Monitoring deforestation in the Serranía de Chiribiquete in northern Colombian Amazon using time series analysis of satellite data
(2023) In Student thesis series INES NGEM01 20231Dept of Physical Geography and Ecosystem Science
- Abstract
- Deforestation monitoring is of significant importance for the ecosystem, climate change,and policy-making. The availability of optical and synthetic aperture radar (SAR) satellite remote sensing images, along with the development of time series change detection methods, has contributed to the increasing popularity of time series analysis in forest disturbance monitoring. However, there are few studies that compare the performance of optical and SAR imagery for this purpose. In this study, the Landsat and Sentinel-1 time series imagery from 2016 to 2021 was used to detect forest cover loss in the northern Colombian Amazon, which has experienced great deforestation following the signing of the Colombian peace agreement in 2016. The time... (More)
- Deforestation monitoring is of significant importance for the ecosystem, climate change,and policy-making. The availability of optical and synthetic aperture radar (SAR) satellite remote sensing images, along with the development of time series change detection methods, has contributed to the increasing popularity of time series analysis in forest disturbance monitoring. However, there are few studies that compare the performance of optical and SAR imagery for this purpose. In this study, the Landsat and Sentinel-1 time series imagery from 2016 to 2021 was used to detect forest cover loss in the northern Colombian Amazon, which has experienced great deforestation following the signing of the Colombian peace agreement in 2016. The time series change detection method applied in this study is the Continuous Change Detection and Classification (CCDC) algorithm, as it flags land cover changes by differencing the predicted and observed data. The deforestation detected by 1040 Landsat and 1378 Sentinel-1 images indicates that deforestation gradually increased from 2016 to 2018 and then exhibited a fluctuating trend. The peak years of deforestation were observed in 2018 and 2020. The paired-samples t-test revealed that the difference between detected forest loss area by Landsat and Sentinel-1 data is statistically significant in study region 1, while it is not statistically significant in study region 2. Furthermore, the spatial distribution analysis indicated that the detected forest loss from 2016 to 2021 roughly followed the direction of the boundaries of the protected area. After assessing the accuracy using stratified random sampling, the overall accuracy values of 62.7% for Landsat and 43.3% for Sentinel-1 in detecting deforestation were obtained. Subsequently, a temporal accuracy assessment of the forest disturbance pixels successfully detected by Landsat and Sentinel-1 was conducted. The results showed that 62.8% of Landsat pixels and 74.9% of Sentinel-1 pixels accurately matched the corresponding actual years of deforestation. This study suggests that integrating Landsat and Sentinel-1 data for forest disturbance monitoring may potentially yield better results in both spatial and temporal domains. (Less)
- Popular Abstract
- Deforestation monitoring is crucial for understanding its impact on the environment, climate change, and policymaking. To achieve this, researchers have been using satellite remote sensing images, such as optical and synthetic aperture radar (SAR), along with time series analysis. However, there is limited research comparing the performance of optical and SAR imagery for this purpose. In this study, Landsat and Sentinel-1 satellite data from 2016 to 2021 were utilized to detect forest cover loss in the northern Colombian Amazon, which has experienced significant deforestation since the Colombian peace agreement was signed in 2016.
The study employed the Continuous Change Detection and Classification (CCDC) algorithm to detect forest... (More) - Deforestation monitoring is crucial for understanding its impact on the environment, climate change, and policymaking. To achieve this, researchers have been using satellite remote sensing images, such as optical and synthetic aperture radar (SAR), along with time series analysis. However, there is limited research comparing the performance of optical and SAR imagery for this purpose. In this study, Landsat and Sentinel-1 satellite data from 2016 to 2021 were utilized to detect forest cover loss in the northern Colombian Amazon, which has experienced significant deforestation since the Colombian peace agreement was signed in 2016.
The study employed the Continuous Change Detection and Classification (CCDC) algorithm to detect forest loss. Analysis of Landsat and Sentinel-1 images revealed a gradual increase in deforestation from 2016 to 2018, followed by a fluctuating trend. The peak deforestation years were observed in 2018 and 2020. A statistical test showed that the difference in detected forest loss area between Landsat and Sentinel-1 data was statistically significant in one study region but not in another.
Spatial distribution analysis indicated that the detected forest loss from 2016 to 2021 roughly followed the boundaries of the protected area. The accuracy assessment showed Landsat performed better. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9129455
- author
- Qian, Yuyang LU
- supervisor
-
- Jesica López LU
- Lars Eklundh LU
- organization
- course
- NGEM01 20231
- year
- 2023
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Physical Geography and Ecosystem analysis, CCDC, Forest loss, Landsat, Sentinel-1, Change detection, NNP, FARC
- publication/series
- Student thesis series INES
- report number
- 614
- language
- English
- id
- 9129455
- date added to LUP
- 2023-06-22 14:20:13
- date last changed
- 2023-06-22 14:20:13
@misc{9129455,
abstract = {{Deforestation monitoring is of significant importance for the ecosystem, climate change,and policy-making. The availability of optical and synthetic aperture radar (SAR) satellite remote sensing images, along with the development of time series change detection methods, has contributed to the increasing popularity of time series analysis in forest disturbance monitoring. However, there are few studies that compare the performance of optical and SAR imagery for this purpose. In this study, the Landsat and Sentinel-1 time series imagery from 2016 to 2021 was used to detect forest cover loss in the northern Colombian Amazon, which has experienced great deforestation following the signing of the Colombian peace agreement in 2016. The time series change detection method applied in this study is the Continuous Change Detection and Classification (CCDC) algorithm, as it flags land cover changes by differencing the predicted and observed data. The deforestation detected by 1040 Landsat and 1378 Sentinel-1 images indicates that deforestation gradually increased from 2016 to 2018 and then exhibited a fluctuating trend. The peak years of deforestation were observed in 2018 and 2020. The paired-samples t-test revealed that the difference between detected forest loss area by Landsat and Sentinel-1 data is statistically significant in study region 1, while it is not statistically significant in study region 2. Furthermore, the spatial distribution analysis indicated that the detected forest loss from 2016 to 2021 roughly followed the direction of the boundaries of the protected area. After assessing the accuracy using stratified random sampling, the overall accuracy values of 62.7% for Landsat and 43.3% for Sentinel-1 in detecting deforestation were obtained. Subsequently, a temporal accuracy assessment of the forest disturbance pixels successfully detected by Landsat and Sentinel-1 was conducted. The results showed that 62.8% of Landsat pixels and 74.9% of Sentinel-1 pixels accurately matched the corresponding actual years of deforestation. This study suggests that integrating Landsat and Sentinel-1 data for forest disturbance monitoring may potentially yield better results in both spatial and temporal domains.}},
author = {{Qian, Yuyang}},
language = {{eng}},
note = {{Student Paper}},
series = {{Student thesis series INES}},
title = {{Monitoring deforestation in the Serranía de Chiribiquete in northern Colombian Amazon using time series analysis of satellite data}},
year = {{2023}},
}