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-
+

-12.2 About the underlying data +13.2 About the underlying data

In the NDVI project for natopen systems, we use two kinds of data (coming as four datasets) for building indicators for ecological condition:

@@ -109,9 +110,9 @@

-
+

-15 Areal av isbreer +16 Areal av isbreer


Author and date:

@@ -136,93 +137,93 @@




-
+

-15.1 Introduction +16.1 Introduction

This indicator describes the relative change in glacial extent for each of fire regions in Norway. A slightly different indicator was used in the alpine assessment.

-
+

-15.2 About the underlying data +16.2 About the underlying data

The indicator uses the maps of glacier extent ( breatlas ) from 2018-2019 and compares it to a similar map from 1947-1985. Both maps are owned by NVE (Norsk vassdrags og energidirektorat). The new map is based on Sentinel images, and the old map is a digitised version of the N50 maps, with some additional areal photo interpretation on top.

-
+

-15.2.1 Representativity in time and space +16.2.1 Representativity in time and space

The data set (map) covers the entire Norwegian mainland. There are only two time steps, but we expect changes in glacial extent to be relatively linear or consistent over time. The older map may have underestimated the glacial extent due to a better chance of smaller snow and ice patches being captured by Sentinel contra aerial photo interpretations.

-
+

-15.2.2 Original units +16.2.2 Original units

Area units (e.g. km2)

-
+

-15.2.3 Temporal coverage +16.2.3 Temporal coverage

1947-2019

-
+

-15.2.4 Aditional comments about the dataset +16.2.4 Aditional comments about the dataset

None

-
+

-15.3 Ecosystem characteristic +16.3 Ecosystem characteristic

-
+

-15.3.1 Norwegian standard +16.3.1 Norwegian standard

The indicator was initially assigned the Abiotiske forhold characteristic, but now it is considered under Landskapsøkologiske mønstre. The initial justificaton was that glacial melting would influence water availability downstream. Now we put more weight on glaciers as not just resevoirs of ice and water, but as a habitat, and the presence or abundance of a specific habitat can be though of as an aspect of landscape state.

-
+

-15.3.2 SEEA ECT +16.3.2 SEEA ECT

The indicator is similarly assiged to the SEEA ECT class C1 Landscape characteristics.

-
+

-15.4 Collinearities with other indicators +16.4 Collinearities with other indicators

None that are known.

-
+

-15.5 Reference state and values +16.5 Reference state and values

-
+

-15.5.1 Reference state +16.5.1 Reference state

The indicator uses the same reference state as all or most of the indicators developed in this and in similar projects, and is defined in Nybø et al (2017): Fagsystem for fastsetting av god økologisk tilstand. As glacial extent is primarily, or solely, dependent on climate, we are in a case where we have actual data from the climatic reference period which is the previous normal period 1961-1990.

-
+

-15.5.2 Reference values, thresholds for defining good ecological condition, minimum and/or maximum values +16.5.2 Reference values, thresholds for defining good ecological condition, minimum and/or maximum values

The reference value is zero change in glacial extent over time. The threshold for defining good ecological condition as set at a 40% reduction in glacial extent, as a default solution. This normalization follows a linear model, although a sigmoidal weibull function might be more appropriate.

The minimum value is set to zero.

-
+

-15.6 Uncertainties +16.6 Uncertainties

Here we add a 3% error on each side of the estimates, same as what is reported for the glacier maps from NVE.

-
+

-15.7 References +16.7 References

  • Andreassen, L.M., & Winsvold, S.H. (eds.). 2012. Inventory of Norwegian glaciers. NVE Rapport 38, Norges Vassdrags- og energidirektorat, 236 s

  • @@ -231,13 +232,13 @@

  • Winsvold, S.H., Andreassen, L.M. & Kienholz, C.. 2014. Glacier area and length changes in Norway from repeat inventories. The Cryosphere 8: 1885-1903

-
+

-15.8 Analyses +16.8 Analyses

-
+

-15.8.1 Importing the data sets +16.8.1 Importing the data sets

There are four data sets:

    @@ -247,12 +248,12 @@

  • an outline of mainland Norway

We are assuming all glaciers are found in the mountins, se we do not need to mask the glacial atlas data using an ecosystem delineation map.

-
+

-15.8.1.1 Glacial atlas +16.8.1.1 Glacial atlas

This is the new glacial atlas from 2018 and 2019 based on Sentinell (ref. Liss Marie Andreassen, NVE)

-
+
 
 # server path
 breatlas_path <- '/data/P-Prosjekter/41201042_okologisk_tilstand_fastlandsnorge_2020_dataanaly/fjell2021/data/breatlas/breatlas_2018_2019/Breatlas_20182019_temp20210922_lma.shp'
@@ -272,7 +273,7 @@ 

All maps made in this project need to have the same CRS: EPSG:25833 - ETRS89 / UTM zone 33N. This one is in UTM 33N ETRS89, even if it is defined in the old Proj.4 way.

There are 6915 polygons. Let’s calculate the area of the polygons and study the distribution of size classes.

-
+
 breatlas$area <- st_area(breatlas)
 par(mfrow=c(1,2))
 hist(breatlas$area)
@@ -280,24 +281,24 @@ 

Most polygons are small, and then there are a couple of very big ones. The largest polygon is 50 km2, but note that some polygons are somewhat arbitrarily split.

-
+

-15.8.1.2 Map of Norway +16.8.1.2 Map of Norway

This map is stored on the GitHub repository under indicators/data/.

-
+
 nor <- sf::read_sf("data/outlineOfNorway_EPSG25833.shp")
 
 plot(nor$geometry, axes=T, main = "Breatlas 2018-2019")
   plot(breatlas$geometry, add=T, border = "blue")
-
+

-15.8.1.3 N50 +16.8.1.3 N50

This is the old glacial extent map. Import it and transform it, same as before.

-
+
 
 # server path
 n50_path <- '/data/P-Prosjekter/41201042_okologisk_tilstand_fastlandsnorge_2020_dataanaly/fjell2021/data/breatlas/n50/cryoclim_GAO_NO_1952_1985_UTM_33N.shp'
@@ -314,12 +315,12 @@ 

#> Dimension: XY #> Bounding box: xmin: -7278.562 ymin: 6623024 xmax: 812207.8 ymax: 7841654 #> Projected CRS: WGS 84 / UTM zone 33N

-
+
 plot(nor$geometry, axes=T, main = "n50 - 1952-1985")
   plot(n50$geometry, add=T, border = "red")

Let’s now plot it all on top of each other.

-
+
 
 # define some boundary boxes for zooming in on specific regions
 myExt <- raster::extent(c(0, 100000, 6840000, 6900000))
@@ -349,12 +350,12 @@ 

The red areas in the map to the far right is where the ice has retreated for a selected glacier on the west coast. Now we need to devide the map into regions to then compare the area in n50 with the new glacial extent map breatlas.

-
+

-15.8.1.4 Regions +16.8.1.4 Regions

Importing a shape file with the regional delineation.

-
+
 reg <- st_read("data/regions.shp")
 #> Reading layer `regions' from data source 
 #>   `/data/Egenutvikling/41001581_egenutvikling_anders_kolstad/github/ecosystemCondition/data/regions.shp' 
@@ -419,7 +420,7 @@ 

#> BBOX[46.4,12,84.42,18]], #> ID["EPSG",25833]]

Let’s plot it to make sure it is correct.

-
+
 plot(nor$geometry, axes=T)
   plot(reg$geometry, add=T, border = "black", 
        col = scales::alpha(c("blue", 
@@ -429,23 +430,23 @@ 

"brown"), .2))

Get the region names

-
+
 unique(reg$region)
 #> [1] "Nord-Norge"      "Midt-Norge"      "Ã\u0098stlandet"
 #> [4] "Vestlandet"      "Sørlandet"

These are odd. Let me fix the Norwegian letters manually.

-
+
 reg$region[reg$region=="Ã\u0098stlandet"] <- "Østlandet"
 reg$region[reg$region=="Sørlandet"] <- "Sørlandet"

Before going any further I need to fix a problem with glacier polygons crossing themselves.

-
+
 table(st_is_valid(breatlas))
 #> 
 #> FALSE  TRUE 
 #>    68  6847

68 polygons are not valid. Let’s look at the first four reasons for why that is.

-
+
 unique(st_is_valid(breatlas, reason=T))[1:5]
 #> [1] "Valid Geometry"                                       
 #> [2] "Ring Self-intersection[100236.4931 6912507.5864]"     
@@ -453,7 +454,7 @@ 

#> [4] "Ring Self-intersection[91965.6612 6883260.4912]" #> [5] "Ring Self-intersection[120595.9254 6853573.4955]"

There is a convinient function to fix this.

-
+
 # requires lwgeom
 breatlas2 <- st_make_valid(breatlas)
 table(st_is_valid(breatlas2))
@@ -465,7 +466,7 @@ 

#breatlas2 <- st_buffer(breatlas, 0.0) #table(st_is_valid(breatlas2))

Let me plot the new, fixed map on top of the old to see if they match. I give the new valid map a blue transparent colour, and the old map a yellow and transparent colour.

-
+
 plot(nor$geometry, xlim=c(5000, 10000),
           ylim=c(6880000, 6885000),
      axes=T)
@@ -475,21 +476,21 @@ 

This seems to have worked. Notice also how some of these larger polygons are somewhat arbitrarely divided into several adjoining polygons. This is only so for the new map, not the old n50 map.

Delete the invalid data set.

-
+
 breatlas <- breatlas2
 rm(breatlas2)

-
+

-15.8.2 Glacial area per region +16.8.2 Glacial area per region

-
+

-15.8.2.1 Contemporary area +16.8.2.1 Contemporary area

Here is one method for getting the glacial area for each region.

-
+
 # This intersection operation is a bit heavy, so I do it once and store it locally so that I can import it again quickly. 
 #brealtas_reg <- st_intersection(breatlas, reg)
 #saveRDS(brealtas_reg, "indicators/data/brealtas_reg_helperfile.rds")
@@ -497,7 +498,7 @@ 

# Importing what I create above brealtas_reg <- readRDS("data/brealtas_reg_helperfile.rds")

Checking what happens to polygons that span to regions. Central Norway in red and western Norway in green. 50% transparency means we should get a third colour where and if they overlap.

-
+
 plot(brealtas_reg$geometry[brealtas_reg$region=="Midt-Norge"], 
     col = scales::alpha("red",0.5), border=NA, axes=T, 
      ylim=c(6900000, 6905000),
@@ -508,7 +509,7 @@ 

It worked fine.

Calculating the area of the polygons

-
+
 #Calculating the area of the polygons
 brealtas_reg$area_crop <- st_area(brealtas_reg)
 
@@ -529,16 +530,16 @@ 

Southern Norway has <1km2 of glaciers in 2018-2019.

