Detection of damaged areas caused by the oil extraction in a steppe region using winter landsat imagery

Mjachina, Ksenya V.; Hu, Zhiyong; Chibilyev, Alexander A.

doi:10.1117/1.jrs.12.016017

Cited by 8 publications

(9 citation statements)

References 26 publications

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“…For the extraction of linear features, the method was well-suited to identify dirt roads and infrastructure from PlanetScope and RapidEye imagery, which obtained accurate results and opened up possibilities to apply the methodology to other areas. Consequently, the classification model achieved affordable results with high accuracy by the basis of different satellite data, which is consistent with the findings of other studies [22][23][24]52]. However, as could be seen in the performance of the different classifications, the accuracies of the dirt road and linear infrastructure were strongly related to the spatial resolution of the images through the influence of boundary pixels and influence of finer spatial resolution that increases the spectral-radiometric variation of land cover types [53].…”

Section: Relevance Of the Approachsupporting

confidence: 91%

Monitoring Oil Exploitation Infrastructure and Dirt Roads with Object-Based Image Analysis and Random Forest in the Eastern Mongolian Steppe

2020

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Information on the spatial distribution of human disturbance is important for assessing and monitoring land degradation. In the Eastern Mongolian Steppe Ecosystem, one of the major driving factors of human-induced land degradation is the expansion of road networks mainly due to intensifications of oil exploration and exploitation. So far, neither the extents of road networks nor the extent of surrounding grasslands affected by the oil industry are monitored which is generally labor consuming. This causes that no information on the changes in the area which is affected by those disturbance drivers is available. Consequently, the study aim is to provide a cost-effective methodology to classify infrastructure and oil exploitation areas from remotely sensed images using object-based classifications with Random Forest. By combining satellite data with different spatial and spectral resolutions (PlanetScope, RapidEye, and Landsat ETM+), the product delivers data since 2005. For the classification variables, segmentation, spectral characteristics, and indices were extracted from all above mentioned imagery and used as predictors. Results show that overall accuracies of land use maps ranged 73%–93% mainly depending on satellites’ spatial resolution. Since 2005, the area of grassland disturbed by dirt roads and oil exploitation infrastructure increased by 88% with its highest expansion by 47% in the period 2005–2010. Settlements and croplands remained relatively constant throughout the 13 years. Comparison of multiscale classification suggests that, although high spatial resolutions are clearly beneficial, all datasets were useful to delineate linear features such as roads. Consequently, the results of this study provide an effective evaluation for the potential of Random Forest for extracting relatively narrow linear features such as roads from multiscale satellite images and map products that are possible to use for detailed land degradation assessments.

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Section: Relevance Of the Approachsupporting

confidence: 91%

Monitoring Oil Exploitation Infrastructure and Dirt Roads with Object-Based Image Analysis and Random Forest in the Eastern Mongolian Steppe

2020

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“…Recently, a comprehensive review summarized different methods to detect paved roads from high resolution imagery and found generally high accuracies for machine learning techniques [21]. The literature review confirmed that a few studies achieved to extract the oil exploitation infrastructures and dirt roads using different methods through Landsat ETM+ and RapidEye data [22][23][24]. To our knowledge, there is no study on land cover classification of linear features which was conducted combining PlanetScope and RapidEye as high spatial resolution data with Landsat data providing a longer time series.…”

Section: Introductionmentioning

confidence: 82%

Monitoring Oil Exploitation Infrastructure and Dirt Roads with Object-Based Image Analysis and Random Forest in the Eastern Mongolian Steppe

