scholarly journals A survey of proximal methods for monitoring leaf phenology in temperate deciduous forests

2020 ◽  
Author(s):  
Kamel Soudani ◽  
Nicolas Delpierre ◽  
Daniel Berveiller ◽  
Gabriel Hmimina ◽  
Jean-Yves Pontailler ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Leaf Senescence ◽  
Flux Measurement ◽  
List Type ◽  
Spring Phenology ◽  
Area Index ◽  
Tree Phenology ◽  
Temperate Deciduous ◽  
Energy Exchanges ◽  
Best Estimates

AbstractTree phenology is a major driver of forest-atmosphere mass and energy exchanges. Yet tree phenology has historically not been recorded at flux measurement sites. Here, we used seasonal time-series of ground-based NDVI (Normalized Difference Vegetation Index), RGB camera GCC (Greenness Chromatic Coordinate), broad-band NDVI, LAI (Leaf Area Index), fAPAR (fraction of Absorbed Photosynthetic Active Radiation), CC (Canopy Closure), fRvis (fraction of Reflected Radiation) and GPP (Gross Primary Productivity) to predict six phenological markers detecting the start, middle and end of budburst and of leaf senescence in a temperate deciduous forest. We compared them to observations of budburst and leaf senescence achieved by field phenologists over a 13-year period. GCC, NDVI and CC captured very well the interannual variability of spring phenology (R2 > 0.80) and provided the best estimates of the observed budburst dates, with a mean absolute deviation (MAD) less than 4 days. For the CC and GCC methods, mid-amplitude (50%) threshold dates during spring phenological transition agreed well with the observed phenological dates. For the NDVI-based method, on average, the mean observed date coincides with the date when NDVI reaches 25% of its amplitude of annual variation. For the other methods, MAD ranges from 6 to 17 days. GPP provides the most biased estimates. During the leaf senescence stage, NDVI- and CC-derived dates correlated significantly with observed dates (R2 =0.63 and 0.80 for NDVI and CC, respectively), with MAD less than 7 days. Our results show that proximal sensing methods can be used to derive robust phenological indexes. They can be used to retrieve long-term phenological series at flux measurement sites and help interpret the interannual variability and decadal trends of mass and energy exchanges.HighlightsWe used 8 indirect methods to predict the timing of phenological events.GCC, NDVI and CC captured very well the interannual variation of spring phenology.GCC, NDVI and CC provided the best estimates of observed budburst dates.NDVI and CC derived-dates correlated with observed leaf senescence dates.

scholarly journals A survey of proximal methods for monitoring leaf phenology in temperate deciduous forests

2020 ◽  
Author(s):  
Kamel Soudani ◽  
Nicolas Delpierre ◽  
Daniel Berveiller ◽  
Gabriel Hmimina ◽  
Jean-Yves Pontailler ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Leaf Senescence ◽  
Vegetation Index ◽  
Broad Band ◽  
Flux Measurement ◽  
Spring Phenology ◽  
Area Index ◽  
Tree Phenology ◽  
Temperate Deciduous ◽  
Energy Exchanges

Abstract. Tree phenology is a major driver of forest-atmosphere mass and energy exchanges. Yet tree phenology has historically not been recorded at flux measurement sites. Here, we used seasonal time-series of ground-based NDVI (Normalized Difference Vegetation Index), RGB camera GCC (Greenness Chromatic Coordinate), broad-band NDVI, LAI (Leaf Area Index), fAPAR (fraction of Absorbed Photosynthetic Active Radiation), CC (Canopy Closure), fRvis (fraction of Reflected Radiation) and GPP (Gross Primary Productivity) to predict six phenological markers detecting the start, middle and end of budburst and of leaf senescence in a temperate deciduous forest. We compared them to observations of budburst and leaf senescence achieved by field phenologists over a 13-year period. GCC, NDVI and CC captured very well the interannual variability of spring phenology (R2 > 0.80) and provided the best estimates of the observed budburst dates, with a mean absolute deviation (MAD) less than 4 days. For the CC and GCC methods, mid-amplitude (50 %) threshold dates during spring phenological transition agreed well with the observed phenological dates. For the NDVI-based method, on average, the mean observed date coincides with the date when NDVI reaches 25 % of its amplitude of annual variation. For the other methods, MAD ranges from 6 to 17 days. GPP provides the most biased estimates. During the leaf senescence stage, NDVI- and CC-derived dates correlated significantly with observed dates (R2 = 0.63 and 0.80 for NDVI and CC, respectively), with MAD less than 7 days. Our results show that proximal sensing methods can be used to derive robust phenological metrics. They can be used to retrieve long-term phenological series at flux measurement sites and help interpret the interannual variability and trends of mass and energy exchanges.

