scholarly journals Satellite image based vegetation classification of a large area using limited ground reference data: a case study in the Usa Basin, north-east European Russia

2004 ◽  
Vol 23 (1) ◽  
pp. 51-66 ◽  
Author(s):  
Tarmo Virtanen ◽  
Kari Mikkola ◽  
Ari Nikula
2017 ◽  
Vol 32 (7) ◽  
pp. 976-988 ◽  
Author(s):  
Hui Zhang ◽  
Matthew J. Amesbury ◽  
Tiina Ronkainen ◽  
Dan J. Charman ◽  
Angela V. Gallego-Sala ◽  
...  

2017 ◽  
Vol 19 (1) ◽  
pp. 1
Author(s):  
Beny Harjadi

Work criteria and indicator of Catchments Area need to be determined because the success and the failure of cultivating Catchments Area can be monitored and evaluated through the determined criteria. Criteria Indicators in utilizing land, one of them is determined based on the erosion index and the ability of utilizing land, for analyzing the land critical level. However, the determination of identification and classification of land critical level has not been determined; as a result the measurement of how wide the real critical land is always changed all the year. In this study, it will be tried a formula to determine the land critical/eve/ with various criteria such as: Class KPL (Ability of Utilizing Land) and the difference of the erosion tolerance value with the great of the erosion compared with land critical level analysis using remote sensing devices. The aim of studying land critical level detection using remote sensing tool and Geographic Information System (SIG) are:1. The backwards and the advantages of critical and analysis method2. Remote Sensing Method for critical and classification3. Critical/and surveyed method in the field (SIG) Collecting and analyzing data can be found from the field survey and interpretation of satellite image visually and using computer. The collected data are analyzed as:a. Comparing the efficiency level and affectivity of collecting biophysical data through field survey, sky photo interpretation, and satellite image analysis.b. Comparing the efficiency level and affectivity of land critical level data that are found from the result of KPL with the result of the measurement of the erosion difference and erosion tolerance.


IJARCCE ◽  
2017 ◽  
Vol 6 (4) ◽  
pp. 297-301
Author(s):  
Mayur Kolhe ◽  
Yogesh Kadam ◽  
Ninad Chousalkar ◽  
Sunny Chordiya

2021 ◽  
Vol 2114 (1) ◽  
pp. 012017
Author(s):  
Bushra A. Ahmed ◽  
Ghaida S. Hadi

Abstract This study compared and classified of land use and land cover changes by using Remote Sensing (RS) and Geographic Information Systems (GIS) on two cities (Al-Saydiya city and Al-Hurriya) in Baghdad province, capital of Iraq. In this study, Landsat satellite image for 2020 were used for (Land Use/Land Cover) classification. The change in the size of the surface area of each class in the Al-Saydiya city and Al-Hurriya cities was also calculated to estimate their effect on environment. The major change identified, in the study, was in agricultural area in Al-Saydiya city compare with Al-Hurriya city in Baghdad province. The results of the research showed that the percentage of the green area from the total area in Al-Saydiya city is 34.95%, while in Al-Hurriya is 27.53%. Therefore, available results of land use and land cover changes can provide critical input to decision-making of environmental management and planning the future.


2021 ◽  
Vol 38 ◽  
pp. 00065
Author(s):  
Ilya Kucherov ◽  
Galina Grishutkina ◽  
Victoria Teleganova ◽  
Alexey Potemkin

The article reveals that epiphytic and epixylic bryophytes could be successfully used as differentials in classification of forest communities together with vascular plants and epigeic cryptogams, the fact proved for broadleaved forests in European Russia.


Geografie ◽  
1997 ◽  
Vol 102 (1) ◽  
pp. 17-30
Author(s):  
Jaromír Kolejka ◽  
Jásim K. Shallal

Surface soil data have been processed using the unsupervised classification (cluster analysis). Three soil categories with different erosional characteristics have been detected: heavily, moderately and slightly/no damaged soils. The supervised satellite image classification (MLC) was based on the data taken from case study areas in the proximity of classified soil sample sites on the vegetation free-fields.


2007 ◽  
Vol 42 (3) ◽  
pp. 449-461 ◽  
Author(s):  
Elena B. Fefilova ◽  
Olga A. Loskutova ◽  
Sergey V. Pestov

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