Set of tools to apply the probabilistic approach of Reiche et al. (2015, 2018) to combine multiple optical and/or Radar satellite time series and to detect deforestation/forest cover loss in near real-time. The package includes functions to apply the approach to both, single pixel time series and raster time series. Examples and test data are provided below.
License: Other
I have a dataset similar to the tutorial and I am trying to recreate the plot as the tutorial shows, but I am unable to do so for one input series (as opposed to having 2 data streams for S1 and L8 in the tutorial). It seems this is occurring for 2 main reasons:
I've tried going through the source code but I have been unable to understand why I'm unable to recreate the tutorial. Any advice you can give would be appreciated.
The data I'm using is attached here: testBaytsData.txt
I am using R 3.6.3
Data set-up
library(data.table)
dt <- fread("../testBaytsData.txt")
pre <- dt[date <= as.Date("2018-06-30"), ]
post <- dt[date > as.Date("2018-06-30"), ]
preMean <- mean(pre[,DI])
preSD <- sd(pre[,DI])
postMean <- mean(post[,DI])
postSD <- sd(post[,DI])
chi <- 0.9
PNFmin <- 0.75
start <- 2018.5
Create the timeseries
library(bayts)
pt1ts <- bfastts(data=dt$DI, dates=dt$date, type=c("irregular"))
plotts(tsL=list(pt1ts),labL=list("DI"))
Warning message:
In plot.xy(xy.coords(x, y), type = type, ...) :
"axes" is not a graphical parameter
Note that I receive this warning message multiple times regardless if I'm plotting or not. For example, you can see below.
pt_pdf <- c(c("gaussian","gaussian"),c(preMean,preSD),c(postMean,postSD))
Warning messages:
1: In doTryCatch(return(expr), name, parentenv, handler) :
"axes" is not a graphical parameter
2: In doTryCatch(return(expr), name, parentenv, handler) :
"axes" is not a graphical parameter
Run the bayts function
bts <- bayts(tsL=list(pt1ts),pdfL=list(pt_pdf), chi=chi, PNFmin=PNFmin, start=start)
Warning messages:
1: In min(which(bayts$Flag == "Flag")) :
no non-missing arguments to min; returning Inf
2: In `[.zoo`(bayts, min(which(bayts$Flag == "Flag"))) :
NAs introduced by coercion to integer range
bts$bayts[index(bts$bayts)>2018.5] # I've truncated the results a bit for brevity
ts1 PNF PChange Flag
2018(295) -0.0103698274580355 0.146422182935227 <NA> 0
2018(296) -0.0109193449193541 0.146697055524646 <NA> 0
2018(297) -0.0113541214173635 0.146921833344499 <NA> 0
2018(298) -0.0116772473208953 0.147093080750404 <NA> 0
2018(299) -0.217396186322773 0.9 0.608173186131117 Change
2018(300) -0.210618789323509 0.9 0.933196749881069 Change
2018(301) -0.20351408921374 0.9 <NA> <NA>
2018(302) -0.19614418378033 0.9 <NA> <NA>
2018(303) -0.188548135003774 0.9 <NA> <NA>
2018(304) -0.180768253936085 0.9 <NA> <NA>
2018(305) -0.172832891918802 0.9 <NA> <NA>
2018(306) -0.154140020871061 0.817288220874064 <NA> <NA>
2018(307) -0.146443843702741 0.766186565563637 <NA> <NA>
2018(308) -0.138759555931493 0.709224537071085 <NA> <NA>
2018(309) -0.11070331698536 0.486083974497615 <NA> 0
2018(310) -0.103861086197172 0.436457341611282 <NA> 0
2018(311) -0.0971769215157538 0.391975796009425 <NA> 0
2018(312) -0.0906775752067858 0.352920450624893 <NA> 0
2018(313) -0.084404840612845 0.319263771132842 <NA> 0
2018(314) -0.0543654049076308 0.207896178415362 <NA> 0
2018(315) -0.0494076443785434 0.196103846211521 <NA> 0
