Proceeding Optical Remote Sensing Method for Detecting Urban Green Space as Indicator Serving City Sustainable Development

Similar documents
Optical Remote Sensing Method for Detecting Urban Green Space as Indicator Serving City Sustainable Development

Monitoring Urban Space Expansion Using Remote Sensing Data in Ha Long City, Quang Ninh Province in Vietnam

Vietnam Coastal Erosion - Cause and Challenges -

Nguyen Hoang Khanh Linh, Le Ngoc Phuong Quy, Truong Do Minh Phuong and Nguyen Trac Ba An

SESSION C: OPTICS, LASER AND APPLICATIONS November 27, 2012 (Thuesday)

Current Status of Vietnam Coastal Erosion and Major Measures for Mitigation

Satellite image-based quantitative assessment of surface urban heat island supporting environmental management at the city level

Populations effected by flood top ten districts

ANALYSIS OF CHANGES IN THE RIVERBANKS OF MEKONG RIVER - VIETNAM BY USING MULTI-TEMPORAL REMOTE SENSING DATA


APPLICATION OF REMOTE SENSING IN LAND USE CHANGE PATTERN IN DA NANG CITY, VIETNAM

Evaluating Flood Hazard Potential in Danang City, Vietnam Using FOSS4G

HỘI NGHỊ QUANG HỌC QUANG PHỔ TOÀN QUỐC LẦN THỨ 10 ( HNQHQP-10) Hall C. November 12, 2018 (Monday)

VNREDSat-1. Vietnam Natural Resources, Environment and Disaster monitoring Satellite. Lai Anh Khoi SPACE TECHNOLOGY INSTITUTE

Ajoke Onojeghuo & Alex Onojeghuo (Nigeria)

NAG2015 International Seminar, 20 August 2015, Hanoi, Vietnam Numerical Analysis in Geotechnics

Applying ArcGIS Online for Establishing Hanoi Agriculture Map

THE FIRST EXPERIENCE OF USING ALOS DATA FOR DISASTER MONITORING

Combination of Microwave and Optical Remote Sensing in Land Cover Mapping

SÁNG TH T, NGÀY

1. Introduction. Chaithanya, V.V. 1, Binoy, B.V. 2, Vinod, T.R. 2. Publication Date: 8 April DOI: /cloud.ijarsg.

SIMULATION OF A WORST CASE TSUNAMI SCENARIO FROM THE MANILA TRENCH TO VIETNAM

EROSION MECHANISM OF NGA BAY RIVERBANKS, HO CHI MINH CITY, VIETNAM

APPLICATION OF LAND CHANGE MODELER FOR PREDICTION OF FUTURE LAND USE LAND COVER A CASE STUDY OF VIJAYAWADA CITY

Mapping Landslide Events in Vietnam Using the Global Landslide Catalog and GIS

Progress and Land-Use Characteristics of Urban Sprawl in Busan Metropolitan City using Remote sensing and GIS

Module 3 Indicator Land Consumption Rate to Population Growth Rate

DAMAGE DETECTION OF THE 2008 SICHUAN, CHINA EARTHQUAKE FROM ALOS OPTICAL IMAGES

KHI X L T SÔNG H NG VÀO SÔNG ÁY

ASSESSING THEMATIC MAP USING SAMPLING TECHNIQUE

Monitoring influence of urbanization on urban thermal environment using multi-temporal LANDSAT imagery: application to Da Nang city

Overview of Remote Sensing in Natural Resources Mapping

SUPPLEMENTAL MATERIAL. Modulation of Daily Rainfall in Southern Vietnam by the Madden-Julian. Oscillation and Convectively Coupled Equatorial Waves

LAND COVER CATEGORY DEFINITION BY IMAGE INVARIANTS FOR AUTOMATED CLASSIFICATION

Using Reanalysis SST Data for Establishing Extreme Drought and Rainfall Predicting Schemes in the Southern Central Vietnam

Monitoring and Change Detection along the Eastern Side of Qena Bend, Nile Valley, Egypt Using GIS and Remote Sensing

