An integrated method for validating spatially non-continuous remotely sensed forest patch dataset for West Africa

Iheaturu, Chima; Wingate, Vladimir; Akinyemi, Felicia; Ifejika Speranza, Chinwe (27 May 2022). An integrated method for validating spatially non-continuous remotely sensed forest patch dataset for West Africa. In: EGU General Assembly 2022. 10.5194/egusphere-egu22-7260

Remote sensing products of medium to high spatial resolution have emerged as promising datasets for environmental modelling and policymaking across scales. Despite the recent increase in their availability and accessibility, questions often remain on how to best assess the accuracy of these products, since it is pivotal that these be rigorously validated before they are used for scientific investigation and decision making. There are several methods for validating spatially continuous remote sensing-derived products, including comparisons to field surveys, cross-comparisons and verification of physical consistency using reference data. However, there exist few or no validation strategies for validating spatially non-continuous products such as forest patches. In effect, forest patches, as with many other thematic maps, contain information that is discrete, not spatially continuous, and not normally distributed; thus, a validation strategy that makes these assumptions may be inappropriate for such a product.

We present an integrative approach for assessing the accuracy of a remote sensing-derived product identifying forest patches found within agricultural landscapes of West Africa. The method is based on the well-established error matrix approach and uses a spatial sampling strategy that determines the sample size based on spatial autocorrelation, select sample points based on spatial uniformity and heterogeneity, and assesses the accuracy by comparing sample points and reference data. Compared to other random sampling approaches, ours ensures that a representative sample is used for the accuracy assessment. This representativeness was achieved by utilizing a stratification method that enabled different categories of forest patches across different ecoregions in the map to be included in the sample size.

While further tests are required, the preliminary results show that our method has the potential to effectively assess the accuracy of forest patches in West Africa and can therefore be adapted for validating other spatially non-continuous remote sensing products.

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