Dashpurev¹,

Bendix²,

Lehnert³

2020

Preprint

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<p>Rapid changes in land use due to intensifications of oil exploration and exploitation adversely affect the Eastern Mongolian steppe ecosystem. The expansion of supporting infrastructure and dirt road networks for oil production contribute to accelerate the human-induced land degradation process in the grasslands. So far, neither the extents of road networks nor the extent of surrounding grasslands affected by the oil industry are monitored which is generally labor consuming. This causes that no information on the changes in the area which is affected by those disturbance drivers is available. Therefore, the major aim of this study is to provide a cost-effective model to map the supporting infrastructure, sites and dirt roads of oil exploitation through classifying remotely sensed images using object-based classifications with Random Forest. By combining satellite data with different spatial and spectral resolutions (PlanetScope, RapidEye, and Landsat ETM+), the product delivers data since 2005. In the image classification, the variables of segmentation, spectral characteristics, and indices were extracted from all above mentioned imagery and used as predictors. Within this study examined the comparison analysis in order to quantify the uncertainty arising from the combination of data from different sensors in their spectral and spatial configurations. Besides that, this study analyzed the consequence of supporting infrastructure and dirt roads on surrounding ecosystems combining data from field vegetation surveys and drone imagery. Results show that overall accuracies of land use maps ranged 73%&#8211;93% mainly depending on satellites&#8217; spatial resolution. Since 2005, the area of grassland disturbed by dirt roads and oil exploitation infrastructure increased by 88% with its highest expansion by 47% in the period 2005&#8211;2010. Consequently, the comparison of multiscale classification suggests that, although high spatial resolutions are clearly beneficial, all datasets were useful to delineate linear features such as roads. Furthermore, the results of this study provide an effective evaluation for the potential of Random Forest based model for extracting relatively narrow linear features such as roads from multiscale satellite images and map products that are possible to use for detailed land degradation assessments.</p>

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“…Such infrastructure fragments the steppes in various ways and is subject to soil and air pollution (Dobrinsky, 1995;Hebblewhite, 2008;Jones et al, 2015). Also, both official (formal) and unofficial (informal) roads are of additional concern in the Eurasian steppes, as they may fragment the steppe grasslands and, therefore, affect the migration of animals (Bekenov et al, 1998;Jones et al, 2015;Mjachina et al, 2018). The road networks may also facilitate the ignition of wildfires due to the accessibility of remote steppe patches (Dlamini, 2010).…”

mentioning

confidence: 99%

“…Very high-resolution (VHR) imagery, such as IKONOS and WorldView, available through the GoogleEarth mapping service, are very appealing data sets to quantify the listed land-uses. For instance, previous case studies have shown the suitability of VHR imagery in mapping the complete and abandonment of settlements in northern Kazakhstan (Baumann et al, 2020) and the fragmentation of steppes due to oil and gas development in the Orenburg Province of Russia (Mjachina et al, 2014;Mjachina et al, 2018). Yet, to date, no study has reported the systematic observation of various fragmentation processes in steppe landscapes using VHR imagery.…”

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confidence: 99%

Multiple trajectories of grassland fragmentation, degradation, and recovery in Russia's steppes

et al. 2021

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Over the 20th century, the Eurasian steppes underwent drastic land-cover changes.Much progress was made studying cropland expansion and the post-1990 (i.e., post-Soviet) agricultural land abandonment in Eurasia. However, the alteration of steppe landscapes may include other disturbances, such as oil and gas development, formal and informal roads and garbage dumps, which were not systematically documented.Considering the example of the steppe Orenburg Province in Russia, we reconstructed agricultural land-cover change dynamics using Landsat and Sentinel-2 imagery from 1990 to 2018. Furthermore, we used very high-resolution imagery and assessed the patterns and determinants of other steppe landscape anthropogenic disturbances. Our study showed that, despite steppe recovery due to widespread cropland abandonment from 1990 to 2018, the steppes, including the recovered steppe patches, underwent fragmentation due to informal roads, oil and gas development, shrub encroachment, garbage dumps and quarries, as well as abandonment of settlements and buildings. Only 6.4% of the sampled 7859 1Â1 km blocks in 2018 showing grassland extent had no documented disturbances. The mapped disturbances occurred primarily near settlements and roads, while some disturbances occurred in remote areas. Given the accessibility of steppes, our study calls for a urgent need to systematically document alternatives to agricultural land uses in the steppes of Eurasia and other parts of the global grassland biome.

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Detection of damaged areas caused by the oil extraction in a steppe region using winter landsat imagery

Cited by 8 publications

References 26 publications

Monitoring Oil Exploitation Infrastructure and Dirt Roads with Object-Based Image Analysis and Random Forest in the Eastern Mongolian Steppe

Monitoring Oil Exploitation Infrastructure and Dirt Roads with Object-Based Image Analysis and Random Forest in the Eastern Mongolian Steppe

Monitoring Oil Exploitation Infrastructure and Dirt Roads with Object-Based Image Analysis and Random Forest in the Eastern Mongolian Steppe

Multiple trajectories of grassland fragmentation, degradation, and recovery in Russia's steppes

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