Nevin Manimala Statistics

An evaluation of different approaches which use Google Street View imagery to ground truth land degradation assessments

Environ Monit Assess. 2022 Sep 6;194(10):732. doi: 10.1007/s10661-022-10438-5.


Member states of the United Nations Convention to Combat Desertification are required to report on the proportion of land that is degraded in their countries, a requirement that is also tied into the UN Sustainable Development Goals (SDGs). National land degradation assessments are often conducted with the use of remote sensing data which are not always ground truthed. Google Street View (GSV) provides high resolution, panoramic imagery across large parts of the world that has the potential to be used to ground truth land degradation assessments. We apply three different methodologies (visual interpretation of GSV images, GSV image classification and vegetation index extraction) to derive vegetation cover estimates from Google Street View imagery for the Hardeveld bioregion of the Succulent Karoo biome in South Africa. Visual estimates of cover best predict known habitat condition values (adjusted R2 = 0.86), whilst estimates derived from an unsupervised classification of GSV images also predict habitat condition relatively well (adjusted R2 = 0.52). These results show the potential for using GSV imagery, and other large collections of ground-level landscape photographs, as a rough ground-truthing tool, especially in instances where more traditional ground-truthing approaches are not possible.

PMID:36066776 | DOI:10.1007/s10661-022-10438-5

By Nevin Manimala

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