Estimation of regeneration coverage in a temperate forest by 3D segmentation using airborne laser scanning data

Amiri, N.; Yao, Wei; Heurich, Marco; Krzystek, Peter; Skidmore, Andrew K.

doi:10.1016/j.jag.2016.06.022

Cited by 30 publications

(38 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One drawback of the single tree approach is that the LiDAR beam loses some signal strength on its way through the canopy. This results in an underestimation of individual trees in the understory [117]. To overcome this problem, methods have been developed to predict stem diameter distributions of forest stands based on the detectable trees in the upper canopy and LiDAR derived information about the vertical forest structure and density [118].…”

Section: Light Detection and Ranging (Lidar)mentioning

confidence: 99%

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

et al. 2017

Self Cite

View full text Add to dashboard Cite

Stress in forest ecosystems (FES) occurs as a result of land-use intensification, disturbances, resource limitations or unsustainable management, causing changes in forest health (FH) at various scales from the local to the global scale. Reactions to such stress depend on the phylogeny of forest species or communities and the characteristics of their impacting drivers and processes. There are many approaches to monitor indicators of FH using in-situ forest inventory and experimental studies, but they are generally limited to sample points or small areas, as well as being time-and labour-intensive. Long-term monitoring based on forest inventories provides valuable information about changes and trends of FH. However, abrupt short-term changes cannot sufficiently be assessed through in-situ forest inventories as they usually have repetition periods of multiple years. Furthermore, numerous FH indicators monitored in in-situ surveys are based on expert judgement. Remote sensing (RS) technologies offer means to monitor FH indicators in an effective, repetitive and comparative way. This paper reviews techniques that are currently used for monitoring, including close-range RS, airborne and satellite approaches. The implementation of optical, RADAR and LiDAR RS-techniques to assess spectral traits/spectral trait variations (ST/STV) is described in detail. We found that ST/STV can be used to record indicators of FH based on RS. Therefore, the ST/STV approach provides a framework to develop a standardized monitoring concept for FH indicators using RS techniques that is applicable to future monitoring programs. It is only through linking in-situ and RS approaches that we will be able to improve our understanding of the relationship between stressors, and the associated spectral responses in order to develop robust FH indicators.

show abstract

Section: Light Detection and Ranging (Lidar)mentioning

confidence: 99%

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent years, mean shift-based clustering techniques have been applied in individual tree delineation using LiDAR data [16][17][18][19]. Mean shift is a powerful and versatile feature-space analysis technique and has found much interest in the image processing and computer vision community [20,21].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it can handle arbitrarily shaped clusters. It is confirmed that the mean shift clusters can better indicate smaller or suppressed trees in the understory, which is not possible to do using other kinds of methods [19]. For simultaneous segmentation of vertical and horizontal structures of forest canopies, Ferraz et al proposed a multi-scale mean shift method where modes are computed with a stratum-dependent kernel bandwidth whose values are increased as a function of the vegetation layer's height [16,17].…”

Section: Introductionmentioning

confidence: 99%

“…A mean shift-based two-staged recursive computing scheme on the data sets in a defined complex multimodal feature space was employed to cluster points with similar vegetation modes together and an overall detection accuracy of more than 80% was reported in [18]. Mean shift clustering was also utilized to construct small parts of trees in a two-tiered 3D segmentation of forest point clouds into segments associated with regeneration trees and up to 70% of the regeneration coverage in a multilayered temperate forest could be correctly estimated [19].…”

Section: Introductionmentioning

confidence: 99%

“…Obviously, an adaptive selection scheme for the kernel bandwidth considering local structure information outperforms that using a single bandwidth for the whole feature space. Methods proposed in [16][17][18][19] are all fixed bandwidth mean shift procedures, where a single kernel bandwidth in a forest stand or a specific vegetation stratum is not adapted to heterogeneous crown sizes and thus could result in obvious under-segmentations or over-segmentations. Ferraz et al designed a technique called 3D adaptive mean shift (AMS3D), in which three bandwidths of different sizes were applied to three forest layers: overstory, understory and ground vegetation, to decompose the entire point cloud into 3D clusters that correspond to individual tree crowns [17].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Mean Shift-Based Identiﬁcation of Individual Trees Using Airborne LiDAR Data

Chen

2017

Remote Sensing

View full text Add to dashboard Cite

Abstract:Identifying individual trees and delineating their canopy structures from the forest point cloud data acquired by an airborne LiDAR (Light Detection And Ranging) has significant implications in forestry inventory. Once accurately identified, tree structural attributes such as tree height, crown diameter, canopy based height and diameter at breast height can be derived. This paper focuses on a novel computationally efficient method to adaptively calibrate the kernel bandwidth of a computational scheme based on mean shift-a non-parametric probability density-based clustering technique-to segment the 3D (three-dimensional) forest point clouds and identify individual tree crowns. The basic concept of this method is to partition the 3D space over each test plot into small vertical units (irregular columns containing 3D spatial features from one or more trees) first, by using a fixed bandwidth mean shift procedure and a small square grouping technique, and then rough estimation of crown sizes for distinct trees within a unit, based on an original 2D (two-dimensional) incremental grid projection technique, is applied to provide a basis for dynamical calibration of the kernel bandwidth for an adaptive mean shift procedure performed in each partition. The adaptive mean shift-based scheme, which incorporates our proposed bandwidth calibration method, is validated on 10 test plots of a dense, multi-layered evergreen broad-leaved forest located in South China. Experimental results reveal that this approach can work effectively and when compared to the conventional point-based approaches (e.g., region growing, k-means clustering, fixed bandwidth or multi-scale mean shift), its accuracies are relatively high: it detects 86 percent of the trees ("recall") and 92 percent of the identified trees are correct ("precision"), showing good potential for use in the area of forest inventory.

show abstract

Improved Method to Calculate Urban Forest Vertical Structure Using Airborne Laser Scanning Data

Popov,

Semko,

Kozak

et al. 2024

Studies in Systems, Decision and Control

View full text Add to dashboard Cite

Estimation of regeneration coverage in a temperate forest by 3D segmentation using airborne laser scanning data

Cited by 30 publications

References 23 publications

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

Adaptive Mean Shift-Based Identiﬁcation of Individual Trees Using Airborne LiDAR Data

Improved Method to Calculate Urban Forest Vertical Structure Using Airborne Laser Scanning Data

Contact Info

Product

Resources

About