scholarly journals Possibilities of GIS-technologies in implementing large-scale mapping during field practices of students-geographers

2019 ◽  
Keyword(s):  
Large Scale ◽  
Remote Sensing Data ◽  
Geological Structure ◽  
Reference Object ◽  
Research Experience ◽  
Data Set ◽  
Gis Technologies ◽  
Nature Park ◽  
Landscape Monitoring ◽  
Sentinel 2

Experimental results of remote satellite data processing with different resolution from 3 to 60 m of bands are discussed in the article. The purpose of the article is to present and justify various options for using satellite imagery data and technologies of geographic information systems (GIS technologies) to solve various problems, taking into account previous research experience. The main material. The author suggests using Sentinel-2 and PlanetScope to compile large-scale maps of territories of different sizes. Based on the improvement of the methodology (previously used by the author), it is proposed to distinguish plant groups as indicative objects of indicative contours using remote sensing data. The second reference object is the contours of water bodies. We propose using colors (RGB), shapes and roughness to identify the contours of objects, but given the actual material of the field outputs to key areas. These characteristics can indirectly determine geomorphology. Based on spectral characteristic images, we consider the seasons, vegetation periods, and territory. During the filed practice students process a data set for different periods and analyze this information to study landscape changes. Based on studies from 2015 to 2019, a database for landscape monitoring of the protected area is being formed. The author with students and other researchers have determined that it is necessary to separately analyze northern and southern parts of the Slobozhansky National Nature Park. QGis and ArcGis tools allow you to prepare data and do overlay analysis to compile a hypothesis map, and then the resulting map. Conclusions and further research. It is established that the number of classes and the classification method depend on the properties of the objects of study. The best results were shown by isolating the contours of plant communities by the method of automatic classification by identifying key areas. It has been experimentally established that the decoding of satellite images PlanetScope gives the best results in small areas. For decoding of a larger area, Sentinel-2 gives the best results, the thematic image data of which is more generalized. Based on the information received from thematic maps, we have attributive data on the topography, geological structure, soil for each contour. All information will be used for the landscape monitoring base in the Slobozhansky National Nature Park.

scholarly journals Indicative landscape monitoring of the national nature park (case study the territory of Slobozhansky National Nature Park)

2018 ◽  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Terrestrial Ecosystems ◽  
Gis Technology ◽  
Landscape Indicators ◽  
Monitoring Method ◽  
Sensing Data ◽  
The Usa ◽  
Nature Park ◽  
Landscape Monitoring

Formulation of the problem. National Natural Parks (NNP) – protected areas where anthropogenic and natural landscapes are combined in the same territory. In addition, the main functions of such objects are significantly competitive, which requires monitoring of changes in existing landscapes. It is necessary to define the local objects which, being the most sensitive, at the same time have small plasticity, therefore, are capable to react quickly and adequately to any changes. That is what we call indicative. Analysis of recent research and publications. Many researchers of the USA, Great Britain, Germany, Australia conduct landscape monitoring using remote sensing data and GIS technologies. For example, D. Keith, S. Rodoreda, L. Holman, R. Noss, U. Walz, and others. The National Inventory of Landscapes in Sweden studies development of modern landscape monitoring in countries of Europe. Landscape Monitoring of Terrestrial Ecosystems, studied by researches R. Kennedy, J. Jons, K. Jones and others allow using data of satellite for selection of plant contours using Gis-technology. Landscape monitoring of the territory of NNP «Slobozhanskiy» has never been carried out. The aim of the study is to choose satellite images, taking into account the area of the study, the choice of optimal methods of their processing for the compilation of a database of landscape structure facies for landscape monitoring based on long-term observations on the ground, comparing their results with geodata. We have determined wetlands, as landscape indicators. Presentation of the main material of the study. Comprehensive analysis of remote sensing data carried out by the authors, allowed us to make sure that vegetation cover is the most indicative, except for the contours of wetlands, which are clearly identified and easily compared in multi-spectral images. It is reliably determined by the characteristic features combine with the corresponding spectral ranges and the image structure. In addition, changes in vegetation allows you to visually determine changes in landscape groupings and the speed of these changes. Summary. The indicative features of landscape monitoring are wetlands, and there are two direct indicators: the contours of wetlands and the change in the aspect of vegetation. The monitoring method is a multispectral analysis of images obtained by processing combinations of spectral channels, which showed the ability to determine the changes in the selection, taking into account reflectivity of the surface. Limitations of the method are the following: there is no established method of meticulous analysis of changes in the structure of vegetation, which is observed visually, but is not reflected instrumentally; inability to take into account random features of the territory conditions and space scanning at a certain point, which is interesting for the study. Finally, the types of monitoring objects, indicative signs of changes and ways to track them according to high-precision and generally available satellite information are determined.

