Vegetation cover fraction (fCover) and related quantities are basic yet critical vegetation structure variables in various disciplines and applications. Ground- and aerial-based proximal and remote sensing techniques have been widely adapted across multiple spatial extents. However, the definitions of fCover-related nomenclatures have not yet been fully standardized, leading to confusing terms and making comparing historic measures difficult. With the issues potentially arising from an increasing diversity of fCover and related quantities estimation methods and corresponding uncertainties, there is also a growing need to spread knowledge on the current advances, challenges, and perspectives, especially in the context of no such existing review for ground- and aerial- based estimation. This paper provides the current knowledge mainly concerning passive image-based methods and active light detection and ranging (LiDAR) -based methods. We first harmonized the definitions of fCover and its related quantities (e.g., effective canopy cover, crown cover, stratified vegetation cover, and canopy fraction). Secondly, the typical applications of fCover and related quantities over a range of scales, fields, and ecosystems were summarized. Thirdly yet importantly, we offered a comprehensive review of traditional non-imaging methods, image-based methods (e.g., segmentation, unmixing, and spectral retrieval), point cloud-based methods (e.g., rasterization), and LiDAR return-based methods (e.g., return number index and return intensity retrieval) across different platforms (i.e., ground, unmanned aerial vehicle (UAV) and airplane). Our investigation of fCover and related quantities estimation touches upon various vegetation ecosystems, including agriculture cropland, grassland, wetland, and forest. Finally, the current challenges and future directions were discussed, such as image signal processing under complex heterogeneous surfaces and stratified cover and non-photosynthesis cover retrieval. We, therefore, expect that this review may offer an insight into fCover and related quantities estimation and serve as a reference for remote sensing scientists, agronomists, silviculturists, and ecologists. 

Review of ground and aerial methods for vegetation cover fraction (fCover) and related quantities estimation: definitions, advances, challenges, and future perspectives

Forest canopy cover (FCC) is an important ecological parameter of forest ecosystems, and is correlated with forest characteristics, including plant growth, regeneration, biodiversity, light regimes, and hydrological properties. Here, we present an approach of combining Sentinel-2 data, high-resolution aerial images, and machine learning (ML) algorithms to model FCC in the Hyrcanian mixed temperate forest, Northern Iran. Sentinel-2 multispectral bands and vegetation indices were used as variables for modeling and mapping FCC based on UAV ground truth to a wider spatial extent. Random forest (RF), support-vector machine (SVM), elastic net (ENET), and extreme gradient boosting (XGBoost) were the ML algorithms used to learn and generalize on the remotely sensed variables. Evaluation of variable importance indicated that vegetation indices including NDVI, NDVI-A, NDRE, and NDI45 were the dominant predictors in most of the models. Model accuracy estimation results showed that among the tested models, RF (R2 = 0.67, RMSE = 18.87%, MAE = 15.35%) and ENET (R2 = 0.63, RMSE = 20.04%, MAE = 16.44%) showed the best and the worst performance, respectively. In conclusion, it was possible to prove the suitability of integrating UAV-obtained RGB images, Sentinel-2 data, and ML models for the estimation of FCC, intended for precise and fast mapping at landscape-level scale.

/pdf/modeling-forest-canopy-cover-a-synergistic-use-of-sentinel-2-2y4oergt.pdf

Modeling Forest Canopy Cover: A Synergistic Use of Sentinel-2, Aerial Photogrammetry Data, and Machine Learning

Terrestrial laser scanning (TLS) accompanied by quantitative tree-modeling algorithms can potentially acquire branching data non-destructively from a forest environment and aid the development and ...

/pdf/assessing-branching-structure-for-biomass-and-wood-quality-2grqs9lfd1.pdf

Assessing branching structure for biomass and wood quality estimation using terrestrial laser scanning point clouds

Airborne Laser Scanning

A LiDAR biomass index-based approach for tree- and plot-level biomass mapping over forest farms using 3D point clouds

In estimating aboveground forest biomass (AGB), three sources of error that interact and propagate include (i) measurement error, the quality of the tree-level measurement data used as inputs for the individual-tree equations; (ii) model error, the uncertainty about the equations of the individual trees; and (iii) sampling error, the uncertainty due to having obtained a probabilistic or purposive sample, rather than a census, of the trees on a given area of forest land. Monte Carlo simulations were used to examine measurement, model, and sampling errors and to compare total uncertainty between models and between a phase-based terrestrial laser scanner (TLS) and traditional forest inventory instruments. Input variables for the equations were diameter at breast height, total tree height (defined the height from the uphill side of the tree to the tree top), and height to crown base; these were extracted from the terrestrial LiDAR data. Relative contributions for measurement, model, and sampling errors were 5...

/pdf/examination-of-uncertainty-in-per-unit-area-estimates-of-2rl0okagwd.pdf

Examination of uncertainty in per unit area estimates of aboveground biomass using terrestrial LiDAR and ground data

Predicting canopy cover of diverse forest types from individual tree measurements

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Examination of uncertainty in per unit area estimates of aboveground biomass using terrestrial LiDAR and ground data

Predicting canopy cover of diverse forest types from individual tree measurements