Chinyere Ottah 25/September/2023
Table of contents
- Project description
- Software used
- Setup
- Project status
The goal of this project is to estimate Carbon loss from disturbance using remotely sensed data from Canada’s boreal peatlands and forests (Figure 1). Peatlands (Sphagnum) are wetlands 1 containing partly decomposed plant materials (Wieder, et al., 2008), while forests are defined as areas with canopy cover < 10% and tree heights < 3 m. Both peatlands and trees serve as significant reservoirs of carbon stocks in boreal forests, with a substantial portion of this zone located in Canada. However, these ecosystems are releasing accumulated biomass from both trees and below ground, primarily due to climate-induced changes in wildfires. This human-induced climate change is causing fuels in forests and peatlands to dry out, rendering this ecosystem more susceptible to wildfires. These fires release carbon dioxide and other harmful gases into the atmosphere, impacting both health and plant biomass stocks.
Understanding and accurately predicting the areas vulnerable to burning is crucial for effective wildfire prevention measures (Parks, 2014). Additionally, reporting the amount of biomass lost from fires provides essential feedback for climate-carbon modeling and improvement. In that case, don’t forget to commit and push the resulting figure files, so they display on GitHub.
Motivated by these considerations, Chinyere’s research interests encompass wildfire, biomass, carbon, peatlands, forests, and remote sensing. I am particularly intrigued by exploring how remotely sensed data from instruments like Landsat, which employ optical sensors and active sensors such as lidar, can be employed to map biomass loss resulting from major disturbances like wildfires.
More about my research can be found here: link
Figure 1: Map of Canada boreal forest
Canada’s forested and peatlands ecosystems
The software used for this project includes ArcGIS, Google earth engine and R
To run this project, you have to have the latest windows verion. For Mac users, you have yo.
This project is still ongoing and wit is expected to end by 2026.
The table and scatterplot showing carbon loss and dNBR for fires between 2000 to 2010.
carbon_loss <- read.csv("/Users/chinyereottah/Desktop/Mac/R_course/Session3_Projects_and_Reproducible_Environments/Carbon_loss_from_disturbances/Response_variable/carbon_loss.csv")
library(knitr)
library(dplyr)
kable(carbon_loss[, c("Carbonloss", "dNBR")], caption = "Carbon Loss Table") %>%
kable_styling(full_width = FALSE) | Carbonloss | dNBR |
|---|---|
| 1953.836 | 714 |
| 1019.236 | 466 |
| 1835.740 | 200 |
| 877.229 | 303 |
| 447.045 | 597 |
| 904.129 | 537 |
| 1039.196 | 435 |
| 1033.456 | 448 |
| 858.031 | 552 |
| 430.362 | 351 |
| 250.247 | 263 |
| 632.152 | 798 |
| 1091.289 | 636 |
| 1719.306 | 139 |
| 1582.658 | 709 |
| 917.505 | 552 |
| 548.847 | 283 |
| 740.378 | 511 |
| 481.517 | 651 |
| 1187.024 | 563 |
| 706.971 | 632 |
| 416.707 | 323 |
| 2252.180 | 242 |
| 787.162 | 416 |
| 5460.000 | 534 |
| 508.951 | 488 |
| 1281.476 | 620 |
| 1293.367 | 324 |
| 332.046 | 429 |
| 1038.077 | 629 |
| 714.375 | 108 |
| 147.046 | 652 |
| 249.375 | 353 |
| 556.656 | 847 |
| 794.981 | 732 |
| 99.470 | 598 |
| 89.239 | 617 |
| 260.490 | 551 |
| 1420.485 | 526 |
| 292.508 | 688 |
| 55.269 | 693 |
| 81.880 | 629 |
| 1302.828 | 629 |
| 291.076 | 376 |
| 617.448 | 376 |
| 594.111 | 354 |
| 696.484 | 354 |
| 141.619 | 345 |
| 161.460 | 811 |
| 211.133 | 816 |
| 284.632 | 787 |
| 201.063 | 787 |
| 46.669 | 568 |
| 27.012 | 587 |
| 69.566 | 588 |
| 54.124 | 713 |
| 82.578 | 713 |
| 36.190 | 255 |
| 37.588 | 255 |
| 67.183 | 280 |
| 70.938 | 216 |
| 140.203 | 216 |
| 380.517 | 461 |
| 152.103 | 318 |
| 242.810 | 281 |
| 384.624 | 590 |
| 459.857 | 395 |
| 419.725 | 395 |
# Load necessary libraries
library(readr)
# Perform linear regression
model <- lm(Carbonloss ~ dNBR, data = carbon_loss)
# Summarize the regression results
summary(model)##
## Call:
## lm(formula = Carbonloss ~ dNBR, data = carbon_loss)
##
## Residuals:
## Min 1Q Median 3Q Max
## -684.3 -476.9 -216.8 246.5 4797.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 773.5874 275.4023 2.809 0.00653 **
## dNBR -0.2083 0.5190 -0.401 0.68944
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 793.9 on 66 degrees of freedom
## Multiple R-squared: 0.002435, Adjusted R-squared: -0.01268
## F-statistic: 0.1611 on 1 and 66 DF, p-value: 0.6894
# Create a scatter plot
plot(carbon_loss$dNBR, carbon_loss$carbonloss, main = "Scatter Plot of dNBR vs Carbon Loss",
xlab = "dNBR", ylab = "Carbon Loss")
# regression line
abline(model, col = "red")
# legend
legend("topright", legend = "Regression Line", col = "red", lty = 1, cex = 0.8)
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R_course/-
Session3_Projects_and_Reproducible_Environments/Carbon_loss_from_disturbances/Data/raw_data.csv(Sample raw data file)
Response_variable/carbon_loss.csv(The response variable data I am using to predict to predict carbon loss)
Scripts/preprocess_data.R(R script for data preprocessing)
Models/model1.RDS(Trained model file)
Results/analysis_results.csv(Sample analysis results)
README.md(This file, explaining the folder structure)
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This project was funded by McMaster University Grdauate Scool. Many thanks to Prof. Antonio Paez for teaching us and giving us an intoduction to Github for version control
Please feel free to contact me on [email protected]
Footnotes
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Marshes, Swamps and peatlands ↩