scholarly journals The Impact of Shale Oil and Gas Development on Rangelands in the Permian Basin Region: An Assessment Using High-Resolution Remote Sensing Data

2021 ◽  
Vol 13 (4) ◽  
pp. 824
Author(s):  
Haoying Wang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Fixed Effects ◽  
Oil And Gas ◽  
Production Volume ◽  
Permian Basin ◽  
Oil And Gas Development ◽  
Basin Region ◽  
The Impact

The environmental impact of shale energy development is a growing concern in the US and worldwide. Although the topic is well-studied in general, shale development’s impact on drylands has received much less attention in the literature. This study focuses on the effect of shale development on land cover in the Permian Basin region—a unique arid/semi-arid landscape experiencing an unprecedented intensity of drilling and production activities. By taking advantage of the high-resolution remote sensing land cover data, we develop a fixed-effects panel (longitudinal) data regression model to control unobserved spatial heterogeneities and regionwide trends. The model allows us to understand the land cover’s dynamics over the past decade of shale development. The results show that shale development had moderate negative but statistically significant impacts on shrubland and grassland/pasture. The effect is more strongly associated with the hydrocarbon production volume and less with the number of oil and gas wells drilled. Between shrubland and grassland/pasture, the impact on shrubland is more pronounced in terms of magnitude. The dominance of shrubland in the region likely explains the result.

scholarly journals Effects of multi-temporal high-resolution remote sensing products on simulated hydrometeorological variables in a cultivated area (southwestern France)

2017 ◽  
Author(s):  
Jordi Etchanchu ◽  
Vincent Rivalland ◽  
Simon Gascoin ◽  
Jérôme Cros ◽  
Aurore Brut ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Land Surface ◽  
Agricultural Landscapes ◽  
Annual Precipitation ◽  
Surface Model ◽  
Area Index ◽  
The Impact ◽  
Sentinel 2

Abstract. Agricultural landscapes often include a patchwork of crop fields whose seasonal evolution is dependent on specific crop rotation patterns and phenologies. This temporal and spatial heterogeneity affects surface hydrometeorological processes as simulated by land surface and distributed hydrological models. Sentinel-2 mission satellite remote sensing products allow for the monitoring of land cover and vegetation dynamics at unprecedented spatial resolutions and revisit frequencies (20 m and 5 days, respectively) that are fully compatible with such heterogeneous agricultural landscapes. Here, we evaluate the impact of Sentinel-2-like remote sensing data on the simulation of surface water and energy flux via the ISBA-SURFEX land surface model. The study area is a 24 km by 24 km agricultural zone in southwestern France. An initial reference simulation was conducted from 2006–2010 using the ECOCLIMAP-II database. This global numerical land ecosystem database was created at a 1 km resolution and includes an ecosystem classification with a consistent set of land surface parameters required for the model, such as the Leaf Area Index (LAI) and albedo measures. The LAI of ECOCLIMAP is climatologic and derived from a 2000–2005 analysis of MODIS satellite products. This low resolution induces that several vegetation covers can be mixed in a model cell. The climatic construction of LAI dynamics also suggests that there is no interannual variability in the vegetation cycle. A second simulation was performed by forcing the same model with annual land cover maps and monthly LAI values derived from a series of 105 8 m-resolution Formosat-2 images for the same period. Both simulations were conducted at the parcel scale, i.e., a computation unit covers an area of connected pixels of the same vegetation type (a crop field, forest patch, etc.). To evaluate our simulations, we used in situ measurements of evapotranspiration and latent and sensible heat flux from two eddy covariance stations in the study area. Our results show that the use of Formosat-2 high-resolution products significantly improves simulated evapotranspiration results with respect to ECOCLIMAP-II, especially when a surface is covered with summer crops (the correlation coefficient with monthly measurements is increased by roughly 0.3 and the root mean square error is decreased by roughly 31 %). This finding is attributable to a better description of LAI evolution processes reflected by Formosat-2 data, which further modify soil water content and drainage levels of deep soil reservoirs. Effects on annual drainage patterns remain small but significant, i.e., an increase roughly equivalent to 4 % of annual precipitation levels from Formosat-2 data in comparison to reference values. In smaller proportions, runoff is also increased by roughly 1 % of annual precipitation when using Formosat-2 data. This study illustrates the potential for the Sentinel-2 mission to better represent effects of crop management on water budgeting for large, anthropized river basins.