Land Use of the Geographical Information System (GIS) and Mathematical Models in Planning Urban Parks & Green Spaces

HA NOI NATIONAL UNIVERSITY PUBLISHING HOUSE

HEAVY RAIN OVER MID-CENTRAL REGION OF VIETNAM

THE MODEL OF SPATIAL ORGANIZATION OF COASTAL FISHERMEN VILLAGES IN THE SOUTH CENTRAL COAST OF VIETNAM

Understanding and Measuring Urban Expansion

REMOTE SENSING APPLICATION IN FOREST MONITORING: AN OBJECT BASED APPROACH Tran Quang Bao 1 and Nguyen Thi Hoa 2

Nguyen Ngoc Thach 1, *, Pham Xuan Canh 2 VNU University of Science, 334 Nguyen Trai, Hanoi, Vietnam

HCMC CLIMATE CHANGE IMPACT AND ADAPTATION ADB/HCMC PPC. Director ICEM International Centre for Environmental Management

EFFECT OF ANCILLARY DATA ON THE PERFORMANCE OF LAND COVER CLASSIFICATION USING A NEURAL NETWORK MODEL. Duong Dang KHOI.

MINISTRY OF NATURAL RESOURCES AND ENVIRONMENT

Updating a medium-scale landslide database for high-risk areas using community surveys in Vietnam

International Symposium on Natural Disaster Mitigation. Local vulnerability assessment of landslides and debris flows

APCTP-ASEAN Workshop on Advanced Materials Science and Nanotechnology 2008

Urban Tree Canopy Assessment Purcellville, Virginia

Megacity Research Project TP. Ho Chi Minh Adaptation to Global Climate Change in Vietnam: Integrative Urban and Environmental Planning Framework

Implementation Status & Results Vietnam Danang Sustainable City Development Project (SCDP) (P123384)

Hong Phuong TRINH, Thi Minh An NGO, Thi Thanh Thuy HOANG, Viet Nam

Contents. Introduction Study area Data and Methodology Results Conclusions

Investigation of the Effect of Transportation Network on Urban Growth by Using Satellite Images and Geographic Information Systems

CAN THO URBAN DEVELOPMENT AND RESILIENCE PROJECT

Urban Growth Analysis: Calculating Metrics to Quantify Urban Sprawl

JOURNAL OF SCIENCE, Hue University, Vol. 67, No. 4A, 2011

Hydraulic modelling for flood vulnerability assessment, case study in river basins in North Central Vietnam

A Mapping of Pond Aquaculture, Mangroves and Coastal Wetlands in Vietnam, Cambodia, Thailand and Myanmar in 1999 and Comparison to 2014

LMI Attendees from Lower Mekong Region (as of 8/15/14)

Impacts of climate change on the flow in Hong-Thai Binh and Dong Nai river basins

Application of Remote Sensing Techniques for Change Detection in Land Use/ Land Cover of Ratnagiri District, Maharashtra

A Method to Improve the Accuracy of Remote Sensing Data Classification by Exploiting the Multi-Scale Properties in the Scene

A Logistic Regression Method for Urban growth modeling Case Study: Sanandaj City in IRAN

Applications of GIS and Remote Sensing for Analysis of Urban Heat Island

THE REVISION OF 1:50000 TOPOGRAPHIC MAP OF ONITSHA METROPOLIS, ANAMBRA STATE, NIGERIA USING NIGERIASAT-1 IMAGERY

FLOOD DAMAGE ASSESSMENT INTEGRATING GEOSPATIAL TECHNOLOGIES. A CASE STUDY IN HUE, VIET NAM

Mapping Coastal Change Using LiDAR and Multispectral Imagery

Proposal for Research and Investment Activities in the Coast and Estuaries (A Case Study in the North Central Coast of Viet Nam)

SCIENTIFIC BACKGROUND

Remote Sensing and GIS Application in Change Detection Study Using Multi Temporal Satellite

RECENT DECLINE IN WATER BODIES IN KOLKATA AND SURROUNDINGS Subhanil Guha Department of Geography, Dinabandhu Andrews College, Kolkata, West Bengal

