Using digital camera images to detect canopy condition of deciduous broad-leaved trees

Nagai, Shin; Maeda, Tsuneaki; Gamo, Minoru; Muraoka, Hiroyuki; Suzuki, Rikie; Nasahara, Kenlo Nishida

doi:10.1080/17550874.2011.579188

Cited by 69 publications

(66 citation statements)

References 36 publications

(56 reference statements)

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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%

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%

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

et al. 2013

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“…The SPAD values are calculated through the amount of light transmitted by the leaf in red light (650 nm) and infrared light (940 nm), which have different absorbance induced by chlorophyll. Although the relationship between SPAD values and chlorophyll contents could be linear, exponential or polynomial correlations for various kinds of plants species have been reported; this relationship could be affected by light conditions, chloroplast distribution or the level of chlorophyll concentrations, but overall, the dynamic curve of SPAD values could be used as the indicator of leaf chlorophyll contents (Markwell et al 1995, v www.esajournals.org Muraoka and Koizumi 2005, Uddling et al 2007, Naus et al 2010, Ling et al 2011, Nagai et al 2011, Djumaeva et al 2012, Scattolin et al 2013, Riccardi et al 2014. Furthermore, the daily leaf area index (LAI) values (lower frequency after the end of leaf expansion) within ROI were collected with the leaf area meter LAI-2200 (Li-COR, Nebraska, USA) at the same time with other measurements.…”

Section: Canopy Images Acquisition and Processingmentioning

confidence: 99%

“…Ahrends et al (2009) have showed that there was a delay between maximum gross primary production (GPP) and the time of maximum Gcc, but how to mathematically adjust Gcc to match GPP is unresolved. Nagai et al (2011) reported that RGB indexes could not detect the different characteristics between forest species in spring. Recently, Yang et al (2014) have found that there was a mismatch of the maximum of Gcc value and the maximum of chlorophyll contents, but they have not quantified how to adjust camera-based indexes to reflect chlorophyll contents and other physiological variables.…”

Section: Introductionmentioning

confidence: 99%

“…To obtain high-resolution phenological information and overcome the limitation of manual observation and satellite remote sensing, an increasing amount of commercial digital cameras have been installed on research towers to capture plant canopy images on a daily basis (Richardson et al 2007, Graham et al 2010, Nagai et al 2011, Sonnentag et al 2012. Digital number of red, green, and blue were extracted in the region of interest (ROI) from images, and then several greenness indexes, such as green chromatic coordinates (Gcc) and excess green (ExG), were used to quantify the timing of the start and end of phenological phase throughout the year (Gillespie et al 1987, Woebbecke et al 1995, Richardson et al 2009a, Mizunuma et al 2011, Henneken et al 2013, Nagai et al 2013.…”

Section: Introductionmentioning

confidence: 99%

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Relationship between leaf physiologic traits and canopy color indices during the leaf expansion period in an oak forest

Liu

et al. 2015

Ecosphere

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Abstract. Plant phenology has a significant impact on the forest ecosystem carbon balance. Detecting plant phenology by capturing the time-series canopy images through digital camera has become popular in recent years. However, the relationship between color indices derived from camera images and plant physiological characters are elusive during the growing season in temperate ecosystems. We collected continuous images of forest canopy, leaf size, leaf area index (LAI) and leaf chlorophyll measured by a soil plant analysis development (SPAD) analyzer in a northern subtropical oak forest in China. Our results show that (1) the spring peak of color indices, Gcc (Green Chromatic Coordinates) and ExG (Excess Green), was 18 days earlier than the 90% maximum SPAD value; (2) the 90% maximum SPAD value coincided with the change point of Gcc and ExG immediately after their spring peak; and (3) the spring curves of Gcc and ExG before their peaks were highly synchronous with the expansion of leaf size and the development of LAI value. We suggest it needs to be adjusted if camera-derived Gcc or ExG is used as a proxy of chlorophyll or gross primary productivity, and images observation should be complemented with field phenological and physiological information to interpret the physiological meaning of leaf seasonality.

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“…Recent studies have reported that the use of digital images for phenological observations is a low-cost, quantitative method for measuring the timings of SLE and ELF (e.g., Richardson et al 2007;Ahrends et al 2009;Nagai et al 2011a). The use of bio-meteorological data recovered by this approach has a shorter history (e.g., 8 years; Ide and Oguma 2010) than the use of traditional in situ observations (e.g., 30 or 48 years; Matsumoto et al 2003;Menzel et al 2006), which were obtained from studies of plants in botanical gardens or meteorological stations.…”

Section: Introductionmentioning

confidence: 99%

Detection of Bio-Meteorological Year-to-Year Variation by Using Digital Canopy Surface Images of a Deciduous Broad-Leaved Forest

Nagai

Saitoh

Kurumado

et al. 2013

SOLA

Self Cite

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Clarification of the year-to-year variations and long-term trends of the timings of the start of leaf-expansion (SLE) and end of leaf-fall (ELF) is an important and challenging task because these timings affect spatial and temporal variations in water, heat and carbon cycles. Here, (1) we examined the relationships between daily mean air temperatures and the timings of SLE and ELF by using digital camera images in a cool-temperate deciduous broad-leaved forest in Japan from 2004 to 2011, and constructed a simple statistical phenology model based on their relationships, and (2) we then evaluated year-to-year variations and long-term trends in the timings of SLE and ELF over the past 51 years (1961−2011) by using that phenology model. We found that (1) the year-to-year variations in daily mean air temperatures over 2°C and below 18°C well affected those in the timings of SLE and ELF, respectively, and (2) a significant long-term linear trend in the timing of ELF (it moved later; 2.7 days decade −1) and the length of the leafy period were observed (it prolonged; 3.4 days decade −1 ), but no long-term trend was evident in the timing of SLE.

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Using digital camera images to detect canopy condition of deciduous broad-leaved trees

Cited by 69 publications

References 36 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

Relationship between leaf physiologic traits and canopy color indices during the leaf expansion period in an oak forest

Detection of Bio-Meteorological Year-to-Year Variation by Using Digital Canopy Surface Images of a Deciduous Broad-Leaved Forest

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