Quantifying the spatial properties of forest canopy gaps using LiDAR imagery and GIS

Koukoulas, Sotirios; Blackburn, George Alan

doi:10.1080/01431160310001657786

Cited by 81 publications

(63 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Accurate segmentation and classification of canopy gaps by a synergistic use of optical images and LiDAR data plays a critical role in understanding forest regeneration dynamics and may help predict future forest condition [16,[47][48][49]. For example, larger canopy gaps may result in the establishment of early and mid-successional species while smaller canopy gaps may promote the establishment of late-successional species [1].…”

Section: Forest Ecosystem Managementmentioning

confidence: 99%

Object-Based Canopy Gap Segmentation and Classification: Quantifying the Pros and Cons of Integrating Optical and LiDAR Data

et al. 2015

View full text Add to dashboard Cite

Abstract:Delineating canopy gaps and quantifying gap characteristics (e.g., size, shape, and dynamics) are essential for understanding regeneration dynamics and understory species diversity in structurally complex forests. Both high spatial resolution optical and light detection and ranging (LiDAR) remote sensing data have been used to identify canopy gaps through object-based image analysis, but few studies have quantified the pros and cons of integrating optical and LiDAR for image segmentation and classification. In this study, we investigate whether the synergistic use of optical and LiDAR data improves segmentation quality and classification accuracy. The segmentation results indicate that the LiDAR-based segmentation best delineates canopy gaps, compared to segmentation with optical data alone, and even the integration of optical and LiDAR data. In contrast, the synergistic use of two datasets provides higher classification accuracy than the independent use of optical or LiDAR (overall accuracy of 80.28%˘6.16% vs. 68.54%˘9.03% and 64.51%˘11.32%, separately). High correlations between segmentation quality and object-based classification accuracy indicate that classification accuracy is largely dependent on segmentation quality in the selected experimental area. The outcome of this study provides valuable insights of the usefulness of data integration into segmentation and classification not only for canopy gap identification but also for many other object-based applications.

show abstract

Section: Forest Ecosystem Managementmentioning

confidence: 99%

Object-Based Canopy Gap Segmentation and Classification: Quantifying the Pros and Cons of Integrating Optical and LiDAR Data

et al. 2015

View full text Add to dashboard Cite

show abstract

“…The Canopy Height Model is a surface model that expresses vegetation distance from the ground and provides a horizontal and vertical distribution of tree canopy (Koukoulas and Blackburn 2004). According to the definition, it is generally generated by DSM and DEM difference calculation.…”

Section: Chm Extractionmentioning

confidence: 99%

Canopy Surface Reconstruction and Tropical Forest Parameters Prediction From Airborne Laser Scanner for Large Forest Area

Chen¹,

Yang²,

Chen³

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Canopy height model(CHM) and tree mean height are critical forestry parameters that many other parameters such as growth, carbon sequestration, standing timber volume, and biomass can be derived from. LiDAR is a new method used to rapidly estimate these parameters over large areas. The estimation of these parameters has been derived successfully from CHM. However, a number of challenges limit the accurate retrieval of tree height and crowns, especially in tropical forest area. In this study, an improved canopy estimation model is proposed based on dynamic moving window that applied on LiDAR point cloud data. DEM, DSM and CHM of large tropical forest area can be derived from LiDAR data effectively and efficiently.

show abstract

“…Barbati et al [2009] have applied the hot-spot analysis based on the calculation of the local statistic variable Gi* of Getis-Ord [Getis and Ord, 1992] in a coastal Mediterranean pine forest. Bottalico et al [2009] did a comparison between the hot-spot analysis and the morphological functions proposed by Koukoulas and Blackburn [2004] to extract gap boundaries. Floris et al [2009] have adopted a rule of spatial contiguity on a minimum surface under alpine conditions.…”

Section: Als-assisted Assessment Of Forest Stand Structurementioning

confidence: 99%

Airborne Laser Scanning to support forest resource management under alpine, temperate and Mediterranean environments in Italy

Corona

Cartisano

Salvati

et al. 2012

European Journal of Remote Sensing

View full text Add to dashboard Cite

This paper aims to provide general considerations, in the form of a scientific review, with reference to selected experiences of ALS applications under alpine, temperate and Mediterranean environments in Italy as case studies. In Italy, the use of ALS data have been mainly focused on the stratification of forest stands and the estimation of their timber volume and biomass at local scale. Potential for ALS data exploitation concerns their integration in forest inventories on large territories, their usage for silvicultural systems detection and their use for the estimation of fuel load in forest and pre-forest stands. Multitemporal ALS may even be suitable to support the assessment of current annual volume increment and the harvesting rates.

show abstract

Quantifying the spatial properties of forest canopy gaps using LiDAR imagery and GIS

Cited by 81 publications

References 39 publications

Object-Based Canopy Gap Segmentation and Classification: Quantifying the Pros and Cons of Integrating Optical and LiDAR Data

Object-Based Canopy Gap Segmentation and Classification: Quantifying the Pros and Cons of Integrating Optical and LiDAR Data

Canopy Surface Reconstruction and Tropical Forest Parameters Prediction From Airborne Laser Scanner for Large Forest Area

Airborne Laser Scanning to support forest resource management under alpine, temperate and Mediterranean environments in Italy

Contact Info

Product

Resources

About