USE OF VERTICAL DIGITAL PHOTOGRAPHY AT THE BAYOU PEROT, LA SPILL FOR OIL MAPPING AND VOLUME ESTIMATION

ABSTRACT The January 2007 release of over 8,000 barrels of a condensate crude oil from a damaged well in Bayou Perot, Louisiana resulted in intermittent oiling of remote mud flats and salt marshes over a 30 square mile area. NOAA'S National Geodetic Survey collected aerial vertical digital photography 17 days after the spill to assist in locating and quantifying areas of oiling. The effective pixel size was 34 centimeters, however, the data were processed to 40 centimeter resolution. Useful products were posted to the web within two days after acquisition. Standard supervised and unsupervised image processing techniques were used in conjunction with oblique photography and field knowledge to define the oiling signatures. Time constraints required that the classification be conducted on mosaiced, non-color balanced images (ideally each image would be classified independently to account for differences in illumination and/or processing). However, the strong visible signature of the oiled areas and ground-truth data from field surveys resulted in high confidence levels for several oil types which in turn were used to enhance the identification of the remaining classes. Five oil types were identified: Black (218,000 ft2), Red (81,000 ft2), Orange (154,000 ft2), Yellow (38,000 ft2), and Light Yellow (349,000 ft2) corresponding to the color and attributes of the oil. The total conservative estimate of oiled area was 840,000 ft2 or nearly 20 acres. Based on estimated thicknesses of the different oils, the total volume of oil present at the time of imagery acquisition was 3,330 barrels. This value was close to the actual amount of oil recovered over the time period between the date of imagery acquisition and the end of cleanup.

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Multi-Temporal Vineyard Monitoring through UAV-Based RGB Imagery

Remote Sensing ◽

10.3390/rs10121907 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1907 ◽

Cited By ~ 18

Author(s):

Luís Pádua ◽

Pedro Marques ◽

Jonáš Hruška ◽

Telmo Adão ◽

Emanuel Peres ◽

...

Keyword(s):

Accuracy Assessment ◽

Low Cost ◽

Ground Truth ◽

Volume Estimation ◽

Height Range ◽

Ground Truth Data ◽

Operational Technique ◽

Digital Elevation ◽

Multi Temporal ◽

Aerial Vehicle

This study aimed to characterize vineyard vegetation thorough multi-temporal monitoring using a commercial low-cost rotary-wing unmanned aerial vehicle (UAV) equipped with a consumer-grade red/green/blue (RGB) sensor. Ground-truth data and UAV-based imagery were acquired on nine distinct dates, covering the most significant vegetative growing cycle until harvesting season, over two selected vineyard plots. The acquired UAV-based imagery underwent photogrammetric processing resulting, per flight, in an orthophoto mosaic, used for vegetation estimation. Digital elevation models were used to compute crop surface models. By filtering vegetation within a given height-range, it was possible to separate grapevine vegetation from other vegetation present in a specific vineyard plot, enabling the estimation of grapevine area and volume. The results showed high accuracy in grapevine detection (94.40%) and low error in grapevine volume estimation (root mean square error of 0.13 m and correlation coefficient of 0.78 for height estimation). The accuracy assessment showed that the proposed method based on UAV-based RGB imagery is effective and has potential to become an operational technique. The proposed method also allows the estimation of grapevine areas that can potentially benefit from canopy management operations.

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ASSESSMENT OF LAND USE AND LAND COVER CHANGE DETECTION BY USING REMOTE SENSING AND GIS TECHNIQUES IN THE COASTAL DESERTS, SOUTH OF IRAN

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w18-489-2019 ◽

2019 ◽

Vol XLII-4/W18 ◽

pp. 489-492

Author(s):

S. A. R. Hosseini ◽

H. Gholami ◽

Y. Esmaeilpoor

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Change Detection ◽

Urban Areas ◽

Ground Truth ◽

Landsat Images ◽

Ground Truth Data ◽

Sensing Technology ◽

Time Period

Abstract. Land use/land cover (LULC) changes have become a central issue in current global change and sustainability research. Due to the large expanse of land change detection by the traditional methods is not sufficient and efficient; therefore, using of new methods such as remote sensing technology is necessary and vital This study evaluates LULC change in chabahar and konarak Coastal deserts, located in south of sistan and baluchestan province from 1988 to 2018 using Landsat images. Maximum likelihood classification were used to develop LULC maps. The change detection was executed using post-classification comparison and GIS. Then, taking ground truth data, the classified maps accuracy were assessed by calculating the Kappa coefficient and overall accuracy. The results for the time period of 1988–2018 are presented. Based on the results of the 30-year time period, vegetation has been decreased in area while urban areas have been developed. The area of saline and sandy lands has also increased.

