Holistic aspects of suburban landscapes: visual image interpretation and landscape metrics

2000 ◽  
Vol 50 (1-3) ◽  
pp. 43-58 ◽  
Author(s):  
Marc Antrop ◽  
Veerle Van Eetvelde
Keyword(s):  
Landscape Metrics ◽  
Image Interpretation ◽  
Visual Image ◽  
Visual Image Interpretation

scholarly journals Estimation of Forest Area and Canopy Cover Based on Visual Interpretation of Satellite Images in Ethiopia

Land ◽  
2018 ◽  
Vol 7 (3) ◽  
pp. 92 ◽  
Author(s):  
Zerihun Asrat ◽  
Habitamu Taddese ◽  
Hans Ørka ◽  
Terje Gobakken ◽  
Ingunn Burud ◽  
...  
Keyword(s):  
Satellite Images ◽  
Forest Cover ◽  
Image Interpretation ◽  
Canopy Cover ◽  
Visual Image ◽  
Forest Area ◽  
Sample Sizes ◽  
High Resolution Satellite Images ◽  
Visual Image Interpretation ◽  
Sentinel 2

Forests, particularly in the tropics, are suffering from deforestation and forest degradations. The estimation of forest area and canopy cover is an essential part of the establishment of a measurement, reporting, and verification (MRV) system that is needed for monitoring carbon stocks and the associated greenhouse gas emissions and removals. Information about forest area and canopy cover might be obtained by visual image interpretation as an alternative to expensive fieldwork. The objectives of this study were to evaluate different types of satellite images for forest area and canopy cover estimation though visual image interpretation, and assess the influence of sample sizes on the estimates. Seven sites in Ethiopia with different vegetation systems were subjectively identified, and visual interpretations were carried out in a systematical design. Bootstrapping was applied to evaluate the effects of sample sizes. The results showed that high-resolution satellite images (≤5 m) (PlanetScope and RapidEye) images produced very similar estimates, while coarser resolution imagery (10 m, Sentinel-2) estimates were dependent on forest conditions. Estimates based on Sentinel-2 images varied significantly from the two other types of images in sites with denser forest cover. The estimates from PlanetScope and RapidEye were less sensitive to changes in sample size.

scholarly journals A multisite analysis of the concordance between visual image interpretation and quantitative analysis of [ 18 F]flutemetamol PET images

2020 ◽  
Vol 16 (S5) ◽  
Author(s):  
Gill Farrar ◽  
Agneta K Nordberg ◽  
Cyrille Sur ◽  
David Scott ◽  
Renaud Lhommel ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Image Interpretation ◽  
Visual Image ◽  
Visual Image Interpretation

Visual Image Interpretation

2019 ◽  
pp. 163-190
Author(s):  
James S. Aber ◽  
Irene Marzolff ◽  
Johannes B. Ries ◽  
Susan E.W. Aber
Keyword(s):  
Image Interpretation ◽  
Visual Image ◽  
Visual Image Interpretation

scholarly journals LANDSAT 8 MULTISPECTRAL AND PANSHARPENED IMAGERY PROCESSING ON THE STUDY OF CIVIL ENGINEERING ISSUES

2016 ◽  
Vol XLI-B8 ◽  
pp. 941-945 ◽  
Author(s):  
M. A. Lazaridou ◽  
A. Ch. Karagianni
Keyword(s):  
Land Cover ◽  
Image Classification ◽  
Spatial Resolution ◽  
Civil Engineering ◽  
Image Interpretation ◽  
Visual Image ◽  
Multispectral Image ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Visual Image Interpretation

Scientific and professional interests of civil engineering mainly include structures, hydraulics, geotechnical engineering, environment, and transportation issues. Topics included in the context of the above may concern urban environment issues, urban planning, hydrological modelling, study of hazards and road construction. Land cover information contributes significantly on the study of the above subjects. Land cover information can be acquired effectively by visual image interpretation of satellite imagery or after applying enhancement routines and also by imagery classification. The Landsat Data Continuity Mission (LDCM – Landsat 8) is the latest satellite in Landsat series, launched in February 2013. Landsat 8 medium spatial resolution multispectral imagery presents particular interest in extracting land cover, because of the fine spectral resolution, the radiometric quantization of 12bits, the capability of merging the high resolution panchromatic band of 15 meters with multispectral imagery of 30 meters as well as the policy of free data. In this paper, Landsat 8 multispectral and panchromatic imageries are being used, concerning surroundings of a lake in north-western Greece. Land cover information is extracted, using suitable digital image processing software. The rich spectral context of the multispectral image is combined with the high spatial resolution of the panchromatic image, applying image fusion – pansharpening, facilitating in this way visual image interpretation to delineate land cover. Further processing concerns supervised image classification. The classification of pansharpened image preceded multispectral image classification. Corresponding comparative considerations are also presented.

scholarly journals LANDSAT 8 MULTISPECTRAL AND PANSHARPENED IMAGERY PROCESSING ON THE STUDY OF CIVIL ENGINEERING ISSUES

2016 ◽  
Vol XLI-B8 ◽  
pp. 941-945 ◽  
Author(s):  
M. A. Lazaridou ◽  
A. Ch. Karagianni
Keyword(s):  
Land Cover ◽  
Image Classification ◽  
Spatial Resolution ◽  
Civil Engineering ◽  
Image Interpretation ◽  
Visual Image ◽  
Multispectral Image ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Visual Image Interpretation

