Estimating vegetation biomass and cover across large plots in shrub and grass dominated drylands using terrestrial lidar and machine learning

Anderson, Kelly J.; Glenn, Nancy F.; Spaete, L.; Shinneman, Douglas J.; Pilliod, David S.; Arkle, Robert S.; McIlroy, Susan K.; Derryberry, DeWayne R.

doi:10.1016/j.ecolind.2017.09.034

Cited by 87 publications

(57 citation statements)

References 49 publications

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“…Prior studies using snow depth probes or GNSS observations noted that airborne lidar systematically underestimated snow depth in areas with significant ground vegetation (Hopkinson et al, ), or ALS errors increased where there were shrubs (DeBeer & Pomeroy, ; Spaete et al, ). In contrast, our results suggested that aerial lidar did penetrate at least partly through the shrubs, while the terrestrial lidar was partially occluded by the shrubs (Anderson et al, ), ultimately biasing the TLS‐derived snow depth too low. The ability for ALS to see through the shrubs in our study is likely explained by differences in ground vegetation type between Grand Mesa and previous studies, the relatively low altitude and high point density of the ALS survey, and the development in recent years of more advanced algorithms that extract discrete lidar returns from the return energy waveforms (Pfennigbauer & Ullrich, ; Ullrich & Pfennigbauer, ).…”

Section: Discussioncontrasting

confidence: 61%

“…• Supporting Information S1 regions where vegetation cover occludes (or partially occludes) the laser from penetrating to the ground (Anderson et al, 2018).…”

Section: 1029/2018wr024533mentioning

confidence: 99%

“…A rigorous error propagation effort showed that discrete lidar returns with incidence angles greater than 60° at a 500‐m range have an additional vertical and horizontal uncertainty introduced, which ranges from 2–10 cm (Hartzell et al, ). This uncertainty will rise in regions where vegetation cover occludes (or partially occludes) the laser from penetrating to the ground (Anderson et al, ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparing Aerial Lidar Observations With Terrestrial Lidar and Snow‐Probe Transects From NASA's 2017 SnowEx Campaign

Currier

Pflug

Mazzotti³

et al. 2019

Water Resources Research

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NASA's 2017 SnowEx field campaign at Grand Mesa, CO, generated Airborne Laser Scans (ALS), Terrestrial Laser Scans (TLS), and snow‐probe transects, which allowed for a comparison between snow depth measurement techniques. At six locations, comparisons between gridded ALS and TLS observations, at 1‐m resolution, had a median snow depth difference of 5 cm, root‐mean‐square difference of 16 cm, mean‐absolute difference of 10 cm, and 3‐cm difference in standard deviation. ALS generally had greater but similar snow depth values to TLS, and results were not sensitive to the gridded cell size between 0.5 and 5 m. The greatest disagreements were where snow‐off TLS scans had shrubs and high incidence angles, leading to deeper snow depths (>10 cm) from ALS than TLS. The low vegetation and oblique angles caused occlusion in the TLS data and thus produced higher snow‐off bare Earth models relative to the ALS. Furthermore, in subcanopy areas where both ALS and TLS data existed, snow depth differences were comparable to differences in the open. Meanwhile, median values from 52 snow‐probe transects and near‐coincident ALS data had a mean difference of 6 cm, root‐mean‐square difference of 8 cm, mean‐absolute difference of 7 cm, and a mean difference in the standard deviation of 1 cm. Snow depth probes had greater but similar snow depth values to ALS. Therefore, based on comparisons with TLS and snow depth probes, ALS captured snow depth magnitude with better than or equal agreement to what has been reported in previous studies and showed the ability to capture high‐resolution spatial variability.

