Measuring seasonal dynamics of leaf area index in a mixed conifer-broadleaved forest with direct and indirect methods

Remote and non-destructive estimation of leaf area index (LAI) has been a challenge in the last few decades as the direct and indirect methods available are laborious and time-consuming. The recent emergence of high-throughput plant phenotyping platforms has increased the need to develop new phenotyping tools for better decision-making by breeders. In this paper, a novel model based on artificial intelligence algorithms and nadir-view red green blue (RGB) images taken from a terrestrial high throughput phenotyping platform is presented. The model mixes numerical data collected in a wheat breeding field and visual features extracted from the images to make rapid and accurate LAI estimations. Model-based LAI estimations were validated against LAI measurements determined non-destructively using an allometric relationship obtained in this study. The model performance was also compared with LAI estimates obtained by other classical indirect methods based on bottom-up hemispherical images and gaps fraction theory. Model-based LAI estimations were highly correlated with ground-truth LAI. The model performance was slightly better than that of the hemispherical image-based method, which tended to underestimate LAI. These results show the great potential of the developed model for near real-time LAI estimation, which can be further improved in the future by increasing the dataset used to train the model.

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A comparison of retrieval approaches for estimating the seasonal dynamics of rice leaf area index from simulated Sentinel-2 data

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI ◽

10.1117/12.2533304 ◽

2019 ◽

Author(s):

Oluseun Adeluyi ◽

Angela Harris ◽

Gareth Clay ◽

Timothy Foster

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Seasonal Dynamics ◽

Area Index ◽

Rice Leaf ◽

Sentinel 2

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Variation in leaf area index in complex mixed-conifer forests in California’s Sierra Nevada: implications for stocking control

Forestry An International Journal of Forest Research ◽

10.1093/forestry/cpaa007 ◽

2020 ◽

Vol 93 (5) ◽

pp. 641-651

Author(s):

Kevin L O’Hara ◽

John J Battles

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Sierra Nevada ◽

Timber Harvest ◽

Forest Community ◽

Conifer Forests ◽

Mixed Conifer ◽

Area Index ◽

Mixed Conifer Forests ◽

Study Sites

Abstract The mixed-conifer forests in California’s Sierra Nevada include species from several genera (Pinus, Abies, Pseudotsuga, Calocedrus and Sequoiadendron). These forests have complex disturbance regimes dominated by low to moderate severity fire that often resulted in patchy spatial patterns and multiaged stands. Leaf area index (LAI) describes the total leaf surface area per unit area in a forest community and is related to wood and biomass production and ecosystem values such as water usage, water yields and carbon sequestration. LAI can also serve as a representation of growing space occupancy and the basis for stocking control, including in multiaged stands. Nine study sites were sampled with 22–37 0.05 ha plots per study site to estimate LAI and other metrics. LAI was highest in study sites with greater proportions of shade tolerant Abies and Calocedrus species and on higher productivity sites. Recent drought-related mortality has reduced stocking and LAI. The combination of fire suppression and timber harvest over the past century has resulted in stands with higher densities, and greater proportions of shade tolerant species. Managing these structures to restore their presettlement character will involve reducing overall stocking, increasing proportions of intolerant species and increasing fine-scale heterogeneity. LAI allocation—allocating leaf area to age classes, species or canopy strata—can be used to design new structures that resemble presettlement structures and are resilient to disturbances.

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