Leveraging missing-data remote sensing for forest inventory

Remote sensing plays a pivotal role in forest inventory by enabling efficient large-scale monitoring while minimizing fieldwork costs. However, missing values pose a critical challenge in remote sensing applications, as ignoring or mishandling such data gaps can introduce systematic bias into the estimation of target variables for natural resource monitoring. This can lead to cascading errors that propagate through forest and ecosystem management decisions, ultimately hindering progress toward sustainable forest management, biodiversity conservation, and climate change mitigation strategies. This study aims to propose and demonstrate a procedure that employs hybrid estimators to address the limitations of missing remotely sensed data in forest inventory, using Landsat 7 ETM+ SLC-off data as an archived source for forest resource monitoring as a case in point. We compared forest inventory estimates from the hybrid estimator with those from a conventional model-based (CMB) estimator using Sentinel-2 data without missing values. Monte Carlo simulations revealed three key findings: (1) The hybrid estimator, leveraging missing-data remote sensing represented by Landsat 7 ETM+ SLC-off data, achieved a sampling precision of over 90%, meeting China's national standard for the National Forest Inventory (NFI); (2) The hybrid estimator demonstrated comparable efficiency to the CMB estimator; (3) The uncertainty associated with hybrid estimators was primarily dominated by model parameter estimation, which could be effectively mitigated by slightly increasing the training sample size or refining model specification. Overall, in forest inventory, the hybrid estimator can surmount the limitations posed by missing values in remotely sensed auxiliary data, effectively balancing cost-effectiveness and flexibility.