Measuring Urban Sprawl A Case Study of China

ESTIMATING LAND VALUE AND DISASTER RISK IN URBAN AREA IN YANGON, MYANMAR USING STEREO HIGH-RESOLUTION IMAGES AND MULTI-TEMPORAL LANDSAT IMAGES

IPL Project (IPL - 175) Annual Report Form 2017

Estimation of the area of sealed soil using GIS technology and remote sensing

An analysis on the relationship between land subsidence and floods at the Kujukuri Plain in Chiba Prefecture, Japan

PREDICTION OF FUTURE LAND USE LAND COVER CHANGES OF VIJAYAWADA CITY USING REMOTE SENSING AND GIS

M.C.PALIWAL. Department of Civil Engineering NATIONAL INSTITUTE OF TECHNICAL TEACHERS TRAINING & RESEARCH, BHOPAL (M.P.), INDIA

to low order streams, or percolated down to the water table (Yang et al., 2009). The prevalence of

Object-based feature extraction of Google Earth Imagery for mapping termite mounds in Bahia, Brazil

Vu Ngoc Ky Academy of Mining and Geology Hanoi, Vietnam

INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 7, No 1, 2016

Assessment of climate change impacts on salinity intrusion in Hong-Thai Binh and Dong Nai river basins

Proceedings Combining Water Indices for Water and Background Threshold in Landsat Image

Model development for web - atlas system applying in administration management

AUTOMATED BUILDING DETECTION FROM HIGH-RESOLUTION SATELLITE IMAGE FOR UPDATING GIS BUILDING INVENTORY DATA

Landuse and Landcover change analysis in Selaiyur village, Tambaram taluk, Chennai

2014 Summer training course for slope land disaster reduction Taipei, Taiwan, Aug

VISUALIZATION URBAN SPATIAL GROWTH OF DESERT CITIES FROM SATELLITE IMAGERY: A PRELIMINARY STUDY

Hua Zhang, Steven M. Gorelick, Nicolas Avisse, Amaury Tilmant, Deepthi Rajsekhar, and Jim Yoon

Development of an urban classification method using a built-up index

EO Information Services. Assessing Vulnerability in the metropolitan area of Rio de Janeiro (Floods & Landslides) Project

Modeling urban growth pattern for sustainable archaeological sites: A case study in Siem Reap, Cambodia

Coastal urban climate resilience planning in Quy Nhon, Vietnam

DROUGHT RISK EVALUATION USING REMOTE SENSING AND GIS : A CASE STUDY IN LOP BURI PROVINCE

URBAN EXPANSION OF CAN THO CITY, VIETNAM: A STUDY BASED ON MULTI-TEMPORAL SATELLITE IMAGES

AND THE COOPERATION WITH SENTINEL ASIA FOR DISASTER MANAGEMENT

Using Geographic Information Systems and Remote Sensing Technology to Analyze Land Use Change in Harbin, China from 2005 to 2015

Transcription:

