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You searched for subject:(dryland forest). Showing records 1 – 3 of 3 total matches.

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Virginia Tech

1. Walker, Jessica. Analysis of Dryland Forest Phenology using Fused Landsat and MODIS Satellite Imagery.

Degree: PhD, Geography, 2012, Virginia Tech

This dissertation investigated the practicality and expediency of applying remote sensing data fusion products to the analysis of dryland vegetation phenology. The objective of the first study was to verify the quality of the output products of the spatial and temporal adaptive reflectance fusion method (STARFM) over the dryland Arizona study site. Synthetic 30 m resolution images were generated from Landsat-5 Thematic Mapper (TM) data and a range of 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance datasets and assessed via correlation analysis with temporally coincident Landsat-5 imagery. The accuracy of the results (0.61 < R2 < 0.94) justified subsequent use of STARFM data in this environment, particularly when the imagery were generated from Nadir Bi-directional Reflectance Factor (BRDF)-Adjusted Reflectance (NBAR) MODIS datasets. The primary objective of the second study was to assess whether synthetic Landsat data could contribute meaningful information to the phenological analyses of a range of dryland vegetation classes. Start-of-season (SOS) and date of peak greenness phenology metrics were calculated for each STARFM and MODIS pixel on the basis of enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) time series over a single growing season. The variability of each metric was calculated for all STARFM pixels within 500 m MODIS extents. Colorado Plateau Pinyon Juniper displayed high amounts of temporal and spatial variability that justified the use of STARFM data, while the benefit to the remaining classes depended on the specific vegetation index and phenology metric. The third study expanded the STARFM time series to five years (2005-2009) to examine the influence of site characteristics and climatic conditions on dryland ponderosa pine (Pinus ponderosa) forest phenological patterns. The results showed that elevation and slope controlled the variability of peak timing across years, with lower elevations and shallower slopes linked to higher levels of variability. During drought conditions, the number of site variables that controlled the timing and variability of vegetation peak increased. Advisors/Committee Members: Thomas, Valerie A. (committee member), Seiler, John R. (committee member), Resler, Lynn M. (committee member), de Beurs, Kirsten M. (committeecochair), Wynne, Randolph H. (committeecochair).

Subjects/Keywords: Remote sensing; satellite data fusion; dryland forest; phenology

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APA (6th Edition):

Walker, J. (2012). Analysis of Dryland Forest Phenology using Fused Landsat and MODIS Satellite Imagery. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/39403

Chicago Manual of Style (16th Edition):

Walker, Jessica. “Analysis of Dryland Forest Phenology using Fused Landsat and MODIS Satellite Imagery.” 2012. Doctoral Dissertation, Virginia Tech. Accessed September 23, 2019. http://hdl.handle.net/10919/39403.

MLA Handbook (7th Edition):

Walker, Jessica. “Analysis of Dryland Forest Phenology using Fused Landsat and MODIS Satellite Imagery.” 2012. Web. 23 Sep 2019.

Vancouver:

Walker J. Analysis of Dryland Forest Phenology using Fused Landsat and MODIS Satellite Imagery. [Internet] [Doctoral dissertation]. Virginia Tech; 2012. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/10919/39403.

Council of Science Editors:

Walker J. Analysis of Dryland Forest Phenology using Fused Landsat and MODIS Satellite Imagery. [Doctoral Dissertation]. Virginia Tech; 2012. Available from: http://hdl.handle.net/10919/39403


University of Hawaii

2. Libby, Riko. Factors That Affect Natural Regeneration, Growth, And Survival Rates Of Threatened And Endangered Species In Dryland Forests In Hawai’i.

Degree: 2019, University of Hawaii

Subjects/Keywords: Dryland Forest; Natural Regeneration; Recruitment

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APA (6th Edition):

Libby, R. (2019). Factors That Affect Natural Regeneration, Growth, And Survival Rates Of Threatened And Endangered Species In Dryland Forests In Hawai’i. (Thesis). University of Hawaii. Retrieved from http://hdl.handle.net/10125/62218

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Libby, Riko. “Factors That Affect Natural Regeneration, Growth, And Survival Rates Of Threatened And Endangered Species In Dryland Forests In Hawai’i.” 2019. Thesis, University of Hawaii. Accessed September 23, 2019. http://hdl.handle.net/10125/62218.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Libby, Riko. “Factors That Affect Natural Regeneration, Growth, And Survival Rates Of Threatened And Endangered Species In Dryland Forests In Hawai’i.” 2019. Web. 23 Sep 2019.

