Abstract
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.
Highlights
Land cover (LC) is a critical feature of the environment because the study of different processes and cycles, such as water and carbon, in a changing environment often requires a detailed characterization of this environment in space and time [1,2,3,4]
We introduce the original framework from Spatiotemporal Categorical Map Fusion (SCaMF) and describe the proposed modifications to address the prediction of LC in large space–time regions that present nonstationarities and to further its flexibility by allowing a more detailed LC classification in the predicted LC product
LC products are essential inputs for many studies, including hydrological, climatological, and ecological. Their resolution in space and/or time, their temporal duration, and their LC classification are often inadequate for long-term studies
Summary
Land cover (LC) is a critical feature of the environment because the study of different processes and cycles, such as water and carbon, in a changing environment often requires a detailed characterization of this environment in space and time [1,2,3,4]. Both natural processes and human activities cause short- and long-term changes in LC, and they have a direct influence on biogeochemical cycling, hydrologic processes, soil erosion, ecological community composition, ecosystem productivity, and rainfall patterns [5,6,7,8,9,10]. Studies ranging from a global to a local scale, and comprising a large variety of applications such as ecological analyses and urban dynamics, would benefit from a high spatial resolution characterization of the LC [26,27,28,29]
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