Spatiotemporal variations of eco-environment in the Guangxi Beibu Gulf Economic Zone based on remote sensing ecological index and granular computing

LIAO Weihua, JIANG Weiguo, HUANG Ziqian

Journal of Geographical Sciences ›› 2022, Vol. 32 ›› Issue (9) : 1813-1830.

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Journal of Geographical Sciences ›› 2022, Vol. 32 ›› Issue (9) : 1813-1830. DOI: 10.1007/s11442-022-2024-3
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Spatiotemporal variations of eco-environment in the Guangxi Beibu Gulf Economic Zone based on remote sensing ecological index and granular computing

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Abstract

Accurate and rapid evaluation of the regional eco-environment is critical to policy formulation. The remote sensing ecological index (RSEI) model of the Guangxi Beibu Gulf Economic Zone (GBGEZ) during 2001-2020 was established and evaluated using four indices: dryness, wetness, greenness, and heat. This paper proposes an information granulation method for remote sensing based on the RSEI index value that uses granular computing. We found that: (1) From 2001 to 2020, the eco-environmental quality (EEQ) of GBGEZ tended to improve, and the spatial difference tended to expand. The regional spatial distribution of the eco-environment is primarily in the second-level and third-level areas, and the EEQ in the east and west is better than that in the middle. The contribution of greenness, wetness, and dryness to the improvement of EEQ in the study region increased year by year. (2) From 2001 to 2020, the order of the contribution of the EEQ index in the GBGEZ was dryness, wetness, greenness, and heat. (3) The social and economic activities in the study region had a certain inhibitory effect on the improvement of the EEQ.

Key words

remote sensing eco-environment / spatiotemporal change / remote sensing information granules / remote sensing information granulation / Guangxi Beibu Gulf Economic Zone

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LIAO Weihua, JIANG Weiguo, HUANG Ziqian. Spatiotemporal variations of eco-environment in the Guangxi Beibu Gulf Economic Zone based on remote sensing ecological index and granular computing[J]. Journal of Geographical Sciences, 2022, 32(9): 1813-1830 https://doi.org/10.1007/s11442-022-2024-3

1 Introduction

The Guangxi Beibu Gulf Economic Zone (GBGEZ) is a key area for China’s western development, its opening up and its cooperation with ASEAN. The GBGEZ is the outlet of the primary channel of the new western land-sea channel and is important for China implementing the master strategy of areal development and the mutually beneficial strategy of opening up (Yu and Lim, 2014). The study of spatiotemporal variation in the eco-environment at the regional scale plays a positive role in the sustainable economic development and eco-environment management of the GBGEZ. Earth observation systems via satellite remote sensing have been extensively used in the field of eco-environments due to their macroscopic, rapid, and real-time advantages. These systems have been extensively used in the evaluation and monitoring of eco-environment systems in forests (Yang et al., 2022), grasslands (Wang et al., 2018), cities (Xu et al., 2009; Zhou et al., 2020), and even the entire river basin (Ren et al., 2021). After the RSEI comprehensive evaluation index of remote sensing technology was proposed, many scholars used the RSEI index to evaluate the eco-environment in different areas (Xu et al., 2019; Huang et al., 2021; Wang, 2021; Wang et al., 2021). Using remote sensing techniques can accurately and quickly evaluate the history, present situation, and development trend of the regional eco-environment, which is useful for policy makers to understand the current situation of the areal eco-environment to describe the harmonious and sustainable development of humans and nature in a study region.
Remote sensing eco-environment evaluation is a comprehensive evaluation of a study region using remote sensing ecological indices. The weight of each index has a strong influence on the evaluation result (Liao and Jiang, 2020). The evaluation methods of indices include the subjective weighting method (Saaty and Kearns, 1980), objective weighting method (Chen et al., 2021), and the subjective and objective weighting method. The existing RSEI evaluation studies use principal component analysis to compute the weight, which belongs to the objective weighting method. Although principal component analysis mitigates result deviations caused by weighting, which varies with the individual and methods, principal component analysis is a type of orthogonal transformation with numerical value as the core, which requires many calculations and produces weak explanatory ability (Dikshit-Ratnaparkhi et al., 2019). The histogram composed of pixel values of RSEI describes the pixel value distribution of this index, and all pixels of each value constitute a type of information granule, which conforms to the application category of granule computing. Currently, the calculation methods of improved index weights for granular computing primarily include concept lattices, three-way decisions, and rough sets.
In this study, we assumed that there is clear spatial differentiation in the eco-environment of the GBGEZ, and the overall eco-environment is gradually improving; and the current index weight establishment method of the RSEI in the study region is not beneficial for decision-making cognition and calculation. To test this assumption, we used remote sensing data from 2001-2020 in the GBGEZ to investigate the long-term evaluation of the eco-environment, which is important to eco-environment management. The goals of this study were to (1) use remote sensing data from the Google Earth Engine (GEE) platform to obtain the greenness index, wetness index, heat and dryness index data to build a remote sensing eco-environmental quality (EEQ) assessment framework for the GBGEZ; (2) ameliorate the current evaluation weight establishment method based on numerical numbers, and determine the RSEI weight method based on knowledge granulation entropy; and (3) analyze the correlation between GDP, population and eco-environment in the GBGEZ.

2 Study region and data

2.1 Study region

The GBGEZ is situated in the coastal areas of Southwest China and consists of the administrative regions of Chongzuo, Yulin, Qinzhou, Fangchenggang, Beihai, and Nanning (Figure 1). The land area is 42,500 square kilometers, and it had a population of 24.56 million at the end of 2019. In January 2008, China proposed building the GBGEZ into an important regional and international economic cooperation zone. The GBGEZ is China's first regional and international economic cooperation zone and is intended to become China's fourth economic growth pole. The construction goal of the GBGEZ is to become the China-ASEAN business, cooperation logistics, manufacturing and processing bases, and information exchange center to drive and support the development of the western region of strategically important open highland with economic prosperity, vibrant culture, healthy ecology, social harmony, and an important international economic cooperation area.
Figure 1 Location and administrative divisions of the Guangxi Beibu Gulf Economic Zone

