Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology

Motohka, Takeshi; Nasahara, Kenlo Nishida; Oguma, Hiroyuki; Tsuchida, Seigi

doi:10.3390/rs2102369

Cited by 433 publications

(304 citation statements)

References 26 publications

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“…Compared with the date of start of green up, the date of end of senescence was less certain. This concurs with other studies (using satellite data) that suggest it is more challenging to measure this as a single date [44] but that this can be overcome using a different index of the data (e.g., green-red vegetation index (GRVI) [45]). In order to fully understand why there was error in the predicted dates the values of greenness would need disaggregating to the component vegetation types and further research into different methods of data smoothing and date extraction would be required (similar to that applied to satellite data [29,46]).…”

Section: Computed Phenological Datessupporting

confidence: 75%

“…Although further work is to be conducted using this network of cameras, this study joins others who have used cameras to capture leaf emergence and green-up (e.g., [34,50,53]) as well as senescence (e.g., [36,50]). Such studies point to the possibility of using other combinations of spectral channels (e.g., Zhao et [45]). Other suggested research avenues include a focus on understorey vegetation [16,61], though this would not be directly applicable using this network, though at different times of year the leaf area of certain trees may allow "background" vegetation to be observed [48,50].…”

Section: Discussionmentioning

confidence: 99%

“…The images captured by these traffic cameras offer extended analysis possibilities, particularly when coupled with similar resolution observations of precipitation and temperature, carbon flux measurements and soil properties [47]. This will benefit the understanding of budgets of carbon and water exchange (e.g., gross primary productivity (GPP) [50,51]) and moreover shed light on any interannual variability in the growing season caused by weather conditions (e.g., windstorms [52]) and other occasional but often severe biotic stresses that influence the physiological status of trees [45,53]. Beyond this there is scope for the data to be used in the understanding of ecosystem interactions, food chains, pollination, breeding and migration [54,55].…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Exploring the Potential for Automatic Extraction of Vegetation Phenological Metrics from Traffic Webcams

et al. 2013

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Phenological metrics are of potential value as direct indicators of climate change. Usually they are obtained via either satellite imaging or ground based manual measurements; both are bespoke and therefore costly and have problems associated with scale and quality. An increase in the use of camera networks for monitoring infrastructure offers a means of obtaining images for use in phenological studies, where the only necessary outlay would be for data transfer, storage, processing and display. Here a pilot study is described that uses image data from a traffic monitoring network to demonstrate that it is possible to obtain usable information from the data captured. There are several challenges in using this network of cameras for automatic extraction of phenological metrics, not least, the low quality of the images and frequent camera motion. Although questions remain to be answered concerning the optimal employment of these cameras, this work illustrates that, in principle, image data from camera networks such as these could be used as a means of tracking environmental change in a low cost, highly automated and scalable manner that would require little human involvement.

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Section: Computed Phenological Datessupporting

confidence: 75%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Exploring the Potential for Automatic Extraction of Vegetation Phenological Metrics from Traffic Webcams

et al. 2013

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“…Ren et al (2014) found that trial-and-error threshold-based estimation was comparable to that of Win-CAM classification and was more efficient than WinCAM. In addition to the EGI-based threshold method, we also tried green relative vegetation index (GRVI = (G-R)/(G + R); Motohka et al, 2010). We did not try the normalized difference vegetation index (NDVI = (NIR-R)/(NIR + R), where NIR is near infrared band), due to lack of NIR band in a common camera.…”

Section: Image Analysismentioning

confidence: 99%

The burying and grazing effects of plateau pika on alpine grassland are small: a pilot study in a semiarid basin on the Qinghai-Tibet Plateau

Chen

Qin

et al. 2016

Biogeosciences

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Abstract. There is considerable controversy about the effects of plateau pika (Ochotona curzoniae, hereafter pika) on alpine grassland on the Qinghai-Tibet Plateau (QTP). On the one hand, pika is considered a keystone species. On the other hand, it is being poisoned. Although significant efforts have been made to study the effects of pika at a quadrat scale ( ∼ m 2 ), our knowledge about its distribution and effects at a larger scale is very limited. In this study, we investigated the direct effects, i.e., burying and grazing, of pika by upscaling field sampling at a quadrat scale to a plot scale (∼ 1000 m 2 ) by aerial photographing. Altogether 168 plots were set on four different types of alpine grassland in a semiarid basin on the QTP. Results showed that (1) the effects of pika pile burying on the reduction of vegetation cover, biomass, soil carbon, and nitrogen were less than 10 %, which was much smaller than the effects of bald patches; and (2) pika consumed 8-21 % of annual net primary production of grassland. We concluded that the direct burying and grazing effects of pika on alpine grassland were minor in this region. The quadcopter is an efficient and economic tool for longterm repeated monitoring over large regions for further understanding the role of pika.

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“…Therefore, an orthophoto mosaic with R, G, and B bands was used to compute RGB indices for the delineation of crop and soil pixels [32]. The Red-Green index [53,54] was computed using DN values (unsigned integer) to classify the crop and bare ground pixels. The output index values ranged between −1.0 to +1.0.…”

Section: Vegetation Indices and Bare Ground Pixel Extractionmentioning

confidence: 99%

Poppy Crop Height and Capsule Volume Estimation from a Single UAS Flight

et al. 2017

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Abstract:The objective of this study was to estimate poppy plant height and capsule volume with remote sensing using an Unmanned Aircraft System (UAS). Data were obtained from field measurements and UAS flights over two poppy crops at Cambridge and Cressy in Tasmania. Imagery acquired from the UAS was used to produce dense point clouds using structure from motion (SfM) and multi-view stereopsis (MVS) techniques. Dense point clouds were used to generate a digital surface model (DSM) and orthophoto mosaic. An RGB index was derived from the orthophoto to extract the bare ground spaces. This bare ground space mask was used to filter the points on the ground, and a digital terrain model (DTM) was interpolated from these points. Plant height values were estimated by subtracting the DSM and DTM to generate a Crop Height Model (CHM). UAS-derived plant height (PH) and field measured PH in Cambridge were strongly correlated with R 2 values ranging from 0.93 to 0.97 for Transect 1 and Transect 2, respectively, while at Cressy results from a single flight provided R 2 of 0.97. Therefore, the proposed method can be considered an important step towards crop surface model (CSM) generation from a single UAS flight in situations where a bare ground DTM is unavailable. High correlations were found between UAS-derived PH and poppy capsule volume (CV) at capsule formation stage (R 2 0.74), with relative error of 19.62%. Results illustrate that plant height can be reliably estimated for poppy crops based on a single UAS flight and can be used to predict opium capsule volume at capsule formation stage.

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Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology

Cited by 433 publications

References 26 publications

Exploring the Potential for Automatic Extraction of Vegetation Phenological Metrics from Traffic Webcams

Exploring the Potential for Automatic Extraction of Vegetation Phenological Metrics from Traffic Webcams

The burying and grazing effects of plateau pika on alpine grassland are small: a pilot study in a semiarid basin on the Qinghai-Tibet Plateau

Poppy Crop Height and Capsule Volume Estimation from a Single UAS Flight

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