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Forest cover change is considered one of the serious issues in the last several decades for the
global environment. This scenario has mainly depended to ever-increasing socio-economic activities.
The present article has revealed major changes of VFC in relation to human activities during
the years of 2000 to 2010 in the Guangdong province of China....
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... fact we can conclude any socio-economic activity such as urbanization, urban development and increasing population density affected VFC in the whole period from 2000 to 2010 in the study area. Also in Figure 10, perhaps we see a small amount of harmony between VFC and Light Index in 2005 and 2006 that each one has its own ecological and socio-economic reasons totally. Figure 11 shows Time Series Modelling of Light Index. The graph in Figure 11, there is strong evidence of our result based on increasing Light Index and growing ur- ban development annually and more confirms our previous results. Figure 12 shows ACF and partial ACF modelling of Light Index in a time interval approximately 11 years. Amounts of Light Index are at roughly situated between ranges −0.5 to 0.5, and vertical axis of ACF and partial ACF show this range well. Also chart has taken harmonious state in positive and negative directions but with equal amounts, thus it can be concluded Light Index has suitable thoroughness altogether and in this paper it could be appropriate index for evaluating VFC and VFC change dynamic annually. ...
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... fact we can conclude any socio-economic activity such as urbanization, urban development and increasing population density affected VFC in the whole period from 2000 to 2010 in the study area. Also in Figure 10, perhaps we see a small amount of harmony between VFC and Light Index in 2005 and 2006 that each one has its own ecological and socio-economic reasons totally. Figure 11 shows Time Series Modelling of Light Index. The graph in Figure 11, there is strong evidence of our result based on increasing Light Index and growing ur- ban development annually and more confirms our previous results. Figure 12 shows ACF and partial ACF modelling of Light Index in a time interval approximately 11 years. Amounts of Light Index are at roughly situated between ranges −0.5 to 0.5, and vertical axis of ACF and partial ACF show this range well. Also chart has taken harmonious state in positive and negative directions but with equal amounts, thus it can be concluded Light Index has suitable thoroughness altogether and in this paper it could be appropriate index for evaluating VFC and VFC change dynamic annually. ...
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... fact we can conclude any socio-economic activity such as urbanization, urban development and increasing population density affected VFC in the whole period from 2000 to 2010 in the study area. Also in Figure 10, perhaps we see a small amount of harmony between VFC and Light Index in 2005 and 2006 that each one has its own ecological and socio-economic reasons totally. Figure 11 shows Time Series Modelling of Light Index. The graph in Figure 11, there is strong evidence of our result based on increasing Light Index and growing ur- ban development annually and more confirms our previous results. Figure 12 shows ACF and partial ACF modelling of Light Index in a time interval approximately 11 years. Amounts of Light Index are at roughly situated between ranges −0.5 to 0.5, and vertical axis of ACF and partial ACF show this range well. Also chart has taken harmonious state in positive and negative directions but with equal amounts, thus it can be concluded Light Index has suitable thoroughness altogether and in this paper it could be appropriate index for evaluating VFC and VFC change dynamic annually. ...
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... fact we can conclude any socio-economic activity such as urbanization, urban development and increasing population density affected VFC in the whole period from 2000 to 2010 in the study area. Also in Figure 10, perhaps we see a small amount of harmony between VFC and Light Index in 2005 and 2006 that each one has its own ecological and socio-economic reasons totally. Figure 11 shows Time Series Modelling of Light Index. The graph in Figure 11, there is strong evidence of our result based on increasing Light Index and growing ur- ban development annually and more confirms our previous results. Figure 12 shows ACF and partial ACF modelling of Light Index in a time interval approximately 11 years. Amounts of Light Index are at roughly situated between ranges −0.5 to 0.5, and vertical axis of ACF and partial ACF show this range well. Also chart has taken harmonious state in positive and negative directions but with equal amounts, thus it can be concluded Light Index has suitable thoroughness altogether and in this paper it could be appropriate index for evaluating VFC and VFC change dynamic annually. ...
