The Sustainable Development of Green Space in the Tourism Zone of Moc Chau Mountains (Son La, Vietnam)45273

The Sustainable Development of Green Space in the Tourism Zone of Moc Chau Mountains (Son La, Vietnam) Phạm Anh Tuan(1), Duong Thi Loi(2)(*) Tay Bac University, Son La, Vietnam Hanoi National University of Education, Hanoi, Vietnam (1 (2) *Correspondence: duongloi1710@gmail.com Abstract: Green space is an inseparable part of ecotourism, it is also an important index of sustainable development The aim of this research is to examine the change of green space quality through values of NDVI in the period of 2005 - 2018 in Moc Chau national tourism zone, Son La Four satellite images from Landsat and Landsat were pre-processing first to calculate the vegetation index The NDVI was derived from post-processing Landsat data based on the difference between near-infrared and red bands, then classified to produce green maps with four classes: non-green, low green, moderate green, and dense green This classification was applied according to the NDVI threshold values such as 0.2, 0.4 and 0.6 The results indicated a drastic decrease in the dense and moderate green while a significant increase in low green and non-green within 13 years (2005 – 2018) The green space tends to narrow continuously due to the negative impact form human being This result is considered as an important base in the management and planning of ecotourism sustainable development in the study area Keywords: Green space; sustainable development; tourism zone; Moc Chau Introduction The term “green space” may be derived from the urban nature conservation movement and European thinking about green space planning which started in the UK (Swanwick, 2003) Green areas consist of open spaces, generally covered with natural or planted vegetation (Rakhshandehroo et al, 2017), comprised of vegetation and associated with natural elements (Lucy et al, 2017) or covered by plants (naturally or artificially) including trees, shrubs, and grasses (Fam, 2008) (Campbell, 2001) given the definition about green space consist of any vegetated land or structure, water or geological features found in a given area In general, green space refers to public access areas within open spaces that involve green elements It covers any vegetated land or structure such as parkland, greenways, open space, a natural heritage or environment lands, vacant lands, conservation lands or green infrastructure such as drainage ditches (Rakhshandehroo et al, 2017) Water systems such as open water, wetlands, floodplains, rivers, and streams are also considered as a part of green space Green space keeps an important role in physical, social and mental development It affects the environmental quality, helps in stress restoration, enhances feeling of social safety, increases social interaction and property values (Matthias, 2017), cleans the air, adjusts urban climate and eliminates noise (Duong et al, 2015), contributes to encourage biodiversity of flora and fauna (David et al, 1999) Therefore, green space needs to be carefully researched and planned to maximize its contribution to the environment of urban areas while minimizing its negative aspects (David et al, 1999) The sustainable development of Green Space is a crucial strategy in urban development to provide comfortable living conditions with fresh air and a beautiful, friendly ecologically environment Most recent studies tend to assess the green space based on classified land cover images or on values of Normalized Difference Vegetation Index (NDVI) (Wei Li et al, 2015), (Donovan, 2010) It can be found that NDVI is the most popular index in remote sensing to measure biomass or vegetative vigor It separates green vegetation from other surfaces because the chlorophyll of green vegetation absorbs red light for photosynthesis and reflects the near-infrared (NIR) wavelengths The difference in NDVI values is a reliable basis to assess the quality of the green and applied in many studies (Ahl et al, 2006), (Jeevalakshmi et al, 2016), (Afriah et al, 2017) (Ahl, 2006) NDVI has found a wide application in vegetative studies as it has been used to estimate crop yields, pasture performance, and rangeland carrying capacities among others It is often directly related to other ground parameters such as percent of ground cover, photosynthetic activity of the plant, surface water, leaf area index and the amount of biomass Up to 12633 results are found from ScienceDirect with keyword “NDVI”, and more than 5800 results found from this website with keyword “NDVI-green space” up to 2019 