2018(316) -0.0447516673303344 0.186380172564815 <NA> 0
2018(317) -0.040412965286588 0.17840255657346 <NA> 0
2018(318) -0.0533460185563043 0.205344014641303 <NA> 0
2018(319) -0.0490462215152801 0.195303686360202 <NA> 0
2018(320) -0.0450323056907596 0.186931237881271 <NA> 0
2018(321) -0.0412974297042931 0.179948027867292 <NA> 0
2018(322) -0.0378323412253186 0.17412039985751 <NA> 0
2018(323) -0.011107730268842 0.146793656697783 <NA> 0
2018(324) -0.00893639888036777 0.145753340619918 <NA> 0
2018(325) -0.00702393039844238 0.144968361845371 <NA> 0
2018(326) -0.00535043126629303 0.144381033717584 <NA> 0
2018(327) -0.00389818479056342 0.143945913735848 <NA> 0
2018(328) -0.00264926090844979 0.143626706341285 <NA> 0
2018(329) 0.0281074515148046 0.151751670081593 <NA> 0
2018(330) 0.0280045824120031 0.151670942057999 <NA> 0
2018(331) 0.0277209637361161 0.151450388469223 <NA> 0
2018(332) 0.0272769307546597 0.151111025910394 <NA> 0
2018(333) 0.0266924577887062 0.150675317006953 <NA> 0
2018(334) 0.0555142561135827 0.18916389178469 <NA> 0
2018(335) 0.0985780819752415 0.344696468173231 <NA> 0
2018(336) 0.0951602331312242 0.326372572235416 <NA> 0
2018(337) 0.0915969816182215 0.308510307791028 <NA> 0
2018(338) 0.0879226664279991 0.291388624170535 <NA> 0
2018(339) 0.115503976244305 0.452536318206864 <NA> 0
2018(340) 0.11063174244274 0.418678107807219 <NA> 0
2018(341) 0.105690457374186 0.386605546439634 <NA> 0
2018(342) 0.102818356792821 0.369068583240284 <NA> 0
2018(343) 0.0977686923405921 0.340250861970259 <NA> 0
2018(344) 0.0927368306516509 0.314087886854492 <NA> 0
2018(345) 0.0877489812322722 0.290611216034045 <NA> 0
2018(346) 0.0828297760317749 0.269754526640656 <NA> 0
2018(347) 0.125774602450901 0.530035543363182 <NA> <NA>
2018(348) 0.119474552510288 0.481615724114275 <NA> 0
2018(349) 0.113231809374945 0.436482584247636 <NA> 0
2018(350) 0.107094574469821 0.395477760575791 <NA> 0
2018(351) 0.101083572329726 0.358877755376623 <NA> 0
2018(352) 0.0952176660770799 0.326670797475777 <NA> 0
2018(353) 0.0894854954627703 0.298512608459057 <NA> 0
2018(354) 0.112721613650906 0.432940325092588 <NA> 0
2018(355) 0.106418786267728 0.391183368320544 <NA> 0
2018(356) 0.10028734921144 0.354302236732953 <NA> 0
2018(357) 0.0943398272266948 0.322148474858418 <NA> 0
2018(358) 0.0885871446778369 0.29438916190609 <NA> 0
2018(359) 0.116444942971422 0.459313551039741 <NA> 0
2018(360) 0.109984076992457 0.414340567334918 <NA> 0
2018(361) 0.103707841570158 0.374411415916444 <NA> 0
2018(362) 0.0976253126928801 0.339470236415147 <NA> 0
2018(363) 0.091744553739018 0.309225242771776 <NA> 0
2018(364) 0.086072585346437 0.283253254318169 <NA> 0
2018(365) 0.0806114277832553 0.261062254417706 <NA> 0
2019(1) 0.059169109652869 0.196985914599432 <NA> 0
2019(2) 0.0547003807389136 0.187527110668438 <NA> 0
2019(3) 0.050473585141166 0.179610954002321 <NA> 0
2019(4) 0.0464878746141911 0.172997740752971 <NA> 0
2019(5) 0.04274096557831 0.167484089375414 <NA> 0
2019(6) 0.0392291916642345 0.16289766299394 <NA> 0
2019(7) 0.0359371306326694 0.159081039905596 <NA> 0
2019(8) 0.0328805064069418 0.155935481319106 <NA> 0
2019(9) 0.0300405284126067 0.153341890730969 <NA> 0
2019(10) 0.027409020236721 0.151211223134586 <NA> 0
2019(11) 0.0249770250841742 0.149467890850071 <NA> 0
2019(12) 0.0227408130221291 0.148051235369864 <NA> 0
