Assessment of the vineyard water footprint by using ancillary data and EEFlux satellite images. Examples in the Chilean central zone

2021 ◽  
pp. 152452
Author(s):  
Marcos Carrasco-Benavides ◽  
Samuel Ortega-Farías ◽  
Pilar M. Gil ◽  
Daniel Knopp ◽  
Luis Morales-Salinas ◽  
...  
Keyword(s):  
Satellite Images ◽  
Water Footprint ◽  
Central Zone ◽  
Ancillary Data

Vegetation mapping of Svalbard utilising Landsat TM/ETM+ data

Polar Record ◽  
2011 ◽  
Vol 48 (1) ◽  
pp. 47-63 ◽  
Author(s):  
Bernt E. Johansen ◽  
Stein Rune Karlsen ◽  
Hans Tømmervik
Keyword(s):  
Satellite Images ◽  
Satellite Image ◽  
Landsat Tm ◽  
Inland Waters ◽  
Vegetation Density ◽  
Ancillary Data ◽  
Vegetation Map ◽  
Digital Format ◽  
Different Types ◽  
Classified Image

ABSTRACTThe overall objective of this paper is to present and discuss the most recently developed vegetation map for Svalbard, Arctic Norway. The map is based on satellite images in which several Landsat TM/ETM+ images were processed through six operational stages involving: (1) automatic image classification, (2) spectral similarity analysis, (3) generation of classified image mosaics, (4) ancillary data analysis, (5) contextual correction, and (6) standardisation of the final map products. The developed map is differentiated into 18 map units interpreted from 37 spectral classes. Among the 18 units separated, six of the units comprise rivers, lakes and inland waters, glaciers, as well as non- to sparsely vegetated areas. The map unit 7 is a result of shadow effects and different types of distortions in the satellite image. The vegetation of the remaining eleven units varies from dense marshes and moss tundra communities to sparsely vegetated polar deserts and moist gravel snowbeds. The accuracy of the map is evaluated in areas were access to traditional maps have been available. The vegetation density and fertility is reflected in computed NDVI values. The map product is in digital format, which gives the opportunity to produce maps in different scales. A map sheet portraying the entire archipelago is one of the main products from this study, produced at a scale of 1:500,000.

Generation of fuel type maps from Landsat TM images and ancillary data in Mediterranean ecosystems

2002 ◽  
Vol 32 (8) ◽  
pp. 1301-1315 ◽  
Author(s):  
David Riaño ◽  
Emilio Chuvieco ◽  
Javier Salas ◽  
Alicia Palacios-Orueta ◽  
Aitor Bastarrika
Keyword(s):  
Satellite Images ◽  
Structural Characteristics ◽  
Remote Sensing Data ◽  
Mediterranean Ecosystems ◽  
Landsat Tm ◽  
Fuel Type ◽  
Ancillary Data ◽  
Vegetation Height ◽  
Digital Elevation ◽  
Elevation Data

This paper presents methods to generate fuel type maps from remote sensing data at a spatial and temporal scale adequate for operational fire management applications. Fuel type maps account for structural characteristics of vegetation related to fire behaviour and fire propagation. A fuel type classification system adapted to the ecological characteristics of the European Mediterranean basin was adopted for this study. The Cabañeros National Park (in central Spain) area was selected for testing and validating the methods. Fuel type maps were derived from two Landsat TM satellite images and digital elevation data. Atmospheric and topographic corrections of the satellite images were performed to reduce spectral variability. A sensitivity analysis was carried out to determine the most appropriate bands for fuel type mapping. The final classification was checked by an intense field survey, the final classification accuracy being estimated at 83%. The main problem was discriminating among those fuel types that differ only in vegetation height or composition of the understory layer. The mean mapping accuracy was 15 m (0.6 pixels), and no areal discrepancy or boundary displacement with vegetation maps was apparent.

scholarly journals A METHOD FOR ESTIMATING THE NUMBER OF HOUSEHOLDS IN A REGION FROM THE NUMBER OF BUILDINGS ESTIMATED BY DEEP LEARNING WITH THE ADJUSTMENT OF ITS NUMBER USING ANCILLARY DATASETS: CASE STUDY IN DJAKARTA

