Understanding the variability in ground-based methods for retrieving canopy openness, gap fraction, and leaf area index in diverse forest systems

Woodgate, William; Jones, Simon; Suárez, Lola; Hill, Michael J.; Armston, John; Wilkes, Phil; Soto‐Berelov, Mariela; Haywood, Andrew; Mellor, Andrew

doi:10.1016/j.agrformet.2015.02.012

Cited by 76 publications

(81 citation statements)

References 71 publications

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“…A large FOV might lead to a dramatic decrease in gap fraction, and this is a major reason why the observed values in PocketLAI were generally lower than those produced by LAISmart and LAI-2000. This phenomenon is similar to the result of the 3D canopy simulation performed by Woodgate et al [44]. They found that as the viewing angle increased, the proportion of trunk (woody) components in the visual field increased.…”

Section: Discussionsupporting

confidence: 79%

“…Another reason for gap fraction overestimation could have been the global optimum threshold method for automatic image segmentation [44]. Due to gap fraction overestimation, the estimated value of LAI was lower than the real LAI.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, when a digital image-based (e.g., DHP) method is segmented into multi-viewing-angle sub-image, a small zenith bin size is often used [44] to ensure a good correspondence between the gap fraction and the given sensor's viewing angle. However, another question must then be answered: why can a more accurate result be achieved in LAISmart when the same wide-FOV sensor is used?…”

Section: Discussionmentioning

confidence: 99%

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Potential and Limits of Retrieving Conifer Leaf Area Index Using Smartphone-Based Method

Qu¹,

Wang²,

Song³

et al. 2017

Forests

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Forest leaf area index (LAI) is a key characteristic affecting a field canopy microclimate. In addition to traditional professional measuring instruments, smartphone-based methods have been used to measure forest LAI. However, when smartphone methods were used to measure conifer forest LAI, very different performances were obtained depending on whether the smartphone was held at the zenith angle or at a 57.5 • angle. To further validate the potential of smartphone sensors for measuring conifer LAI and to find the limits of this method, this paper reports the results of a comparison of two smartphone methods with an LAI-2000 instrument. It is shown that the method with the smartphone oriented vertically upwards always produced better consistency in magnitude with LAI-2000. The bias of the LAI between the smartphone method and the LAI-2000 instrument was explained with regards to four aspects that can affect LAI: gap fraction; leaf projection ratio; sensor field of view (FOV); and viewing zenith angle (VZA). It was concluded that large FOV and large VZA cause the 57.5 • method to overestimate the gap fraction and hence underestimate conifer LAI. For the vertically upward method, the bias caused by the overestimated gap fraction is compensated for by an underestimated leaf projection ratio.

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Section: Discussionsupporting

confidence: 79%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Potential and Limits of Retrieving Conifer Leaf Area Index Using Smartphone-Based Method

Qu¹,

Wang²,

Song³

et al. 2017

Forests

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show abstract

“…The images were acquired using a Canon 5D equipped with a fisheye lens leveled on a tripod at around breast height (1.3 m above the ground) near dawn or dusk. Two-corner classification was applied on the obtained images, and combined Lang and Xiang clumping correction were used to estimate the LAI as outlined in Woodgate et al [68], Macfarlane [69], and Leblanc et al [70], respectively.…”

Section: Study Area and Field Datamentioning

confidence: 99%

Vegetation Indices for Mapping Canopy Foliar Nitrogen in a Mixed Temperate Forest

et al. 2016

Self Cite

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Hyperspectral remote sensing serves as an effective tool for estimating foliar nitrogen using a variety of techniques. Vegetation indices (VIs) are a simple means of retrieving foliar nitrogen. Despite their popularity, few studies have been conducted to examine the utility of VIs for mapping canopy foliar nitrogen in a mixed forest context. In this study, we assessed the performance of 32 vegetation indices derived from HySpex airborne hyperspectral images for estimating canopy mass-based foliar nitrogen concentration (%N) in the Bavarian Forest National Park. The partial least squares regression (PLSR) was performed for comparison. These vegetation indices were classified into three categories that are mostly correlated to nitrogen, chlorophyll, and structural properties such as leaf area index (LAI). %N was destructively measured in 26 broadleaf, needle leaf, and mixed stand plots to represent the different species and canopy structure. The canopy foliar %N is defined as the plot-level mean foliar %N of all species weighted by species canopy foliar mass fraction. Our results showed that the variance of canopy foliar %N is mainly explained by functional type and species composition. The normalized difference nitrogen index (NDNI) produced the most accurate estimation of %N (R 2 CV = 0.79, RMSE CV = 0.26). A comparable estimation of %N was obtained by the chlorophyll index Boochs2 (R 2 CV = 0.76, RMSE CV = 0.27). In addition, the mean NIR reflectance (800-850 nm), representing canopy structural properties, also achieved a good accuracy in %N estimation (R 2 CV = 0.73, RMSE CV = 0.30). The PLSR model provided a less accurate estimation of %N (R 2 CV = 0.69, RMSE CV = 0.32). We argue that the good performance of all three categories of vegetation indices in %N estimation can be attributed to the synergy among plant traits (i.e., canopy structure, leaf chemical and optical properties) while these traits may converge across plant species for evolutionary reasons. Our findings demonstrated the feasibility of using hyperspectral vegetation indices to estimate %N in a mixed temperate forest which may relate to the effect of the physical basis of nitrogen absorption features on canopy reflectance, or the biological links between nitrogen, chlorophyll, and canopy structure.

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“…Another reason for gap fraction overestimation could have been the global optimum thresholding method for automatic image segmentation [28]. Due to gap fraction overestimation, the estimated value of LAI was lower than the real LAI.…”

Section: Discussionmentioning

confidence: 99%

Potential and Limits of Retrieving Conifer Leaf Area Index Using Smartphone Camera Sensors

Wang

Song

2017

Preprint

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Plant leaf area index (LAI) is a key characteristic affecting field canopy microclimate. In addition to traditional professional measuring instruments, smartphone camera sensors have been used to measure plant LAI. However, when smartphone methods were used to measure conifer forest LAI, very different performances were obtained depending on whether the smartphone was held at the zenith angle or at a 57.5° angle. To validate further the potential of smartphone sensors for measuring conifer LAI and to find the limits of this method, this paper reports the results of a comparison of two smartphone methods with an LAI-2000 instrument. It is shown that both methods can be used to reveal the conifer leaf-growing trajectory. However, the method with the phone oriented vertically upwards always produced better consistency in magnitude with LAI-2000. The bias of the LAI between the smartphone method and the LAI-2000 instrument was explained with regard to four aspects that can affect LAI: gap fraction, leaf projection ratio, sensor field of view (FOV), and viewing zenith angle (VZA). It was concluded that large FOV and large VZA cause the 57.5° method to overestimate the gap fraction and hence underestimate conifer LAI, especially when tree height is greater than 2.0 m. For the vertically upward method, the bias caused by the overestimated gap fraction is compensated for by an underestimated leaf projection ratio.

show abstract

Understanding the variability in ground-based methods for retrieving canopy openness, gap fraction, and leaf area index in diverse forest systems

Cited by 76 publications

References 71 publications

Potential and Limits of Retrieving Conifer Leaf Area Index Using Smartphone-Based Method

Potential and Limits of Retrieving Conifer Leaf Area Index Using Smartphone-Based Method

Vegetation Indices for Mapping Canopy Foliar Nitrogen in a Mixed Temperate Forest

Potential and Limits of Retrieving Conifer Leaf Area Index Using Smartphone Camera Sensors

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