scholarly journals 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

2018 ◽  
Vol 11 (1) ◽  
pp. 43 ◽  
Author(s):  
Masoud Mahdianpari ◽  
Bahram Salehi ◽  
Fariba Mohammadimanesh ◽  
Saeid Homayouni ◽  
Eric Gill
Keyword(s):  
Large Scale ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Wetland Mapping ◽  
Object Based ◽  
Wetland Inventory ◽  
Inventory Map ◽  
Google Earth Engine ◽  
Provincial Scale ◽  
Sentinel 2

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.

scholarly journals Use of optical absorption indices to assess seasonal variability of dissolved organic matter in Amazon floodplain lakes

2020 ◽  
Vol 17 (21) ◽  
pp. 5355-5364
Author(s):  
Maria Paula da Silva ◽  
Lino A. Sander de Carvalho ◽  
Evlyn Novo ◽  
Daniel S. F. Jorge ◽  
Claudio C. F. Barbosa
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Optical Absorption ◽  
Dissolved Organic Matter ◽  
Seasonal Variability ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Floodplain Lakes ◽  
Sentinel 2

Abstract. Given the importance of dissolved organic matter (DOM) in the carbon cycling of aquatic ecosystems, information on its seasonal variability is crucial. In this study we assess the use of optical absorption indices available in the literature based on in situ data to both characterize the seasonal variability of DOM in a highly complex environment and for application in large-scale studies using remote sensing data. The study area comprises four lakes located in the Mamirauá Sustainable Development Reserve (MSDR). Samples for the determination of colored dissolved organic matter (CDOM) and measurements of remote sensing reflectance (Rrs) were acquired in situ. The Rrs was used to simulate the response of the visible bands of the Sentinel-2 MultiSpectral Instrument (MSI), which was used in the proposed models. Differences between lakes were tested using the CDOM indices. The results highlight the role of the flood pulse in the DOM dynamics at the floodplain lakes. The validation results show that the use of the absorption coefficient of CDOM (aCDOM) as a proxy of the spectral slope between 275 and 295 nm (S275–295) during rising water is worthwhile, demonstrating its potential application to Sentinel-2 MSI imagery data for studying DOM dynamics on the large scale.

scholarly journals Towards Global-Scale Seagrass Mapping and Monitoring Using Sentinel-2 on Google Earth Engine: The Case Study of the Aegean and Ionian Seas

2018 ◽  
Vol 10 (8) ◽  
pp. 1227 ◽  
Author(s):  
Dimosthenis Traganos ◽  
Bharat Aggarwal ◽  
Dimitris Poursanidis ◽  
Konstantinos Topouzelis ◽  
Nektarios Chrysoulakis ◽  
...  
Keyword(s):  
Large Scale ◽  
Anthropogenic Impacts ◽  
Global Scale ◽  
Google Earth ◽  
Support Vector ◽  
Learning Approaches ◽  
Data Set ◽  
Independent Field ◽  
Google Earth Engine ◽  
Sentinel 2

