期刊
REMOTE SENSING
卷 9, 期 8, 页码 -出版社
MDPI AG
DOI: 10.3390/rs9080801
关键词
net primary production; Landsat; Normalised Difference Vegetation Index (NDVI); vegetation mapping; habitat condition monitoring; remote sensing
类别
资金
- GMEP (Glastir Monitoring & Evaluation Programme) [C147/2010/11]
- NERC/Centre for Ecology Hydrology [NEC05782]
- NERC/CEH National Capability Project Land Cover Monitoring with Satellites [NEC05567]
- NERC Macronutrients program as part of the Turf2Surf project [NE/J011991/1]
- NERC [NE/J011991/1, ceh020015] Funding Source: UKRI
- Natural Environment Research Council [ceh020015, NE/J011991/1] Funding Source: researchfish
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 (R-2 = 0.706), which increased when only grasslands were considered (R-2 = 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.
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