Land cover (LC) is a critical variable driving many environmental processes, so its assessment, monitoring, and characterization are essential. However, existing LC products, derived primarily from satellite spectral imagery, each have different temporal and spatial resolutions and different LC classes. Most effort is focused on either fusing a pair of LC products over a small space‐time region or on interpolating missing values in an individual LC product. Here, we review the complexities of LC identification and propose a method for fusing multiple existing LC products to produce a single LC record for a large spatial‐temporal grid, referred to as spatiotemporal categorical map fusion. We first reconcile the LC classes of different LC products and then present a probabilistic weighted nearest neighbor estimator of LC class. This estimator depends on three unknown parameters that are estimated using numerical optimization to maximize an agreement criterion that we define. We illustrate the method using six LC products over the Rocky Mountains and show the improvement gained by supplying the optimization with data‐driven information describing the spatial‐temporal behavior of each LC class. Given the massive size of the LC products, we show how the optimal parameters for a given year are often optimal for other years, leading to shorter computing times.
Land cover (LC) products, derived primarily from satellite spectral imagery, are essential inputs for environmental studies because LC is a critical driver of processes involved in hydrology, ecology, and climatology, among others. However, existing LC products each have different temporal and spatial resolutions and different LC classes that rarely provide the detail required by these studies. Using multiple existing LC products, we implement our Spatiotemporal Categorical Map Fusion (SCaMF) methodology over a large region of the Rocky Mountains (RM), encompassing sections of six states, to create a new LC product, SCaMF-RM. To do this, we must adapt SCaMF to address the prediction of LC in large space-time regions that present nonstationarities, and we add more flexibility in the LC classifications of the predicted product. SCaMF-RM is produced at two high spatial resolutions, 30 and 50 m, and a yearly frequency for the 30-year period 1983-2012. When multiple products are available in time, we illustrate how SCaMF-RM captures relevant information from the different LC products and improves upon flaws observed in other products. Future work needed includes an exhaustive validation not only of SCaMF-RM but also of all input LC products.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.