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2.2 Data

The RSEI index data in this study were Landsat 7 and MODIS data for the GBGEZ, all of which were downloaded from the Cloud Computing Website (https://code.earthngine. google.com/), and the effective data time was 2001.01.01-2020.12.31. The greenness, wetness, and dryness were computed from the Landsat 7 dataset. The LST data were downloaded from the QC_Day band of MOD11A2.006 Terra Land Surface Temperature dataset. The LST is a stable product in the MODIS dataset that can be reduced and calculated directly. The greenness, wetness, and dryness index data were reduced with GEE, which took the average pixel value for each index every year. The administrative boundary, population, and GDP spatial data of the study region are from the Resource and Environmental Science and Data Center of the Institute of Geographical Sciences and Natural Resources Research. The detailed data sources and descriptions are shown in Table 1.
Table 1 Brief table of data description
Data item Time Source Dataset provider Resolution
Greenness 2001.01.01-2020.12.31 Landsat 7 Collection 1 Tier 1 and Real-Time data TOA Reflectance USGS/Google 30 m, 60 m
Wetness 2001.01.01-2020.12.31 Landsat 7 Collection 1 Tier 1 and Real-Time data TOA Reflectance USGS/Google 30 m, 60 m
Heat 2001.01.01-2020.12.31 MOD11A2.006 Terra Land Surface Temperature and Emissivity 8-Day Global 1 km NASA LP DAAC at the USGS EROS Center 1200 m
Dryness 2001.01.01-2020.12.31 Landsat 7 Collection 1 Tier 1 and Real-Time data TOA Reflectance USGS/Google 30 m, 60 m
GDP 2001, 2005, 2010, 2015 http://www.resdc.cn/Default.aspx Resource and Environmental Science and Data Center of the Institute of Geographic Sciences and Natural Resources Research 1000 m
Population 2001, 2005, 2010, 2015 http://www.resdc.cn/Default.aspx Resource and Environmental Science and Data Center of the Institute of Geographical Sciences and Natural Resources Research 1000 m

3 Methods

In this study, the evaluation of the RSEI in the GBGEZ was performed using a three-stage framework (Figure 2). First, four indices (dryness, wetness, heat, and greenness) were efficiently calculated based on Landsat images and MODIS images. Second, with the assistance of the concept of granular computing, the remote sensing information was granulated, and the weight of each RSEI index was calculated. Finally, based on the RSEI results, spatial-temporal analysis and related influencing analysis of EEQ in the GBGEZ were performed.
Figure 2 Methodological framework of the remote sensing ecological index (RSEI) analysis in the Guangxi Beibu Gulf Economic Zone during 2001-2020

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3.1 Indices of remote sensing ecological index

The four indices used in the RSEI were wetness, heat, dryness, and greenness, which are often used in evaluating ecological status, are typically associated with eco-environmental status, and can be instantly understood by person (Mishra et al., 2015; Coutts et al., 2016). RSEI indicates that the adjustment of the four indices will cause the transformation of the land surface ecological environment. Accordingly, we can use a four-index function to define the RSEI:
RSEI=f (dryness, greenness, wetness, heat)
(1)
The greenness index is represented by the normalized difference vegetation indicator (NDVI), which is typically calculated using satellite remote sensing data to evaluate the growth status of green vegetation in the study region (De Araujo et al., 2015). The index method is calculated based on the reflection of red and near-infrared light, which can describe productivity, ecosystem vitality, plant growth, and other information. The larger the value, the greater the plant growth. The formula of NDVI is as follows (Rouse et al., 1973):
NDVI=(Nir−Red)(Nir+Red)
(2)
where Red is the pixel value of the red light reflection band and Nir is the pixel value of the near-infrared light reflection band. There is a maximum value of 1 when Red = 0. In contrast, there is a minimum value of -1 when Nir = 0; thus, the output value of the index is between -1.0 and 1.0.
The wetness index is represented by the humidity obtained by remote sensing tasseled cap transformation. The wetness index reflects the humidity of soil, water and vegetation, which is closely related to ecology (Kauth and Thomas, 1976; Huang et al., 2002). For Landsat TM images, the formula of wetness is as follows (Crist and Cicone, 1984):
WET=0.0315Blue+0.2021Green+0.3102Red+0.1594Nir−0.6806Swir1−0.6109Swir2
(3)
where Blue, Green, Swir1 and Swir2 are the blue light reflection, green light reflection, short infrared band 1 reflection, and short infrared band 2 reflection band values of pixels, respectively. The greater the value of wetness is, the greater the contribution to the EEQ.
Dryness was expressed by the NDBI index composed of the index-based built-up index (IBI) (Xu, 2008) and bare soil index (BI) (Rikimaru et al., 2002), which are defined as follows:
BI=(Swir1+Red)−(Nir+Blue)(Swir1+Red)+(Nir+Blue)
(4)
IBI=2Swir2Swir1+Nir−(NirNir+Red+GreenGreen+Swir1)2Swir2Swir1+Nir+(NirNir+Red+GreenGreen+Swir1)
(5)
NDBI=(BI+IBI)/2
(6)
The larger the NDBI value is, the smaller the contribution to the EEQ.
Thermal sensor data of MODIS LST product data represented heat in this study. LST is an important index that is typically used to examine climate change and ecological processes, as well as to study evapotranspiration, drought, surface energy balance, and vegetation density (Rhee et al., 2010; Weng et al., 2014; Xu, 2014).