scholarly journals A survey of proximal methods for monitoring leaf phenology in temperate deciduous forests

2021 ◽  
Vol 18 (11) ◽  
pp. 3391-3408
Author(s):  
Kamel Soudani ◽  
Nicolas Delpierre ◽  
Daniel Berveiller ◽  
Gabriel Hmimina ◽  
Jean-Yves Pontailler ◽  
...  
Keyword(s):  
Time Series ◽  
Interannual Variability ◽  
Leaf Senescence ◽  
Vegetation Index ◽  
Sigmoid Function ◽  
Spring Phenology ◽  
Area Index ◽  
Tree Phenology ◽  
Temperate Deciduous ◽  
Energy Exchanges

Abstract. Tree phenology is a major driver of forest–atmosphere mass and energy exchanges. Yet, tree phenology has rarely been monitored in a consistent way throughout the life of a flux-tower site. Here, we used seasonal time series of ground-based NDVI (Normalized Difference Vegetation Index), RGB camera GCC (greenness chromatic coordinate), broadband NDVI, LAI (leaf area index), fAPAR (fraction of absorbed photosynthetic active radiation), CC (canopy closure), fRvis (fraction of reflected radiation) and GPP (gross primary productivity) to predict six phenological markers detecting the start, middle and end of budburst and of leaf senescence in a temperate deciduous forest using an asymmetric double sigmoid function (ADS) fitted to the time series. We compared them to observations of budburst and leaf senescence achieved by field phenologists over a 13-year period. GCC, NDVI and CC captured the interannual variability of spring phenology very well (R2>0.80) and provided the best estimates of the observed budburst dates, with a mean absolute deviation (MAD) of less than 4 d. For the CC and GCC methods, mid-amplitude (50 %) threshold dates during spring phenological transition agreed well with the observed phenological dates. For the NDVI-based method, on average, the mean observed date coincides with the date when NDVI reaches 25 % of its amplitude of annual variation. For the other methods, MAD ranges from 6 to 17 d. The ADS method used to derive the phenological markers provides the most biased estimates for the GPP and GCC. During the leaf senescence stage, NDVI- and CC-derived dates correlated significantly with observed dates (R2=0.63 and 0.80 for NDVI and CC, respectively), with an MAD of less than 7 d. Our results show that proximal-sensing methods can be used to derive robust phenological metrics. They can be used to retrieve long-term phenological series at eddy covariance (EC) flux measurement sites and help interpret the interannual variability and trends of mass and energy exchanges.

Rapid Measurement of Potato Canopy Coverage and Leaf Area Index Inversion

2020 ◽  
Vol 36 (4) ◽  
pp. 557-564
Author(s):  
LingHan Cai ◽  
Yuan Zhao ◽  
Zhuojue Huang ◽  
Yang Gao ◽  
Han Li ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Color Space ◽  
List Type ◽  
Area Index ◽  
Average Value ◽  
Rapid Measurement ◽  
Canopy Coverage ◽  
Lab Color Space ◽  
Extraction Model

Highlights This article calculates the canopy coverage (Cc) and inverts it to the leaf area index (LAI) of the collected images through a portable device such as a mobile phone, which is convenient for researchers. The Lab color model has been used for plant area extraction, which has achieved good results. Steps such as weed removal make the algorithm more universal. The inversion results of LAI based on canopy coverage has high accuracy, which indicates that it can be used for LAI calculation. Abstract . Canopy coverage (Cc) and leaf area index (LAI) are important parameters for qualitative and quantitative descriptions of plant growth trends. Meanwhile, LAI can be reflected by Cc. Therefore, it is of great significance to observe Cc and establish the relationship between Cc and LAI for monitoring the growth of plants. In July 2019, in Shang Zhuang experimental field of China Agricultural University, 30 potato canopy images were taken vertically by camera, and the actual LAI data of the corresponding images were measured and recorded by LAI-2200C. Image extraction algorithms of different models, such as ExG, ExGR, NDIGR, and Lab color space extraction model are evaluated and compared. After that, estimating the parameters of the logarithmic model of LAI-Cc by minimizing errors, evaluating the inversion model by Hold-Out. Besides, the result shows Cc can be calculated efficiently by using Lab color space extraction model. In the training set, the average value of R2 between the predicted LAI and the actual LAI reaches 0.940, and the RMSE reaches 0.144. In the test set, the average value of R2 reaches 0.937, the RMSE reaches 0.197. And the average time consumption of the entire process is 2.989 s on an image. It suggests that the study can provide a basis for dynamic monitoring of potato and other crops. Keywords: Canopy coverage (Cc), Leaf area index (LAI), Image processing, Potato, Rapid measurement.

