Spatial and Temporal Distribution of Mikania micarantha Kunth in Chitwan Annapurna Landscape (CHAL) Area with Application of Satellite Imageries

Abstract

Mikania micrantha is a fast-growing neotropical, and the most problematic terrestrial invasive plant species rapidly invading tropical parts of Nepal. Remote Sensing offers synoptic view for detecting and mapping invasive plant species and record changes in actual and potential distribution across wide region over time period. Knowledge based classification approach was used for mapping M. micrantha distribution in Chitwan Annapurna Landscape using multispectral Landsat and WorldView-2 imageries. For Knowledge Based classification, information on elevation, slope, aspect, maximum temperature, minimum temperature, rainfall, unsupervised classified image based on digital number (DN) value NDVI from reflectance and supervised classified image of land use that is suitable for M. micrantha were used as variables for rules. Results have shown increasing trend i.e. 0.1%, 0.19 %, 0.65% and 1.39% of total area of CHAL covered by M. micrantha in 1990, 2000, 2008 and 2018 respectively in Landsat image. WorldView 2 images of different small patch of Chitwan, Nawalparasi, Chitwan-Makwanpur, Chitwan- Tanahu, Makwanpur (Hetauda) were classified and accuracy assessment was done. WorldView-2 images with high spatial resolution than the Landsat images show higher accuracy. Overall accuracy varied from 68.75% to 76% and 79% to 82.5% in Landsat and WorldView-2 imageries respectively. Kappa coefficient varied between 0.37to 0.52 and 0.49 to 0.65 for Landsat and WorldView-2 imageries, respectively. WorldView-2 imageries of high spatial resolution are more effective than Landsat imageries in delineation of Mikania micrantha however Landsat imageries can also be useful in detecting the herbaceous weed. Keywords: Invasive plant species, Knowledge based classification, Supervised classification, Accuracy assessment