Microelevational Differences Affect Longleaf Pine (Pinus palustris Mill.) Sensitivity to Tropical Cyclone Precipitation: A Case Study Using Lidar

2020 ◽  
Vol 76 (2) ◽  
pp. 89
Author(s):  
Evan E. Montpellier ◽  
Paul A. Knapp ◽  
Peter T. Soulé ◽  
Justin T. Maxwell
Keyword(s):  
Tropical Cyclone ◽  
Longleaf Pine ◽  
Pinus Palustris

Longleaf Pine Management on the DeSoto National Forest—A Case Study

1989 ◽  
Vol 13 (1) ◽  
pp. 34-40
Author(s):  
Gene A. Sirmon ◽  
Roger W. Dennington
Keyword(s):  
Natural Regeneration ◽  
Longleaf Pine ◽  
Pinus Palustris ◽  
National Forest ◽  
Culture Care ◽  
Artificial Regeneration ◽  
Pine Regeneration ◽  
Trained Personnel ◽  
Bareroot Seedlings

Abstract Longleaf pine (Pinus palustris Mill) reforestation efforts were successful on the National Forest in south Mississippi when foresters began applying the proper technology. Artificial regeneration by planting bareroot seedlings and natural regeneration by the shelterwood system bothresulted in a plantation success rate consistently above 90%. This success can be attributed to better site preparation, better seedling culture, care, and handling, and more highly trained personnel committed to excellence in longleaf pine regeneration. South. J. Appl. For. 13(1):34-40.

scholarly journals Estimating Forest Characteristics for Longleaf Pine Restoration Using Normalized Remotely Sensed Imagery in Florida USA

Forests ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 426 ◽  
Author(s):  
John Hogland ◽  
David L.R. Affleck ◽  
Nathaniel Anderson ◽  
Carl Seielstad ◽  
Solomon Dobrowski ◽  
...  
Keyword(s):  
Longleaf Pine ◽  
Pinus Palustris ◽  
Remotely Sensed ◽  
Accurate Information ◽  
Remotely Sensed Data ◽  
Image Normalization ◽  
Remotely Sensed Imagery ◽  
Generalized Additive Modeling ◽  
Additive Modeling

Effective forest management is predicated on accurate information pertaining to the characteristics and condition of forests. Unfortunately, ground-based information that accurately describes the complex spatial and contextual nature of forests across broad landscapes is cost prohibitive to collect. In this case study we address technical challenges associated with estimating forest characteristics from remotely sensed data by incorporating field plot layouts specifically designed for calibrating models from such data, applying new image normalization procedures to bring images of varying spatial resolutions to a common radiometric scale, and implementing an ensemble generalized additive modeling technique. Image normalization and ensemble models provided accurate estimates of forest types, presence/absence of longleaf pine (Pinus palustris), and tree basal areas and tree densities over a large segment of the panhandle of Florida, USA. This study overcomes several of the major barriers associated with linking remotely sensed imagery with plot data to estimate key forest characteristics over large areas.

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