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Combined HMM post-processing analyses of river plots and bar plots of the gross (grey) and net (colour) change in each land cover class (i.e. NVF, Aliens, Indigenous Forest, Grassland, and Mixed Woody Grassland) under their respective management classes (i.e. Barloworld/Commercial, Communal, Forestry/Conservation, and Plantations) between 1990 and 2020

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Bibliographic Details
Title: Combined HMM post-processing analyses of river plots and bar plots of the gross (grey) and net (colour) change in each land cover class (i.e. NVF, Aliens, Indigenous Forest, Grassland, and Mixed Woody Grassland) under their respective management classes (i.e. Barloworld/Commercial, Communal, Forestry/Conservation, and Plantations) between 1990 and 2020
Authors: Glenn Moncrieff, Keletso Moilwe, Jasper Slingsby, Vernon Visser
Publication Year: 2024
Collection: University of Cape Town: Figshare
Subject Terms: Terrestrial ecology, Cartography and digital mapping, Photogrammetry and remote sensing, Biosecurity science and invasive species ecology, Spatial data and applications, Sankey plot, Repeatability, Land cover change quantification, Hidden Markov Model, post-processing algorithms
Description: This dataset was used to produce land cover change analyses for Figures 3.8 (A-D) and Figures 3.9 (A -D) as part of a master's thesis titled- Repeatable methods for classification of alien and native vegetation in the Montane grasslands (2024). This dataset encompasses forty-five data entries. This includes 11 Hidden Markov Model (HMM) post-processed GeoTIFFS for 1990 until 2020 under each management class (i.e. Barloworld/Commercial, Communal, Forestry/Conservation, and Plantations). An r. script is also included to combine all GeoTIFFS then format and run code to produce river plots and bar graphs for the four management classes. The dataset aims to illustrate the changes among each land cover class (i.e. NVF, Aliens, Indigenous Forest, Grassland, and Mixed Woody Grassland) under the different management classes. This helps identify drivers of land cover change in the various management classes and the entire study area. It is also important to note that the land cover changes will portray realistic changes because they are HMM post-processed. Date of data collection: February 2020 Location of data collection: Blyde River Canyon Conservancy and its surrounds, in Mpumalanga/Limpopo Provinces, South Africa.
Document Type: dataset
Language: unknown
DOI: 10.25375/uct.24885267.v1
Availability: https://doi.org/10.25375/uct.24885267.v1
https://figshare.com/articles/dataset/Combined_HMM_post-processing_analyses_of_river_plots_and_bar_plots_of_the_gross_grey_and_net_colour_change_in_each_land_cover_class_i_e_NVF_Aliens_Indigenous_Forest_Grassland_and_Mixed_Woody_Grassland_under_their_respective_management_class/24885267
Rights: CC BY 4.0
Accession Number: edsbas.4BC944F8
Database: BASE
Description
Abstract:This dataset was used to produce land cover change analyses for Figures 3.8 (A-D) and Figures 3.9 (A -D) as part of a master's thesis titled- Repeatable methods for classification of alien and native vegetation in the Montane grasslands (2024). This dataset encompasses forty-five data entries. This includes 11 Hidden Markov Model (HMM) post-processed GeoTIFFS for 1990 until 2020 under each management class (i.e. Barloworld/Commercial, Communal, Forestry/Conservation, and Plantations). An r. script is also included to combine all GeoTIFFS then format and run code to produce river plots and bar graphs for the four management classes. The dataset aims to illustrate the changes among each land cover class (i.e. NVF, Aliens, Indigenous Forest, Grassland, and Mixed Woody Grassland) under the different management classes. This helps identify drivers of land cover change in the various management classes and the entire study area. It is also important to note that the land cover changes will portray realistic changes because they are HMM post-processed. Date of data collection: February 2020 Location of data collection: Blyde River Canyon Conservancy and its surrounds, in Mpumalanga/Limpopo Provinces, South Africa.
DOI:10.25375/uct.24885267.v1