Mapping land-based oil spills using high spatial resolution unmanned aerial vehicle imagery and electromagnetic induction survey data

Mahdianpari, Masoud; Salehi, Bahram; Mohammadimanesh, Fariba; Larsen, Glen; Peddle, Derek R.

doi:10.1117/1.jrs.12.036015

Cited by 10 publications

(3 citation statements)

References 38 publications

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“…Assigning a label to each pixel is based on the majority vote of trees. RF can be tuned by adjusting two input parameters [70], namely the number of trees (Ntree), which is generated by randomly selecting samples from the training data, and the number of variables (Mtry), which is used for tree node splitting [71]. In this study, these parameters were selected based on (a) direction from previous studies (e.g., [56,69,72]) and (b) a trial-and-error approach.…”

Section: Random Forestmentioning

confidence: 99%

The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform

et al. 2018

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Wetlands are one of the most important ecosystems that provide a desirable habitat for a great variety of flora and fauna. Wetland mapping and modeling using Earth Observation (EO) data are essential for natural resource management at both regional and national levels. However, accurate wetland mapping is challenging, especially on a large scale, given their heterogeneous and fragmented landscape, as well as the spectral similarity of differing wetland classes. Currently, precise, consistent, and comprehensive wetland inventories on a national- or provincial-scale are lacking globally, with most studies focused on the generation of local-scale maps from limited remote sensing data. Leveraging the Google Earth Engine (GEE) computational power and the availability of high spatial resolution remote sensing data collected by Copernicus Sentinels, this study introduces the first detailed, provincial-scale wetland inventory map of one of the richest Canadian provinces in terms of wetland extent. In particular, multi-year summer Synthetic Aperture Radar (SAR) Sentinel-1 and optical Sentinel-2 data composites were used to identify the spatial distribution of five wetland and three non-wetland classes on the Island of Newfoundland, covering an approximate area of 106,000 km2. The classification results were evaluated using both pixel-based and object-based random forest (RF) classifications implemented on the GEE platform. The results revealed the superiority of the object-based approach relative to the pixel-based classification for wetland mapping. Although the classification using multi-year optical data was more accurate compared to that of SAR, the inclusion of both types of data significantly improved the classification accuracies of wetland classes. In particular, an overall accuracy of 88.37% and a Kappa coefficient of 0.85 were achieved with the multi-year summer SAR/optical composite using an object-based RF classification, wherein all wetland and non-wetland classes were correctly identified with accuracies beyond 70% and 90%, respectively. The results suggest a paradigm-shift from standard static products and approaches toward generating more dynamic, on-demand, large-scale wetland coverage maps through advanced cloud computing resources that simplify access to and processing of the “Geo Big Data.” In addition, the resulting ever-demanding inventory map of Newfoundland is of great interest to and can be used by many stakeholders, including federal and provincial governments, municipalities, NGOs, and environmental consultants to name a few.

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Section: Random Forestmentioning

confidence: 99%

The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform

et al. 2018

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“…About two thirds (i.e., in-bag) of the training samples are selected to produce trees with high variance and low bias using a bootstrap aggregating (bagging) approach. The remaining one third (i.e., out-of-bag, OOB) of the training samples are employed for an internal cross-validation accuracy assessment [71]. The best splitting of the nodes is determined by minimizing the correlation between trees and the final label is based on the majority vote of the trees [70].…”

Section: Classification Schemementioning

confidence: 99%

Full and Simulated Compact Polarimetry SAR Responses to Canadian Wetlands: Separability Analysis and Classification

Mohammadimanesh¹,

Salehi

Mahdianpari

et al. 2019

Remote Sensing

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Detailed information on spatial distribution of wetland classes is crucial for monitoring this important productive ecosystem using advanced remote sensing tools and data. Although the potential of full- and dual-polarimetric (FP and DP) Synthetic Aperture Radar (SAR) data for wetland classification has been well examined, the capability of compact polarimetric (CP) SAR data has not yet been thoroughly investigated. This is of great significance, since the upcoming RADARSAT Constellation Mission (RCM), which will soon be the main source of SAR observations in Canada, will have CP mode as one of its main SAR configurations. This also highlights the necessity to fully exploit such important Earth Observation (EO) data by examining the similarities and dissimilarities between FP and CP SAR data for wetland mapping. Accordingly, this study examines and compares the discrimination capability of extracted features from FP and simulated CP SAR data between pairs of wetland classes. In particular, 13 FP and 22 simulated CP SAR features are extracted from RADARSAT-2 data to determine their discrimination capabilities both qualitatively and quantitatively in three wetland sites, located in Newfoundland and Labrador, Canada. Seven of 13 FP and 15 of 22 CP SAR features are found to be the most discriminant, as they indicate an excellent separability for at least one pair of wetland classes. The overall accuracies of 87.89%, 80.67%, and 84.07% are achieved using the CP SAR data for the three wetland sites (Avalon, Deer Lake, and Gros Morne, respectively) in this study. Although these accuracies are lower than those of FP SAR data, they confirm the potential of CP SAR data for wetland mapping as accuracies exceed 80% in all three sites. The CP SAR data collected by RCM will significantly contribute to the efforts ongoing of conservation strategies for wetlands and monitoring changes, especially on large scales, as they have both wider swath coverage and improved temporal resolution compared to those of RADARSAT-2.

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“…In order to meet the requirements of land survey, it is particularly important to apply digital mapping technology, such as computer and satellite positioning. In the traditional land survey, many surveying and mapping links and jobs need to be carried out manually, which is greatly influenced by human factors, and the accuracy is very limited, and the workload is huge, which brings huge burden to the staff [3]. Traditional land surveying technology mainly uses remote sensing technology and surveying technology to map and survey land, but with the continuous growth of society, people's requirements for surveying technology and accuracy of land surveying are becoming higher and higher [4].…”

Section: Introductionmentioning

confidence: 99%

Research on Land Renovation Planning Strategy based on Digital Surveying and Mapping Technology

Murong¹

2022

FSE

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With the rapid growth of new surveying and mapping technology and surveying and mapping instruments, engineering surveying has also been developed rapidly. Land survey is a foundation for the country to effectively manage and plan land. When land is used, relevant information can be found in the database established in previous land surveys, so its accurate measurement method is particularly important. On the one hand, there are more and more land surveying work, and the surveying and mapping data of land surveying units are more accurate. On the other hand, with the increasing difficulty of land surveying, surveying units have put forward higher requirements for surveying and mapping technology. Under the background of the continuous growth of China's economy and society, the level of network information technology has improved rapidly, and digital information technology is widely used in land survey, which can improve the efficiency of land survey. This paper analyzes the application of digital mapping technology in land consolidation planning, hoping to provide some reference and suggestions for the smooth growth of land consolidation planning and the improvement of the accuracy of survey results.

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Mapping land-based oil spills using high spatial resolution unmanned aerial vehicle imagery and electromagnetic induction survey data

Cited by 10 publications

References 38 publications

The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform

The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform

Full and Simulated Compact Polarimetry SAR Responses to Canadian Wetlands: Separability Analysis and Classification

Research on Land Renovation Planning Strategy based on Digital Surveying and Mapping Technology

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