Abstract
Recent guidance on environmental modeling and global land-cover validation stresses the need for a probability-based design. Additionally, spatial balance has also been recommended as it ensures more efficient sampling, which is particularly relevant for understanding land use change. In this paper I describe a global sample design and database called the Global Grid (GG) that has both of these statistical characteristics, as well as being flexible, multi-scale, and globally comprehensive. The GG is intended to facilitate collaborative science and monitoring of land changes among local, regional, and national groups of scientists and citizens, and it is provided in a variety of open source formats to promote collaborative and citizen science. Since the GG sample grid is provided at multiple scales and is globally comprehensive, it provides a universal, readily-available sample. It also supports uneven probability sample designs through filtering sample locations by user-defined strata. The GG is not appropriate for use at locations above ±85° because the shape and topological distortion of quadrants becomes extreme near the poles. Additionally, the file sizes of the GG datasets are very large at fine scale (resolution ~600 m × 600 m) and require a 64-bit integer representation.
Highlights
A number of comprehensive and long-term monitoring programs have been developed to provide a deeper understanding of the conditions and changes of human and natural systems
In this paper I focus on survey design—often called sample design or spatial design [3]—and how best to generate a rigorous, useful, and flexible survey design that specifies where environmental data will be collected
Stehman [2] suggests that a good survey design should be probability-based; have a low and known estimated variance; be spatially-balanced, simple, and cost-effective; and have flexibility as a key characteristic because of real-world, practical challenges that environmental monitoring programs inevitably face [4]
Summary
A number of comprehensive and long-term monitoring programs have been developed to provide a deeper understanding of the conditions and changes of human and natural systems. In this paper I focus on survey design—often called sample design or spatial design [3]—and how best to generate a rigorous, useful, and flexible survey design that specifies where environmental data will be collected. Stehman [2] suggests that a good survey design should be probability-based; have a low and known estimated variance; be spatially-balanced, simple, and cost-effective; and have flexibility as a key characteristic because of real-world, practical challenges that environmental monitoring programs inevitably face [4]. Four aspects of developing a sample design for monitoring landscape change are discussed here: probability-based design, spatial balance, cartographic projections, and sampling intensity (i.e., frequency)
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