Role of LiDAR remote sensing in identifying physiognomic traits of alpine treeline: a global review

Mathew, Jincy Rachel; Singh, Chandra Prakash; Solanki, Hitesh; Sedha, Dhruvi; Pandya, Mehul R.; Bhattacharya, Bimal K.

doi:10.1007/s42965-023-00317-6

Cited by 1 publication

(1 citation statement)

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“…Optical multispectral datasets can be supported with digital elevation models (DEMs) created using light detection and ranging (LiDAR) instruments to aid in orthorectification, visualisation and spatial analysis [13]. The use of LiDAR technology for treeline research has gained popularity over time, owing to its ability to capture the structural variability of vegetation at treeline in remarkable detail [21]. The integration of remote sensing technologies can substantially support regional treeline monitoring studies and enables the quantification of ecosystem migration with a reduced need for field work [18] as well as improved terrain modelling using airborne LiDAR [22,23].…”

Section: Introductionmentioning

confidence: 99%

Linking High-Resolution UAV-Based Remote Sensing Data to Long-Term Vegetation Sampling—A Novel Workflow to Study Slow Ecotone Dynamics

Döweler,

Fransson,

Bader

2024

Remote Sensing

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Unravelling slow ecosystem migration patterns requires a fundamental understanding of the broad-scale climatic drivers, which are further modulated by fine-scale heterogeneities just outside established ecosystem boundaries. While modern Unoccupied Aerial Vehicle (UAV) remote sensing approaches enable us to monitor local scale ecotone dynamics in unprecedented detail, they are often underutilised as a temporal snapshot of the conditions on site. In this study in the Southern Alps of New Zealand, we demonstrate how the combination of multispectral and thermal data, as well as LiDAR data (2019), supplemented by three decades (1991–2021) of treeline transect data can add great value to field monitoring campaigns by putting seedling regeneration patterns at treeline into a spatially explicit context. Orthorectification and mosaicking of RGB and multispectral imagery produced spatially extensive maps of the subalpine area (~4 ha) with low spatial offset (Craigieburn: 6.14 ± 4.03 cm; Mt Faust: 5.11 ± 2.88 cm, mean ± standard error). The seven multispectral bands enabled a highly detailed delineation of six ground cover classes at treeline. Subalpine shrubs were detected with high accuracy (up to 90%), and a clear identification of the closed forest canopy (Fuscospora cliffortioides, >95%) was achieved. Two thermal imaging flights revealed the effect of existing vegetation classes on ground-level thermal conditions. UAV LiDAR data acquisition at the Craigieburn site allowed us to model vegetation height profiles for ~6000 previously classified objects and calculate annual fine-scale variation in the local solar radiation budget (20 cm resolution). At the heart of the proposed framework, an easy-to-use extrapolation procedure was used for the vegetation monitoring datasets with minimal georeferencing effort. The proposed method can satisfy the rapidly increasing demand for high spatiotemporal resolution mapping and shed further light on current treeline recruitment bottlenecks. This low-budget framework can readily be expanded to other ecotones, allowing us to gain further insights into slow ecotone dynamics in a drastically changing climate.

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