Optimizing GLCNMO version 2 method to detect Vietnam's urban expansion

Optimizing GLCNMO version 2 method to detect Vietnam’s urban expansion Pham Tuan Dung1, Man Duc Chuc1, Nguyen Thi Nhat Thanh1, Bui Quang Hung1, Doan Minh Chung2 1Center of Multidiscipli

Optimizing GLCNMO version 2 method to detect

Vietnam’s urban expansion

Pham Tuan Dung1, Man Duc Chuc1, Nguyen Thi Nhat Thanh1, Bui Quang Hung1, Doan Minh Chung2

1Center of Multidisciplinary Integrated Technology for Field Monitoring, University of Engineering and Technology, Vietnam National University, Hanoi, Vietnam

2Space Technology Institute, Vietnam Academy of Science and Technology, Hanoi, Vietnam

dungpt@fimo.edu.vn

Abstract—No global scale land cover classification method

performs with high accuracy at local scale This study tries to

develop a classification algorithm for urban area in Vietnam

This is the first assessment of the Global Land Cover by National

Mapping Organizations (GLCNMO) version 2 method for

producing global urban map in 2008 An improved and

optimized algorithm is then developed based on the GLCNMO

method for Vietnam taking into account of local natural and

social conditions Improving method is then applied to produce

urban maps of Vietnam for the years of 2008 and 2015 Accuracy

assessment showed that the improved method can achieve up to

13% higher precision and 10% higher F1 measure as compared

to the global GNCNMO method Also, an increasing trend was

observed in population density in urban area in the period from

2008 to 2015 in Vietnam which may correspond to fast

urbanization process in the country The cities also tend to

become less green in 2015 than 2008 as indicated by comparing

the Normalized Difference Vegetation Index (NDVI) between the

two years

Keywords—optimizing; GLCNMO version2; Vietnam’s urban

map; urbanization; urban expansion

I INTRODUCTION Despite of huge achievements in economic growth,

Vietnam’s government has also implemented various long term

policies in an attempt to boost its economy Urbanization is a

necessary effect of economic and urban development, which is

related to functional and spatial transformations and its form

will have long-lasting consequences on the lives of urban

residents [1] Because urbanization might cause many

environmental problems, such as vegetation loss, air pollution,

water shortage and contamination, and urban heat island, it has

been recognized as an important factor affecting the functions

of terrestrial ecosystems and climate change [2] This study

aimed to answer questions about how urban population growth

relates to urban spatial expansion; and the relationship between

urbanization, energy consumption growth, and green areas

reduction in Vietnam from 2008 to 2015

Remote sensing is a useful source for mapping the

expansion of urban land Recent coarse resolution urban

mapping from satellite imagery are also unsatisfactory, because

of the tedious process of training data collection and

inadequacies of in classification algorithm [3]

In Vietnam, there are few researches in urban classification with limited scope, such as the relation between surface temperature and land cover types using thermal infrared remote sensing in Hochiminh city [4], study about land use change pattern in Danang city [5], optimizing spatial resolution of imagery for urban form detection [6], and assessing the impact of urbanization on urban climate by remote sensing perspective [7]

This paper presents results from a research to create urban maps, which serves as the first stage in our development of a comprehensive database of urban land surface characteristics for Vietnam The primary goal of this paper is to provide urban maps at 500m spatial resolution, by combining gridded population density, nighttime lights and MODIS-NDVI data From those maps, physical extents of urban areas are detected

II DATA Data used in this research are described in Table I

A High Resolution Population Distribution Maps for Vietnam in 2009 and 2015

Landsat images and land cover information were combined with other datasets to model population distributions for 2010 and 2015 for countries in the Southeast Asia region including Vietnam [8] Vietnam’s population distribution datasets for

2010 and 2015 were already generated at a fine-scale spatial resolution (100m) and projected to a geographic coordinate system and WGS 84 The data was freely downloaded from website http://www.worldpop.org.uk/