3.2 Remote sensing image information granulation

Granular computing is an intelligent research method that divides and simplifies complex problems into several simple problems. The key assignment of granular computing is to construct, represent and process information granules (Artiemjew, 2020). Information granulation refers to the decomposition of an object space into many subspaces or the aggregation of individuals in the space into different classes based on useful information and knowledge. These classes are called granules, and the elements in the granules can be described as instances of corresponding concepts (Liang et al., 2015). Remote sensing images are an object space that conforms to the study and computing category of granular computing. Combined with the definition of granular computing, this study defines remote sensing information particles. In a remote sensing image, there are equivalence relations, similarity relations, and close relations among remote sensing pixels, and the set of remote sensing pixels that exhibit a certain relationship is called a remote sensing information particle. The remote sensing image decomposition that creates different spatial unit aggregates according to a certain relationship is called remote sensing image information granulation. For example, in Figure 3, the value of an index (e.g., a land type) of the image composed of 12 remote sensing pixels is {1, 2, 3}. According to the index value equality relationship, the remote sensing image information is granulated into remote sensing pixel information granule 1, remote sensing pixel information granule 2, and remote sensing pixel information granule 3. Using remote sensing information granulation, each index of the remote sensing image can be granulated into different spatial information particles.
Figure 3 Process of spatial information granulation

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Each index of the RSEI is a remote sensing image that is composed of continuous values. Different index values have the corresponding spatial distribution of remote sensing pixels, and all index values and the corresponding remote sensing pixels form the index histogram. For example, Figure 4a shows a spatial distribution map of the greenness index that is composed of continuous values; Figure 4b shows the corresponding histogram of the greenness index, where the ordinate represents the number of spatial pixels of the greenness index value; and Figure 4c shows the spatial remote sensing pixel information granules of several values of the greenness index. Each index of the RSEI can be granulated into different spatial units aggregated through pixel value-histogram-equal relation-remote sensing pixel information granules so that the complex calculation of continuous remote sensing image values can be transformed into a simple granular computing problem.
Figure 4 Granulation process of remote sensing spatial information based on the index attribute value histogram

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3.3 Weight generation for the RSEI

The weight reflects the position of each index in the eco-environmental evaluation index system and represents the contribution of the index itself to the RSEI evaluation. Most existing studies of the areal environmental quality evaluation of the RSEI use principal component analysis to determine the weights (Xu, 2013; Xu et al., 2019; Yue et al., 2019). Principal component analysis does not produce clear results when interpreted as a weighting method but can produce good results compared to high-dimensional evaluation indices. The distribution of remote sensing pixel values is random and uncertain. The greater the uncertainty, the greater the information entropy of the remote sensing index, the greater the amount of information contained, and the smaller the uncertainty, the smaller the information entropy of the remote sensing index, and the smaller the amount of information contained. The amount of remote sensing index information is a measure to judge the randomness and dispersion of the index spatial distribution. The greater the dispersion of the index is, the greater the impact of the index on the comprehensive evaluation of the RSEI. This study used the information entropy weight method to calculate each index weight and uses the spatial distribution of all pixel values of each RSEI index to determine the index weight. The relevant formula is as follows:
Er=−ln(n)−1∑i=1npijln⁡(pij)
(7)
where Er is the value of information entropy for one index, n is the number of distinct numbers in each RSEI index, and pij is the probability value of each pixel value of remote sensing pixel information particles in all remote sensing pixel information particles in all indices. The pixels with different pixel values in each index constitute remote sensing information granules. In this study, combined with the concept of knowledge granularity and knowledge granularity entropy in granular computing, a method to establish RSEI weights based on remote sensing pixel values and remote sensing spatial information particles was proposed. In the evaluation of the RSEI, evaluation indices are refined into different hierarchical partitions according to different pixel values. Different zones and levels were subdivided by different pixel values for the evaluation of the RSEI. The knowledge granularity is a type of value of this type of zone variation, which can be calculated by the following formula (Liang et al., 2004):
Gk(R)=∑i=1n⁡|Xi|2|U|2
(8)
where Gk is the knowledge granularity, |Xi|   is the number of samples of remote sensing pixel information particles for each pixel value, |U|   is the total number of samples in the area, and n is the number of pixel values for each index in an area (i.e., the total number of remote sensing pixel information particles). The smaller the value of knowledge granularity is, the stronger the resolution of the index; otherwise, the larger the value of knowledge granularity is, the weaker the resolving ability of the index. The knowledge granularity entropy based on knowledge granularity is defined as follows:
Er(R)=k∑i=1n | Xi |  | U | log2|Xi|
(9)
where k = 1/log2 |U|   , Er(R) is the knowledge granularity entropy of the index. The smaller the value of index knowledge granularity entropy, the finer it divides the evaluation area, the greater its contribution to RSEI, and the stronger its resolution. According to the law of the entropy weight method and the knowledge granularity entropy of every index, the weight formula of every index in the RSEI is defined as follows:
W(Ci)=1−Eri(R)m−∑i=1m⁡Eri(R)
(10)
where m is the number of indices and W(Ci) is the final weight value of each index.

4 Results

4.1 Analysis of the index

In this study, the spatiotemporal variations in dryness, wetness, and greenness were calculated from 2001 to 2020. From 2001 to 2020, the maximum greenness mean value of each year in the GBGEZ was 0.3568, which occurred in 2013, and the minimum greenness mean value of each year was 0.257, which occurred in 2003. From 2001 to 2020, the greenness mean value of each year showed a growth trend, and the greenness contribution to EEQ increased year by year, as shown in Figure 5. The maximum standard deviation of greenness was 0.0949, which occurred in 2011, and the minimum standard deviation was 0.0502, which occurred in 2002. The standard deviation showed an increasing trend, and the spatial difference in greenness in the study region increased year by year, as detailed in Figure 6. The GBGEZ considered ecological and environmental protection during the study period; the vegetation coverage in GBGEZ showed an increasing trend; and the increased area of vegetation in all local regions levels is greater than the reduced area of vegetation, indicating that in the process of economic development, local municipal governments paid attention to environmental protection, put the ecological environment in the first place, and encourage people to perform afforestation and returning farmland to forest activities. Also, the process had effectively implemented the national policy of ecological civilization construction.
Figure 5 Mean variations in greenness, dryness, and wetness in the Guangxi Beibu Gulf Economic Zone

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Figure 6 Standard deviation (Std) variations of greenness, dryness, and wetness in the Guangxi Beibu Gulf Economic Zone

