Using Ordinary Digital Cameras in Place of Near-Infrared Sensors to Derive Vegetation Indices for Phenology Studies of High Arctic Vegetation

Anderson, Helen; Nilsen, Lennart; Tømmervik, Hans; Karlsen, Stein Rune; Nagai, Shin; Cooper, Elisabeth J.

doi:10.3390/rs8100847

Cited by 66 publications

(56 citation statements)

References 63 publications

(94 reference statements)

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“…For NDFI NIR-G , NDVI 1 , and NDVI 2 , all calculated from NIR, the R 2 of combinations (NDFI NIR-G and NDVI 1 ) and (NDFI NIR-G and NDVI 2 ) were higher than 0.87 for both combinations. Anderson et al (2016) [38] also found that the vegetation indices from active sensors had high correlations. They reported that the vegetation indices measured from active and passive sensors were correlated (R 2 > 0.70) for the same plant species.…”

Section: Discussionmentioning

confidence: 97%

Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System

Teo

2017

Applied Sciences

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Featured Application: The featured application of this study is to combine the geometrical and spectral features from advanced multi-wavelength LiDAR in land cover classification. Abstract:The airborne multi-wavelength light detection and ranging (LiDAR) system measures different wavelengths simultaneously and usually includes two or more active channels in infrared and green to acquire both topographic and hydrographic information. The reflected multi-wavelength energy can also be used to identify different land covers based on physical properties of materials. This study explored the benefits of multi-wavelength LiDAR in object-based land cover classification, focusing on three major issues: (1) the evaluation of single-and multi-wavelength LiDARs for land cover classification; (2) the performance of spectral and geometrical features extracted from multi-wavelength LiDAR; and (3) the comparison of the vegetation index derived from active multi-wavelength LiDAR and passive multispectral images. The three-wavelength test data were acquired by Optech Titan in green, near-infrared, and mid-infrared channels, and the reference data were acquired from Worldview-3 image. The experimental results show that the multi-wavelength LiDAR provided higher accuracy than single-wavelength LiDAR in land cover classification, with an overall accuracy improvement rate about 4-14 percentage points. The spectral features performed better compared to geometrical features for grass, road, and bare soil classes, and the overall accuracy improvement is about 29 percentage points. The results also demonstrated the vegetation indices from Worldview-3 and Optech Titan have similar characteristics, with correlations reaching 0.68 to 0.89. Overall, the multi-wavelength LiDAR system improves the accuracy of land cover classification because this system provides more spectral information than traditional single-wavelength LiDAR.

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Section: Discussionmentioning

confidence: 97%

Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System

Teo

2017

Applied Sciences

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“…To complement the use of airborne and satellite imagery to measure vegetation pigments and classify biophysical properties in Arctic ecosystems, the use of true‐color digital photography and red, green, and blue (RGB) indices to infer photosynthetic activity and foliar condition is a promising area of study and should be examined (Anderson et al. , Beamish et al. ).…”

Section: Introductionmentioning

confidence: 99%

“…, Anderson et al. ), as well as containing a sensitivity to changes in plant photosynthetic pigments (Ide and Oguma ).…”

Section: Introductionmentioning

confidence: 99%

“…Remote locations, challenges associated with field-based monitoring, and atmospheric and illumination conditions of optical satellite imagery of these areas limit available optical data, hindering phenological studies. To complement the use of airborne and satellite imagery to measure vegetation pigments and classify biophysical properties in Arctic ecosystems, the use of true-color digital photography and red, green, and blue (RGB) indices to infer photosynthetic activity and foliar condition is a promising area of study and should be examined (Anderson et al 2016, Beamish et al 2016). Research has shown that Arctic vegetation changes are complex with strong site and speciesspecific responses through space and time (Walker et al 2006, Bhatt et al 2010, Elmendorf et al 2012b, Bjorkman et al 2015, Prev ey et al 2017.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring pigment‐driven vegetation changes in a low‐Arctic tundra ecosystem using digital cameras

