Regional-scale high spatial resolution mapping of aboveground net primary productivity (ANPP) from field survey and Landsat data: a case study for the country of Wales

Tebbs, Emma J.; Rowland, Clare S. ORCID: https://orcid.org/0000-0002-0459-506X; Smart, Simon M. ORCID: https://orcid.org/0000-0003-2750-7832; Maskell, Lindsay C. ORCID: https://orcid.org/0000-0003-4006-7755; Norton, Lisa R. ORCID: https://orcid.org/0000-0002-1622-0281. 2017 Regional-scale high spatial resolution mapping of aboveground net primary productivity (ANPP) from field survey and Landsat data: a case study for the country of Wales. Remote Sensing, 9 (8), 801. 19, pp. 10.3390/rs9080801

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Official URL: https://doi.org/10.3390/rs9080801

Abstract/Summary

This paper presents an alternative approach for high spatial resolution vegetation productivity mapping at a regional scale, using a combination of Normalised Difference Vegetation Index (NDVI) imagery and widely distributed ground-based Above-ground Net Primary Production (ANPP) estimates. Our method searches through all available single-date NDVI imagery to identify the images which give the best NDVI–ANPP relationship. The derived relationships are then used to predict ANPP values outside of field survey plots. This approach enables the use of the high spatial resolution (30 m) Landsat 8 sensor, despite its low revisit frequency that is further reduced by cloud cover. This is one of few studies to investigate the NDVI–ANPP relationship across a wide range of temperate habitats and strong relationships were observed (R2 = 0.706), which increased when only grasslands were considered (R2 = 0.833). The strongest NDVI–ANPP relationships occurred during the spring “green-up” period. A reserved subset of 20% of ground-based ANPP estimates was used for validation and results showed that our method was able to estimate ANPP with a RMSE of 15–21%. This work is important because we demonstrate a general methodological framework for mapping of ANPP from local to regional scales, with the potential to be applied to any temperate ecosystems with a pronounced green up period. Our approach allows spatial extrapolation outside of field survey plots to produce a continuous surface product, useful for capturing spatial patterns and representing small-scale heterogeneity, and well-suited for modelling applications. The data requirements for implementing this approach are also discussed.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.3390/rs9080801
UKCEH and CEH Sections/Science Areas:	Soils and Land Use (Science Area 2017-)
ISSN:	2072-4292
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	net primary production, Landsat, Normalised Difference Vegetation Index (NDVI), vegetation mapping, habitat condition monitoring, remote sensing
NORA Subject Terms:	Ecology and Environment Data and Information
Date made live:	01 Nov 2017 15:02 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/518190

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.3390/rs9080801

UKCEH and CEH Sections/Science Areas:

Soils and Land Use (Science Area 2017-)

ISSN:

2072-4292

Additional Information. Not used in RCUK Gateway to Research.:

Open Access paper - full text available via Official URL link.

Additional Keywords:

net primary production, Landsat, Normalised Difference Vegetation Index (NDVI), vegetation mapping, habitat condition monitoring, remote sensing