Determining the onset of autumn grass senescence in subtropical sour-veld grasslands using remote sensing proxies and the breakpoint approach
Ecological Informatics, ISSN: 1574-9541, Vol: 69, Page: 101651
2022
- 10Citations
- 5Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Information on the onset of autumn grass senescence in subtropical grasslands is essential for ascertaining the duration of poor forage quality. It is well-established that during senescence, grass leaves lose their nutrients to the rooting systems; which affects the quality and quantity of forage resources. The timing of the onset of autumn grass senescence is critical in determining the potential lifespan through which the provision of quality forage can be sustained in a grazing area. However, objective and robust methods for estimating the onset of autumn grass senescence at a rangeland scale are limited. Hence, this study sought to characterize the onset of autumn senescence in mesic subtropical sour-veld grasslands using remotely sensed data. Ten monthly vegetation indices were generated from the Sentinel 2 data and used as proxies to explain the onset of autumn grass senescence. The performance of the proxies was validated using the corresponding field-measured monthly grass chlorophylls. Results showed that the Chlorophyll Red-Edge (CHL-RED-EDGE) and the Normalized Difference Red Edge Index (NDVI 705 ) were the most important proxies for characterizing the autumn grass senescence. In addition, monthly (i.e., January to June) mean values of the two best proxies were fitted in a piecewise linear regression model with a breakpoint approach to determine the start of the autumn grass senescence. The first proxy (i.e., NDVI 705 ) predicted that the grass in the study area starts senescing on day number ± 98 of the year (R 2 = 0.97, RMSE = 0.024) while the second (i.e., CHL-RED-EDGE) suggested day number ± 106 of the year (R 2 = 0.96, RMSE = 0.052). Overall, this study demonstrated the value of remote sensing proxies in estimating the autumn grass senescence and in determining its onset. These results provide a basis for understanding the impact of autumn senescence on foraging resource provision in rangeland ecosystems.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1574954122001005; http://dx.doi.org/10.1016/j.ecoinf.2022.101651; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85128989406&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S1574954122001005; https://dx.doi.org/10.1016/j.ecoinf.2022.101651
Elsevier BV
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know