Continuous estimation of leaf area index and the woody-to-total area ratio of two deciduous shrub canopies using fisheye webcams in a semiarid loessial region of China

Niu, Xiaotao; Fan, Jun; Luo, Ruihua; Fu, Wei; Yuan, Hongyou; Du, Mengge

doi:10.1016/j.ecolind.2021.107549

Cited by 12 publications

(3 citation statements)

References 48 publications

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“…Our results echo others' in finding that the prevalence of woody material in the tree canopy, and therefore α, is dynamic and varies by species as well as senescence, crown health and, in the case of deciduous forests, leaf phenology (Gower et al, 1999). The use of single α value in a plot or region (Olivas et al, 2013;Woodgate et al, 2016), invariant of species, size and forest structure, to convert PAI to LAI is therefore problematic (Niu et al, 2021). Our study demonstrates the importance of taking species mix and structural variation into account when correcting for non-photosynthetic material in ground-based LAI estimates.…”

Section: Correcting For Non-photosynthetic Elements In Lai Estimates ...supporting

confidence: 60%

Quantifying vegetation indices using TLS: methodological complexities and ecological insights from a Mediterranean forest

Flynn

Owen

Grieve

et al. 2022

Preprint

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Abstract. Accurate measurement of vegetation density metrics including plant, wood and leaf area indices (PAI, WAI and LAI) is key to monitoring and modelling carbon storage and uptake in forests. Traditional passive sensor approaches, such as Digital Hemispherical Photography (DHP), cannot separate leaf and wood material, nor individual trees, and require many assumptions in processing. Terrestrial Laser Scanning (TLS) data offer new opportunities to improve understanding of tree and canopy structure. Multiple methods have been developed to derive PAI and LAI from TLS data, but there is little consensus on the best approach, nor are methods benchmarked as standard. Using TLS data collected in 33 plots containing 2472 trees of five species in Mediterranean forests, we compare three TLS methods (LiDAR Pulse, 2D Intensity Image and Voxel-Based) to derive PAI and compare with co-located DHP. We then separate leaf and wood in individual tree point clouds to calculate wood to total plant area (α), a metric to correct for non-photosynthetic material in LAI estimates. We use individual tree TLS point clouds to estimate how α varies with species, tree height and stand density. We find the LiDAR Pulse method agrees most closely with DHP, but is limited to single scan data so cannot determine individual tree α. The Voxel-Based method shows promise for ecological studies as it can be applied to individual tree point clouds. Using the Voxel-Based method, we show that species explain some variation in α, however, height and density were stronger predictors. Our findings highlight the value of TLS data to improve fundamental understanding of tree form and function, but also the importance of rigorous testing of TLS data processing methods at a time when new approaches are being rapidly developed. New algorithms need to be compared against traditional methods, and existing algorithms, using common reference data. Whilst promising, our results show that metrics derived from TLS data are not yet reliably calibrated and validated to the extent they are ready to replace traditional approaches for large scale monitoring of PAI and LAI.

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Section: Correcting For Non-photosynthetic Elements In Lai Estimates ...supporting

confidence: 60%

Quantifying vegetation indices using TLS: methodological complexities and ecological insights from a Mediterranean forest

Flynn

Owen

Grieve

et al. 2022

Preprint

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“…Precipitation was found to be the only source of soil water in the CLP, and the relationship between precipitation and evapotranspiration determined the fluctuation in θ v (Bai et al, 2020). The temperature in the study area increased in April (Figure S1), and plant growth and development accelerated with a gradual increase in evapotranspiration (Niu et al, 2021). As a result, the θ v showed a decreasing trend from April to June due to low precipitation and increased evapotranspiration (Figure 6).…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the soil water content of two managed ecosystems using cosmic‐ray neutron sensing on China's Loess Plateau

