Remote sensing methods to detect land‐use/cover changes in New Zealand's ‘indigenous’ grasslands

We compared four remote sensing methods to detect changes in New Zealand's grasslands (image differencing, normalised difference vegetation index (NDVI) differencing post‐classification and visual interpretation). The visual interpretation resulted in the best classification results, with a 98% overall accuracy when compared with ground‐truthed data. The tests on automatic classification (image differencing, NDVI differencing) and post classification had much lower accuracies, ranging from 47% to 56%. In the New Zealand grassland landscape, automatic detection methods were not able to differentiate between variations of soil moisture and vegetation phenology from variations in land‐use change. This, in combination with topographic effects, which have hampered the automated mapping of vegetation, is the main reason why visual interpretation of high‐resolution imagery is still needed.