Remote sensing and GIS for mapping and management of invasive shrub Chromolaena odorata in Nepal

The applications of remote sensing have been restricted to those species dominating the canopy of ecosystems. Dominant invasive species determine the spectral signature received by the remote sensing scanner and thus allow detection using this spectral signature in a straightforward manner. Many invasive species however, do not dominate the canopy. They are rare or hidden below the ecosystem canopy. The aim of this study was to map the seed productivity of such hidden invasive species. We have selected Chromolaena odorata which is one of the world's worst invasive species invading the entire tropical region of the world. We applied ETM+ imagery, GIS and statistics to map and model its seed productivity. Light intensity and biomass of other forest under-storey vegetation significantly determined the productivity of the species. Data analysis showed that other variables such as grazing intensity and distance from forest edge and road to forest interior had an indirect effect on productivity of Chromolaena. Populations of Chromolaena were present, but failed to flower below a light intensity of 6.5 mJ m^-2 day ^-1. This suggests that light intensity segregates the species in reproductive and non-reproductive populations. These patterns in Chromolaena were mapped using remote sensing imagery and GIS. Localization of such populations could significantly reduce eradication/control costs. This may prove particularly valuable when implementing control measures under circumstances of limited capital and manpower.