scholarly journals Meta-Analysis of Wetland Classification Using Remote Sensing: A Systematic Review of a 40-Year Trend in North America

2020 ◽  
Vol 12 (11) ◽  
pp. 1882 ◽  
Author(s):  
Masoud Mahdianpari ◽  
Jean Elizabeth Granger ◽  
Fariba Mohammadimanesh ◽  
Bahram Salehi ◽  
Brian Brisco ◽  
...  
Keyword(s):  
Remote Sensing ◽  
North America ◽  
Spatial Resolution ◽  
Meta Analysis ◽  
Future Research ◽  
Wetland Classification ◽  
Classification Methods ◽  
Wetland Ecosystems ◽  
Sensing Technology ◽  
Recreational Benefits

North America is covered in 2.5 million km2 of wetlands, which is the remainder of an estimated 56% of wetlands lost since the 1700s. This loss has resulted in a decrease in important habitat and services of great ecological, economic, and recreational benefits to humankind. To better manage these ecosystems, since the 1970s, wetlands in North America have been classified with increasing regularity using remote sensing technology. Since then, optimal methods for wetland classification by numerous researchers have been examined, assessed, modified, and established. Over the past several decades, a large number of studies have investigated the effects of different remote sensing factors, such as data type, spatial resolution, feature selection, classification methods, and other parameters of interest on wetland classification in North America. However, the results of these studies have not yet been synthesized to determine best practices and to establish avenues for future research. This paper reviews the last 40 years of research and development on North American wetland classification through remote sensing methods. A meta-analysis of 157 relevant articles published since 1980 summarizes trends in 23 parameters, including publication, year, study location, application of specific sensors, and classification methods. This paper also examines is the relationship between several remote sensing parameters (e.g., spatial resolution and type of data) and resulting overall accuracies. Finally, this paper discusses the future of remote sensing of wetlands in North America with regard to upcoming technologies and sensors. Given the increasing importance and vulnerability of wetland ecosystems under the climate change influences, this paper aims to provide a comprehensive review in support of the continued, improved, and novel applications of remote sensing for wetland mapping across North America and to provide a fundamental knowledge base for future studies in this field.

scholarly journals Status and Trends of Wetland Studies in Canada Using Remote Sensing Technology with a Focus on Wetland Classification: A Bibliographic Analysis

2021 ◽  
Vol 13 (20) ◽  
pp. 4025
Author(s):  
S. Mohammad Mirmazloumi ◽  
Armin Moghimi ◽  
Babak Ranjgar ◽  
Farzane Mohseni ◽  
Arsalan Ghorbanian ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Meta Analysis ◽  
Classification Systems ◽  
Machine Learning Algorithms ◽  
Wetland Classification ◽  
Wetland Mapping ◽  
Sensing Technology ◽  
Status And Trends ◽  
The Status ◽  
Supervised Classifiers

A large portion of Canada is covered by wetlands; mapping and monitoring them is of great importance for various applications. In this regard, Remote Sensing (RS) technology has been widely employed for wetland studies in Canada over the past 45 years. This study evaluates meta-data to investigate the status and trends of wetland studies in Canada using RS technology by reviewing the scientific papers published between 1976 and the end of 2020 (300 papers in total). Initially, a meta-analysis was conducted to analyze the status of RS-based wetland studies in terms of the wetland classification systems, methods, classes, RS data usage, publication details (e.g., authors, keywords, citations, and publications time), geographic information, and level of classification accuracies. The deep systematic review of 128 peer-reviewed articles illustrated the rising trend in using multi-source RS datasets along with advanced machine learning algorithms for wetland mapping in Canada. It was also observed that most of the studies were implemented over the province of Ontario. Pixel-based supervised classifiers were the most popular wetland classification algorithms. This review summarizes different RS systems and methodologies for wetland mapping in Canada to outline how RS has been utilized for the generation of wetland inventories. The results of this review paper provide the current state-of-the-art methods and datasets for wetland studies in Canada and will provide direction for future wetland mapping research.

scholarly journals Detection of Collapsed Buildings in Post-Earthquake Remote Sensing Images Based on the Improved YOLOv3

2019 ◽  
Vol 12 (1) ◽  
pp. 44 ◽  
Author(s):  
Haojie Ma ◽  
Yalan Liu ◽  
Yuhuan Ren ◽  
Jingxian Yu
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Detection Methods ◽  
Remote Sensing Images ◽  
Sensing Technology ◽  
Collapsed Buildings ◽  
Improved Model ◽  
Detection Speed

