-
Notifications
You must be signed in to change notification settings - Fork 3
/
Copy pathCBM_dataPrep_SK.Rmd
166 lines (125 loc) · 7.95 KB
/
CBM_dataPrep_SK.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
---
title: "CBM_dataPrep_SK"
author:
- Celine Boisvenue
- Alex Chubaty
date: "September 2021"
output:
html_document:
keep_md: yes
editor_options:
chunk_output_type: console
markdown:
wrap: sentence
---
# CBM_dataPrep_SK
## Overview
## List of input objects
| Name | Class | Description | Source |
|-----------------|-----------------|-----------------------|-----------------|
## List of output objects
| Name | Class | Description |
|------------------|------------------|-------------------------------------|
## Module flow (rename this section later)
1.
2.
3.
4.
#OLD VERSION
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, eval = FALSE, results = "hold")
```
## Overview
This module is to read-in user-provided information or provide defaults.
It reads-in rasters (`ageRaster`, `ecoRaster`, `gcIndexRaster`, `spuRaster`, and `masterRaster`) from either defaults of provided by the user.
From the rasters, `pixelGroup` are created which are unique combinations of the rasters values.
`pixelGroup` is a main processing unit in SpaDES `CBM` simulations.
In a first step, a `spatialDT` which is a `data.table` listing all pixels with their respective values of `raster`, `pixelIndex` and `pixelGroup` is created (`sim$spatialDT`).
From the `spatialDT`, a reduced `data.table` is create (`sim$level3DT`) which is the data.table from which processing will start in `CBM_core`.
The number of records in this data.table (`sim$level3DT`) should equal the number of pixel groups that will be processed in the spinup event of the `CBM_core` module.
This present module also creates variables of the same length as the rows in `level3DT` for use in other events of the `CBM_core` module.
These are: `returnIntervals`, `maxRotations`, `minRotations`, `lastPassDMIDs`, `historicDMIDs`, and delays all stored in the `simList.`
Another important object created in this module is `mySpuDmids`.
This `data.table` links the user-defined disturbances (`$userDist`) with a spatial unit and a disturbance matrix.
This will be used to apply disturbances to pixel groups in the annual event of the `CBM_core` module.
The `mySpuDmids` object is created starting from a user provided list of disturbances (`userDist`) that matches the `rasterId` of the disturbance raster to the disturbance name, and speficies if the disturbance is stand-replacing (`userDist$wholeStand == 1`) or not (`userDist$wholeStand == 1`).
The disturbance names (`userDist$distName`) and their location of the disturbance (linked via the rasterID to the `sim$mySpuDmids$spatial_unit id`) are used to associate a disturbance matrix identification number to the disturbed `pixelGroup`.
Disturbance Matrices (DM) determine what proportion of a carbon pool gets transferred to another carbon pool via disturbance.
There are 426 matrix IDs in the present default data (`sim$processes$disturbanceMatrices`).
DMIDs (Disturbance Matrix IDs) are part of the default data of CBM-CFS3.
DMs are specific to spatial units which are a numbering (48 of them `sim$cbmData@spatialUnitIds`) of the overlay of the administrative boundaries and ecozones in Canada.
Spatial units are central units in CBM-CFS3, as are ecozones because both determining various ecological and other parameters that will be used in simulations via the `CBM_core` module.
The proportion of carbon transferred by a specific DMID can be found here `sim$cbmData@disturbanceMatrixValues`.
A series of R-functions were built to help users associate the correct disturbance matrices (`spuDist()`, `mySpu()`, `seeDist()`, `simDist()`) and are searchable in this package.
Note: \* CBM_defaults objects are recreated in the `.inputObject` of this module \* nothing is in carbon or carbon increments at this point.
This module feeds into the CBM_core module as does the CBM_vol2biomass.R module.
