Incorporating LiDAR metrics into a structure-based habitat model for a canopy-dwelling species

Hagar, Joan C.; Yost, Andrew; Haggerty, Patricia K.

doi:10.1016/j.rse.2019.111499

Cited by 32 publications

(27 citation statements)

References 36 publications

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“…MaxEnt version 3.3.4 k (Phillips et al 2006) was used to create predictive habitat models for the occurrence and nest locations from 2012 to 2017 for Akikiki and Akekee at 10-, 100-, and 250 m spatial resolutions (Approach 2). Feature types were limited to linear, product, or quadratic features to reduce model complexity as recommended for presence-only modeling (Hagar et al 2020), and default options for background sampling were used. The nine metrics derived from 1 m lidar were used as environmental layer inputs for each model (canopy height, canopy density, elevation, slope, topographic wetness index, and four relative height metrics).…”

Section: Predictive Habitat Modelingmentioning

confidence: 99%

Application of lidar for critical endangered bird species conservation on the island of Kauai, Hawaii

Fricker

Crampton²,

Gallerani³

et al. 2021

Ecosphere

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The Akikiki (Oreomystis bairdi) and Akekee (Loxops caeruleirostris) are two honeycreepers endemic to Kauai, Hawaii, that were listed as federally endangered in 2010. Both species are rare, little-studied, and occur in a remote, roadless area. We analyzed high-resolution airborne lidar data to identify forest structure and topography metrics associated with Akikiki and Akekee nest locations (88 for Akikiki and 22 for Akekee) and occurrences (3706 for Akikiki and 1581 for Akekee) from 2012 to 2017 on the Alakai Plateau to predict their distribution in unsurveyed areas. Akikiki and Akekee nested in areas with similar forest structure at 10 m resolution, but different maximum tree heights. Akikiki and Akekee foraged in areas with significantly different forest structure (maximum tree height, mean canopy height, relative heights) and topography (slope) based on occurrences. Elevation was consistently one of the most important metrics for predicting both species nest locations and occurrences across scales (10, 100, 250 m) and it appears that both species are at the upper limits of their elevational range. We estimate that the area of suitable nesting habitat for Akikiki is 17.59 km 2 while the area of suitable nesting habitat for Akekee is 11.10 km 2 at 10 m resolution. The Akikiki has a potential range of 38 km 2 while the Akekee has a range of 58 km 2 at 100 m resolution. We produce predictive nest and occurrence maps at 10 m and 100 m resolutions to spatially target conservation actions. Results suggest that if avian malaria cannot be controlled and both species populations do not stabilize over the coming years, translocation may be needed to insure their viability.

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Section: Predictive Habitat Modelingmentioning

confidence: 99%

Application of lidar for critical endangered bird species conservation on the island of Kauai, Hawaii

Fricker

Crampton²,

Gallerani³

et al. 2021

Ecosphere

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“…In forest monitoring, detailed canopy information is more useful than other remote sensing approaches (Maltamo et al 2006). The ALS-derived metrics describe the key characteristics of a forest and are valuable for the prediction and monitoring of various attributes, such as tree species (Van Aardt et al 2008), height (Maltamo et al 2004), diameter distribution (Räty et al 2018), volume (Naesset, 1997), spatial patterns of the trees (Packalen et al 2013), structural complexity of the forests (Valbuena et al 2013), biomass and carbon stocks (Naesset and Gobakken 2008;Valbuena et al 2017a), and wildlife habitats (Hagar et al 2020). Moreover, ALS data is also reliable for the evaluation of canopy changes and to compare different forest areas (McInerney et al 2010).…”

Section: Assessment Of Forest Structural Attributes From Alsmentioning

confidence: 99%

“…Forest structural types assessment is important for wildlife habitat management (Hagar et al 2020), biodiversity (Lelli et al 2019), biomass and carbon storage (Clark and Clark 2000;Marvin et al 2014), natural dynamics in forests, such as thinning and disturbances (Coomes and Allen 2007a) or if these dynamics are artificially modified (Valbuena et al 2016a). In this study, various FST were identified in a simple two-tier approach by utilising four forest attributes -, , andobtained from the Boreal, Mediterranean and Atlantic biogeographical regions, which made it feasible for a regional assessment of the FST.…”

Section: Simplifying the Cross-bioregional Assessment Of Fst (Ii)mentioning

confidence: 99%

Improvements in forest structural type assessment using airborne laser scanning

Adnan¹

2020

Diss. For.