Proceeding Optical Remote Sensing Method for Detecting Urban Green Space as Indicator Serving City Sustainable Development Tran Thi Van *, Nguyen Dang Huyen Tran, Ha Duong Xuan Bao, Dinh Thi Thanh Phuong, Pham Khanh Hoa, Nguyen Le Nhat Hanh, Tham Thi Ngoc Han Ho Chi Minh City University of Technology, Vietnam National University Ho Chi Minh City; nguyendanghuyentran@gmail.com (N.D.H.T.); hdxbao@hcmut.edu.vn (H.D.X.B.); thanhphuong.dinhthi@gmail.com (D.T.T.P.); 91301390@hcmut.edu.vn (P.K.H.); 91201003@hcmut.edu.vn (N.L.N.H.); thamhan.3001@gmail.com (T.T.N.H.) * Correspondence: tranthivankt@hcmut.edu.vn; Tel.: +84-028-091-918-8485 Presented at the 4th International Electronic Conference on Sensors and Applications (ECSA 2017), 15 30 November 2017; Available online: https://sciforum.net/conference/ecsa-4. Published: 14 November 2017 Abstract: Urban green space, discovered by optical remote sensors, is the area covered by terrestrial vegetation in urban areas, and is considered an important factor in urban sustainability. Two sensors ALOS/AVNIR-2 and Landsat/OLI&TIR were used in this study to determine green space by Maximum Likelihood Classification method. The investigated area was Nha Trang city, located in the central Vietnam. This was found that the impervious surfaces were rapidly increased leading to significantly reduce urban green space within 10 years from 2007-2017. In urban areas, the green index was very low compared to the TCXDVN 9257: 2012. Based on the Markov chain, it is projected that over the next 10 years, the total vegetation cover of the city will continue to decline compared to that of today. This is likely to lead to increase catastrophe and environmental risks, especially floods and erosion in the coastal city of Nha Trang. The process could be very useful in mapping urban green space as indicator serving city sustainable development. Keywords: green index; optical remote sensing sensor; sustainable development; urban green space 1. Introduction Green space is the area in urban covered by mainly vegetation and is regarded as an important element in Urban Sustainable Development (Haq & Shah Md Atiqul, 2015). At the same time, Green Space is the signal to identify environment quality and residents life quality (Hofmann et al., 2011; Liu et al., 2016). Currently, there are two main methods to assess and quantify the urban green space. The conventional method is used to assess the quality or urban green space by using perception and self-awareness of residents with questionnaire and field survey. Nevertheless, this method requires a large amount of time to conduct survey and the outcomes will be objective because the self-awareness towards green space is different among each person. The second method uses Earth observation system with satellites, which has become an indispensable tool for researching and managing land use status as well as the coverage of urban green space (Liu et al., 2016). Recently, with the rapid development of remote sensing technology along with the improvement of satellites image processing, many cities in the world have applied the former to collect information about real coverage to determine urban green space and optimize the structure of city green space (Yao & Song, 2002). The research purpose of this article is to analyze space with the application of remote sensing technology to determine the urban green space distribution at Nha Trang (located in the central

2 of 7 Vietnam), thus assessing and predict the Change of such distribution with time and space. From such, the proper management methods aligning with the city s targeted urbanization will be proposed. 2. Methodology 2.1. Data The satellite images used for research consist of: the ALOS AVNIR2 captured in July 17, 2007 (space resolution: 10m) and the LANDSAT 8 OLI&TIRS captured in April 21, 2017 (space resolution 30m, full color-channeled resolution: 15m). Both images were captured at daylight, with transparency and low cloud s coverage (below 10%). 2.2. Method 2.1.1. Supervised Classification Classification is verified on sample pixels which are selected by analysts. By choosing sample, the analyst has helped the computer to determine the pixels with the same features of reflection spectrum. In this research, the Verified classification method with MLC (Maximum Likelihood Classifier) has been used. The system employed in this case includes 4 main targets: water, unoccupied land, vegetation and impermeable surface 2.1.2. Change Analysis after Classification The techniques and algorithms which detect changes at different stages of objects in satellite images have been established based on the development of remote sensing technology featuring characteristics of space, spectrum, temperature and time. Currently, many methods have been proposed to identify the development of coverage using data, which include: comparation of coverage s classifications; multi-time images classification; subtraction image or division image; difference in real coverage index; main elements analysis (Nguyen Thi Hong Diep et al., 2013). In this article, the authors used the Change analysis after classification method to determine changes of real coverage status at Nha Trang city, especially green coverage and impermeable surface 2.1.3. Development Prediction Method using Markov Chain Development prediction model using Markov chain relies on two map layers which are introduced to build transition matrix. Therefore, the model will check every real coverage and determine to transit pixel number of real coverages at time t (year 2017) to that at time t+1 (year 2017), thus calculating the transition pixel quantity and converting to the percentage change of every type of coverage (Trinh Le Hung et at., 2017). Pij is the matrix transiting probability of real coverage i to real coverage j. Suppose that this matrix is stable with time, it can be used to predict the distribution of real coverage layers in the future with the recent data. This means in the future time, from 2017 to 2027, the urbanization at Nha Trang will occur as similarly as it did from 2007 to 2017. In this research, the probability transition matrix has the 4x4 from (including water, unoccupied land, vegetation and impermeable surface). The input of Markov chain s analysis consists of 2 variables: the area of 4 real coverage layers in 2017 and probability transition matrix among real coverage layers from 2007 to 2017. The total area of these 4 layers in 2017 were calculated directly after the satellite images process was conducted. Probability transition matrix among real coverage layers were identified on ArcGIS software after the process of collapsing two real coverage classification s maps in 2007 and 2017 was practiced.