-
+

-15.8.2.2 Same for N50 +16.8.2.2 Same for N50

-
+
 n50x <- st_make_valid(n50)
 #n50_reg <- st_intersection(n50x, reg)
 #saveRDS(n50_reg, "indicators/data/n50_helperfile.rds")
 n50_reg <- readRDS("data/n50_helperfile.rds")
-
+
 plot(n50_reg$geometry[n50_reg$region=="Midt-Norge"], 
     col = scales::alpha("red",0.5), border=NA, axes=T, 
      ylim=c(6900000, 6905000),
@@ -549,7 +550,7 @@ 

Thats good. Now I will plot the reference values (n50) and the contemporary data together.

-
+
 n50_reg$area_crop <- st_area(n50_reg)
 
 n50tabell <- tapply(
@@ -575,12 +576,12 @@ 

There was a little more glaicers in southern Norway in the reference period, but only 4 km2. The condition here will be very poor! I’m not entirely sure this is representative. The alternative is to set it to be NA, and I think I will do this - one could argue that glaciers are not as important of central to the alpine ecosystems in the south.

-
+

-15.8.3 Scaled indicator values +16.8.3 Scaled indicator values

Making a table with the indicator values.

-
+
 myTbl <- tibble(region           = names(bretabell),
                 indikator        = bretabell,
                 referanseverdi   = n50tabell)
@@ -628,7 +629,7 @@ 

Scaling and clipping the regions against the outline of Norway

-
+
 # paste the scaled indicato values into the shape file with the regions, using the match function to make sure the align correctly
 reg$skalert_indikator <- myTbl$skalert_indikator[match(reg$region, myTbl$region)]
 
@@ -636,11 +637,11 @@ 

reg_clipped <- st_intersection(reg, nor) #> Warning: attribute variables are assumed to be spatially #> constant throughout all geometries

-
+
 # round of to 2 decimals
 reg_clipped$skalert_indikator_r <- round(reg_clipped$skalert_indikator, 2)

Plotting a static map with scaled indicator values

-
+
 tmap_mode("plot") # mode 'view' takes a lot longer to render, but looks better
 #> tmap mode set to plotting
 tm_shape(reg_clipped) + 
@@ -650,36 +651,36 @@ 

tm_shape(nor)+ tm_polygons(alpha = 0,border.col = "black")

-
+
 # removing the value for souther norway
 reg_clipped$skalert_indikator[reg_clipped$region=="Sørlandet"] <- NA

Calculating total change of glacier extent

-
+
 sum(bretabell)/sum(n50tabell)
 #> [1] 0.7003536

We’ve lost 30% of the glacial area over this time.

-
+

-15.8.4 Uncertainty +16.8.4 Uncertainty

NVE reports a 3% uncertainty with their method. This is interpret as if +- 3% represent a 99.9%CI for the estimate (ca 2 SD). In the original indicator I re-sampled the indicator value 10000 times here, with a 0.015sd, to get a distribution of indicator values. This time I will settle for reporting the sd as a single value below.

-
+

-15.9 Prepare export +16.9 Prepare export

Now I can prepare the final exported data file. It will be a shape file with raw and scaled indicator values, reference reference values, and uncertainties.

The names of the columns are standardised (region, v_YYYY, sd_YYYY, i_YYYY, and reference).

-
+
 names(reg_clipped)
 #> [1] "id"                  "region"             
 #> [3] "skalert_indikator"   "GID_0"              
 #> [5] "NAME_0"              "geometry"           
 #> [7] "skalert_indikator_r"
-
+
 # paste the raw variable estimates
 reg_clipped$v_2019 <- myTbl$indikator[match(reg_clipped$region, myTbl$region)]              
 
@@ -697,13 +698,13 @@ 

reference, i_2019 = skalert_indikator, geometry)

-
+

-15.9.1 Eksport shape file (final product) +16.9.1 Eksport shape file (final product)

-
+
 sf::st_write(exp, "indicators/indicatorMaps/breareal.shp")
-
+
 knitr::kable(exp[,1:5])
@@ -772,46 +773,46 @@

-
+

-16 Areal uten død eller skadet røsslyng i kystlynghei +17 Areal uten død eller skadet røsslyng i kystlynghei


Author and date:

@@ -121,39 +122,39 @@

- -

+ +
- - - + + + - - - + + +

Ecosystem

Økologisk egenskap

ECT class

Ecosystem

Økologisk egenskap

ECT class

Semi-naturlig mark

Primærproduksjon

Structual state characteristic

Semi-naturlig mark

Primærproduksjon

Structual state characteristic




-
+

-16.1 Introduction +17.1 Introduction

Here I quickly go through the ANO data set for the years 2019-2021 to investigate the frequeny of poins that fall within coastal heathlands (kystlynghei). This will decide whether we can use this data to inform an indicator about the proportion of dead Calluna vulgaris (røsslyng).

-
+

-16.2 Analyses +17.2 Analyses

-
+

-16.2.0.1 ANO data +17.2.0.1 ANO data

Data is downloaded from here.

Import and list terms:

-
+
 ano <- sf::st_read("/data/P-Prosjekter2/41201785_okologisk_tilstand_2022_2023/data/Naturovervaking_eksport.gdb",
                    layer = "ANO_SurveyPoint")
 #> Reading layer `ANO_SurveyPoint' from data source 
@@ -239,26 +240,26 @@ 

#> [71] "editdate" #> [72] "SHAPE"

Table of the number of sampling points per year:

-
+
 table(ano$aar)
 #> 
 #> 2019 2020 2021 
 #> 1111 3411 4452
-
+
 par(mar=c(15,5,0,0))
 barplot(table(ano$hovedoekosystem_punkt), las=2)
The distribution of ANO points that fall within different main ecosystems.

-Figure 16.1: The distribution of ANO points that fall within different main ecosystems. +Figure 17.1: The distribution of ANO points that fall within different main ecosystems.

Sub-setting the data to only consider seni natural areas.

-
+
 snat <- ano[ano$hovedoekosystem_punkt == "semi_naturlig_mark",]
 snat2 <- snat[!is.na(snat$GlobalID),]

230 points are semi-natural.

-
+
 table(snat$hovedtype_1m2)
 #> 
 #>       Åpen jordvannsmyr              Boreal hei 
@@ -274,7 +275,7 @@ 

#> Skogsmark #> 4

Only 49 1m2 samples are kystlynghei.

-
+
 table(ano$hovedtype_250m2)
 #> 
 #>                                      Åker 
@@ -366,9 +367,9 @@ 

Only 26 of the 250m2 circles are kystlynghei. The variable of interest is called Dekning av død/skadet røsslyng and is only recorded on these 26 circles.

-
+

-16.3 Conclusion +17.3 Conclusion

Because of the random allocation of sampling points, not enough points fall in coastal heathlands to allow us to create a trustworthy indicator for this nature type. With a few more years of data we might end up with about 100 points, which is borderline what we need in order to calculate condition values at a regional level with some level of precision.

@@ -378,15 +379,15 @@

-
+

-21 Climate indicators explained +22 Climate indicators explained


Author and date:

@@ -133,79 +134,79 @@




-
+

-21.1 Introduction +22.1 Introduction

This chapters describes the development of a workflow for generating or preparing indicators based on interpolated climate data from SeNorge. I describe the process using dummy data and also discuss alternate approaches and dead ends that occured in the development process. For a succinct workflow example for a specific indicator, see Median summer temperature.

-
+

-21.2 About the underlying data +22.2 About the underlying data

The data is in a raster format and extends back to 1957 in the form of multiple interpolated climate variables. The spatial resolution is 1 x 1 km.

-
+

-21.2.1 Representativity in time and space +22.2.1 Representativity in time and space

The data includes the last normal period (1961-1990) which defines the reference condition for climate variables. Therefore the temporal resolution is very good. Also considering the daily resolution of the data.

Spatially, a 1x1 km resolution is sufficient for most climate variables, esp. in homogeneous terrain, but this needs to be evaluation for each variable and scenario specifically.

-
+

-21.2.2 Original units +22.2.2 Original units

Varied. Specified below for each parameter.

-
+

-21.2.3 Temporal coverage +22.2.3 Temporal coverage

1957 - present

-
+

-21.2.4 Additional comments about the data set +22.2.4 Additional comments about the data set

The data format has recently changed from .BIL to .nc (netcdf) and now a single file contains all the rasters for one year (365 days), and sometimes for multiple variables also.

-
+

-21.3 Ecosystem characteristic +22.3 Ecosystem characteristic

-
+

-21.3.1 Norwegian standard +22.3.1 Norwegian standard

These variables typically will fall under the abiotiske egenskaper class.

-
+

-21.3.2 SEEA EA +22.3.2 SEEA EA

In SEEA EA, these variables will typically fall under A1 - Physical state characteristics.

-
+

-21.4 Collinearities with other indicators +22.4 Collinearities with other indicators

Climate variables are most likely to be correlated with each other (e.g. temperature and snow). Also, some climate variables are better classed as pressure indicators, and these might have a causal association with several condition indicators.

-
+

-21.5 Reference condition and values +22.5 Reference condition and values

-
+

-21.5.1 Reference condition +22.5.1 Reference condition

The reference condition for climate variables is defined as the normal period 1961-1990.

-
+

-21.5.2 Reference values, thresholds for defining good ecological condition, minimum and/or maximum values +22.5.2 Reference values, thresholds for defining good ecological condition, minimum and/or maximum values

  • Un-scaled indicator value = median value over 5 year periods (5 years being a pragmatic choice. It is long enough to smooth out a lot of inter-annual variation, and it’s long enough to enable estimating errors)

  • @@ -216,62 +217,62 @@

    The choice to use 2 SD units as the threshold values is a subjective choice in many ways, and not founded in any empirical or known relationship between the indicators and ecosystem integrity. The value comes from the common practice of calling something extreme weather when it is outside 2 SD units of the current running average. So, if the indicator value today is <0.6 it implies that the mean for that variable over the last year would have been referred to as extreme if it had occurred between 1961 and 1990. This is I think a conservative threshold, since one would/could call it extreme if only one year is outside the 2SD range, and having the mean of 5 years being outside this range is really extreme.

-
+

-21.6 Uncertainties +22.6 Uncertainties

For the indicator map (1 x 1 km raster) there is no uncertainty associated with the indicator values. For aggregated indicator values (e.g. for regions), the uncertainty in the indicator value is calculated from the spatial variation in the indicator values via bootstrapping. This might, however, be changed later to the temporal variation between the five years of each period.

-
+

-21.7 References +22.7 References

https://senorge.no/

rr and tm are being download from: https://thredds.met.no/thredds/catalog/senorge/seNorge_2018/Archive/catalog.html

-
+

-21.7.1 Additional resources +22.7.1 Additional resources

Stars package

R as a GIS for economists chapter 7

-
+

-21.8 Analyses +22.8 Analyses

-
+

-21.8.1 Data set +22.8.1 Data set

The data is downloaded to a local NINA server, and updated regularly.

