Forest canopy stratification based on fused, imbalanced and collinear LiDAR and Sentinel-2 metrics

Wernicke, Jakob; Seltmann, Christian Torsten; Wenzel, Ralf; Becker, Carina; Körner, Michael

doi:10.1016/j.rse.2022.113134

Cited by 5 publications

(1 citation statement)

References 48 publications

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“…The review on forest monitoring in Germany based on earth observation data by Holzwarth et al, 2020 concluded that there is a lack of nationwide studies at multi-temporal scale [19]. Preliminary work on the forest structure of German forests is mainly focused on regional-scale [41][42][43][44][45][46][47][48] or based on coarse resolution imagery [49]. To address these short-comings, the present study builds up on the modeling workflow developed by Kacic et al, 2021 generating data products of complementary attributes of forest structure for the Paraguayan Chaco for a single year (2019) [50].…”

Section: Introductionmentioning

confidence: 99%

Forest Structure Characterization in Germany: Novel Products and Analysis Based on GEDI, Sentinel-1 and Sentinel-2 Data

et al. 2023

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Monitoring forest conditions is an essential task in the context of global climate change to preserve biodiversity, protect carbon sinks and foster future forest resilience. Severe impacts of heatwaves and droughts triggering cascading effects such as insect infestation are challenging the semi-natural forests in Germany. As a consequence of repeated drought years since 2018, large-scale canopy cover loss has occurred calling for an improved disturbance monitoring and assessment of forest structure conditions. The present study demonstrates the potential of complementary remote sensing sensors to generate wall-to-wall products of forest structure for Germany. The combination of high spatial and temporal resolution imagery from Sentinel-1 (Synthetic Aperture Radar, SAR) and Sentinel-2 (multispectral) with novel samples on forest structure from the Global Ecosystem Dynamics Investigation (GEDI, LiDAR, Light detection and ranging) enables the analysis of forest structure dynamics. Modeling the three-dimensional structure of forests from GEDI samples in machine learning models reveals the recent changes in German forests due to disturbances (e.g., canopy cover degradation, salvage logging). This first consistent data set on forest structure for Germany from 2017 to 2022 provides information of forest canopy height, forest canopy cover and forest biomass and allows estimating recent forest conditions at 10 m spatial resolution. The wall-to-wall maps of the forest structure support a better understanding of post-disturbance forest structure and forest resilience.

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Section: Introductionmentioning

confidence: 99%

Forest Structure Characterization in Germany: Novel Products and Analysis Based on GEDI, Sentinel-1 and Sentinel-2 Data

et al. 2023

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LiDAR Data Fusion to Improve Forest Attribute Estimates: A Review

Balestra,

Marselis,

Sankey

et al. 2024

Curr. For. Rep.

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Purpose of the Review Many LiDAR remote sensing studies over the past decade promised data fusion as a potential avenue to increase accuracy, spatial-temporal resolution, and information extraction in the final data products. Here, we performed a structured literature review to analyze relevant studies on these topics published in the last decade and the main motivations and applications for fusion, and the methods used. We discuss the findings with a panel of experts and report important lessons, main challenges, and future directions. Recent Findings LiDAR fusion with other datasets, including multispectral, hyperspectral, and radar, is found to be useful for a variety of applications in the literature, both at individual tree level and at area level, for tree/crown segmentation, aboveground biomass assessments, canopy height, tree species identification, structural parameters, and fuel load assessments etc. In most cases, gains are achieved in improving the accuracy (e.g. better tree species classifications), and spatial-temporal resolution (e.g. for canopy height). However, questions remain regarding whether the marginal improvements reported in a range of studies are worth the extra investment, specifically from an operational point of view. We also provide a clear definition of “data fusion” to inform the scientific community on data fusion, combination, and integration. Summary This review provides a positive outlook for LiDAR fusion applications in the decade to come, while raising questions about the trade-off between benefits versus the time and effort needed for collecting and combining multiple datasets.

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Comparing phenocam color indices with phenological observations of black spruce in the boreal forest

Khare

et al. 2023

Ecological Informatics

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Forest canopy stratification based on fused, imbalanced and collinear LiDAR and Sentinel-2 metrics

Cited by 5 publications

References 48 publications

Forest Structure Characterization in Germany: Novel Products and Analysis Based on GEDI, Sentinel-1 and Sentinel-2 Data

Forest Structure Characterization in Germany: Novel Products and Analysis Based on GEDI, Sentinel-1 and Sentinel-2 Data

LiDAR Data Fusion to Improve Forest Attribute Estimates: A Review

Comparing phenocam color indices with phenological observations of black spruce in the boreal forest

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