scholarly journals Effects of Externally Applied IBA Doses on Rooting and Sapling Characteristics of Autumn Olive Berry Cuttings Taken at Different Periods

2021 ◽  
Vol 5 (1) ◽  
pp. 33-40
Author(s):  
Hüseyin Çelik ◽  
Damla Çil
Keyword(s):  
Autumn Olive

Elaeagnus umbellata (autumn olive).

2021 ◽  
Author(s):  
Julissa Rojas-Sandoval ◽  
Nick Pasiecznik
Keyword(s):  
Sand Dunes ◽  
Pinus Thunbergii ◽  
Shaanxi Province ◽  
Annual Rainfall ◽  
Coastal Sand Dunes ◽  
Elaeagnus Umbellata ◽  
Autumn Olive ◽  
Pioneer Tree ◽  
Coastal Sand ◽  
Deciduous Shrub

Abstract E. umbellata is an important deciduous shrub which reaches up to 5 m high and 10 cm in d.b.h. It is found in thickets and sparse woods of Japan, Korea and China. E. umbellata is a shade intolerant pioneer tree and is also commonly found along riversides and seashores in Japan. This species is growing in humid areas with 1000-4000 mm of annual rainfall in Japan. In China it is reported to grow even in semi-arid areas of Nei Menggu, Gansu and Shaanxi province, where annual rainfall is around 400 mm (Niu, 1990). E. umbellata can fix nitrogen and it is tolerant to salt winds, this species is therefore used for fixation of coastal sand dunes in Japan, and is frequently planted mixed with Pinus thunbergii as a soil improving tree. E. umbellata is also planted in eroded areas of mountainous zones to re-establish and develop vegetation. In China, E. umbellata is occasionally cultivated in gardens (Zhang, 1997).

Invasive autumn olive performance varies in different reclamation conditions: implications for restoration

2018 ◽  
Vol 27 (3) ◽  
pp. 600-606
Author(s):  
Morgan E. Franke ◽  
Carl Zipper ◽  
Jacob N. Barney
Keyword(s):  
Autumn Olive

scholarly journals Identifying woody vegetation on coal surface mines using phenological indicators with multitemporal Landsat imagery

2014 ◽  
Vol XL-1 ◽  
pp. 339-345 ◽  
Author(s):  
A. J. Oliphant ◽  
J. Li ◽  
R. H. Wynne ◽  
P. F. Donovan ◽  
C. E. Zipper
Keyword(s):  
Land Cover ◽  
Growing Season ◽  
Forest Recovery ◽  
Support Vector ◽  
Landsat 8 ◽  
Coal Surface ◽  
Ecosystem Recovery ◽  
Surface Mines ◽  
Autumn Olive ◽  
Native Forests

Surface mining for coal has disturbed large land areas in the Appalachian Mountains. Better information on mined lands' ecosystem recovery status is necessary for effective environmental management in mining-impacted regions. Because record quality varies between state mining agencies and much mining occurred prior to widespread use of geospatial technologies, accurate maps of mining extents, durations, and land cover effects are often not available. Landsat data are well suited to mapping and characterizing land cover and forest recovery on former coal surface mines. Past mine reclamation techniques have often failed to restore premining forest vegetation but natural processes may enable native forests to re-establish on mined areas with time. However, the invasive species autumn olive (Elaeagnus umbellate) is proliferating widely on former coal surface mines, often inhibiting reestablishment of native forests. Autumn olive outcompetes native vegetation because it fixes atmospheric nitrogen and benefits from a longer growing season than native deciduous trees. This longer growing season, along with Landsat 8's high signal to noise ratio, has enabled species-level classification of autumn olive using multitemporal Landsat 8 data at accuracy levels usually only obtainable using higher spatial or spectral resolution sensors. We have used classification and regression tree (CART<sup>®</sup>) and support vector machine (SVM) to classify five counties in the coal mining region of Virginia for presence and absence of autumn olive. The best model found was a CART<sup>®</sup> model with 36 nodes which had an overall accuracy of 84% and kappa of 0.68. Autumn olive had conditional kappa of 0.65 and a producers and users accuracy of 86% and 83% respectively. The best SVM model used a second order polynomial kernel and had an overall accuracy of 77%, an overall kappa of 0.54 and a producers and users accuracy of 60% and 90% respectively.

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