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... previous theoretical arguments and field survey we assume that the vegetation fractional cover in the study area is significantly affected by human socio-economic activities as urbanization, development urban and ex- pending rural area and increasing population density. Also was identified the potential of DMSP data for esti- mating urban population as well as the technical to monitor urban population dynamics annually in a region where data are scare and the demographic dynamic are unique. Altogether in our study results showed Light In- dex obtained from DMSP/OLS images enables to use as an indicator to detect human presence and socio- economic activities for analysis of the spatial-temporal patterns in most parts of the developing countries. On the other hands we argue that variation of VFC is indeed driven by human socio-economic factors and results of Figure 13 shows the trends of VFC and Light Index has same direction in [2005][2006] (marked with dotted red), Because Guangdong province had a major shift in its population between the years of 2005 and 2006, So that scientific resources report that Guang- dong had surpassed Henan and Sichuan province to become the most populous province in China in January 2005, registering 79.1 million permanent residents and 31 million migrants who lived in the province for at least six months of the year [22]. Thus, it can be concluded that increasing and decreasing population density leads to change VFC undoubtedly. In fact in Figure 13, the trend of Light Index must was decreased in 2005 to 2006 and was found inverse direction with VFC actually [1]. They were concluded in general, deforestation is highly correlated with the logarithm of population density. Again the trends of VFC and Light Index have inverse rela-tionship together in 2007 to 2010. Figure 13 shows sharp decline of VFC and sudden increase of Light Index in 2009 to 2010 actually. In fact we face with the most increasing of human activity in 2009 to 2010 surly and on the other hand forest protection and grassland improvement has been on the decline. Indeed during the more than 10 years, the population of Guangdong Province increased from 85, 225,007 people in 2000 to 104,303,132 person in 2010. With the increase of the population, human activities such as urbanization, development of rural areas and land utilization increase additionally and this issue will affects vegetation fractional cover. But human activities such as agriculture production, ecological construction significantly drove the improvement of vegeta- tion cover [8]. In addition with development of social economy, people become increasingly aware of the im- portance of environmental protection and sustainable development to restore forestland or grassland [19]. Ac- cording to Figure 13, VFC increases in 2001to 2003, in 2005to 2007and eventually 2008. In can be concluded that beside to reducing of human activities and diminishes Light Index in these years, driven by eco- logical benefits in the recent years, reforestation, rangelands improvement and other ecological project have been increasing. Also controlling land use, planting trees and agroforestry were closely related to expand VFC and improve vegetation coverage in the study area. Additionally, it cannot be ignored impacts of climatic factors and physiographic with VFC variation annually [23]. They also were reported that vegetation index and frac- tional vegetation cover have good correlation with climate variables such as precipitation, temperature and eva- poration [8]. They were concluded the average VFC increased from 40.17% in 1989 to 53.6% in 2006 mainly due to the increase of temperature, annual runoff and population as well as improved land utilization. Howsoev- er we faced with seven years increasing of VFC and four years decreasing of VFC from 2000 to 2010 in the study area. It was found that the VFC in Guangdong province shows an increasing trend in the 11 ...
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... previous theoretical arguments and field survey we assume that the vegetation fractional cover in the study area is significantly affected by human socio-economic activities as urbanization, development urban and ex- pending rural area and increasing population density. Also was identified the potential of DMSP data for esti- mating urban population as well as the technical to monitor urban population dynamics annually in a region where data are scare and the demographic dynamic are unique. Altogether in our study results showed Light In- dex obtained from DMSP/OLS images enables to use as an indicator to detect human presence and socio- economic activities for analysis of the spatial-temporal patterns in most parts of the developing countries. On the other hands we argue that variation of VFC is indeed driven by human socio-economic factors and results of Figure 13 shows the trends of VFC and Light Index has same direction in [2005][2006] (marked with dotted red), Because Guangdong province had a major shift in its population between the years of 2005 and 2006, So that scientific resources report that Guang- dong had surpassed Henan and Sichuan province to become the most populous province in China in January 2005, registering 79.1 million permanent residents and 31 million migrants who lived in the province for at least six months of the year [22]. Thus, it can be concluded that increasing and decreasing population density leads to change VFC undoubtedly. In fact in Figure 13, the trend of Light Index must was decreased in 2005 to 2006 and was found inverse direction with VFC actually [1]. They were concluded in general, deforestation is highly correlated with the logarithm of population density. Again the trends of VFC and Light Index have inverse rela-tionship together in 2007 to 2010. Figure 13 shows sharp decline of VFC and sudden increase of Light Index in 2009 to 2010 actually. In fact we face with the most increasing