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Accurate forest structural type (FST) assessment provides a valuable support tool to distinguish the different structures in forest stands, achieve sustainable forest management and formulate effective decisions. Data from four research sites within three biogeographical regions-Boreal, Mediterranean and Atlanticwere used in this study, and reliable methodologies were developed for FST assessment. First, the Gini coefficient () of tree size inequality was used for the structural characterisation, and the effects of plot size, stand density and point density of airborne laser scanning (ALS) on the ALS-assisted estimations were evaluated for the Boreal region. Second, four forest structural attributesquadratic mean diameter (), , basal area larger than the mean () and stand density ()from the three biogeographical regions were used to develop regionindependent methods for FST assessment. Lastly, a threshold value to represent maximum entropy was determined and was used to classify the various FST directly from ALS data using L-coefficient of variation and L-skewness of ALS echo heights. Aboveground biomass (AGB) was predicted for each FST and was compared with the AGB predictions without prestratification. The results showed that (a) plot size had a greater effect on the ALS-assisted estimation compared to stand size and point density, and that 250-450 m 2 plot size (radius 9-12 m for circular plots) is the optimal plot size for reliable ALS-assisted estimations, (b) and are the most reliable bivariate descriptors for FST assessment, and single storey, multi-storey and reversed-J type forest structures can be separated by lower, medium and upper and values, respectively, while and are relevant for the separation of young/mature and sparse/dense subtypes, and (c) based on the mathematical proofs, the threshold values calculated from ALS echo heights and tree basal areas to represent maximum entropy should be 0.33 and 0.50, respectively. Moderate improvements were observed in the AGB predictions from FST classified directly from ALS data compared to the full dataset but critical differences were identified in the selection of ALS metrics by the prediction models. For example, higher percentiles were more relevant in uneven-sized structures and open canopy areas, while cover metrics and average percentiles were important in the even-sized structures and closed canopy areas. Thus, these results are very useful in improving our understanding of the relationships that underpin the choice of ALS predictors in structurally complex forests.

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“…For example, Kueppers et al (2005) used discriminant analysis to study the potential ranges of two California endemic oaks in response to regional climate change. Hagar et al (2020) used maximum entropy (MAXENT) to predict the habitat suitability of northern spotted owl in Oregon with forest structural attributes derived from airborne light detection and ranging data. When both observed presence and absence data are available, it is straightforward to apply standard binary classifiers such as logistic regression and neural network to predict the conditional probability of species occurrence at given locations (Guisan et al, 2002;Li et al, 2011;Marmion et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Plotting receiver operating characteristic and precision–recall curves from presence and background data

Li¹

2021

Ecology and Evolution

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1. The receiver operating characteristic (ROC) and precision-recall (PR) plots have been widely used to evaluate the performance of species distribution models.Plotting the ROC/PR curves requires a traditional test set with both presence and absence data (namely PA approach), but species absence data are usually not available in reality. Plotting the ROC/PR curves from presence-only data while treating background data as pseudo absence data (namely PO approach) may provide misleading results.2. In this study, we propose a new approach to calibrate the ROC/PR curves from presence and background data with user-provided information on a constant c, namely PB approach. Here, c defines the probability that species occurrence is detected (labeled), and an estimate of c can also be derived from the PB-based ROC/ PR plots given that a model with good ability of discrimination is available. We used five virtual species and a real aerial photography to test the effectiveness of the proposed PB-based ROC/PR plots. Different models (or classifiers) were trained from presence and background data with various sample sizes. The ROC/ PR curves plotted by PA approach were used to benchmark the curves plotted by PO and PB approaches. Experimental resultsshow that the curves and areas under curves by PB approach are more similar to that by PA approach as compared with PO approach.The PB-based ROC/PR plots also provide highly accurate estimations of c in our experiment.4. We conclude that the proposed PB-based ROC/PR plots can provide valuable complements to the existing model assessment methods, and they also provide an additional way to estimate the constant c (or species prevalence) from presence and background data.

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Incorporating LiDAR metrics into a structure-based habitat model for a canopy-dwelling species

Cited by 32 publications

References 36 publications

Application of lidar for critical endangered bird species conservation on the island of Kauai, Hawaii

Application of lidar for critical endangered bird species conservation on the island of Kauai, Hawaii

Improvements in forest structural type assessment using airborne laser scanning

Plotting receiver operating characteristic and precision–recall curves from presence and background data

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