Vegetation index can be used as an indicator to quantify the greenness of plants within satellite data There are several vegetation indices, but the most frequently used index is the Normalized Difference Vegetation Index (NDVI) (Jiaguo, 2005) Moc Chau national tourism zone located on Moc Chau plateau and has potentials for ecotourism development However, population growth and ineffective land management have caused much negative consequence to green space Land-use conversion processing is one of the reasons for reducing significantly forest area and ecological diversity in this study area The relationship between green space, the social economy, and the environment is a dynamic and complex process Understanding how to achieve economic development while maintaining the needs of green space has become a major concern of managers at Moc Chau national tourism zone To so, understanding about standards of green space and conversion and change of green space in the study area is very important, thereby proposing the appropriate solutions and strategies for sustainable development The aim of this study is to examine the change of green space in the study area using Remote sensing (RS) and Geographic Information System (GIS) technology from 2005 to 2018 Study Area Moc Chau national tourism zone includes whole of Moc Chau and Van Ho districts, south of Son La province with the area at 206150 hectares Moc Chau tourism zone is considered as a key area for developing tourism in Son La in particular and the whole northern midland and mountainous region in general Moc Chau tourism zone shares its border with Hoa Binh province to the east and south-east, and Yen Chau district to the west and north-west, and Phu Yen district to the north It shares its border with Thanh Hoa province to the south The zone includes three key tourism centers: the Moc Chau relaxation center, the Moc Chau eco-tourism center and the Moc Chau recreational center Figure 1: Location of study area Methodology 3.1 Data use The satellite images taken for the time period of 2005, 2010, 2015 and 2018 were used to assess the change of green space in the study area Landsat and Landsat were freely downloaded from website https://earthexplorer.usgs.gov/ Landsat was launched March 1, 1984, and observed the Earth until 15 January 2013 and carried the Multispectral Scanner (MSS) and the new Thematic Mapper (TM) sensors Landsat was launch on February 11, 2013, and carried Operational Land Imager and the Thermal Infrared Sensor (Survey, 2019) Both of Landsat TM and Landsat OLI-TIRS sensors have a spatial resolution of 30 m The characteristics of Landsat data are shown in Table To avoid cloud and unwanted shadefree imagery, we selected imageries at the October and November for this study This is the transitional period of summer to winter in Moc Chau, Son La, so the sky is quite clean and less cloudy Table Detail information about satellite data used in this research Satellite Sensor Path/Row Acquisition Date Spatial resolution (m) Landsat OLI/TIRS 127/46 30/11/2018 21/10/2015 30 Landsat TM 127/46 08/11/2010 09/10/2005 30 The overall methodology of this study is briefly presented below: Landsat imageries 2005 2010 Classification 2015 2018 Pre-processing NDVI calculation NDVI thresholds NDVI reclassify Classified Result Post classification OVERLAY Green space quality change map Figure Methodological Flow chart 3.2 Preprocessing This process was performed on four images including geometric and radiometric corrections - Geometric correction: All images were rectified to a common Universal Transverse Mercator (UTM) WGS84 Datum, Zone 48 Because different satellite imageries were used for the study time frame, it should be registered first through proper Ground Control Point (GCP) Landsat-8 scene of 2018 (path 127, row 46) was considered as the reference image based on which imageries of 2005, 2010 and 2015 were registered Twenty well-distributed GCPs were used to register the remaining data sets (2005, 2010 and 2015) using a second-degree - Atmospheric correction: Atmospheric correction is an important processing step that needs to be followed The signal measured at the satellite could be affected due to the presence of gases, solids, and liquid particles from the atmosphere This process includes two steps: firstly, converting digital numbers (DNs) into radiance value by using standard calibration values to remove temporal differences in sensor calibration and in environmental factors between image acquisitions (López-Serrano, 2016) Secondly, the images were taken from different dates, thus, the atmospheric