B Nighttime light data for Vietnam in 2008 and 2015

The Version 4 Defense Meteorological Satellite Program - Operational Linescan System (DMSP-OLS) nighttime light imagery is available in http://ngdc.noaa.gov/eog/dmsp/ For the present study, the stable night light data within 2008 DMSP-OLS (F16 satellite) composite product was used Ephemeral detections of fires, gas flares, volcanoes or aurora in DMSP-OLS nighttime imagery have already removed Also, the background noise has been subtracted The original spatial resolution of the products was 500m, and the DN (Digital number) values range from 0 to 63 [9]

2016 Eighth International Conference on Knowledge and Systems Engineering (KSE)

978-1-4673-8929-7/16/$31.00 ©2016 IEEE 309

TABLE I REMOTE SENSING DATA USED IN THIS RESEARCH

Abbreviation Data Description Spatial

Resolution Time

2015

NPP-VIIRS Suomi NPP nighttime

Vegetation Indices

16-Day L3 Global

2015

EstISA Impervious surface

area

1km 2010

Suomi National Polar-Orbiting Partnership - Visible

Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime

light imagery was downloaded from the website

http://ngdc.noaa.gov/eog/viirs/ Twelve month composites

generated from the observations between 2015/1/1 and

2015/12/31 was used in this study

C MODIS-NDVI data for Vietnam in 2008 and 2015

MODIS/Terra Vegetation Indices 16-Day L3 Global 250m

SIN Grid images were downloaded from the NASA Land

Processes Distributed Active Archive Center

(http://earthexplorer.usgs.gov/) 23 composite images for each

year in 2008 and 2015 were used in this study A Maximum

Value Composition (MVC) was then applied to all NDVI

images with the aim of selecting pixels less affected by clouds

and other atmospheric perturbations [10]

D Estimate the density of constructed Impervious Surface

Area (EstISA) data for Vietnam in 2010

The estimate of ISA is derived solely from the brightness of

satellite observed nighttime lights and population count The

initial global ISA density grid was produced on a 30 arc second

grid since that is the native grid of both the Landscan and

nighttime lights This was then converted to a 1 km equal area

grid in a WGS 84 projection A threshold of 0.4 percent was

applied to eliminate the salt and pepper noise present at the

very low end of the ISA scale ISA values over 100 were reset

to 100 Extractions of the digital values were run to tally the

quantity of ISA for countries, sub-national units (states/

provinces) and major watersheds [11] The global grid of ISA

at a resolution of 1km is freely available at:

http://www.ngdc.noaa.gov/dmsp/

E Water body data for Vietnam

The MODIS land-water mask at 250 meter spatial

resolution (Short Name: MOD44W) is a new product using the

Shuttle Radar Topography Mission Water Body Data (SWBD)

in combination with MODIS 250m data to create a complete global map of surface water MOD44W data was downloaded

at https://lpdaac.usgs.gov/data_access/

III METHODOLOGY

A Urban definition

Recently, in order to have an urban definition widely accepted by most of researchers is a challenge There are various ways to define an “urban area” in different studies and countries The United Nations itself recognizes the difficulty of defining urban areas globally, stating that, “because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population

is not amenable to a single definition that would be applicable

to all countries” [12]

Urban classification is the identification and delimitation of urban areas and their assignment to classes Urban areas are heterogeneous mixtures of land cover types, and may contain any number of vegetated and man-made surfaces As defined

by the Food and Agriculture Organization-United Nations Environment Programme (FAO-UNEF) Land Cover Classification System (LCCS) [13], urban areas are non-linear built up areas (i.e urban, industrial and other areas related to trade, manufacturing, distribution and commerce) covered by impervious structures adjacent to or connected by streets Linear elements like roads, railways and communication lines/pipelines occur but are not dominant features Impervious surfaces are dominated implies which coverage greater than or equal to 50% of a given landscape unit (here, the pixel) [14] Pixels that are predominantly vegetated (e.g a park) are not considered urban, even though in terms of land use, they may function as urban space

In GLCNMO version 2 method, a definition of urban areas based on physical attributes: urban areas are places that are dominated by the built environment The “built environment” includes all non-vegetative, human-constructed elements, such

as buildings, roads, runways [15]

The Vietnam urban classification system, established in

2001 and updated in 2009 with the inception of Decree No 42/2009/ND-CP [1], serves as an important part of urban policy and management in Vietnam It is a hierarchical system constituted by six classes of urban centers that are defined by different levels of economic activities, physical development, population, population density, and infrastructure provision In