Seagrasses are traversing the epoch of intense anthropogenic impacts that significantly decrease their coverage and invaluable ecosystem services, necessitating accurate and adaptable, global-scale mapping and monitoring solutions. Here, we combine the cloud computing power of Google Earth Engine with the freely available Copernicus Sentinel-2 multispectral image archive, image composition, and machine learning approaches to develop a methodological workflow for large-scale, high spatiotemporal mapping and monitoring of seagrass habitats. The present workflow can be easily tuned to space, time and data input; here, we show its potential, mapping 2510.1 km2 of P. oceanica seagrasses in an area of 40,951 km2 between 0 and 40 m of depth in the Aegean and Ionian Seas (Greek territorial waters) after applying support vector machines to a composite of 1045 Sentinel-2 tiles at 10-m resolution. The overall accuracy of P. oceanica seagrass habitats features an overall accuracy of 72% following validation by an independent field data set to reduce bias. We envision that the introduced flexible, time- and cost-efficient cloud-based chain will provide the crucial seasonal to interannual baseline mapping and monitoring of seagrass ecosystems in global scale, resolving gain and loss trends and assisting coastal conservation, management planning, and ultimately climate change mitigation.

scholarly journals Mapping an Urban Boundary Based on Multi-Temporal Sentinel-2 and POI Data: A Case Study of Zhengzhou City

2020 ◽  
Vol 12 (24) ◽  
pp. 4103
Author(s):  
Zhe Wang ◽  
Haiying Wang ◽  
Fen Qin ◽  
Zhigang Han ◽  
Changhong Miao
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Reference Value ◽  
Training Sample ◽  
Data Set ◽  
Construction Land ◽  
Urban Construction ◽  
Multi Temporal ◽  
Extraction Model ◽  
Sentinel 2

Accurately identifying and delineating urban boundaries are the premise for and foundation of the control of disorderly urban sprawl, which is helpful for us to accurately grasp the scale and form of cities, optimize the internal spatial structure and pattern of cities, and guide the expansion of urban spaces in the future. At present, the concept and delineation of urban boundaries do not follow a unified method or standard. However, many scholars have made use of multi-source remote sensing images of various scales and social auxiliary data such as point of interest (POI) data to achieve large-scale, high-resolution, and high-precision land cover mapping and impermeable water surface mapping. The accuracy of small- and medium-scale urban boundary mapping has not been improved to an obvious extent. This study uses multi-temporal Sentinel-2 high-resolution images and POI data that can reflect detailed features of human activities to extract multi-dimensional features and use random forests and mathematical morphology to map the urban boundaries of the city of Zhengzhou. The research results show that: (1) the urban construction land extraction model established with multi-dimensional features has a great improvement in accuracy; (2) when the training sample accounts for 65% of the sample data set, the urban construction land extraction model has the highest accuracy, reaching 96.25%, and the Kappa coefficient is 0.93; (3) the optimized boundary of structural elements with a size of 13 × 13 is selected, which is in good agreement in terms of scope and location with the boundary of FROM-GLC10 (Zhengzhou) and visual interpretations. The results from the urban boundary delineation in this paper can be used as an important database for detailed basic land use mapping within cities. Moreover, the method in this paper has some reference value for other cities in terms of delineating urban boundaries.

scholarly journals Multi-Source and Multi-Temporal Image Fusion on Hypercomplex Bases

2020 ◽  
Vol 12 (6) ◽  
pp. 943
Author(s):  
Andreas Schmitt ◽  
Anna Wendleder ◽  
Rüdiger Kleynmans ◽  
Maximilian Hell ◽  
Achim Roth ◽  
...  
Keyword(s):  
Image Fusion ◽  
Optical Sensors ◽  
Remote Sensing Data ◽  
Image Data ◽  
Machine Learning Algorithms ◽  
Alos Palsar ◽  
Data Set ◽  
Multi Temporal ◽  
The Stability ◽  
Sentinel 2