3 of 7 2.1.4. Urban Green Space Index Assessment Method (Per Capita) Based on many researches which built urban environment quality index in Vietnam, the index green area/capita is an important index to evaluate environment quality and urban space (Tran Quang Loc, 2012). This index is built to assess the quality of urban green coverage. The urban green space index after calculation is compared with TCXDVN 9257:2012 (Ministry of Construction, 2012) to evaluate the status of urban green space distribution/ 3. Results and Discussion 3.1. Land Cover Distribution in 2007 and 2017 To evaluate the accuracy of classification results, the authors used a verified sample set, randomly picked similarly with choosing from the classified sample set. The result of classification on ALOS AVNIR2 2007 is shown with the total accuracy up to 90.43% and the Kappa coefficient at 0.89. Likewise, in 2007, the result of classification on Landsat 8 OLI is also shown with the total accuracy up to 91.21% and Kappa coefficient at 0.89. The results showed, in 2007, that the vegetation layer majorly accounted for 73.87% of total coverage, followed by unoccupied land layer (12.23%), impermeable surface layer (9.34%) and water layer (4.06%). Until 2017, the distribution of layers in percentage has changed. Particularly, the vegetation layer still largely accounted for 72.81%, followed by impermeable surface area (15.15%), unoccupied land layer (8.18%) and water layer (3.86%). As a result, after 10-year time, the distribution of coverage layers has changed apparently. The majority of Nha Trang city s area is covered with vegetation (more than 70%), especially in the northern and southern area of the city. Therefore, the area of vegetation coverage (throughout the metropolitan area) slightly reduced 1.44% from 2007-2017. However, when we examined inner-city area and other surrounding areas during urbanization, the urban green space area is decreasing rapidly. From 2007 to 2017, the area of urban green space reduced around 21.69% (Table 1 and Fig 1) Based on the real coverage layers distribution s maps in 2007, Nha Trang city prioritized urban development at coastal area (ranging from Tran Phu Street to Le Hong Phong Street). This area had low proportion of urban green space, while most green space distributed closely in northern, southern mountainous area and a small western area of the city (Figure 2). Meanwhile, until 2017, although urban green space is mainly distributed in northern mountainous area and southern area, the western part has started the urbanization and the urban green space here is gradually replaced by impermeable surface (Figure 2 ) Table 1. Statistics of real coverage in inner-city areas and urbanization from 2007-2017. 2007 2017 Biến động Coverage Type Area (ha) Proportion (%) to Total Area Area (ha) Proportion (%) to Total Area Increase/Decrease Area (ha) Change (%) to 2007 Proportion Water 689,8 13,10 556,94 10,58-132,86-19,26 Impermeable surface 1.142,5 21,70 2147,46 40,79 1.004,96 87,96 Bare land 1.037,88 19,71 685,09 13,01-352,79-33,99 Vegetation 2.394,42 45,48 1875,11 35,62-519,31-21,69

