Using Geographic Information Systems (GIS) to Inventory Coastal Prairie Wetlands Along the Upper Gulf Coast, Texas

Enwright, Nicholas M.; Forbes, Margaret G.; Doyle, Robert D.; Hunter, Bruce Allan; Forbes, William

doi:10.1007/s13157-011-0184-5

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…subsp. occidentalis) cover (Davies et al 2010), and inventory of coastal prairie wetlands (Enwright et al 2011). Although we demonstrated that NAIP images could be used to map mesquite cover, the results obtained here might not apply to other species, plant communities and/or seasons.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Ground-Measured and Image-Classified Mesquite (Prosopis glandulosa) Canopy Cover

Mırık¹,

Ansley²

2012

Rangeland Ecology & Management

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Remote sensing has long been recognized as a rapid, inexpensive, nondestructive, and synoptic technique to study rangeland vegetation and soils. With respect to the worldwide phenomenon of woody plant invasion on many grasslands and rangelands, there is increasing interest in accurate and cost-effective quantification of woody plant cover and distribution over large land areas. Our objectives were to 1) investigate the relationship between ground-measured and image-classified honey mesquite (Prosopis glandulosa Torr.) canopy cover at three sites in north Texas using high spatial resolution (0.67-m) aerial images, and 2) examine the suitability of aerial images with different spatial resolutions (0.67-m, 1-m, and 2-m) for accurate estimation of mesquite canopy cover. The line intercept method and supervised maximum likelihood classifier were used to measure mesquite cover on the ground and on images, respectively. Images all were taken in September when mesquite foliage was photosynthetically active and most herbaceous vegetation was dormant. The results indicated that there were robust agreements between classified and ground-measured mesquite cover at all three sites with the coefficients of determination (r 2) $ 0.95. Accuracy of lower spatial resolution images ranged from r 2 5 0.89-0.93, with the 2-m spatial resolution image on one of the sites at r 2 5 0.89. For all sites, the overall, producer's, and user's accuracies, and kappa statistics were 92% and 97%, 91% and 99%, 85% and 96%, and 0.82 and 0.95 for 2-m and 0.67-m spatial resolution images, respectively. Results showed that images at all three spatial resolution levels were effective for estimating mesquite cover over large and remote or inaccessible areas. Resumen La te´cnica de sensores remotos ha sido reconocida como una te´cnica rápida, económica, no destructiva, y sinóptica para el estudio de la vegetación de los pastizales y suelos. Con respecto al fenómeno mundial de invasión de plantas leñ osas praderas y pastizales, existe un creciente de interés en la cuantificación precisa y efectiva en costo, del alcance y distribución de las plantas leñ osas en grandes extensiones de tierra. Nuestros objetivos fueron: 1) investigar la relación entre la medición en tierra y la clasificación de imágenes de la cobertura del mezquite (Prosopis glandulosa Torr), en tres sitios al norte de Texas, usando imágenes de alta resolución espacial (0.67-m), y 2) examinando las imágenes aéreas más apropiadas con diferentes resoluciones espaciales (0.67-m, 1-m y 2-m) para una exacta estimación de la cobertura del mezquite. Se usaron el me´todo de la línea de intercepción y el clasificador de máxima probabilidad para medir la cubierta del mezquite en el suelo en las imágenes respectivamente. Todas las imágenes se tomaron en Septiembre cuando el follaje del mezquite estaba fotosinte´ticamente activo y la mayoría de la vegetación herbácea perennes estaba inactiva. Los resultados indicaron que hubo sólidos coincidencias entre las medidas clasificadas y las del suelo en la med...

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Section: Discussionmentioning

confidence: 99%

Comparison of Ground-Measured and Image-Classified Mesquite (Prosopis glandulosa) Canopy Cover

Mırık¹,

Ansley²

2012

Rangeland Ecology & Management

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“…We generated a normalized difference vegetation index (NDVI) from the August 2009 National Agriculture Imagery Program (NAIP) 1-m resolution color aerial imagery (USDA 2009; see Enwright et al 2011) and rescaled this to 30-m resolution. NDVI is a measure of surface greenness, generally correlating well with live green vegetation and aboveground biomass.…”

Section: Spatial Predictor Variablesmentioning

confidence: 99%

Identifying Greater Sage‐Grouse source and sink habitats for conservation planning in an energy development landscape