conditions were different As the requirements for change detection analysis, it is necessary to standardize the effect of the atmosphere (USGS., 2013) All pre-processing operations were performed with Landsat TM on red band: band and near-infrared band: band and Landsat OLI on red band: band and near-infrared band: band The subset steps were also carried out to reduce the size of the scene to include only the study area and speed up processing All these steps were processed with the support of ERDAS and ArcGIS 10.2 software 3.3 NDVI calculation This index is calculated based on the difference between the near-infrared band and red band and expressed as Equation (1): (NDVI) = (NIR-RED) / (NIR+RED) where: (1) NIR= near-infrared band RED = red band 3.4 Defining the indices NDVI value ranges between -1 to +1 Higher values of NDVI present healthy vegetation in the area while lower values refer to sparse vegetation (Ravi Prakash Singh, 2016) The water, cloud, and snow reflect more in the visible band than they in the nearinfrared band and therefore, they have negative NDVI values, whereas, bare soil and rock have an NDVI value of around zero Based on the (Afirah, 2017), (USGS) NDVI value was classified into four classes: non-green, low green, moderate green and dense green (Table 2) As the result, low green corresponds with NDVI values between 0.2 and 0.4, and it refers to shrub and grassland; moderate green values range between 0.4 and 0.6 representing for agriculture land and orchard, while dense green corresponds with NDVI values range greater than 0.6 and referring to forest areas Similarly, NDVI values less than 0.2 represent water body, built-up and bare-land It is assigned for non-green class Table 2: Value of NDVI Values of NDVI Description < 0.2 Non- green 0.2- 0.4 Low green 0.4 – 0.6 Moderate green > 0.6 Dense green To obtain an annual rate of change for each green class, the difference between a final year to initial year which represents the magnitude of change between corresponding years was divided by the number of years It was calculated using Equation (2) Annual Rate of Change= ( ) (2) Results NDVI in the period of 2005 – 2018 is shown in figure In which, the value range of NDVI change significantly and the upper value tends to be smaller Accordingly, the value ranges of 2005, 2010, 2015 and 2018 are corresponded (from -0.46 to 0.76), (from -0.15 to 0.68), (from -0.19 to 0.65) and (from -0.13 to 0.61) respectively (figure 3) In order to avoid errors caused by the database, the data of the years are collected between October and November, the weather conditions and seasons are relatively homogeneous for the development of vegetation Therefore, the decline in green space quality is determined by the impact of socio-economic activities This process is continuous and tends to be faster in recent years Figure NDVI in 2005, 2010, 2015 and 2018 NDVI was categorized into four classes: non-green, low green, moderate green, and dense green as figure Table indicates the area of classes derived from Landsat 2005, 2010, 2015 and 2018 It is found that from 2005 to 2018, there is an increase continuously of nongreen and low green areas while the area at moderate and dense green tends to decrease dramatically Even the dense green area was only 23.1 in 2015 and almost disappeared in 2018 with only 0.5 while this area was more than 77 thousand in 2005 In addition, the area of non-green increased times and the area of low green increased 10 times in 13 years (figure 5) By 2018, the low green keeps the largest area with approximately 70 %, the second position is moderate green with 22.5% and non-green also keeps to 7.8% while dense green areas are almost disappeared It can be seen that the rate of green space degradation is happening with high-speed Figure Classified NDVI Table Area of classes derived from Landsat data 2005 2010 2015 2018 Type of green Area (ha) (%) Area (ha) (%) Area (ha) (%) Area (ha) (%) Non veg 3661.6 1.8 4154.0 2.0 13499.9 6.6 15985.0 7.8 Low veg 13646.1 6.7 67652.5 33.2 87608.7 42.9 142115.4 69.7 Moderate veg 109312.0 53.6 130829.4 64.1 102846.7 50.4 45867.1 22.5 Dense veg 77344.2 37.9 1331.9 0.7 23.1 0.0 0.5 0.0 160000 140000 120000 2005 100000 2010 80000 2015 60000 2018 40000 20000 Non veg Low veg Moderate veg Dense veg Figure Change of each vegetation class from 2005 to 2018 The matrix table shows the conversion process among the green space classes Accordingly, many moderate greens and dense green areas were converted into non-green and low-green Specifically, up to 88420.32 (accounting for 88.4%) of the area was converted