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From 2001 to 2020, the maximum dryness mean value of each year in the GBGEZ was -0.0987, which occurred in 2020, and the minimum dryness mean value of each year was -0.1625, which occurred in 2003. From 2001 to 2020, the dryness mean value of every year in the GBGEZ showed a decreasing trend, the surface dryness decreased year by year, and the contribution of dryness to the EEQ increased year by year, as shown in Figure 5. The maximum standard deviation of dryness was 0.0632, which occurred in 2019, and the minimum standard deviation was 0.0322, which occurred in 2001. The standard deviation showed an increasing trend, and the spatial difference in dryness in the study region increased year by year (Figure 6). In the early study period of GBGEZ, the rural residential area occupied a lot of land, and construction land was extensive, which posed a certain threat to the maintenance of the regional ecological environment, particularly the coastal ecological environment. With the continuous promotion of urban-rural integration, the intensity of construction in the study area increased. During urban construction, increasing attention has been given to the restoration of the ecological environment.
From 2001 to 2020, the maximum wetness mean value of each year in the GBGEZ was -0.0847, which occurred in 2013, and the minimum wetness mean value of each year was -0.1132, which occurred in 2003. From 2001 to 2020, the wetness mean value of each year in the GBGEZ showed a decreasing trend, while the land surface wetness increased from year to year, and the contribution of wetness to the EEQ increased, as shown in Figure 5. The maximum standard deviation of wetness was 0.0412, which occurred in 2012, and the minimum standard deviation was 0.0267, which occurred in 2002. The standard deviation showed an increasing trend, and the spatial difference in wetness in the study region increased year by year (Figure 6). Compared with greenness and dryness, the change in wetness in the GBGEZ slowed. The GBGEZ once encouraged the planting of eucalyptus and other fast-growing forests. Sparse forestland and high coverage grassland were planted into timber forest and economic forest, which had a certain impact on soil water conservation. With the gradual cancellation of the subsidy policy for fast-growing forests, the soil water conservation capacity has been gradually improved.
From 2001 to 2020, the maximum heat mean value of each year in the GBGEZ was 21.0143, which occurred in 2009, and the minimum heat mean value of each year was 15.1025, which occurred in 2002. From 2001 to 2020, the mean heat value every year in the GBGEZ showed a marginal decreasing trend. The maximum standard deviation of heat was 6.5516, which occurred in 2003, and the minimum standard deviation was 4.9479, which occurred in 2002. The standard deviation showed an increasing trend, and the spatial difference in heat in the study region increased year by year, as shown in Figure 7.
Figure 7 Standard deviation (Std) and mean variations in heat in the Guangxi Beibu Gulf Economic Zone

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4.2 Weight and ecological zoning

This study performed a comprehensive evaluation of the GBGEZ over five years: 2001, 2005, 2010, 2015, and 2020. In these five evaluations, the granularity entropy of the heat index is the largest, the spatial differentiation of the heat index in the study region is relatively small, its contribution to the EEQ is the smallest, and its weight is relatively small. The overall dryness granularity entropy is the smallest, and the spatial differentiation of the dryness index in the study region is relatively large, which contributes the most to the EEQ and has a relatively large weight. The “dryness” caused by economic activities in the study region has a strong effect on the EEQ. From 2001 to 2020, the contribution order of the RSEI indices for the eco-environment in the GBGEZ was dryness, wetness, greenness, and heat (Table 2). The detailed values of knowledge granulation entropy and the weight value of every index in 2001, 2005, 2010, 2015, and 2020 are listed in Table 2. The change in land use mode and the change in soil water conservation brought by economic forest had the strongest impact on the eco-environmental quality of the GBGEZ, and the vegetation coverage in the study area was high.
Table 2 Granularity entropy and weight of the ecological remote sensing index in different years in the Guangxi Beibu Gulf Economic Zone
Year Greenness Dryness Wetness Heat
2001 Granularity entropy 0.7831 0.7695 0.7776 0.8102
weight 0.2524 0.2682 0.2587 0.2208
2005 Granularity entropy 0.7997 0.7819 0.8013 0.8282
weight 0.2539 0.2765 0.2518 0.2178
2010 Granularity entropy 0.7916 0.7742 0.7962 0.8262
weight 0.2567 0.2782 0.2510 0.2140
2015 Granularity entropy 0.8005 0.7830 0.8029 0.8316
weight 0.2551 0.2775 0.2520 0.2154
2020 Granularity entropy 0.7928 0.8139 0.7997 0.8275
weight 0.2705 0.2429 0.2614 0.2252
During the 2001-2020 period, the mean ecological remote sensing quality in the GBGEZ did not change much but showed a marginal upward trend. The maximum value is 0.8120, which occurred in 2001, and the minimum value is 0.1151, which occurred in 2001. The difference between each year was low, and the spatial trend fluctuates. The maximum mean was 0.4110, which occurred in 2005, and the minimum mean was 0.3937, which occurred in 2020. The mean value slowly increased and declined slowly, as shown in Table 3. The overall fluctuation of ecological remote sensing quality in the GBGEZ is thus weak. During the study period, ecological damage behaviors such as soil erosion, desertification, desertification, sharp decline of forests, land degradation and reduction of biodiversity were rare.
Table 3 Statistical information about ecological remote sensing quality in the Guangxi Beibu Gulf Economic Zone in different years
Year Max Min Mean Standard deviation
2001 0.8120 0.1151 0.3991 0.0468
2005 0.7591 0.1391 0.4110 0.0416
2010 0.8062 0.1411 0.4107 0.0523
2015 0.7469 0.1475 0.4102 0.0408
2020 0.7355 0.1347 0.3937 0.0441
From 2001 to 2020, the minimum and maximum means were considered to be the upper and lower limits of the eco-environmental zoning, and the GBGEZ was divided into five ecological zones. The GBGEZ is primarily composed of second- and third-level ecological zones. The first-level ecological zones with extremely poor ecological conditions and the fourth- and fifth-level ecological zones with excellent ecological conditions account for a small proportion, and these areas typically are at higher altitudes, have higher temperatures, and exhibit slow soil development and fragile ecological environment. The overall proportion of second- and third-level ecological zones remained relatively constant, and the spatial difference was not apparent overall, as shown in Table 4. The first-level ecological areas are scattered in some areas in northwestern Nanning and northeastern Chongzuo. The second-level ecological areas are distributed in the middle of the GBGEZ, which primarily consists of cities, farmland, and other economically active areas. The third-level ecological areas are distributed in the east and west of the GBGEZ, and the west and east are primarily mountainous areas. In terms of spatial distribution, the EEQ of the GBGEZ in the west and east was better than that in the middle, as shown in Figure 8.
Table 4 Area percentage values of ecological region levels according to the RSEI in the Guangxi Beibu Gulf Economic Zone in different years (%)
Year Level of ecological zones
1 2 3 4 5
2001 0.0014 0.4521 0.5406 0.0057 0.0002
2005 0.0006 0.3305 0.6639 0.005 0
2010 0.0047 0.4895 0.4986 0.0073 0
2015 0.0002 0.3351 0.6596 0.0051 0
2020 0.0026 0.4878 0.5065 0.0032 0
Figure 8 RSEI spatial distribution of the Guangxi Beibu Gulf Economic Zone in 2001, 2005, 2010, 2020 (the types in the legend corresponds to Table 4)

