Forest vegetation assessment using geoinformation tools: a case of the Burla pine forest, Novosibirsk Region, Russia

The paper presents the vegetation thematic classification of the Burla banded pine forest carried on using "Canopus-V" remote sensing data and the supervised classification technique by a spectral angle mapper. Areas of selected elements have been assessed: 1. Pine forests, 2. Birch forests; 3. Meadows; 4. Anthropogenic objects (roads, etc.); 5. Agricultural lands; 6. Water objects. Sites of anthropogenic disturbed forests are identified according to remote sensing data. The results show that the data obtained in the classification by a spectral angle can be used to compile geobotanical maps, but due to low spectral resolution of Canopus-V satellite data, it is not always possible to classify individual objects validlys.

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Comparative Evaluation Threshold Parameters of Spectral Angle Mapper (SAM) for Mapping of Chhabadiya Talc Minerals, Jahajpur, Bhilwara, India using Hyperion hyperspectral Remote Sensing Data

2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT) ◽

10.1109/icct46177.2019.8969015 ◽

2019 ◽

Author(s):

Mahesh Kumar Tripathi ◽

Himanshu Govil ◽

Prabhat Diwan

Keyword(s):

Remote Sensing ◽

Comparative Evaluation ◽

Remote Sensing Data ◽

Hyperspectral Remote Sensing ◽

Spectral Angle Mapper ◽

Sensing Data ◽

Spectral Angle

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Application of the behavioral approach for supervised classification of remote sensing data

Proceedings of IGARSS '93 - IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.1993.322759 ◽

2002 ◽

Cited By ~ 3

Author(s):

F. Orban-Ferauge ◽

J.P. Rassom ◽

V. Granville

Keyword(s):

Remote Sensing ◽

Supervised Classification ◽

Remote Sensing Data ◽

Behavioral Approach ◽

Sensing Data

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Spectral derivative features for supervised classification of remote sensing data: An experimental evaluation

2012 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS) ◽

10.1109/whispers.2012.6874247 ◽

2012 ◽

Author(s):

Jiangfeng Bao ◽

Mingmin Chi ◽

Jon Atli Benediktsson

Keyword(s):

Remote Sensing ◽

Experimental Evaluation ◽

Supervised Classification ◽

Remote Sensing Data ◽

Sensing Data

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COMPUTING COMPONENTS OF CLOUD WEB-SERVICES FOR REMOTE SENSING DATA PROCESSING

Interexpo GEO-Siberia ◽

10.33764/2618-981x-2021-4-1-66-71 ◽

2021 ◽

Vol 4 (1) ◽

pp. 66-71

Author(s):

Aleksey A. Buchnev ◽

Valery P. Pyatkin ◽

Evgeny V. Rusin

Keyword(s):

Remote Sensing ◽

Web Services ◽

Data Processing ◽

Supervised Classification ◽

Remote Sensing Data ◽

Natural Environments ◽

Interactive Communication ◽

Sensing Data ◽

Movement Tracking

The organization of computations in cloud Web services for satellite data processing is considered. Computing component of almost every service is a batch version of the corresponding technology of the PlanetaMonitoring software for processing remote sensing data. The exceptions are the technologies that require interactive communication with user, i.e., supervised classification of remote sensing data and movement tracking of natural environments by the coordinates of identifiable objects, each of which consists of two parts, i.e., an interactive Windows application running on the user's computer and the part hidden in the cloud.

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Indices Extraction from Multitemporal Remote Sensing Data for Mapping Urban Built-Up

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/887/1/012004 ◽

2021 ◽

Vol 887 (1) ◽

pp. 012004

Author(s):

A. K. Hayati ◽

Y.F. Hestrio ◽

N. Cendiana ◽

K. Kustiyo

Keyword(s):