Rapid Measurement of Potato Canopy Coverage and Leaf Area Index Inversion

2020 ◽  
Vol 36 (4) ◽  
pp. 557-564
Author(s):  
LingHan Cai ◽  
Yuan Zhao ◽  
Zhuojue Huang ◽  
Yang Gao ◽  
Han Li ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Color Space ◽  
List Type ◽  
Area Index ◽  
Average Value ◽  
Rapid Measurement ◽  
Canopy Coverage ◽  
Lab Color Space ◽  
Extraction Model

Highlights This article calculates the canopy coverage (Cc) and inverts it to the leaf area index (LAI) of the collected images through a portable device such as a mobile phone, which is convenient for researchers. The Lab color model has been used for plant area extraction, which has achieved good results. Steps such as weed removal make the algorithm more universal. The inversion results of LAI based on canopy coverage has high accuracy, which indicates that it can be used for LAI calculation. Abstract . Canopy coverage (Cc) and leaf area index (LAI) are important parameters for qualitative and quantitative descriptions of plant growth trends. Meanwhile, LAI can be reflected by Cc. Therefore, it is of great significance to observe Cc and establish the relationship between Cc and LAI for monitoring the growth of plants. In July 2019, in Shang Zhuang experimental field of China Agricultural University, 30 potato canopy images were taken vertically by camera, and the actual LAI data of the corresponding images were measured and recorded by LAI-2200C. Image extraction algorithms of different models, such as ExG, ExGR, NDIGR, and Lab color space extraction model are evaluated and compared. After that, estimating the parameters of the logarithmic model of LAI-Cc by minimizing errors, evaluating the inversion model by Hold-Out. Besides, the result shows Cc can be calculated efficiently by using Lab color space extraction model. In the training set, the average value of R2 between the predicted LAI and the actual LAI reaches 0.940, and the RMSE reaches 0.144. In the test set, the average value of R2 reaches 0.937, the RMSE reaches 0.197. And the average time consumption of the entire process is 2.989 s on an image. It suggests that the study can provide a basis for dynamic monitoring of potato and other crops. Keywords: Canopy coverage (Cc), Leaf area index (LAI), Image processing, Potato, Rapid measurement.

Post-clearcut dynamics of carbon, water and energy exchanges in a midlatitude temperate, deciduous broadleaf forest environment

2013 ◽  
Vol 20 (3) ◽  
pp. 992-1007 ◽  
Author(s):  
Christopher A. Williams ◽  
Melanie K. Vanderhoof ◽  
Myroslava Khomik ◽  
Bardan Ghimire
Keyword(s):  
Forest Environment ◽  
Broadleaf Forest ◽  
Temperate Deciduous ◽  
Energy Exchanges ◽  
Deciduous Broadleaf Forest

Defoliation, leaf area index, and the water use of four temperate pasture species under irrigated and dryland conditions

1973 ◽  
Vol 24 (6) ◽  
pp. 783 ◽  
Author(s):  
GG Johns ◽  
A Lazenby
Keyword(s):  
Soil Moisture ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Water Use ◽  
Leaf Senescence ◽  
Late Spring ◽  
Stomatal Control ◽  
Area Index ◽  
Autumn Period ◽  
Dry Conditions

Measurements were made over a 12-month period of the water use and leaf area index (LAI) of both dryland and irrigated monoculture swards of four temperate pasture species under two defoliation regimes. All four species used similar quantities of water on the dryland plots despite large differences in their ability to grow under such conditions. Even though very dry conditions prevailed during part of the study, the dryland swards generally failed to exploit reserves of soil moisture below a depth of c. 120 cm. The water use of the irrigated swards was sensitive to the manipulation of LAI by defoliation, while in contrast, dryland water use was not. On the irrigated swards, at an LAI of 1, a 1% decrease in LAI was associated with a 1% decrease in water use. This sensitivity of water use decreased as LAI increased until, at an LAI of 3 and above, water use appeared to be insensitive to charges in LAI. During the late spring to early autumn period both irrigated and dryland water use were significantly related to LAI. In this period, those irrigated and dryland swards which had common values of LAI generally used similar quantities of water. This finding indicated that stomatal control was ineffective in reducing water use per unit of leaf area. The quantity of dead herbage present in the swards suggests that pronounced leaf senescence (and hence reduction of leaf area) may have been a consequence of ineffective stomatal control of transpiration.

scholarly journals Does drought advance the onset of autumn leaf senescence in temperate deciduous forest trees?