4 of 7 UGS in 2007 UGS in 2017 Figure 1. Distribution of UGS in urban core. 3.2. Change of Urban Green Space and Prediction Land cover in 2007 Land cover in 2017 Figure 2. Distribution of Land cover of Nha Trang city. 3.2.1. Change of Urban Green Space from 2007 to 2017 The result showed that throughout 10 years (from 2007 to 2017), only the impermeable surface s area has increased 1255.61 ha on the total research area. Meanwhile, the other coverages such as water, unoccupied land and vegetation s areas have decreased respectively around 173.34ha, 853.72 ha and 229.15 ha. Therein, most of vegetation coverage has reduced because this coverage transformed into impermeable surface (transited 1,149.89 ha) and unoccupied land (transited 997.83 ha) which is covered for the coming new urban project. Besides, the research results also showed that the increment in impermeable surface area is largely due to the addition from vegetation coverage (1,149.89 ha) and part of unoccupied land (547.66 ha). From the analysis above, we realized that urban development (urbanization) has severely decreased the vegetation coverage area and dramatically increased impermeable surface area. At inner-city area and urbanization area, the vegetation coverage Change s trend is similar with the situation above. From 2007 to 2017 (Table 2), only the impermeable surface s area has increased 1004.96 ha on the total research area. Meanwhile, the other coverages such as water, unoccupied land and vegetation s areas have decreased respectively around 133.86 ha, 352.79 ha and 519.31 ha. From the mentioned results, throughout the metropolitan area, the unoccupied land area decreased the most. However, when examining on inner-city area and urbanization area, the vegetation coverage area reduced the most. Especially, when comparing with the whole area of the city, the urban green

5 of 7 space at inner-city areas and urbanization areas have decreased more severely, with respective value of 229.15 ha and 519.31 ha. This is explained by the increase of green space area in suburban areas such as Vinh Luong, Vinh Hai and Vinh Phuong, thus causing the urban green space in inner-city areas and urbanization areas to decrease more than that in metropolitan area Table 2. Fluctuation of real coverage in inner-city areas and urbanization from 2007-2017 (unit: ha). 2007 Impermeable Water 2017 Surface Bare land Vegetation Total Water 428,73 18,56 90,15 19,5 556,94 Impermeable surface 100,53 963,82 396,26 686,85 2147,46 Unoccupied land 114,92 65,95 265,14 239,08 685,09 Vegetation 45,62 94,17 286,33 1448,99 1875,11 Total of layers 689,8 1142,5 1037,88 2394,42 0 Change in layers 261,07 178,68 772,74 945,43 0 Fluctuation -132,86 1004,96-352,79-519,31 0 3.2.2. Land use Change prediction in 2007 From the data of vegetation coverage Change during 2007-2017 and status of real coverage in 2017, the authors conducted the prediction of real coverage distribution in 2027. The input variable of this process includes area of real coverage at current time (2017) and matrix transiting probability from this coverage to the other coverage (transit probability among coverages from 2007 to 2017). The output variables of this calculation process is the area of prospective real coverage (2027). The results showed that, in the prediction of year 2027, the urban green space area of Nha Trang City will continue decreasing 437.47 ha (2.78%) from 2017 to 2027. Similarly, the water coverage and unoccupied land will continue decreasing respectively 131.35 ha and 108.54 ha. However, the impermeable surface in Nha Trang city have propensity to increase 677.33 ha from 2017 to 2027. Therefore, if urbanization continues, much as similarly as from 2007 to 2017, in the future (2027), the urban green space will continue decreasing while impermeable surface area increase. As a result, to preserve and conserve the urban green space in metropolitan area, the related offices must re-adjust construction plans for the city in the future. 3.3. Urban Green Space Index and the Consequences of Reducing Urban Green Space The urban green space index per capita has been used to evaluate environment quality and urban space all over the world (A. R. Maman Poshesh, 2007; Dang Trung Tu et al., 2015). Based on the population statistics of research area, in the urbanization area with many residences being built, the new urban areas show positive result with the urban green space index per capita over 100m2/capita. However, in the inner-city area, most of the urban green space index per capita are low, under 10m2/capita (apart from Ward Loc Tho). Especially, in Ward Van Thanh, Phuong Sai, Phuoc Tien, the green space index per capita is under 1m2/capita (Fig 3). The results above showed that these inner-city areas are severely short of urban green space, based on TCXDVN 9257:2012. Thus, the green space index per capita in the inner-city areas of Nha Trang city is much lower than that of mentioned cities. These areas are even in severe shortage of green space compared with the equivalent areas of Ho Chi Minh City and Hanoi. Rapidly increasing land in urban area and heavily reducing vegetation in city have caused many negative impacts on living environment quality of residents and tourists in Nha Trang city. According to the research overview of author Dang Trung Tu, the expansion and increment in impermeable surface area and reduction in urban green space may lead to many consequences such as increasing air temperature in the city due to the thermal radiation of concrete; raise the probability of flood; weakening the quality and amount of groundwater resource due to natural permeable

