Environ Monit Assess (2011) 177:353–373 DOI 10.1007/s10661-010-1639-5 Monitoring and identification of spatiotemporal landscape changes in multiple remote sensing images by using a stratified conditional Latin hypercube sampling approach and geostatistical simulation Yu-Pin Lin · Hone-Jay Chu · Yu-Long Huang · Chia-Hsi Tang · Shahrokh Rouhani Received: 10 January 2010 / Accepted: 29 July 2010 / Published online: 17 August 2010 © Springer Science+Business Media B.V. 2010 Abstract This study develops a stratified con- ditional Latin hypercube sampling (scLHS) ap- proach for multiple, remotely sensed, normalized difference vegetation index (NDVI) images. The objective is to sample, monitor, and delineate spa- tiotemporal landscape changes, including spatial heterogeneity and variability, in a given area. The scLHS approach, which is based on the variance quadtree technique (VQT) and the conditional Latin hypercube sampling (cLHS) method, selects samples in order to delineate landscape changes from multiple NDVI images. The images are then Y.-P. Lin (B ) · H.-J. Chu · Y.-L. Huang · C.-H. Tang Department of Bioenvironmental Systems Engineering, National Taiwan University, 1, Sec. 4, Roosevelt Rd., Da-an District, Taipei City 106, Taiwan e-mail: yplin@ntu.edu.tw H.-J. Chu e-mail: honejaychu@gmail.com Y.-L. Huang e-mail: morris0109@hotmail.com C.-H. Tang e-mail: jesst17@hotmail.com S. Rouhani NewFields, 1349 W. Peachtree St. Suite 2000, Atlanta, GA 30309, USA e-mail: SRouhani@newfields.com mapped for calibration and validation by using sequential Gaussian simulation (SGS) with the scLHS selected samples. Spatial statistical results indicate that in terms of their statistical distribu- tion, spatial distribution, and spatial variation, the statistics and variograms of the scLHS samples resemble those of multiple NDVI images more closely than those of cLHS and VQT samples. Moreover, the accuracy of simulated NDVI im- ages based on SGS with scLHS samples is sig- nificantly better than that of simulated NDVI im- ages based on SGS with cLHS samples and VQT samples, respectively. However, the proposed ap- proach efficiently monitors the spatial characteris- tics of landscape changes, including the statistics, spatial variability, and heterogeneity of NDVI im- ages. In addition, SGS with the scLHS samples effectively reproduces spatial patterns and land- scape changes in multiple NDVI images. Keywords Stratified conditional Latin hypercube sampling · Sequential Gaussian simulation · Landscape change · Remotely sensed images Introduction Patterns of landscape change are attributed to complex interactions between physical, biolog- ical, and social forces (Turner 1987), such as fires, hurricanes, earthquakes, urbanization, and