scholarly journals Designing a Validation Protocol for Remote Sensing Based Operational Forest Masks Applications. Comparison of Products Across Europe

2020 ◽  
Vol 12 (19) ◽  
pp. 3159
Author(s):  
Angel Fernandez-Carrillo ◽  
Antonio Franco-Nieto ◽  
Erika Pinto-Bañuls ◽  
Miguel Basarte-Mena ◽  
Beatriz Revilla-Romero
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Forest Cover ◽  
Performance Metrics ◽  
Temporal Dynamics ◽  
Forest Products ◽  
Future Research ◽  
Forest Types ◽  
Visual Interpretation ◽  
Sampling System

The spatial and temporal dynamics of the forest cover can be captured using remote sensing data. Forest masks are a valuable tool to monitor forest characteristics, such as biomass, deforestation, health condition and disturbances. This study was carried out under the umbrella of the EC H2020 MySustainableForest (MSF) project. A key achievement has been the development of supervised classification methods for delineating forest cover. The forest masks presented here are binary forest/non-forest classification maps obtained using Sentinel-2 data for 16 study areas across Europe with different forest types. Performance metrics can be selected to measure accuracy of forest mask. However, large-scale reference datasets are scarce and typically cannot be considered as ground truth. In this study, we implemented a stratified random sampling system and the generation of a reference dataset based on visual interpretation of satellite images. This dataset was used for validation of the forest masks, MSF and two other similar products: HRL by Copernicus and FNF by the DLR. MSF forest masks showed a good performance (OAMSF = 96.3%; DCMSF = 96.5), with high overall accuracy (88.7–99.5%) across all the areas, and omission and commission errors were low and balanced (OEMSF = 2.4%; CEMSF = 4.5%; relBMSF = 2%), while the other products showed on average lower accuracies (OAHRL = 89.2%; OAFNF = 76%). However, for all three products, the Mediterranean areas were challenging to model, where the complexity of forest structure led to relatively high omission errors (OEMSF = 9.5%; OEHRL = 59.5%; OEFNF = 71.4%). Comparing these results with the vision from external local stakeholders highlighted the need of establishing clear large-scale validation datasets and protocols for remote sensing-based forest products. Future research will be done to test the MSF mask in forest types not present in Europe and compare new outputs to available reference datasets.

scholarly journals Opportunistic Large Array Propagation Models: A Comprehensive Survey

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4206
Author(s):  
Farhan Nawaz ◽  
Hemant Kumar ◽  
Syed Ali Hassan ◽  
Haejoon Jung
Keyword(s):  
Energy Efficient ◽  
Large Scale ◽  
Performance Metrics ◽  
Experimental Studies ◽  
Cooperative Transmission ◽  
Analytical Models ◽  
Future Research ◽  
Large Array ◽  
Propagation Models ◽  
Comprehensive Survey

Enabled by the fifth-generation (5G) and beyond 5G communications, large-scale deployments of Internet-of-Things (IoT) networks are expected in various application fields to handle massive machine-type communication (mMTC) services. Device-to-device (D2D) communications can be an effective solution in massive IoT networks to overcome the inherent hardware limitations of small devices. In such D2D scenarios, given that a receiver can benefit from the signal-to-noise-ratio (SNR) advantage through diversity and array gains, cooperative transmission (CT) can be employed, so that multiple IoT nodes can create a virtual antenna array. In particular, Opportunistic Large Array (OLA), which is one type of CT technique, is known to provide fast, energy-efficient, and reliable broadcasting and unicasting without prior coordination, which can be exploited in future mMTC applications. However, OLA-based protocol design and operation are subject to network models to characterize the propagation behavior and evaluate the performance. Further, it has been shown through some experimental studies that the most widely-used model in prior studies on OLA is not accurate for networks with networks with low node density. Therefore, stochastic models using quasi-stationary Markov chain are introduced, which are more complex but more exact to estimate the key performance metrics of the OLA transmissions in practice. Considering the fact that such propagation models should be selected carefully depending on system parameters such as network topology and channel environments, we provide a comprehensive survey on the analytical models and framework of the OLA propagation in the literature, which is not available in the existing survey papers on OLA protocols. In addition, we introduce energy-efficient OLA techniques, which are of paramount importance in energy-limited IoT networks. Furthermore, we discuss future research directions to combine OLA with emerging technologies.