Consideration of Geopolitical Challenges within the Analysis of Geoenvironmental Risks for Oil and Gas Development of the Russian Arctic

2019 ◽  
Vol 16 (6) ◽  
pp. 50-59
Author(s):  
O. P. Trubitsina ◽  
V. N. Bashkin
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Optimal Operation ◽  
The Arctic ◽  
Stage Model ◽  
Oil And Gas Development ◽  
Gas Industry ◽  
The Impact ◽  
Further Development ◽  
Geopolitical Risks

The article is devoted to the consideration of geopolitical challenges for the analysis of geoenvironmental risks (GERs) in the hydrocarbon development of the Arctic territory. Geopolitical risks (GPRs), like GERs, can be transformed into opposite external environment factors of oil and gas industry facilities in the form of additional opportunities or threats, which the authors identify in detail for each type of risk. This is necessary for further development of methodological base of expert methods for GER management in the context of the implementational proposed two-stage model of the GER analysis taking to account GPR for the improvement of effectiveness making decisions to ensure optimal operation of the facility oil and gas industry and minimize the impact on the environment in the geopolitical conditions of the Arctic.The authors declare no conflict of interest

scholarly journals Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3232 ◽  
Author(s):  
Yan Liu ◽  
Qirui Ren ◽  
Jiahui Geng ◽  
Meng Ding ◽  
Jiangyun Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Large Scale ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Training Data ◽  
Land Resources ◽  
Remote Sensing Images ◽  
Training Strategy ◽  
The Impact

Efficient and accurate semantic segmentation is the key technique for automatic remote sensing image analysis. While there have been many segmentation methods based on traditional hand-craft feature extractors, it is still challenging to process high-resolution and large-scale remote sensing images. In this work, a novel patch-wise semantic segmentation method with a new training strategy based on fully convolutional networks is presented to segment common land resources. First, to handle the high-resolution image, the images are split as local patches and then a patch-wise network is built. Second, training data is preprocessed in several ways to meet the specific characteristics of remote sensing images, i.e., color imbalance, object rotation variations and lens distortion. Third, a multi-scale training strategy is developed to solve the severe scale variation problem. In addition, the impact of conditional random field (CRF) is studied to improve the precision. The proposed method was evaluated on a dataset collected from a capital city in West China with the Gaofen-2 satellite. The dataset contains ten common land resources (Grassland, Road, etc.). The experimental results show that the proposed algorithm achieves 54.96% in terms of mean intersection over union (MIoU) and outperforms other state-of-the-art methods in remote sensing image segmentation.

scholarly journals Leveraging machine learning to produce a cost-effective national building height map of Ireland

2021 ◽  
Author(s):  
Eoghan Keany ◽  
Geoffrey Bessardon ◽  
Emily Gleeson
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Urban Areas ◽  
Cost Effective ◽  
Land Cover Classification ◽  
National Scale ◽  
Building Height ◽  
Height Data

