Authors

Hai Khuong Van 1* , Trang Tran Huong 2

Affiliations

1 Water Resources Institute; khuongvanhai@gmail.com

2 Ha Noi University of Natural Resources and Environment; tranhuongtrang2608gmail.com

*Correspondence: khuongvanhai@gmail.com; Tel.: +84-974183835

Abstracts

Today, the environmental situation in urban areas becoming polluted, people are increasingly interested in and want to live in green cities. This paper uses the satellite image Landsat 8 and the method of calculating the vegetation index (NDVI) combined with the multivariate regression analysis to study and evaluate the change of greenery area for the inner districts of Hanoi period 2013–2016. The study results show that the greenery area is strongly correlated in the central districts and the average correlation in districts with high urbanization or agricultural areas. The green tree density in Ha Noi city is quite different between the central districts and suburbs. In the suburb such as Long Bien, Ha Dong, Nam Tu Liem, North Tu Liem, Tay Ho, Hoang Mai the green tree density in the people is quite high, exceeding TCVN 9257:2012. To be specific, Long Bien district has the highest green tree density, with 134.2 m2/person up to 11 times national standards. Meanwhile, central districts such as Dong Da, Hai Ba Trung, Ba Dinh, Hoan Kiem, Thanh Xuan have very low green tree density, lower than the minimum standard of TCVN 9257: 2012. To be specific, Dong Da is the lowest green tree density with 2.5 m2/person, lower than the TCVN 9257:2012 (> 12 m2/person) to 4.8 times national standards.

Keywords

NDIV; Green tree; Remote sensing; GIS; Ha Noi City

Cite this paper

Van, H.K.; Trang, T.H. Green space study in 12 urban districts of Ha Noi using remote sensing data. VN J. Hydrometeorol. 2021, 7, 53-64. 

References

1. Chang, Q.; Liu, X.W.; Wu J.S.; He, P. MSPA–based urban green infrastructure planning and management approach for urban sustainability: Case study of Longgang in China. J. Urban Plan. Dev. 2015, 141, 3.
2. Ossola, A.;  Hopton, M.E. Measuring urban tree loss dynamics across residential landscapes. Sci. Total Environ. 2018, 612, 940–949, doi: 10.1016/j.scitotenv.2017.08.103.
3. Pu, R.L.;  Landry, S. A Comparative analysis of high spatial resolution IKONOS and WorldView–2 imagery for mapping urban tree species. Remote Sens. Environ. 2012, 124, 516–533.
4. Parmehr, E.G.; Amati, M. Estimation of urban tree canopy cover using random point sampling and remote sensing methods. Urban For. Urban Green. 2016, 20, 160–171.
5. Shojanoori, R.; Ismail, M.H.; Mansor, S.; Shafri, H. Generic rule–sets for automated detection of urban tree species from very high–resolution satellite data. Geocarto Int.  2018, 33, 1–36. 
6. Chance, C.M.; Coops, N.C.; Plowright, A.A.; Tooke, T.R.; Christen, A.; Aven, N. Invasive shrub mapping in an urban environment from hyperspectral and LiDAR–Derived attributes. Front. Plant Sci. 2016, 1–19. https://doi.org/10.3389/fpls.2016.01528
7. Nasi, R.; Eija, H.; Minna, B.; Paivi, L.S. Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft. Urban For. Urban Green 2018, 30, 72–83. https://doi.org/10.1016/j.ufug.2018.01.010.
8. Harris, R.; Baumann, I. Open data policies and satellite Earth observation. Space Policy 2015, 32, 44–53. https://doi.org/1010.1016/j.spacepol.2015.01.001.
9. Zhu, Z.; Michael, A.W.; David, P.R.; Curtis, E.W.; Matthew, C.H.; Volker, C.R.; Sean P.H.; Crystal, S.; Patrick, H.; Peter, S.; Jean–Francois, P.; Leo, L.; Nima, P.; Ted, A.S. Benefits of the free and open Landsat data policy. Remote Sens. Environ. 2019, 224, 382–385. 
10. https://hanoi.gov.vn/home
11. http://www.tnmtnd.hanoi.gov.vn/
12. https://Landsat.usgs.gov/Landsat8.php
13. https://www.usgs.gov
14. http://vietgis.com.vn/
15. Chi, P.K. New satellite generation – LDCM or Landsat 8, GeoViet. 2013.
16. Esau, I.; Miles, V.V.; Davy, R.; Miles, M.W.; Kurchatova, A. Victoria, V.M. Trends in normalize difference vegetation index (NDVI) associated with urban development in northern West Siberia. Atmos. Chem. Phys. 2016, 16, 9563–9577. https://doi.org/10.5194/acp–16–9563–2016.
17. Rumiana, V.; Monika, K. Mapping urban green spaces based on remote sensing data: Case studies in Bulgaria  and Slovakia. Proceeding of the 6th International Conference on Cartography and GIS. 2015.
18. Hanoi People’s Committee. Land use planning up to 2020 and 5–year land use plan (2011–2015) for Hanoi districts.
19. Kham, D.V. et al. Building a model for forecasting rice yield and yield in the Red River Delta using Modis image data. 2011. 
20. http://climatechangegis.blogspot.com/2012/03/cac–chi–so–thuc–vat–trong–vien–tham_4603.html
21. Hung, T.H.; Chi, P.K. Pilot study on application of remote sensing and GIS to manage urban green spaces and trees in Ha Tinh and Tra Vinh cities, 2011.