scholarly journals EXPERIENCE OF DATA APPLICATION OF REMOTE SENSING IN STUDIES OF STEPPE FIRES

2018 ◽  
Vol 2 (№11 2018) ◽  
pp. 377-382
Author(s):  
V.M. Pavleychik
Keyword(s):  
Remote Sensing ◽  
Data Application ◽  
Steppe Fires

scholarly journals Mangrove Distribution in Riau Islands Using Remote Sensing Technology

2017 ◽  
Vol 1 (2) ◽  
pp. 58-62 ◽  
Author(s):  
Sudra Irawan ◽  
Dwi Ely Kurniawan ◽  
Wenang Anurogo ◽  
Muhammad Zainuddin Lubis
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Data ◽  
Research Data ◽  
Resolution Image ◽  
Mangrove Area ◽  
High Resolution Image ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Data Application

Mangrove mapping is done with remote sensing technology using high-resolution image data. Application and information are then presented in web form. This study aims to map the mangrove distribution in Riau Islands, Indonesia. Based on the analysis, from the research data obtained the total area of mangrove in Riau Islands in 2011 and 2017 amounted to 71,504.83 Ha and 64,218.90 Ha, decreased by 7,285, 93 Ha or decreased by 10.19%. Based on the regency, the largest mangrove area in 2017 is located in Batam City of 22,964.77 Ha, then Karimun Regency (13,659,58 Ha), Lingga Regency (11,881.61 Ha), Regency of Bintan (9,701.49) Ha, Natuna Regency (2,477.16 Ha), Tanjungpinang City (1,847.65 Ha), and Anambas Regency (1,686.61 Ha). The magnitude of the widespread change (widespread reduction) occurring over the years between 2011 and 2017 by district, Natuna Regency experienced the largest reduction of 1,949.69 Ha or around 41.39%, followed by Lingga Regency of 1,947.15 Ha (14.08%), Tanjungpinang Municipality of 284.13 Ha (13.33%), Karimun Regency 1,920.93 Ha (12.33%), Anambas Regency of 195.90 Ha (10.40%), Batam City 1,094.83 Ha (4.55%) and Bintan Regency with 93.29 Ha (0, 95%). Opportunities that the pixels classified on the mangrove image are truly mangrove on the facts in the field.

scholarly journals Determination of the Pyro-Factor Ecological Successions Duration in the Zonal Landscapes of the Volgograd Region According to the Remote Sensing Data

2020 ◽  
pp. 44-53
Author(s):  
Stanislav Shinkarenko ◽  
◽  
Asel Berdengalieva ◽  
Valeriya Doroshenko ◽  
Kseniya Oleynikova ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spectral Characteristics ◽  
Remote Sensing Data ◽  
Field Studies ◽  
Spatial Characteristics ◽  
Sensing Data ◽  
Volgograd Region ◽  
Burnt Areas ◽  
Steppe Fires

The aim of the work is to determine the spatial characteristics of the distribution of the burnt areas of natural zonal landscapes of the Volgograd region with different duration of pyro-factor successions, taking into account the frequency of fires. Based on the previously developed thematic geo-information layers of the steppe fires in the region using overlay operations, the duration of post-pyrogenic periods in the municipal districts of the region was determined, taking into account the total number of fires from 1998–2018. The largest areas covered by fire have a succession duration of 2–3 years and 12–14 years at the beginning of 2019, which corresponds to the fires of 2016–2017 and 2005–2007, respectively. Large areas after the fires of 2001–2002 are located in Ilovlinsky, Kletsky, Pallasovsky and Surovikinsky districts. The largest area of land covered by fire in 2004–2006 is located in the Danilovsky, Ilovlinsky, Olkhovsky and Pallas districts. In our opinion, landscapes affected by fire no more than 5–7 years ago are suitable for the analysis of pyrogenic shifts. These territories are located in Frolovsky, Chernyshkovsky, Kotovsky, Ilovlinsky, Pallasovsky, Leninsky, Kamyshinsky, Staropoltavsky districts. The results will serve as the basis for field studies and the analysis of the spectral characteristics of overgrowing burns from remote sensing materials.

Water level monitoring using radar remote sensing data: Application to Lake Kivu, central Africa

2009 ◽  
Vol 34 (13-16) ◽  
pp. 722-728 ◽  
Author(s):  
Omar Munyaneza ◽  
Umaru G. Wali ◽  
Stefan Uhlenbrook ◽  
Shreedhar Maskey ◽  
McArd J. Mlotha
Keyword(s):  
Remote Sensing ◽  
Water Level ◽  
Central Africa ◽  
Remote Sensing Data ◽  
Radar Remote Sensing ◽  
Sensing Data ◽  
Data Application ◽  
Lake Kivu ◽  
Level Monitoring ◽  
Water Level Monitoring

scholarly journals FORECASTING OF STEPPE FIRES USING REMOTE SENSING DATA OF TIME SERIES

2020 ◽  
Vol 1 (№12 2020) ◽  
pp. 15-19
Author(s):  
V.M. Goryaev ◽  
D.B. Bembitov ◽  
E.V. Sumyanova ◽  
E.O. Uchurova ◽  
S.S. Sarginov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Steppe Fires

scholarly journals Experience of Applying Remote Sensing Earth Data in Environmental Forensics

2019 ◽  
Vol 14 (1) ◽  
pp. 56-65
Author(s):  
S. Yu. Rozov ◽  
N. D. Kutuzova ◽  
T. N. Bolysheva
Keyword(s):  
Remote Sensing ◽  
Data Interpretation ◽  
Landscape Changes ◽  
Environmental Forensics ◽  
Environmental Objects ◽  
Data Application ◽  
Modern Image ◽  
Expert Practice

The opportunity of remote sensing Earth data application in performing of environmental forensics is contemplated. Using examples from expert practice, the progress of soil and vegetation mantle examination on the remote sensing materials is analyzed. It is demonstrated that existing methods of data interpretation enable the non-contact reconnaissance of environmental objects on an immense territory signifcantly reducing the time and expense of the expertise. With the help of this method an expert gets an opportunity to perform backcasting of landscape changes and the features of the modern image software make both visual and automated ways of analysis possible. The latest enables subjectivity elimination from interpretation of the images which is essential in getting reasonable expert conclusions.

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