Remote sensing of animals

2001 ◽  
Vol 25 (3) ◽  
pp. 355-362 ◽  
Author(s):  
Thomas W. Gillespie
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
First Century ◽  
Future Research ◽  
Species Dispersal ◽  
Biogeographic Patterns ◽  
Environmental Correlates ◽  
Accuracy And Precision ◽  
Gis Data ◽  
Improved Accuracy

The improved accuracy and precision of animal tracking via satellites has made a significant impact on quantifying large-scale biogeographic patterns for a variety of taxa with important implications for conservation and natural resource management. This paper reviews research undertaken from 1995 to 1999 to provide an overview of advances in the remote sensing of animal movements in both terrestrial and marine environments and to identify promising trends for biogeographic research in the twenty-first century. Remote sensing of animals by satellite provides a new method to test a number of biogeographic hypotheses related to migration and can identify a number of environmental correlates associated with the distributions of species. Tracking of smaller species and increases in sample size are sure to occur as transmitter size and cost continue to decrease in the next decade. Geographers can significantly contribute to the understanding of species dispersal and distributional patterns by combining real-time and archived global and regional datasets with existing data from past studies and future research projects. Only four studies used GIS data or remote sensed imagery in this review, while the remaining studies cited used simple digital line graphs of countries, topography, land and sea boundaries.

scholarly journals STANDARDIZING QUALITY ASSESSMENT OF FUSED REMOTELY SENSED IMAGES

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 863-869 ◽  
Author(s):  
C. Pohl ◽  
J. Moellmann ◽  
K. Fries
Keyword(s):  
Remote Sensing ◽  
Image Quality ◽  
Image Fusion ◽  
Quality Assessment ◽  
Quantitative Assessment ◽  
Visual Assessment ◽  
Future Research ◽  
Visual Interpretation ◽  
Fused Image ◽  
The University

The multitude of available operational remote sensing satellites led to the development of many image fusion techniques to provide high spatial, spectral and temporal resolution images. The comparison of different techniques is necessary to obtain an optimized image for the different applications of remote sensing. There are two approaches in assessing image quality: 1. Quantitatively by visual interpretation and 2. Quantitatively using image quality indices. However an objective comparison is difficult due to the fact that a visual assessment is always subject and a quantitative assessment is done by different criteria. Depending on the criteria and indices the result varies. Therefore it is necessary to standardize both processes (qualitative and quantitative assessment) in order to allow an objective image fusion quality evaluation. Various studies have been conducted at the University of Osnabrueck (UOS) to establish a standardized process to objectively compare fused image quality. First established image fusion quality assessment protocols, i.e. Quality with No Reference (QNR) and Khan's protocol, were compared on varies fusion experiments. Second the process of visual quality assessment was structured and standardized with the aim to provide an evaluation protocol. This manuscript reports on the results of the comparison and provides recommendations for future research.

Comparison of Coastline Extraction Methods and Block Classification Method to Extract Coastlines based on Remote Sensing

2021 ◽  
Author(s):  
Zongmei Li ◽  
Hongmei Chen ◽  
Qin Nie
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Extraction Methods ◽  
Classification Method ◽  
Segmentation Method ◽  
Visual Interpretation ◽  
Threshold Segmentation ◽  
Detection Algorithms ◽  
Block Classification ◽  
Interpretation Method

Abstract Coastlines change with urbanization, and methods to extract coastlines have been previously reported. However, comparisons of these methods are rare. Based on remote sensing image, methods of coastline extraction, namely, the visual interpretation method, the threshold segmentation method, improved normalized water indexes and edge detection algorithms and were studied in Xiamen City, China. The best method to extract coastlines was then determined. The results show that the visual interpretation method for coastline extraction was inefficient. The threshold segmentation method was suitable for small-scale, but not large-scale, coastline extraction, based on coastline area. Improved normalized water indexes were insensitive to sediment shadows. The Sobel method (edge detection algorithms) was suitable for large-scale coastline extraction but could yield false edges. Finally, the block classification method, which combines the advantages of different extraction methods, specifically the threshold segmentation method and improved normalized water indexes, was studied. The results of this study show that coastline extraction by the block classification method is easier and produces better results than coastline extraction by other methods. Therefore, block classification is recommended for the study of coastlines and coastal ecology in large areas.

scholarly journals Pakistan�s Timberline Forest Ecosystem Dynamics vis-a-vis Changing Climate Across Three Provinces (KPK, AJK and GB) Using GIS/RS Techniques