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4.3 Spatial transfer

From 2001 to 2020, the transformation of the eco-environment in the GBGEZ was primarily between levels 2 and 3, and 3 and 4. In general, there are more areas of improved remote sensing EEQ than areas of degraded EEQ. Remote sensing eco-environment degradation areas are distributed in the east and west, while remote sensing ecological environment improvement areas are distributed in the middle of the study region. The remote sensing EEQ in the GBGEZ improved markedly from 2010-1015, as shown in Figure 9.
Figure 9 RSEI Spatial transfer distributions in the Guangxi Beibu Gulf Economic Zone (the types in the legend corresponds to Table 4)

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From 2001 to 2005, 27.446% of the EEQ results of the GBGEZ were converted from level 2 to level 3; 15.269% of the regions were converted from level 3 to level 2; and at this stage, the EEQ was converted to the direction of ecological environment improvement. From 2005 to 2010, 12.412% of the EEQ results of the GBGEZ were converted from level 2 to level 3; 28.471% of the regions were converted from level 3 to level 2; and the EEQ was converted to the direction of ecological environment degradation. From 2010 to 2015, 25.677% of the EEQ results of the GBGEZ were converted from level 2 to level 3; 9.93% of the regions were converted from level 3 to level 2; and the EEQ was converted to the direction of ecological environment improvement. From 2015 to 2020, 11.104% of the EEQ results of the GBGEZ were converted from level 2 to level 3; 26.549% of the regions were converted from level 3 to level 2; and the EEQ was converted to the direction of ecological environment degradation. Detailed values are listed in Table 5.
Table 5 Spatial transformation matrix of the RSEI in the Guangxi Beibu Gulf Economic Zone (%)
2001-2005 2005-2010
1 2 3 4 5 1 2 3 4 5
1 0.000 0.072 0.066 0.000 0.000 0.000 0.052 0.007 0.000 0.000
2 0.042 17.698 27.446 0.024 0.000 0.212 20.407 12.412 0.014 0.000
3 0.018 15.269 38.568 0.205 0.000 0.255 28.470 37.218 0.445 0.001
4 0.000 0.006 0.308 0.254 0.001 0.000 0.015 0.219 0.269 0.001
5 0.000 0.000 0.000 0.021 0.001 0.000 0.000 0.000 0.002 0.000
2010-2015 2015-2020
1 2 3 4 5 1 2 3 4 5
1 0.001 0.331 0.135 0.000 0.000 0.000 0.010 0.005 0.000 0.000
2 0.012 23.246 25.677 0.011 0.000 0.188 22.212 11.104 0.007 0.000
3 0.002 9.930 39.745 0.178 0.000 0.072 26.548 39.194 0.149 0.000
4 0.000 0.004 0.406 0.320 0.001 0.000 0.006 0.343 0.161 0.000
5 0.000 0.000 0.000 0.002 0.000 0.000 0.000 0.000 0.001 0.000

4.4 Analysis of the relationship with GDP and population

Population and GDP are closely related to EEQ. For data reasons, this study used the Pearson correlation coefficient to study the relationship between population, GDP, and the RSEI in the GBGEZ in 2001, 2005, 2010, and 2015. The population density and GDP of the GBGEZ increased during the study period. The population and GDP in the city and surrounding areas are larger than those in the mountainous and coastal areas, and the central part is larger than the eastern, western, and northern areas. In 2001-2015, there was a negative correlation coefficient value between population, GDP, and RSEI (Table 6). Population and GDP inhibited the improvement of the EEQ in the GBGEZ to a certain extent.
Table 6 Correlation between population, GDP, and the RSEI in the Guangxi Beibu Gulf Economic Zone
Year GDP Population
2001 -0.1943 -0.2333
2005 -0.2249 -0.3553
2010 -0.2283 -0.3115
2015 -0.1013 -0.1485
Because the economic growth of the GBGEZ still belonged to the resource-dependent growth mode, the improvement of resource use efficiency cannot offset the increase in the demand for natural resources in economic development. Therefore, the ecological environment was not well protected during social and economic development. The GBGEZ should rely on advanced technical means to continuously promote the resource-saving production mode, improve regional green competitiveness and practice the concept of sustainable development. The correlation coefficient value first increased and then decreased, indicating that the government of the study region is paying increasing attention to the management of eco-environment quality while developing the economy.

5 Discussion

5.1 Improvement of methods

In existing studies of spatiotemporal evaluation of RSEI, most weights were determined by principal component analysis (Xiong et al., 2021; Ning et al., 2020). Principal component analysis is a type of orthogonal transformation calculation based on the continuous value of the index, which requires a lot of computational resources and is difficult to explain. The RSEI spatiotemporal evaluation based on remote sensing pixel information granulation proposed in this paper granulates the remote sensing image information into different remote sensing spatial information granules according to the index values. This method preserves the information of all the index values, avoids the information profit and loss caused by information granulation (Liao and Jiang, 2020), makes calculations easier, and exhibits strong interpretability. Through the granules and granulation of remote sensing information, complex spatial computing problems can be abstracted and divided into several simpler problems to simplify the spatial computing problems of remote sensing and improve the efficiency of solving the problems. As a form of remote sensing spatial information, remote sensing information granules have become a basic concept for researchers to process and perceive complex spatial information. Remote sensing EEQ is the result that is directly driven by each index. Based on the evaluation result of the RSEI, existing studies randomly sampled a certain number of spatial sample points in the study region to analyze the impact of each index on the remote sensing eco-environment (Cheng and He, 2019; Song et al., 2019). The evaluation framework of this study, which considers all remote sensing spatial pixels and index values, can directly measure the magnitude and ranking of the impact of evaluation indices on the remote sensing eco-environment.