<p>To represent surface thermal, turbulent and humidity exchanges, Numerical Weather Prediction (NWP) systems require a land-cover classification map to calculate sur-face parameters used in surface flux estimation. The latest land-cover classification map used in the HARMONIE-AROME configuration of the shared ALADIN-HIRLAMNWP system for operational weather forecasting is ECOCLIMAP-SG (ECO-SG). The first evaluation of ECO-SG over Ireland suggested that sparse urban areas are underestimated and instead appear as vegetation areas (1). While the work of (2) on land-cover classification helps to correct the horizontal extent of urban areas, the method does not provide information on the vertical characteristics of urban areas. ECO-SG urban classification implicitly includes building heights (3), and any improvement to ECO-SG urban area extent requires a complementary building height dataset.</p><p>Openly accessible building height data at a national scale does not exist for the island of Ireland. This work seeks to address this gap in availability by extrapolating the preexisting localised building height data across the entire island. The study utilises information from both the temporal and spatial dimensions by creating band-wise temporal aggregation statistics from morphological operations, for both the Sentinel-1A/B and Sentinel-2A/B constellations (4). The extrapolation uses building height information from the Copernicus Urban Atlas, which contains regional coverage for Dublin at 10 m x10 m resolution (5). Various regression models were then trained on these aggregated statistics to make pixel-wise building height estimates. These model estimates were then evaluated with an adjusted RMSE metric, with the most accurate model chosen to map the entire country. This method relies solely on freely available satellite imagery and open-source software, providing a cost-effective mapping service at a national scale that can be updated more frequently, unlike expensive once-off private mapping services. Furthermore, this process could be applied by these services to reduce costs by taking a small representative sample and extrapolating the rest of the area. This method can be applied beyond national borders providing a uniform map that does not depends on the different private service practices facilitating the updates of global or continental land-cover information used in NWP.</p><p> </p><p>(1) G. Bessardon and E. Gleeson, “Using the best available physiography to improve weather forecasts for Ireland,” in Challenges in High-Resolution Short Range NWP at European level including forecaster-developer cooperation, European Meteorological Society, 2019.</p><p>(2) E. Walsh, et al., “Using machine learning to produce a very high-resolution land-cover map for Ireland, ” Advances in Science and Research,  (accepted for publication).</p><p>(3) CNRM, "Wiki - ECOCLIMAP-SG" https://opensource.umr-cnrm.fr/projects/ecoclimap-sg/wiki</p><p>(4) D. Frantz, et al., “National-scale mapping of building height using sentinel-1 and sentinel-2 time series,” Remote Sensing of Environment, vol. 252, 2021.</p><p>(5) M. Fitrzyk, et al., “Esa Copernicus sentinel-1 exploitation activities,” in IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, IEEE, 2019.</p>

scholarly journals APLIKASI PENGINDERAAN JAUH DAN SISTEM INFORMASI GEOGRAFI UNTUK PEMETAAN DAERAH BERPOTENSI TSUNAMI DI KABUPATEN KUPANG PROVINSI NUSA TENGGARA TIMUR

2018 ◽  
Vol 3 (2) ◽  
pp. 170-178
Author(s):  
Lidia Agustina Rumaal ◽  
Jehunias L. Tanesib ◽  
Jonshon Tarigan
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Geographic Information System ◽  
Population Density ◽  
Land Cover ◽  
Arrival Time ◽  
Land Cover Map ◽  
The Impact ◽  
Monitoring Area ◽  
Wave Arrival

