scholarly journals coveR: An R package for processing Digital Cover Photography images to retrieve forest canopy attributes

2022 ◽  
Author(s):  
Francesco Chianucci ◽  
Carlotta Ferrara ◽  
Nicola Puletti

Digital Cover Photography (DCP) is an increasingly popular tool for estimating canopy cover and leaf area index (LAI). However, existing solutions to process canopy images are predominantly tailored for fisheye photography, whereas open-access tools for DCP are lacking. We developed an R package (coveR) to support the whole processing of DCP images in an automated, fast and reproducible way. The package functions, which are designed for step-by-step single-image analysis, can be performed sequentially in a pipeline and also allow simple implementation of batch-processing bunches of images. A case study is presented to demonstrate the reliability of canopy attributes derived from coveR in pure beech (Fagus sylvatica L.) stands with variable canopy density and structure. Estimates of gap fraction and effective LAI from DCP were validated against reference measurements obtained from terrestrial laser scanning. By providing a simple, transparent and flexible image processing procedure, coveR supported the use of DCP for routine measurements and monitoring of forest canopy attributes. This, combined with the implementability of DCP in many devices, including smartphones, micro-cameras, and remote trail cameras, can greatly expand the accessibility of the method also to non-experts.

Author(s):  
K. C. Chen ◽  
C. K. Wang

The quantification of forest carbon sequestration is helpful to understand the carbon storage on the Earth. The estimation of forest carbon sequestration can be achieved by the use of leaf area index (LAI), which is derived from forest gap fraction. The hemispherical image-based technique is the most popular non-destructive means for obtaining such information. However, only the gap fraction of the top canopy is derived due to the limitation of imaging technique. The gap fraction information of understory is thus neglected. In this study, we evaluate the use of a terrestrial laser scanner (TLS) to obtain the forest canopy and understory gap fraction. The forest TLS data were manually classified as the top canopy and understory layers to facilitate the estimation of top canopy and understory gap fraction, respectively.


Author(s):  
K. C. Chen ◽  
C. K. Wang

The quantification of forest carbon sequestration is helpful to understand the carbon storage on the Earth. The estimation of forest carbon sequestration can be achieved by the use of leaf area index (LAI), which is derived from forest gap fraction. The hemispherical image-based technique is the most popular non-destructive means for obtaining such information. However, only the gap fraction of the top canopy is derived due to the limitation of imaging technique. The gap fraction information of understory is thus neglected. In this study, we evaluate the use of a terrestrial laser scanner (TLS) to obtain the forest canopy and understory gap fraction. The forest TLS data were manually classified as the top canopy and understory layers to facilitate the estimation of top canopy and understory gap fraction, respectively.


2021 ◽  
Author(s):  
Gastón Mauro Díaz

1) Hemispherical photography (HP) is a long-standing tool for forest canopy characterization. Currently, there are low-cost fisheye lenses to convert smartphones into high-portable HP equipment; however, they cannot be used whenever since HP is sensitive to illumination conditions. To obtain sound results outside diffuse light conditions, a deep-learning-based system needs to be developed. A ready-to-use alternative is the multiscale color-based binarization algorithm, but it can provide moderate-quality results only for open forests. To overcome this limitation, I propose coupling it with the model-based local thresholding algorithm. I call this coupling the MBCB approach. 2) Methods presented here are part of the R package CAnopy IMage ANalysis (caiman), which I am developing. The accuracy assessment of the new MBCB approach was done with data from a pine plantation and a broadleaf native forest. 3) The coefficient of determination (R^2) was greater than 0.7, and the root mean square error (RMSE) lower than 20 %, both for plant area index calculation. 4) Results suggest that the new MBCB approach allows the calculation of unbiased canopy metrics from smartphone-based HP acquired in sunlight conditions, even for closed canopies. This facilitates large-scale and opportunistic sampling with hemispherical photography.


Forests ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1324
Author(s):  
Xi Peng ◽  
Anjiu Zhao ◽  
Yongfu Chen ◽  
Qiao Chen ◽  
Haodong Liu ◽  
...  

Knowledge of forest structure is vital for sustainable forest management decisions. Terrestrial laser scanning cannot describe the canopy trees in a large area, and it is unclear whether unmanned aerial vehicle-light detection and ranging (UAV-LiDAR) data have the ability to capture the forest canopy structural parameters in tropical forests. In this study, we estimated five forest canopy structures (stand density (N), basic area (G), above-ground biomass (AGB), Lorey’s mean height (HL), and under-crown height (hT)) with four modeling algorithms (linear regression (LR), bagged tree (BT), support vector regression (SVR), and random forest (RF)) based on UAV-LiDAR data and 60 sample plot data from tropical forests in Hainan and determined the optimal algorithms for the five canopy structures by comparing the performance of the four algorithms. First, we defined the canopy tree as a tree with a height ≥70% HL. Then, UAV-LiDAR metrics were calculated, and the LiDAR metrics were screened by recursive feature elimination (RFE). Finally, a prediction model of the five forest canopy structural parameters was established by the four algorithms, and the results were compared. The metrics’ screening results show that the most important LiDAR indexes for estimating HL, AGB, and hT are the leaf area index and some height metrics, while the most important indexes for estimating N and G are the kurtosis of heights and the coefficient of variation of height. The relative root mean squared error (rRMSE) of five structure parameters showed the following: when modeling HL, the rRMSEs (10.60%–12.05%) obtained by the four algorithms showed little difference; when N was modeled, BT, RF, and SVR had lower rRMSEs (26.76%–27.44%); when G was modeled, the rRMSEs of RF and SVR (15.37%–15.87%) were lower; when hT was modeled, BT, RF, and SVR had lower rRMSEs (10.24%–11.07%); when AGB was modeled, RF had the lowest rRMSE (26.75%). Our results will help facilitate choosing LiDAR indexes and modeling algorithms for tropical forest resource inventories.


