Abstract
In 2016, in response to forest loss, the Myanmar government banned logging operations for 1 year throughout the entire country and for 10 years in specific regions. However, it is unclear whether this measure will effectively reduce forest loss, because disturbance agents other than logging may have substantial effects on forest loss. In this study, we investigated an approach to attribute disturbance agents to forest loss, and we characterized the attribution of disturbance agents, as well as the areas affected by these agents, in tropical seasonal forests in the Bago Mountains, Myanmar. A trajectory-based analysis using a Landsat time series was performed to detect change pixels. After the aggregation process that grouped adjacent change pixels in the same year as patches, a change attribution was implemented using the spectral, geometric, and topographic information of each patch via random forest modeling. The attributed agents of change include "logging", "plantation", "shifting cultivation", "urban expansion", "water invasion", "recovery", "other change", and "no change". The overall accuracy of the attribution model at the patch and area levels was 84.7% and 96.0%, respectively. The estimated disturbance area from the attribution model accounted for 10.0% of the study area. The largest disturbance agent was found to be logging (59.8%), followed by water invasion (14.6%). This approach quantifies disturbance agents at both spatial and temporal scales in tropical seasonal forests, where limited information is available for forest management, thereby providing crucial information for assessing forest conditions in such environments.
| Original language | English |
|---|---|
| Article number | 218 |
| Journal | Forests |
| Volume | 8 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - Jan 1 2017 |
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All Science Journal Classification (ASJC) codes
- Forestry
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Attribution of disturbance agents to forest change using a Landsat time series in tropical seasonal forests in the Bago Mountains, Myanmar. / Shimizu, Katsuto; Ahmed, Oumer S.; Ponce-Hernandez, Raul; Ota, Tetsuji; Win, Zar Chi; Mizoue, Nobuya; Yoshida, Shigejiro.
In: Forests, Vol. 8, No. 6, 218, 01.01.2017.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Attribution of disturbance agents to forest change using a Landsat time series in tropical seasonal forests in the Bago Mountains, Myanmar
AU - Shimizu, Katsuto
AU - Ahmed, Oumer S.
AU - Ponce-Hernandez, Raul
AU - Ota, Tetsuji
AU - Win, Zar Chi
AU - Mizoue, Nobuya
AU - Yoshida, Shigejiro
PY - 2017/1/1
Y1 - 2017/1/1
N2 - In 2016, in response to forest loss, the Myanmar government banned logging operations for 1 year throughout the entire country and for 10 years in specific regions. However, it is unclear whether this measure will effectively reduce forest loss, because disturbance agents other than logging may have substantial effects on forest loss. In this study, we investigated an approach to attribute disturbance agents to forest loss, and we characterized the attribution of disturbance agents, as well as the areas affected by these agents, in tropical seasonal forests in the Bago Mountains, Myanmar. A trajectory-based analysis using a Landsat time series was performed to detect change pixels. After the aggregation process that grouped adjacent change pixels in the same year as patches, a change attribution was implemented using the spectral, geometric, and topographic information of each patch via random forest modeling. The attributed agents of change include "logging", "plantation", "shifting cultivation", "urban expansion", "water invasion", "recovery", "other change", and "no change". The overall accuracy of the attribution model at the patch and area levels was 84.7% and 96.0%, respectively. The estimated disturbance area from the attribution model accounted for 10.0% of the study area. The largest disturbance agent was found to be logging (59.8%), followed by water invasion (14.6%). This approach quantifies disturbance agents at both spatial and temporal scales in tropical seasonal forests, where limited information is available for forest management, thereby providing crucial information for assessing forest conditions in such environments.
AB - In 2016, in response to forest loss, the Myanmar government banned logging operations for 1 year throughout the entire country and for 10 years in specific regions. However, it is unclear whether this measure will effectively reduce forest loss, because disturbance agents other than logging may have substantial effects on forest loss. In this study, we investigated an approach to attribute disturbance agents to forest loss, and we characterized the attribution of disturbance agents, as well as the areas affected by these agents, in tropical seasonal forests in the Bago Mountains, Myanmar. A trajectory-based analysis using a Landsat time series was performed to detect change pixels. After the aggregation process that grouped adjacent change pixels in the same year as patches, a change attribution was implemented using the spectral, geometric, and topographic information of each patch via random forest modeling. The attributed agents of change include "logging", "plantation", "shifting cultivation", "urban expansion", "water invasion", "recovery", "other change", and "no change". The overall accuracy of the attribution model at the patch and area levels was 84.7% and 96.0%, respectively. The estimated disturbance area from the attribution model accounted for 10.0% of the study area. The largest disturbance agent was found to be logging (59.8%), followed by water invasion (14.6%). This approach quantifies disturbance agents at both spatial and temporal scales in tropical seasonal forests, where limited information is available for forest management, thereby providing crucial information for assessing forest conditions in such environments.
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U2 - 10.3390/f8060218
DO - 10.3390/f8060218
M3 - Article
AN - SCOPUS:85021172186
VL - 8
JO - Forests
JF - Forests
SN - 1999-4907
IS - 6
M1 - 218
ER -