5.2 Implications for policy making

The GBGEZ government must establish ecological perspectives and ecological values and construct a land and space planning system from the perspective of ecological civilization epistemology. Ecological environmental monitoring must be expanded from past monitoring primarily on construction land to cultivated land, forest, wetland, water area, mangroves, and other categories, and a satellite remote sensing monitoring system with all elements, all-weather, all-time, and all-scale landscapes, forests, lakes, and grass should be established. Ecological environment monitoring should be converted from quantity monitoring to quality monitoring. Concurrently, time-series observations should be performed, and the change in ecological status and the analysis of ecosystem function should be investigated and monitored by integrating the factors of geography, climate, economy and humanity. The GBGEZ should strengthen the construction of the concept of regional integrated development of the ecological environment and promote the construction of the guarantee system of coordinated governance of ecological environment.

5.3 Limitations and future research

Due to the limitation of remote sensing data, this study only calculated the spatiotemporal evaluation of the RSEI in the GBGEZ from 2001 to 2020 and did not calculate the RSEI of the study region with longer time series. Although the RSEI based on the RSEI can quickly and accurately evaluate the regional EEQ, more testing is required to determine whether the four indices can fully describe the EEQ of the study region. This study only calculated the correlation between GDP, population density, and the RSEI and did not calculate more related factors to find the driving factors of eco-environmental change in the GBGEZ. In future studies, the discovery of an association between land-use change and RSEI change should be found in combination with land-use change.

6 Conclusions

This paper uses the idea of spatial information granulation to calculate the RSEI evaluation problem, and establishes their own weight from the spatial distribution of each RSEI index value. This method is a new perspective to think about the calculation of remote sensing spatial data, and it is the concrete embodiment of the method of geographical stratification and scale transformation, and there are three primary findings in this study:
(1) By introducing granular computing, the definitions of remote sensing information granules and information granulation are proposed. The granulation method of remote sensing image information is based on the continuous value of indices, and the weight determination method based on the knowledge granularity entropy is proposed according to the definitions. This method allows each remote sensing pixel and each index value to participate in the calculation and is simpler and easier to understand than existing RSEI evaluation methods.
(2) From 2001 to 2020, the EEQ in the GBGEZ tended to improve, and the spatial difference tended to expand. The spatial distribution of the eco-environment is primarily composed of second- and third-level ecological zones, and the EEQ in the east and west is better than that in the middle.
(3) During 2001-2020, the social and economic activities in the study region had a specific inhibitory effect on the improvement of the EEQ. However, the government of GBGEZ was paying increasing attention to the governance of the eco-environment while developing the economy.

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis
[1]
Artiemjew P, 2020. About granular rough computing: Overview of decision system approximation techniques and future perspectives. Algorithms, 13(4): 79.
https://doi.org/10.3390/a13040079
https://www.mdpi.com/1999-4893/13/4/79
Cited in this article [1]
[2]
Chen Z G, Lu M, Zhou Y M, 2021. Information synergy entropy based multi-feature information fusion for the operating condition identification in aluminium electrolysis. Information Sciences, 548: 275-294.
https://doi.org/10.1016/j.ins.2020.07.031
https://linkinghub.elsevier.com/retrieve/pii/S0020025520306915
Cited in this article [1]
[3]
Cheng Z F, He Q S, 2019. Remote sensing evaluation of the ecological environment of Su-Xi-Chang city group based on remote sensing ecological index (RSEI). Remote Sensing Technology and Application, 34(3): 531-539. (in Chinese)
Cited in this article [1]
[4]
Coutts A M, Harris R J, Phan T et al., 2016. Thermal infrared remote sensing of urban heat: Hotspots, vegetation, and an assessment of techniques for use in urban planning. Remote Sensing of Environment, 186: 637-651.
https://doi.org/10.1016/j.rse.2016.09.007
https://linkinghub.elsevier.com/retrieve/pii/S0034425716303509
Cited in this article [1]
[5]
Crist E P, Cicone R C, 1984. A physically-based transformation of Thematic Mapper data: The TM Tasseled Cap. IEEE Transactions on Geoscience and Remote Sensing, 22(3): 256-263.
Cited in this article [1]
[6]
De Araujo Barbosa C C, Atkinson P M, Dearing J A, 2015. Remote sensing of ecosystem services: A systematic review. Ecological Indicators, 52: 430-443.
https://doi.org/10.1016/j.ecolind.2015.01.007
https://linkinghub.elsevier.com/retrieve/pii/S1470160X15000084
Cited in this article [1]
[7]
Dikshit-Ratnaparkhi A, Bormane D, Ghongade R A, 2019. A framework for optimal attribute evaluation and selection in hesitant fuzzy environment based on enhanced ordered weighted entropy approach for medical dataset. Journal of biomedical Physics and Engineering, 9(3): 327-334.
https://doi.org/10.31661/jbpe.v0i0.1033
https://www.ncbi.nlm.nih.gov/pubmed/31341878
Cited in this article [1] Abstract
In this paper, a generic hesitant fuzzy set (HFS) model for clustering various ECG beats according to weights of attributes is proposed. A comprehensive review of the electrocardiogram signal classification and segmentation methodologies indicates that algorithms which are able to effectively handle the nonstationary and uncertainty of the signals should be used for ECG analysis. Extensive research that focuses on incorporating vagueness in the form of fuzzy sets, fuzzy rough sets and hesitant fuzzy sets (HFS) has been in past decades.The paper aims to develop an enhanced entropy based on the clustering technique for calculating the weights of the attributes to finally generate appropriately clustered attributes.Finding optimal attributes to make a decision has always been a matter of concern for the researchers. Metrics used for optimal attribute generation can be broadly classified into mutual dependency, similarity, correlation and entropy based metrics in fuzzy domain.The experimentation has been carried out on ECG dataset in a hesitant fuzzy framework with four attributes.We propose a novel correlation based on an algorithm that takes entropy based weighted attributes as input which effectively generates a relevant and non-redundant set of attributes. We have also derived correlation coefficient formulas for HFSs and applied them to clustering analysis under framework of hesitant fuzzy sets. The results show the comparison of the proposed mathematical model with the existing similarity based on algorithms.The selection of optimal relevant attributes certainly highlights the robustness and efficacy of the proposed approach. The entire experimentation and comparative results help us conclude that selection of optimal attributes in hesitant fuzzy domain certainly prove to be a powerful tool in order to express uncertainty in the process of data acquisition and classification.
[8]
Huang C Q, Wylie B K, Yang L M et al., 2002. Derivation of a tasselled cap transformation based on Landsat 7 at-satellite reflectance. International Journal of Remote Sensing, 23(8): 1741-1748.
https://doi.org/10.1080/01431160110106113
https://www.tandfonline.com/doi/full/10.1080/01431160110106113
Cited in this article [1]
[9]
Huang H, Chen W, Zhang Y et al., 2021. Analysis of ecological quality in Lhasa Metropolitan Area during 1990-2017 based on remote sensing and Google Earth Engine platform. Journal of Geographical Sciences, 31(2): 265-280.
https://doi.org/10.1007/s11442-021-1846-8
Cited in this article [1] Abstract

