Geology topics

Erika Sanchez-Chopitea

Publications and source records attributed to Erika Sanchez-Chopitea.

4 recordsLinked to original sources

USGS2021

A customized framework for regional classification of conifers using automated feature extraction

Pinyon and juniper expansion into sagebrush ecosystems is one of the major challenges facing land managers in the Great Basin. Effective pinyon and juniper treatment requires maps that accurately and precisely depict tree location and degree of woodland development so managers can target restoration efforts for early stages of pinyon and juniper expansion. However, available remotely sensed layers that cover a regional spatial extent lack the spatial resolution or accuracy to meet this need. Accuracy can be improved using object-based image analysis methods such as automated feature extraction, which has proven successful in accurately classifying land cover at the site-level but to date has yet to be applied to regional extents due to time and computational limitations. Using Feature Analyst™, we implement our framework with 1-m 2 reference imagery provided by National Agricultural Imagery Program to classify conifers across Nevada and northeastern California. Our resulting binary conifer map has an overall accuracy of 86%. We discuss the advantages to accuracy and precision our framework provides compared to other classification methods

Oregon, California, Nevada, Utah↗

USGS2017

Using object-based image analysis to conduct high-resolution conifer extraction at regional spatial scales

The distribution and abundance of pinyon ( Pinus monophylla ) and juniper ( Juniperus osteosperma, J. occidentalis ) trees (hereinafter, "pinyon-juniper") in sagebrush ( Artemisia spp.) ecosystems of the Great Basin in the Western United States has increased substantially since the late 1800s. Distributional expansion and infill of pinyon-juniper into sagebrush ecosystems threatens the ecological function and economic viability of these ecosystems within the Great Basin, and is now a major contemporary challenge facing land and wildlife managers. Particularly, pinyon-juniper encroachment into intact sagebrush ecosystems has been identified as a primary threat facing populations of greater sage-grouse ( Centrocercus urophasianus ; hereinafter, "sage-grouse"), which is a sagebrush obligate species. Even seemingly innocuous scatterings of isolated pinyon-juniper in an otherwise intact sagebrush landscape can negatively affect survival and reproduction of sage-grouse. Therefore, accurate and high-resolution maps of pinyon-juniper distribution and abundance (indexed by canopy cover) across broad geographic extents would help guide land management decisions that better target areas for pinyon-juniper removal projects (for example, fuel reduction, habitat improvement for sage-grouse, and other sagebrush species) and facilitate science that further quantifies ecological effects of pinyon-juniper encroachment on sage-grouse populations and sagebrush ecosystem processes . Hence, we mapped pinyon-juniper (referred to as conifers for actual mapping) at a 1 × 1-meter (m) high resolution across the entire range of previously mapped sage-grouse habitat in Nevada and northeastern California. We used digital orthophoto quad tiles from National Agriculture Imagery Program (2010, 2013) as base imagery, and then classified conifers using automated feature extraction methodology with the program Feature Analyst ™ . This method relies on machine learning algorithms that extract features from imagery based on their spectral and spatial signatures. We classified conifers in 6,230 tiles and then tested for errors of omission and commission using confusion matrices. Accuracy ranged from 79.1 to 96.8, with an overall accuracy of 84.3 percent across all mapped areas. An estimated accuracy coefficient (kappa) indicated substantial to nearly perfect agreement, which varied across mapped areas. For this mapping process across the entire mapping extent, four sets of products are available at https://doi.org/10.5066/F7348HVC , including (1) a shapefile representing accuracy results linked to mapping subunits; (2) binary rasters representing conifer presence or absence at a 1 × 1 m resolution; (3) a 30 × 30 m resolution raster representing percentages of conifer canopy cover within each cell from 0 to 100; and (4) 1 × 1 m resolution canopy cover classification rasters derived from a 50-m-radius moving window analysis. The latter two products can be reclassified in a geographic information system (GIS) into user-specified bins to meet different objectives, which include approximations for phases of encroachment. These products complement, and in some cases improve upon, existing conifer maps in the Western United States, and will help facilitate sage-grouse habitat management and sagebrush ecosystem restoration.

California, Nevada↗

USGS2016

Spatially explicit modeling of annual and seasonal habitat for greater sage-grouse ( Centrocercus urophasianus ) in Nevada and Northeastern California—An updated decision-support tool for management