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Section: Discussioncontrasting

confidence: 61%

“…• Supporting Information S1 regions where vegetation cover occludes (or partially occludes) the laser from penetrating to the ground (Anderson et al, 2018).…”

Section: 1029/2018wr024533mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparing Aerial Lidar Observations With Terrestrial Lidar and Snow‐Probe Transects From NASA's 2017 SnowEx Campaign

Currier

Pflug

Mazzotti³

et al. 2019

Water Resources Research

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“…, Anderson et al. ). Random Forests has the ability to model complex non‐linear interactions across predictors, leveraging the large quantity of high spatial resolution plot cover estimates with the vast suite of spatiotemporal and static predictor variables.…”

Section: Methodsmentioning

confidence: 99%

Innovation in rangeland monitoring: annual, 30 m, plant functional type percent cover maps for U.S. rangelands, 1984–2017

et al. 2018

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Innovations in machine learning and cloud‐based computing were merged with historical remote sensing and field data to provide the first moderate resolution, annual, percent cover maps of plant functional types across rangeland ecosystems to effectively and efficiently respond to pressing challenges facing conservation of biodiversity and ecosystem services. We utilized the historical Landsat satellite record, gridded meteorology, abiotic land surface data, and over 30,000 field plots within a Random Forests model to predict per‐pixel percent cover of annual forbs and grasses, perennial forbs and grasses, shrubs, and bare ground over the western United States from 1984 to 2017. Results were validated using three independent collections of plot‐level measurements, and resulting maps display land cover variation in response to changes in climate, disturbance, and management. The maps, which will be updated annually at the end of each year, provide exciting opportunities to expand and improve rangeland conservation, monitoring, and management. The data open new doors for scientific investigation at an unprecedented blend of temporal fidelity, spatial resolution, and geographic scale.

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“…cover and height to biomass and carbon estimates (e.g., Anderson et al 2018). In the Great Basin, the most widespread of the shrub-steppe communities is the basin big sagebrush steppe, and we originally hypothesized that these communities would have higher productivity and carbon storage than the other shrub types.…”

Section: Land Cover Classifications In the Great Basinmentioning

confidence: 99%

Accounting for aboveground carbon storage in shrubland and woodland ecosystems in the Great Basin

Fusco

Rau²,

Falkowski

et al. 2019

Ecosphere

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Improving the accuracy of carbon accounting in terrestrial ecosystems is critical for understanding carbon fluxes associated with land cover change, with significant implications for global carbon cycling and climate change. Semi-arid ecosystems account for an estimated 45% of global terrestrial ecosystem area and are in many locations experiencing high degrees of degradation. However, aboveground carbon accounting has largely focused on tropical and forested ecosystems, while drylands have been relatively neglected. Here, we used a combination of field estimates, remotely sensed data, and existing land cover maps to create a spatially explicit estimate of aboveground carbon storage within the Great Basin, a semi-arid region of the western United States encompassing 643,500 km 2 of shrubland and woodland vegetation. We classified the region into seven distinct land cover categories: pinyon-juniper woodland, sagebrush steppe, salt desert shrub, low sagebrush, forest, non-forest, and other/excluded, each with an associated carbon estimate. Aboveground carbon estimates for pinyon-juniper woodland were continuous values based on tree canopy cover. Carbon estimates for other land cover categories were based on a mean value for the land cover type. The Great Basin ecosystems contain an estimated 295.4 Tg in aboveground carbon, which is almost double the previous estimates that only accounted for forested ecosystems in the same area. Aboveground carbon was disproportionately stored in pinyon-juniper woodland (43.7% carbon, 16.9% land area), while the shrubland systems accounted for roughly half of the total land area (49.1%) and one-third of the total carbon. Our results emphasize the importance of distinguishing and accounting for the distinctive contributions of shrubland and woodland ecosystems when creating carbon storage estimates for dryland regions.

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Estimating vegetation biomass and cover across large plots in shrub and grass dominated drylands using terrestrial lidar and machine learning

Cited by 87 publications

References 49 publications

Comparing Aerial Lidar Observations With Terrestrial Lidar and Snow‐Probe Transects From NASA's 2017 SnowEx Campaign

Comparing Aerial Lidar Observations With Terrestrial Lidar and Snow‐Probe Transects From NASA's 2017 SnowEx Campaign

Innovation in rangeland monitoring: annual, 30 m, plant functional type percent cover maps for U.S. rangelands, 1984–2017

Accounting for aboveground carbon storage in shrubland and woodland ecosystems in the Great Basin

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