2009 there were 2 special cities (Hanoi and Hochiminh city), 5 class I cities, 12 class II cities, 40 class III towns, 47 class IV provincial towns, and 625 class V small townships The most important indicators are as follows: (i) Population of an urban center is at least 4000; (ii) The population density is at least 2000/km2

This study defines “urban area” as population density distribution, percentage of impervious surface, and nighttime light Green fields and water bodies (such as a big park or a golf course) are not considered as urban Minimum mapping unit of an urban area is 1 km2

TABLE II THRESHOLD VALUE OF POPULATION DENSITY,

NIGHTTIME LIGHT, NDVI_MAX, AND ISA

B Sample selection methods

In order to calculate thresholds, polygon samples were

chosen and checked by comparing with Google Earth and

Landsat ETM+ images The numbers of pixel samples of each

class (except urban class) were decided by percentages of

GLCNMO’s classes 1046 non-urban pixels and 540 urban

pixels were chosen totally Urban class has higher priority than

others in deciding thresholds Population density threshold was

based on the Vietnam urban classification system

C Data processing

The method divided in two main steps as shown in Fig1

Preprocessing step: the population distribution maps with

100 m spatial resolution was aggregated to generate

proportional settlement values in a new data set with a pixel

size of 500 m by 500 m DMSP-OLS and EstISA data at spatial

resolution of 1 km were resembled to spatial resolution of 500

m NDVI_MAX data was generated from MODIS-NDVI data

of 23 periods in 2008 with the maximum algorithm and

aggregated to match the same spatial resolution with MODIS

and DMSP-OLS other data The Vietnam’s administrative

boundaries were used to extract study area Clipping the study area was done by using the ArcMap’s Extract by Mask with the Vietnam’s shapefile set as an analysis mask

Processing step: for population density, DMSP-OLS, and EstISA datasets, high threshold values generate a small urban area, and vice versa Potential urban map is derived from population density data using threshold Because EstISA data

is not available for 2008, the data for 2010 were used in this research DMSP-OLS, EstISA, and NDVI_MAX thresholds were used to exclude low NTL data, low ISA data, and green areas from potential urban map, respectively Finally, the MODIS land-water mask was used to exclude inland water bodies

Due to the lack of EstISA data for 2015, this data was not used to generate the urban map of 2015 as in Fig2

IV RESULTS

A Accuracy assessment

Accuracy assessment was performed independently for the urban class For each urban map, an equalized, random set of test points was selected The test points within a sample were further randomized to avoid bias in the reference labeling of those pixels This set of points was exported into a shape file and used to assess the accuracy of this method classification In this research, Google Earth and Landsat ETM+ images were used as independent sources of reference data of higher precision and known accuracy for validating the classifications GLCNMO 2008, urban maps of 2008 and 2015 (VN 2008, VN 2015) are assessed Precision and Recall measures are used to quantify map accuracy Also, F1 measure (the harmonic mean

of precision and recall) is applied to provide additional information on the effectiveness of each urban map

Thresholds per pixel

NDVI_MAX 0.69

Fig 1 Flowchart of urban mapping in 2008

311

In this section, accuracies of three urban maps including

Fig 2 Flowchart of urban mapping in 2015

Fig 3 Vietnam’s urban maps

TABLE III MAPS’ ACCURACY ASSESSMENT

GLCNMO

To assess recall, 140 urban pixels (R set) were randomly

selected based on ground truth images (collected from field

trips and Global Geo-Referenced Field Photo Library which

allows downloading freely in http://www.eomf.ou.edu/photos/)