2019(13) 0.0206781138058781 0.146899258497611 <NA> 0
2019(14) 0.0187850639485467 0.145970303107675 <NA> 0
2019(15) 0.0170355678793249 0.145219287869532 <NA> 0
2019(16) 0.0154519337952479 0.144627404270771 <NA> 0
2019(17) 0.0140065719320316 0.144159398433461 <NA> 0
2019(18) -0.0179575895489548 0.151147179368772 <NA> 0
2019(19) -0.0181030882140769 0.151257838372718 <NA> 0
2019(20) -0.018132785447423 0.151280519924538 <NA> 0
2019(21) 0.010949032132212 0.14339398791346 <NA> 0
2019(22) 0.0101846571102187 0.143249932148884 <NA> 0
2019(23) 0.0094922407690351 0.143135679844599 <NA> 0
2019(24) -0.0397983170134051 0.177352217893418 <NA> 0
2019(25) -0.0387396181208487 0.175587900088209 <NA> 0
2019(26) -0.0375837253829888 0.173725343765663 <NA> 0
2019(27) -0.0363437672108415 0.171799845363409 <NA> 0
2019(28) -0.0350328273522696 0.169844055994227 <NA> 0
2019(29) -0.203641222732472 0.9 <NA> <NA>
2019(30) -0.196506707678658 0.9 <NA> <NA>
2019(31) -0.18917341734071 0.9 <NA> <NA>
2019(32) -0.181667907169265 0.9 <NA> <NA>
2019(33) -0.174031556013066 0.9 <NA> <NA>
2019(34) -0.375599166806213 0.9 <NA> <NA>
2019(35) -0.360774013153237 0.9 <NA> <NA>
2019(36) -0.345727197763821 0.9 <NA> <NA>
2019(37) -0.330526746612581 0.9 <NA> <NA>
2019(38) -0.315243014553276 0.9 <NA> <NA>
2019(39) -0.533950546121899 0.9 <NA> <NA>
2019(40) -0.510839511005354 0.9 <NA> <NA>
2019(41) -0.487625001844107 0.9 <NA> <NA>
2019(42) -0.508716529869376 0.9 <NA> <NA>
2019(43) -0.484096560068751 0.9 <NA> <NA>
2019(44) -0.702635241068076 0.9 <NA> <NA>
2019(45) -0.670245887585986 0.9 <NA> <NA>
2019(46) -0.637962987860921 0.9 <NA> <NA>
2019(47) -0.605912118126666 0.9 <NA> <NA>
Plot the function
plotBayts(bts$bayts)
Error in merge.zoo(zoo(coredata(x), tt), zoo(, tt2)) :
series cannot be merged with non-unique index entries in a series
In addition: Warning messages:
1: In min(which(bayts$Flag == "Flag")) :
no non-missing arguments to min; returning Inf
2: In `[.zoo`(bayts, min(which(bayts$Flag == "Flag"))) :
NAs introduced by coercion to integer range
3: In zoo(data, 1900 + as.POSIXlt(dates)$year + (yday365(dates) - 1)/365, :
some methods for “zoo” objects do not work if the index entries in ‘order.by’ are not unique
4: In zoo(coredata(x), tt) :
some methods for “zoo” objects do not work if the index entries in ‘order.by’ are not unique
Plotting here does not work. The error message appears to suggest that there is a timeseries that is being merged with the one I supplied, but there are duplicates even though I've already removed the Leap Day (29 Feb 2016). This also is confusing for Error messages 3 and 4.
This is the last argument in the function detectBayts. From the help file, I'm understanding chi to be "Threshold of Pchange at which the change is confirmed", and PNFmin as "threshold of pNF above which the first observation is flagged." Why is the conditional for chi here taken as the first step to be labeled as change? If chi is used only for when the change is confirmed, shouldn't the first conditional be when the first observation is flagged?
On that note, is there a reason 0.5 is used here instead of PNFmin?
if ((as.double(bayts$PChange[t])) >= chi) {
if ((as.double(bayts$PNF[t])) >= 0.5) {
bayts$Flag[min(which(bayts$Flag == "Flag")):t] <- "Change"
return(bayts)
}
}
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