Based on a total of 519 images, the composite images with the lowest possible cloud cover were generated at pixel level with image synthesis method on Google Earth Engine (GEE) platform. The Remote Sensing Ecological Index (RSEI) was adopted, and calculated in an efficient way with the assistance of parallel cloud computing of the GEE platform. The RSEI was used in this paper to evaluate and monitor the eco-environmental quality of the Lhasa Metropolitan Area. Results show that: (1) The ecological quality is better in the west than in the east of Lhasa Metropolitan Area, with Lhasa as an approximate dividing point. The ecological quality improved and then deteriorated dramatically before 2000, with the mean RSEI value dropping from 0.51 to 0.46; the trend was followed by a gradual increase up until 2017, with the mean RSEI value increased from 0.46 to 0.55. (2) The RSEI is weakly and positively correlated with socioeconomic indicators. This indicates that the population growth and economic development did not negatively influence the ecological quality, but actually boosted it. (3) The GEE can serve as an efficient computing platform for the assessment and monitoring of eco-environmental quality in vast regions.
[10]
Kauth R J, Thomas G S, 1976. The tasseled cap graphic description of the spectral-temporal development of agricultural crops as seen in Landsat. In:Proceedings of the Symposium on Machine Processing of Remotely Sensed Data, West Lafayette, IN, USA, 29 June-1 July 1976; 41-51.
Cited in this article [1]
[11]
Li N, Wang J Y, Qin F, 2020. The improvement of ecological environment index model RSEI. Arabian Journal of Geosciences, 13: 403.
https://doi.org/10.1007/s12517-020-05414-7
https://doi.org/10.1007/s12517-020-05414-7
[12]
Liang J Y, Qian Y H, LI D Y et al., 2015. Theory and method of granular computing for big data mining. Scientia Sinica Informationis, 45(11): 1355-1369. (in Chinese)
https://doi.org/10.1360/N112015-00092
http://engine.scichina.com/doi/10.1360/N112015-00092
Cited in this article [1]
[13]
Liang J Y, Shi Z Z, 2004. The information entropy, rough entropy and knowledge granulation in rough set theory. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 12(1): 37-46.
https://doi.org/10.1142/S0218488504002631
https://www.worldscientific.com/doi/abs/10.1142/S0218488504002631
Cited in this article [1]
[14]
Liao W H, Jiang W G, 2020. Evaluation of the spatiotemporal variations in the eco-environmental quality in China based on the remote sensing ecological index. Remote Sensing, 12(15): 2462.
https://doi.org/10.3390/rs12152462
https://www.mdpi.com/2072-4292/12/15/2462
Cited in this article [2]
[15]
Mishra N B, Crews K A, Thoralf N M et al., 2015. MODIS derived vegetation greenness trends in African Savanna: Deconstructing and localizing the role of changing moisture availability, fire regime anthropogenic impact. Remote Sensing of Environment, 169: 192-204.
https://doi.org/10.1016/j.rse.2015.08.008
https://linkinghub.elsevier.com/retrieve/pii/S0034425715300961
Cited in this article [1]
[16]
Ren Y S, Lu L, Yu H et al., 2021. Game strategies in government-led eco-compensation in the Xin’an River Basin from the perspective of the politics of scale. Journal of Geographical Sciences, 31(8): 1205-1221.
https://doi.org/10.1007/s11442-021-1893-1
https://doi.org/10.1007/s11442-021-1893-1
Cited in this article [1]
[17]
Rhee J, Im J, Carbone G J, 2010. Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sensing of Environment, 114(12): 2875-2887.
https://doi.org/10.1016/j.rse.2010.07.005
https://linkinghub.elsevier.com/retrieve/pii/S003442571000221X
Cited in this article [1]
[18]
Rikimaru A, Roy P S, Miyatake S, 2002. Tropical forest cover density mapping. Tropical Ecology, 43(1): 39-47.
Cited in this article [1]
[19]
Rouse J W, Haas R H, Schell J A et al., 1973. Monitoring vegetation systems in the Great Plains with ERTS. In:Proceedings of the Third ERTS Symposium, NASA SP-351, Washington, DC, USA, 10-14 December 1973;309-317.
Cited in this article [1]
[20]
Saaty T L, Kearns K P, 1980. The Analytic Hierarchy Process. New York: McGraw-Hill.
Cited in this article [1]
[21]
Shahrood A J, Menberu M W, Darabi H et al., 2020. RiMARS: An automated river morphodynamics analysis method based on remote sensing multispectral datasets. Science of The Total Environment, 719: 137336.
https://doi.org/10.1016/j.scitotenv.2020.137336
https://linkinghub.elsevier.com/retrieve/pii/S0048969720308469
[22]
Song M J, Luo Y Y, Duan L M, 2019. Evaluation of ecological environment in the Xinlin Gol steppe based on modified remote sensing ecological index model. Arid Zone Research, 36(6): 1521-1527. (in Chinese)
Cited in this article [1]
[23]
Wang J, Liu D, Ma J et al., 2021. Development of a large-scale remote sensing ecological index in arid areas and its application in the Aral Sea Basin. Journal of Arid Land, 13(1): 40-55.
https://doi.org/10.1007/s40333-021-0052-y
Cited in this article [1] Abstract