Successful adaptive management hinges largely upon integrating new and improved sources of information as they become available. As a timely example of this tenet, we updated a management decision support tool that was previously developed for greater sage-grouse ( Centrocercus urophasianus , hereinafter referred to as “sage-grouse”) populations in Nevada and California. Specifically, recently developed spatially explicit habitat maps derived from empirical data played a key role in the conservation of this species facing listing under the Endangered Species Act. This report provides an updated process for mapping relative habitat suitability and management categories for sage-grouse in Nevada and northeastern California (Coates and others, 2014, 2016). These updates include: (1) adding radio and GPS telemetry locations from sage-grouse monitored at multiple sites during 2014 to the original location dataset beginning in 1998; (2) integrating output from high resolution maps (1–2 m 2 ) of sagebrush and pinyon-juniper cover as covariates in resource selection models; (3) modifying the spatial extent of the analyses to match newly available vegetation layers; (4) explicit modeling of relative habitat suitability during three seasons (spring, summer, winter) that corresponded to critical life history periods for sage-grouse (breeding, brood-rearing, over-wintering); (5) accounting for differences in habitat availability between more mesic sagebrush steppe communities in the northern part of the study area and drier Great Basin sagebrush in more southerly regions by categorizing continuous region-wide surfaces of habitat suitability index (HSI) with independent locations falling within two hydrological zones; (6) integrating the three seasonal maps into a composite map of annual relative habitat suitability; (7) deriving updated land management categories based on previously determined cut-points for intersections of habitat suitability and an updated index of sage-grouse abundance and space-use (AUI); and (8) masking urban footprints and major roadways out of the final map products. Seasonal habitat maps were generated based on model-averaged resource selection functions (RSF) derived for 10 project areas (813 sage-grouse; 14,085 locations) during the spring season, 10 during the summer season (591 sage-grouse, 11,743 locations), and 7 during the winter season (288 sage-grouse, 4,862 locations). RSF surfaces were transformed to HSIs and averaged in a GIS framework for every pixel for each season. Validation analyses of categorized HSI surfaces using a suite of independent datasets resulted in an agreement of 93–97 percent for habitat versus non-habitat on an annual basis. Spring and summer maps validated similarly well at 94–97 percent, while winter maps validated slightly less accurately at 87–93 percent. We then provide an updated example of how space use models can be integrated with habitat models to help inform conservation planning. We used updated lek count data to calculate a composite abundance and space use index (AUI) that comprised the combination of probabilistic breeding density with a non-linear probability of occurrence relative to distance to nearest lek. The AUI was then classified into two categories of use (high and low-to-no) and intersected with the HSI categories to create potential management prioritization scenarios based on information about sage-grouse occupancy coupled with habitat suitability. Compared to Coates and others (2014, 2016), the amount of area classified as habitat across the region increased by 6.5 percent (approximately 1,700,000 acres). For management categories, core increased by 7.2 percent (approximately 865,000 acres), priority increased by 9.6 percent (approximately 855,000 acres), and general increased by 9.2 percent (approximately 768,000 acres), while non-habitat decreased (that is, classified non-habitat occurring outside of areas of concentrated use) by 11.9 percent (approximately 2,500,000 acres). Importantly, seasonal and annual maps represent habitat for all age and sex classes of sage-grouse (that is, sample sizes of marked grouse were insufficient to only construct models for reproductive females). This revised sage-grouse habitat mapping product helps improve adaptive application of conservation planning tools based on intersections of spatially explicit habitat suitability, abundance, and space use indices.

California, Nevada↗

USGS2014

Spatially explicit modeling of greater sage-grouse ( Centrocercus urophasianus ) habitat in Nevada and northeastern California: a decision-support tool for management

Greater sage-grouse ( Centrocercus urophasianus , hereafter referred to as “sage-grouse”) populations are declining throughout the sagebrush ( Artemisia spp.) ecosystem, including millions of acres of potential habitat across the West. Habitat maps derived from empirical data are needed given impending listing decisions that will affect both sage-grouse population dynamics and human land-use restrictions. This report presents the process for developing spatially explicit maps describing relative habitat suitability for sage-grouse in Nevada and northeastern California. Maps depicting habitat suitability indices (HSI) values were generated based on model-averaged resource selection functions informed by more than 31,000 independent telemetry locations from more than 1,500 radio-marked sage-grouse across 12 project areas in Nevada and northeastern California collected during a 15-year period (1998–2013). Modeled habitat covariates included land cover composition, water resources, habitat configuration, elevation, and topography, each at multiple spatial scales that were relevant to empirically observed sage-grouse movement patterns. We then present an example of how the HSI can be delineated into categories. Specifically, we demonstrate that the deviation from the mean can be used to classify habitat suitability into three categories of habitat quality (high, moderate, and low) and one non-habitat category. The classification resulted in an agreement of 93–97 percent for habitat versus non-habitat across a suite of independent validation datasets. Lastly, we provide an example of how space use models can be integrated with habitat models to help inform conservation planning. In this example, we combined probabilistic breeding density with a non-linear probability of occurrence relative to distance to nearest lek (traditional breeding ground) using count data to calculate a composite space use index (SUI). The SUI was then classified into two categories of use (high and low-to-no) and intersected with the HSI categories to create potential management prioritization scenarios based oninformation about sage-grouse occupancy coupled with habitat suitability. This provided an example of a conservation planning application that uses the intersection of the spatially-explicit HSI and empirically-based SUI to identify potential spatially explicit strategies for sage-grouse management. Importantly, the reported categories for the HSI and SUI can be reclassified relatively easily to employ alternative conservation thresholds that may be identified through decision-making processes with stake-holders, managers, and biologists. Moreover, the HSI/SUI interface map can be updated readily as new data become available.

California;Nevada↗