Modeling and analysis of temporal and spatial processes in landscapes are based on large amounts of collected data today. These data, however, are often stored at multiple locations and are only accessible from distributed repositories. This chapter discusses techniques for efficient and effective data integration, and presents an approach for accessing distributed data based on open standards. Data from different providers may have different levels of accuracies. Therefore, data interoperability implies comprehensive reports on data accuracy and other data quality elements (metadata). We illustrate the importance of metadata within the scope of long-term monitoring surveys, where technological and scientific progress leads to permanent improvement of the sampling procedures and estimation techniques, and discuss resampling techniques and model building with existing data aiming at the optimization of data collection and the enhancement of comparability, reliability and accuracy of estimates of population parameters over a long period.