Recommendations to enhance breeding bird diversity in managed plantation forests determined using LiDAR

Tew, Eleanor R.; Henderson, Ian; Milodowski, David T.; Swinfield, Tom; Sutherland, William J.

doi:10.1002/eap.2678

Cited by 4 publications

(3 citation statements)

References 92 publications

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“…Tree species and canopy structure can influence the biases observed in ALS observations of height and can have an effect on the vertical penetration of ALS returns, which can be complicated when considering leaf-on and leaf-off data (e.g., [137,138]). In addition, considerations over differences in the acquisition parameters of the ALS data between studies pose problems [139,140], as do seasonal differences between field and remote sensing data capture, for example, models calibrated using leaf-on field and ALS data can produce erroneous results when applied to leaf-off ALS data (e.g., [64]). Different canopy penetration capabilities when comparing sensors have been demonstrated, even when applied at the same location [141,142].…”

Section: Concerns and Wider Implicationsmentioning

confidence: 99%

“…Other studies have combined ALS datasets with multi-or hyper-spectral data which have allowed the classification of overstorey species [60,61]. Alternatively, general species characteristics have been correlated with ALS derived metrics, such as mean vegetation return height and the diversity of forest species or land cover (e.g., [62][63][64]).…”

Section: Introductionmentioning

confidence: 99%

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Towards Forest Condition Assessment: Evaluating Small-Footprint Full-Waveform Airborne Laser Scanning Data for Deriving Forest Structural and Compositional Metrics

2022

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Spatial data on forest structure, composition, regeneration and deadwood are required for informed assessment of forest condition and subsequent management decisions. Here, we estimate 27 forest metrics from small-footprint full-waveform airborne laser scanning (ALS) data using a random forest (RF) and automated variable selection (Boruta) approach. Modelling was conducted using leaf-off (April) and leaf-on (July) ALS data, both separately and combined. Field data from semi-natural deciduous and managed conifer plantation forests were used to generate the RF models. Based on NRMSE and NBias, overall model accuracies were good, with only two of the best 27 models having an NRMSE > 30% and/or NBias > 15% (Standing deadwood decay class and Number of sapling species). With the exception of the Simpson index of diversity for native trees, both NRMSE and NBias varied by less than ±4.5% points between leaf-on only, leaf-off only and combined leaf-on/leaf-off models per forest metric. However, whilst model performance was similar between ALS datasets, model composition was often very dissimilar in terms of input variables. RF models using leaf-on data showed a dominance of height variables, whilst leaf-off models had a dominance of width variables, reiterating that leaf-on and leaf-off ALS datasets capture different aspects of the forest and that structure and composition across the full vertical profile are highly inter-connected and therefore can be predicted equally well in different ways. A subset of 17 forest metrics was subsequently used to assess favourable conservation status (FCS), as a measure of forest condition. The most accurate RF models relevant to the 17 FCS indicator metrics were used to predict each forest metric across the field site and thresholds defining favourable conditions were applied. Binomial logistic regression was implemented to evaluate predicative accuracy probability relative to the thresholds, which varied from 0.73–0.98 area under the curve (AUC), where 11 of 17 metrics were >0.8. This enabled an index of forest condition (FCS) based on structure, composition, regeneration and deadwood to be mapped across the field site with reasonable certainty. The FCS map closely and consistently corresponded to forest types and stand boundaries, indicating that ALS data offer a feasible approach for forest condition mapping and monitoring to advance forest ecological understanding and improve conservation efforts.

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Section: Concerns and Wider Implicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards Forest Condition Assessment: Evaluating Small-Footprint Full-Waveform Airborne Laser Scanning Data for Deriving Forest Structural and Compositional Metrics

2022

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“…The knowledge on forest successional stages and their associated ecological processes is crucial for understanding and mitigating for example climate change or anthropogenic disturbances (Corona et al, 2011;Poorter et al, 2023). Such an understanding can furthermore improve monitoring and is fundamental for the development of adequate conservation strategies (Hilmers et al, 2018;Tew et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Leveraging readily available heterogeneous LiDAR data to enhance modeling of successional stages at tree species level in temperate forests

Bald,

Ziegler,

Gottwald

et al. 2024

Preprint

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In the context of the ongoing biodiversity crisis, understanding forest ecosystems, their tree species composition and especially the successional stages of their development is crucial. They collectively shape the biodiversity within forests and thereby influence the ecosystem services that forests provide, yet this information is not readily available on a large-scale. Remote sensing techniques offer promising solutions for obtaining area-wide information on tree species composition and their successional stages. While optical data are often freely available in appropriate quality over large-scales, obtaining Light Detection And Ranging (LiDAR) data, which provide valuable information about forest structure, is more challenging. LiDAR data are mostly acquired by public authorities across several years and therefore heterogeneous in quality. This study aims to assess if heterogeneous LiDAR data can support area-wide modeling of forest successional stages at tree species group level. Different combinations of spectral satellite data (Sentinel-2) and heterogeneous airborne LiDAR data, collected by the federal government of Rhineland-Palatinate, Germany, were utilized to model up to three different successional stages of seven tree species groups. When incorporating heterogeneous LiDAR data into random forest models with spatial variable selection and spatial cross-validation, significant accuracy improvements of up to 0.23 were observed. This study shows the potential of not dismissing initially seemingly unusable heterogeneous LiDAR data for ecological studies. We advocate for a thorough examination to determine its usefulness for model enhancement. A practical application of this approach is demonstrated, in the context of mapping successional stages of tree species groups at a regional level.

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