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- 1. EVALUATING THE EFFICACYOF MULTISPECTRAL DATA FOR DETECTING AND ESTIMATING PATCH SIZES OF PARTHENIUM WEED: Preliminary results Mahlatse Kganyago, John Odindi & Clement Adjorlolo AARSE conference 2014
- 2. - Parthenium hysterophorus(Asteraceae) – commonly: Famine weed, ragweed (Astralia), (Ethiopia-sign off and leave your land), Congress grass, ‘‘Scourge of India’’, etc. - Native: Gulf of Mexico, central and South America (Kumar et al. 2009). - Highly aggressive & colonises disturbed sites (road sides, railway-tracks, waste lands, croplands, building peripheries, irrigation canals, & cultivated fields (Chatterjee, 2006) - Top 7 problematic, devastating and hazardous weeds in the globe (Patel et al. 2011). - Fast & sporadic growth in wet (>500mm) and warm (10 to 250C) summer conditions (Dogra et al. 2011) - A seed bank which can survive for many years, e.g. recorded 1880 prevalent in the 1980s, after cyclone Demoina. - 25 000 dark brown, light weight seeds /plant - can dispersed by vehicles, water, animals, farm machinery, water and wind (Javaid et al. 2009; Mcconnachie et al. 2010; Dogra et al. 2011). Introduction
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- 4. CLIMAX – climaticsuitability model Mcconnachie et al. 2010
- 5. Phenological stages 1. Juvenile– sporadic growth 2. Adult/vegetative – whitish, hairy stem & bipinnate leaves 3. Mature – (>97 flowers per plant) Structural & Phenological characteristics of Parthenium weed
- 6. Impacts of Partheniumweed • Reduces density of grass (Dhileepan 2007; Nigatu et al. 2010) – reduced pasture production • Sheep and buffalo, it causes degenerative changes in liver and kidney (Patel, 2011) – death in 30 days (10%- 50%) Biodiversity • Health problems, incl. hay fever, bronchitis, dermatitis, allergic rhinitis, black spots, diarrhoea, skin inflammation and asthma (Mcconnachie et al. 2010, Lalitha et al. 2012) • Increased Poverty Society • Inhibiting germination and growth of pasture grasses, legumes, cereals, vegetables, other weeds, and trees (Mcconnachie et al. 2010) • Tainted milk and meat products – below standard quality livestock products Agriculture • Increases in food prices • Increased health investment • Total investment in the infested areas since 2005 - R248 130 000 (Braack , 2014) Economy
- 7. • An attractiveoption - capabilities to provide detailed, quantitative characteristics of the land surface information, frequent revisits, local to global scales • Integration of mathematical and statistical models – provides an objective and powerful ways to monitor the dynamics in the landscape composition (IAS) • Plant characteristics such as, e.g., chlorophyll content, nutrients deficiency, water content and stress, distribution of leaves, and phenology, can be derived through remote sensing techniques. • An understanding of distribution & patch sizes of Parthenium will enable proper allocation of eradication and containment resources. Remote sensing of IAS
- 8. Objectives • (i) Todetermine the distribution and patch sizes P. hysterophorus, • (ii) To assess the efficacy of multispectral sensors in detecting P. hysterophorus, and Objectives of the study
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- 10. Data & Methods Datacollection Pre-processing Training Validation SPOT 6 Landsat 8 GPS presence Uncertainty Reject Alternative Aerial Photos Digitisation Visual interpretation GE Confusion Matrix SVM classification FLAASH- MODTRAN4
- 11. Support Vector Machines(SVM) • Kernel function - Projects data into higher dimensional space • Locates complex decision boundaries • Kernel function affect the performance of SVM (Huang et al. 2002) Sources: Lennon et al. 2002; Pouteau et al. 2011 SVM Parameterisation Landsat 8 • Gaussian RBF Kernel Parameters g: 0.100000 • Regularization Parameters C: 100.000 • Number of Support Vectors: 179 • Class-wise Support Vectors: 31, 9, 16, 23, 12, 32, 27, 16, 13 SPOT 6 • Gaussian RBF Kernel Parameters g: 10.0000 • Regularization Parameters C: 10.0000 • Number of Support Vectors: 432 • Class-wise Support Vectors: 25, 5, 34, 11, 48, 214, 26, 27, 25, 17
- 12. Preliminary Results Photo: KayMontgomery
- 13. Preliminary Results… Photo: KayMontgomery
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- 15. • The totalarea infested • 11,748.2848 Hectares (7.18%; SPOT 6) • 21,845.340 Hectares (13.31%; Landsat 8) • Patch sizes • Minimum: 0.036 hectares Maximum: 7.15 hectares (SPOT 6) • Minimum: 0.09 hectares Maximum:56.43 hectares (Landsat 8) • Estimates of patch sizes would enable proper allocation of resources • Detection is possible, optimisation is needed for correct patch size • The species spectral variability, can be accounted for. Spectral separability with other inactive veg. unknown. • Landsat 8 suitable for detecting large patches, SPOT 6 – can detect smaller patches • SVM - robust algorithm, works well with small training data & can be used effectively to detect Patches of Parthenium weed Discussion and Conclusion
- 16. Future Work • Multi-temporaldatasets will be incorporated, the possibility for early detection • Ground reference datasets for training and validation using RS “rules of thumb” • Pontius Matrix for accuracy assessment –AD & QD • Statistical comparisons of maps using McNemar’s test
- 17. • The Authoracknowledges financial support through a sponsorship from the South African National Space Agency (SANSA) • I also acknowledge the data provided by Ian Ruthworth (Ezemvelo Wildlife); Lesley Henderson (ARC-Plant Protection Institute); and Kay Montgomery (Environmental Programmes-Biosecurity Unit) Acknowledgements
- 18. Thank you Email: mkganyago@sansa.org.za Tel:012 844 0424