of human activity in 2009 to 2010 surly and on the other hand forest protection and grassland improvement has been on the decline. Indeed during the more than 10 years, the population of Guangdong Province increased from 85, 225,007 people in 2000 to 104,303,132 person in 2010. With the increase of the population, human activities such as urbanization, development of rural areas and land utilization increase additionally and this issue will affects vegetation fractional cover. But human activities such as agriculture production, ecological construction significantly drove the improvement of vegeta- tion cover [8]. In addition with development of social economy, people become increasingly aware of the im- portance of environmental protection and sustainable development to restore forestland or grassland [19]. Ac- cording to Figure 13, VFC increases in 2001to 2003, in 2005to 2007and eventually 2008. In can be concluded that beside to reducing of human activities and diminishes Light Index in these years, driven by eco- logical benefits in the recent years, reforestation, rangelands improvement and other ecological project have been increasing. Also controlling land use, planting trees and agroforestry were closely related to expand VFC and improve vegetation coverage in the study area. Additionally, it cannot be ignored impacts of climatic factors and physiographic with VFC variation annually [23]. They also were reported that vegetation index and frac- tional vegetation cover have good correlation with climate variables such as precipitation, temperature and eva- poration [8]. They were concluded the average VFC increased from 40.17% in 1989 to 53.6% in 2006 mainly due to the increase of temperature, annual runoff and population as well as improved land utilization. Howsoev- er we faced with seven years increasing of VFC and four years decreasing of VFC from 2000 to 2010 in the study area. It was found that the VFC in Guangdong province shows an increasing trend in the 11 ...
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... previous theoretical arguments and field survey we assume that the vegetation fractional cover in the study area is significantly affected by human socio-economic activities as urbanization, development urban and ex- pending rural area and increasing population density. Also was identified the potential of DMSP data for esti- mating urban population as well as the technical to monitor urban population dynamics annually in a region where data are scare and the demographic dynamic are unique. Altogether in our study results showed Light In- dex obtained from DMSP/OLS images enables to use as an indicator to detect human presence and socio- economic activities for analysis of the spatial-temporal patterns in most parts of the developing countries. On the other hands we argue that variation of VFC is indeed driven by human socio-economic factors and results of Figure 13 shows the trends of VFC and Light Index has same direction in [2005][2006] (marked with dotted red), Because Guangdong province had a major shift in its population between the years of 2005 and 2006, So that scientific resources report that Guang- dong had surpassed Henan and Sichuan province to become the most populous province in China in January 2005, registering 79.1 million permanent residents and 31 million migrants who lived in the province for at least six months of the year [22]. Thus, it can be concluded that increasing and decreasing population density leads to change VFC undoubtedly. In fact in Figure 13, the trend of Light Index must was decreased in 2005 to 2006 and was found inverse direction with VFC actually [1]. They were concluded in general, deforestation is highly correlated with the logarithm of population density. Again the trends of VFC and Light Index have inverse rela-tionship together in 2007 to 2010. Figure 13 shows sharp decline of VFC and sudden increase of Light Index in 2009 to 2010 actually. In fact we face with the most increasing of human activity in 2009 to 2010 surly and on the other hand forest protection and grassland improvement has been on the decline. Indeed during the more than 10 years, the population of Guangdong Province increased from 85, 225,007 people in 2000 to 104,303,132 person in 2010. With the increase of the population, human activities such as urbanization, development of rural areas and land utilization increase additionally and this issue will affects vegetation fractional cover. But human activities such as agriculture production, ecological construction significantly drove the improvement of vegeta- tion cover [8]. In addition with development of social economy, people become increasingly aware of the im- portance of environmental protection and sustainable development to restore forestland or grassland [19]. Ac- cording to Figure 13, VFC increases in 2001to 2003, in 2005to 2007and eventually 2008. In can be concluded that beside to reducing of human activities and diminishes Light Index in these years, driven by eco- logical benefits in the recent years, reforestation, rangelands improvement and other ecological project have been increasing. Also controlling land use, planting trees and agroforestry were closely related to expand VFC and improve vegetation coverage in the study area. Additionally, it cannot be ignored impacts of climatic factors and physiographic with VFC variation annually [23]. They also were reported that vegetation index and frac- tional vegetation cover have good correlation with climate variables such as precipitation, temperature and eva- poration [8]. They were concluded the average VFC increased from 40.17% in 1989 to 53.6% in 2006 mainly due to the increase of temperature, annual runoff and population as well as improved land utilization. Howsoev- er we faced with seven years increasing of VFC and four years decreasing of VFC from 2000 to 2010 in the study area. It was found that the VFC in Guangdong province shows an increasing trend in the 11 ...