-
+
 path <- ifelse(dir == "C:", 
       "R:/GeoSpatialData/Meteorology/Norway_SeNorge2018_v22.09/Original",
       "/data/R/GeoSpatialData/Meteorology/Norway_SeNorge2018_v22.09/Original")

This folder contains folder for the different parameters

-
+
 (files <- list.files(path))
 #>  [1] "age"        "fsw"        "gwb_eva"    "gwb_gwtcl" 
 #>  [5] "gwb_q"      "gwb_sssdev" "gwb_sssrel" "lwc"       
 #>  [9] "qsw"        "rr_tm"      "sd"         "sdfsw"     
 #> [13] "swe"

This table explains them in more detail

-
+
 senorgelayers <- read_delim("data/senorgelayers.txt", 
     delim = "\t", escape_double = FALSE, 
     trim_ws = TRUE)
 DT::datatable(senorgelayers)
-
-

-Figure 16.1: Table explaining the available climate parameters in the data set. +

+

+Figure 17.1: Table explaining the available climate parameters in the data set.

Here is the content of one of these folders (first 10 entries)

-
+
 (files_tm <- list.files(paste0(path, "/rr_tm")))[1:10]
 #>  [1] "seNorge2018_1957.nc" "seNorge2018_1958.nc"
 #>  [3] "seNorge2018_1959.nc" "seNorge2018_1960.nc"
@@ -280,29 +281,29 @@ 

#> [9] "seNorge2018_1965.nc" "seNorge2018_1966.nc"

There are 67 files, i.e. 67 years of data, one file per year.

Importing (proxy) file:

-
+
 tg_1957 <- stars::read_ncdf(paste0(path, "/rr_tm/seNorge2018_1957.nc"),
                             var="tg")
 #> Large netcdf source found, returning proxy object.

I know the CRS, so setting it manually. Although I cannot rule out 32633, I don’t think it matters.

-
+
 st_crs(tg_1957) <- 25833

The data has three dimension

-
+
 dim(tg_1957)
 #>    X    Y time 
 #> 1195 1550  365

Initially the data had four attributes, but I subsettet to only include tg.

-
+
 names(tg_1957)
 #> [1] "tg"

tg_1957 is a stars proxy object and most commands will not initiate any change until later, typically I write to file and all the lazy operations are done in squeal. Therefore, I will prepare a test data set, which is smaller and which I can import to memory. Then I can perform the operations on that data set and we can see the results.

-
+

-21.8.1.1 Dummy data +22.8.1.1 Dummy data

This test data is included in the {stars} package

-
+
 tif = system.file("tif/L7_ETMs.tif", package = "stars")
 t1 = as.Date("1970-05-31")
 x = read_stars(c(tif, tif, tif, tif), along = 
@@ -313,48 +314,48 @@ 

nYSize = 50, bands = c(1:6)))

A single attribute

-
+
 names(x)
 #> [1] "L7_ETMs.tif"

I can rename it like this

-
+
 x <- setNames(x, "Attribute_A")

And I can add another dummy attribute.

-
+
 x <- x %>%
   mutate("Attribute_B" = Attribute_A/2)

The dummy data also has four dimensions

-
+
 dim(x)
 #>    x    y band time 
 #>   50   50    6    4

X and y area the coordinates. Band is an integer:

-
+
 st_get_dimension_values(x, "band")
 #> [1] 1 2 3 4 5 6

I will remove the band dimension.

-
+
 x <- x %>% filter(band==1) %>% adrop()

Time is four dates covering four months in 1970:

-
+
 (oldTime <- st_get_dimension_values(x, "time"))
 #> [1] "1970-05-31" "1970-06-01" "1970-07-20" "1970-09-08"

I’ll add some extra seasonal variation between to the mix

-
+
 x$Attribute_A[,,2] <- x$Attribute_A[,,1]*1.2
 x$Attribute_A[,,3] <- x$Attribute_A[,,1]*1.4
 x$Attribute_A[,,4] <- x$Attribute_A[,,1]*1.1

Now I create another four copies of this data, adding some random noise and a continuous decreasing trend.

-
+
 # Function to add random noise
 myRandom <- function(x) x*rnorm(1,1,.05)
-
+
 y1 <- x
 y2 <- x %>% st_apply(1:3, myRandom) %>% st_set_dimensions("time", values = oldTime %m+% years(-1)) %>% mutate(Attribute_A = Attribute_A*0.95)
 y3 <- x %>% st_apply(1:3, myRandom) %>% st_set_dimensions("time", values = oldTime %m+% years(-2)) %>% mutate(Attribute_A = Attribute_A*0.90)
 y4 <- x %>% st_apply(1:3, myRandom) %>% st_set_dimensions("time", values = oldTime %m+% years(-3)) %>% mutate(Attribute_A = Attribute_A*0.85)
 y5 <- x %>% st_apply(1:3, myRandom) %>% st_set_dimensions("time", values = oldTime %m+% years(-4)) %>% mutate(Attribute_A = Attribute_A*0.80)
-
+
 # We combine the data into one cube for plotting:
 temp <- c(y1, y2, y3, y4, y5) 
 
@@ -368,19 +369,19 @@ 

facet_wrap(~time, ncol=4)

Saving these to file.

-
+
 path <- "P:/41201785_okologisk_tilstand_2022_2023/data/climate_indicators/dummy_data"
 saveRDS(y1, paste0(path, "/y1.rds"))
 saveRDS(y2, paste0(path, "/y2.rds"))
 saveRDS(y3, paste0(path, "/y3.rds"))
 saveRDS(y4, paste0(path, "/y4.rds"))
 saveRDS(y5, paste0(path, "/y5.rds"))
-
+
-21.8.1.1.1 Dummy regions and ET map +22.8.1.1.1 Dummy regions and ET map

Here is some dummy data for accounting areas (regions) and ecosystem occurrences as well, in case I need them later.

-
+
 dummy_aa <- x %>%
   filter(time == oldTime[1]) %>%
   adrop() %>%
@@ -392,10 +393,10 @@ 
Showing the example account area delineation (raster format)

-Figure 17.7: Showing the example account area delineation (raster format) +Figure 18.7: Showing the example account area delineation (raster format)

-
+
 dummy_et <- x %>%
   filter(time == oldTime[1]) %>%
   adrop() %>%
@@ -407,17 +408,17 @@ 
Showing the example Ecosystem type delineation data.

-Figure 21.1: Showing the example Ecosystem type delineation data. +Figure 22.1: Showing the example Ecosystem type delineation data.

-
+

-21.8.1.2 Regions +22.8.1.2 Regions

Importing a shape file with the regional delineation.

-
+
 reg <- sf::st_read("data/regions.shp", options = "ENCODING=UTF8")
 #> options:        ENCODING=UTF8 
 #> Reading layer `regions' from data source 
@@ -430,7 +431,7 @@ 

#> Projected CRS: ETRS89 / UTM zone 33N #st_crs(reg)

Outline of Norway

-
+
 nor <- sf::st_read("data/outlineOfNorway_EPSG25833.shp")
 #> Reading layer `outlineOfNorway_EPSG25833' from data source 
 #>   `/data/Egenutvikling/41001581_egenutvikling_anders_kolstad/github/ecosystemCondition/data/outlineOfNorway_EPSG25833.shp' 
@@ -441,11 +442,11 @@ 

#> Bounding box: xmin: -113472.7 ymin: 6448359 xmax: 1114618 ymax: 7939917 #> Projected CRS: ETRS89 / UTM zone 33N

Remove marine areas from regions

-
+
 reg <- st_intersection(reg, nor)
 #> Warning: attribute variables are assumed to be spatially
 #> constant throughout all geometries
-
+
 tm_shape(reg) +
   tm_polygons(col="region")
@@ -455,17 +456,17 @@

-
+

-21.8.1.3 Ecosystem map +22.8.1.3 Ecosystem map

Coming soon ….

The climate indicators need to be masked with ecosystem type maps. This step is part of this chapter.

-
+

-21.8.2 Conceptual workflow +22.8.2 Conceptual workflow

The general, the conceptual workflow is like this:

    @@ -503,38 +504,38 @@

  1. Make trend figure and spatially aggregated maps,

-
+

-21.8.3 Collate variable data series +22.8.3 Collate variable data series

I include a for loop for importing the .nc files at the end.

-
+

-21.8.4 Filter data by dates +22.8.4 Filter data by dates

Say we want to calculate the mean summer temperature. We then want to exclude the data for the times that is not withing our definition of summer.

Let’s try it on the dummy data. Remember this data had four time steps.

-
+
 st_get_dimension_values(x, "time")
 #> [1] "1970-05-31" "1970-06-01" "1970-07-20" "1970-09-08"

Our real data has 365 time steps for each year.

First I define my start and end dates. I want to keep only the summer data, defined as jun - aug.

-
+
 start_month <-  "06"
 end_month <- "08"

Then for each iteration I need to get the year as well

-
+
 (year_temp <- year(st_get_dimension_values(x, "time")[1]))
 #> [1] 1970

Then I can create a time interval object

-
+
 start <- ym(paste(year_temp, start_month, sep="-"))
 end <-  ym(paste(year_temp, end_month, sep="-"))
 (myInterval <- interval(start, end))
 #> [1] 1970-06-01 UTC--1970-08-01 UTC

Then I filter

-
+
 x_aug <- x %>%
   select(Attribute_A) %>%
   adrop() %>%
@@ -543,12 +544,12 @@ 

#> [1] "1970-06-01" "1970-07-20"

Later I discover that `dplyr::filter` work after reading the whole object to file, and therefor it is too slow for our use. I therefore filter data a different way further down.

-
+

-21.8.5 Aggregate across time within a year +22.8.5 Aggregate across time within a year

For this example I want to calculate the mean temperature over the summer. I therefore need to aggregate over time. Many climate indicators will need this functionality. There are two ways, either using st_apply or using aggregate.

-
+
 temp_names <- year(st_get_dimension_values(x_aug, "time")[1])
 
 microOut <- microbenchmark(
@@ -560,29 +561,29 @@ 

times=30 )

The time dimension is now gone as we have aggregated across it.

-
+
 autoplot(microOut)
 #> Coordinate system already present. Adding new coordinate
 #> system, which will replace the existing one.
Comparing computation tome for two spatial aggregation functions.

-Figure 21.2: Comparing computation tome for two spatial aggregation functions. +Figure 22.2: Comparing computation tome for two spatial aggregation functions.

st_apply is slightly faster, but aggregate has the advantage of that it retains the time dimension, whereas for st_apply I need to set the attribute name to be the year. I will try to use st_apply.

But let me see if I can save time by masking to ET before doing this operation.

