Mapping trees outside forests using high-resolution aerial imagery: a comparison of pixel- and object-based classification approaches

Discrete trees and small groups of trees in nonforest settings are considered an essential resource around the world and are collectively referred to as trees outside forests (ToF). ToF provide important functions across the landscape, such as protecting soil and water resources, providing wildlife habitat, and improving farmstead energy efficiency and aesthetics. Despite the significance of ToF, forest and other natural resource inventory programs and geospatial land cover datasets that are available at a national scale do not include comprehensive information regarding ToF in the United States. Additional ground-based data collection and acquisition of specialized imagery to inventory these resources are expensive alternatives. As a potential solution, we identified two remote sensing-based approaches that use free high-resolution aerial imagery from the National Agriculture Imagery Program (NAIP) to map all tree cover in an agriculturally dominant landscape. We compared the results obtained using an unsupervised per-pixel classifier (independent component analysis—[ICA]) and an object-based image analysis (OBIA) procedure in Steele County, Minnesota, USA. Three types of accuracy assessments were used to evaluate how each method performed in terms of: (1) producing a county-level estimate of total tree-covered area, (2) correctly locating tree cover on the ground, and (3) how tree cover patch metrics computed from the classified outputs compared to those delineated by a human photo interpreter. Both approaches were found to be viable for mapping tree cover over a broad spatial extent and could serve to supplement ground-based inventory data. The ICA approach produced an estimate of total tree cover more similar to the photo-interpreted result, but the output from the OBIA method was more realistic in terms of describing the actual observed spatial pattern of tree cover.     

International law regulation of offshore oil and gas exploitation

In the current scenario, the exploitation of oil and natural gas deposits on and offshore still represents the most significant global energy needs. For decades, offshore hydrocarbon resources have been explored and exploited among the traditional commercial uses of coastal continental shelter. However, this activity presents considerable potential threats to the marine environment. Surprisingly, a global multilateral environment does not govern the offshore gas and oil sectors, although public international and regional law rules are generally applicable. The maritime oil & gas industry is instead regulated primarily by national legislation. Besides, it is subject to a mostly independent sector, which traditionally uses its contractual solutions in a capital-intensive industry. This paper aims to give an innovative Support Vector Machine (SVM) approach to discuss the unique regulatory framework on amplitude (2012L), frequency (28 Hz), velocity (6325 m/s), line curvature (0.62), polarity (3.2), layer thickness (25 mm) in the industry when compared to other methods.     

An environmental risk assessment framework for enhanced oil recovery solutions from offshore oil and gas industry

Environmental risk assessments are necessary to understand the risk associated with enhanced oil recovery (EOR) solutions and to provide decision support for choosing the best technology and implementing risk-reducing measures. This study presents a review of potentially relevant environmental/ecological risk assessment (ERA) guidelines and, based on this review, proposes an initial suggestion of an ERA framework for understanding the environmental impacts from EOR solutions. We first shortlist the important elements necessary for conducting an ERA of EOR solutions from the selected guidelines. These elements are then used to build the suggested ERA framework for produced water discharges, drilling discharges and emissions to air from EOR solutions, which is the primary objective of the present study. Furthermore, the emphasis is placed on identifying the knowledge gaps that exist for conducting ERA of EOR processes. In order to link the framework with the current best environmental practices, a review of environmental policies applicable to the marine environment around the European Union (EU) was conducted. Finally, some major challenges in the application of ERA methods for novel EOR technologies, i.e. uncertainties in the ERA due to lack of data and aggregation of risk from different environmental impacts, are discussed in detail. The frameworks suggested in this study should be possible to use by relevant stakeholders to assess environmental risk from enhanced oil recovery solutions.     

