What is it about?
two-phase sampling strategy for large-scale assessment of the total area and the total wood volume of fast-growing forest tree crops within agricultural land is presented. The first phase is performed using tessellation stratified sampling on high-resolution remotely sensed imagery and is sufficient for estimating the total area of plantations by means of a Monte Carlo integration estimator. The second phase is performed using stratified sampling of the plantations selected in the first phase and is aimed at estimating total wood volume by means of an approximation of the first-phase Horvitz-Thompson estimator. Vegetation indices from Sentinel-2 are exploited as freely available auxiliary information in a linear regression estimator to improve the design-based precision of the estimator based on the sole sample data.
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Why is it important?
As distinctively concerns fast-growing tree plantations within agricultural farms, characterized by relatively short rotations and an intrinsic high dynamism in relation to the temporal variability of farmland destinations for cultivation, there is the need for a frequent updating of accurate and spatially detailed statistical data about tree species composition, stand structure and wood supply attributes. Such needs may exceed the scope of conventional forest inventories, making room for ad-hoc information sources.
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This page is a summary of: Large-scale two-phase estimation of wood production by poplar plantations exploiting Sentinel-2 data as auxiliary information, Silva Fennica, January 2020, Finnish Society of Forest Science,
DOI: 10.14214/sf.10247.
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