-
+
 x_masked <- x_aug
 x_masked[is.na(dummy_et)] <- NA
 plot(x_masked[,,,1])
Demonstrating the effect of maskig the dummy data using a perfectly aligne raster ET map.

-Figure 21.3: Demonstrating the effect of maskig the dummy data using a perfectly aligne raster ET map. +Figure 22.3: Demonstrating the effect of maskig the dummy data using a perfectly aligne raster ET map.

-
+
 autoplot(
   microbenchmark(
 No_masking =  x_aug %>%
@@ -598,12 +599,12 @@ 

Comparing computation time before and after masking of raster data. There is no performance increase by masking before aggregating.

-Figure 21.4: Comparing computation time before and after masking of raster data. There is no performance increase by masking before aggregating. +Figure 22.4: Comparing computation time before and after masking of raster data. There is no performance increase by masking before aggregating.

Since there is no increase in performance from masking, as we see in Fig. @ref(fig: withMasking) then it basically means we it is slower to mask beforehand, since the masking was not past of the benchmark assessment.

So, this is my solution:

-
+
 # setNames dont work on stars proxy obejct, so I use rename instead
 lookup <- setNames("mean", paste0("v_", temp_names))
 
@@ -611,7 +612,7 @@ 

st_apply(1:2, mean) %>% rename(all_of(lookup))

The v_ follows the naming convention we have developed for ourselves.

-
+
 ggarrange(
 ggplot() + 
   geom_stars(data = x_aug) +
@@ -634,11 +635,11 @@ 

Plotting the dummy data showing Attribute_A for the two dates as small maps, and a larger map showing the mean for year 2018

-Figure 21.5: Plotting the dummy data showing Attribute_A for the two dates as small maps, and a larger map showing the mean for year 2018 +Figure 22.5: Plotting the dummy data showing Attribute_A for the two dates as small maps, and a larger map showing the mean for year 2018

Here’s the whole first step of the conceptual workflow in action, on the dummy data.

-
+
 
 # Setting up the parameters
 dir <- substr(getwd(), 1,2)
@@ -681,9 +682,9 @@ 

setNames("Attribute_A") }) #> user system elapsed -#> 0.125 0.004 0.141

+#> 0.141 0.000 0.153

And this is the result.

-
+
 ggplot()+
   geom_stars(data = summerTemp_fullSeries) +
   coord_equal() +
@@ -695,13 +696,13 @@ 

Temporal aggregation on the dummy data set.

-Figure 21.6: Temporal aggregation on the dummy data set. +Figure 22.6: Temporal aggregation on the dummy data set.

Now let’s try it on the real data.

I initially tried to read stars proxy object and add steps to the call_list and execute these with st_as_stars, but this still returned proxy objects. Therefore I read the whole file in without proxy. To save time I can subset based on Julian dates.

I will use parallelization to speed things up.

-
+
 path <- path <- ifelse(dir == "C:", 
       "R:/",
       "/data/R/")
@@ -765,7 +766,7 @@ 

stopCluster(cl)

This takes about 20 sec per file/year, or 22 min on total. That is not too bad. About 6000 raster are read into memory. Here’s a test for the effect of splitting over more cores.

-
+
 cl <- makeCluster(2L)
 cl2 <- makeCluster(6L)
 cl3 <- makeCluster(10L)
@@ -810,25 +811,25 @@ 

  • Ten clusters (second run): 13.617 sec elapsed

  • The RStudio server has 48 cores. More parallel cores is probably on average faster. The NINA guidelines is to use max 10 cores, and to remember to close parallel cluster when done.

    -
    +
     #saveRDS(real_temp_summer, "/data/P-Prosjekter2/41201785_okologisk_tilstand_2022_2023/data/climate_indicators/aggregated_climate_time_series/real_temp_summer.rds")
     #write_stars(real_temp_summer, "/data/P-Prosjekter2/41201785_okologisk_tilstand_2022_2023/data/climate_indicators/aggregated_climate_time_series/real_temp_summer.tiff")

    These files are about 500 MB, but the tiff file reads in a lot quicker then the rds file, so from now on I’ll just export as tiff. (This could perhaps have been solved with a gc(). Later I tried saving as RData with no speed issues problems, but then I had a problem with RData files seemingly becoming corrupted over time - stars objects saved as RData files would not bind together using c.stars() with other matching stars object and I think this could be related to a time stamp in the RData format. So, stick to tiff).

    -
    +
     summer_median_temp <- read_stars(paste0(pData, "climate_indicators/aggregated_climate_time_series/real_temp_summer.tiff"),
                                      proxy=F)
    -
    +
     names(summer_median_temp)
     #> [1] "real_temp_summer.tiff"
    -
    +
     dim(summer_median_temp)
     #>    x    y band 
     #> 1195 1550   66
    -
    +
     st_get_dimension_values(summer_median_temp, "Year")
     #> NULL

    Note that GTiff automatically renames the third dimension band and also renames the attribute.

    -
    +
     st_get_dimension_values(summer_median_temp, "band")
     #>  [1] "v_2022" "v_2021" "v_2020" "v_2019" "v_2018" "v_2017"
     #>  [7] "v_2016" "v_2015" "v_2014" "v_2013" "v_2012" "v_2011"
    @@ -842,14 +843,14 @@ 

    #> [55] "v_1968" "v_1967" "v_1966" "v_1965" "v_1964" "v_1963" #> [61] "v_1962" "v_1961" "v_1960" "v_1959" "v_1958" "v_1957"

    I can rename them.

    -
    +
     summer_median_temp <- summer_median_temp %>% 
       st_set_dimensions(names = c("x", "y", "v_YEAR")) %>%
       setNames("temperature")
     dim(summer_median_temp)
     #>      x      y v_YEAR 
     #>   1195   1550     66
    -
    +
     ggplot()+
       geom_stars(data = summer_median_temp[,,,c(1,11,22)], downsample = c(10, 10, 0)) +
       coord_equal() +
    @@ -865,16 +866,16 @@ 

    -
    +

    -21.8.6 Calculate reference values +22.8.6 Calculate reference values

    -
    +

    -21.8.6.1 Aggregate across reference years +22.8.6.1 Aggregate across reference years

    We need to first to define a reference period and then to subset our data summer_median_temp. We can practice on the dummy data y.

    -
    +
     dir <- substr(getwd(), 1,2)
     path <- ifelse(dir == "C:",
            "P:/", 
    @@ -889,37 +890,37 @@ 

    temp <- readRDS(myFiles[i]) y <- c(temp, y) }

    -
    +
     st_get_dimension_values(y, "time")
     #>  [1] "1966-05-31" "1966-06-01" "1966-07-20" "1966-09-08"
     #>  [5] "1967-05-31" "1967-06-01" "1967-07-20" "1967-09-08"
     #>  [9] "1968-05-31" "1968-06-01" "1968-07-20" "1968-09-08"
     #> [13] "1969-05-31" "1969-06-01" "1969-07-20" "1969-09-08"
     #> [17] "1970-05-31" "1970-06-01" "1970-07-20" "1970-09-08"
    -
    +
     y_filtered <- y %>%
       select(Attribute_A) %>%
       filter(time %within% interval("1961-01-01", "1990-12-31"))

    Now we aggregate across years, again using st_apply.

    -
    +
     median_sd <- function(x) { c(median = median(x), sd = sd(x))}
    -
    +
     y_ref <- y_filtered %>%
       st_apply(c("x", "y"), FUN =  median_sd)
    -
    +
     dim(y_ref)
     #> median_sd         x         y 
     #>         2        50        50
    -
    +
     st_get_dimension_values(y_ref, "median_sd")
     #> [1] "median" "sd"

    It’s perhaps easier if median and sd are unique attributes, rather than levels of a dimension.

    -
    +
     y_ref <- y_ref %>% split("median_sd")

    The attribute name mean we can change this to something more meaningful.

    -
    +
     y_ref <- setNames(y_ref, c("reference_upper", "sd"))
    -
    +
     tmap_arrange(
     tm_shape(y_ref)+
       tm_raster("reference_upper")
    @@ -935,13 +936,13 @@ 

    Let’s transfer this over to the real data. First we need to rename our dimension values and turn them back into dates.

    -
    +
     new_dims <- as.Date(paste0(
       substr(st_get_dimension_values(summer_median_temp, "v_YEAR"), 3, 6), "-01-01"))
     summer_median_temp_ref <- summer_median_temp %>%
       st_set_dimensions("v_YEAR", values = new_dims)

    Then I can filter to leave only the reference period.

    -
    +
     summer_median_temp_ref <- summer_median_temp_ref %>%
       filter(v_YEAR %within% interval("1961-01-01", "1990-12-31"))
     st_get_dimension_values(summer_median_temp_ref, "v_YEAR")
    @@ -954,7 +955,7 @@ 

    #> [25] "1966-01-01" "1965-01-01" "1964-01-01" "1963-01-01" #> [29] "1962-01-01" "1961-01-01"

    And then we calculate the median and sd like above

    -
    +
     system.time({
     cl <- makeCluster(10L)
     summer_median_temp_ref <- summer_median_temp_ref %>%
    @@ -976,19 +977,19 @@ 

    This is also something I can export.

    -
    +
     # Note that write_stars can only export a single attribute, but we have put the 'attributes' as dimensions, and these will be read in as 'band' when importing tiff.
     write_stars(summer_median_temp_ref, "/data/P-Prosjekter2/41201785_okologisk_tilstand_2022_2023/data/climate_indicators/aggregated_climate_time_series/summer_median_temp_ref.tiff")
     
     # I initially saved as RData, like this, but ran into problems later when trying to bind stars objects using c.stars(). It appears RData becomes corrutpt in a sense, over time, perhaps due to some time stamp. If I recreated the object in the global environment and saved as RDatam I could read it in and work with it, but the next day it would not work again.
     #saveRDS(summer_median_temp_ref, "/data/P-Prosjekter2/41201785_okologisk_tilstand_2022_2023/data/climate_indicators/aggregated_climate_time_series/summer_median_temp_ref.RData")

    Pivot and turn dimension into attributes:

    -
    +
     summer_median_temp_ref_long <- summer_median_temp_ref %>% split("band")

    The attribute name is mean. We can change this to something more meaningful.

    -
    +
     summer_median_temp_ref_long <- setNames(summer_median_temp_ref_long, c("reference_upper", "sd"))
    -
    +
     tmap_arrange(
     tm_shape(st_downsample(summer_median_temp_ref_long, 10))+
       tm_raster("reference_upper")
    @@ -1000,31 +1001,31 @@ 

    Showing the upper reference levels and the standard deviation from actual data of median summer temperatures.

    -Figure 21.7: Showing the upper reference levels and the standard deviation from actual data of median summer temperatures. +Figure 22.7: Showing the upper reference levels and the standard deviation from actual data of median summer temperatures.