Assessing and monitoring semi-arid shrublands using object-based image analysis and multiple endmember spectral mixture analysis

Arid and semi-arid shrublands have significant biological and economical values and have been experiencing dramatic changes due to human activities. In California, California sage scrub (CSS) is one of the most endangered plant communities in the US and requires close monitoring in order to conserve this important biological resource. We investigate the utility of remote-sensing approaches—object-based image analysis applied to pansharpened QuickBird imagery (QBPS/OBIA) and multiple endmember spectral mixture analysis (MESMA) applied to SPOT imagery (SPOT/MESMA)—for estimating fractional cover of true shrub, subshrub, herb, and bare ground within CSS communities of southern California. We also explore the effectiveness of life-form cover maps for assessing CSS conditions. Overall and combined shrub cover (i.e., true shrub and subshrub) were estimated more accurately using QBPS/OBIA (mean absolute error or MAE, 8.9 %) than SPOT/MESMA (MAE, 11.4 %). Life-form cover from QBPS/OBIA at a 25?×?25 m grid cell size seems most desirable for assessing CSS because of its higher accuracy and spatial detail in cover estimates and amenability to extracting other vegetation information (e.g., size, shape, and density of shrub patches). Maps derived from SPOT/MESMA at a 50?×?50 m scale are effective for retrospective analysis of life-form cover change because their comparable accuracies to QBPS/OBIA and availability of SPOT archives data dating back to the mid-1980s. The framework in this study can be applied to other physiognomically comparable shrubland communities.     

A critique of the performance of EIA within the offshore oil and gas sector

The oil and gas sector is a key driver of the offshore economy. Yet, it is also associated with a number of unwanted environmental impacts which potentially threaten the long term economic and environmental viability of marine ecosystems. Environmental Impact Assessment (EIA) can potentially make a significant contribution to the identification and management of adverse impacts through the promotion of evidence based decision making. However, the extent to which EIA has been embraced by key stakeholders is poorly understood. On this basis, this paper provides an initial evaluation of EIA performance within the oil and gas sector. The methodology adopted for the paper consisted of the structured review of 35 Environmental Statements (ESs) along with interviews with regulators, operators, consultants and advisory bodies. The findings reveal a mixed picture of EIA performance with a significant number of ESs falling short of satisfactory quality and a tendency for the process to be driven by compliance rather than best practice.     

Land cover mapping based on random forest classification of multitemporal spectral and thermal images

Thematic mapping of complex landscapes, with various phenological patterns from satellite imagery, is a particularly challenging task. However, supplementary information, such as multitemporal data and/or land surface temperature (LST), has the potential to improve the land cover classification accuracy and efficiency. In this paper, in order to map land covers, we evaluated the potential of multitemporal Landsat 8’s spectral and thermal imageries using a random forest (RF) classifier. We used a grid search approach based on the out-of-bag (OOB) estimate of error to optimize the RF parameters. Four different scenarios were considered in this research: (1) RF classification of multitemporal spectral images, (2) RF classification of multitemporal LST images, (3) RF classification of all multitemporal LST and spectral images, and (4) RF classification of selected important or optimum features. The study area in this research was Naghadeh city and its surrounding region, located in West Azerbaijan Province, northwest of Iran. The overall accuracies of first, second, third, and fourth scenarios were equal to 86.48, 82.26, 90.63, and 91.82 %, respectively. The quantitative assessments of the results demonstrated that the most important or optimum features increase the class separability, while the spectral and thermal features produced a more moderate increase in the land cover mapping accuracy. In addition, the contribution of the multitemporal thermal information led to a considerable increase in the user and producer accuracies of classes with a rapid temporal change behavior, such as crops and vegetation.     

Desert disturbance assessments of regional oil exploitation by Aster and ETM+ images in Taklimakan Desert China

To feed its rapidly growing energy demand, oil exploitation in China has never been more intensive. The most obvious characteristics of oil exploitation are progressive and regional, which can be monitored by remote sensing, such as land use and cover change, either perpetual or temporary, during oil field development such as construction of oil well, roads, transportation systems and other facilities. In this paper, the oil field located on the north edge of Taklimakan Desert, in the Tarim River watershed in northwest of China. The disturbance effects of regional oil exploitation were the main content of regional environmental managements and monitoring. Based on Enhanced Thematic Mapper Plus (ETM+) and Aster images, analyzed regional land use and landscape change from 2001 to 2003. By the comparison, it can be concluded that the ecological quality was deteriorating in these 3 years. The woodland was degrading to grass and dessert. The area of woodland dropped from 9.06 km(2) in 2001 to 3.24 km(2) in 2003 with a 64.23% decrease. At the same time, the area of shrubbery lessened 18.23%. On the other hand, the whole area of dessert and Saline soils inflated from 15.08 km(2) in 2001 to 25.36 km(2) in 2003. The patch number of bare land did climb dramatically, but single patch area increased. The research demonstrated that dessert and Saline soils patches were activated by the human behavior and climate change. The information from the ETM+ and Aster images was proved be an effective and efficient way to be applied in regional environmental managements.     

