One of the most common problems in estimating trends in image time series is the presence of contaminants such as clouds. There are many techniques for estimating robust trends but evaluating the significance of the trends can be difficult due to this increased variance. This article presents a novel approach called the Contextual Mann‐Kendall (CMK) test for assessing significant trends. This test uses the principle of spatial autocorrelation to characterize geographical phenomena, according to which a pixel would not be expected to exhibit a radically different trend from neighboring pixels. The procedure removes serial correlation through a prewhitening process. Then, similar to the logic of the Regionally Averaged Mann‐Kendall (RAMK) test, it combines the information from neighboring pixels while adjusting for cross‐correlation. CMK was compared with the Mann‐Kendall (MK) test in which contextual information was not involved for the mean annual NDVI over 22 years (1982–2003) in West Africa. With the MK test, ∼11% of the study area showed significant (p < 0.001) trends which increased to 16% when tested using the CMK test. Thus the CMK test produces a result that makes intuitive sense from a geographical perspective and enhances the ability to detect trends in relatively short time series.

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The fate of live forest biomass is largely controlled by growth and disturbance processes, both natural and anthropogenic. Thus, biomass monitoring strategies must characterize both the biomass of the forests at a given point in time and the dynamic processes that change it. Here, we describe and test an empirical monitoring system designed to meet those needs. Our system uses a mix of field data, statistical modeling, remotely-sensed time-series imagery, and small-footprint lidar data to build and evaluate maps of forest biomass. It ascribes biomass change to specific change agents, and attempts to capture the impact of uncertainty in methodology. We find that: • A common image framework for biomass estimation and for change detection allows for consistent comparison of both state and change processes controlling biomass dynamics. • Regional estimates of total biomass agree well with those from plot data alone.• The system tracks biomass densities up to 450-500 Mg ha −1 with little bias, but begins underestimating true biomass as densities increase further. • Scale considerations are important. Estimates at the 30 m grain size are noisy, but agreement at broad scales is good. Further investigation to determine the appropriate scales is underway. • Uncertainty from methodological choices is evident, but much smaller than uncertainty based on choice of allometric equation used to estimate biomass from tree data. • In this forest-dominated study area, growth and loss processes largely balance in most years, with loss processes dominated by human removal through harvest. In years with substantial fire activity, however, overall biomass loss greatly outpaces growth. Taken together, our methods represent a unique combination of elements foundational to an operational landscape-scale forest biomass monitoring program.