Using mobile surface in situ and remote sensing and airborne remote sensing to derive emissions from a producing central California oil field in complex terrain

2021 ◽  
pp. 101145
Author(s):  
Ira Leifer ◽  
Christopher Melton
Keyword(s):  
Remote Sensing ◽  
Complex Terrain ◽  
Oil Field ◽  
Airborne Remote Sensing ◽  
Central California

scholarly journals Integrating National Ecological Observatory Network (NEON) Airborne Remote Sensing and In-Situ Data for Optimal Tree Species Classification

2020 ◽  
Vol 12 (9) ◽  
pp. 1414
Author(s):  
Victoria M. Scholl ◽  
Megan E. Cattau ◽  
Maxwell B. Joseph ◽  
Jennifer K. Balch
Keyword(s):  
Remote Sensing ◽  
Tree Species ◽  
Training Data ◽  
Data Sets ◽  
Airborne Remote Sensing ◽  
Species Classification ◽  
Crown Diameter ◽  
External Data ◽  
Data Products

Accurately mapping tree species composition and diversity is a critical step towards spatially explicit and species-specific ecological understanding. The National Ecological Observatory Network (NEON) is a valuable source of open ecological data across the United States. Freely available NEON data include in-situ measurements of individual trees, including stem locations, species, and crown diameter, along with the NEON Airborne Observation Platform (AOP) airborne remote sensing imagery, including hyperspectral, multispectral, and light detection and ranging (LiDAR) data products. An important aspect of predicting species using remote sensing data is creating high-quality training sets for optimal classification purposes. Ultimately, manually creating training data is an expensive and time-consuming task that relies on human analyst decisions and may require external data sets or information. We combine in-situ and airborne remote sensing NEON data to evaluate the impact of automated training set preparation and a novel data preprocessing workflow on classifying the four dominant subalpine coniferous tree species at the Niwot Ridge Mountain Research Station forested NEON site in Colorado, USA. We trained pixel-based Random Forest (RF) machine learning models using a series of training data sets along with remote sensing raster data as descriptive features. The highest classification accuracies, 69% and 60% based on internal RF error assessment and an independent validation set, respectively, were obtained using circular tree crown polygons created with half the maximum crown diameter per tree. LiDAR-derived data products were the most important features for species classification, followed by vegetation indices. This work contributes to the open development of well-labeled training data sets for forest composition mapping using openly available NEON data without requiring external data collection, manual delineation steps, or site-specific parameters.

scholarly journals Improved Atmospheric Characterization through Fused Mobile Airborne & Surface <i>In Situ</i> Surveys: Methane Emissions Quantification from a Producing Oil Field

2017 ◽  
Author(s):  
Ira Leifer ◽  
Christopher Melton ◽  
Marc L. Fischer ◽  
Matthew Fladeland ◽  
Jason Frash ◽  
...  
Keyword(s):  
Sierra Nevada ◽  
Oil Field ◽  
Oil Fields ◽  
Trace Gas ◽  
Central California ◽  
Multiple Parameters ◽  
Surface Data ◽  
Sierra Nevada Mountains ◽  
Data Collection Approach

Abstract. Methane (CH4) inventory uncertainties are large, requiring robust emission derivation approaches. We report on a fused airborne/surface data collection approach to derive emissions from an active oil field near Bakersfield, central California. The approach characterizes the atmosphere from the surface to above the planetary boundary layer (PBL) and combines downwind trace gas concentration anomaly (plume) above background with normal winds to derive flux. This approach does not require a well-mixed PBL, allows explicit, data based, uncertainty evaluation, and was applied to complex topography and wind flows. In Situ airborne (collected by AJAX – the Alpha Jet Atmospheric eXperiment) and mobile surface (collected by AMOG – the AutoMObile trace Gas – Surveyor) data were collected on 19 August 2015 to assess source strength. Data included an AMOG and AJAX intercomparison transect profiling from the San Joaquin Valley (SJV) floor into the Sierra Nevada Mountains (0.1–2.2 km altitude), validating a novel surface approach for atmospheric profiling by leveraging topography. The profile intercomparison found good agreement in multiple parameters for the overlapping altitude range from 500 to 1500 m, for the upper 5 % of surface winds, which accounts for wind-impeding structures, i.e., terrain, trees, buildings, etc. Annualized emissions from the active oil fields were 31.3 ±16 Gg methane and 2.4 ± 1.2 Tg carbon dioxide. Data showed the PBL was not well-mixed at distances of 10–20 km downwind, highlighting the importance of the experimental design.