Metals in water, sediments, and biota of an offshore oil exploration area in the Potiguar Basin, Northeastern Brazil

Metal concentrations were evaluated in water, bottom sediments, and biota in four field campaigns from 2002 to 2004 in the Potiguar Basin, northeastern Brazil, where offshore oil exploration occurs. Analyses were performed by inductively coupled plasma mass spectrometry and inductively coupled plasma optical emission spectrometry. Total metal concentrations in water (dissolved?+?particulate) and sediments were in the range expected for coastal and oceanic areas. Abnormally high concentrations in waters were only found for Ba (80 μg?l^?1) and Mn (12 μg?l^?1) at the releasing point of one of the outfalls, and for the other metals, concentrations in water were found in stations closer to shore, suggesting continental inputs. In bottom sediments, only Fe and Mn showed abnormal concentrations closer to the effluent releasing point. Metal spatial distribution in shelf sediments showed the influence of the silt–clay fraction distribution, with deeper stations at the edge of the continental shelf, which are much richer in silt–clay fraction showing higher concentrations than shallower sediments typically dominated by carbonates. Metal concentrations in estuarine (mollusks and crustaceans) and marine (fish) organisms showed highest concentrations in oysters (Crassostrea rhizophorae). Fish tissues metal concentrations were similar between the continental shelf influenced by the oil exploration area and a control site. The results were within the range of concentrations reported for pristine environments without metals contamination. The global results suggest small, if any, alteration in metal concentrations due to the oil exploration activity in the Potiguar Basin. For monitoring purposes, the continental inputs and the distribution of the clay–silt fraction need to be taken into consideration for interpreting environmental monitoring results.     

Advancing strategic environmental assessment in the offshore oil and gas sector: Lessons from Norway,Canada, and the United Kingdom

Abstract: Strategic environmental assessment (SEA) for offshore oil and gas planning and development is utilized in select international jurisdictions, but the sector has received limited attention in the SEA literature. While the potential benefits of and rationale for SEA are well argued, there have been few empirical studies of SEA processes for the offshore sector. Hence, little is known about the efficacy of SEA offshore, in particular its influence on planning and development decisions. This paper examines SEA practice and influence in three international offshore systems: Norway, Atlantic Canada and the United Kingdom, with the intent to identify the challenges, lessons and opportunities for advancing SEA in offshore planning and impact assessment. Results demonstrate that SEA can help inform and improve the efficacy and efficiency of project-based assessment in the offshore sector, however weak coordination between higher and lower tiers limit SEA's ability to influence planning and development decisions in a broad regional environmental and socioeconomic context.     

Effectiveness and potential ecological effects of offshore surface dispersant use during the Deepwater Horizon oil spill: a retrospective analysis of monitoring data

The Special Monitoring of Applied Response Technologies (SMART) program was used during the Deepwater Horizon oil spill as a strategy to monitor the effectiveness of sea surface dispersant use. Although SMART was implemented during aerial and vessel dispersant applications, this analysis centers on the effort of a special dispersant missions onboard the M/V International Peace, which evaluated the effectiveness of surface dispersant applications by vessel only. Water samples (n?=?120) were collected from background sites, and under naturally and chemically dispersed oil slicks, and were analyzed for polycyclic aromatic hydrocarbons (TPAHs), total petroleum hydrocarbons (TPH), and a chemical marker of Corexit® (dipropylene glycol n-butyl ether, DPnB). Water chemistry results were analyzed relative to SMART field assessments of dispersant effectiveness (“not effective,” “effective,” and “very effective”), based on in situ fluorometry. Chemistry data were also used to indirectly determine if the use of dispersants increased the risk of acute effects to water column biota, by comparison to toxicity benchmarks. TPAH and TPH concentrations in background, and naturally and chemically dispersed samples were extremely variable, and differences were not statistically detected across sample types. Ratios of TPAH and TPH between chemically and naturally dispersed samples provided a quantitative measure of dispersant effectiveness over natural oil dispersion alone, and were in reasonable agreement with SMART field assessments of dispersant effectiveness. Samples from “effective” and “very effective” dispersant applications had ratios of TPAH and TPH up to 35 and 64, respectively. In two samples from an “effective” dispersant application, TPHs and TPAHs exceeded acute benchmarks (0.81 mg/L and 8 μg/L, respectively), while none exceeded DPnB’s chronic value (1,000 μg/L). Although the primary goal of the SMART program is to provide near real-time effectiveness data to the response, and not to address concerns regarding acute biological effects, the analyses presented here demonstrate that SMART can generate information of value to a larger scientific audience. A series of recommendations for future SMART planning are also provided.     