## Usage
```{r module_dataprep_usage, eval=FALSE}
library(igraph)
library(SpaDES.core)
moduleDir <- getwd()
inputDir <- file.path(moduleDir, "inputs") %>% reproducible::checkPath(create = TRUE)
outputDir <- file.path(moduleDir, "outputs")
cacheDir <- file.path(outputDir, "cache")
times <- list(start = 0, end = 10)
parameters <- list(
#CBM_dataPrep = list(.useCache = ".inputObjects")
#.progress = list(type = "text", interval = 1), # for a progress bar
## If there are further modules, each can have its own set of parameters:
#module1 = list(param1 = value1, param2 = value2),
#module2 = list(param1 = value1, param2 = value2)
)
modules <- list("CBM_dataPrep_SK")
objects <- list(
userDistFile = file.path(moduleDir, "CBM_dataPrep_SK", "data", "userDist.csv")
)
paths <- list(
cachePath = cacheDir,
modulePath = moduleDir,
inputPath = inputDir,
outputPath = outputDir
)
myInputs <- simInit(times = times, params = parameters, modules = modules,
objects = objects, paths = paths)
outInputs <- spades(myInputs)
```
## Events
There is only when event (init) is this module.
## Data dependencies
### Module parameters
```{r moduleParams, echo = FALSE, eval = FALSE}
df_params <- SpaDES.core::moduleParams("CBM_dataPrep_SK", "..")
knitr::kable(df_params)
```
### Input data
```{r moduleInputs, echo = FALSE, eval = FALSE}
df_inputs <- SpaDES.core::moduleInputs("CBM_dataPrep_SK", "..")
knitr::kable(df_inputs)
```
An example with all the user-provided rasters and `.csv` files is provided by default.
The example simulates a region of the managed forests of SK.
All rasters and data frames for this example are on a cloud-drive (`userDefaultData_CBM_SK`).
Unless using this example, the user most provide:
- a raster of the study area at the desired resolution for simulation (`sim$masterRaster`)
- an age raster (`sim$ageRaster`)
- a raster indicating which growth curve should be applied to which pixels (`sim$gcIndexRaster`) or a URL for this raster (`sim$gcIndexRasterURL`).
- raster of disturbances for each year the user wants disturbances to be simulated. This information could come from other SpaDES modules (fireSence, other fire modules, insects modules, etc.). For retrospective simulation (past to present), rasters found here can be used anywhere in Canada <https://opendata.nfis.org/downloads/forest_change/CA_forest_harvest_mask_year_1985_2015.zip>.
- a `.csv` file of the growth curve for the study area (with links to the `sim$gcIndexRaster`), `sim$userGcM3.csv` or the location of this file (`sim$userGcM3File`). The `sim$userGcM3.csv` file is required to have three columns:
- "GrowthCurveComponentID", which will be the link to the raster `sim$gcIndexRaster`,
- "Age" ranging from 0 to the maximum age of the growth curve, and
- "MerchVolume" which is the cumulative value of m3/ha at each age along each growth curve.
- a file with the disturbances to be applied as well as their raster values (`sim$userDist`) or its location (`sim$userDistFile`). The `userDist.csv` file must have three columns:
- "distName" representing a simple description of the disturbance type (e.g., fire, clearcut, deforestation, etc.).
- "rasterId" which indications the value that this specific disturbance will have on the disturbance raster.
- "wholeStand" indicating if the disturbance is stand-replacing disturbance (1) or a partial disturbance (0).
The user could provide: \* a raster of the ecozones in their study area (`sim$ecoRaster`), but the script will calculate this raster based on the `sim$masterRaster` if it is not provided.
\* a raster of the spatial units (`sim$spuRaster`) but the script will calculate this raster based on the `sim$masterRaster` if it is not provided.
### Output data
```{r moduleOutputs, echo = FALSE, eval = FALSE}
df_outputs <- SpaDES.core::moduleOutputs("CBM_dataPrep_SK", "..")
knitr::kable(df_outputs)
```
## Links to other modules
- [`CBM_core`](https://github.com/PredictiveEcology/CBM_core)
- [`CBM_defaults`](https://github.com/PredictiveEcology/CBM_defaults)
- [`CBM_vol2biomass`](https://github.com/PredictiveEcology/CBM_vol2biomass)