The normalized difference water index (NDWI) of McFeeters ( 1996 McFeeters, S. K. 1996. The use of normalized difference water index (NDWI) in the delineation of open water features.. International Journal of Remote Sensing, 17: 1425–1432. [Taylor & Francis Online], [Web of Science ®] [Google Scholar] ) was modified by substitution of a middle infrared band such as Landsat TM band 5 for the near infrared band used in the NDWI. The modified NDWI (MNDWI) can enhance open water features while efficiently suppressing and even removing built‐up land noise as well as vegetation and soil noise. The enhanced water information using the NDWI is often mixed with built‐up land noise and the area of extracted water is thus overestimated. Accordingly, the MNDWI is more suitable for enhancing and extracting water information for a water region with a background dominated by built‐up land areas because of its advantage in reducing and even removing built‐up land noise over the NDWI.

A new index derived from existing indices – an index‐based built‐up index (IBI) – is proposed for the rapid extraction of built‐up land features in satellite imagery. The IBI is distinguished from conventional indices by its first‐time use of thematic index‐derived bands to construct an index rather than by using original image bands. The three thematic indices used in constructing the IBI are the soil adjusted vegetation index (SAVI), the modified normalized difference water index (MNDWI) and the normalized difference built‐up index (NDBI). Respectively, these represent the three major urban components of vegetation, water and built‐up land. The new index has been verified using the Landsat ETM+ image of Fuzhou City in southeastern China. The result shows that the IBI can significantly enhance the built‐up land feature while effectively suppressing background noise. A statistical analysis indicates that the IBI possesses a positive correlation with land surface temperature, but negative correlations with the NDVI and the MNDWI.

Increasing human activities have caused significant global ecosystem disturbances at various scales. There is an increasing need for effective techniques to quantify and detect ecological changes. Remote sensing can serve as a measurement surrogate of spatial changes in ecological conditions. This study has improved a newly-proposed remote sensing based ecological index (RSEI) with a sharpened land surface temperature image and then used the improved index to produce the time series of ecological-status images. The Mann–Kendall test and Theil–Sen estimator were employed to evaluate the significance of the trend of the RSEI time series and the direction of change. The change vector analysis (CVA) was employed to detect ecological changes based on the image series. This RSEI-CVA approach was applied to Fujian province, China to quantify and detect the ecological changes of the province in a period from 2002 to 2017 using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The result shows that the RSEI-CVA method can effectively quantify and detect spatiotemporal changes in ecological conditions of the province, which reveals an ecological improvement in the province during the study period. This is indicated by the rise of mean RSEI scores from 0.794 to 0.852 due to an increase in forest area by 7078 km². Nevertheless, CVA-based change detection has detected ecological declines in the eastern coastal areas of the province. This study shows that the RSEI-CVA approach would serve as a prototype method to quantify and detect ecological changes and hence promote ecological change detection at various scales.