Remote Sensing ◽

Supervised Classification ◽

Remote Sensing Data ◽

Landsat 8 ◽

Sensing Data ◽

Remote Sensing Technology ◽

Sensing Technology ◽

Multitemporal Remote Sensing ◽

Multitemporal Data

Abstract Remote sensing data analysis in the cloudy area is still a challenging process. Fortunately, remote sensing technology is fast growing. As a result, multitemporal data could be used to overcome the problem of the cloudy area. Using multitemporal data is a common approach to address the cloud problem. However, most methods only use two data, one as the main data and the other as complementary of the cloudy area. In this paper, a method to harness multitemporal remote sensing data for automatically extracting some indices is proposed. In this method, the process of extracting the indices is done without having to mask the cloud. Those indices could be further used for many applications such as the classification of urban built-up. Landsat-8 data that is acquired during 2019 are stacked, therefore each pixel at the same position creates a list. From each list, indices are extracted. In this study, NDVI, NDBI, and NDWI are used to mapping built-up areas. Furthermore, extracted indices are divided into four categories by their value (maximum, quantile 75, median, and mean). Those indices are then combined into a simple formula to mapping built-up to see which produces better accuracy. The Pleiades as high-resolution remote sensing data is used to assist supervised classification for assessment. In this study, the combination of mean NDBI, maximum NDVI, and mean NDWI result highest Kappa coefficient of 0.771.

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Scalable semi-supervised classification of hyperspectral remote sensing data with spectral and spatial information

2011 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2011.6049562 ◽

2011 ◽

Cited By ~ 1

Author(s):

Mingmin Chi ◽

Jun Liu ◽

Jiangfeng Bao ◽

Jon Atli Benediktsson

Keyword(s):

Remote Sensing ◽

Supervised Classification ◽

Spatial Information ◽

Remote Sensing Data ◽

Hyperspectral Remote Sensing ◽

Sensing Data

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The Support Vector Machine (SVM) Algorithm for Supervised Classification of Hyperspectral Remote Sensing Data

Kernel Methods for Remote Sensing Data Analysis ◽

10.1002/9780470748992.ch3 ◽

2009 ◽

pp. 49-83 ◽

Cited By ~ 3

Author(s):

J. Anthony Gualtieri

Keyword(s):

Remote Sensing ◽

Support Vector Machine ◽

Supervised Classification ◽

Remote Sensing Data ◽

Hyperspectral Remote Sensing ◽

Support Vector ◽

Sensing Data ◽

Svm Algorithm

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Estimation of debris cover and its temporal variation using optical satellite sensor data: a case study in Chenab basin, Himalaya

Journal of Glaciology ◽

10.3189/002214309788816632 ◽

2009 ◽

Vol 55 (191) ◽

pp. 444-452 ◽

Cited By ~ 62

Author(s):

A. Shukla ◽

R.P. Gupta ◽

M.K. Arora

Keyword(s):

Remote Sensing ◽

Supervised Classification ◽

Remote Sensing Data ◽

Image Data ◽

Optical Remote Sensing ◽

Glacier Area ◽

Sensing Data ◽

Debris Cover ◽

Topographical Maps

AbstractDebris cover over glaciers greatly affects their rate of ablation and is a sensitive indicator of glacier health. This study focuses on estimation of debris cover over Samudratapu glacier, Chenab basin, Himalaya, using optical remote-sensing data. Remote-sensing image data of IRS-1C LISS-III (September 2001), IRS-P6 AWiFS (September 2004) and Terra ASTER (September 2004) along with Survey of India topographical maps (1963) were used in the study. Supervised classification of topographically corrected reflectance image data was systematically conducted to map six land-cover classes in the glacier terrain: snow, ice, mixed ice and debris, debris, valley rock, and water. An accuracy assessment of the classification was conducted using the ASTER visible/near-infrared data as the reference. The overall accuracies of the glacier-cover maps were found to range from 83.7% to 89.1%, whereas the individual class accuracy of debris-cover mapping was found to range from 82% to 95%. This shows that supervised classification of topographically corrected reflectance data is effective for the extraction of debris cover. In addition, a comparative study of glacier-cover maps generated from remote-sensing data (supervised classification) of September 2001 and September 2004 and Survey of India topographical maps (1963) has highlighted the trends of glacier depletion and recession. The glacier snout receded by about 756 m from 1963 to 2004, and the total glacier area was reduced by 13.7 km2 (from 110 km2 in 1963). Further, glacier retreat is found to be accompanied by a decrease in mixed ice and debris and a marked increase in debris-cover area. The area covered by valley rock is found to increase, confirming an overall decrease in the glacier area. The results from this study demonstrate the applicability of optical remote-sensing data in monitoring glacier terrain, and particularly mapping debris-cover area.

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