et al. 2018

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Abstract. Arctic vegetation phenology is a sensitive indicator of a changing climate, and rapid assessment of vegetation status is necessary to more comprehensively understand the impacts on foliar condition and photosynthetic activity. Airborne and space-borne optical remote sensing has been successfully used to monitor vegetation phenology in Arctic ecosystems by exploiting the biophysical and biochemical changes associated with vegetation growth and senescence. However, persistent cloud cover and low sun angles in the region make the acquisition of high-quality temporal optical data within one growing season challenging. In the following study, we examine the capability of "near-field" remote sensing technologies, in this case digital, true-color cameras to produce surrogate in situ spectral data to characterize changes in vegetation driven by seasonal pigment dynamics. Simple linear regression was used to investigate relationships between common pigment-driven spectral indices calculated from field-based spectrometry and red, green, and blue (RGB) indices from corresponding digital photographs in three dominant vegetation communities across three major seasons at Toolik Lake, North Slope, Alaska. We chose the strongest and most consistent RGB index across all communities to represent each spectral index. Next, linear regressions were used to relate RGB indices and extracted leaf-level pigment content with a simple additive error propagation of the root mean square error. Results indicate that the green-based RGB indices had the strongest relationship with chlorophyll a and total chlorophyll, while a red-based RGB index showed moderate relationships with the chlorophyll to carotenoid ratio. The results suggest that vegetation color contributes strongly to the response of pigment-driven spectral indices and RGB data can act as a surrogate to track seasonal vegetation change associated with pigment development and degradation. Overall, we find that low-cost, easy-to-use digital cameras can monitor vegetation status and changes related to seasonal foliar condition and photosynthetic activity in three dominant, low-Arctic vegetation communities.

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“…This damage was shown to be caused by stressful conditions during winter [6]. Usually, lower NDVI values (<0.6) are measured for species and sites operating within more adverse environmental conditions, while species and sites in good health had a NDVI of 0.6-0.7 with species and sites in very good condition having NDVI values > 0.7 [6,52]. Red edge modification of SR.…”

Section: Calculation Of Vegetation Indicesmentioning

confidence: 99%

Intraspecific Differences in Spectral Reflectance Curves as Indicators of Reduced Vitality in High-Arctic Plants

et al. 2017

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Remote sensing is a suitable candidate for monitoring rapid changes in Polar regions, offering high-resolution spectral, spatial and radiometric data. This paper focuses on the spectral properties of dominant plant species acquired during the first week of August 2015. Twenty-eight plots were selected, which could easily be identified in the field as well as on RapidEye satellite imagery. Spectral measurements of individual species were acquired, and heavy metal contamination stress factors were measured contemporaneously. As a result, a unique spectral library of dominant plant species, heavy metal concentrations and damage ratios were achieved with an indication that species-specific changes due to environmental conditions can best be differentiated in the 1401-2400 nm spectral region. Two key arctic tundra species, Cassiope tetragona and Dryas octopetala, exhibited significant differences in this spectral region that were linked to a changing health status. Relationships between field and satellite measurements were comparable, e.g., the Red Edge Normalized Difference Vegetation Index (RENDVI) showed a strong and significant relationship (R 2 = 0.82; p = 0.036) for the species Dryas octopetala. Cadmium and Lead were below detection levels while manganese, copper and zinc acquired near Longyearbyen were at concentrations comparable to other places in Svalbard. There were high levels of nickel near Longyearbyen (0.014 mg/g), while it was low (0.004 mg/g) elsewhere.

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Using Ordinary Digital Cameras in Place of Near-Infrared Sensors to Derive Vegetation Indices for Phenology Studies of High Arctic Vegetation

Cited by 66 publications

References 63 publications

Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System

Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System

Monitoring pigment‐driven vegetation changes in a low‐Arctic tundra ecosystem using digital cameras

Intraspecific Differences in Spectral Reflectance Curves as Indicators of Reduced Vitality in High-Arctic Plants

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