to low-green About 1776.8 (2.3%) dense green area was converted to nongreen More than 42 thousands (54.6%) this class changed to low-green, more than 33 thousands (approximately 43.1%) into the moderate green Table Conversion matrix of green area in the period of 2005 - 2018 2018 Year 2005 Non green Low green Moderated green Dense green Grand Total Non green 2746.22 750.54 165.06 3661.82 Low green 0.26 10656.8 845.21 11502.27 Moderate green 0.17 88420.32 11561.74 0.09 99982.32 Dense green 1776.81 42262.95 33305.3 0.4 77345.46 Grand Total 4523.46 142090.61 45877.31 0.49 192491.87 Table indicated the severe degradation in green space quality in the study area from 2005 to 2018 through Annual Rate of Change The data showed a negative change of moderated green (-84.4%) and dense green (-99.8%) while it also showed a positive change of non-green (25%) and low green (7.4%) The result showed that there was a considerable expansion of non-green and low green That is the consequence of the spread of the builtup area and deforestation from 2005 to 2018 Especially, shifting cultivation has been still quite common in agricultural production, which causes an increase in soil erosion rapidly Table Green space quality change assessment based on time frame data (2005–2018) Types of green Years 2005 - 2018 Magnitude Area % change (hectare) The annual rate of change Non-green (+) 915.6 (+) 25.0 (+) 70.4 Low green (+) 845.8 (+) 7.4 (+) 65.1 Moderate green (-) 88420.5 (-) 84.4 (-) 6801.5 Dense green (-)77344.0 (-) 99.8 (-) 5949.5 (+) Sign denotes Increase and (-) sign denotes decrease of magnitude of change of green space classes Figure shows the degradation degree of green among administrative units This change has taken place in all communes in the study area Accordingly, the areas with the most fluctuations include Xuan Nha, Long Sap, Chiang Khua and Muong Sang Specifically, the area from green to non-green corresponds to the above communes: 441.6 ha, 140.2 ha, 121.9 ha, and 103.8 respectively (figure 7a) In addition, there is a large area of moderate green being turned into non-green and low green in the period of 2005 - 2018 The communes with the most degradation levels are: Xuan Nha, Muong Sang, Tan Lap In which, the fastest change is given to Xuan Nha commune with the changed area from moderate green to non-green and low green are 948.1 and 11896 respectively Similarly, the degradation from moderate green to non -green and low green also takes place quickly at some typical communes such as Muong Sang (915.5 and 6825.9 ha), Tan Lap (813.1 and 4637.8 ha), Tan Hop (473.7 and 4637.2 ha) At Moc Chau town and Phieng Luong, the degradation is slower than other communes (figure 7b) The result is found that the process of change is concentrated significantly in communes located south and southwest of the study area Figure 6: Green space change map from 2005 - 2018 Ha 10000 9000 8000 7000 6000 5000 4000 3000 2000 1000 0 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Dense green > non green Dense green > low green Dense green > moderate green (a) From Dense level green to other levels Ha 14000.0 12000.0 10000.0 8000.0 6000.0 4000.0 2000.0 0.0 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Moderate green > non green Moderate green > low green (b) From Moderate-level green to other levels Ordinal number and corresponding administrative unit Cho Long Long Luong 13 Phien Luong 19 Tan Hop Chieng Khoa Long Sap 14 Quang Minh 20 Tan Lap Chieng Hac Muong Men 15 Quy Huong 21 Moc Chau town Chieng Khua 10 Muong Sang 16 Song Khua 22 Van Ho Chieng Yen 11 Muong Te 17 Suoi Bang 23 Xuan Nha Hua Pang 12 Na Muong 18 To Mua Figure 7: Green space change by administrative units in period of 2005 - 2018 Conclusion and Discussion The use of multi-temporal satellite images has a positive effect on the study of green space change Accordingly, the dense green areas which represent forest have been under pressures from the surrounding population and economic activities They have been degraded seriously and land has been fragmented and converted into various land uses Due to people's awareness and loose management, the vacant land area increased due to indiscriminate deforestation that causes negative consequences for the environment The trend of green space changes found in this study, especially percentage increase in low green and non-green and decrease in dense green will be helpful for decision making to revert the situation to conserve the natural habitat of wildlife to ensure ecotourism development Socio-economic variables are considered highly related to the changes in