Modeling vegetation heights from high resolution stereo aerial photography: An application for broad-scale rangeland monitoring

Gillan, Jeffrey K.; Karl, Jason W.; Duniway, Michael C.; Elaksher, Ahmed

doi:10.1016/j.jenvman.2014.05.028

Cited by 35 publications

(42 citation statements)

References 37 publications

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“…Korpela, 2004;Véga, 2006;Magnusson et al, 2007), our results show that canopy height tends to be underestimated by imagery measurements. These inaccuracies in height measurements based on aerial photographs are widely observed in photogrammetry (Worley and Landis, 1954;Spencer and Hall, 1988;Brown and Arbogast, 1999;Miller et al, 2000;Naesset, 2002;Saint-Onge et al, 2004;Gillan et al, 2014). Even if the probability of detecting a tree increases with its relative height (Anttila, 2005), underestimation is expected because in photogrammetry the model algorithms tend to smooth the representation of vegetation cover.…”

Section: Accuracy Of Photogrammetric Digital Modelsmentioning

confidence: 99%

“…Traditionally, photogrammetry was used as a tool to create DTMs, for example for the modelling of terrestrial landform evolution (Chandler, 1999;Brown and Arbogast, 1999;Lane, 2000;Baily et al, 2003;Veyrat-Charvillon and Memier, 2006;Walstra et al, 2007;Baldi et al, 2008;Lane et al, 2010). Photogrammetric analysis has also been used to determine vegetation height of forests (Nakashizuka et al, 1995;Gong et al, 2000;Saint-Onge et al, 2004;Véga and Saint-Onge, 2008), mangroves (Mitchell et al, 2007) or semi-arid shrublands (Gillan et al, 2014). The use of recent high resolution digital stereopair aerial images taken for example by small aircrafts provides promising results in determining accurate vegetation heights (Gillan et al, 2014), but archival aerial images have not been exploited to their full potential.…”

Section: Introductionmentioning

confidence: 99%

“…Photogrammetric analysis has also been used to determine vegetation height of forests (Nakashizuka et al, 1995;Gong et al, 2000;Saint-Onge et al, 2004;Véga and Saint-Onge, 2008), mangroves (Mitchell et al, 2007) or semi-arid shrublands (Gillan et al, 2014). The use of recent high resolution digital stereopair aerial images taken for example by small aircrafts provides promising results in determining accurate vegetation heights (Gillan et al, 2014), but archival aerial images have not been exploited to their full potential. Given the existence of large archives of stereo aerial photograph databases that are available worldwide (Aschenwald et al, 2001;Rhemtulla et al, 2002;Fox and Czifersky, 2008), stereophotogrammetry represents a method of crucial importance for 3D cartography of environments and for retrospective landscape studies (Gong et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Monitoring and reconstructing past biogeomorphic succession within fluvial corridors using stereophotogrammetry

Vautier

Corenblit

Hortobágyi

et al. 2016

Earth Surf Processes Landf

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Section: Accuracy Of Photogrammetric Digital Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Monitoring and reconstructing past biogeomorphic succession within fluvial corridors using stereophotogrammetry

Vautier

Corenblit

Hortobágyi

et al. 2016

Earth Surf Processes Landf

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“…The overall classification accuracy increased with the inclusion of 3D data from both image matching and laser scanning compared to using spectral and elevation data alone. Gillan et al [14] estimated the height of low-growing rangeland vegetation from aerial stereo images with 3-cm pixels and found a good correlation between field measurements and the vegetation height estimated from the images, even though the latter tended to be underestimated. Rampi et al [15] combined LiDAR data (digital elevation model, DSM, compound topographic index, and intensity) and aerial imagery to map wetlands versus other land cover types in three ecoregions with an object-based image analysis approach and achieved high classification accuracies (>90%).…”

Section: Introductionmentioning

confidence: 99%

Combining Spectral Data and a DSM from UAS-Images for Improved Classification of Non-Submerged Aquatic Vegetation