An important and effective method for the preliminary mitigation and relief of an earthquake is the rapid estimation of building damage via high spatial resolution remote sensing technology. Traditional object detection methods only use artificially designed shallow features on post-earthquake remote sensing images, which are uncertain and complex background environment and time-consuming feature selection. The satisfactory results from them are often difficult. Therefore, this study aims to apply the object detection method You Only Look Once (YOLOv3) based on the convolutional neural network (CNN) to locate collapsed buildings from post-earthquake remote sensing images. Moreover, YOLOv3 was improved to obtain more effective detection results. First, we replaced the Darknet53 CNN in YOLOv3 with the lightweight CNN ShuffleNet v2. Second, the prediction box center point, XY loss, and prediction box width and height, WH loss, in the loss function was replaced with the generalized intersection over union (GIoU) loss. Experiments performed using the improved YOLOv3 model, with high spatial resolution aerial remote sensing images at resolutions of 0.5 m after the Yushu and Wenchuan earthquakes, show a significant reduction in the number of parameters, detection speed of up to 29.23 f/s, and target precision of 90.89%. Compared with the general YOLOv3, the detection speed improved by 5.21 f/s and its precision improved by 5.24%. Moreover, the improved model had stronger noise immunity capabilities, which indicates a significant improvement in the model’s generalization. Therefore, this improved YOLOv3 model is effective for the detection of collapsed buildings in post-earthquake high-resolution remote sensing images.

The Research Review of Remote Sensing Survey on Geological Landscape Tourism Resources

2013 ◽  
Vol 726-731 ◽  
pp. 4705-4708 ◽  
Author(s):  
Wei Luo ◽  
Ling Mao ◽  
Bao Li Liu ◽  
Zhi Wu Yan
Keyword(s):  
Remote Sensing ◽  
Dynamic Change ◽  
Resource Planning ◽  
Research Review ◽  
Future Research ◽  
Research Issues ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Tourism Resources ◽  
Space Layout

Geological landscape is a kind of important tourism resources whose survey is the forerunner of tourism resource planning and development. The application of remote sensing technology develops a revolution of investigation method of geological landscape tourism resources. By analyzing domestic and international investigations and researches of remote sensing of geological landscape tourism resources, this paper considers that remote sensing technology is mainly applied to the investigation of regional geological environment background. The macro characteristics of regional geological landscape tourism resources such as resources area, type identification, dynamic change, space layout, formation lithology, topography distribution are hot research issues, and the analysis and measurement of micro characteristic values of regional geological landscape tourism resources is an important direction of future research.

scholarly journals UAS-Based Archaeological Remote Sensing: Review, Meta-Analysis and State-of-the-Art

Drones ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 46 ◽  
Author(s):  
Efstathios Adamopoulos ◽  
Fulvio Rinaudo
Keyword(s):  
Remote Sensing ◽  
State Of The Art ◽  
Meta Analysis ◽  
Visible Spectrum ◽  
Unmanned Aircraft ◽  
Future Research ◽  
Radar Sensors ◽  
Technological Developments ◽  
Aerial Platforms ◽  
Historical Sites

Over the last decade, we have witnessed momentous technological developments in unmanned aircraft systems (UAS) and in lightweight sensors operating at various wavelengths, at and beyond the visible spectrum, which can be integrated with unmanned aerial platforms. These innovations have made feasible close-range and high-resolution remote sensing for numerous archaeological applications, including documentation, prospection, and monitoring bridging the gap between satellite, high-altitude airborne, and terrestrial sensing of historical sites and landscapes. In this article, we track the progress made so far, by systematically reviewing the literature relevant to the combined use of UAS platforms with visible, infrared, multi-spectral, hyper-spectral, laser, and radar sensors to reveal archaeological features otherwise invisible to archaeologists with applied non-destructive techniques. We review, specific applications and their global distribution, as well as commonly used platforms, sensors, and data-processing workflows. Furthermore, we identify the contemporary state-of-the-art and discuss the challenges that have already been overcome, and those that have not, to propose suggestions for future research.

scholarly journals Estimation of Chlorophyll Fluorescence at Different Scales: A Review

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 3000 ◽  
Author(s):  
Zhuoya Ni ◽  
Qifeng Lu ◽  
Hongyuan Huo ◽  
Huili Zhang
Keyword(s):  
Remote Sensing ◽  
Chlorophyll Fluorescence ◽  
Multiple Scales ◽  
Future Research ◽  
Passive Remote Sensing ◽  
Advantages And Disadvantages ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Key Issues ◽  
Non Destructive

