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The climate change and related impacts are experienced around the world. There arise different triggering factors to climate change and impact. The purpose of this study is to figure out how changes in vegetation cover may or may not have an impact to climate change. The research will produce ideas for vegetation preservation and replant.

The investigation was probed for 34 years’ time period starting from the year 1981 to 2015. After testing and checking for serial autocorrelation in the vegetation data series, Mann–Kendal nonparametric statistical evaluation was carried out to investigate vegetation cover trends. Sen’s method was deployed to investigate the magnitude of vegetation cover change in natural differential vegetation index (NDVI) unit per year. Furthermore, the ArcGIS spatial analysis tools were used for the calculation of mean NDVI distribution and also for carrying out the spatial investigation of trends at each specific location within the study region.

The yearly mean NDVI during the study period was observed to have a decreasing trend. The mean NDVI value ranges between 0.32 and 0.98 NDVI unit, and hence, this means from less or poor vegetated zones to higher or healthier vegetated zones. The mean NDVI value was seen decreasing toward the highlands regions. The NDVI-rainfall correlation was observed to be stronger than the NDVI-temperature correlation. The % area coverage of NDVI-rainfall positive correlation was higher than the negative correlation. The % area coverage of NDVI-temperature negative correlation was higher than the positive correlation within the study region. Rainfall is seen as a highly influencing climatic factor for vegetation growth than the temperature within the study region.

This study in this country is a new approach for climate change monitoring and planning for the survival of the people of Papua New Guinea, especially for the farmer and those who is living in the coastal area.