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 673-678
Author(s):  
R. Ito ◽  
K. Hara ◽  
Y. Shimazaki ◽  
N. Mori ◽  
Y. Kani ◽  
...  
Keyword(s):  
Deep Learning ◽  
Satellite Images ◽  
Census Data ◽  
Statistical Data ◽  
Aerial Images ◽  
Disaster Prevention ◽  
Ancillary Data ◽  
Small Areas ◽  
The Difference

Abstract. The high resolution statistical data such as the number of households in small areas are indispensable for urban planning, disaster prevention and many kinds of business activities. However, it is difficult to obtain the number of households in small areas because census data are usually aggregated in municipal districts. Techniques for automatically analyzing statistical data, e.g., land cover, population density, and the number of households obtained from satellite/aerial images have been continuously studied. In recent years, many methods using deep learning have been proposed in the related literature. In estimating the number of households, the use of buildings, the number of floors and that of rooms are also important information, but it is difficult to obtain such information from only image analysis using deep learning. This study proposes a method for estimating the number of households in 100 meter grid cells from satellite images using deep learning, and adjusting it using ancillary data obtained from a few statistical datasets. The application of this method to Djakarta shows that the difference between the estimated values and the corresponding values of census is less than 10%.

Investigation into Cigarette Smoking and Atmospheric Pollution in the Aetiology of Lung Cancer

1963 ◽  
Vol 2 (01) ◽  
pp. 13-19 ◽  
Author(s):  
R. Doll
Keyword(s):  
Lung Cancer ◽  
Cigarette Smoking ◽  
Atmospheric Pollution ◽  
Direct Evidence ◽  
Retrospective Studies ◽  
Common Factor ◽  
Animal Experiments ◽  
Smoking Habits ◽  
Careful Examination ◽  
Ancillary Data

The evidence that cigarette smoking and atmospheric pcllution are causes of lung cancer is largely statistical. The first evidence was indirect; that is, i1. was noticed that in many countries the incidence of lung cancer had increased and that the increase could be correlated with changes in the prevalence of cigarette smoking and of certain types of atmospheric pollution.Since then much direct evidence has been obtained. The relationship between cigarette smoking and lung cancer has been demonstrated retrospectively by comparing the smoking habits of patients with and without lung cancer and prospectively by observing the mortality from lung cancer in groups of persons of known smoking habits. Conclusions can be drawn from these studies only after careful examination of the results. In particular it is important in retrospective studies to test a) the reproducibility of the data, b) the representativeness of the data, and c) the comparability of the special series and their controls. The resul1.s of retrospective studies are all similar and all show a close relationship between cigarette smoking and the disease.The results have been confirmed by pro~pective studies which are lesF. open to bias. The results can be explained if cigarette smoking causes lung cancer or if both are related to some third common factor. Ancillary data (pathological changes in the bronchial mucosa, animal experiments, etc.) support the causal hypothesis.The evidence relating to atmospheric pollution is less definite and it is difficult to get direct evidence of a relationship in the individual. It is clear that pollution has little effect in the absence of smoking, but the mortality associated with a given amount of smoking is generally greater in large towns than in the countryside and among men who have emigrated from Britain than among men who have lived all their lives in less polluted countries.

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

Multitemporal distribution analysis of Dodonaea viscosa (L.) Jacq. by remote sensing in Durango, Mexico

2020 ◽  
Vol 13 (8) ◽  
Author(s):  
Marco, A. Márquez-Linares ◽  
Jonathan G. Escobar--Flores ◽  
Sarahi Sandoval- Espinosa ◽  
Gustavo Pérez-Verdín
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Design Methodology ◽  
Supervised Classification ◽  
Satellite Images ◽  
Ground Cover ◽  
Distribution Analysis ◽  
Dodonaea Viscosa ◽  
Artificial Neural ◽  
Landsat Satellite

Objective: to determine the distribution of D. viscosa in the vicinity of the Guadalupe Victoria Dam in Durango, Mexico, for the years 1990, 2010 and 2017.Design/Methodology/Approach: Landsat satellite images were processed in order to carry out supervised classifications using an artificial neural network. Images from the years 1990, 2010 and 2017 were used to estimate ground cover of D. viscosa, pastures, crops, shrubs, and oak forest. This data was used to calculate the expansion of D. viscosa in the study area.Results/Study Limitations/Implications: the supervised classification with the artificial neural network was optimal after 400 iterations, obtaining the best overall precision of 84.5 % for 2017. This contrasted with the year 1990, when overall accuracy was low at 45 % due to less training sites (fewer than 100) recorded for each of the land cover classes.Findings/Conclusions: in 1990, D. viscosa was found on only five hectares, while by 2017 it had increased to 147 hectares. If the disturbance caused by overgrazing continues, and based on the distribution of D. viscosa, it is likely that in a few years it will have the ability to invade half the study area, occupying agricultural, forested, and shrub areas

