The availability of very-high-resolution (VHR) imagery and techniques for processing those data into indicators of ecosystem function has opened the door for VHR imagery to be used in rangeland monitoring and assessment. However, VHR imagery can be expensive and, like any survey measurement, studies that rely on VHR-image-based indicators will be subject survey errors that lead to biased results or inflated standard errors. To yield defensible monitoring and assessment data for managers, indicators derived from VHR imagery must be implemented with a statistically valid sampling design. We discuss the use of VHR imagery from the perspectives of achieving unbiased estimates with minimal survey errors in the context of rangeland monitoring and assessment. Our discussion includes study design considerations such as the importance of probability-based sampling for generating credible estimates; measurement errors in VHR-derived indicators; and model-assisted estimation to integrate data from multiple sources, improve precision and adjust for sources of errors. We demonstrate the effects of these design considerations using example datasets where VHR imagery and concurrent field data were collected. Finally, we propose practical ways to conduct statistically-sound rangeland monitoring and assessment using VHR imagery.