2020 ◽  
Author(s):  
Bertold Mariën ◽  
Inge Dox ◽  
Hans J. De Boeck ◽  
Patrick Willems ◽  
Sebastien Leys ◽  
...  
Keyword(s):  
Leaf Senescence ◽  
Deciduous Forest ◽  
Seasonal Pattern ◽  
Additive Models ◽  
Deciduous Trees ◽  
Severe Drought ◽  
Temperate Deciduous Forest ◽  
Mature Trees ◽  
Temperate Deciduous ◽  
Autumn Leaf

Abstract. Severe droughts are expected to become more frequent and persistent. However, their effect on autumn leaf senescence, a key process for deciduous trees and ecosystem functioning, is currently unclear. We hypothesized that (I) severe drought advances the onset of autumn leaf senescence in temperate deciduous trees and that (II) tree species show different dynamics of autumn leaf senescence under drought. We tested these hypotheses using a manipulative experiment on beech saplings and three years of monitoring mature beech, birch and oak trees in Belgium. The autumn leaf senescence was derived from the seasonal pattern of the chlorophyll content index and the loss of canopy greenness using generalized additive models and piece-wise linear regressions. Drought did not affect the onset of autumn leaf senescence in both saplings and mature trees, even if the saplings showed a high mortality and the mature trees a high leaf mortality (due to accelerated leaf senescence and early leaf abscission). We did not observe major differences among species. Synthesis: The timing of autumn leaf senescence appears conservative across years and species, and even independent on drought stress. Therefore, to study autumn senescence, seasonal chlorophyll dynamics and loss of canopy greenness should be considered separately.

Improved methods for predicting individual leaf area and leaf senescence in maize (Zea mays)

1998 ◽  
Vol 49 (2) ◽  
pp. 249 ◽  
Author(s):  
C. J. Birch ◽  
G. L. Hammer ◽  
K. G. Rickert
Keyword(s):  
Leaf Area ◽  
Leaf Senescence ◽  
Leaf Length ◽  
Thermal Time ◽  
Leaf Number ◽  
Area Index ◽  
Plant Leaf ◽  
Individual Leaf ◽  
Total Leaf ◽  
Maximum Temperatures

The ability to predict leaf area and leaf area index is crucial in crop simulation models that predict crop growth and yield. Previous studies have shown existing methods of predicting leaf area to be inadequate when applied to a broad range of cultivars with different numbers of leaves. The objectives of the study were to (i) develop generalised methods of modelling individual and total plant leaf area, and leaf senescence, that do not require constants that are specific to environments and/or genotypes, (ii) re-examine the base, optimum, and maximum temperatures for calculation of thermal time for leaf senescence, and (iii) assess the method of calculation of individual leaf area from leaf length and leaf width in experimental work. Five cultivars of maize differing widely in maturity and adaptation were planted in October 1994 in south-eastern Queensland, and grown under non-limiting conditions of water and plant nutrient supplies. Additional data for maize plants with low total leaf number (12-17) grown at Katumani Research Centre, Kenya, were included to extend the range in the total leaf number per plant. The equation for the modified (slightly skewed) bell curve could be generalised for modelling individual leaf area, as all coefficients in it were related to total leaf number. Use of coefficients for individual genotypes can be avoided, and individual and total plant leaf area can be calculated from total leaf number. A single, logistic equation, relying on maximum plant leaf area and thermal time from emergence, was developed to predict leaf senescence. The base, optimum, and maximum temperatures for calculation of thermal time for leaf senescence were 8, 34, and 40ºC, and apply for the whole crop-cycle when used in modelling of leaf senescence. Thus, the modelling of leaf production and senescence is simplified, improved, and generalised. Consequently, the modelling of leaf area index (LAI) and variables that rely on LAI will be improved. For experimental purposes, we found that the calculation of leaf area from leaf length and leaf width remains appropriate, though the relationship differed slightly from previously published equations.

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