Using satellite imagery toevaluate the impacts of oilpipeline on vegetation
Başlık çevirisi mevcut değil.
- Tez No: 757442
- Danışmanlar: DR. M.T MARSHALL, DR. T.A GROEN
- Tez Türü: Yüksek Lisans
- Konular: Jeoloji Mühendisliği, Geological Engineering
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2021
- Dil: İngilizce
- Üniversite: University of Twente
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 46
Özet
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Özet (Çeviri)
Oil pipelines have various effects on vegetation both during and after construction. Oil pipeline construction activities and their impacts over time pose a threat to the sustainability of plant species and other biodiversity conservation, especially as it passes through protected or adjacent areas (Castell et al., 1994). These impacts from the pipeline vary according to different land cover types and elevations. This study aims to evaluate the magnitude of change and the duration of the recovery process in forest and grass fields, depending on elevation, using the LandTrendr algorithm and Landsat images after pipeline construction. For this study, a part of the Baku Tbilisi Ceyhan (BTC) pipeline remaining in Georgia was selected. After the construction activities covering the years 2003-2005, the change in the vegetation was studied using the Normalized Difference Vegetation Index (NDVI) with the help of remote sensing. The magnitude of the disturbance in the vegetation experienced in this period and the time required for the recovery of the vegetation afterwards were determined. First, preprocessing was performed for data preparation, which included cloud masking, harmonization, and measurement of surface reflectance to ensure continuity over time. In the second phase, LandTrendr segmentation parameters were established to detect changes in which time series algorithm was run, temporal interruptions, abrupt changes and temporal segmentation were performed. After determining the spatial and temporal changes through LandTrendr, the magnitude of the change in our sample areas, forests and grasslands, and the duration of vegetation recovery were evaluated using 500 samples (pixel values). We also created a regression model based on three variables to understand the impact of elevation, distance to pipeline, and different land cover classes on the magnitude of changes and recovery period. Finally, we evaluated our model using K-fold cross validation and interpreting the spatial temporal changes of land cover due to pipeline disturbance. According to the disturbance result, the changes in NDVI increase with altitude, and the regression result shows that forest and grass land cover, distance to pipeline, and elevation can explain 36.8% (RMSE=0.067) of the changes in NDVI. Also, NDVI variation in grass increases while forest decreases with altitude. The regression results in forest and grass were R2=0.374 (RMSE=0.062) and R2=0.403 (RMSE=0.066), respectively. For the duration of recovery period, the change in NDVI increases with elevation, and the regression result shows that forest and grass land covers, distance to pipeline, and elevation can explain 6.5% (RMSE=4.425) of the changes in NDVI. Also, the recovery period in forest is longer than in grass, and the regression results in forest and grass show R2 = 0.0344 (RMSE=4.07), R2 = 0.091 (RMSE=3.39), respectively. As a result, K-fold cross validation was run for the disturbance and recovery period, showing R2=0.415 (RMSE=0.057) for the disturbance and R2=0.286 (RMSE=4.080) for the recovery period. Therefore, this study shows that LandTrendr algorithm is suitable to estimate the magnitude of change and duration of recovery in vegetation greenness after pipeline disturbance.
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