Heat wave hazard classification and risk assessment using artificial intelligence fuzzy logic

The average summer temperatures as well as the frequency and intensity of hot days and heat waves are expected to increase due to climate change. Motivated by this consequence, we propose a methodology to evaluate the monthly heat wave hazard and risk and its spatial distribution within large cities. A simple urban climate model with assimilated satellite-derived land surface temperature images was used to generate a historic database of urban air temperature fields. Heat wave hazard was then estimated from the analysis of these hourly air temperatures distributed at a 1-km grid over Athens, Greece, by identifying the areas that are more likely to suffer higher temperatures in the case of a heat wave event. Innovation lies in the artificial intelligence fuzzy logic model that was used to classify the heat waves from mild to extreme by taking into consideration their duration, intensity and time of occurrence. The monthly hazard was subsequently estimated as the cumulative effect from the individual heat waves that occurred at each grid cell during a month. Finally, monthly heat wave risk maps were produced integrating geospatial information on the population vulnerability to heat waves calculated from socio-economic variables.     

Yield and leaf area index estimations for sunflower plants using unmanned aerial vehicle images

Vegetation is commonly monitored to improve efficiency of various agricultural practices. Spatial and temporal changes in plant growth and development can be monitored with the aid of remote sensing techniques employing ground, aerial, and satellite platforms. Unmanned aerial vehicles (UAV) and multi-spectral cameras developed for UAVs have an important potential for agricultural management activities with high-resolution spatial and temporal images. However, UAV images should be assessed based on ground measurements for using these images as a decision-support tool in agriculture. This study was conducted to estimate sunflower leaf area index (LAI) and yield with the aid of Normalized Difference Vegetation Index (NDVI) images generated from raw UAV images. Furthermore, UAV-based NDVI values were compared with NDVI values calculated by using hyper-spectral measurements carried out with a ground-based spectroradiometer. Between July and August of 2017, six flight missions were conducted and spectral measurements were made simultaneously. A significant correlation (R²?=?0.77) was determined between NDVI values that belong to UAV platform and spectroradiometer. Also, regression models developed for sunflower LAI and yield estimation depending UAV-based NDVI have R² values of 0.88 and 0.91, respectively.     

Feature extraction using Hough transform for solid waste bin level detection and classification

This paper deals with the solid waste image detection and classification to detect and classify the solid waste bin level. To do so, Hough transform techniques is used for feature extraction to identify the line detection based on image’s gradient field. The feedforward neural network (FFNN) model is used to classify the level content of solid waste based on learning concept. Numbers of training have been performed using FFNN to learn and match the targets of the testing images to compute the sum squared error with the performance goal met. The images for each class are used as input samples for classification. Result from the neural network and the rules decision are used to build the receiver operating characteristic (ROC) graph. Decision graph shows the performance of the system waste system based on area under curve (AUC), WS-class reached 0.9875 for excellent result and WS-grade reached 0.8293 for good result. The system has been successfully designated with the motivation of solid waste bin monitoring system that can applied to a wide variety of local municipal authorities system.     

植物油烟组分的色质联机分析

将植物油烟采集在玻璃纤维滤筒中 ,用环己烷处理样品 ,样品经过净化处理 ,然后进行色质联机分析。对油烟化学成分的分析有助于人们研究油烟对人体健康的影响