green space quality in the study area Thus incorporating socio-economic and demographic data of the study area along with temporal change pattern would give critical reasoning for green space quality assessment Acknowledgments This research was supported by the Ministry of Education and Training under the Project No CT.2019.06.06 References Ahl, D.G (2006) Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS Remote Sensing of Environment, 104, 88 - 95 Cabral I., Costa S., Weiland U., Bonn A (2017) Urban gardens as multi-functional nature-based solutions for societal goals in a changing climate Theory and Practice of Urban Sustainability Transitions, Editors: Nadja Kabisch, Horst Korn, Jutta Stadler, Aletta Bonn, Springer publisher, 187- 205 Campbell, K (2001) Rethinking open space, open space provision and management: a way forward Report Presented by Scottish Executive Central Research Unit, Edinburgh, Scotland, United Kingdom Availble at: https://www.webarchive.org.uk/wayback/archive/20180518031705/http://www.gov.scot/P ublications/2001/03/8576/File-1 Donovan, G H (2010) Trees in the city: Valuing street trees in Portland, Oregon Landscape and Urban Planning, 94(2), 77–83 Duong, T.L., Chou, T.Y., Fang, Y.M (2015) Development of an index for assessment of urban green spaces at city level International journal of remote sensing application, 5, 78 - 88 Fam, D., E Mosley, A Lopes, L Mathieson, J Morison, G Connellan (2008) Irrigation of Urban Green Spaces: a review of the Environmental, Social and Economic benefits Cooperative Research Centre for Irrigation Futures Technical Report No 04/08, Technologies Pty Ltd Harrison, D., S Schipani (2007) Lao tourism and poverty alleviation: Communitybased tourism and the private sector Current Issues in Tourism, 10 (2–3), 194–230 Jeevalakshmi, D.S.N (2016) Land cover classification based on NDVI using LANDSAT Time series: A case study Tiruputi Region International conference on Communication and Signal Procssing, India, 6- Jiaguo, W C (2005) Assessment of tropical forest degradation with canopy fractional cover from landsat ETM+ and IKONOS imagery Earth Interactions, (22), 1-17 Li, W., Jean-Daniel M Saphores, Thomas W Gillespie (2015) A comparison of the economic benefits of urban green spaces estimated with NDVI and with high-resolution land cover data Landscape and Urban Planning, 133, 105–117 López-Serrano, José J Corral-Rivas, Ramón A Díaz-Varela, Juan G ÁlvarezGonzález, Carlos A López-Sánchez (2016) Evaluation of radiometric and atmospheric correction algorithms for above ground forest biomass estimation using Landsat TM data Remote Sensing, 8, 1- 19 Mehhdi, R.M.Y (2017) Terminology of Urban Open and Green Spaces ASEAN Postgraduate Seminar, APGS Malaysia, 11, 1- Ravi Prakash Singh, N.S (2016) Normalized Difference Vegetation Index (NDVI) Based Classification to Assess the Change in Land Use/Land Cover (LULC) in Lower Assam, India International Journal of Advanced Remote Sensing and GIS, (10), 1963-1970 Survey, U.G (2019) Available at: https://landsat.gsfc.nasa.gov/a-landsat-timeline/ Swanwick, C.D (2003) Nature, role and value of green space in towns and cities: An overview Built Environment, 29 (2), 94–106 Taufik, A., Ahmad, S.S.S., Ahmad, A (2017) Classification of Landsat satellite data using NDVI thresholds Telecommunication, Electronic and Computer Engineering, (4), 37 - 40 Taylor, L.D.F (2017) Defining greenspace: Multiple uses across multiple disciplines Landscape and Urban planning, 158, 25-38 USGS (2013) Landsat Missions: Frequently Asked Questions About the Landsat Missions USGS Available at: http://www.gisagmaps.com/landsat-8-atco-guide/ USGS (n.d.) NDVI, the Foundation for Remote Sensing Phenology Available at: https://www.usgs.gov/land-resources/eros/phenology/science/ndvi-foundation-remotesensing-phenology?qt-science_center_objects=0#qt-science_center_objects William, A.M., Aveling, R., Brockington, D (2004) Biodiversity conservation and the eradication of poverty Science, 306 (5699), 1146–1149  ... maintaining the needs of green space has become a major concern of managers at Moc Chau national tourism zone To so, understanding about standards of green space and conversion and change of green. .. province to the south The zone includes three key tourism centers: the Moc Chau relaxation center, the Moc Chau eco -tourism center and the Moc Chau recreational center Figure 1: Location of study... green space in the study area is very important, thereby proposing the appropriate solutions and strategies for sustainable development The aim of this study is to examine the change of green space