scholarly journals Automatic Proba-V Processor: TREX—Tool for Raster Data Exploration

2019 ◽  
Vol 11 (21) ◽  
pp. 2538 ◽  
Author(s):  
Joanna Suliga ◽  
Joy Bhattacharjee ◽  
Jarosław Chormański ◽  
Ann van Griensven ◽  
Boud Verbeiren
Keyword(s):  
Open Source ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Satellite Data ◽  
Original Data ◽  
Data Exploration ◽  
Plant Canopy ◽  
Raster Data ◽  
Northeastern Poland

The processing tool TREX, standing for ‘Tool for Raster data EXploration’ is presented and evaluated in the Biebrza wetlands in northeastern Poland. TREX was designed for the automatization of processing satellite data from the Proba-V satellite into maps of NDVI or LAI in any defined by the user projection, spatial resolution, or extent. The open source and access concept of TREX encourages the potential community of users to collaborate, develop, and integrate the tool with other satellite imagery and models. TREX reprojects, shifts, and resamples original data obtained from the Proba-V satellite to deliver reliable maps of NDVI and LAI. Validation of TREX in Biebrza wetlands resulted in correlations between 0.79 and 0.92 for NDVI data (measured with ASD Field Spec 4) and 0.92 for LAI data (measured with LiCOR—LAI-2000 Plant Canopy Analyzer).

scholarly journals UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health

2018 ◽  
Vol 10 (8) ◽  
pp. 1216 ◽  
Author(s):  
Jonathan Dash ◽  
Grant Pearse ◽  
Michael Watt
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Satellite Data ◽  
Physiological Stress ◽  
Abiotic Factors ◽  
Vegetation Indices ◽  
Detection Threshold ◽  
Forest Trees ◽  
Multispectral Imagery

The development of methods that can accurately detect physiological stress in forest trees caused by biotic or abiotic factors is vital for ensuring productive forest systems that can meet the demands of the Earth’s population. The emergence of new sensors and platforms presents opportunities to augment traditional practices by combining remotely-sensed data products to provide enhanced information on forest condition. We tested the sensitivity of multispectral imagery collected from time-series unmanned aerial vehicle (UAV) and satellite imagery to detect herbicide-induced stress in a carefully controlled experiment carried out in a mature Pinus radiata D. Don plantation. The results revealed that both data sources were sensitive to physiological stress in the study trees. The UAV data were more sensitive to changes at a finer spatial resolution and could detect stress down to the level of individual trees. The satellite data tested could only detect physiological stress in clusters of four or more trees. Resampling the UAV imagery to the same spatial resolution as the satellite imagery revealed that the differences in sensitivity were not solely the result of spatial resolution. Instead, vegetation indices suited to the sensor characteristics of each platform were required to optimise the detection of physiological stress from each data source. Our results define both the spatial detection threshold and the optimum vegetation indices required to implement monitoring of this forest type. A comparison between time-series datasets of different spectral indices showed that the two sensors are compatible and can be used to deliver an enhanced method for monitoring physiological stress in forest trees at various scales. We found that the higher resolution UAV imagery was more sensitive to fine-scale instances of herbicide induced physiological stress than the RapidEye imagery. Although less sensitive to smaller phenomena the satellite imagery was found to be very useful for observing trends in physiological stress over larger areas.

Weather forecasting by interactive analysis of radar and satellite imagery

1988 ◽  
Vol 324 (1579) ◽  
pp. 299-315 ◽  
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Satellite Data ◽  
Weather Forecasting ◽  
Display System ◽  
Time Data ◽  
Interactive Analysis ◽  
Short Period ◽  
Weather Situation ◽  
Real Time Data

At the Meteorological Office, Bracknell, quantitative rainfall maps from a network of ground-based radars, augmented by cloud images from Meteosat , are used to produce analyses and very-short-period forecasts of precipitation. These remotely sensed images provide the only way of presenting the current weather situation quickly enough and with the required spatial resolution and areal coverage. The processing of the radar and satellite data is highly automated, but there are some tasks that require judgements based upon many strands of information and an understanding of meteorological processes. To this end, forecasters use a specially developed display system to interact with the imagery. The facilities for interacting with the pictures have been optimized so that the forecaster, who is kept very busy in active weather situations, can keep pace with the flow of real-time data. Even so, as more radars are added to the network, ways must be found of reducing the burden of the forecaster’s interpretive and judgemental functions by automating some of them and making others easier to perform.