Vancouver:

Libby R. Factors That Affect Natural Regeneration, Growth, And Survival Rates Of Threatened And Endangered Species In Dryland Forests In Hawai’i. [Internet] [Thesis]. University of Hawaii; 2019. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/10125/62218.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Libby R. Factors That Affect Natural Regeneration, Growth, And Survival Rates Of Threatened And Endangered Species In Dryland Forests In Hawai’i. [Thesis]. University of Hawaii; 2019. Available from: http://hdl.handle.net/10125/62218

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Florida

3. Herrero, Hannah V. Using Random Forest Classification to Improve Savanna Landscape Analysis a Case Study of Chobe National Park, Botswana from 1990 to 2009.

Degree: MS, Geography, 2015, University of Florida

This paper evaluates the change in savanna vegetation along the Chobe Riverfront within Chobe National Park Botswana from 1990 to 2009. Classifying land cover in savanna environments is challenging because the vegetation signatures are similar across distinct vegetation covers, e.g. shrub versus tree covers, due to the specific composition of savanna vegetation. Therefore, there are difficulties in making discrete clarifications in such landscapes. To address the issue of difficulty in classifying this landscape, the Random Forest Algorithm was applied to predict land-cover classes, and it is compared to more traditional classification techniques and vegetation indices methods. This study indicates that there has been a transformation of vegetation in Chobe Riverfront towards an increasing amount of bush from grassland. This could be, in part, due to an increasing number of elephants utilizing the Riverfront. The forested area at a further distance from the River has also had areas of loss of percent cover. The Random Forest technique had an overall accuracy from 79-80%, Support Vector Machine 75-78%, Minimum Distance 53-72%, and Parallelepiped supervised classification from 67-72%. Trends were also evaluated for the Random Forest classifiers based on weightings across input variables and these were related to changes in vegetation indices over time to verify patterns. This study provides land use planners and managers with a more reliable, efficient and relatively inexpensive tool for analyzing land-cover change across these highly sensitive regions, and thus to link to necessary changes in management in a more timely manner. ( en ) Advisors/Committee Members: SOUTHWORTH,JANE (committee chair), CHILD,BRIAN ANTHONY (committee member).

Subjects/Keywords: Forests; Grasslands; Image classification; Land cover; Modeling; Remote sensing; Savannas; Shrubs; Vegetation; Woodlands; bush  – chobe  – degradation  – dryland  – encroachment  – forest  – park  – random  – remote  – savanna  – sensing

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Herrero, H. V. (2015). Using Random Forest Classification to Improve Savanna Landscape Analysis a Case Study of Chobe National Park, Botswana from 1990 to 2009. (Masters Thesis). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0047666

Chicago Manual of Style (16th Edition):

Herrero, Hannah V. “Using Random Forest Classification to Improve Savanna Landscape Analysis a Case Study of Chobe National Park, Botswana from 1990 to 2009.” 2015. Masters Thesis, University of Florida. Accessed September 23, 2019. http://ufdc.ufl.edu/UFE0047666.

MLA Handbook (7th Edition):

Herrero, Hannah V. “Using Random Forest Classification to Improve Savanna Landscape Analysis a Case Study of Chobe National Park, Botswana from 1990 to 2009.” 2015. Web. 23 Sep 2019.

Vancouver:

Herrero HV. Using Random Forest Classification to Improve Savanna Landscape Analysis a Case Study of Chobe National Park, Botswana from 1990 to 2009. [Internet] [Masters thesis]. University of Florida; 2015. [cited 2019 Sep 23]. Available from: http://ufdc.ufl.edu/UFE0047666.

Council of Science Editors:

Herrero HV. Using Random Forest Classification to Improve Savanna Landscape Analysis a Case Study of Chobe National Park, Botswana from 1990 to 2009. [Masters Thesis]. University of Florida; 2015. Available from: http://ufdc.ufl.edu/UFE0047666

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