2020 ◽  
Vol 71 (5) ◽  
pp. 86-98
Author(s):  
Tariq Mahmood ◽  
Sarwat Naz Mirza ◽  
Saeed Gulzar ◽  
Mohammad Hanif
Keyword(s):  
Supervised Classification ◽  
Forest Cover ◽  
Temporal Dynamics ◽  
Climatic Factors ◽  
Ecosystem Dynamics ◽  
Primary Data ◽  
Climate Change Scenarios ◽  
Visual Interpretation ◽  
Classification Technique ◽  
Anthropogenic Threats

World`s forests have always been under anthropogenic threats leading to instantaneous and sound supervision using satellite-based data collecting capabilities through GIS/RS. Timber line in Pakistan consists of sub-alpine forests and alpine pastures of KPK, AJK and GB. Research findings showed that earth`s climate was changing rapidly than previously assumed, demanding the spatio-temporal dynamics analysis using GIS/RS techniques in prevalent climate change scenarios. Two separate methods were used; 1) Visual Interpretation of Supervised Classification Technique for AJK & GB and 2) object Oriented Classification of Supervised Classification Technique for KPK. The primary data for T (max), T (min) & Ppt. (1980-2013) was taken from PMD, Islamabad. Province wise data showed 15 % change in forest cover area for timber line in both KPK (142780 Ha) & AJK (24990 Ha), followed by GB (39267 Ha) as13 % across 2005-2014. The results calculated that the average upward shift in timberline was highest in KPK (285 m), followed by AJK (233 m) and GB (170m) across 2005-2014.The data also showed change in avg. T (max) was 3.4�, -1.8� and 0.6� C, avg. T (min) was -2�, -1.5� and -2� C while change in total precipitation was 88.5, -3.7 and 75.5 mm for AJK, GB and KPK respectively. The Pearson`s Correlation Co-efficient chart concluded that climatic factors showed a strong and positive correlation among themselves as well as with change in elevation. However, the correlation among climatic factors and change in forest cover area was weak concluding deforestation to be exclusively an anthropogenic phenomena. Change in elevation showed a weak and negative correlation with change in area while all other correlations were non-significant.

Study and analysis of various sentiment classification strategies: A challenging overview

2021 ◽  
Author(s):  
Mandar Kundan Keakde ◽  
Akkalakshmi Muddana
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Performance Metrics ◽  
Research Area ◽  
Sentiment Classification ◽  
Future Research ◽  
Classification Schemes ◽  
Social Events ◽  
Election Prediction ◽  
Media Sentiment

In large-scale social media, sentiment classification is a significant one for connecting gaps among social media contents as well as real-world actions, including public emotional status monitoring, political election prediction, and so on. On the other hand, textual sentiment classification is well studied by various platforms, like Instagram, Twitter, etc. Sentiment classification has many advantages in various fields, like opinion polls, education, and e-commerce. Sentiment classification is an interesting and progressing research area due to its applications in several areas. The information is collected from various people about social, products, and social events by web in sentiment analysis. This review provides a detailed survey of 50 research papers presenting sentiment classification schemes such as active learning-based approach, aspect learning-based method, and machine learning-based approach. The analysis is presented based on the categorization of sentiment classification schemes, the dataset used, software tools utilized, published year, and the performance metrics. Finally, the issues of existing methods considering conventional sentiment classification strategies are elaborated to obtain improved contribution in devising significant sentiment classification strategies. Moreover, the probable future research directions in attaining efficient sentiment classification are provided.

scholarly journals Tracking Forest Change and Development Using Low-Cost Remote Sensing Imagery and GIS Integration

2009 ◽  
Vol 26 (4) ◽  
pp. 148-155
Author(s):  
Nathan A. Briggs ◽  
Steven A. Sader
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Forest Cover ◽  
Low Cost ◽  
Human Populations ◽  
Forest Cover Change ◽  
Visual Interpretation ◽  
Forest Change ◽  
Remote Sensing Imagery ◽  
High Resolution Images

Abstract Conversion of forestland to other uses is occurring in Maine as growing human populations and desire for second homes are exerting development pressures on privately owned forestland. This study was performed to assess forest cover change and conversion to developed uses in a 636,000-hastudy area in Maine. A three-date time series (2000, 2002, and 2006) of Landsat Thematic Mapper data was analyzed to detect forest cover losses, and overall mapping accuracy was determined to be 91%. Forest cover losses (percentage per year) were aggregated for 81 townships and reported foreach time sequence. Rates of forest cover loss differ among townships and for the same township in different time periods. Visual interpretation of forestland conversion using high-resolution images for a subsample of 24 townships showed that 305 of 4,716 harvested forest hectares (6.47%)was converted to developed uses. The study demonstrates the practical use of low-cost remote-sensing imagery and routine interpretation methods for accurate tracking of forest change and quantification of land use conversion. The methods are adaptable to other states to assist decisionmakersin assessing regional and local land use and planning forest conservation measures.

