Name
Title | ||
|---|---|---|
The assessment of data mining algorithms for modelling Savannah Woody cover using multi-frequency (X-, C- and L-band) synthetic aperture radar (SAR) datasets |
Abstract | ||
|---|---|---|
The woody component in African Savannahs provides essential ecosystem services such as fuel wood and construction timber to large populations of rural communities. Woody canopy cover (i.e. the percentage area occupied by woody canopy or CC) is a key parameter of the woody component. Synthetic Aperture Radar (SAR) is effective at assessing the woody component, because of its capacity to image within-canopy properties of the vegetation while offering an all-weather capacity to map relatively large extents of the woody component. This study compared the modelling accuracies of woody canopy cover (CC), in South African Savannahs, through the assessment of a set of modelling approaches (Linear Regression, Support Vector Machines, REPTree decision tree, Artificial Neural Network and Random Forest) with the use of X-band (TerraSAR-X), C-band (RADARSAT-2) and L-band (ALOS PALSAR) datasets. This study illustrated that the ANN, REPTree and RF non-parametric modelling algorithms were the most ideal with high CC prediction accuracies throughout the different scenarios. Results also illustrated that the acquisition of L-band data be prioritized due to the high accuracies achieved by the L-band dataset alone in comparison to the individual shorter wavelengths. The study provides promising results for developing regional savannah woody cover maps using limited LiDAR training data and SAR images. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IGARSS.2014.6946608 | Geoscience and Remote Sensing Symposium |
Keywords | Field | DocType |
data mining,decision trees,neural nets,regression analysis,remote sensing by radar,support vector machines,vegetation,vegetation mapping,ALOS PALSAR datasets,ANN,C-band,L-band,RADARSAT-2,REPTree decision tree,RF nonparametric modelling algorithms,South African savannahs,TerraSAR-X,X-band,artificial neural network,data mining algorithm assessment,essential ecosystem services,limited LiDAR training data,linear regression,multifrequency synthetic aperture radar,random forest,savannah woody cover modelling,support vector machines,within-canopy properties,woody canopy cover,woody component,Multi-frequency,Non-parametric,Savannahs,Synthetic Aperture Radar,Woody canopy cover | Decision tree,Vegetation,L band,Computer science,Synthetic aperture radar,Remote sensing,Support vector machine,Lidar,Random forest,Canopy | Conference |
ISSN | Citations | PageRank |
2153-6996 | 1 | 0.38 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Naidoo, L. | 1 | 1 | 0.38 |
| Renaud Mathieu | 2 | 125 | 17.29 |
| russell main | 3 | 6 | 2.90 |
| Waldo Kleynhans | 4 | 111 | 27.45 |