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Vine Identification and Characterization in Goblet-Trained Vineyards Using Remotely Sensed Images

Remote Sensing ◽

10.3390/rs13152992 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2992

Author(s):

Chantal Hajjar ◽

Ghassan Ghattas ◽

Maya Kharrat Sarkis ◽

Yolla Ghorra Chamoun

Keyword(s):

Mortality Rate ◽

Spatial Patterns ◽

Ground Truth ◽

Aerial Images ◽

Color Intensity ◽

Ground Truth Data ◽

Green Color ◽

Novel Approach ◽

Processing Techniques ◽

Identification And Characterization

This paper proposes a novel approach for living and missing vine identification and vine characterization in goblet-trained vine plots using aerial images. Given the periodic structure of goblet vineyards, the RGB color coded parcel image is analyzed using proper processing techniques in order to determine the locations of living and missing vines. Vine characterization is achieved by implementing the marker-controlled watershed transform where the centers of the living vines serve as object markers. As a result, a precise mortality rate is calculated for each parcel. Moreover, all vines, even the overlapping ones, are fully recognized providing information about their size, shape, and green color intensity. The presented approach is fully automated and yields accuracy values exceeding 95% when the obtained results are assessed with ground-truth data. This unsupervised and automated approach can be applied to any type of plots presenting similar spatial patterns requiring only the image as input.

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Angle count sampling reliability as ground truth for area-based LiDAR applications in forest inventories

Canadian Journal of Forest Research ◽

10.1139/cjfr-2014-0408 ◽

2015 ◽

Vol 45 (4) ◽

pp. 506-514 ◽

Cited By ~ 13

Author(s):

Gianfranco Scrinzi ◽

Fabrizio Clementel ◽

Antonio Floris

Keyword(s):

Ground Truth ◽

Visual Assessment ◽

Volume Estimation ◽

Tree Canopy ◽

Volume Data ◽

Ground Truth Data ◽

Forest Inventories ◽

Fixed Area ◽

Fixed Radius ◽

Angle Count Sampling

LiDAR-based techniques to estimate forest variables at the stand level require accurate calibration through ground truth data. One purpose of this study was to verify whether angle count samples can be used as suitable ground truth to calibrate LiDAR-based models for timber volume estimation. Volume data were acquired on the ground for 79 plots in the Latemar forest (province of Bolzano, Italian Alps). A simple linear regression model, using the sum of all of the tree canopy heights in the plot as the explanatory variable, was adopted. As angle count samples have no fixed area, three different methods to approximate their size were compared. The angle count sample area can be properly approximated by visual assessment of the tree size classes and by callipering the largest tree in the plot. The results show that angle count sampling can be an efficient solution to calibrate LiDAR-based models: they produced fair estimates at the plot level (relative root mean square error (RMSE), 26.6%) that were better than fixed-radius plot estimates with full callipering (RMSE, 29.7%). Estimate uncertainty at increasingly large forest stand areas was also calculated by means of a simulation procedure. It showed that low uncertainty (standard error of estimate = approximately 2%) could be reached at a forest compartment level (19 ha on average).

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Multi-Frequency and Multi-Temporal InSAR Analysis for Monitoring Land Subsidence, Aswan City, Egypt

10.5194/egusphere-egu21-292 ◽

2021 ◽

Author(s):

Noha Medhat ◽

Masa-yuki Yamamoto ◽

Gad El-Qady

Keyword(s):

Land Subsidence ◽

Urban Areas ◽

Ground Deformation ◽

Anthropogenic Impacts ◽

Ground Truth ◽

Subsurface Water ◽

Ground Truth Data ◽

Time Period ◽

Management Plans ◽

Multi Temporal

<p>Land deformation due to natural and anthropogenic impacts considered to be one of the challenging environmental problems in the Aswan area located in the southern part of Egypt. Specifically, we applied multi-sensor analysis in order to record the slow rate of subsidence with a high spatial resolution of COSMO-SkyMed (X-band) and Sentinel-1 TOPSAR (C-band) scenes. We proposed multi-temporal DInSAR data analysis by means of ascending and descending orbit tracks during the recent time period of 2012-2017. The stacked DInSAR results reported the occurrence of land subsidence of active urban areas. A strong correlation between the ground truth data, ground leveling, and the estimated Line of Sight (LOS) displacement time series values are reached, assuming the ground deformation controlled by seasonal surface water loading, lithological units, and subsurface water activity. The detection of short-term displacement highlights the priority of groundwater management plans in the affected urban areas.</p>