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... forest cover change due to increased human activities is one of the most important issues in climate change and global warming. It has been especially considerable in the last few decades. Increasing rate of de- forestation due to pressure is caused by the population growth and it is very important to compute the trend of global environment as deforestation with the satellites products, meteorological datasets and socio-economic in- formation based on population growth [1]. Use of remote sensing in monitoring forest cover change dates back to the early 1980s [2]. Remote sensing data provide an alternative data source to quantify forest cover. Informa- tion is derived from satellite imageries and there are suitable tools for mapping land-cover, tree canopy cover and dominant tree species composition [3]. At the same time, remote sensing-based forest cover change analysis requires less effort and time than ground surveys and can be performed in areas of limited ground access. This is why remote sensing-based products are widely used for multi-national forest assessment and change estimations [4]. Their results showed as a baseline for carbon modeling, fire management and socio-economic analysis as well as for studies of forest cover dynamic and biodiversity patterns [5]. Effective monitoring of forest cover requires longer-term data set with fine spatial resolution ideally at sub-hectare spatial resolutions spanning mul- tiple decades [6]. In this context, satellite borne sensors can detect forest cover change in the visible, thermal and mid-infrared signature during the days, nights, months and seasons [2]. In this study, we use one of the most common satellite systems as MODIS (Moderate Resolution Imaging Spectroradiometer) from NASA which provides visible and thermal images and also it can be evaluated forest cover changes. There are a lot of projects that are defined start and end of the growing season using MODIS-based 16-days NDVI profiles derived within MODIS-based forest cover mask for each Landsat footprint [3]. The growing season was defined as the sum of all 16-day intervals having an NDVI equal to or above 90% of the maximum annual NDVI. The NDVI images of MODIS (1 month-Terra) from the NEO (Nasa Earth Observations) data archive can be used as based datasets [7]. Using NDVI images are computed Vegetation Fractional Cover. VFC is the vertical projection of vegetation including leaves, stems, and also shoots to the ground surface and is expressed as the fraction or percentage of the reference area [8]. In fact, VFC enables to couple natural environment changes and human activities and also it is an essential index to study the ecological systems [9]. In addition, vegetation change attaches a great impor- tance to global energy circulation and geo-biochemical cycle of substance, thus evaluating VFC contains great significant for both ecology and society exactly [10]. On the other side, DMSP/OLS (Defense Meteorological Satellite Program/Optical Linescan System) datasets from NOAA (National Oceanic and Atmospheric Adminis- tration) makes daily over flights and routinely collects visible images during its nighttime pass [11]. In fact, DMSP/OLS is possible to detect human presence, urban settlements and light-demanding activities, estimating urban population and density, socio-economic activities, energy and electricity consumption and gas emissions [12]. Also [13] reported that popular applications of the DMSP/OLS night time images included measuring im- pacts of urban growth on the environment, mapping nighttime sky brightness and specially evaluating damage from natural disasters and forest fires. Generally data available of DMSP/OLS nighttime images can be present by Nasa Earth Observation and National Center for Environmental Information Archive from NOAA. In this paper, we calculate VFC and VFC levels for a period of eleven years annually. Also, we explain significant cover changes in this period dynamically. In addition, we assess the quality of theforest cover and changes by mean of NDVI, VFC, VFC levels and Hot Spot-Cold Spot maps. We aim to show that DMSP/ OLS images in- geminate population density and human activities dynamically. Totally, this paper demonstrates relationship between Light Index with VFC, to detect trend of VFC changes and Light Index and it describes driving forces of VFC variation in the whole study area during 2000 to 2010. Finally, we discuss about these validation results with each other. Figure 1 shows the political map of China with different state boundaries and specially study area. Guangdong is a province on the South China Sea coast of the People's Republic of China. The district occupies an area of 179,800 km 2 and it is bounded