scholarly journals Supplementary material to "Methane emissions from a Californian landfill, determined from airborne remote sensing and in-situ measurements"

2016 ◽  
Author(s):  
Sven Krautwurst ◽  
Konstantin Gerilowski ◽  
Haflidi H. Jonsson ◽  
David R. Thompson ◽  
Richard W. Kolyer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Methane Emissions ◽  
In Situ Measurements ◽  
Airborne Remote Sensing ◽  
Supplementary Material

scholarly journals Airborne remote sensing and in-situ measurements of atmospheric CO<sub>2</sub> to quantify point source emissions

2016 ◽  
Author(s):  
Thomas Krings ◽  
Bruno Neininger ◽  
Konstantin Gerilowski ◽  
Sven Krautwurst ◽  
Michael Buchwitz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Carbon Dioxide ◽  
Mass Balance ◽  
Power Plants ◽  
Greenhouse Gas Emission ◽  
Remote Sensing Data ◽  
In Situ Measurements ◽  
Emission Rates ◽  
Airborne Remote Sensing

Abstract. Reliable techniques to infer greenhouse gas emission rates from localised sources require accurate measurement and inversion approaches. In this study airborne remote sensing observations by the MAMAP instrument and airborne in-situ measurements are used to infer emission estimates of carbon dioxide released from a cluster of coal fired power plants. For the analysis of in-situ data, a mass balance approach is described and applied. Whereas for the remote sensing observations an inverse Gaussian plume model is used in addition to a mass balance technique. A comparison between methods shows that results for all methods agree within a few percent for cases where in-situ measurements were made for the complete vertical plume extent. Even though the power plants are partly in close proximity and the associated carbon dioxide plumes are overlapping it is possible to derive emission rates from remote sensing data for individual power plants that agree well with results derived from emission factors and energy production data for the time of the overflight.

scholarly journals Airborne remote sensing and in situ measurements of atmospheric CO<sub>2</sub> to quantify point source emissions

2018 ◽  
Vol 11 (2) ◽  
pp. 721-739 ◽  
Author(s):  
Thomas Krings ◽  
Bruno Neininger ◽  
Konstantin Gerilowski ◽  
Sven Krautwurst ◽  
Michael Buchwitz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Carbon Dioxide ◽  
Mass Balance ◽  
Power Plants ◽  
Greenhouse Gas Emission ◽  
In Situ Measurements ◽  
Emission Rates ◽  
Airborne Remote Sensing ◽  
In Situ Data

Abstract. Reliable techniques to infer greenhouse gas emission rates from localised sources require accurate measurement and inversion approaches. In this study airborne remote sensing observations of CO2 by the MAMAP instrument and airborne in situ measurements are used to infer emission estimates of carbon dioxide released from a cluster of coal-fired power plants. The study area is complex due to sources being located in close proximity and overlapping associated carbon dioxide plumes. For the analysis of in situ data, a mass balance approach is described and applied, whereas for the remote sensing observations an inverse Gaussian plume model is used in addition to a mass balance technique. A comparison between methods shows that results for all methods agree within 10 % or better with uncertainties of 10 to 30 % for cases in which in situ measurements were made for the complete vertical plume extent. The computed emissions for individual power plants are in agreement with results derived from emission factors and energy production data for the time of the overflight.