    -
    +

    -21.8.6.2 Combine +22.8.6.2 Combine

    I need to combine the variables and the ref values in one data cube

    -
    +
     y_var <- summer_median_temp %>%
       split(3) %>%
       c(summer_median_temp_ref_long)
    -
    +

    -21.8.7 Normalise variable +22.8.7 Normalise variable

    -
    +
     # select the columns to normalise
     cols <- names(y_var)[!names(y_var) %in% c("reference_upper", "sd") ]
     cols_new <- cols
     names(cols_new) <- gsub("v_", "i_", cols)
    -
    +
     # Mutate
     
     # The break point scaling is actually not needed here, since 
    @@ -1066,10 +1067,10 @@ 

    Writing to file, this time as RData (could use rds) because tiff don’t allow multiple attributes, and because we don’t need to use c.stars() in this object.

    -
    +
     gc()
     saveRDS(y_var_norm, "/data/P-Prosjekter2/41201785_okologisk_tilstand_2022_2023/data/climate_indicators/aggregated_climate_time_series/summer_median_temp_normalised.RData")
    -
    +
     
     lims <- c(-5, 22)
     
    @@ -1125,18 +1126,18 @@ 

    Example showing median summer tempretur in 1970, the upper and lwoer reference temperture, i.e. median and 5 SD units of the temperature between  1961-1990, and finally, the scaled indicator values.

    -Figure 21.8: Example showing median summer tempretur in 1970, the upper and lwoer reference temperture, i.e. median and 5 SD units of the temperature between 1961-1990, and finally, the scaled indicator values. +Figure 22.8: Example showing median summer tempretur in 1970, the upper and lwoer reference temperture, i.e. median and 5 SD units of the temperature between 1961-1990, and finally, the scaled indicator values.

    The real indicator values should be means over 5 year periods. Calculating a running mean for all time steps is too time consuming. Therefore, the scaled, regional indicator values will be calculated for distinct time steps. The time series can perhaps best be presented with yearly resolution.

    -
    +

    -21.8.8 Plot time series as line graphs +22.8.8 Plot time series as line graphs

    We could also plot a time series as a series of maps, but then we need to average over these 5 year time periods first. We can get to that later.

    To plot time series I first need to do a spatial aggregation. One option is to spatially aggregate the stars object directly, using the reg data set:

    -
    +
     temp <- y_var_norm[1,,] %>%
       aggregate(reg, mean, na.rm=T)
     plot(temp)
    @@ -1144,15 +1145,15 @@ 

    st_as_sf() # too slow

    However, this does no preserve the region name, and a subsequent st_intersection taks a little while and gives some boundary problems.

    I can also convert pixels to points and then do the intersection.

    -
    temp_points <- y_var_norm[1,,] %>%
    -  st_as_sf(as_points = TRUE, merge = FALSE)
    -temp_points2 <- temp_points %>%
    -  st_intersection(reg) " too slow"
    +
    temp_points <- y_var_norm[1,,] %>%
    +  st_as_sf(as_points = TRUE, merge = FALSE)
    +temp_points2 <- temp_points %>%
    +  st_intersection(reg) " too slow"

    But this also took too long.

    I can perhaps aggregate, and then turn to points and intersect, but that did not work perfect either.

    I can rasterize the regions data set, turn it into a coded dimension and use st_apply, but that is tedious.

    Let me try using exact_extract, as an alternative to aggregate. This requires us to turn to the terra package for a bit.

    -
    +
     system.time(
     regional_means <- rast(y_var_norm) %>%
       exact_extract(reg, fun = 'mean', append_cols = "region", progress=T) %>%
    @@ -1161,18 +1162,18 @@ 

    user; system; elapsed: 7.603; 3.071; 10.697

    That was fast!

    I should also get the sd:

    -
    +
     system.time(
     regional_sd <- rast(y_var_norm) %>%
       exact_extract(reg, fun = 'stdev', append_cols = "region", progress=T) %>%
       setNames(c("region", names(y_var_norm)))
       )

    user; system; elapsed: 7.420; 2.921; 10.355

    -
    +
     saveRDS(regional_means, "temp/regional_means.rds")
     saveRDS(regional_sd, "temp/regional_sd.rds")

    Reshape and plot

    -
    +
     div <- c("reference_upper",
              "reference_low",
              "reference_low2",
    @@ -1226,7 +1227,7 @@ 

    #> # threshold_low_centered <dbl>, #> # threshold_high_centered <dbl>, and abbreviated variable #> # names ¹​reference_upper, ²​reference_low

    -
    +
     regOrder = c("Østlandet","Sørlandet","Vestlandet","Midt-Norge","Nord-Norge")
     
     regional_means_long %>%
    @@ -1252,11 +1253,11 @@ 

    Times series for median summer temperature centered on the median value during the reference period. The reference period is indicated with a thick horizontal line. Dottet horisontal lines are 2 sd units for the reference period.

    -Figure 21.9: Times series for median summer temperature centered on the median value during the reference period. The reference period is indicated with a thick horizontal line. Dottet horisontal lines are 2 sd units for the reference period. +Figure 22.9: Times series for median summer temperature centered on the median value during the reference period. The reference period is indicated with a thick horizontal line. Dottet horisontal lines are 2 sd units for the reference period.

    Then we can take the mean over the last 5 years and add to a spatial representation.

    -
    +
     (
     i_aggregatedToPeriods <- regional_means_long %>%
       mutate(year = as.numeric(year)) %>%
    @@ -1300,7 +1301,7 @@ 

    #> 9 Nord-Norge 2018-2022 5 0.625 0.118 #> 10 Nord-Norge pre 2003 1 0.625 0.0266 #> # … with 15 more rows

    -
    +
     labs <- unique(i_aggregatedToPeriods$period[order(i_aggregatedToPeriods$period_rank)])
     
     i_aggregatedToPeriods %>%
    @@ -1320,18 +1321,18 @@ 

    Scaled indicator values, aggregated over 5 year intervals. Errors represent spatial variation within regions and across years.

    -Figure 21.10: Scaled indicator values, aggregated over 5 year intervals. Errors represent spatial variation within regions and across years. +Figure 22.10: Scaled indicator values, aggregated over 5 year intervals. Errors represent spatial variation within regions and across years.

    Finally, I can add these values to the sp object.

    -
    +
     reg2 <- reg %>%
       left_join(i_aggregatedToPeriods[i_aggregatedToPeriods$period_rank==5,], by=join_by(region))
     names(reg2)
     #> [1] "id"          "region"      "GID_0"       "NAME_0"     
     #> [5] "period"      "period_rank" "indicator"   "spatial_sd" 
     #> [9] "geometry"
    -
    +
     myCol <- "RdYlGn"
     myCol2 <- "-RdYlGn"
     
    @@ -1354,7 +1355,7 @@ 

    Summer tempreature indicator values for five accounting areas in Norway. SD is the spatial variation.

    -Figure 21.11: Summer tempreature indicator values for five accounting areas in Norway. SD is the spatial variation. +Figure 22.11: Summer tempreature indicator values for five accounting areas in Norway. SD is the spatial variation.

    @@ -1362,48 +1363,48 @@

    -
    +

    -19 Homogeneous Impact Areas +20 Homogeneous Impact Areas


    Author and date:

    Anders Kolstad


    -
    +

    -19.1 Introduction +20.1 Introduction

    This chapter documents the creation of a wall-to-wall map of homogeneous impact areas in Norway. The map is produced by binning values in the infrastructure index into four discrete categories. It is likely to be a good predictor for some indicators, such as slitasje and the presence of alien species.

    -
    +

    -19.2 About the underlying data +20.2 About the underlying data

    The infrastructure index is explained here. It is a wall-to-wall raster over Norway with 100 m resolution. Each pixel is given a value along a continuous gradient from 0 to around 15.4, representing the frequency of surrounding cells within 500 m with human infrastructure (houses, roads, ect).

    -
    +

    -19.2.1 Representativity in time and space +20.2.1 Representativity in time and space

    The infrastructure index is calculated with data that have slightly different dates, but can be said to represent year 2015. The data covers all of mainland Norway.

    -
    +

    -19.2.2 References +20.2.2 References

    Bakkestuen, V., Dervo, B.K., Bærum, K.M. og Erikstad, L. 2022. Prediksjonsmodellering av naturtyper i ferskvann. @@ -150,74 +151,74 @@

    Norsk institutt for naturforskning.

    -
    +

    -19.3 Analyses +20.3 Analyses

    -
    +

    -19.3.1 Import data +20.3.1 Import data

    The path must be conditional:

    -
    +
     dir <- substr(getwd(), 1,2)
    -
    +
     path <- ifelse(dir == "C:", 
             "R:/GeoSpatialData/Utility_governmentalServices/Norway_Infrastructure_Index/Original/Infrastrukturindeks_UTM33/infra_tiff.tif",
             "/data/R/GeoSpatialData/Utility_governmentalServices/Norway_Infrastructure_Index/Original/Infrastrukturindeks_UTM33/infra_tiff.tif")

    Import a stars proxy (no data imported yet)

    -
    +
     infra <- stars::read_stars(path)

    Print the coordinate reference system:

    -
    +
     st_crs(infra)$input
     #> [1] "WGS_1984_UTM_Zone_33N"
    -
    +

    -19.3.2 Trondheim example +20.3.2 Trondheim example

    It’s easier to see what’s happening if we zoom in a bit. Lets get a boundary box around Trondheim.

    -
    +
     myBB <- st_bbox(c(xmin=260520.12, xmax = 278587.56,
                     ymin = 7032142.5, ymax = 7045245.27),
                     crs = st_crs(infra))

    Cropping the raster to the bbox

    -
    +
     infra_trd <- sf::st_crop(infra, myBB)
    -
    +

    -19.3.2.1 Get OSM highways +20.3.2.1 Get OSM highways

    Lets download some base maps to help validate and contextualize the infrastructure index.

    Transform to lat long due to osm requirements

    -
    +
     infra_trd_ll <- sf::st_transform(infra_trd, 4326)

    Get the boundary box of the cropped raster

    -
    +
     myBB_ll <- sf::st_bbox(infra_trd_ll)

    Download highways using the above bbox.

    -
    +
     hw <- 
       osmplotr::extract_osm_objects(
         bbox = myBB_ll,
         key = "highway",
         sf = T)

    Transforming highway data back into utm, although not strictly necessary.

    -
    +
     hw_utm <- sf::st_transform(hw, sf::st_crs(infra_trd)) 

    This object contains too many roads (about 30k). I’ll take out the unnamed roads.

    -
    +
     hw_utm <- hw_utm[!is.na(hw_utm$name),]
    -
    +

    -19.3.2.2 Discretize +20.3.2.2 Discretize

    Here I define a simplified categorical typology for the infrastructure index using four classes.

