SAPIA: A Model Based Satellite Image Interpretation System

1999 ◽  
pp. 580-589
Author(s):  
F. Ramparany ◽  
F. Sandt
Keyword(s):  
Satellite Image ◽  
Image Interpretation ◽  
Model Based ◽  
Interpretation System

GIS mapping to solve environment education tasks: interactive map of vegetation “Along the Doppelmair’s trail”

2020 ◽  
Vol 955 (1) ◽  
pp. 7-18
Author(s):  
A.V. Myadzelets ◽  
N.M. Luzhkova
Keyword(s):  
Protected Areas ◽  
Scientific Information ◽  
Satellite Image ◽  
Image Interpretation ◽  
Field Research ◽  
Gis Mapping ◽  
Environment Protection ◽  
Gis Technologies ◽  
Interactive Map ◽  
Unique Species

Environmental education is an important function of Protected Areas among them nature reserves. It includes development of visible and available materials on tours and routes, on geographical and environmental features of a territory, and on unique species of flora and fauna. Interactive map of vegetation “Along Doppelmair’s trail” is an example of scientific information visualization. It was made for a distant and restricted core area in Barguzinsky Nature Reserve. We applied traditional geographical approaches and methods (field research, geobotanical descriptions) and modern GIS technologies (creation of unit database on landscape foundation, satellite image interpretation, infogram visualization) to create the map. As a result a GIS product is created with ArcMAP and located on the ArcGIS Online platform. This map shows characteristic vegetation types, succession stages of pyrogenic dynamics of forest geosystems formed during a century period. Infograms demonstrate information on sites with wild animal encounters, vegetation distribution, landscape features, and photographical materials. This interactive map is a way for environment protection popularization and solving some educational tasks for Protected Areas. It gives an opportunity to study changes in vegetation from Lake Baikal shoreline to mountain peaks of Bаrguzinskii Range, learn typical flora and fauna species, including endangered ones, find interesting historical facts about the reserve, and, thus, get an idea of uniqueness and fragility of nature and the importance of protection attempts.

Assessment of mangrove forest change in Ca Mau province using satellite images in the period of 1988 - 2018

2021 ◽  
Vol 66 (1) ◽  
pp. 175-187
Author(s):  
Duong Phung Thai ◽  
Son Ton
Keyword(s):  
Satellite Images ◽  
Satellite Image ◽  
Image Interpretation ◽  
Image Data ◽  
Vegetation Coverage ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Forest Change ◽  
Shrimp Ponds ◽  
Area Classification

On the basis of using practical methods, satellite image processing methods, the vegetation coverage classification system of the study area, interpretation key for the study area, classification and post-classification pro cessing, this research introduces how to exploit and process multi-temporal satellite images in evaluating the changes of forest area. Landsat 4, 5 TM and Landsat 8 OLI remote sensing image data were used to evaluate the changes in the area of mangrove forests (RNM) in Ca Mau province in the periods of 1988 - 1998, 1998 - 2013, 2013 - 2018, and 1988 - 2018. The results of the image interpretation in 1988, 1998, 2013, 2018 and the overlapping of the above maps show: In the 30-year period from 1988 to 2018, the total area of mangroves in Ca Mau province was decreased by 28% compared to the beginning, from 71,093.3 ha in 1988 reduced to 51,363.5 ha in 2018, decreasing by 19,729.8 ha. The recovery speed of mangroves is 2 times lower than their disappearance speed. Specifically, from 1988 to 2018, mangroves disappeared on an area of 42,534.9 hectares and appeared on the new area of 22,805 hectares, only 12,154.5 hectares of mangroves remained unchanged. The fluctuation of mangrove area in Ca Mau province is related to the process of deforestation to dig shrimp ponds, coastal erosion, the formation of mangroves on new coastal alluvial lands and soil dunes in estuaries, as well as planting new mangroves in inefficient shrimp ponds.

Assessing components of the model-based mean square error estimator for remote sensing assisted forest applications

2018 ◽  
Vol 48 (6) ◽  
pp. 642-649 ◽  
Author(s):  
Ronald E. McRoberts ◽  
Erik Næsset ◽  
Terje Gobakken ◽  
Gherardo Chirici ◽  
Sonia Condés ◽  
...  
Keyword(s):  
Mean Square Error ◽  
Spatial Correlation ◽  
Laser Scanning ◽  
Satellite Image ◽  
Error Estimator ◽  
Residual Variance ◽  
Mean Square ◽  
Model Parameter ◽  
Model Based ◽  
Residual Covariance

Model-based inference is an alternative to probability-based inference for small areas or remote areas for which probability sampling is difficult. Model-based mean square error estimators incorporate three components: prediction covariance, residual variance, and residual covariance. The latter two components are often considered negligible, particularly for large areas, but no thresholds that justify ignoring them have been reported. The objectives of the study were threefold: (i) to compare analytical and bootstrap estimators of model parameter covariances as the primary factors affecting prediction covariance; (ii) to estimate the contribution of residual variance to overall variance; and (iii) to estimate thresholds for residual spatial correlation that justify ignoring this component. Five datasets were used, three from Europe, one from Africa, and one from North America. The dependent variable was either forest volume or biomass and the independent variables were either Landsat satellite image bands or airborne laser scanning metrics. Three conclusions were noteworthy: (i) analytical estimators of the model parameter covariances tended to be biased; (ii) the effects of residual variance were mostly negligible; and (iii) the effects of spatial correlation on residual covariance vary by multiple factors but decrease with increasing study area size. For study areas greater than 75 km2 in size, residual covariance could generally be ignored.

The Use of Local Landscape as a Field Laboratory for Geography of Education

2019 ◽  
Vol 19 (2) ◽  
pp. 1-7
Author(s):  
Ruli As'ari ◽  
Erni Mulyanie
Keyword(s):  
Cultural Geography ◽  
Satellite Image ◽  
Image Interpretation ◽  
Geography Education ◽  
Physical Field ◽  
Learning Needs ◽  
Field Laboratory ◽  
Testing And Measurement ◽  
Education Field

Geographical skills that need to be shared by each geographer in general are map skills, field skills, and satellite image interpretation skills. To achieve field skills competency, a location is needed to be used as material for practicum studies for each subject. The Geography Education Field Laboratory can be studied in depth based on an analysis of the level of learning needs. The basis of the lab location requirements as a laboratory is seen from the laboratory function as an area to carry out careful and accurate testing and measurement of the phenomenon under study. The study was carried out through the identification of local landscapes by delineating the area through the utilization of satellite citera, and identifying potential from each area that was chosen descriptively. In this study, the Gunung Galunggung area can be used as a Physical Field Laboratory for Geography and Kampung Naga Education can be used as a Field Laboratory for Social and Cultural Geography.

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.

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