Correction for light scattering combined with sub-pixel classification improves estimation of gap fraction from digital cover photography

Here, we compare the efficiency and accuracy of remote sensing and plot-based methods for measuring vegetation cover for the understory and canopy of banksia woodland in an urban area of Western Australia. Methods compared were visual estimation, foliage cover computation from photographs, satellite imagery and aerial photographs. Observations and images from 1 m 2 , 100 m 2 and 625 m 2 quadrats measured cover of small plants, understory plants and trees respectively. Aerial photography and satellite imagery allowed the number, height and cover of trees to be estimated in 625 m 2 and 1 ha plots. The accuracy of methods was compared using a 28 month time series commencing before and after an intense bushfire that removed all foliage cover. Directly comparable methods were in close agreement and in combination allowed plant recovery to be quantified in great detail. Visual estimation of cover in the field was time-consuming but necessary to measure the contribution of individual species. Visual estimates from 1 m 2 downward photos allowed functional groups of plants to be measured. The number of green pixels selected manually in photographs confirmed that cover calculated from ground-based photographs using algorithms was accurate, except when cover was very low. We developed a new algorithm for computing cover from photographs that was accurate at low cover (Gperc). Canopy cover estimation by algorithm from upward photographs was subject to more errors, requiring exclusion of some images. Landsat satellite images allowed the impacts of severe drought and previous fires to be identified against a background of relatively consistent seasonal variations since 1988. Aerial photographs from 1953 onwards showed gradual recolonisation by banksia woodland trees over 60 years following tree felling. These methods provide a toolkit for monitoring vegetation recovery after disturbance and baseline data for monitoring banksia woodland. This toolkit should also be suitable for most other plant communities.

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“…As was also noted by Hwang et al. (), photography on uniformly overcast days provided the greatest contrast between the canopy and sky.…”

Section: Resultssupporting

confidence: 66%

A monitoring toolkit for banksia woodlands: comparison of different scale methods to measure recovery of vegetation after fire

Brundrett

Dongen²,

Huntley³

et al. 2018

Remote Sens Ecol Conserv

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“…Typically, the quantification of UVC and OCC can be conducted by using nadir (downward-looking) and zenith (upward-looking) photography methods in the field, respectively [17]. Previous studies confirmed the effectiveness and accuracy of the commonly used photography methods [18,19] to segment green vegetation from the background (i.e., soil or sky). The ground-based measurement is generally treated as ground truth or reference value in validating other remote sensing estimates [20].…”

Section: Introductionmentioning

confidence: 99%

Quantifying Understory and Overstory Vegetation Cover Using UAV-Based RGB Imagery in Forest Plantation

et al. 2020

Remote Sensing

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Vegetation cover estimation for overstory and understory layers provides valuable information for modeling forest carbon and water cycles and refining forest ecosystem function assessment. Although previous studies demonstrated the capability of light detection and ranging (LiDAR) in the three-dimensional (3D) characterization of forest overstory and understory communities, the high cost inhibits its application in frequent and successive survey tasks. Low-cost commercial red–green–blue (RGB) cameras mounted on unmanned aerial vehicles (UAVs), as LiDAR alternatives, provide operational systems for simultaneously quantifying overstory crown cover (OCC) and understory vegetation cover (UVC). We developed an effective method named back-projection of 3D point cloud onto superpixel-segmented image (BAPS) to extract overstory and forest floor pixels using 3D structure-from-motion (SfM) point clouds and two-dimensional (2D) superpixel segmentation. The OCC was estimated from the extracted overstory crown pixels. A reported method, called half-Gaussian fitting (HAGFVC), was used to segement green vegetation and non-vegetation pixels from the extracted forest floor pixels and derive UVC. The UAV-based RGB imagery and field validation data were collected from eight forest plots in Saihanba National Forest Park (SNFP) plantation in northern China. The consistency of the OCC estimates between BAPS and canopy height model (CHM)-based methods (coefficient of determination: 0.7171) demonstrated the capability of the BAPS method in the estimation of OCC. The segmentation of understory vegetation was verified by the supervised classification (SC) method. The validation results showed that the OCC and UVC estimates were in good agreement with reference values, where the root-mean-square error (RMSE) of OCC (unitless) and UVC (unitless) reached 0.0704 and 0.1144, respectively. The low-cost UAV-based observation system and the newly developed method are expected to improve the understanding of ecosystem functioning and facilitate ecological process modeling.

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“…In the field, if one alters the 512 camera exposure to gain contrast between leaf and woody elements, then the image is likely 513 to be over-exposed, leading to higher Pgap than from 'optimally' exposed images. Analternative to HP is to capture multi-angular images using narrow FOV digital cover 515 photography methods (Hwang et al, 2016;Macfarlane et al, 2007b;Macfarlane et al, 2014;516 Piayda et al, 2015). The comparatively higher image resolution over the same image sampling 517 domain has the advantage of being less sensitive to exposure (Blennow, 1995).…”

mentioning

confidence: 99%

Quantifying the impact of woody material on leaf area index estimation from hemispherical photography using 3D canopy simulations

Woodgate

Armston

Disney

et al. 2016

Agricultural and Forest Meteorology

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Correction for light scattering combined with sub-pixel classification improves estimation of gap fraction from digital cover photography

Abstract: and for calibrating and evaluating satellite remote sensing LAI products.

Cited by 32 publications

References 44 publications

A monitoring toolkit for banksia woodlands: comparison of different scale methods to measure recovery of vegetation after fire

A monitoring toolkit for banksia woodlands: comparison of different scale methods to measure recovery of vegetation after fire

Quantifying Understory and Overstory Vegetation Cover Using UAV-Based RGB Imagery in Forest Plantation

Quantifying the impact of woody material on leaf area index estimation from hemispherical photography using 3D canopy simulations

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