Kirol

Beck

Huzurbazar

et al. 2015

Ecological Applications

106

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Conserving a declining species that is facing many threats, including overlap of its habitats with energy extraction activities, depends upon identifying and prioritizing the value of the habitats that remain. In addition, habitat quality is often compromised when source habitats are lost or fragmented due to anthropogenic development. Our objective was to build an ecological model to classify and map habitat quality in terms of source or sink dynamics for Greater Sage-Grouse (Centrocercus urophasianus) in the Atlantic Rim Project Area (ARPA), a developing coalbed natural gas field in south-central Wyoming, USA. We used occurrence and survival modeling to evaluate relationships between environmental and anthropogenic variables at multiple spatial scales and for all female summer life stages, including nesting, brood-rearing, and non-brooding females. For each life stage, we created resource selection functions (RSFs). We weighted the RSFs and combined them to form a female summer occurrence map. We modeled survival also as a function of spatial variables for nest, brood, and adult female summer survival. Our survival-models were mapped as survival probability functions individually and then combined with fixed vital rates in a fitness metric model that, when mapped, predicted habitat productivity (productivity map). Our results demonstrate a suite of environmental and anthropogenic variables at multiple scales that were predictive of occurrence and survival. We created a source-sink map by overlaying our female summer occurrence map and productivity map to predict habitats contributing to population surpluses (source habitats) or deficits (sink habitat) and low-occurrence habitats on the landscape. The source-sink map predicted that of the Sage-Grouse habitat within the ARPA, 30% was primary source, 29% was secondary source, 4% was primary sink, 6% was secondary sink, and 31% was low occurrence. Our results provide evidence that energy development and avoidance of energy infrastructure were probably reducing the amount of source habitat within the ARPA landscape. Our source-sink map provides managers with a means of prioritizing habitats for conservation planning based on source and sink dynamics. The spatial identification of high value (i.e., primary source) as well as suboptimal (i.e., primary sink) habitats allows for informed energy development to minimize effects on local wildlife populations.

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“…If there has been a large disturbance event between the two dates, the data should be used with caution. For instance, LiDAR data collected prior to a hurricane or tropical storm may be less useful because storm events can cause extreme erosion, deposition, and damage to vegetation (Enwright et al 2011). Other natural disturbances, such as wildfires, avalanches, mudslides, or beaver activity, may increase or decrease wetland area (Environmental Laboratory 1987), making older LiDAR datasets obsolete.…”

Section: Discussionmentioning

confidence: 99%

Use of LiDAR to Assist in Delineating Waters of the United States, Including Wetlands

Gillrich¹,

Lichvar²

2014

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During preliminary delineations of an Ordinary High Water Mark (OHWM) boundary, LiDAR data or products may be used to view the OHWM signature across a project area and to estimate the height and location of two primary OHWM indicators: changes in vegetation and breaks in slope. At this time, most LiDAR data or products cannot detect changes in sediment texture. The point spacing, horizontal resolution, and vertical accuracy of the data or products determine if landscape features, such as the OHWM break in slope, can be measured with sufficient accuracy. All information gathered from LiDAR data or products should be verified in the field. During the preliminary, data-gathering stage of wetland delineations, LiDAR data and products may be used to view vegetative, topographic, and hydrologic patterns across a project area and to focus the investigation on transitional areas. They cannot provide evidence of hydrophytic vegetation or hydric soils. Although LiDAR intensity data may provide information on inundation extent, they contain no information regarding inundation frequency or duration and should not be used as a primary hydrology indicator. Intensity data collected during the growing season could be used as a secondary indicator of wetland hydrology. LiDAR data or products are not an adequate substitute for a field investigation. DISCLAIMER: The contents of this report are not to be used for advertising, publication, or promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product names and trademarks cited are the property of their respective owners. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents.

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Using Geographic Information Systems (GIS) to Inventory Coastal Prairie Wetlands Along the Upper Gulf Coast, Texas

Cited by 9 publications

References 17 publications

Comparison of Ground-Measured and Image-Classified Mesquite (Prosopis glandulosa) Canopy Cover

Comparison of Ground-Measured and Image-Classified Mesquite (Prosopis glandulosa) Canopy Cover

Identifying Greater Sage‐Grouse source and sink habitats for conservation planning in an energy development landscape

Use of LiDAR to Assist in Delineating Waters of the United States, Including Wetlands

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