Mapping of the built-up areas of Russian Arctic biggest cities using satellite imagery of various spatial resolution

2018 ◽  
Vol 933 (3) ◽  
pp. 23-34 ◽  
Author(s):  
M.Yu. Grischchenko ◽  
Yu.V. Ermilova
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Russian Arctic ◽  
Mapping Process

The paper deals the mapping process of the built-up areas features of the Russian Arctic biggest cities. For the study, the following cities were selected

scholarly journals Mineral Detection Using Sharpened VNIR and SWIR Bands of Worldview-3 Satellite Imagery

2021 ◽  
Vol 13 (10) ◽  
pp. 5518
Author(s):  
Honglyun Park ◽  
Jaewan Choi
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Near Infrared ◽  
False Negative ◽  
Similarity Score ◽  
Majority Voting ◽  
Similarity Analysis ◽  
Spectral Angle Mapper ◽  
Mineral Detection ◽  
Shortwave Infrared

Worldview-3 satellite imagery provides panchromatic images with a high spatial resolution and visible near infrared (VNIR) and shortwave infrared (SWIR) bands with a low spatial resolution. These images can be used for various applications such as environmental analysis, urban monitoring and surveying for sustainability. In this study, mineral detection was performed using Worldview-3 satellite imagery. A pansharpening technique was applied to the spatial resolution of the panchromatic image to effectively utilize the VNIR and SWIR bands of Worldview-3 satellite imagery. The following representative similarity analysis techniques were implemented for the mineral detection: the spectral angle mapper (SAM), spectral information divergence (SID) and the normalized spectral similarity score (NS3). In addition, pixels that could be estimated to indicate minerals were calculated by applying an empirical threshold to each similarity analysis result. A majority voting technique was applied to the results of each similarity analysis and pixels estimated to indicate minerals were finally selected. The results of each similarity analysis were compared to evaluate the accuracy of the proposed methods. From that comparison, it could be confirmed that false negative and false positive rates decreased when the methods proposed in the present study were applied.

scholarly journals An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery

2021 ◽  
Vol 13 (10) ◽  
pp. 1868
Author(s):  
Martina Deur ◽  
Mateo Gašparović ◽  
Ivan Balenović
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Tree Species ◽  
Classification Accuracy ◽  
New Technologies ◽  
Deciduous Forest ◽  
Species Classification ◽  
Tree Species Classification ◽  
Object Based ◽  
Very High

Quality tree species information gathering is the basis for making proper decisions in forest management. By applying new technologies and remote sensing methods, very high resolution (VHR) satellite imagery can give sufficient spatial detail to achieve accurate species-level classification. In this study, the influence of pansharpening of the WorldView-3 (WV-3) satellite imagery on classification results of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) has been evaluated. In order to increase tree species classification accuracy, three different pansharpening algorithms (Bayes, RCS, and LMVM) have been conducted. The LMVM algorithm proved the most effective pansharpening technique. The pixel- and object-based classification were applied to three pansharpened imageries using a random forest (RF) algorithm. The results showed a very high overall accuracy (OA) for LMVM pansharpened imagery: 92% and 96% for tree species classification based on pixel- and object-based approach, respectively. As expected, the object-based exceeded the pixel-based approach (OA increased by 4%). The influence of fusion on classification results was analyzed as well. Overall classification accuracy was improved by the spatial resolution of pansharpened images (OA increased by 7% for pixel-based approach). Also, regardless of pixel- or object-based classification approaches, the influence of the use of pansharpening is highly beneficial to classifying complex, natural, and mixed deciduous forest areas.

scholarly journals EXTRACTING BUILT-UP FEATURES IN COMPLEX BIOPHYSICAL ENVIRONMENTS BY USING A LANDSAT BANDS RATIO

2020 ◽  
Vol XLIV-M-2-2020 ◽  
pp. 79-85
Author(s):  
A. H. Ngandam Mfondoum ◽  
P. G. Gbetkom ◽  
R. Cooper ◽  
S. Hakdaoui ◽  
M. B. Mansour Badamassi
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Satellite Data ◽  
Kappa Coefficient ◽  
Landsat 8 ◽  
Experimental Site ◽  
Soil Features ◽  
Mixed Pixels ◽  
Shortwave Infrared ◽  
Normalization Process

Abstract. This paper addresses the remote sensing challenging field of urban mixed pixels on a medium spatial resolution satellite data. The tentatively named Normalized Difference Built-up and Surroundings Unmixing Index (NDBSUI) is proposed by using Landsat-8 Operational Land Imager (OLI) bands. It uses the Shortwave Infrared 2 (SWIR2) as the main wavelength, the SWIR1 with the red wavelengths, for the built-up extraction. A ratio is computed based on the normalization process and the application is made on six cities with different urban and environmental characteristics. The built-up of the experimental site of Yaoundé is extracted with an overall accuracy of 95.51% and a kappa coefficient of 0.90. The NDBSUI is validated over five other sites, chosen according to Cameroon’s bioclimatic zoning. The results are satisfactory for the cities of Yokadouma and Kumba in the bimodal and monomodal rainfall zones, where overall accuracies are up to 98.9% and 97.5%, with kappa coefficients of 0.88 and 0.94 respectively, although these values are close to those of three other indices. However, in the cities of Foumban, Ngaoundéré and Garoua, representing the western highlands, the high Guinea savannah and the Sudano-sahelian zones where built-up is more confused with soil features, overall accuracies of 97.06%, 95.29% and 74.86%, corresponding to 0.918, 0.89 and 0.42 kappa coefficients were recorded. Difference of accuracy with EBBI, NDBI and UI are up to 31.66%, confirming the NDBSUI efficiency to automate built-up extraction and unmixing from surrounding noises with less biases.

