaks are economically valuable species. Our workflow includes the CIR/NIR image acquisition, mosaicking, georeferencing and pixel-based image enhancement followed by object-based image classification techniques. A modified Normalized Difference Vegetation Index (NDVImod) derived classification was used to distinguish between five vegetation health classes, i.e., infested, healthy or dead branches, other vegetation and canopy gaps. We achieved an overall Kappa Index of Agreement (KIA) OPEN ACCESSForests 2015, 6 595 of 0.81 and 0.77 for each study site, respectively. This approach offers a low-cost alternative to private forest owners who pursue a sustainable management strategy.

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Section: Introductionmentioning

confidence: 99%

Analysis of Unmanned Aerial System-Based CIR Images in Forestry—A New Perspective to Monitor Pest Infestation Levels

Lehmann

Nieberding

Prinz

et al. 2015

Forests

133

128

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“…; Shuman & Ambrose ; Brown et al. ). These obstacles hinder the semi‐automatic identification of plant species assemblages or individuals (Chambers et al.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, multi-spectral imagery derived from high flying aircraft (Medlin et al 2000;Yu et al 2006), commercial satellites or multi-temporal public data sets, like the ASTER or Landsat Thematic Mapper satellite system (Lawes & Wallace 2008), has increasingly attracted attention for conservation and restoration monitoring of peatlands during the last decade (Milton et al 2005;Ecker et al 2008). However, cut-over bog surfaces pose significant challenges to conventional remote sensing approaches due to for example small spectral differences in characteristics of monitored indicators, and limited spatial and temporal resolution (Thomas et al 2002;Shuman & Ambrose 2003;Brown et al 2007). These obstacles hinder the semi-automatic identification of plant species assemblages or individuals (Chambers et al 2007).…”

Section: Introductionmentioning

confidence: 99%

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

Knoth

Klein

Prinz

et al. 2013

Applied Vegetation Science

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Question Can UAV‐based NIR remote sensing support restoration monitoring of cut‐over bogs by providing valid information on species distribution and surface structure? Location Restored polders of the Uchter Moor, a bog complex in NW Germany. Methods We used autonomously flying quadrocopters, supplied with either a panchromatic or colour infrared calibrated small frame digital camera to generate high resolution images of the restored bog surface. We performed a two‐step classification process of automatic image segmentation and object‐based classification to distinguish between four pre‐defined classes (waterlogged bare peat, Sphagnum spp., Eriophorum vaginatum and Betula pubescens. An independent validation procedure was performed to evaluate the accuracy of the classification. Results A set‐up composed of decision rules for reflectance, geometry and textural features was applied for identification of the four classes. The presented classification revealed an overall accuracy level of 91%. Most reliable attribution was obtained for waterlogged bare peat and Sphagnum‐covered surfaces, revealing producer accuracies of 95% and 91%, respectively. Lower but still feasible accuracy levels were obtained for Eriophorum vaginatum and Betula pubescens individuals (89% and 84%, respectively). Conclusions UAV‐based NIR remote sensing is a promising tool for monitoring the restoration of cut‐over bogs and has the potential to significantly reduce laborious field surveys. UAVs may increasingly play a significant role in future ecological monitoring studies, since they are small in size, highly flexible, easy to handle, non‐emissive and available at a comparatively low cost.

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“…Brown et al . () used principal component analysis and Landsat ETM+ data to classify blanket peat in the Isle of Lewis. They found that per‐pixel methods of classification alone could not distinguish between certain peat types.…”

Section: Introductionmentioning

confidence: 99%

“…Glaser (1989) used false colour composites from Landsat TM data to indicate the presence of peat communities. Brown et al (2007) used principal component analysis and Landsat ETM+ data to classify blanket peat in the Isle of Lewis. They found that per-pixel methods of classification alone could not distinguish between certain peat types.…”

Section: Introductionmentioning

confidence: 99%

Characterizing peatland carbon balance estimates using freely available Landsat ETM+ data

et al. 2014

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International audienceWe demonstrate the potential of using freely available satellite data from the Landsat ETM+ sensor for generating carbon balance estimates for lowland peatlands. We used a lowland ombrotrophic peatland in the UK as our test site representing a range of peatland conditions. A literature survey was undertaken to identify the simplest classification schema that could be used to distinguish ecohydrological classes for carbon sequestration on the peatland surface. These were defined as: active raised bog, Eriophorum-dominated bog, milled unvegetated peat and drained or degraded bog, with bracken and Carr woodland to define the bog edges. A maximum likelihood classifier (MLC) was used to map the spatial distribution of the six classes on the peatland surface. A Landsat ETM+ band-5 derived brightness-texture layer created using geostatistical methods greatly improved classification accuracies. The results showed the best accuracy of the MLC, when compared to finer scale methods, with Landsat ETM+ bands alone was 74%, which increased to 93% when including the brightness-texture layer. An estimate of carbon sequestration status of the site was performed that showed good agreement with the results of a finer-scale-based estimate. The coarse-scale map estimating −12 000 kg carbon and fine scale map estimating +23 000 kg carbon per annum. We conclude that with further development of our tool, if textural measures are used alongside optical data in MLC, it is possible to achieve good quality estimates of carbon balance status for peatland landscapes. This represents a potentially powerful operational toolkit for land managers and policy makers who require spatially distributed information on carbon storage and release for carbon pricing and effective land management. Copyright © 2014 John Wiley & Sons, Ltd

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Mapping and classification of Peatland on the Isle of Lewis using Landsat ETM+

Cited by 18 publications

References 10 publications

Analysis of Unmanned Aerial System-Based CIR Images in Forestry—A New Perspective to Monitor Pest Infestation Levels

Analysis of Unmanned Aerial System-Based CIR Images in Forestry—A New Perspective to Monitor Pest Infestation Levels

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

Characterizing peatland carbon balance estimates using freely available Landsat ETM+ data

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