An Accuracy Assessment of Tree Detection Algorithms in Juniper Woodlands

Poznanovic, Aaron J.; Falkowski, Michael J.; Maclean, Ann L.; Smith, Alistair M. S.

doi:10.14358/pers.80.7.627

Cited by 26 publications

(21 citation statements)

References 39 publications

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“…OBIA methods extract objects of interest from digital imagery by first grouping together neighboring pixels with similar spectral and spatial properties and then classifying these pixel groups into objects of interest (e.g., trees). When using VHSR data for mapping woody plants, OBIA outputs are typically polygons delineating specified objects of interest (e.g., woody plants or patches of woody plants; Poznanovic et al, 2014).…”

Section: Contents Lists Available At Sciencedirectmentioning

confidence: 99%

“…Among the various OBIA methods available, spatial wavelet analysis (SWA) is the most efficient method because it requires the least user input and the least amount of processing time to characterize tree and shrub cover, while preserving relatively high accuracies (Poznanovic et al, 2014). In SWA, individual trees are identified by both reflectance and shape, marked with spatial coordinates (x, y), assigned with an image-derived tree crown diameter value, and converted to points and circular buffers indicating tree location and crown area (Falkowski et al, 2006;Smith et al, 2008;Poznanovic et al, 2014).…”

Section: Contents Lists Available At Sciencedirectmentioning

confidence: 99%

“…In SWA, individual trees are identified by both reflectance and shape, marked with spatial coordinates (x, y), assigned with an image-derived tree crown diameter value, and converted to points and circular buffers indicating tree location and crown area (Falkowski et al, 2006;Smith et al, 2008;Poznanovic et al, 2014). The detailed output provided by SWA can be used to calculate useful metrics including canopy cover, tree density, canopy configuration, and crown diameter distributions, many of which have been identified as important drivers of sage-grouse lek activity (Baruch-Mordo et al, 2013) and prairiechicken space use (Lautenbach et al, this issue; Boggie et al, this issue).…”

Section: Contents Lists Available At Sciencedirectmentioning

confidence: 99%

“…The SWA algorithm is often used to estimate the size and location of individual trees from remote sensing data including both LiDAR data and highresolution imagery such as NAIP (e.g., Falkowski et al, 2006Falkowski et al, , 2008Strand et al, 2006;Smith et al, 2008;Poznanovic et al, 2014). SWA uses a dynamically scaled, wavelet-based image filter to decompose digital images into individual objects or features, which in this case corresponds to individual woody plants.…”

Section: Conifer Detection and Mapping-spatial Wavelet Analysismentioning

confidence: 99%

“…Following previously published methods (e.g., Falkowski et al, 2006;Strand et al, 2006;Poznanovic et al, 2014), we convolved a series of two-dimensional (2D) Mexican hat wavelets of progressively larger sizes (1−10 m in 0.5-m increments) with NAIP-derived NDVI images. This wavelet size range was chosen to match the spatial resolution of the NAIP imagery at the low end and to approximately equal the maximum expected juniper crown diameter at the high end (i.e., juniper crown diameters rarely exceed 10 m).…”

Section: Conifer Detection and Mapping-spatial Wavelet Analysismentioning

confidence: 99%

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Root Biomass and Distribution Patterns in a Semi-Arid Mesquite Savanna: Responses to Long-Term Rainfall Manipulation

Ansley

Boutton

Jacoby

2014

Rangeland Ecology & Management

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Section: Contents Lists Available At Sciencedirectmentioning

confidence: 99%

Section: Contents Lists Available At Sciencedirectmentioning

confidence: 99%

Section: Contents Lists Available At Sciencedirectmentioning

confidence: 99%

Section: Conifer Detection and Mapping-spatial Wavelet Analysismentioning

confidence: 99%

Section: Conifer Detection and Mapping-spatial Wavelet Analysismentioning

confidence: 99%

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Root Biomass and Distribution Patterns in a Semi-Arid Mesquite Savanna: Responses to Long-Term Rainfall Manipulation

Ansley

Boutton

Jacoby

2014

Rangeland Ecology & Management

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Next‐generation restoration for sage‐grouse: a framework for visualizing local conifer cuts within a landscape context

et al. 2017

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. 2017. Next-generation restoration for sage-grouse: a framework for visualizing local conifer cuts within a landscape context. Ecosphere 8 (7): e01888. 10.1002/ecs2.1888Abstract. The expansion of coniferous trees into sagebrush ecosystems is a major driver of habitat loss and fragmentation, resulting in negative impacts to wildlife. Greater sage-grouse (Centrocercus urophasianus) respond directly to conifer expansion through decreased breeding activity, nesting, and overall survival; thus, small amounts of conifer expansion can have significant impacts on sage-grouse habitat and populations. To this end, conservation partners have collaborated across private and public lands to reduce the threat of conifer expansion through targeted removal of conifer trees. Here, we demonstrate the use of the Marxan framework to incorporate important ecosystem attributes in the prioritization of conifer removal within the Oregon range of sage-grouse. We prioritized conifer removal relative to three separate goals: (1) enhancement of existing sage-grouse breeding, nesting, and early brood-rearing habitats; (2) facilitation of sage-grouse movement between breeding and brood-rearing habitats; and (3) improvement of connectivity among sage-grouse priority areas for conservation (PACs). Optimization models successfully identified areas with low conifer canopy cover, high resilience and resistance to wildfire and annual grass invasion, and high bird abundance to enhance sage-grouse habitat. The inclusion of mesic resources resulted in further prioritization of areas that were closer to such resources, but also identified potential pathways that connected breeding habitats to the late brood-rearing habitats associated with mesic areas. Examining areas outside of PACs resulted in the selection of potential corridors to facilitate connectivity; although areas with low conifer cover were selected similarly to the other optimization models, areas with high cover were also chosen to be able to enhance connectivity. Areas identified by optimization models were largely consistent with and overlapped ongoing conifer removal efforts in the Warner Mountains of south-central Oregon. Land ownership of preferential areas selected by models varied with priority goals and followed general ownership patterns of the region, with public lands managed by the Bureau of Land Management and private lands being selected the most. The increased availability of landscape-level datasets and assessment tools in sagebrush ecosystems can reduce the time and cost of both planning and implementation of habitat projects involving conifer removal. Most importantly, incorporating these new datasets and tools can supplement expert-based knowledge to maximize benefits to sagebrush and sage-grouse conservation.

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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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An Accuracy Assessment of Tree Detection Algorithms in Juniper Woodlands

Cited by 26 publications

References 39 publications

Root Biomass and Distribution Patterns in a Semi-Arid Mesquite Savanna: Responses to Long-Term Rainfall Manipulation

Root Biomass and Distribution Patterns in a Semi-Arid Mesquite Savanna: Responses to Long-Term Rainfall Manipulation

Next‐generation restoration for sage‐grouse: a framework for visualizing local conifer cuts within a landscape context

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

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