by 20˚13ꞌ -25˚31ꞌ North latitudes and 109˚39ꞌ -117˚19ꞌ East longitudes. Guang- dong neighbors Jiangxi and Hunan provinces in the north, Fujian province in the east, and the Guangxi Zhuang autonomous region in the west. The provincial capital Guangzhou and economic hub Shenzhen are among the most populous and important cities in China. The Guangdong had 106,440,000 people in 2013 and it to have followed many economic and social developments. Guangdong province is divided into 21 prefecture-level ci- ties, 33 county-level cities, 43 counties and 3 autonomous counties [14]. Crossed by the tropic of Cancer in the central part of its continental portion, tropical and sub-tropical Guangdong has a climate marked by high tem- perature and plentiful rainfall. It has the highest mean temperature of 28˚C in July (the hottest month in a year) and 13˚C in January (the coldest month in a year). The greater part of the province has a mean annual precipita- tion of about 1500 -2000 mm and with 140 -160 rainy days. Guangdong province has vegetation varies from north to south. In the north, the Nanling Mountain Ranges is covered by the subtropical montane evergreen broadleaved forest; in the middle, it is the subtropical evergreen broadleaved forest, and in the south the tropical monsoon forest. Guangdong abounds in the fauna and flora resources; there are more than 7055 species of vas- cular plant here, 4000 species of which are woody plant or 80% of the total woody plant in China ...
Citations
... where ε v and ε s are the emissivities of the vegetation and bare soil, respectively, and f v is the fractional vegetation coverage (FVC) [43]: ...
The role of land surface temperature (LST) is of the utmost importance in multiple academic disciplines, such as climatology, hydrology, ecology, and meteorology. To date, many methods have been proposed to estimate LST from satellite thermal infrared data. The single-channel (SC) algorithm can provide an accurate result in retrieving LST based on prior knowledge of known land surface emissivity (LSE). The SC algorithm is extensively employed for retrieving LST from Landsat series data due to its simplicity and its reliance on just one thermal infrared channel. The Thermal Infrared Sensor (IRS) on the Chinese ZY1-02E satellite is a pivotal instrument employed for gathering thermal infrared (TIR) data of land surfaces. The objective of this research is to evaluate the feasibility of a single-channel approach based on water vapor scaling (WVS) for deriving LST from ZY1-02E IRS data because of its wide spectrum range, i.e., 7~12 μm, which is affected strongly by both atmospheric water vapor and ozone. Three study areas, namely the Baotou, Heihe River Basin, and Yantai Sea sites, were selected as validation sites to evaluate the LST inversion accuracy. This evaluation was also conducted via cross-comparison between the retrieved LST and MODIS LST products. The results revealed that the WVS-based method exhibited an average bias of 0.63 K and an RMSE of 1.62 K compared to the in situ LSTs. The WVS-based method demonstrated reasonable accuracy through cross-validation with the MODIS LST product, with an average bias of 0.77 K and an RMSE of 2.0 K. These findings indicate that the WVS-based method is effective in estimating LST from ZY1-02E IRS data.
... where v and s are the emissivity of the vegetation and bare soil, respectively, and v f is the Fractional Vegetation Coverage (FVC) [37] : ...
The role of land surface temperature (LST) is of utmost importance in multiple academic disciplines such as climatology, hydrology, ecology, and meteorology. Until to now, many methods have been proposed to estimate LST from satellite thermal infrared data. The Thermal Infrared Sensor (IRS) on the Chinese ZY1-02E satellite is a pivotal instrument employed for gathering thermal infrared (TIR) data of land surfaces. The objective of this research is to evaluate the feasibility of a single-channel approach based on water vapor scaling (WVS) for deriving LST from ZY1-02E IRS data because of its wide spectrum range, i.e., 7~12μm, affected strongly by both atmospheric water vapor and ozone. Three study areas, namely Baotou, Heihe River Basin, and Yantai-sea sites, were selected as validation sites to evaluate the LST inversion accuracy. This evaluation was also conducted by cross-comparison between the retrieved LST and MODIS LST product. The results revealed that the WVS-based method exhibited an average bias of 0.63K and an RMSE of 1.62K compared to the in-situ LSTs. The WVS-based method demonstrated reasonable accuracy through cross-validation with the MODIS LST product, with an average bias of 0.77K and RMSE of 2.0K. These findings provide that the WVS-based method is effective to estimate LST from ZY1-02E IRS data.
