bfast01classify {bfast} | R Documentation |
Change type analysis of the bfast01 function
Description
A function to determine the change type
Usage
bfast01classify(
object,
alpha = 0.05,
pct_stable = NULL,
typology = c("standard", "drylands")
)
Arguments
object |
|
alpha |
threshold for significance tests, default 0.05 |
pct_stable |
threshold for segment stability, unit: percent change per unit time (0-100), default NULL |
typology |
classification legend to use: |
Details
bfast01classify
Value
bfast01classify
returns a data.frame with the following
elements:
flag_type |
Type of shift: (1) monotonic increase, (2) monotonic decrease, (3) monotonic increase (with positive break), (4) monotonic decrease (with negative break), (5) interruption: increase with negative break, (6) interruption: decrease with positive break, (7) reversal: increase to decrease, (8) reversal: decrease to increase |
flag_significance |
SIGNIFICANCE FLAG: (0) both segments significant (or no break and significant), (1) only first segment significant, (2) only 2nd segment significant, (3) both segments insignificant (or no break and not significant) |
flag_pct_stable |
STABILITY FLAG: (0) change in both segments is substantial (or no break and substantial), (1) only first segment substantial, (2) only 2nd segment substantial (3) both segments are stable (or no break and stable) |
and also significance and percentage of both segments before and after the potentially detected break: "p_segment1", "p_segment2", "pct_segment1", "pct_segment2".
Author(s)
Rogier de Jong, Jan Verbesselt
References
Bernardino PN, De Keersmaecker W, Fensholt R, Verbesselt J, Somers B, Horion S (2020).
“Global-scale characterization of turning points in arid and semi-arid ecosystem functioning.”
Global Ecology and Biogeography, 29(7), 1230–1245.
doi: 10.1111/geb.13099, https://doi.org/10.1111/geb.13099.
De Jong R, Verbesselt J, Zeileis A, Schaepman ME (2013).
“Shifts in Global Vegetation Activity Trends.”
Remote Sensing, 5(3), 1117–1133.
ISSN 2072-4292, doi: 10.3390/rs5031117, https://doi.org/10.3390/rs5031117.
See Also
Examples
library(zoo)
## define a regular time series
ndvi <- as.ts(zoo(som$NDVI.a, som$Time))
## fit variations
bf1 <- bfast01(ndvi)
bfast01classify(bf1, pct_stable = 0.25)