scholarly journals STEAD: A high-resolution daily gridded temperature dataset for Spain

2019 ◽  
Author(s):  
Roberto Serrano-Notivoli ◽  
Santiago Beguería ◽  
Martín De Luis
Keyword(s):  
Quality Control ◽  
Spatial Resolution ◽  
Minimum Temperature ◽  
Grid Point ◽  
Temporal Distribution ◽  
Original Data ◽  
Accurate Estimate ◽  
Absolute Temperature ◽  
Data Series ◽  
Absolute Maximum

Abstract. Using the full total of available 5520 observatories covering the whole territory of Spain, a daily gridded maximum and minimum temperature was built covering a period from 1901 to 2014 in peninsular Spain and 1971–2014 in Balearic and Canary Islands. A comprehensive quality control was applied to the original data and the gaps were filled on each day and location independently. Using the filled data series, a grid of 5 × 5 km spatial resolution was created by estimating daily temperatures and their corresponding uncertainties at each grid point. Four daily temperature indices were calculated to describe the spatial distribution of absolute maximum and minimum temperature, number of frost days and number of summer days in Spain. The southern plateau showed the maximum values of maximum absolute temperature and summer days, while the minimum absolute temperature and frost days reached their maximums at northern plateau. The use of all the available information, the complete quality control and the high spatial resolution of the grid allowed for an accurate estimate of temperature that represents a precise spatial and temporal distribution of daily temperatures in Spain. STEAD dataset is publicly available at http://dx.doi.org/10.20350/digitalCSIC/8622.

scholarly journals An open source database for the synthesis of soil radiocarbon data: ISRaD version 1.0

2019 ◽  
Author(s):  
Corey R. Lawrence ◽  
Jeffery Beem-Miller ◽  
Alison M. Hoyt ◽  
Grey Monroe ◽  
Carlos A. Sierra ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Open Source ◽  
Soil Surface ◽  
Original Data ◽  
R Package ◽  
Direct Access ◽  
Temporal Scales ◽  
Spatial And Temporal Scales ◽  
Starting Point ◽  
Radiocarbon Data

Abstract. Radiocarbon is a critical constraint on our estimates of the timescales of soil carbon cycling that can aid in identifying mechanisms of carbon stabilization and destabilization, and improve forecast of soil carbon response to management or environmental change. Despite the wealth of soil radiocarbon data that has been reported over the past 75 years, the ability to apply these data to global scale questions is limited by our capacity to synthesis and compare measurements generated using a variety of methods. Here we describe the International Soil Radiocarbon Database (ISRaD, soilradiocarbon.org), an open-source archive of soils data that include data from bulk soils, or whole-soils; distinct soil carbon pools isolated in the laboratory by a variety of soil fractionation methods; samples of soil gas or water collected interstitially from within an intact soil profile; CO2 gas isolated from laboratory soil incubations; and fluxes collected in situ from a soil surface. The core of ISRaD is a relational database structured around individual datasets (entries) and organized hierarchically to report soil radiocarbon data, measured at different physical and temporal scales, as well as other soil or environmental properties that may also be measured at one or more levels of the hierarchy that may assist with interpretation and context. Anyone may contribute their own data to the database by entering it into the ISRaD template and subjecting it to quality assurance protocols. ISRaD can be accessed through: (1) a web-based interface, (2) an R package (ISRaD), or (3) direct access to code and data through the GitHub repository, which hosts both code and data. The design of ISRaD allows for participants to become directly involved in the management, design, and application of ISRaD data. The synthesized dataset is available in two forms: the original data as reported by the authors of the datasets; and an enhanced dataset that includes ancillary geospatial data calculated within the ISRaD framework. ISRaD also provides data management tools in the ISRaD-R package that provide a starting point for data analysis. This community-based dataset and platform for soil radiocarbon and a wide array of additional soils data information in soils where data are easy to contribute and the community is invited to add tools and ideas for improvement. As a whole, ISRaD provides resources that can aid our evaluation of soil dynamics and improve our understanding of controls on soil carbon dynamics across a range of spatial and temporal scales. The ISRaD v1.0 dataset (Lawrence et al., 2019) is archived and freely available at https://doi.org/10.5281/zenodo.2613911.

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