Multi-level filtering segmentation to measure individual tree parameters based on Lidar data: Application to a mountainous forest with heterogeneous stands

Abstract

This paper presents a method for individual tree crown extraction and characterisation from a Canopy Surface Model (CSM). The method is based on a conventional algorithm used for localising LM on a smoothed version of the CSM and subsequently for modelling the tree crowns around each maximum at the plot level. The novelty of the approach lies in the introduction of controls on both the degree of CSM filtering and the shape of elliptic crowns, in addition to a multi-filtering level crown fusion approach to balance omission and commission errors. The algorithm derives the total tree height and the mean crown diameter from the elliptic tree crowns generated. The method was tested and validated on a mountainous forested area mainly covered by mature and even-aged black pine (Pinus nigra ssp. nigra [Arn.]) stands. Mean stem detection per plot, using this method, was 73.97 %.Algorithm performance was affected slightly by both stand density and heterogeneity (i.e. tree diameter classes' distribution). The total tree height and the mean crown diameter were estimated with root mean squared error values of 1.83 m and 1.48 m respectively. Tree heights were slightly underestimated in flat areas and overestimated on slopes. The average crown diameter was underestimated by 17.46 % on average.

Quantifying Multi-Decadal Change of Planted Forest Cover Using Airborne LiDAR and Landsat Imagery

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Detection and Characterization of Hedgerows Using TerraSAR-X Imagery

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Remote Sensing and Cropping Practices: A Review

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“…The use of a mono-date decametric multispectral image to obtain information at the field level is outdated, and we observe now: (1) very high spatial resolution images (metric and sub-metric resolutions), acquired in mono or stereo mode, to identify intercropping and mixed-cropping, tree crop and agroforestry plot structure, or agro-ecological infrastructures such as hedgerows or riparian forests; (2) high image acquisition frequency of one to three cloud-free images biweekly to identify practices whose recognition is based on the extraction of phenology-based The emergence of remotely sensed data from improved sensors has created opportunities to better characterize agro-ecological infrastructures in a whole landscape. For example, LiDAR sensors acquire high-resolution 3D measurements, which are useful to estimate a variety of tree features, such as tree height, volume, and biomass [207]. Nonetheless, LiDAR data is generally acquired at one moment in time because of cost considerations.…”