Measuring chlorophyll fluorescence is a direct and non-destructive way to monitor vegetation. In this paper, the fluorescence retrieval methods from multiple scales, ranging from near the ground to the use of space-borne sensors, are analyzed and summarized in detail. At the leaf-scale, the chlorophyll fluorescence is measured using active and passive technology. Active remote sensing technology uses a fluorimeter to measure the chlorophyll fluorescence, and passive remote sensing technology mainly depends on the sun-induced chlorophyll fluorescence filling in the Fraunhofer lines or oxygen absorptions bands. Based on these retrieval principles, many retrieval methods have been developed, including the radiance-based methods and the reflectance-based methods near the ground, as well as physically and statistically-based methods that make use of satellite data. The advantages and disadvantages of different approaches for sun-induced chlorophyll fluorescence retrieval are compared and the key issues of the current sun-induced chlorophyll fluorescence retrieval algorithms are discussed. Finally, conclusions and key problems are proposed for the future research.

scholarly journals Analyzing the Effects of Spatial Resolution for Small Landslide Susceptibility and Hazard Mapping

2014 ◽  
Vol XL-1 ◽  
pp. 293-300 ◽  
Author(s):  
O. E. Mora ◽  
M. G. Lenzano ◽  
C. K. Toth ◽  
D. A. Grejner-Brzezinska
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Hazard Mapping ◽  
Surface Features ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Landslide Morphology

Spatial resolution plays an important role in remote sensing technology as it defines the smallest scale at which surface features may be extracted, identified, and mapped. Remote sensing technology has become a vital component in recent developments for landslide susceptibility mapping. The spatial resolution is essential, especially when landslides are small and the dimensions of slope failures vary. If the spatial resolution is relevant to the surface features found in the landslide morphology, it will help improve the extraction, identification and mapping of landslide surface features. Although, the spatial resolution is a well-known issue, few studies have demonstrated the potential effects it may have on small landslide susceptibility mapping. For these reasons, an evaluation to assess the impact of spatial resolution was performed using data acquired along a transportation corridor in Zanesville, Ohio. Using a landslide susceptibility mapping algorithm, landslide surface features were extracted and identified on a cell-by-cell basis from Digital Elevation Models (DEM) generated at 50, 100, 200 and 400 cm spatial resolution. The performance of the landslide surface feature extraction algorithm was then evaluated using an inventory map and a confusion matrix to assess the effects of spatial resolution. In addition to assessing the performance of the algorithm, we statistically analyzed the surface features and their relevant patterns. The results from this evaluation reveal patterns caused by the varying spatial resolution. From this study we can conclude that the spatial resolution has an effect on the accuracy and surface features extracted for small landslide susceptibility mapping, as the performance is dependent on the scale of the landslide morphology.

scholarly journals Decision Support System for Hyperspectral Remote-Sensing Data of Yellow River Estuary, China

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Wang Ping ◽  
Jie Fu ◽  
Wenyu Qiao ◽  
Muhammad Yasir ◽  
Sheng Hui ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Coupling Coefficient ◽  
Yellow River ◽  
River Estuary ◽  
Hyperspectral Data ◽  
Yellow River Estuary ◽  
The Yellow River ◽  
Wetland Classification ◽  
Classification Methods ◽  
Machine Method

There are many wetland resources in the area where the Yellow River enters the sea. This area has good ecological and economic value. Therefore, wetlands are precious resources. The accuracy of traditional wetland classification methods is low (for example, based on the support machine method). In order to explore ways to improve the accuracy of wetland classification, this paper selected the wetland at the mouth of the Yellow River as the study area. And, we used the hyperspectral data of “Zhuhai No. 1” as the research data. Then, we used the logarithmic transformation method to enhance the spectral characteristics of remote-sensing images. Finally, we used Markov random field method (MRF) and support vector machine method (SVM) to finely classify the wetlands in the Yellow River estuary area. We used these experiments to explore wetland classification methods for hyperspectral data. The results showed that the settings of the coupling coefficient and the initial value in the Markov model had a greater impact on the classification results. We found that the best result was when the initial classification number is 50 and the coupling coefficient is 0.5. Compared with the SVM classification method, the overall classification accuracy of our proposed method was improved by 3.9672%, and the Kappa coefficient was improved by 0.042.

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