Laser Pulse Interaction with Forest Canopy: Geometric and Radiometric Issues

Roncat, Andreas; Morsdorf, Felix; Briese, Christian; Wagner, Wolfgang; Pfeifer, Norbert

doi:10.1007/978-94-017-8663-8_2

Cited by 9 publications

(18 citation statements)

References 34 publications

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“…Because of the hot-spot geometry, the first-order scattering in LiDAR is not affected by mutual shading (Kuusk 1991). For the same reason, the contribution of multiple scattering is often considered to be relatively low compared to passive optical data (Disney et al 2006, Roncat et al 2014b). However, this depends on vegetation characteristics, and multiply scattered photons can account for as much as 43% of the total energy reaching the receiver (study III).…”

Section: Transmitted Pulsementioning

confidence: 99%

“…If the aim is to quantitatively deduce the target characteristics from LiDAR data, the user must know the function that relates the instantaneous at-sensor signal with the actual observations recorded. WF recording has made the measurement process more transparent, but there are still many uncertainties, especially because the response functions of commercial sensors are not exactly known (Roncat et al 2014b). The incoming signal is collected from within the receiver's iFOV through an aperture, and directed through collimating optics to a photosensitive detector.…”

Section: Receiving and Recording The Signalmentioning

confidence: 99%

“…in explaining and understanding the measurement, and in the inversion of the measurements in order to derive physical target properties, which is called calibration (Roncat et al 2014b). The most commonly used model for LiDAR measurement is the radar equation (Jelalian 1992).…”

Section: Theoretical Formulation and Radiometric Calibration Of Lidarmentioning

confidence: 99%

“…Small-footprint WF sensors have gained a foothold in markets over recent years, although most customers need point clouds for their applications and the post processing software for WF data is scarce. Owing to the fact that the shapes of the transmitted and received pulse are stored, it can be argued that WF sensors provide a more transparent view of the measurement process, and this is therefore beneficial for the better understanding and modeling of LiDAR measurements (Roncat et al 2014b).…”

Section: Introductionmentioning

confidence: 99%

“…Algorithm and sensor development also improves the quality of the inventory product. The need for a thorough understanding of the physical linkage between vegetation variables and the LiDAR signal has been addressed by several authors (Lim et al 2003;Wulder et al 2013;Roncat et al 2014b), and this could contribute to the development of both data interpretation algorithms and sensors (Lim et al 2003;Disney et al 2010;Roncat et al 2014b).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Towards an enhanced understanding of airborne LiDAR measurements of forest vegetation

Hovi¹

2015

Diss. For.

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Section: Transmitted Pulsementioning

confidence: 99%

Section: Receiving and Recording The Signalmentioning

confidence: 99%

Section: Theoretical Formulation and Radiometric Calibration Of Lidarmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards an enhanced understanding of airborne LiDAR measurements of forest vegetation

Hovi¹

2015

Diss. For.

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Delineating forest stands from grid data

Pukkala

2020

For. Ecosyst.

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Background: Forest inventories are increasingly based on airborne laser scanning (ALS). In Finland, the results of these inventories are calculated for small grid cells, 16 m by 16 m in size. Use of grid data in forest planning results in the additional requirement of aggregating management prescriptions into large enough continuous treatment units. This can be done before the planning calculations, using various segmentation techniques, or during the planning calculations, using spatial optimization. Forestry practice usually prefers reasonably permanent segments created before planning. These segments are expected to be homogeneous in terms of site properties, growing stock characteristics and treatments. Recent research has developed methods for partitioning grids of ALS inventory results into segments that are homogeneous in terms of site and growing stock characteristics. The current study extended previous methods so that also the similarity of treatments was considered in the segmentation process. The study also proposed methods to deal with biases that are likely to appear in the results when grid data are aggregated into large segments. Methods: The analyses were conducted for two datasets, one from southern and the other from northern Finland. Cellular automaton (CA) was used to aggregate the grid cells into segments using site characteristics with (1) growing stock attributes interpreted from ALS data, (2) predicted cutting prescriptions and (3) both stand attributes cutting prescriptions. The CA was optimized for each segmentation task. A method based on virtual stands was used to correct systematic errors in variable estimates calculated for segments. Results: The segmentation was rather similar in all cases. The result is not surprising since treatment prescriptions depend on stand attributes. The use of virtual stands decreased biases in growth prediction and in the areas of different fertility classes. Conclusions: Automated stand delineation was not sensitive to the type of variables that were used in the process. Virtual stands are an easy method to decrease systematic errors in calculations.

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Estimating the Threshold of Detection on Tree Crown Defoliation Using Vegetation Indices from UAS Multispectral Imagery

Otsu

Pla

Duane

et al. 2019

Drones

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Periodical outbreaks of Thaumetopoea pityocampa feeding on pine needles may pose a threat to Mediterranean coniferous forests by causing severe tree defoliation, growth reduction, and eventually mortality. To cost–effectively monitor the temporal and spatial damages in pine–oak mixed stands using unmanned aerial systems (UASs) for multispectral imagery, we aimed at developing a simple thresholding classification tool for forest practitioners as an alternative method to complex classifiers such as Random Forest. The UAS flights were performed during winter 2017–2018 over four study areas in Catalonia, northeastern Spain. To detect defoliation and further distinguish pine species, we conducted nested histogram thresholding analyses with four UAS-derived vegetation indices (VIs) and evaluated classification accuracy. The normalized difference vegetation index (NDVI) and NDVI red edge performed the best for detecting defoliation with an overall accuracy of 95% in the total study area. For discriminating pine species, accuracy results of 93–96% were only achievable with green NDVI in the partial study area, where the Random Forest classification combined for defoliation and tree species resulted in 91–93%. Finally, we achieved to estimate the average thresholds of VIs for detecting defoliation over the total area, which may be applicable across similar Mediterranean pine stands for monitoring regional forest health on a large scale.

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Laser Pulse Interaction with Forest Canopy: Geometric and Radiometric Issues

Cited by 9 publications

References 34 publications

Towards an enhanced understanding of airborne LiDAR measurements of forest vegetation

Towards an enhanced understanding of airborne LiDAR measurements of forest vegetation

Delineating forest stands from grid data

Estimating the Threshold of Detection on Tree Crown Defoliation Using Vegetation Indices from UAS Multispectral Imagery

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