very high resolution images from Google Earth (using

historical imagery function) It should be noted that the pixels

are not included in the training sets which are used to estimate

algorithm’s thresholds GLCNMO 2008, VN 2008, VN 2015

maps are then compared to R set For precision, 100 urban

pixels are randomly extracted from each urban map

This is resulted in three sets: GLCNMO 2008 (P1 set), VN

2008 (P2 set), and VN 2015 (P3 set) in which each set is

independent from the other Distribution of pixels in R, P1, P2,

P3 sets are provided in Fig 4 Assessment results of urban maps

are reported in Table III

From the results, there is a small difference between recalls

of the three urban maps, around 3.58% All three methods can

reach to a recall of above 85% However, it is remarkable that

the precision of GLCNMO 2008 map over Vietnam is very

low, around 57%, which is 13% and 29% lower than precisions

of VN 2008 and VN 2015 respectively This may be due to

very rough thresholds of the indexes (NDVI, population

density, nighttime light, and impervious surface) which are not

suitable to social and natural conditions in Vietnam For

example, Vietnam has a population of above 90 millions and a

higher population density in urban regions As a result, a global

threshold of population density applied on Vietnam is not

suitable

Fig 5 Population density trending

Similar observations are also applied for other indexes Also, this study adopted the water mask to assist the classification because of the natural distributions of lakes and rivers in urban areas in Vietnam By optimizing the thresholds, significant higher precision on urban map of 2008 could reach This is also reflected in 10.01% improvement of F1 measures between GLCNMO 2008 and VN 2008

It is observed that VN 2015 urban map has highest recall and precision of 89.29% and 86% respectively It should be noted that the thresholds of 2015 map are estimated from the same set of pixels as 2008 map Impervious surface data was not used in the production of 2015 urban map This change may help to explain the improved result, because NPP Nighttime light data has much higher spatial resolution than DMSP-OLS thus providing more detailed information than DMSP-OLS data

B Trends in population density and the greenness of urban area in Vietnam

Population density

Analyzing of urban pixels in the training data showed a significant improvement of population density in urban areas From Fig 5, it could be also observed that the highest increase belongs to regions that already have very high population density such as the core or center of cities Considering the population density is not normally distributed, Wilcoxon rank sum test over the data in 2008 and 2015 were performed The p-value is 0.0016 which indicates statistical

Hanoi

Hochiminh city

Fig 6 Urban expansion in Hanoi and HCMC, 2008-2015

313

significant improvement of population density over urban

regions in Vietnam

The greenness of urban areas in Vietnam

The difference of NDVI values of pixels in the training data

is not clearly seen as the population density data There is an

increase of NDVI in some areas and decrease in other areas

However, by considering some descriptive indicator of the data

such as mean and standard deviation, small lower threshold of

NDVI in 2015 (0.66) is obtained comparing to those in 2008

(0.69) This is also easy to understand that in most urban areas,

tree density tend to become less dense over time especially in

regions suffering from fast urbanization process

C Vietnam’s urban expansion from 2008 to 2015

This study used satellite imagery and demographic data to

measure expansion in urban areas of Vietnam between 2008

and 2015 Two urban maps for two time periods were

calculated and analyzed as in Fig 3 Because DMSP-OLS data

is not available for 2015, NPP-VIIRS nighttime light images

are alternatively used The urban mapping method for 2015 is

described in Fig 4 Threshold for NDVI_MAX, NPP-VIIRS

nighttime light are 0.66, 1.1, respectively Vietnam’s urban

expansion in period 2008-2015 is about 600 km2 Fig 6

describes the urban extension of Vietnam’s two largest city:

Hanoi and Hochiminh city

V CONCLUSION The results presented here indicate that application of

population distribution in conjunction with the yearly

vegetation photosynthesis product from MODIS images, the

nighttime data, and the estimate of ISA, which can be used as

an effective tool to compare the expansion of human

settlements The methodology is easily implemented since it

relies only on remote sensing data Even though this

methodology is not the substitution for ground monitoring and

field analysis, its scale of applicability makes it particularly

interesting for regional planning purposes and to increase

public and political perception of the problems connected with

unregulated development

The Gridded population density data, nighttime lights and

NDVI offers a useful perspective for monitoring the extent and

level of urbanization accurately and regularly at normal scale

Compared with GLCNMO method, new thresholds are

appropriate to create Vietnam’s urban maps more accurate,

simpler and less costly

ACKNOWLEDGMENT The authors would like to thank the ARC 2015 research

project “Research, transfer, develop the Global land cover

datasets - GLCNMO and CERes Gaia system for collection,

management, analysis, and sharing of geospatial data” for

financial support

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