This article spanned a new, consistent framework for production, archiving, and provision of analysis ready data (ARD) from multi-source and multi-temporal satellite acquisitions and an subsequent image fusion. The core of the image fusion was an orthogonal transform of the reflectance channels from optical sensors on hypercomplex bases delivered in Kennaugh-like elements, which are well-known from polarimetric radar. In this way, SAR and Optics could be fused to one image data set sharing the characteristics of both: the sharpness of Optics and the texture of SAR. The special properties of Kennaugh elements regarding their scaling—linear, logarithmic, normalized—applied likewise to the new elements and guaranteed their robustness towards noise, radiometric sub-sampling, and therewith data compression. This study combined Sentinel-1 and Sentinel-2 on an Octonion basis as well as Sentinel-2 and ALOS-PALSAR-2 on a Sedenion basis. The validation using signatures of typical land cover classes showed that the efficient archiving in 4 bit images still guaranteed an accuracy over 90% in the class assignment. Due to the stability of the resulting class signatures, the fuzziness to be caught by Machine Learning Algorithms was minimized at the same time. Thus, this methodology was predestined to act as new standard for ARD remote sensing data with an subsequent image fusion processed in so-called data cubes.

scholarly journals Analysis of Combining SAR and Optical Optimal Parameters to Classify Typhoon-Invasion Lodged Rice: A Case Study Using the Random Forest Method

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7346
Author(s):  
Jinning Wang ◽  
Kun Li ◽  
Yun Shao ◽  
Fengli Zhang ◽  
Zhiyong Wang ◽  
...  
Keyword(s):  
Random Forest ◽  
Large Scale ◽  
Remote Sensing Data ◽  
High Sensitivity ◽  
Spectral Indices ◽  
Optimal Parameters ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Typhoon Event ◽  
Sentinel 2

Lodging, a commonly occurring rice crop disaster, seriously reduces rice quality and production. Monitoring rice lodging after a typhoon event is essential for evaluating yield loss and formulating suitable remedial policies. The availability of Sentinel-1 and Sentinel-2 open-access remote sensing data provides large-scale information with a short revisit time to be freely accessed. Data from these sources have been previously shown to identify lodged crops. In this study, therefore, Sentinel-1 and Sentinel-2 data after a typhoon event were combined to enable monitoring of lodging rice to be quickly undertaken. In this context, the sensitivity of synthetic aperture radar (SAR) features (SF) and spectral indices (SI) extracted from Sentinel-1 and Sentinel-2 to lodged rice were analyzed, and a model was constructed for selecting optimal sensitive parameters for lodging rice (OSPL). OSPL has high sensitivity to lodged rice and strong ability to distinguish lodged rice from healthy rice. After screening, Band 11 (SWIR-1) and Band 12 (SWIR-2) were identified as optimal spectral indices (OSI), and VV, VV + VH and Shannon Entropy were optimal SAR features (OSF). Three classification results of lodging rice were acquired using the Random Forest classification (RFC) method based on OSI, OSF and integrated OSI–OSF stack images, respectively. Results indicate that an overall level of accuracy of 91.29% was achieved with the combination of SAR and optical optimal parameters. The result was 2.91% and 6.05% better than solely using optical or SAR processes, respectively.

scholarly journals Detailed geological mapping of the Olon-Ovoot gold-ore cluster (South Mongolia) based on the interpretation of satellite imagery of medium and high spatial resolution.

2019 ◽  
pp. 59-71
Author(s):  
M. V. Zadorozhnyy ◽  
I. D. Zolnikov ◽  
N. V. Glushkova
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Geological Structure ◽  
Google Earth ◽  
Geological Mapping ◽  
Geological Interpretation ◽  
Gold Ore ◽  
Sentinel 2