scholarly journals A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6442 ◽  
Author(s):  
Panagiotis Barmpoutis ◽  
Periklis Papaioannou ◽  
Kosmas Dimitropoulos ◽  
Nikos Grammalidis
Keyword(s):  
Remote Sensing ◽  
Early Warning ◽  
Forest Fires ◽  
Large Scale ◽  
Fire Detection ◽  
Future Research ◽  
Optical Remote Sensing ◽  
Effective Prevention ◽  
Detection Systems ◽  
The World

The environmental challenges the world faces nowadays have never been greater or more complex. Global areas covered by forests and urban woodlands are threatened by natural disasters that have increased dramatically during the last decades, in terms of both frequency and magnitude. Large-scale forest fires are one of the most harmful natural hazards affecting climate change and life around the world. Thus, to minimize their impacts on people and nature, the adoption of well-planned and closely coordinated effective prevention, early warning, and response approaches are necessary. This paper presents an overview of the optical remote sensing technologies used in early fire warning systems and provides an extensive survey on both flame and smoke detection algorithms employed by each technology. Three types of systems are identified, namely terrestrial, airborne, and spaceborne-based systems, while various models aiming to detect fire occurrences with high accuracy in challenging environments are studied. Finally, the strengths and weaknesses of fire detection systems based on optical remote sensing are discussed aiming to contribute to future research projects for the development of early warning fire systems.

Application of Remote Sensing Technology in Tianjin Binhai New Area Coastal Zone Monitoring

2010 ◽  
Author(s):  
Juan Wang ◽  
Zhiguo Bu ◽  
Zhongqiang Li
Keyword(s):  
Remote Sensing ◽  
Coastal Zone ◽  
Large Scale ◽  
Influential Factor ◽  
Visual Interpretation ◽  
Economic Activities ◽  
The Past ◽  
Sensing Technology ◽  
Temporal Aspects ◽  
Tianjin City

The coastal zone is the belt influenced by land and ocean interactions, as well as human factors. So its evolution depends not only on natural factors but also on human socio-economic activities. It has very good instructive meaning to provide timely accurate coastal zone changeing information for exploiting and protecting the coast. Using 5 periods’ remote sensing images covering 20 years from 1987 to 2008 of Tianjin city, this paper extracted the coastline and the wetlands from different years utilizing different methods and techniques of data image processing and visual interpretation based on the characteristic of each RS image. The paper analyzed the law of the coastline and the wetlands changes in both spatial and temporal aspects, and then discussed the major influential factor to the changes by analyzing natural and artificial factors. The results indicated that the total coastline and the natural coastline increased, while the artificial seashore and wetlands decreased in large scale in the 20 years, due to the development of the coastal industry. Thanks to the protection and reinstatement for wetlands, the area of wetlands increased in the past two years.

The use of notary deeds to estimate the changes in the composition of 19th century forests, in Haut-Saint-Laurent, Quebec

1989 ◽  
Vol 19 (9) ◽  
pp. 1146-1150 ◽  
Author(s):  
André Bouchard ◽  
Stéphan Dyrda ◽  
Yves Bergeron ◽  
Alain Meilleur
Keyword(s):  
19Th Century ◽  
Relative Abundance ◽  
Forest Cover ◽  
Market Value ◽  
Forest Products ◽  
Forest Types ◽  
Forest Exploitation ◽  
The Past ◽  
The 19Th Century

Notary deeds are a new and useful tool for studying the past composition of forest cover, especially when other sources of detailed historical information are not available. When their biases, particularly their nonsystematic survey of the landscape or the influence of market value on the cords sold, are properly taken into consideration, these documents can be used to indicate past forest types and some of the major component species. A case study was developed in the Haut-Saint-Laurent, Quebec, which describes the relative abundance of tree species utilized for forest products in the middle of the 19th century. After the valuable pines and oaks were selectively lumbered at the start of the 19th century, five other species, hemlock, spruce, maple, yellow birch, and beech, were lumbered in a second generation of forest exploitation.

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