Update of seagrass cover and species diversity in Southern Viet Nam by remote sensing data and molecular analyses

2021 ◽  
pp. 101803
Author(s):  
Xuan-Vy Nguyen ◽  
Va-Khin Lau ◽  
Nhu-Thuy Nguyen-Nhat ◽  
Trung-Hieu Nguyen ◽  
Kim-Hoang Phan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Species Diversity ◽  
Remote Sensing Data ◽  
Viet Nam ◽  
Molecular Analyses ◽  
Sensing Data ◽  
Seagrass Cover

scholarly journals Characterization of Species Diversity and Forest Health using AVIRIS-NG Hyperspectral Remote Sensing Data

2019 ◽  
Vol 116 (7) ◽  
pp. 1124 ◽  
Author(s):  
C. S. Jha ◽  
, Rakesh ◽  
J. Singhal ◽  
C. S. Reddy ◽  
G. Rajashekar ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Species Diversity ◽  
Forest Health ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Sensing Data

scholarly journals Time-series modelling of dengue incidence in the Mekong Delta region of Viet Nam using remote sensing data

2020 ◽  
Vol 11 (1) ◽  
pp. 13-19
Author(s):  
Nga TT Pham ◽  
Cong Nguyen ◽  
Maria Ruth Pineda-Cartel
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Mekong Delta ◽  
Remote Sensing Data ◽  
Viet Nam ◽  
Data Sets ◽  
Delta Region ◽  
Study Region ◽  
Monthly Data ◽  
Sensing Data

Objective: This study aims to enhance the capacity of dengue prediction by investigating the relationship of dengue incidence with climate and environmental factors in the Mekong Delta region (MDR) of Viet Nam by using remote sensing data. Methods: To produce monthly data sets for each province, we extracted and aggregated precipitation data from the Global Satellite Mapping of Precipitation project and land surface temperatures and normalized difference vegetation indexes from the Moderate Resolution Imaging Spectroradiometer satellite observations. Monthly data sets from 2000 to 2016 were used to construct autoregressive integrated moving average (ARIMA) models to predict dengue incidence for 12 provinces across the study region. Results: The final models were able to predict dengue incidence from January to December 2016 that concurred with the observation that dengue epidemics occur mostly in rainy seasons. As a result, the obtained model presents a good fit at a regional level with the correlation value of 0.65 between predicted and reported dengue cases; nevertheless, its performance declines at the subregional scale. Conclusion: We demonstrated the use of remote sensing data in time-series to develop a model of dengue incidence in the MDR of Viet Nam. Results indicated that this approach could be an effective method to predict regional dengue incidence and its trends.

Assessment of the national market demand for high-resolution remote sensing data

2002 ◽  
Vol 8 (1) ◽  
pp. 15-22
Author(s):  
V.N. Astapenko ◽  
◽  
Ye.I. Bushuev ◽  
V.P. Zubko ◽  
V.I. Ivanov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Market Demand ◽  
Sensing Data ◽  
National Market

Processing of remote sensing data for detection and recognition of object classes

2004 ◽  
Vol 10 (5-6) ◽  
pp. 175-177
Author(s):  
S.L. Kravtsov ◽  
◽  
L.V. Oreshkina ◽  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Object Classes ◽  
Detection And Recognition

A procedure for integrated assessment of landscape fire risk using remote sensing data

2011 ◽  
Vol 17 (6) ◽  
pp. 30-44
Author(s):  
Yu.V. Kostyuchenko ◽  
◽  
M.V. Yushchenko ◽  
I.M. Kopachevskyi ◽  
S. Levynsky ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Integrated Assessment ◽  
Remote Sensing Data ◽  
Fire Risk ◽  
Sensing Data

Estimation of PM10 Distribution using Landsat 7 ETM+ Remote Sensing Data

2017 ◽  
Vol 6 (1) ◽  
pp. 2246-2252 ◽  
Author(s):  
Ajay Roy ◽  
◽  
Anjali Jivani ◽  
Bhuvan Parekh ◽  
◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Landsat 7
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