    -
    +
     names(infra_trd) <- "infrastructureIndex"
     infra_trd_reclassed <-  infra_trd %>%
       mutate(infrastructureIndex = case_when(
    @@ -227,7 +228,7 @@ 

    infrastructureIndex >= 12 ~ 3 ))

    Lets plot these two maps side by side

    -
    +
     map_trd_reclassed <- tm_shape(infra_trd_reclassed)+
       tm_raster(title="Infrastructure Index",
                 #palette = "viridis",
    @@ -235,7 +236,7 @@ 

    tm_layout(legend.outside = T)+ tm_shape(hw_utm)+ tm_lines(col="white")

    -
    +
     map_trd <- tm_shape(infra_trd)+
       tm_raster(title="Infrastructure Index",
                 style="cont",
    @@ -243,7 +244,7 @@ 

    tm_layout(legend.outside = T)+ tm_shape(hw_utm)+ tm_lines(col="white")

    -
    +
     tmap_arrange(map_trd,
                  map_trd_reclassed,
                  ncol=1)
    @@ -252,7 +253,7 @@ 

    Infrastructure index over Trondheim, comparing the continous scale with the ordinal four-step scale. Major roads are in white.

    -Figure 17.4: Infrastructure index over Trondheim, comparing the continous scale with the ordinal four-step scale. Major roads are in white. +Figure 18.4: Infrastructure index over Trondheim, comparing the continous scale with the ordinal four-step scale. Major roads are in white.

    I tweaked the thresholds for the bins so that the categories match my knowledge about the land use intensity in Trondheim, for example that most of the forest next to Trondheim (to the left in the map) was in the second lowest class. @@ -260,28 +261,28 @@

    It depicts a gradient in human presence from high within the built zone, to no to very low in the forests and mountains to the west. Note that there is still considerable human activity also there in the form of outdoor recreation and even forestry.

    -
    +

    -19.3.2.3 Aggregate +20.3.2.3 Aggregate

    The resolution in this map is more than we need, and the size of the data implies that the next step, the vectorization, would take too long. We therefore aggregate, or reduce the resolution.

    -
    +
     dim <- st_dimensions(infra)
     paste("Resolution is", dim$x$delta, "by", dim$x$delta, "meters")
     #> [1] "Resolution is 100 by 100 meters"
    -
    +
     infra_trd_reclassed_agg <- st_warp(infra_trd_reclassed, cellsize = c(1000, 1000), 
                                        crs = st_crs(infra_trd_reclassed), 
                                        use_gdal = TRUE,
                                        method = "average")
    -
    +
     dim <- st_dimensions(infra_trd_reclassed_agg)
     paste("Resolution is", dim$x$delta, "by", dim$x$delta, "meters")
     #> [1] "Resolution is 1000 by 1000 meters"

    Each cell is now the average of the aggregated cells, and hence the value is continuous again. Let’s make it discrete.

    -
    +
     names(infra_trd_reclassed_agg) <- "infrastructureIndex"
     infra_trd_reclassed_agg <-  infra_trd_reclassed_agg %>%
       mutate(infrastructureIndex = case_when(
    @@ -290,7 +291,7 @@ 

    infrastructureIndex < 12 ~ 2, infrastructureIndex >= 12 ~ 3 ))

    -
    +
     tm_shape(infra_trd_reclassed_agg)+
       tm_raster(title="Infrastructure Index",
                 style="cat")+
    @@ -300,12 +301,12 @@ 

    Infrastrcture index over Trondheim on a four-step discrete scale, aggregated to 1x1 km resolution using the mean function.

    -Figure 17.6: Infrastrcture index over Trondheim on a four-step discrete scale, aggregated to 1x1 km resolution using the mean function. +Figure 18.6: Infrastrcture index over Trondheim on a four-step discrete scale, aggregated to 1x1 km resolution using the mean function.

    This resolution is more than good enough for our purpose here. I see that the map extends a bit into the fjord. We want to cut away these areas. We can use a map of the outline of Norway to do this.

    -
    +
     outline <- st_read("data/outlineOfNorway_EPSG25833.shp")
     #> Reading layer `outlineOfNorway_EPSG25833' from data source 
     #>   `/data/Egenutvikling/41001581_egenutvikling_anders_kolstad/github/ecosystemCondition/data/outlineOfNorway_EPSG25833.shp' 
    @@ -316,13 +317,13 @@ 

    #> Bounding box: xmin: -113472.7 ymin: 6448359 xmax: 1114618 ymax: 7939917 #> Projected CRS: ETRS89 / UTM zone 33N

    The CRS needs to be identical

    -
    +
     infra_trd_reclassed_agg <- st_transform(infra_trd_reclassed_agg, 25833)
    -
    +
     infra_trd_reclassed_agg_terrestrial <- st_crop(infra_trd_reclassed_agg, outline)
     #> Warning in st_crop.stars(infra_trd_reclassed_agg, outline):
     #> crop only crops regular grids
    -
    +
     tm_shape(infra_trd_reclassed_agg_terrestrial)+
       tm_raster(title="Infrastructure Index",
                 style="cat")+
    @@ -341,11 +342,11 @@ 

    I have treated the raster cells as points when cropping, so that cells where the center of the cell is outside the terrestrial delineation are removed. I think this makes the most sense for unbiased area statistics, but some coastal indicator data could also be excluded because of this. The errors would be smaller with a finer resolution raster, but computation time is a problem.

    -
    +

    -19.3.3 Aggregate the entire map to 1x1 km +20.3.3 Aggregate the entire map to 1x1 km

    -
    +
     # runtime about 30 sec
     infra_agg <- st_warp(infra, cellsize = c(1000, 1000), 
                                        crs = st_crs(infra), 
    @@ -354,20 +355,20 @@ 

    # The CRS needs to be identical for st_crop infra_agg <- st_transform(infra_agg, 25833)

    -
    +

    -19.3.4 Cut out marine areas +20.3.4 Cut out marine areas

    I see that the map extends a bit into the fjord. We want to cut away these areas. We can use a map of the outline of Norway to do this.

    -
    +
     infra_agg_terrestrial <- st_crop(infra_agg, outline)
     saveRDS(infra_agg_terrestrial, "P:/41201785_okologisk_tilstand_2022_2023/data/cache/infra_agg_terrestrial.rds")
    -
    +

    -19.3.5 Discretize the entire map +20.3.5 Discretize the entire map

    -
    +
     names(infra_agg_terrestrial) <- "infrastructureIndex"
     
     infra_agg_discrete <- infra_agg_terrestrial %>%
    @@ -378,9 +379,9 @@ 

    infrastructureIndex >= 12 ~ 3 ))

    -
    +

    -19.3.6 Vectorize +20.3.6 Vectorize

    This step might seem rather stupid. We want to vectorize a rather large raster. @@ -388,7 +389,7 @@

    The reason is that there is no really good way to burn polygon data on to raster grid cells after the disuse of the raster package. It was not straight forward then either. But calculating intersections between polygons is very fast and easy.

    -
    +
     infra_agg_discrete_vect <- 
       eaTools::ea_homogeneous_area(infra_agg_discrete, 
                                     groups = infrastructureIndex)
    @@ -399,7 +400,7 @@ 

    saveRDS(infra_agg_discrete_vect, paste0(path, "infra_agg_discrete_vect.rds"))

    Let’s plot this now. First, lets crop it to reduce computation time.

    -
    +
     myBB <- st_bbox(c(xmin=260520.12, xmax = 278587.56,
                     ymin = 7032142.5, ymax = 7045245.27),
                     crs = st_crs(infra_agg_discrete_vect))
    @@ -407,7 +408,7 @@ 

    infra_agg_discrete_vect_trd <- st_crop(infra_agg_discrete_vect, myBB) #> Warning: attribute variables are assumed to be spatially #> constant throughout all geometries

    -
    +
     tm_shape(infra_agg_discrete_vect_trd)+
       tm_polygons(col="infrastructureIndex",
                   style = "cat")+
    @@ -417,33 +418,33 @@ 

    A vectorized version of the Infrastructure index over Trondheim on a four-step discrete scale.

    -Figure 19.1: A vectorized version of the Infrastructure index over Trondheim on a four-step discrete scale. +Figure 20.1: A vectorized version of the Infrastructure index over Trondheim on a four-step discrete scale.

    As can be seen in the figure above, st_warp merges grid cells that have the same value, and the vectorized raster doesn’t end up being that big in the end.

    -
    +

    -19.4 Check national distribution +20.4 Check national distribution

    -
    +
     regions <- sf::read_sf("data/regions.shp", options = "ENCODING=UTF8")
     unique(regions$region)
     #> [1] "Nord-Norge" "Midt-Norge" "Østlandet"  "Vestlandet"
     #> [5] "Sørlandet"
    -
    +
     st_crs(infra_agg_discrete_vect) == st_crs(regions)
     #> [1] TRUE

    Since the two layers are completely overlapping, we can get the intersections

    -
    +
     infra_stats <- eaTools::ea_homogeneous_area(infra_agg_discrete_vect,
                                  regions,
                                  keep1 = "infrastructureIndex",
                                  keep2 = "region")
     saveRDS(infra_stats, "P:/41201785_okologisk_tilstand_2022_2023/data/cache/infra_stats.rds")

    Let’s calculate the areas of these polygons and compare the HIF in the five regions.

    -
    +
     infra_stats$area_km2 <- units::drop_units(sf::st_area(infra_stats))
     infra_stats$area_km2 <- infra_stats$area_km2/1000
     
    @@ -475,50 +476,50 @@ 

    This distribution looks reasonable. The relative proportions are similar in the five regions, but Østlandet and Sørlandet have more infrastructure in general.

    Here are the numbers behind the figure above.

    -
    +
     temp$area_km2 <- round(temp$area_km2,0)
     DT::datatable(temp)
    -
    - +
    +
    -
    +

    -19.5 Export +20.5 Export

    I will write this data to the NINA P server.

    -
    +
     saveRDS(infra_agg_discrete_vect, "P:/41201785_okologisk_tilstand_2022_2023/data/infrastrukturindeks/homogeneous_impact_areas.rds")
    -
    +
     HIA_returned <- readRDS("P:/41201785_okologisk_tilstand_2022_2023/data/infrastrukturindeks/homogeneous_impact_areas.rds")

    Alternatively, we could use a reference point in the time series itself. Say for example that we want to use the values for species richness of pollinators in semi-natural land i Østlandet 2020 as a reference point. We can then calculate the difference (the contrast) between this level and all the other levels. We do this by the function boot_contrast()

    NB! This functionality is in development. It currently works for single rows as reference points, but needs updating to allow for referencing several values simultaneously, e.g using the start values for all regions and habitat types as their own reference points.