Scientific and professional interests of civil engineering mainly include structures, hydraulics, geotechnical engineering, environment, and transportation issues. Topics included in the context of the above may concern urban environment issues, urban planning, hydrological modelling, study of hazards and road construction. Land cover information contributes significantly on the study of the above subjects. Land cover information can be acquired effectively by visual image interpretation of satellite imagery or after applying enhancement routines and also by imagery classification. The Landsat Data Continuity Mission (LDCM – Landsat 8) is the latest satellite in Landsat series, launched in February 2013. Landsat 8 medium spatial resolution multispectral imagery presents particular interest in extracting land cover, because of the fine spectral resolution, the radiometric quantization of 12bits, the capability of merging the high resolution panchromatic band of 15 meters with multispectral imagery of 30 meters as well as the policy of free data. In this paper, Landsat 8 multispectral and panchromatic imageries are being used, concerning surroundings of a lake in north-western Greece. Land cover information is extracted, using suitable digital image processing software. The rich spectral context of the multispectral image is combined with the high spatial resolution of the panchromatic image, applying image fusion – pansharpening, facilitating in this way visual image interpretation to delineate land cover. Further processing concerns supervised image classification. The classification of pansharpened image preceded multispectral image classification. Corresponding comparative considerations are also presented.

Working with images

2010 ◽  
Author(s):  
Ned Horning ◽  
Julie A. Robinson ◽  
Eleanor J. Sterling ◽  
Woody Turner ◽  
Sacha Spector
Keyword(s):  
Satellite Image ◽  
Image Interpretation ◽  
Visual Image ◽  
Human Observer ◽  
Intensity Data ◽  
Reflected Light ◽  
Automated Processing ◽  
Blue Line ◽  
Color Scheme ◽  
Professional Photographer

There are two very different ways to envision a satellite image: as a photograph taken with a camera, or as a visual representation of spectral intensity data quantifying the light reflecting off of objects on a planet’s surface. In working with satellite images, sometimes the objective is to highlight and accent the information in the image using tools to enhance the way the image looks—the same goal that a professional photographer might have when working in the darkroom with film or using Photoshop to manipulate digital photographs. Another objective could be to manipulate the image using automated processing methods within a remote sensing package that rely on a set of equations that quantify information about reflected light. With either approach the goal is to gain information about conditions observed on the ground. At first glance, the image in Fig. 3.1 bears little resemblance to what most people would recognize as a terrestrial landscape. After all, its predominant colors are orange and bright turquoise. The use of colors in creating a visual image allows great breadth in the types of things one can identify on the ground, but also makes image interpretation an art. Even an inexperienced interpreter can make some sense of the image; more experienced interpreters with knowledge of the color scheme in use are able to determine finer details. For example, in Fig. 3.1 some of the more prominent features are a river (blue line on the left side of the image) a gradient of different vegetation (orange colors throughout the image that go from light to dark), and burn scars (turquoise patches). Fig. 3.2 shows a portion of landscape represented in the satellite image in Fig. 3.1. The red dot in Fig. 3.1 indicates the location where the photograph was taken. This photograph shows what a human observer would see looking south (in this case toward the top of the satellite image) from the point represented by the red dot. The view in the photograph differs from the satellite image in two important ways.

scholarly journals The Use of Spot-HRV Data for the Mapping of the Land Cover (applied upon East-Mono, Central Togo)

Afrika Focus ◽  
1991 ◽  
Vol 7 (1) ◽  
Author(s):  
Beata Maria De Vliegher
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Image Interpretation ◽  
Remote Sensing Data ◽  
Image Data ◽  
Visual Image ◽  
Data Set ◽  
Land Cover Map ◽  
Colour Composite

The mapping of the land use in a tropical wet and dry area (East-Mono, Central Togo) is made using remote sensing data, recorded by the satellite SPOT. The negative, multispectral image data set has been transferred into positives by photographical means and afterwards enhanced using the diazo technique. The combination of the different diazo coloured images resulted in a false colour composite, being the basic document for the visual image interpretation. The image analysis, based upon differences in colour and texture, resulted in a photomorphic unit map. The use of a decision tree including the various image characteristics allowed the conversion of the photomorphic unit map into a land cover map. For this, six main land cover types could be differentiated resulting in 16 different classes of the final map. KEY WORDS :Remote sensing, SPOT, Multispectral view, Visual image interpre- tation, Mapping, Vegetation, Land use, Togo. 

scholarly journals Population Estimation Using Land-Use Change Data from Multi-Sensor Images in Maha Sarakham Province, Thailand

2021 ◽  
pp. 81-95
Author(s):  
S.R. Suwanlee ◽  
J. Som-ard
Keyword(s):  
Land Use ◽  
Land Use Changes ◽  
Image Interpretation ◽  
Visual Image ◽  
Population Based ◽  
Population Estimation ◽  
Aerial Photographs ◽  
Residential Areas ◽  
Landsat 7 ◽  
Sentinel 2A

Land-use changes surrounding Mahasarakham University in Thailand were investigated using multi-sensor images from 2002 and 2019. This study used aerial photographs and Landsat-7 satellite images captured in 2002, and aerial photographs from an unmanned aerial vehicle and Sentinel-2A data observed in 2019. Visual image interpretation (VII), object-based image analysis (OBIA), and random forest (RF) methods were applied to classify building areas from the multi-sensor images. Population was estimated using buildings and field-survey data, and population samples. The samples were obtained by point-, pixel-, and area-based methods. The different population estimation approaches were then compared with the actual population based on field surveys. VII yielded accuracies of 97% in 2002 and 97.5% in 2019. Built-up extraction using RF yielded accuracies of 86.55 and 90.76%, whereas OBIA was 76.47 and 82.35%, indicating a transformation in the land use from paddy fields to urban and residential areas. The area-based method were highly efficient in 2002 (r2 = 0.92) and 2019 (r2 = 0.93). The proposed area-based method provides more accurate population estimates than existing methods, with accuracies considered to be comparable to those of field data.

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