Improving biome classification of tropical grasslands using fire frequency data
(2025) In Student thesis series INES NGEK01 20251Dept of Physical Geography and Ecosystem Science
- Abstract (Swedish)
- Tropical grasslands are ecosystems of immense value, both ecologically and socially. Despite this, they are commonly misclassified as degraded forests, partly due to the large role of fire, rather than climate, in limiting the establishment of woody vegetation in those ecosystems. Misclassification can cause misguided management practices to be implemented, which explains the need for improved classification schemes for tropical grasslands. This study therefore investigates the possibility of improving biome classification of tropical grasslands by using both climate and fire frequency as input variables. To this end, two different classification maps for northern Africa were produced, using clustering with (1) only climate variables and... (More)
- Tropical grasslands are ecosystems of immense value, both ecologically and socially. Despite this, they are commonly misclassified as degraded forests, partly due to the large role of fire, rather than climate, in limiting the establishment of woody vegetation in those ecosystems. Misclassification can cause misguided management practices to be implemented, which explains the need for improved classification schemes for tropical grasslands. This study therefore investigates the possibility of improving biome classification of tropical grasslands by using both climate and fire frequency as input variables. To this end, two different classification maps for northern Africa were produced, using clustering with (1) only climate variables and (2) climate and fire frequency variables as input. The classifications were then compared against MODIS land cover data to get an estimate of the accuracy for the different approaches. There was a clearly distinguishable difference between the two resulting maps, indicating that the inclusion of fire does have an impact on biome classification schemes. While both classifications had classes for which they performed better than the other, the one including the fire variable was significantly more accurate for the classes located at the boundary between tropical forests and grasslands. This is a promising result, indicating potential to further develop the method to achieve both better accuracy and global coverage. This could allow for better identification of tropical grasslands, which is an important step in order to properly manage those highly valuable ecosystems. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9199216
- author
- Nilsson, Ylva LU
- supervisor
- organization
- course
- NGEK01 20251
- year
- 2025
- type
- M2 - Bachelor Degree
- subject
- keywords
- Tropical grasslands, Fire, Biomes, Alternative stable states, Forest, Savanna, Clustering
- publication/series
- Student thesis series INES
- report number
- 701
- language
- English
- id
- 9199216
- date added to LUP
- 2025-06-16 09:31:28
- date last changed
- 2025-06-16 09:31:28
@misc{9199216, abstract = {{Tropical grasslands are ecosystems of immense value, both ecologically and socially. Despite this, they are commonly misclassified as degraded forests, partly due to the large role of fire, rather than climate, in limiting the establishment of woody vegetation in those ecosystems. Misclassification can cause misguided management practices to be implemented, which explains the need for improved classification schemes for tropical grasslands. This study therefore investigates the possibility of improving biome classification of tropical grasslands by using both climate and fire frequency as input variables. To this end, two different classification maps for northern Africa were produced, using clustering with (1) only climate variables and (2) climate and fire frequency variables as input. The classifications were then compared against MODIS land cover data to get an estimate of the accuracy for the different approaches. There was a clearly distinguishable difference between the two resulting maps, indicating that the inclusion of fire does have an impact on biome classification schemes. While both classifications had classes for which they performed better than the other, the one including the fire variable was significantly more accurate for the classes located at the boundary between tropical forests and grasslands. This is a promising result, indicating potential to further develop the method to achieve both better accuracy and global coverage. This could allow for better identification of tropical grasslands, which is an important step in order to properly manage those highly valuable ecosystems.}}, author = {{Nilsson, Ylva}}, language = {{eng}}, note = {{Student Paper}}, series = {{Student thesis series INES}}, title = {{Improving biome classification of tropical grasslands using fire frequency data}}, year = {{2025}}, }