Effects of Placer Mining on the Invertebrate Communities of Interior Alaska Streams

1985 ◽  
Vol 4 (4) ◽  
pp. 208-214 ◽  
Author(s):  
Stephen M. Wagener ◽  
Jacqueline D. LaPerriere
1998 ◽  
Author(s):  
Frederic H. Wilson ◽  
James H. Dover ◽  
Dwight C. Bradley ◽  
Florence R. Weber ◽  
Thomas K. Bundtzen ◽  
...  
Keyword(s):  

2021 ◽  
Vol 13 (10) ◽  
pp. 1966
Author(s):  
Christopher W Smith ◽  
Santosh K Panda ◽  
Uma S Bhatt ◽  
Franz J Meyer ◽  
Anushree Badola ◽  
...  

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.


Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 188
Author(s):  
Cristina Popescu ◽  
Mihaela Oprina-Pavelescu ◽  
Valentin Dinu ◽  
Constantin Cazacu ◽  
Francis J. Burdon ◽  
...  

Stream and terrestrial ecosystems are intimately connected by riparian zones that support high biodiversity but are also vulnerable to human impacts. Landscape disturbances, overgrazing, and diffuse pollution of agrochemicals threaten riparian biodiversity and the delivery of ecosystem services in agricultural landscapes. We assessed how terrestrial invertebrate communities respond to changes in riparian vegetation in Romanian agricultural catchments, with a focus on the role of forested riparian buffers. Riparian invertebrates were sampled in 10 paired sites, with each pair consisting of an unbuffered upstream reach and a downstream reach buffered with woody riparian vegetation. Our results revealed distinct invertebrate community structures in the two site types. Out of 33 invertebrate families, 13 were unique to either forested (6) or unbuffered (7) sites. Thomisidae, Clubionidae, Tetragnathidae, Curculionidae, Culicidae, and Cicadidae were associated with forested buffers, while Lycosidae, Chrysomelidae, Staphylinidae, Coccinellidae, Tettigoniidae, Formicidae, and Eutichuridae were more abundant in unbuffered sites. Despite statistically equivocal results, invertebrate diversity was generally higher in forested riparian buffers. Local riparian attributes significantly influenced patterns in invertebrate community composition. Our findings highlight the importance of local woody riparian buffers in maintaining terrestrial invertebrate diversity and their potential contribution as a multifunctional management tool in agricultural landscapes.


2021 ◽  
Author(s):  
Patrick F. Sullivan ◽  
Annalis H. Brownlee ◽  
Sarah B.Z. Ellison ◽  
Sean M.P. Cahoon

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