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1.
J Environ Manage ; 277: 111418, 2021 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-33080432

RESUMO

Green roofs are among the most popular type of green infrastructure implemented in highly urbanized watersheds due to their low cost and efficient utilization of unused or under-used space. In this study, we examined the effectiveness of green roofs to attenuate stormwater runoff across a large metropolitan area in the Pacific Northwest, United States. We utilized a spatially explicit ecohydrological watershed model called Visualizing Ecosystem Land Management Assessments (VELMA) to simulate the resulting stormwater hydrology of implementing green roofs over 25%, 50%, 75%, and 100% of existing buildings within four urban watersheds in Seattle, Washington, United States. We simulated the effects of two types of green roofs: extensive green roofs, which are characterized by shallow soil profiles and short vegetative cover, and intensive green roofs, which are characterized by deeper soil profiles and can support larger vegetation. While buildings only comprise approximately 10% of the total area within each of the four watersheds, our simulations showed that 100% implementation of green roofs on these buildings can achieve approximately 10-15% and 20-25% mean annual runoff reductions for extensive and intensive green roofs, respectively, over a 28-year simulation. These results provide an upper limit for volume reductions achievable by green roofs in these urban watersheds. We also showed that stormwater runoff reductions are proportionately smaller during higher flow regimes caused by increased precipitation, likely due to the limited storage capacity of saturated green roofs. In general, green roofs can be effective at reducing stormwater runoff, and their effectiveness is limited by both their areal extent and storage capacity. Our results showed that green roof implementation can be an effective stormwater management tool in highly urban areas, and we demonstrated that our modeling approach can be used to assess the watershed-scale hydrologic impacts of the widespread adoption of green roofs across large metropolitan areas.


Assuntos
Hidrologia , Movimentos da Água , Conservação dos Recursos Naturais , Ecossistema , Chuva , Washington
2.
Ecol Evol ; 8(22): 11158-11168, 2018 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-30519433

RESUMO

Top predators have cascading effects throughout the food web, but their impacts on scavenger abundance are largely unknown. Gray wolves (Canis lupus) provide carrion to a suite of scavenger species, including the common raven (Corvus corax). Ravens are wide-ranging and intelligent omnivores that commonly take advantage of anthropogenic food resources. In areas where they overlap with wolves, however, ravens are numerous and ubiquitous scavengers of wolf-acquired carrion. We aimed to determine whether subsidies provided through wolves are a limiting factor for raven populations in general and how the wolf reintroduction to Yellowstone National Park in 1995-1997 affected raven population abundance and distribution on the Yellowstone's Northern Range specifically. We counted ravens throughout Yellowstone's Northern Range in March from 2009 to 2017 in both human-use areas and wolf habitat. We then used statistics related to the local wolf population and the winter weather conditions to model raven abundance during our study period and predict raven abundance on the Northern Range both before and after the wolf reintroduction. In relatively severe winters with greater snowpack, raven abundance increased in areas of human use and decreased in wolf habitat. When wolves were able to acquire more carrion, however, ravens increased in wolf habitat and decreased in areas with anthropogenic resources. Raven populations prior to the wolf reintroduction were likely more variable and heavily dependent on ungulate winter-kill and hunter-provided carcasses. The wolf recovery in Yellowstone helped stabilize raven populations by providing a regular food supply, regardless of winter severity. This stabilization has important implications for effective land management as wolves recolonize the west and global climate patterns change.

3.
Sensors (Basel) ; 14(11): 20304-19, 2014 Oct 28.
Artigo em Inglês | MEDLINE | ID: mdl-25353981

RESUMO

We developed new vegetation indices utilizing terrestrial laser scanning (TLS) to quantify the three-dimensional spatial configuration of plant communities. These indices leverage the novelty of TLS data and rely on the spatially biased arrangement of a TLS point cloud. We calculated these indices from TLS data acquired within an existing long term manipulation of forest structure in Central Oregon, USA, and used these data to test for differences in vegetation structure. Results provided quantitative evidence of a significant difference in vegetation density due to thinning and burning, and a marginally significant difference in vegetation patchiness due to grazing. A comparison to traditional field sampling highlighted the novelty of the TLS based method. By creating a linkage between traditional field sampling and landscape ecology, these indices enable field investigations of fine-scale spatial patterns. Applications include experimental assessment, long-term monitoring, and habitat characterization.


Assuntos
Agricultura/métodos , Monitoramento Ambiental/métodos , Florestas , Imageamento Tridimensional/métodos , Lasers , Árvores/anatomia & histologia , Árvores/classificação , Agricultura/instrumentação , Algoritmos , Monitoramento Ambiental/instrumentação , Imageamento Tridimensional/instrumentação
4.
Sensors (Basel) ; 12(8): 10639-58, 2012.
Artigo em Inglês | MEDLINE | ID: mdl-23112620

RESUMO

The characterization of soil attributes using hyperspectral sensors has revealed patterns in soil spectra that are known to respond to mineral composition, organic matter, soil moisture and particle size distribution. Soil samples from different soil horizons of replicated soil series from sites located within Washington and Oregon were analyzed with the FieldSpec Spectroradiometer to measure their spectral signatures across the electromagnetic range of 400 to 1,000 nm. Similarity rankings of individual soil samples reveal differences between replicate series as well as samples within the same replicate series. Using classification and regression tree statistical methods, regression trees were fitted to each spectral response using concentrations of nitrogen, carbon, carbonate and organic matter as the response variables. Statistics resulting from fitted trees were: nitrogen R(2) 0.91 (p < 0.01) at 403, 470, 687, and 846 nm spectral band widths, carbonate R(2) 0.95 (p < 0.01) at 531 and 898 nm band widths, total carbon R(2) 0.93 (p < 0.01) at 400, 409, 441 and 907 nm band widths, and organic matter R(2) 0.98 (p < 0.01) at 300, 400, 441, 832 and 907 nm band widths. Use of the 400 to 1,000 nm electromagnetic range utilizing regression trees provided a powerful, rapid and inexpensive method for assessing nitrogen, carbon, carbonate and organic matter for upper soil horizons in a nondestructive method.


Assuntos
Carbono/análise , Carbonatos/análise , Nitrogênio/análise , Radiometria/métodos , Solo/química , Análise Espectral/métodos , Radiação Eletromagnética , Monitoramento Ambiental/métodos , Oregon , Análise de Regressão , Washington
5.
Sensors (Basel) ; 9(4): 2719-45, 2009.
Artigo em Inglês | MEDLINE | ID: mdl-22574042

RESUMO

The ability to accurately and rapidly acquire leaf area index (LAI) is an indispensable component of process-based ecological research facilitating the understanding of gas-vegetation exchange phenomenon at an array of spatial scales from the leaf to the landscape. However, LAI is difficult to directly acquire for large spatial extents due to its time consuming and work intensive nature. Such efforts have been significantly improved by the emergence of optical and active remote sensing techniques. This paper reviews the definitions and theories of LAI measurement with respect to direct and indirect methods. Then, the methodologies for LAI retrieval with regard to the characteristics of a range of remotely sensed datasets are discussed. Remote sensing indirect methods are subdivided into two categories of passive and active remote sensing, which are further categorized as terrestrial, aerial and satellite-born platforms. Due to a wide variety in spatial resolution of remotely sensed data and the requirements of ecological modeling, the scaling issue of LAI is discussed and special consideration is given to extrapolation of measurement to landscape and regional levels.

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