Detailed geological mapping of Olon-Ovoot gold-ore cluster (South Mongolia) on the basis of interpretation of satellite imagery of medium and high spatial resolution The article presents the results of geological interpretation the territory of the Olon-Ovoot ore cluster by space imagery of medium and high spatial resolution. A Sentinel-2 imagery, chosen for interpretation, was orthorectified and reduced to a common spatial resolution (10m) The iron-hydroxid and ferrous-silicates indices in Sentinel-2 imagery were used to detect the perspective gold-bearing objects. The sub-pixel structure of the imagery Sentinel-2 were analyzed by means of satellite imagery of high spatial resolution by Google Earth for detecting areas concentration of the quartz-carbonate veins. The study of the spectral domain in high-resolution imagery not necessary for detecting lineaments by structural and morphological interpretation. The interpretation of the remote sensing data provide a unique opportunity to substantial specify the geological structure of the territory and change the level of mapping from the scale of 1 : 200 000 to the scale of 1 : 20 000 for the perspective areas. The integration of satellite images of different functional scale provided an tenfold increase for some geological objects (for example dikes). Detailed mapping of the territory allowed to come for geoinformation modeling of geological structural elements and predictive indicators.

scholarly journals SENTINEL-1 AND SENTINEL-2 DATA FUSION FOR WETLANDS MAPPING: BALIKDAMI, TURKEY

2018 ◽  
Vol XLII-3 ◽  
pp. 729-734 ◽  
Author(s):  
G. Kaplan ◽  
U. Avdan
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Economic Benefits ◽  
Future Research ◽  
Wetland Classification ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Sentinel 2

Wetlands provide a number of environmental and socio-economic benefits such as their ability to store floodwaters and improve water quality, providing habitats for wildlife and supporting biodiversity, as well as aesthetic values. Remote sensing technology has proven to be a useful and frequent application in monitoring and mapping wetlands. Combining optical and microwave satellite data can help with mapping and monitoring the biophysical characteristics of wetlands and wetlands` vegetation. Also, fusing radar and optical remote sensing data can increase the wetland classification accuracy.<br> In this paper, data from the fine spatial resolution optical satellite, Sentinel-2 and the Synthetic Aperture Radar Satellite, Sentinel-1, were fused for mapping wetlands. Both Sentinel-1 and Sentinel-2 images were pre-processed. After the pre-processing, vegetation indices were calculated using the Sentinel-2 bands and the results were included in the fusion data set. For the classification of the fused data, three different classification approaches were used and compared.<br> The results showed significant improvement in the wetland classification using both multispectral and microwave data. Also, the presence of the red edge bands and the vegetation indices used in the data set showed significant improvement in the discrimination between wetlands and other vegetated areas. The statistical results of the fusion of the optical and radar data showed high wetland mapping accuracy, showing an overall classification accuracy of approximately 90&amp;thinsp;% in the object-based classification method. For future research, we recommend multi-temporal image use, terrain data collection, as well as a comparison of the used method with the traditional image fusion techniques.

scholarly journals An Improved Forest Structure Data Set for Europe

2022 ◽  
Vol 14 (2) ◽  
pp. 395
Author(s):  
Christoph Pucher ◽  
Mathias Neumann ◽  
Hubert Hasenauer
Keyword(s):  
Structure Data ◽  
Forest Structure ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Inventory Data ◽  
Data Set ◽  
European Forests ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
European Forest

Today, European forests face many challenges but also offer opportunities, such as climate change mitigation, provision of renewable resources, energy and other ecosystem services. Large-scale analyses to assess these opportunities are hindered by the lack of a consistent, spatial and accessible forest structure data. This study presents a freely available pan-European forest structure data set. Building on our previous work, we used data from six additional countries and consider now ten key forest stand variables. Harmonized inventory data from 16 European countries were used in combination with remote sensing data and a gap-filling algorithm to produce this consistent and comparable forest structure data set across European forests. We showed how land cover data can be used to scale inventory data to a higher resolution which in turn ensures a consistent data structure across sub-regional, country and European forest assessments. Cross validation and comparison with published country statistics of the Food and Agriculture Organization (FAO) indicate that the chosen methodology is able to produce robust and accurate forest structure data across Europe, even for areas where no inventory data were available.

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