    @@ -716,11 +717,11 @@

    #> year region_name no_species boot_sd boot_lower2.5 boot_…¹ #> <int> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 2020 Østlandet 0 0 0 0 -#> 2 2021 Trøndelag -18.5 6.74 -32.4 -5.5 -#> 3 2021 Østlandet -6.82 8.37 -23.4 10.2 -#> 4 2022 Sørlandet -30.6 8.50 -47.8 -14.4 -#> 5 2022 Trøndelag -25.8 6.51 -39.1 -13.3 -#> 6 2022 Østlandet -24.6 6.76 -38.5 -11.4 +#> 2 2021 Trøndelag -18.4 6.79 -32.3 -5.9 +#> 3 2021 Østlandet -6.58 8.55 -23.6 9.7 +#> 4 2022 Sørlandet -30.6 8.35 -47.1 -13.9 +#> 5 2022 Trøndelag -25.4 6.56 -39.8 -12.1 +#> 6 2022 Østlandet -24.4 6.77 -38.2 -11.2 #> # … with abbreviated variable name ¹​boot_upper97.5

    @@ -733,15 +734,16 @@

     diff_poll_richness_boot3
     #> # A tibble: 6 × 6
    -#>    year region_name no_species boot_sd boot_lower2.5 boot_…¹
    -#>   <int> <chr>            <dbl>   <dbl>         <dbl>   <dbl>
    +#>    year region_name no_species  boot_sd boot_lower…¹ boot_…²
    +#>   <int> <chr>            <dbl>    <dbl>        <dbl>   <dbl>
     #> 1  2020 Østlandet        1     0               1           1
    -#> 2  2021 Trøndelag        1     0               1           1
    +#> 2  2021 Trøndelag        1.00  0.000746        1           1
     #> 3  2021 Østlandet        1     0               1           1
    -#> 4  2022 Sørlandet        0.919 0.110           0.613       1
    -#> 5  2022 Trøndelag        0.999 0.00643         0.993       1
    -#> 6  2022 Østlandet        0.999 0.00611         1           1
    -#> # … with abbreviated variable name ¹​boot_upper97.5
    +#> 4 2022 Sørlandet 0.919 0.110 0.63 1 +#> 5 2022 Trøndelag 0.999 0.00553 0.997 1 +#> 6 2022 Østlandet 1.00 0.00443 1 1 +#> # … with abbreviated variable names ¹​boot_lower2.5, +#> # ²​boot_upper97.5

    @@ -949,12 +951,12 @@

    #> # A tibble: 6 × 6 #> year region_name avg_wet_weight boot_sd boot_lo…¹ boot_…² #> <int> <chr> <dbl> <dbl> <dbl> <dbl> -#> 1 2020 Østlandet 0.670 0.0797 0.518 0.843 -#> 2 2021 Trøndelag 0.860 0.0999 0.648 1 -#> 3 2021 Østlandet 0.584 0.0731 0.434 0.720 -#> 4 2022 Sørlandet 0.467 0.0648 0.342 0.596 -#> 5 2022 Trøndelag 0.626 0.0702 0.500 0.769 -#> 6 2022 Østlandet 0.373 0.0420 0.296 0.466 +#> 1 2020 Østlandet 0.670 0.0812 0.519 0.836 +#> 2 2021 Trøndelag 0.861 0.0967 0.648 1 +#> 3 2021 Østlandet 0.583 0.0729 0.442 0.727 +#> 4 2022 Sørlandet 0.467 0.0672 0.345 0.600 +#> 5 2022 Trøndelag 0.622 0.0695 0.493 0.772 +#> 6 2022 Østlandet 0.378 0.0406 0.300 0.459 #> # … with abbreviated variable names ¹​boot_lower2.5, #> # ²​boot_upper97.5

    @@ -986,12 +988,12 @@ 

    #> # A tibble: 6 × 7 #> region geom year ind_v…¹ ind_sd #> <chr> <MULTIPOLYGON [m]> <int> <dbl> <dbl> -#> 1 Sørlandet (((-31605 6513854, -31464 … 2022 0.467 0.0648 -#> 2 Trøndelag (((227783 7068646, 227876 … 2021 0.860 0.0999 -#> 3 Trøndelag (((227783 7068646, 227876 … 2022 0.626 0.0702 -#> 4 Østlandet (((179915 6537265, 180246 … 2020 0.670 0.0797 -#> 5 Østlandet (((179915 6537265, 180246 … 2021 0.584 0.0731 -#> 6 Østlandet (((179915 6537265, 180246 … 2022 0.373 0.0420 +#> 1 Sørlandet (((-31605 6513854, -31464 … 2022 0.467 0.0672 +#> 2 Trøndelag (((227783 7068646, 227876 … 2021 0.861 0.0967 +#> 3 Trøndelag (((227783 7068646, 227876 … 2022 0.622 0.0695 +#> 4 Østlandet (((179915 6537265, 180246 … 2020 0.670 0.0812 +#> 5 Østlandet (((179915 6537265, 180246 … 2021 0.583 0.0729 +#> 6 Østlandet (((179915 6537265, 180246 … 2022 0.378 0.0406 #> # … with 2 more variables: ind_lower2.5 <dbl>, #> # ind_upper97.5 <dbl>, and abbreviated variable name #> # ¹​ind_value

    @@ -1022,12 +1024,12 @@

    #> # A tibble: 6 × 6 #> year region_name no_species boot_sd boot_lower2.5 boot_…¹ #> <int> <chr> <dbl> <dbl> <dbl> <dbl> -#> 1 2020 Østlandet 0.516 0.0503 0.425 0.625 -#> 2 2021 Trøndelag 0.695 0.0452 0.599 0.775 -#> 3 2021 Østlandet 0.767 0.0426 0.687 0.854 -#> 4 2022 Sørlandet 0.544 0.0619 0.427 0.666 -#> 5 2022 Trøndelag 0.633 0.0296 0.574 0.691 -#> 6 2022 Østlandet 0.626 0.0211 0.583 0.666 +#> 1 2020 Østlandet 0.516 0.0486 0.428 0.618 +#> 2 2021 Trøndelag 0.695 0.0449 0.598 0.773 +#> 3 2021 Østlandet 0.767 0.0418 0.689 0.850 +#> 4 2022 Sørlandet 0.546 0.0649 0.422 0.672 +#> 5 2022 Trøndelag 0.629 0.0302 0.568 0.684 +#> 6 2022 Østlandet 0.626 0.0207 0.582 0.666 #> # … with abbreviated variable name ¹​boot_upper97.5
     plot(richness_sn_diff)
    @@ -1058,12 +1060,12 @@

    #> # A tibble: 6 × 7 #> region geom year ind_v…¹ ind_sd #> <chr> <MULTIPOLYGON [m]> <int> <dbl> <dbl> -#> 1 Sørlandet (((-31605 6513854, -31464 … 2022 0.544 0.0619 -#> 2 Trøndelag (((227783 7068646, 227876 … 2021 0.695 0.0452 -#> 3 Trøndelag (((227783 7068646, 227876 … 2022 0.633 0.0296 -#> 4 Østlandet (((179915 6537265, 180246 … 2020 0.516 0.0503 -#> 5 Østlandet (((179915 6537265, 180246 … 2021 0.767 0.0426 -#> 6 Østlandet (((179915 6537265, 180246 … 2022 0.626 0.0211 +#> 1 Sørlandet (((-31605 6513854, -31464 … 2022 0.546 0.0649 +#> 2 Trøndelag (((227783 7068646, 227876 … 2021 0.695 0.0449 +#> 3 Trøndelag (((227783 7068646, 227876 … 2022 0.629 0.0302 +#> 4 Østlandet (((179915 6537265, 180246 … 2020 0.516 0.0486 +#> 5 Østlandet (((179915 6537265, 180246 … 2021 0.767 0.0418 +#> 6 Østlandet (((179915 6537265, 180246 … 2022 0.626 0.0207 #> # … with 2 more variables: ind_lower2.5 <dbl>, #> # ind_upper97.5 <dbl>, and abbreviated variable name #> # ¹​ind_value @@ -1100,12 +1102,12 @@

    #> # A tibble: 6 × 6 #> year region_name no_species boot_sd boot_lower2.5 boot_…¹ #> <int> <chr> <dbl> <dbl> <dbl> <dbl> -#> 1 2020 Østlandet 0.927 0.0756 0.743 1 -#> 2 2021 Trøndelag 0.841 0.0611 0.704 0.946 -#> 3 2021 Østlandet 0.908 0.0749 0.752 1 -#> 4 2022 Sørlandet 0.600 0.114 0.376 0.838 -#> 5 2022 Trøndelag 0.691 0.0501 0.59 0.786 -#> 6 2022 Østlandet 0.635 0.0522 0.53 0.741 +#> 1 2020 Østlandet 0.929 0.0740 0.757 1 +#> 2 2021 Trøndelag 0.835 0.0625 0.698 0.94 +#> 3 2021 Østlandet 0.910 0.0739 0.744 1 +#> 4 2022 Sørlandet 0.593 0.114 0.376 0.826 +#> 5 2022 Trøndelag 0.694 0.0501 0.592 0.794 +#> 6 2022 Østlandet 0.639 0.0569 0.533 0.758 #> # … with abbreviated variable name ¹​boot_upper97.5
     plot(pollinators_sn_diff)
    @@ -1136,12 +1138,12 @@

    #> # A tibble: 6 × 7 #> region geom year ind_v…¹ ind_sd #> <chr> <MULTIPOLYGON [m]> <int> <dbl> <dbl> -#> 1 Sørlandet (((-31605 6513854, -31464 … 2022 0.600 0.114 -#> 2 Trøndelag (((227783 7068646, 227876 … 2021 0.841 0.0611 -#> 3 Trøndelag (((227783 7068646, 227876 … 2022 0.691 0.0501 -#> 4 Østlandet (((179915 6537265, 180246 … 2020 0.927 0.0756 -#> 5 Østlandet (((179915 6537265, 180246 … 2021 0.908 0.0749 -#> 6 Østlandet (((179915 6537265, 180246 … 2022 0.635 0.0522 +#> 1 Sørlandet (((-31605 6513854, -31464 … 2022 0.593 0.114 +#> 2 Trøndelag (((227783 7068646, 227876 … 2021 0.835 0.0625 +#> 3 Trøndelag (((227783 7068646, 227876 … 2022 0.694 0.0501 +#> 4 Østlandet (((179915 6537265, 180246 … 2020 0.929 0.0740 +#> 5 Østlandet (((179915 6537265, 180246 … 2021 0.910 0.0739 +#> 6 Østlandet (((179915 6537265, 180246 … 2022 0.639 0.0569 #> # … with 2 more variables: ind_lower2.5 <dbl>, #> # ind_upper97.5 <dbl>, and abbreviated variable name #> # ¹​ind_value @@ -1177,12 +1179,12 @@