Testing the temporal and spatial validity of site-specific models derived from airborne remote sensing of phytoplankton

1995 ◽  
Vol 52 (5) ◽  
pp. 1094-1107 ◽  
Author(s):  
David F. Millie ◽  
Bryan T. Vinyard ◽  
Michael C. Baker ◽  
Craig S. Tucker
Keyword(s):  
Remote Sensing ◽  
Phytoplankton Biomass ◽  
Significant Proportion ◽  
Regression Tree ◽  
Airborne Remote Sensing ◽  
Site Specific ◽  
In Situ Data ◽  
Tree Modeling ◽  
Chlorophyll Distribution

Validations of predictive models are necessary for the accurate application of remotely sensed imagery within ecological research and fisheries management. Multiple regression models derived from airborne imagery on 16 May 1990 accurately depicted phytoplankton biomass and turbidity within aquaculture impoundments. To examine their temporal validity, the exact models, as well as identical model forms, were fit to similar imagery and in situ data collected on 20 June 1990. None of the exact models for 16 May accurately predicted in situ data on 20 June; however, model forms were robust for describing in situ variables. To examine their spatial validity, identical model forms were fit to in situ data partitioned among phytoplankton composition and biomass. The fit of the models and the contribution of imagery variables to the models varied among in situ variables. Although imagery variables explained all of the observed variability for turbidity, regression tree modeling indicated that a significant proportion of the variability in chlorophyll distribution both among and within impoundments was explained through both imagery variables and phytoplankton biomass. Consequently, universal models for the airborne remote sensing of water-quality variables in systems having distinct optical signatures is unlikely. Rather, robust site-specific models will need to be developed.

scholarly journals Atmospheric characterization through fused mobile airborne and surface in situ surveys: methane emissions quantification from a producing oil field

2018 ◽  
Vol 11 (3) ◽  
pp. 1689-1705 ◽  
Author(s):  
Ira Leifer ◽  
Christopher Melton ◽  
Marc L. Fischer ◽  
Matthew Fladeland ◽  
Jason Frash ◽  
...  
Keyword(s):  
Sierra Nevada ◽  
Oil Field ◽  
Oil Fields ◽  
Trace Gas ◽  
Surface Approach ◽  
Central California ◽  
Multiple Parameters ◽  
Surface Data ◽  
Data Collection Approach

Abstract. Methane (CH4) inventory uncertainties are large, requiring robust emission derivation approaches. We report on a fused airborne–surface data collection approach to derive emissions from an active oil field near Bakersfield, central California. The approach characterizes the atmosphere from the surface to above the planetary boundary layer (PBL) and combines downwind trace gas concentration anomaly (plume) above background with normal winds to derive flux. This approach does not require a well-mixed PBL; allows explicit, data-based, uncertainty evaluation; and was applied to complex topography and wind flows. In situ airborne (collected by AJAX – the Alpha Jet Atmospheric eXperiment) and mobile surface (collected by AMOG – the AutoMObile trace Gas – Surveyor) data were collected on 19 August 2015 to assess source strength. Data included an AMOG and AJAX intercomparison transect profiling from the San Joaquin Valley (SJV) floor into the Sierra Nevada (0.1–2.2 km altitude), validating a novel surface approach for atmospheric profiling by leveraging topography. The profile intercomparison found good agreement in multiple parameters for the overlapping altitude range from 500 to 1500 m for the upper 5 % of surface winds, which accounts for wind-impeding structures, i.e., terrain, trees, buildings, etc. Annualized emissions from the active oil fields were 31.3 ± 16 Gg methane and 2.4 ± 1.2 Tg carbon dioxide. Data showed the PBL was not well mixed at distances of 10–20 km downwind, highlighting the importance of the experimental design.

scholarly journals Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data

2021 ◽  
Vol 13 (9) ◽  
pp. 1715
Author(s):  
Foyez Ahmed Prodhan ◽  
Jiahua Zhang ◽  
Fengmei Yao ◽  
Lamei Shi ◽  
Til Prasad Pangali Sharma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
South Asia ◽  
Model Performance ◽  
Remote Sensing Data ◽  
Agricultural Drought ◽  
Gradient Boosting ◽  
Complex Nature ◽  
Sensing Data

Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.

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