2017

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Abstract:Monitoring of aquatic vegetation is an important component in the assessment of freshwater ecosystems. Remote sensing with unmanned aircraft systems (UASs) can provide sub-decimetre-resolution aerial images and is a useful tool for detailed vegetation mapping. In a previous study, non-submerged aquatic vegetation was successfully mapped using automated classification of spectral and textural features from a true-colour UAS-orthoimage with 5-cm pixels. In the present study, height data from a digital surface model (DSM) created from overlapping UAS-images has been incorporated together with the spectral and textural features from the UAS-orthoimage to test if classification accuracy can be improved further. We studied two levels of thematic detail: (a) Growth forms including the classes of water, nymphaeid, and helophyte; and (b) dominant taxa including seven vegetation classes. We hypothesized that the incorporation of height data together with spectral and textural features would increase classification accuracy as compared to using spectral and textural features alone, at both levels of thematic detail. We tested our hypothesis at five test sites (100 m × 100 m each) with varying vegetation complexity and image quality using automated object-based image analysis in combination with Random Forest classification. Overall accuracy at each of the five test sites ranged from 78% to 87% at the growth-form level and from 66% to 85% at the dominant-taxon level. In comparison to using spectral and textural features alone, the inclusion of height data increased the overall accuracy significantly by 4%-21% for growth-forms and 3%-30% for dominant taxa. The biggest improvement gained by adding height data was observed at the test site with the most complex vegetation. Height data derived from UAS-images has a large potential to efficiently increase the accuracy of automated classification of non-submerged aquatic vegetation, indicating good possibilities for operative mapping.

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“…Advances in photogrammetry software have made this process much easier, more accurate, and less expensive. Gillan et al (2014) used stereo aerial photographs to model shrub heights in the Mojave Desert. Gong et al (2000) created digital surface models from VHR stereo imagery to monitor changes in crown closure and tree height in a hardwood rangeland.…”

Section: Developments In Remote-sensing Techniquesmentioning

confidence: 99%

Monitoring Protocols: Options, Approaches, Implementation, Benefits

Karl

Pyke²

2017

Springer Series on Environmental Management

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Monitoring and adaptive management are fundamental concepts to rangeland management across land management agencies and embodied as best management practices for private landowners. Historically, rangeland monitoring was limited to determining impacts or maximizing the potential of specific land uses-typically grazing. Over the past several decades, though, the uses of and disturbances to rangelands have increased dramatically against a backdrop of global climate change that adds uncertainty to predictions of future rangeland conditions. Thus, today's monitoring needs are more complex (or multidimensional) and yet still must be reconciled with the realities of costs to collect requisite data. However, conceptual advances in rangeland ecology and management and changes in natural resource policies and societal values over the past 25 years have facilitated new approaches to monitoring that can support rangeland management's diverse information needs. Additionally, advances in sensor technologies and remote-sensing techniques have broadened the suite of rangeland attributes that can be monitored and the temporal and spatial scales at which they can be monitored. We review some of the conceptual and technological advancements and provide examples of how they have influenced rangeland monitoring. We then discuss implications of these developments for rangeland management and highlight what we see as challenges and opportunities for implementing effective rangeland monitoring. We conclude with a vision for how monitoring can contribute to rangeland information needs in the future.

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Modeling vegetation heights from high resolution stereo aerial photography: An application for broad-scale rangeland monitoring

Cited by 35 publications

References 37 publications

Monitoring and reconstructing past biogeomorphic succession within fluvial corridors using stereophotogrammetry

Monitoring and reconstructing past biogeomorphic succession within fluvial corridors using stereophotogrammetry

Combining Spectral Data and a DSM from UAS-Images for Improved Classification of Non-Submerged Aquatic Vegetation

Monitoring Protocols: Options, Approaches, Implementation, Benefits

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