    #> # A tibble: 6 × 6 #> year region_name no_species boot_sd boot_lower2.5 boot_…¹ #> <int> <chr> <dbl> <dbl> <dbl> <dbl> -#> 1 2020 Østlandet 0.685 0.0469 0.591 0.779 -#> 2 2021 Trøndelag 0.924 0.0542 0.807 1 -#> 3 2021 Østlandet 0.836 0.0491 0.74 0.935 -#> 4 2022 Sørlandet 0.676 0.0670 0.558 0.816 -#> 5 2022 Trøndelag 0.925 0.0424 0.834 1 -#> 6 2022 Østlandet 0.638 0.0446 0.553 0.726 +#> 1 2020 Østlandet 0.681 0.0474 0.586 0.778 +#> 2 2021 Trøndelag 0.922 0.0555 0.789 1 +#> 3 2021 Østlandet 0.834 0.0488 0.736 0.925 +#> 4 2022 Sørlandet 0.677 0.0680 0.554 0.826 +#> 5 2022 Trøndelag 0.923 0.0432 0.822 1 +#> 6 2022 Østlandet 0.636 0.0450 0.548 0.732 #> # … with abbreviated variable name ¹​boot_upper97.5
     plot(dung_sn_diff)
    @@ -1213,12 +1215,12 @@

    #> # A tibble: 6 × 7 #> region geom year ind_v…¹ ind_sd #> <chr> <MULTIPOLYGON [m]> <int> <dbl> <dbl> -#> 1 Sørlandet (((-31605 6513854, -31464 … 2022 0.676 0.0670 -#> 2 Trøndelag (((227783 7068646, 227876 … 2021 0.924 0.0542 -#> 3 Trøndelag (((227783 7068646, 227876 … 2022 0.925 0.0424 -#> 4 Østlandet (((179915 6537265, 180246 … 2020 0.685 0.0469 -#> 5 Østlandet (((179915 6537265, 180246 … 2021 0.836 0.0491 -#> 6 Østlandet (((179915 6537265, 180246 … 2022 0.638 0.0446 +#> 1 Sørlandet (((-31605 6513854, -31464 … 2022 0.677 0.0680 +#> 2 Trøndelag (((227783 7068646, 227876 … 2021 0.922 0.0555 +#> 3 Trøndelag (((227783 7068646, 227876 … 2022 0.923 0.0432 +#> 4 Østlandet (((179915 6537265, 180246 … 2020 0.681 0.0474 +#> 5 Østlandet (((179915 6537265, 180246 … 2021 0.834 0.0488 +#> 6 Østlandet (((179915 6537265, 180246 … 2022 0.636 0.0450 #> # … with 2 more variables: ind_lower2.5 <dbl>, #> # ind_upper97.5 <dbl>, and abbreviated variable name #> # ¹​ind_value @@ -1319,12 +1321,12 @@

    #> # A tibble: 6 × 6 #> year region_name par_per_sym_ri…¹ boot_sd boot_…² boot_…³ #> <int> <chr> <dbl> <dbl> <dbl> <dbl> -#> 1 2020 Østlandet 0.288 0.0249 0.244 0.344 -#> 2 2021 Trøndelag 0.890 0.0948 0.696 1 -#> 3 2021 Østlandet 0.605 0.0517 0.513 0.713 -#> 4 2022 Sørlandet 0.817 0.166 0.530 1 -#> 5 2022 Trøndelag 0.557 0.0661 0.441 0.707 -#> 6 2022 Østlandet 0.698 0.133 0.471 0.994 +#> 1 2020 Østlandet 0.287 0.0225 0.243 0.331 +#> 2 2021 Trøndelag 0.896 0.0914 0.698 1 +#> 3 2021 Østlandet 0.601 0.0523 0.504 0.709 +#> 4 2022 Sørlandet 0.829 0.164 0.522 1 +#> 5 2022 Trøndelag 0.558 0.0628 0.444 0.686 +#> 6 2022 Østlandet 0.706 0.135 0.467 1 #> # … with abbreviated variable names ¹​par_per_sym_richn, #> # ²​boot_lower2.5, ³​boot_upper97.5
    @@ -1356,12 +1358,12 @@ 

    #> # A tibble: 6 × 7 #> region geom year ind_v…¹ ind_sd #> <chr> <MULTIPOLYGON [m]> <int> <dbl> <dbl> -#> 1 Sørlandet (((-31605 6513854, -31464 … 2022 0.817 0.166 -#> 2 Trøndelag (((227783 7068646, 227876 … 2021 0.890 0.0948 -#> 3 Trøndelag (((227783 7068646, 227876 … 2022 0.557 0.0661 -#> 4 Østlandet (((179915 6537265, 180246 … 2020 0.288 0.0249 -#> 5 Østlandet (((179915 6537265, 180246 … 2021 0.605 0.0517 -#> 6 Østlandet (((179915 6537265, 180246 … 2022 0.698 0.133 +#> 1 Sørlandet (((-31605 6513854, -31464 … 2022 0.829 0.164 +#> 2 Trøndelag (((227783 7068646, 227876 … 2021 0.896 0.0914 +#> 3 Trøndelag (((227783 7068646, 227876 … 2022 0.558 0.0628 +#> 4 Østlandet (((179915 6537265, 180246 … 2020 0.287 0.0225 +#> 5 Østlandet (((179915 6537265, 180246 … 2021 0.601 0.0523 +#> 6 Østlandet (((179915 6537265, 180246 … 2022 0.706 0.135 #> # … with 2 more variables: ind_lower2.5 <dbl>, #> # ind_upper97.5 <dbl>, and abbreviated variable name #> # ¹​ind_value

    @@ -1393,12 +1395,12 @@

    #> # A tibble: 6 × 6 #> year region_name GDE_by_asv boot_sd boot_lower2.5 boot_…¹ #> <int> <chr> <dbl> <dbl> <dbl> <dbl> -#> 1 2020 Østlandet 0.871 0.0282 0.819 0.93 -#> 2 2021 Trøndelag 0.753 0.0259 0.704 0.806 -#> 3 2021 Østlandet 0.825 0.0191 0.787 0.862 -#> 4 2022 Sørlandet 0.810 0.0280 0.754 0.866 -#> 5 2022 Trøndelag 0.748 0.0220 0.706 0.793 -#> 6 2022 Østlandet 0.933 0.0154 0.902 0.963 +#> 1 2020 Østlandet 0.871 0.0291 0.814 0.931 +#> 2 2021 Trøndelag 0.756 0.0255 0.7 0.806 +#> 3 2021 Østlandet 0.824 0.0193 0.786 0.861 +#> 4 2022 Sørlandet 0.809 0.0278 0.753 0.866 +#> 5 2022 Trøndelag 0.746 0.0221 0.703 0.789 +#> 6 2022 Østlandet 0.933 0.0150 0.903 0.963 #> # … with abbreviated variable name ¹​boot_upper97.5
     plot(gde_sn_diff)
    @@ -1429,12 +1431,12 @@

    #> # A tibble: 6 × 7 #> region geom year ind_v…¹ ind_sd #> <chr> <MULTIPOLYGON [m]> <int> <dbl> <dbl> -#> 1 Sørlandet (((-31605 6513854, -31464 … 2022 0.810 0.0280 -#> 2 Trøndelag (((227783 7068646, 227876 … 2021 0.753 0.0259 -#> 3 Trøndelag (((227783 7068646, 227876 … 2022 0.748 0.0220 -#> 4 Østlandet (((179915 6537265, 180246 … 2020 0.871 0.0282 -#> 5 Østlandet (((179915 6537265, 180246 … 2021 0.825 0.0191 -#> 6 Østlandet (((179915 6537265, 180246 … 2022 0.933 0.0154 +#> 1 Sørlandet (((-31605 6513854, -31464 … 2022 0.809 0.0278 +#> 2 Trøndelag (((227783 7068646, 227876 … 2021 0.756 0.0255 +#> 3 Trøndelag (((227783 7068646, 227876 … 2022 0.746 0.0221 +#> 4 Østlandet (((179915 6537265, 180246 … 2020 0.871 0.0291 +#> 5 Østlandet (((179915 6537265, 180246 … 2021 0.824 0.0193 +#> 6 Østlandet (((179915 6537265, 180246 … 2022 0.933 0.0150 #> # … with 2 more variables: ind_lower2.5 <dbl>, #> # ind_upper97.5 <dbl>, and abbreviated variable name #> # ¹​ind_value @@ -1868,7 +1870,7 @@

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      @@ -109,9 +110,9 @@

      -
      +

      -12 NDVI - Naturally Open Ecosystems +13 NDVI - Naturally Open Ecosystems


      Norwegian name: Primærproduksjon

      NDVI (greenness) indicator, naturally open ecosystems below the treeline

      @@ -122,25 +123,25 @@


      - -
      + +
      - - - + + + - - - + + +

      Ecosystem

      Økologisk egenskap

      ECT class

      Ecosystem

      Økologisk egenskap

      ECT class

      naturlig åpen

      Primærproduksjon

      Structural state characteristics

      naturlig åpen

      Primærproduksjon

      Structural state characteristics




      -
      +

      -12.1 Introduction +13.1 Introduction

      The Normalized difference vegetation index (NDVI) can be used to describe the greenness of an area, and may thus represent a proxy for the amount of chlorophyll and plant productivity in the given area. Different ecosystem types naturally have greenness signals of different intensity but also with a certain variance. Deviations beyond the natural variation in either direction may indicate a reduction in ecological condition of a given ecosystem. E.g. lower than normal NDVI may indicate excessive disturbance, while higher than normal NDVI may indicate regrowth and transitions towards more densely vegetated systems. In recent assessments of ecological condition for forest and mountain ecosystems, two different NDVI-based indicators were developed and applied. For forests, NDVI-values across the country were compared to a spatial reference NDVI-signal from protected areas (Framstad et al. 2020). For mountains, the extent and consistence of the temporal change of NDVI-values over the last twenty years was assessed (Framstad et al 2021). In both cases, the NDVI-product from the MODIS satellite reflectance imagery was used based on available maps of spatial ecosystem extent in Norway (i.e. national maps for the forest and mountain ecosystems).

      Here, we explore the potential for developing an NDVI-indicator for ecological condition representing naturally open ecosystems below the treeline (called natopen hereafter). For this task, three initial challenges can be identified early on:

      @@ -155,9 +156,9 @@

    1. We include Sentinel-2 imagery for natopen ecosystems as the resolution for the bands underlying the NDVI product here is 10 x 10 m which is smaller than almost all natopen polygons in the NiN-data. However, since Sentinel-2 only goes back to 2015, we lose much of the time series aspect as compared to the MODIS data. In addition, we include Landsat imagery which goes back to 1984 and has a resolution of 30 x 30 m, but also has more variable data due to less frequent seasonal measurements. We also still explore NDVI data from MODIS (see more details below).
    2. 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