... Guangdong Province is located in the southernmost part of the Chinese mainland ( Figure 1), between 20 • 13 N-25 • 31 N and 109 • 45 E-117 • 20 E. It is adjacent to Fujian in the east, Jiangxi, Hunan in the north, Guangxi in the west, Hong Kong and Macao in the south, and faces Hainan across the sea to the southwest [54]. The total land area of the Guangdong Province is 179,800 km 2 . ...
Guangdong Province is an important ecological barrier and the primary pillar of economic development in China. Driven by high-speed urbanization and industrialization, unreasonable land use change in Guangdong Province has exacerbated habitat degradation and loss, seriously affecting habitat quality. Thus, taking Guangdong Province as the study area, this paper quantifies the response of habitat quality on land use change using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and constructs a contribution index (CI). The following conclusions can be drawn from the results: (1) The habitat quality exhibits a spatial distribution pattern of low quality in plain areas and high quality in hilly and mountainous areas. (2) The annual average habitat quality gradually decreases from 1980 to 2020, with a total decrease of 0.0351 and a reduction rate of 4.83%; (3) The impact of land use change on habitat quality is mainly negative, and the habitat quality mainly decreases by the conversion of forest land to orchards, paddy field to urban land, and forest land to dry land, with CI values of −24.09, −11.67, and −8.04, respectively. Preventing the destruction of natural forests, increasing the diversity of plantation orchards, and rationalizing and mitigating the growth rate of construction land are key to maintaining and improving the habitat quality.
... In previous studies, time series NDVI and NTL data have been used for urban land use extraction [36,37] and urban land dynamic analysis [38][39][40]. Correlation analysis is usually used to explore the influence of urbanization on the eco-environment [38,39]. However, while correlation analysis provides the degree of correlation between urbanization and ecological degradation, it is difficult to determine urban development trend types, and the pace of change and spatial distribution patterns of these types. ...
... In previous studies, time series NDVI and NTL data have been used for urban land use extraction [36,37] and urban land dynamic analysis [38][39][40]. Correlation analysis is usually used to explore the influence of urbanization on the eco-environment [38,39]. However, while correlation analysis provides the degree of correlation between urbanization and ecological degradation, it is difficult to determine urban development trend types, and the pace of change and spatial distribution patterns of these types. ...
... In previous studies, time series NDVI and NTL data have been used for urban land mapping [36,37] and urban land dynamic analysis [38][39][40]. Correlation analysis is commonly used to analyze the influence of urbanization on the eco-environment [38,39]. This study used linear model fitting to analyze the trends of urbanization and ecology based on time series NDVI and NTL data. ...
Uncontrolled urban growth detracts from healthy urban development. Understanding urban development trends and predicting future urban spatial states is of great practical significance. In order to comprehensively analyze urbanization and its effect on vegetation cover, we extracted urban development trends from time series DMSP/OLS NTL and NDVI data from 2000 to 2015, using a linear model fitting method. Six urban development trend types were identified by clustering the linear model parameters. The identified trend types were found to accurately reflect the on-ground conditions and changes in the Jinan area. For example, a high-density, stable urban type was found in the city center while a stable dense vegetation type was found in the mountains to the south. The SLEUTH model was used for urban growth simulation under three scenarios built on the urban development analysis results. The simulation results project a gentle urban growth trend from 2015 to 2030, demonstrating the prospects for urban growth from the perspective of environmental protection and conservative urban development.
... It shares boundary with Fujian province in the east, Jiangxi and Hunan provinces in the north, Guangxi in the west, and the South China Sea in the south. Its topography includes rivers, mounatins, plains, and plateaus (Hasan et al., 2019;Shobairi & Li, 2016). ...