Abstrak Telah dilakukan pemetaan daerah rawan tsunami berdasarkan estimasi waktu tiba gelombang dan tutupan lahan di Kabupaten Kupang Provinsi Nusa Tenggara Timur menggunakan aplikasi Penginderaan Jauh dan Sistem Informasi Geografi. Penelitian ini bertujuan untuk mengidentifikasi, memetakan daerah rawan tsunami dan tingkat kerawanannya menurut estimasi waktu tiba gelombang dan tutupan lahan sebagai upaya mitigasi dampak bencana tsunami terhadap kepadatan penduduk. Metode penelitian secara umum dibagi dalam empat tahap utama yaitu pembangunan basis data berupa pembuatan peta tutupan lahan, peta gempa dan peta batimetri. Analisis data kerawanan dari peta tutupan lahan dan etimasi waktu tiba gelombang, penyajian hasil data dalam bentuk tingkat kerawanan masing-masing peta dan analisis hasil penelitian berupa tingkat kerawanan secara kualitatif masing-masing daerah titik pantau menurut peta tutupan lahan maupun estimasi waktu tiba gelombang. Selain itu, dampak kerawanan tsunami diklasifikasikan menurut tingkat kepadatan penduduk untuk kebutuhan mitigasi sebagai berikut Kecamatan Kupang Timur, Kupang Barat, Sulamu, Amfoang Timur, Semau, Semau Selatan, Amfoang Utara, Amfoang Barat Daya, Amfoang Barat Laut dan Fatuleu Barat. Kata kunci : Peta rawan tsunami, Penginderaan Jauh, Sistem Informasi Geografi, Estimasi Waktu Tiba Gelombang  Abstract Mapping of hazard tsunami areas based on estimation of arrival time of wave and land cover in Kupang Regency of East Nusa Tenggara Province using remote sensing application and geographic information system has been done. The  aims of this research are to mapping the hazard tsunami area and tsunami vulnerability level in Kupang Regency East Nusa Tenggara according to the estimated arrival time of the wave and land cover as an effort to mitigate the impact of the tsunami disaster on population density. These generally devided into four main phase namely development of database in the form of land cover map , seismic maps and bathymetry maps, data analysis of research results in the form of qualitative vulnerability of each monitoring area according to land cover map and estimated wave arrival time. Presentation of data results in the form of vulnerability level of each map and analysis and results analysis of research the form of vulnerability level of each map and analysis of research results in the form of qualitative vulnerability of each monitoring area according to land cover map and estimated wave arrival time. And then, the impact of tsunami vulnerability is classified according to population density levels for mitigation needs as follows Kupang Timur, Kupang Barat, Sulamu, Amfoang Timur, Semau, Semau Selatan, Amfoang Utara, Amfoang Barat Daya, Amfoang Barat Laut and Fatuleu Barat. Keywords: Tsunami Hazard Map, Remote Sensing, Geographic Information System, Estimated Time of arrival Wave

scholarly journals Urban Land Cover Classification of High-Resolution Aerial Imagery Using a Relation-Enhanced Multiscale Convolutional Network

2020 ◽  
Vol 12 (2) ◽  
pp. 311 ◽  
Author(s):  
Chun Liu ◽  
Doudou Zeng ◽  
Hangbin Wu ◽  
Yin Wang ◽  
Shoujun Jia ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Land Cover Classification ◽  
Urban Land ◽  
Small Sample ◽  
Connectivity Pattern ◽  
Feature Maps ◽  
Convolutional Network ◽  
Urban Land Cover

Urban land cover classification for high-resolution images is a fundamental yet challenging task in remote sensing image analysis. Recently, deep learning techniques have achieved outstanding performance in high-resolution image classification, especially the methods based on deep convolutional neural networks (DCNNs). However, the traditional CNNs using convolution operations with local receptive fields are not sufficient to model global contextual relations between objects. In addition, multiscale objects and the relatively small sample size in remote sensing have also limited classification accuracy. In this paper, a relation-enhanced multiscale convolutional network (REMSNet) method is proposed to overcome these weaknesses. A dense connectivity pattern and parallel multi-kernel convolution are combined to build a lightweight and varied receptive field sizes model. Then, the spatial relation-enhanced block and the channel relation-enhanced block are introduced into the network. They can adaptively learn global contextual relations between any two positions or feature maps to enhance feature representations. Moreover, we design a parallel multi-kernel deconvolution module and spatial path to further aggregate different scales information. The proposed network is used for urban land cover classification against two datasets: the ISPRS 2D semantic labelling contest of Vaihingen and an area of Shanghai of about 143 km2. The results demonstrate that the proposed method can effectively capture long-range dependencies and improve the accuracy of land cover classification. Our model obtains an overall accuracy (OA) of 90.46% and a mean intersection-over-union (mIoU) of 0.8073 for Vaihingen and an OA of 88.55% and a mIoU of 0.7394 for Shanghai.

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