Characterizing Forest Structure by Means of Remote Sensing: A Review

Latifi, Hooman

doi:10.5772/35143

Cited by 13 publications

(15 citation statements)

References 88 publications

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“…Light detection and ranging (LiDAR) systems provide accurate data for describing forest structure because of their ability to measure the inherent three-dimensional structure of stands via discrete return or full waveform measurements (reviewed in Latifi, 2012). The assets of LiDAR data have been discussed based on case studies on a wide variety of forest ecosystems worldwide, ranging from boreal (Hyyppä et al, 2008) to temperate Tsui et al, 2013) and tropical (Drake et al, 2002;Treuhaft et al, 2010) ecosystems (see also Fassnacht et al, 2014 for a systematic review).…”

Section: Introductionmentioning

confidence: 98%

Forest inventories by LiDAR data: A comparison of single tree segmentation and metric-based methods for inventories of a heterogeneous temperate forest

Latifi

Fassnacht

Müller

et al. 2015

International Journal of Applied Earth Observation and Geoinfor

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Section: Introductionmentioning

confidence: 98%

Forest inventories by LiDAR data: A comparison of single tree segmentation and metric-based methods for inventories of a heterogeneous temperate forest

Latifi

Fassnacht

Müller

et al. 2015

International Journal of Applied Earth Observation and Geoinfor

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“…Various structural parameters, such as crown size [33,34], tree height and location [35,36], canopy cover [37], and LAI [38], have been successfully obtained from ALS data. Latifi [39] found that ALS data outperformed other remotely sensed data in predicting forest structure parameters. Further, the detailed vertical structure [40] and shadowing effects among tree crowns implicitly contained within ALS data allow us to estimate the proportions of four forest components without using a statistical-based method.…”

Section: Introductionmentioning

confidence: 99%

Characterizing the Spatial Variations of Forest Sunlit and Shaded Components Using Discrete Aerial Lidar

et al. 2020

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Forest three-dimensional (3-D) structure, in the vertical dimension, consists of at least two components, including overstory and a forest background matrix (i.e., shrubs, grass, and bare earth). Quantitatively characterizing the proportions of forest sunlit (i.e., sunlit overstory and forest background) and shaded (i.e., shaded overstory and forest background) components is a crucial step in simulating the spatial variations of bidirectional reflectance distribution function (BRDF) of a forest canopy. By developing a Voxel-based sorest sunlit and shaded (VFSS) approach driven by aerial laser scanning data (ALS), we investigated the spatial variations of the forest sunlit and shaded components in a heterogeneous urban forest park (Washington Park Arboretum) with abundant tree species and a homogeneous natural forest area (Panther Creek). Meanwhile, we validated the forest canopy directional reflectance at both solar principal and perpendicular planes at the plot level. Moreover, we explored the effects of ALS data characteristics and forest stand conditions on the estimation accuracy of forest sunlit and shaded components. Our results show that (1) ALS data effectively stratify overstory and forest background with the accuracy decreasing from 87% to 65% as forest densities increase; (2) the root mean square errors (RMSEs) between the modeled- and ALS-based proportions of forest sunlit and shaded components range from 5.8% to 11.1% affected by forest densities; and (3) the scan angles and flight directions have apparent effects on the estimation accuracy of forest sunlit and shaded components. This work provides a solid foundation to investigate the spatial variations of directional forest canopy reflectance with a high spatial resolution of 1 m.

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“…In-doc controls inventory information at unprecedented spatial and temporal resolutions. Specifically, discrete return airborne light detection and ranging (LiDAR) technology has extensively been used in the past two decades in forestry [4,5]. Due to its ability to penetrate vegetation canopy, LiDAR data captured in the shape of 3D point clouds contain vertical information from which vegetation structural characteristics can be retrieved, even from understory canopy layers.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing of Forests using Discrete Return Airborne LiDAR

Hamraz¹,

Contreras²

2018

Recent Advances and Applications in Remote Sensing

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Airborne discrete return light detection and ranging (LiDAR) point clouds covering forested areas can be processed to segment individual trees and retrieve their morphological attributes. Segmenting individual trees in natural deciduous forests however remained a challenge because of the complex and multi-layered canopy. In this chapter, we present (i) a robust segmentation method that avoids a priori assumptions about the canopy structure, (ii) a vertical canopy stratification procedure that improves segmentation of understory trees, (iii) an occlusion model for estimating the point density of each canopy stratum, and (iv) a distributed computing approach for efficient processing at the forest level. When applied to the University of Kentucky Robinson Forest, the segmentation method detected about 90% of overstory and 47% of understory trees with over-segmentation rates of 14% and 2%. Stratifying the canopy improved the detection rate of understory trees to 68% at the cost of increasing their over-segmentations to 16%. According to our occlusion model, a point density of ~170 pt/m² is needed to segment understory trees as accurately as overstory trees. Lastly, using the distributed approach, we segmented about two million trees in the 7,440-ha forest in 2.5 hours using 192 processors, which is 167 times faster than using a single processor.

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Characterizing Forest Structure by Means of Remote Sensing: A Review

Cited by 13 publications

References 88 publications

Forest inventories by LiDAR data: A comparison of single tree segmentation and metric-based methods for inventories of a heterogeneous temperate forest

Forest inventories by LiDAR data: A comparison of single tree segmentation and metric-based methods for inventories of a heterogeneous temperate forest

Characterizing the Spatial Variations of Forest Sunlit and Shaded Components Using Discrete Aerial Lidar

Remote Sensing of Forests using Discrete Return Airborne LiDAR

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