Land use land cover (LULC) of Guangdong, Hong Kong, and Macao (GHKM), south china, has undergone significant changes in the last few decades. This study analyze the spatio-temporal LULC changes and urban expansion during 2010–2017 using Landsat TM, ETM+, and OLI. The Landsat images were classified using support vector machine (SVM) into seven classes as forest, grassland, water, fishponds, built-up, bareland, and farmland. Several socioeconomic factors were also obtained to determine their impact on LULC. The result shows that during the studied period, massive economic development and urbanization has increased the built-up area from 8.26% (16,209.61 km2) to 10.31% (20241.77 km2) and substantial reduction in both farmland from 37.64% (73,897.77 km2) to 33.05% (64932.19 km2) and fishponds from 1.25% (2451.12 km2) 0.85% (1674.71 km2). The most dominant conversion were from farmland to built-up and to forest. Furthermore, forest cover increased to 45.02 % (88384.97 km2) in 2017 from 42.38% (83215.59 km2) in 2010 as a result of different afforestation scheme and policies in order to make Greener study area. The analysis of socioeconomic factors shows that increase in gross domestic product (GDP), total investment in fixed assets, and industrialization has led to urbanization growth on a large scale and reduction of farmland. Therefore, there is pressing need for sustainable development and protection of farmlands.
... where C is the vegetation cover and management factor (dimensionless). VFC is calculated using the NDVI as follows [32]: ...
The Loess Plateau is one of the most fragile areas in the world, where the problem of soil erosion is particularly prominent. The spatial and temporal variation characteristics and mechanisms of soil erosion in this region have always been hot topics for researchers. In this study, Revised Universal Soil Loss Equation (RUSLE) is used to estimate the soil erosion modulus of the Loess Plateau from 2000 to 2015, the dynamic characteristics of its temporal and spatial variations and driving mechanisms are determined, and meteorological data are combined with remote sensing data to quantitatively calculate the contribution rate of human activities. The results show that from 2000 to 2015, the soil erosion modulus of the Loess Plateau had a downward trend as a whole, with a rate of −0.6408 t/ha/a, but the downward trend gradually slowed down. Precipitation mainly resulted in changes in the soil erosion modulus in the northwestern part of the Loess Plateau, where a significant positive correlation was seen. Meanwhile, the Vegetation Fractional Coverage (VFC) mainly affected the southeastern part, where a significant negative correlation was measured. The human-activity contribution rate was −1.0774 on the Loess Plateau, which means human activities effectively reduced the soil erosion modulus while climate change promoted soil erosion combined with the result of the analysis of variance (ANOVA). “Hilly and gully regions” and “Gully region of Loess Plateau” as the main implementation areas of ecological projects, human activities had contribution rate of 0.5513 and 0.7805 toward the declining of soil erosion, respectively. Interestingly, the spatial differentiation characteristic of the soil erosion driving mechanisms and human contribution rates on the Loess Plateau showed the same boundary line from northeast to southwest, which was well explained by the 400-mm isohyetal line and Hu’s Line. This boundary can guide the geographical layout of the ecological management projects and urban development spaces on the Loess Plateau.
... GHKM is located at 20 • 13 N-25 • 31 N, 109 • 39 E-117 • 19 E in the southernmost part of China ( Figure 1) [11,55,56]. It adjoins Fujian province in the east, Jiangxi and Hunan provinces in the north, Guangxi in the west, and the South China Sea in the south [55]. ...
... GHKM is located at 20 • 13 N-25 • 31 N, 109 • 39 E-117 • 19 E in the southernmost part of China ( Figure 1) [11,55,56]. It adjoins Fujian province in the east, Jiangxi and Hunan provinces in the north, Guangxi in the west, and the South China Sea in the south [55]. GHKM covers a total area of approximately 196,342 km 2 . ...
... The area consists of 23 cities, divided into four groups in accordance with their geographical location. This includes 11 cities in the Pearl River Delta (PRD), five cities in mountainous regions, four cities on the eastern side, and three cities on the western side [11,55,56]. ...
