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

Liu, Zhunqiao; Hu, Haibo; Yu, Hua; Yang, Xi; Yang, Hong; Ruan, Cunxin; Wang, Yan; Tang, Jianwu

doi:10.1890/es14-00452.1

Cited by 24 publications

(25 citation statements)

References 43 publications

(65 reference statements)

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“…, Liu et al. ). Therefore, compared with fitting logistic curves, the BCP method could detect the curve change point without smoothing fit curve that may lead to biases in the analyses of phenology transition phases.…”

Section: Methodsmentioning

confidence: 99%

“…, Liu et al. ), leaf chlorophyll concentration (Keenan et al. , Yang ), and gross primary production (GPP; Ahrends et al.…”

Section: Introductionmentioning

confidence: 99%

“…Depending on how the start of the growing season is defined (by increasing LAI or chlorophyll concentration), the spring curve of G cc could well represent the development of LAI (Liu et al. ), but the peak of G cc could be a ~3 weeks earlier than the peak of the chlorophyll concentration in the deciduous forest (Yang et al. ).…”

Section: Introductionmentioning

confidence: 99%

“…Seasonal changes in canopy color indices (such as green chromatic coordinate, G cc ), extracted from the region of interest (ROI) in repeat canopy photography, were closely related to seasonal changes in phenology of deciduous forests, such as bud-break, leaf expansion, and leaf abscission (Woebbecke et al 1995, Richardson et al 2009, Sakamoto et al 2012, Ide and Oguma 2013, Keenan et al 2014, Filippa et al 2015, Moore et al 2016, Nagai et al 2016). In addition, G cc can track plant physiological characters, such as leaf area index (LAI; Ryu et al 2012, Liu et al 2015, leaf chlorophyll concentration (Keenan et al 2014, Yang 2014, and gross primary production (GPP; Ahrends et al 2009, Mizunuma et al 2014, Wingate et al 2015, even though mismatches between G cc and chlorophyll concentration may exist (Yang et al 2017). Based on the widespread application of canopy repeat imagery, regional camera-based phenological networks were initiated and extended, such as the PhenoCam Network (Richardson et al 2009(Richardson et al , 2018, the National Ecological Observatory Network (Kampe et al 2010), EUROPheno Network (Wingate et al 2015), Phenological Eyes Network (PEN; Nasahara and Nagai 2015), and Australian Phenocam Network (Moore et al 2016).…”

Section: Introductionmentioning

confidence: 99%

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Using canopy greenness index to identify leaf ecophysiological traits during the foliar senescence in an oak forest

Liu

Lü

et al. 2018

Ecosphere

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Camera‐based observation of forest canopies allows for low‐cost, continuous, high temporal‐spatial resolutions of plant phenology and seasonality of functional traits. In this study, we extracted canopy color index (green chromatic coordinate, Gcc) from the time‐series canopy images provided by a digital camera in a deciduous forest in Massachusetts, USA. We also measured leaf‐level photosynthetic activities and leaf area index (LAI) development in the field during the growing season, and corresponding leaf chlorophyll concentrations in the laboratory. We used the Bayesian change point (BCP) approach to analyze Gcc. Our results showed that (1) the date of starting decline of LAI (DOY 263), defined as the start of senescence, could be mathematically identified from the autumn Gcc pattern by analyzing change points of the Gcc curve, and Gcc is highly correlated with LAI after the first change point when LAI was decreasing (R2 = 0.88, LAI < 2.5 m2/m2); (2) the second change point of Gcc (DOY 289) started a more rapid decline of Gcc when chlorophyll concentration and photosynthesis rates were relatively low (13.4 ± 10.0% and 23.7 ± 13.4% of their maximum values, respectively) and continuously reducing; and (3) the third change point of Gcc (DOY 295) marked the end of growing season, defined by the termination of photosynthetic activities, two weeks earlier than the end of Gcc curve decline. Our results suggested that with the change point analysis, camera‐based phenology observation can effectively quantify the dynamic pattern of the start of senescence (with declining LAI) and the end of senescence (when photosynthetic activities terminated) in the deciduous forest.