6 of 7 surface (Dang Trung Tu et al., 2015). Besides, this can lead to the increase of disasters and environmental risks, especially flood and erosion in Nha Trang city. This is a warning to which must be paid attention to guarantee the urban green space for sustainably developing urban. 4. Conclusion Figure 3. Distribution of UGS index in urban core of Nha Trang city in 2017. Currently, there are many methods to construct urban green space distribution s maps. However, applying remote sensing technology to research distribution and Change of urban green space is a positive method which lead to effective result, time reduction, manpower savings, thus meeting the demands on recent society. This vegetation classification process leads to a positive result on both satellite images with accuracy over 90% and Kappa coefficient around 0.9. Most area of Nha Trang is covered by vegetation with density over 70%; however, in other inner-city areas, the urban green space is very low (under 10%). With the current urbanization speed, in 2027, the urban green space area will reduce around 2.78%. The urban green space index per capita in inner-city areas is very low, mostly under 10m2/capita, or under 1m2/capita in some areas, much lower than the standards in TCXDVN 9257:2012. Since then, we recognize that inner-city areas are in severe shortage of urban green space, and in need of increasing urban green space such areas in Nha Trang. References 1. Dang Trung Tu, Truong Quang Hai, Nguyen Thi Thu Ha, & Nguyen Thi Mai Ngan. (2015). Using multitime LANDSAT images to research progress of urbanization in Da Nang city for protecting urban environment. Environment Magazine, 8. 2. Haq, & Shah Md Atiqul. (2015). Urban Green Spaces and an Integrative Approach to Sustainable Environment. Urban Ecology: Strategies for Green Infrastructure and Land Use, 147 3. Hofmann, P., Strobl, J., & Nazarkulova, A. (2011). Mapping green spaces in Bishkek how reliable can spatial analysis be? Remote Sensing, 3(6), 1088 1103 4. Liu, Y., Meng, Q., Zhang, J., Zhang, L., Jancso, T., & Vatseva, R. (2016). An effective Building Neighborhood Green Index model for measuring urban green space. International Journal of Digital Earth, 9(4), 387 409 5. Maman Poshesh, A. R.. (2007). Evaluation revealed that the spatial landscape of Isfahan in both 1386 and 1302 period by satellite images and GIS. Paper presented at the 3th national conference of urban green space and landscape, Tehran, Iran 6. Maman Poshesh, A. R.. (2007). Evaluation revealed that the spatial landscape of Isfahan in both 1386 and 1302 period by satellite images and GIS. Paper presented at the 3th national conference of urban green space and landscape, Tehran, Iran 7. Maman Poshesh, A. R.. (2007). Evaluation revealed that the spatial landscape of Isfahan in both 1386 and 1302 period by satellite images and GIS. Paper presented at the 3th national conference of urban green space and landscape, Tehran, Iran 8. Tran Quang Loc, P. K. L. (2012). Researching and building urban environment quality index and applying to some urban areas in Vietnam. Science Magazine, Hue University, 74B

7 of 7 9. Trinh Le Hung, Nguyen Thi Thu Nga, Vu Danh Tuyen & Bui Thu Phuong. (2017). Evaluate and predict urban earthquake fluctuation in inner-city areas of Hanoi with remote sensing material and GIS. Science Magazine University of Pedagogy Ho Chi Minh City, 14 10. Trinh Le Hung, Nguyen Thi Thu Nga, Vu Danh Tuyen & Bui Thu Phuong. (2017). Evaluate and predict urban earthquake fluctuation in inner-city areas of Hanoi with remote sensing material and GIS. Science Magazine University of Pedagogy Ho Chi Minh City, 14 2017 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).