Land use and land cover changes (LULCC) are prime variables that reflect changes in ecological systems. The Guangdong, Hong Kong, and Macau (GHKM) region located in South China has undergone rapid economic development and urbanization over the past three decades (1986–2017). Therefore, this study investigates the changes in LULC of GHKM based on multi-year Landsat and nighttime light (NTL) data. First, a supervised classification technique, i.e., support vector machine (SVM), is used to classify the Landsat images into seven thematic classes: forest, grassland, water, fishponds, built-up, bareland, and farmland. Second, the demographic activities are studied by calculating the light index, using nighttime light data. Third, several socioeconomic factors, derived from statistical yearbooks, are used to determine the impact on the LULCC in the study area. The post-classification change detection shows that the increase in the urban area, from 0.76% (1488.35 km2) in 1986 to 10.31% (20,643.28 km2) in 2017, caused GHKM to become the largest economic segment in South China. This unprecedented urbanization and industrialization resulted in a substantial reduction in both farmland (from 53.54% (105,123.93 km2) to 33.07% (64,932.19 km2)) and fishponds (from 1.25% (2463.35 km2) to 0.85% (1674.61 km2)) during 1986–2017. The most dominant conversion, however, was of farmland to built-up area. The subsequent urban growth is also reflected in the increasing light index trends revealed by NTL data. Of further interest is that the overall forest cover increased from 33.24% (65,257.55 km2) to 45.02% (88,384.19 km2) during the study period, with a significant proportion of farmland transformed into forest as a result of different afforestation programs. An analysis of the socioeconomic indicators shows that the increase in gross domestic product, total investment in real estate, and total sales of consumer goods, combined with the overall industrialization, have led to (1) urbanization on a large scale, (2) an increased light index, and (3) the reduction of farmland. The speed of development suggests that opportunistic development has taken place, which requires a pressing need to improve land policies and regulations for more sustainable urban development and protection of farmland.
The rapid increase in anthropogenic activities, socioeconomic development, and land use land cover (LULC) changes since the opening of economic reforms (1978), have changed the ecosystem service value (ESV) in Guangdong, Hong Kong, and Macao (GKHM) region located in South China. This leads to the requirement of a significant tailored analysis of ecosystem services regarding incisive and relevant planning to ensure sustainability at regional level. This study focuses on the use of Landsat satellite imagery to quantify the precise impact of LULC changes on the ecosystem services in GHKM over the past three decades (1986–2017). The most renowned established unit value transfer method has been employed to calculate the ESV. The results show that the total ecosystem service value in GHKM has decreased from 680.23 billion CNY in 1986 to 668.45 billion CNY in 2017, mainly due to the decrease in farmland and fishponds. This overall decrease concealed the more dynamic and complex nature of the individual ESV. The most significant decrease took place in the values of water supply (-22.20 billion CNY, -14.72%), waste treatment (-20.77 billion CNY, −14.63%), and food production (-7.96 billion CNY, −33.18%). On the other hand, the value of fertile soil formation and retention (6.28 billion CNY, +7.26%) and recreation and culture (5.09 billion CNY, +12.91%) increased. Furthermore, total ESV and ESV per capita decreased significantly with the continuous increase in total gross domestic product (GDP) and GDP per capita. A substantial negative correlation exists between farmland ESV and GDP indicating human encroachment into a natural and semi natural ecosystems. The results suggest that in the rapidly urbanizing region, the protection of farmland and to control the intrusion of urban areas has marked an important societal demand and a challenge to the local government. This required a pressing need for smart LULC planning and to improve policies and regulation to guarantee ecosystem service sustainability for acceptable life quality in the study area and other fast expanding urban areas in China.
Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) data of MODIS 11A2 and Normalized Difference Vegetation Index (NDVI) products of MODIS 13Q1 were used to analyse the spatial and temporal changes of LST and its relationship with vegetation from 2001 to 2015 in the subtropical region of Fujian Province, China. The LST and NDVI products were reconstructed by Savitzky–Golay (S-G) filtering for abnormal values or missing data due to the influence of cloud contamination. The LST of Fujian decreases from its southeast coast to its northwest mountain area, and the annual average highest LST is greater than 30°C while the lowest LST is about 15°C. In the past 15 years, the average LST in Fujian has declined gradually due to an increase in fractional vegetation coverage (FVC), but the highest LST increased from 30.95°C to 33.05°C across south-east coastal zone cities due to rapid urbanization. From 2001 to 2015, the LST of Fujian Province trended slightly downward; the LST area, however, significantly decreased by 18.85% of the total area of the province and was twice that of the (significant) increase in LST. The changing trend distributions of LST are opposite to those of FVC in general, but LST decreases to greater extent as FVC increases when FVC is above 0.2 – every 10% increase in FVC is accompanied by about a 1.1°C decrease in LST.
