Open AccessCamera traps enable the estimation of herbaceous aboveground net primary production ( ANPP ) in an African savanna at high temporal resolution
Author(s) -
Jonge Inger K.,
Veldhuis Michiel P.,
Vrieling Anton,
Olff Han
Publication year - 2022
Publication title -
remote sensing in ecology and conservation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.191
H-Index - 21
ISSN - 2056-3485
DOI - 10.1002/rse2.263
Subject(s) - primary production , environmental science , vegetation (pathology) , productivity , herbaceous plant , biomass (ecology) , remote sensing , normalized difference vegetation index , ecosystem , enhanced vegetation index , physical geography , ecology , leaf area index , geography , vegetation index , biology , medicine , macroeconomics , pathology , economics
Determining the drivers of aboveground net primary production (ANPP), a key ecosystem process, is an important goal of ecosystem ecology. However, accurate estimation of ANPP across larger areas remains challenging, especially for savanna ecosystems that are characterized by large spatiotemporal heterogeneity in ANPP. Satellite remote sensing methods are frequently used to estimate productivity at the landscape scale but generally lack the spatial and temporal resolution to capture the determinants of productivity variation. Here, we developed and tested methods for estimating herbaceous productivity as an alternative to labour‐intensive repeated biomass clipping and caging of small plots. We compared measures of three spectral greenness indices, normalized difference vegetation index derived from Sentinel‐2 (NDVIs) and a handheld radiometer (NDVIg), and green chromatic coordinate derived from digital repeat cameras (GCC) and tested their relationship to biweekly field‐measured herbaceous ANPP using movable exclosures. We found that a satellite‐based model including average NDVIs and its rate of change (ΔNDVIs) over the biweekly productivity measurement interval predicted herbaceous ANPP reasonably well (Jackknife R 2 = 0.26). However, the predictive accuracy doubled (Jackknife R 2 = 0.52) when including the sum of day to day increases in camera trap‐derived vegetation greenness (tGCC). This result can be considered promising, given the current lack of productivity estimation methods at comparable spatiotemporal resolution. We furthermore found that the fine (daily) temporal resolution of GCC time series captured fast vegetation responses to rainfall events that were missed when using a coarser temporal resolution (>2 days). These findings demonstrate the importance of measuring at a fine temporal resolution for predicting herbaceous ANPP in savanna ecosystems. We conclude that camera traps are promising in offering a reliable and cost‐effective method to estimate productivity in savannas and contribute to a better understanding of ecosystem functioning and its drivers.