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Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

Remote Sensing ◽

10.3390/rs13101966 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1966

Author(s):

Christopher W Smith ◽

Santosh K Panda ◽

Uma S Bhatt ◽

Franz J Meyer ◽

Anushree Badola ◽

...

Keyword(s):

Boreal Forest ◽

Ground Truth ◽

Burn Severity ◽

Classification Methods ◽

Spectral Indices ◽

Ground Truth Data ◽

Burn Scar ◽

Interior Alaska ◽

Remote Sensing Methods ◽

The Relationship

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.

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Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision

Remote Sensing ◽

10.3390/rs13010026 ◽

2020 ◽

Vol 13 (1) ◽

pp. 26

Author(s):

Wen-Hao Su ◽

Jiajing Zhang ◽

Ce Yang ◽

Rae Page ◽

Tamas Szinyei ◽

...

Keyword(s):

Fusarium Head Blight ◽

Ground Truth ◽

Wheat Breeding ◽

Head Blight ◽

Detection Rates ◽

Ground Truth Data ◽

Resistant Cultivars ◽

Feature Pyramid ◽

Rater Error ◽

Wheat Lines

In many regions of the world, wheat is vulnerable to severe yield and quality losses from the fungus disease of Fusarium head blight (FHB). The development of resistant cultivars is one means of ameliorating the devastating effects of this disease, but the breeding process requires the evaluation of hundreds of lines each year for reaction to the disease. These field evaluations are laborious, expensive, time-consuming, and are prone to rater error. A phenotyping cart that can quickly capture images of the spikes of wheat lines and their level of FHB infection would greatly benefit wheat breeding programs. In this study, mask region convolutional neural network (Mask-RCNN) allowed for reliable identification of the symptom location and the disease severity of wheat spikes. Within a wheat line planted in the field, color images of individual wheat spikes and their corresponding diseased areas were labeled and segmented into sub-images. Images with annotated spikes and sub-images of individual spikes with labeled diseased areas were used as ground truth data to train Mask-RCNN models for automatic image segmentation of wheat spikes and FHB diseased areas, respectively. The feature pyramid network (FPN) based on ResNet-101 network was used as the backbone of Mask-RCNN for constructing the feature pyramid and extracting features. After generating mask images of wheat spikes from full-size images, Mask-RCNN was performed to predict diseased areas on each individual spike. This protocol enabled the rapid recognition of wheat spikes and diseased areas with the detection rates of 77.76% and 98.81%, respectively. The prediction accuracy of 77.19% was achieved by calculating the ratio of the wheat FHB severity value of prediction over ground truth. This study demonstrates the feasibility of rapidly determining levels of FHB in wheat spikes, which will greatly facilitate the breeding of resistant cultivars.

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Classification of Cattle Behaviours Using Neck-Mounted Accelerometer-Equipped Collars and Convolutional Neural Networks

Sensors ◽

10.3390/s21124050 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4050

Author(s):

Dejan Pavlovic ◽

Christopher Davison ◽

Andrew Hamilton ◽

Oskar Marko ◽

Robert Atkinson ◽

...

Keyword(s):