Designing a coherent land registration system for rural Portugal

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Tiago NUNES ◽  
Miguel COUTINHO
Keyword(s):  
Satellite Images ◽  
Field Research ◽  
Digital Native ◽  
Web Based ◽  
Registration System ◽  
Land Registration ◽  
Daily Rate ◽  
Property Owners ◽  
Property Identification

After almost a century of several attempts to establish a coherent land registration system across the whole country, in 2017 the Portuguese government decided to try a new, digital native approach to the problem. Thus, a web-based platform was created, where property owners from 10 pilot municipalities could manually identify their lands’ properties using a map based on satellite images. After the first month of submissions, it became clear that at the current daily rate, it would take years to achieve the goal of 100% rural property identification across just the 10 municipalities. Field research during the first month after launch enabled us to understand landowners’ relationships with their land, map their struggles with the platform, and prototype ways to improve the whole service. Understanding that these improvements would still not be enough to get to the necessary daily rate, we designed, tested and validated an algorithm that allows us to identify a rural property shape and location without coordinates. Today, we are able to help both Government and landowners identify a rural property location with the click of a button.

Influence of corneal biomechanical properties on IOP indices in patients with keratoconus

The Eye ◽  
2019 ◽  
Vol 21 (128) ◽  
pp. 15-19
Author(s):  
Irina Bubnova ◽  
Veronica Averich ◽  
Elena Belousova
Keyword(s):  
Disease Progression ◽  
Corneal Thickness ◽  
Central Zone ◽  
Biomechanical Properties ◽  
Resistance Factor ◽  
Corneal Resistance Factor ◽  
Mean Values ◽  
Coefficient Of Elasticity ◽  
D Values ◽  
Corneal Biomechanical Properties

Purpose: Evaluation of corneal biomechanical prop¬erties and their influence on IOP indices in patients with keratoconus. Material and methods. The study included 194 eyes with keratoconus (113 patients aged from 23 to 36 years old). Corneal refraction in central zone varied from 48.25 to 56.75 D, values of corneal thickness ranged from 279 to 558 μm. Patients were divided into 4 groups according to Amsler classification: I stage – 40 eyes; II stage – 78 eyes; III stage – 54 eyes and IV stage – 22 eyes. Standard ophthal¬mological examination was carried out including pneumo¬tonometry. IOP indices and values of biomechanical prop¬erties were evaluated by dynamic bidirectional pneumatic applanation and pneumatic impression. Results. Study of corneal biomechanical properties in patients with keratoconus showed a decrease of such biomechanical indices as corneal hysteresis (CH) on aver¬age to 8.42±1.12 mm Hg, corneal resistance factor (CRF) – to 7.45±0.96 mm Hg, coefficient of elasticity (CE) – 5.35± 0.87 mm Hg. Values of these indices strongly depended on the stage of keratoconus. In the whole sample, the aver¬age corneal compensated IOP (IOPcc) amounted to 15.08± 2.43 mm Hg, Goldman IOP (IOPg) was 11.61±2.37 mm Hg and pneumatic tonometry IOP (IOPp) was 10.13±2.94 mm Hg. IOPcc indices didn’t have any statistically significant differ¬ence in dependence on the stage of keratoconus (р>0.473), while in process of disease progression IOPg and IOPp indi¬ces showed statistically significant decrease of mean values. Conclusion. Progression of keratoconus led to a de¬crease in corneal biomechanical properties which deter¬mine reduction of such indices as IOPg and IOPp in contrast to IOPcc.

A Novel Change Detection Method for Unregistered Optical Satellite Images

2012 ◽  
Vol E95.B (5) ◽  
pp. 1890-1893
Author(s):  
Wang LUO ◽  
Hongliang LI ◽  
Guanghui LIU ◽  
Guan GUI
Keyword(s):  
Change Detection ◽  
Satellite Images ◽  
Detection Method ◽  
Optical Satellite Images
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