Growing demands for temporally specific information on land surface change are fueling a new generation of maps and statistics that can contribute to understanding geographic and temporal patterns of change across large regions, provide input into a wide range of environmental modeling studies, clarify the drivers of change, and provide more timely information for land managers. To meet these needs, we are implementing a monitoring capability titled LCMAP - Land Change Monitoring, Assessment, and Projection. This paper describes the methodological foundations and lessons learned during development and testing of the LCMAP approach. Testing and evaluation of a suite of ten annual land cover and land surface change data sets over six diverse study areas across the U.S. revealed both good agreement with other published maps (overall agreement ranged from 73% to 87%) as well as several challenges that needed to be addressed to meet the goals of robust, repeatable, and geographically consistent monitoring results from the Continuous Change Detection and Classification (CCDC) algorithm developed by Zhu and Woodcock (2014). First, the high spatial and temporal variability of observational frequency led to differences in the number of changes found, so we modified CCDC such that change detection is dependent on observational frequency. Second, we modified the CCDC classification methodology to improve its ability to characterize gradual land surface changes. Third, modifications were made to the classification element of CCDC to improve the representativeness of training data, which necessitated replacing the random forest algorithm with a boosted decision tree. Following these modifications, assessment of prototype Version 1 LCMAP results showed improvements in overall agreement (ranging from 85% to 90%).