Neural Network ◽

Model Performance ◽

Ground Truth ◽

Practical Implementation ◽

Ground Truth Data ◽

Battery Lifetime ◽

Implementation Challenges ◽

Memory Footprint ◽

Commercial Farms ◽

Using Data

Monitoring cattle behaviour is core to the early detection of health and welfare issues and to optimise the fertility of large herds. Accelerometer-based sensor systems that provide activity profiles are now used extensively on commercial farms and have evolved to identify behaviours such as the time spent ruminating and eating at an individual animal level. Acquiring this information at scale is central to informing on-farm management decisions. The paper presents the development of a Convolutional Neural Network (CNN) that classifies cattle behavioural states (`rumination’, `eating’ and `other’) using data generated from neck-mounted accelerometer collars. During three farm trials in the United Kingdom (Easter Howgate Farm, Edinburgh, UK), 18 steers were monitored to provide raw acceleration measurements, with ground truth data provided by muzzle-mounted pressure sensor halters. A range of neural network architectures are explored and rigorous hyper-parameter searches are performed to optimise the network. The computational complexity and memory footprint of CNN models are not readily compatible with deployment on low-power processors which are both memory and energy constrained. Thus, progressive reductions of the CNN were executed with minimal loss of performance in order to address the practical implementation challenges, defining the trade-off between model performance versus computation complexity and memory footprint to permit deployment on micro-controller architectures. The proposed methodology achieves a compression of 14.30 compared to the unpruned architecture but is nevertheless able to accurately classify cattle behaviours with an overall F1 score of 0.82 for both FP32 and FP16 precision while achieving a reasonable battery lifetime in excess of 5.7 years.

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A machine learning approach to estimate the strain energy absorption in expanded polystyrene foams

Journal of Cellular Plastics ◽

10.1177/0021955x211021014 ◽

2021 ◽

pp. 0021955X2110210

Author(s):

Alejandro E Rodríguez-Sánchez ◽

Héctor Plascencia-Mora

Keyword(s):

Neural Network ◽

Energy Absorption ◽

Mechanical Energy ◽

Compressive Loading ◽

Ground Truth ◽

Expanded Polystyrene ◽

Polystyrene Foam ◽

Stress Strain ◽

Ground Truth Data ◽

Expanded Polystyrene Foam

Traditional modeling of mechanical energy absorption due to compressive loadings in expanded polystyrene foams involves mathematical descriptions that are derived from stress/strain continuum mechanics models. Nevertheless, most of those models are either constrained using the strain as the only variable to work at large deformation regimes and usually neglect important parameters for energy absorption properties such as the material density or the rate of the applying load. This work presents a neural-network-based approach that produces models that are capable to map the compressive stress response and energy absorption parameters of an expanded polystyrene foam by considering its deformation, compressive loading rates, and different densities. The models are trained with ground-truth data obtained in compressive tests. Two methods to select neural network architectures are also presented, one of which is based on a Design of Experiments strategy. The results show that it is possible to obtain a single artificial neural networks model that can abstract stress and energy absorption solution spaces for the conditions studied in the material. Additionally, such a model is compared with a phenomenological model, and the results show than the neural network model outperforms it in terms of prediction capabilities, since errors around 2% of experimental data were obtained. In this sense, it is demonstrated that by following the presented approach is possible to obtain a model capable to reproduce compressive polystyrene foam stress/strain data, and consequently, to simulate its energy absorption parameters.

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Multi-Temporal Arable Land Monitoring in Arid Region of Northwest China Using a New Extraction Index

Sustainability ◽

10.3390/su13095274 ◽

2021 ◽

Vol 13 (9) ◽

pp. 5274

Author(s):

Xinyang Yu ◽

Younggu Her ◽

Xicun Zhu ◽

Changhe Lu ◽

Xuefei Li

Keyword(s):

Arable Land ◽

Ground Truth ◽

Northwest China ◽

Hexi Corridor ◽

Ground Truth Data ◽

Land Protection ◽

Promising Tool ◽

Study Results ◽

Multi Temporal ◽

The Mean

Development of a high-accuracy method to extract arable land using effective data sources is crucial to detect and monitor arable land dynamics, servicing land protection and sustainable development. In this study, a new arable land extraction index (ALEI) based on spectral analysis was proposed, examined by ground truth data, and then applied to the Hexi Corridor in northwest China. The arable land and its change patterns during 1990–2020 were extracted and identified using 40 Landsat TM/OLI images acquired in 1990, 2000, 2010, and 2020. The results demonstrated that the proposed method can distinguish arable land areas accurately, with the User’s (Producer’s) accuracy and overall accuracy (kappa coefficient) exceeding 0.90 (0.88) and 0.89 (0.87), respectively. The mean relative error calculated using field survey data obtained in 2012 and 2020 was 0.169 and 0.191, respectively, indicating the feasibility of the ALEI method in arable land extracting. The study found that arable land area in the Hexi Corridor was 13217.58 km2 in 2020, significantly increased by 25.33% compared to that in 1990. At 10-year intervals, the arable land experienced different change patterns. The study results indicate that ALEI index is a promising tool used to effectively extract arable land in the arid area.

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