Multi-scale remote sensing sagebrush characterization with regression trees over Wyoming, USA: Laying a foundation for monitoring

Homer, Collin G.; Aldridge, Cameron L.; Meyer, Debra K.; Schell, Spencer J.

doi:10.1016/j.jag.2011.09.012

Cited by 103 publications

(136 citation statements)

References 32 publications

(35 reference statements)

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“…We used a multi-resolution approach, which combines very high spatial resolution data with high temporal resolution data, in order to compensate for the tradeoffs inherent in each individual remote sensing dataset [23]. The datasets and processing steps are illustrated in Figure 3 and will be described in the following sections.…”

Section: Datamentioning

confidence: 99%

Estimation of Tree Cover in an Agricultural Parkland of Senegal Using Rule-Based Regression Tree Modeling

2013

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Abstract:Field trees are an integral part of the farmed parkland landscape in West Africa and provide multiple benefits to the local environment and livelihoods. While field trees have received increasing interest in the context of strengthening resilience to climate variability and change, the actual extent of farmed parkland and spatial patterns of tree cover are largely unknown. We used the rule-based predictive modeling tool Cubist® to estimate field tree cover in the west-central agricultural region of Senegal. A collection of rules and associated multiple linear regression models was constructed from (1) a reference dataset of percent tree cover derived from very high spatial resolution data (2 m Orbview) as the dependent variable, and (2) ten years of 10-day 250 m Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) composites and derived phenological metrics as independent variables. Correlation coefficients between modeled and reference percent tree cover of 0.88 and 0.77 were achieved for training and validation data respectively, with absolute mean errors of 1.07 and 1.03 percent tree cover. The resulting map shows a west-east gradient from high tree cover in the peri-urban areas of horticulture and arboriculture to low tree cover in the more sparsely populated eastern part of the study area. A comparison of current (2000s) tree cover along this gradient with historic cover as seen on Corona images reveals dynamics of change but also areas of remarkable stability of field tree cover since 1968. The proposed modeling approach can help to identify locations of high and low tree cover in dryland environments and guide ground studies and management interventions aimed at promoting the integration of field trees in agricultural systems.

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Section: Datamentioning

confidence: 99%

Estimation of Tree Cover in an Agricultural Parkland of Senegal Using Rule-Based Regression Tree Modeling

2013

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“…Using a Geographic Information System (GIS; ArcGIS 10, ESRI, Redlands, CA), we calculated covariates depicting landscape features that influence resource selection behavior of sagegrouse (Table 1; Doherty et al 2008, Carpenter et al 2010, Dzialak et al 2011a, Homer et al 2012. Raster images (grids) of oil and natural gas wells were updated annually so that we could analyze location data against wells known to be present at the time the relocation occurred (or as a lagged process; Table 1; Harju et al 2010).…”

Section: Covariate Calculation and Inclusionmentioning

confidence: 99%

Temporal and hierarchical spatial components of animal occurrence: conserving seasonal habitat for greater sage‐grouse

Dzialak¹,

Olson²,

Harju³

et al. 2012

Ecosphere

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Abstract. Developing strategies for sustainable management of landscapes requires research that bridges regionally important ecological and socioeconomic issues, and that aims to provide solutions to sustainability problems. We integrated Global Positioning Systems (GPS) telemetry and statistical modeling to quantify hierarchical spatial and temporal components of occurrence among greater sage-grouse (Centrocercus urophasianus; n ¼ 87), a species of conservation concern, with the larger goal of developing spatially-explicit guidance for conservation of important winter habitat in a Wyoming, USA landscape undergoing development for energy resources. The pattern of occurrence at the landscape level (secondorder) and within seasonal use areas (third-order) included selection for shrub vegetation with a prominent sagebrush (Artemisia spp.) component, and avoidance of rough terrain, mesic areas, and human activity. A change in resource selection behavior across the diel cycle was not an apparent component of the higherorder selection process; however, at the finer scale of investigation sage-grouse shifted behavior across the diel cycle in ways likely related to risk aversion or maintaining a favorable thermal environment (i.e., daytime-only avoidance of natural gas wells and night-time-only selection for taller shrubs). At both spatial scales there was considerably more variation among individuals in the sign of their association with anthropogenic features than with vegetation and terrain. The final spatially-explicit model, which depicted lower-order selection (local, patch-level, and seasonal use area) across the diel cycle constrained by selection processes at a higher order (second-order), validated well, offering specific guidance for managing human activity and sage-grouse conservation in the study area, and general guidance in developing sustainable landscape management strategies when animal occurrence reflects multiple spatial and temporal processes.

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“…Based on first-hand experience of the site, habitat in this area is best described as vegetated sand dunes and is comprised of dunes interspersed with vegetated hummocks that include species such as shadscale, spiny hopsage (Grayia spinosa), greasewood, Gardner's saltbush (Atriplex gardneri), and grasses. Although sage-grouse under observation have occurred in this area, its inclusion here as high-priority brood-rearing habitat may be erroneous, reflecting relatively high values for percent shrub in the covariate raster set (Table 1; Homer et al 2012). …”

Section: Resultsmentioning

confidence: 99%

Incorporating within- and between-patch resource selection in identification of critical habitat for brood-rearing greater sage-grouse

Dzialak¹,

Olson²,

Webb

et al. 2015

Ecol Process

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Introduction: Incorporating information on animal behavior in resource-based predictive modeling (e.g., occurrence mapping) can elucidate the relationship between process and spatial pattern and depict habitat in terms of its structure as well as its function. In this paper, we assigned location data on brood-rearing greater sage-grouse (Centrocercus urophasianus) to either within-patch (encamped) or between-patch (traveling) behavioral modes by estimating a movement-based relative displacement index. Objectives were to estimate and validate spatially explicit models of within-versus between-patch resource selection for application in habitat management and compare these models to a non-behaviorally adjusted model.

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Multi-scale remote sensing sagebrush characterization with regression trees over Wyoming, USA: Laying a foundation for monitoring

Cited by 103 publications

References 32 publications

Estimation of Tree Cover in an Agricultural Parkland of Senegal Using Rule-Based Regression Tree Modeling

Estimation of Tree Cover in an Agricultural Parkland of Senegal Using Rule-Based Regression Tree Modeling

Temporal and hierarchical spatial components of animal occurrence: conserving seasonal habitat for greater sage‐grouse

Incorporating within- and between-patch resource selection in identification of critical habitat for brood-rearing greater sage-grouse

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