Full-Waveform Airborne Laser Scanning in Vegetation Studies—A Review of Point Cloud and Waveform Features for Tree Species Classification

Koenig, Kristina; Höfle, Bernhard

doi:10.3390/f7090198

Cited by 53 publications

(40 citation statements)

References 85 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LIDAR applications for assessing settlements are presented among others in publications (Fisher et al, 2016), (Prufer et al, 2015), (Ebert et al, 2017) (FOTOLOT). Laser scanning capabilities are shown in forest areas (Koening and Hoefle, 2016) and (Wallace et al, 2012). The second way shows that by analyzing laser scanning products it is possible to validate the results of previous research on archaeological sites.…”

Section: Problem Statementmentioning

confidence: 53%

Effective Detection of Sub-Surface Archeological Features From Laser Scanning Point Clouds and Imagery Data

Fryśkowska¹,

Kędzierski²,

Walczykowski³

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:The archaeological heritage is non-renewable, and any invasive research or other actions leading to the intervention of mechanical or chemical into the ground lead to the destruction of the archaeological site in whole or in part. For this reason, modern archeology is looking for alternative methods of non-destructive and non-invasive methods of new objects identification. The concept of aerial archeology is relation between the presence of the archaeological site in the particular localization, and the phenomena that in the same place can be observed on the terrain surface form airborne platform. One of the most appreciated, moreover, extremely precise, methods of such measurements is airborne laser scanning. In research airborne laser scanning point cloud with a density of 5 points/sq. m was used. Additionally unmanned aerial vehicle imagery data was acquired. Test area is located in central Europe. The preliminary verification of potentially microstructures localization was the creation of digital terrain and surface models. These models gave an information about the differences in elevation, as well as regular shapes and sizes that can be related to the former settlement/sub-surface feature. The paper presents the results of the detection of potentially sub-surface microstructure fields in the forestry area.

show abstract

Section: Problem Statementmentioning

confidence: 53%

Effective Detection of Sub-Surface Archeological Features From Laser Scanning Point Clouds and Imagery Data

Fryśkowska¹,

Kędzierski²,

Walczykowski³

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…Within the 2D or 3D representation of the individual tree point cloud and waveform features are calculated and with the help of classification techniques, tree species can be determined [90,110]. While the differentiation between deciduous and coniferous trees can be performed with a high accuracy (>80%, up to~97%) the differentiation within these classes is more difficult and leads to a higher classification error [111]. Moreover, it is possible to distinguish between living trees, standing dead trees and snags [112][113][114] or to map dead trees on the plot or stand level [115,116].…”

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

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

“…Limited studies have utilised high spatial resolution LiDAR data to characterise vertical vegetation structure [8][9][10]. The majority have researched structurally simple vegetation communities at course vertical and horizontal spatial resolutions [11].…”

Section: Introductionmentioning

confidence: 99%

Vertical Segmentation of Airborne Light Detection and Ranging (LiDAR) for Select Australian Vegetation Communities

Tasker

Phinn

2018

The 2nd International Electronic Conference on Remote Sensing

View full text Add to dashboard Cite

A quantitative understanding of vegetation structure is vital to inform long-term protection and management of Australia's vegetation communities. Although airborne light detection and ranging (LiDAR) systems are increasingly utilised to provide three-dimensional measures of vegetation structure at high spatial resolutions (1 -10 m 2 ), only limited studies characterise vertical vegetation structure using these datasets. This study assesses the capacity of high spatial resolution LiDAR data to accurately characterise the structural forms of Australian vegetation communities. Four study sites, each covering approximately 25 km 2 , were selected to provide examples across a range of vegetation structural forms, from shrubland to tall closed forest. A novel vertical segmentation methodology was developed to process airborne LiDAR data from each study site at 1 or 2 m vertical and horizontal spatial resolutions. Ratios were applied to standardise point density values, prior to exploratory analysis utilising multi-dimensional clustering algorithms to classify distinct vertical structure patterns. Comparisons were subsequently performed between the exploratory analysis results and established structural classifications for Australian vegetation communities. The use of the vertical segmentation technique was found to improve the identification of sub-canopy features in multi-story vegetation communities, particularly shrubs and herbaceous ground covers 0.5 -4 m tall. Exploratory analysis results saw increased noise in structurally complex and dense vegetation communities due to reduced subcanopy returns. Further development and application of vertical segmentation methods in multistory vegetation communities should be evaluated due to the potential for targeted management and monitoring of vegetation communities and wildlife populations.

show abstract

Full-Waveform Airborne Laser Scanning in Vegetation Studies—A Review of Point Cloud and Waveform Features for Tree Species Classification

Cited by 53 publications

References 85 publications

Effective Detection of Sub-Surface Archeological Features From Laser Scanning Point Clouds and Imagery Data

Effective Detection of Sub-Surface Archeological Features From Laser Scanning Point Clouds and Imagery Data

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

Vertical Segmentation of Airborne Light Detection and Ranging (LiDAR) for Select Australian Vegetation Communities

Contact Info

Product

Resources

About