Liu

Jia

Ren

et al. 2023

European J Soil Science

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Cosmic‐ray neutron sensing (CRNS) is a promising method for the continuous monitoring of soil water content (θv) at the hectometre scale. However, few studies have validated its applicability within the complex terrains of various ecosystems in semiarid loess regions. In this study, CRNS‐based θv was measured on China's Loess Plateau under two managed ecosystems (Caragana korshinskii K. shrubland [KOS] and Medicago sativa L. grassland [SAG]). The accuracy of CRNS measurement was assessed with the traditional oven‐drying method, while its sensitivity to θv variation during the study period was compared with typical point‐scale monitoring sensors (EC‐5, Decagon Devices, Inc.). CRNS was found to be strongly correlated with the oven‐drying based θv under both ecosystems, with coefficients of determination (R2) of 0.99 and 0.93, and root mean square errors of 0.006 and 0.007 cm3/cm3, for KOS and SAG, respectively. The mean monitoring radius of the CRNS was approximately 204 and 231 m for KOS and SAG, with a mean measurement depth of approximately 39 and 45 cm, respectively. Moreover, the θv determined by the CRNS and EC‐5 sensors exhibited similar trends during the observation period, excluding that for April. However, the CRNS‐based θv had a higher spatial representation for both managed ecosystems compared with that for EC‐5. No significant difference was detected in the response range of θv to precipitation between the CRNS and EC‐5 sensors at a depth of 0–10 cm, whereas the response time of CRNS for KOS was much shorter than that of EC‐5 sensors (p < 0.01). Overall, the CRNS measured hectometre‐scale θv had acceptable accuracy and sensitively traced the precipitation response under different ecosystems with high θv heterogeneity in the semiarid loess regions. Highlights CRNS accurately estimates hectometre‐scale θv in ecosystems with complex terrains. The CRNS monitoring radius is ~218 m with ~42 cm measurement depth in the loess region. The θv in the managed shrubland is higher than that of the managed grassland. CRNS has a higher sensitivity in response to precipitation than EC‐5 sensors.

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“…Photos were taken within 3 min per hour, with no more than 20 s between the eight photos taken per hour. All DPWs were adjusted to auto‐exposure to exclude the oversaturation problems (Demarez et al, 2008; Niu et al, 2021) and took pictures at maximum resolutions of 3072 × 2048 pixels and saved in JPG format, as shown in Figure 1e–l. All photos of two grasslands were processed by CAN‐EYE software, and there are four main operational steps.…”

Section: Methodsmentioning

confidence: 99%

LAI‐based reduction in growth of semiarid grasslands and its responses to climate change on China's Loess Plateau

Niu

Fan

2023

Land Degrad Dev

Self Cite

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Understanding the response of vegetation growth to climate change can provide key information for predicting future ecosystem dynamics. Over the past 6 years, the semiarid regions of the Loess Plateau in China have experienced a transition from wet to dry years, but little is known about the response of grass growth to this change. Thus, this study aimed to analyze and evaluate the effect of climate change on grass growth. Using observed climate data and leaf area index (LAI) data obtained with hemispherical photography from 2016 to 2021, we investigated changes in trends of five climatic factors, including precipitation (P), temperature (T), relative humidity (RH), wind speed (WS), and solar radiation (Rs), and their impact on the growth of alfalfa (Medicago sativa) and Stipa bungeana plantations in the semiarid area of the Loess Plateau. The results showed a tendency to become colder in spring and drier in summer and autumn. In response, LAI tended to decrease in alfalfa and S. bungeana plots with interannual variation rates of 0.21 year−1 and 0.12 year−1, respectively. Additionally, linear regression methods showed that P had the greatest effect on LAI of two grasslands during the growing season (R2 > 0.84, p < 0.01), followed by RH and T (0.30 < R2 < 0.50, p < 0.01), a negative effect of WS (R2 = 0.36, p < 0.01), and no significant effect of Rs on LAI were found. Conversely, the path analysis indicated that Rs and P were the largest decision coefficients affecting the monthly LAI of alfalfa and S. bungeana, respectively, due to the different time‐to‐peak LAI and root growth depth of these two grass species. This study can provide theoretical support for ecological restoration and system management of semiarid grasslands.

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Continuous estimation of leaf area index and the woody-to-total area ratio of two deciduous shrub canopies using fisheye webcams in a semiarid loessial region of China

Cited by 12 publications

References 48 publications

Quantifying vegetation indices using TLS: methodological complexities and ecological insights from a Mediterranean forest

Quantifying vegetation indices using TLS: methodological complexities and ecological insights from a Mediterranean forest

Evaluation of the soil water content of two managed ecosystems using cosmic‐ray neutron sensing on China's Loess Plateau

LAI‐based reduction in growth of semiarid grasslands and its responses to climate change on China's Loess Plateau

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