2019 ◽  
Vol 11 (1) ◽  
pp. 92 ◽  
Author(s):  
Danilo Roberti Alves de Almeida ◽  
Scott C. Stark ◽  
Gang Shao ◽  
Juliana Schietti ◽  
Bruce Walker Nelson ◽  
...  

Airborne Laser Scanning (ALS) has been considered as a primary source to model the structure and function of a forest canopy through the indicators leaf area index (LAI) and vertical canopy profiles of leaf area density (LAD). However, little is known about the effects of the laser pulse density and the grain size (horizontal binning resolution) of the laser point cloud on the estimation of LAD profiles and their associated LAIs. Our objective was to determine the optimal values for reliable and stable estimates of LAD profiles from ALS data obtained over a dense tropical forest. Profiles were compared using three methods: Destructive field sampling, Portable Canopy profiling Lidar (PCL) and ALS. Stable LAD profiles from ALS, concordant with the other two analytical methods, were obtained when the grain size was less than 10 m and pulse density was high (>15 pulses m−2). Lower pulse densities also provided stable and reliable LAD profiles when using an appropriate adjustment (coefficient K). We also discuss how LAD profiles might be corrected throughout the landscape when using ALS surveys of lower density, by calibrating with LAI measurements in the field or from PCL. Appropriate choices of grain size, pulse density and K provide reliable estimates of LAD and associated tree plot demography and biomass in dense forest ecosystems.


2020 ◽  
Vol 12 (13) ◽  
pp. 2126 ◽  
Author(s):  
Zhaoshang Xu ◽  
Guang Zheng ◽  
L. Monika Moskal

Accurately mapping forest effective leaf area index (LAIe) at the landscape level is a crucial step to better simulate various ecological and physiological processes such as photosynthesis, respiration, transpiration, and precipitation interception. The LAIe products obtained from two-dimensional (2-D) remotely sensed optical imageries are usually biased due to their inability to identify the vertical forest structure and eliminate the effects of forest background (i.e., shrubs, grass, snow, and bare earth). In this study, we first stratified the forest overstory and background layers and generated a forest background mask layer based on the structural information implicitly contained within the aerial laser scanning (ALS) data. We improved the retrieval accuracy of LAIe by combining light detection and ranging (Lidar)-based three dimensional (3-D) structural and 2-D spectral information. Then, we obtained the improved final LAIe estimation result by masking the forest background pixels from the optical remotely sensed imageries. Our results showed that: (1) Removing forest background information could effectively (R2 increase from 20% to 30%) improve the estimation accuracy of optical-based forest LAIe depending on forest structure characteristics. (2) The forest background in the forest stands with low canopy cover showed more apparent effects on LAIe estimation compared with the forest stands with a high canopy cover. (3) The combination of ALS and optical remotely sensed data could produce the best LAIe retrieval result effectively by removing the forest background information.


2021 ◽  
Author(s):  
Adam Erickson ◽  
Nicholas Coops

Reliable estimates of canopy light transmission are critical to understanding the structure and function of vegetation communities but are difficult and costly to attain by traditional field inventory methods. Airborne laser scanning (ALS) data uniquely provide multi-angular vertically resolved representation of canopy geometry across large geographic areas. While previous studies have proposed ALS indices of canopy light transmission, new algorithms based on theoretical advancements may improve existing models. Herein, we propose two new models of canopy light transmission (i.e., gap fraction, or Po, the inverse of angular canopy closure). We demonstrate the models against a suite of existing models and ancillary metrics, validated against convex spherical densiometer measurements for 950 field plots in the foothills of Alberta, Canada. We also tested the effects of synthetic hemispherical lens models on the performance of the proposed hemispherical Voronoi gap fraction (Phv) index. While vertical canopy cover metrics showed the best overall fit to field measurements, one new metric, point-density-normalized gap fraction (Ppdn), outperformed all other gap fraction metrics by two-fold. We provide suggestions for further algorithm enhancements based on validation data improvements. We argue that traditional field measurements are no longer appropriate for ‘ground-truthing’ modern LiDAR or SfM point cloud models, as the latter provide orders of magnitude greater sampling and coverage. We discuss the implications of this finding for LiDAR applications in forestry.


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