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Section: Methodsmentioning

confidence: 99%

“…, Liu et al. ), leaf chlorophyll concentration (Keenan et al. , Yang ), and gross primary production (GPP; Ahrends et al.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using canopy greenness index to identify leaf ecophysiological traits during the foliar senescence in an oak forest

Liu

Lü

et al. 2018

Ecosphere

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“…The GCC correlates with measurements of GPP, NDVI, and EVI [52][53][54] and is a measure of greenness widely used to monitor canopy development and the changing levels of green pigmentation when only information from the visible spectrum is available. The GCC has been used mostly to follow phenological events of various plants and trees [37,52,[54][55][56]. Another application of indices based on RGB colors is to accurately discriminate plants from non-plant surfaces for agriculture purposes [57], or to measure forest health by detecting disease and mortality occurrence in pine forests [58].…”

Section: Remote Sensing Data Acquisition and Processingmentioning

confidence: 99%

Mapping Wild Leek through the Forest Canopy Using a UAV

Leduc

Knudby

2018

Remote Sensing

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Abstract:Wild leek, an endangered plant species of Eastern North America, grows on forest floors and greens up to approximately three weeks before the trees it is typically found under, temporarily allowing it to be observed through the canopy by remote sensing instruments. This paper explores the accuracy with which wild leek can be mapped with a low-flying UAV. Nadir video imagery was obtained using a commercial UAV during the spring of 2017 in Gatineau Park, Quebec. Point clouds were generated from the video frames with the Structure-from-Motion framework, and a multiscale curvature classification was used to separate points on the ground, where wild leek grows, from above-ground points belonging to the forest canopy. Five-cm resolution orthomosaics were created from the ground points, and a threshold value of 0.350 for the green chromatic coordinate (GCC) was applied to delineate wild leek from wood, leaves, and other plants on the forest floor, with an F1-score of 0.69 and 0.76 for two different areas. The GCC index was most effective in delineating bigger patches, and therefore often misclassified patches smaller than 30 cm in diameter. Although short flight times and long data processing times are presently technical challenges to upscaling, the low cost and high accuracy of UAV imagery provides a promising method for monitoring the spatial distribution of this endangered species.

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Emerging opportunities and challenges in phenology: a review

et al. 2016

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Abstract. Plant phenology research has gained increasing attention because of the sensitivity of phenology to climate change and its consequences for ecosystem function. Recent technological development has made it possible to gather invaluable data at a variety of spatial and ecological scales. Despite our ability to observe phenological change at multiple scales, the mechanistic basis of phenology is still not well understood. Integration of multiple disciplines, including ecology, evolutionary biology, climate science, and remote sensing, with long-term monitoring data across multiple spatial scales is needed to advance understanding of phenology. We review the mechanisms and major drivers of plant phenology, including temperature, photoperiod, and winter chilling, as well as other factors such as competition, resource limitation, and genetics. Shifts in plant phenology have significant consequences on ecosystem productivity, carbon cycling, competition, food webs, and other ecosystem functions and services. We summarize recent advances in observation techniques across multiple spatial scales, including digital repeat photography, other complementary optical measurements, and solar-induced fluorescence, to assess our capability to address the importance of these scale-dependent drivers. Then, we review phenology models as an important component of earth system modeling. We find that the lack of species-level knowledge and observation data leads to difficulties in the development of vegetation phenology models at ecosystem or community scales. Finally, we recommend further research to advance understanding of the mechanisms governing phenology and the standardization of phenology observation methods across networks. With the opportunity for "big data" collection for plant phenology, we envision a breakthrough in processbased phenology modeling.

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

Cited by 24 publications

References 43 publications

Using canopy greenness index to identify leaf ecophysiological traits during the foliar senescence in an oak forest

Using canopy greenness index to identify leaf ecophysiological traits during the foliar senescence in an oak forest

Mapping Wild Leek through the Forest Canopy Using a UAV

Emerging opportunities and challenges in phenology: a review

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