The Aral Sea Basin in Central Asia is an important geographical environment unit in the center of Eurasia. It is of great significance to the ecological protection and sustainable development of Central Asia to carry out dynamic monitoring and effective evaluation of the eco-environmental quality of the Aral Sea Basin. In this study, the arid remote sensing ecological index (ARSEI) for large-scale arid areas was developed, which coupled the information of the greenness index, the salinity index, the humidity index, the heat index, and the land degradation index of arid areas. The ARSEI was used to monitor and evaluate the eco-environmental quality of the Aral Sea Basin from 2000 to 2019. The results show that the greenness index, the humidity index and the land degradation index had a positive impact on the quality of the ecological environment in the Aral Sea Basin, while the salinity index and the heat index exerted a negative impact on the quality of the ecological environment. The eco-environmental quality of the Aral Sea Basin demonstrated a trend of initial improvement, followed by deterioration, and finally further improvement. The spatial variation of these changes was significant. From 2000 to 2019, grassland and wasteland (saline alkali land and sandy land) in the central and western parts of the basin had the worst ecological environment quality. The areas with poor ecological environment quality are mainly distributed in rivers, wetlands, and cultivated land around lakes. During the period from 2000 to 2019, except for the surrounding areas of the Aral Sea, the ecological environment quality in other areas of the Aral Sea Basin has been improved in general. The correlation coefficients between the change in the eco-environmental quality and the heat index and between the change in the eco-environmental quality and the humidity index were -0.593 and 0.524, respectively. Climate conditions and human activities have led to different combinations of heat and humidity changes in the eco-environmental quality of the Aral Sea Basin. However, human activities had a greater impact. The ARSEI can quantitatively and intuitively reflect the scale and causes of large-scale and long-time period changes of the eco-environmental quality in arid areas; it is very suitable for the study of the eco-environmental quality in arid areas.
[24]
Wang L, Yu H Y, Zhang Q et al., 2018. Responses of aboveground biomass of alpine grasslands to climate changes on the Qinghai-Tibet Plateau. Journal of Geographical Sciences, 28(12): 1953-1964.
https://doi.org/10.1007/s11442-019-1573-y
Cited in this article [1] Abstract

Aboveground biomass in grasslands of the Qinghai-Tibet Plateau has displayed an overall increasing trend during 2003-2016, which is profoundly influenced by climate change. However, the responses of different biomes show large discrepancies, in both size and magnitude. By applying partial least squares regression, we calculated the correlation between peak aboveground biomass and mean monthly temperature and monthly total precipitation in the preceding 12 months for three different grassland types (alpine steppe, alpine meadow, and temperate steppe) on the central and eastern Qinghai-Tibet Plateau. The results showed that mean temperature in most preceding months was positively correlated with peak aboveground biomass of alpine meadow and alpine steppe, while mean temperature in the preceding October and February to June was significantly negatively correlated with peak aboveground biomass of temperate steppe. Precipitation in all months had a promoting effect on biomass of alpine meadow, but its correlations with biomass of alpine steppe and temperate steppe were inconsistent. It is worth noting that, in a warmer, wetter climate, peak aboveground biomass of alpine meadow would increase more than that of alpine steppe, while that of temperate steppe would decrease significantly, providing support for the hypothesis of conservative growth strategies by vegetation in stressed ecosystems.
[25]
Wang Y, 2021. Evaluation of lake wetland ecotourism resources based on remote sensing ecological index. Arabian Journal of Geosciences, 14(7): 559.
https://doi.org/10.1007/s12517-021-06892-z
https://doi.org/10.1007/s12517-021-06892-z
Cited in this article [1]
[26]
Weng Q H, Fu P, Gao F, 2014. Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data. Remote Sensing of Environment, 145: 55-67.
https://doi.org/10.1016/j.rse.2014.02.003
https://linkinghub.elsevier.com/retrieve/pii/S0034425714000479
Cited in this article [1]
[27]
Xiong Y, Xu W H, Lu N et al., 2021. Assessment of spatial-temporal changes of ecological environment quality based on RSEI and GEE: A case study in Erhai Lake Basin, Yunnan province, China. Ecological Indicators, 125: 107518.
https://doi.org/10.1016/j.ecolind.2021.107518
https://linkinghub.elsevier.com/retrieve/pii/S1470160X21001837
Cited in this article [1]
[28]
Xu H Q, 2008. A new index for delineating built-up land features in satellite imagery. International Journal of Remote Sensing, 29(14): 4269-4276.
https://doi.org/10.1080/01431160802039957
https://www.tandfonline.com/doi/full/10.1080/01431160802039957
Cited in this article [1]
[29]
Xu H Q, 2013. A remote sensing urban ecological index and its application. Acta Ecologica Sinca, 33(24): 7853-7862. (in Chinese)
Cited in this article [1]
[30]
Xu H Q, 2014. Dynamic of soil exposure intensity and its effect on thermal environment change. International Journal of Climatology, 34(3): 902-910.
https://doi.org/10.1002/joc.3738
https://onlinelibrary.wiley.com/doi/10.1002/joc.3738
Cited in this article [1]
[31]
Xu H, Wang Y, Guan H et al., 2019. Detecting ecological changes with a remote sensing based ecological index (RSEI) produced time series and change vector analysis. Remote Sensing, 11(20): 2345.
https://doi.org/10.3390/rs11202345
https://www.mdpi.com/2072-4292/11/20/2345
Cited in this article [2]
[32]
Xu H Q, Ding F, Wen X L, 2009. Urban expansion and heat island dynamics in the Quanzhou region, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2(2):74-79.
https://doi.org/10.1109/JSTARS.2009.2023088
https://ieeexplore.ieee.org/document/5061500/
Cited in this article [1]
[33]
Yang X H, Jin X B, Yang Y K et al., 2022. Spatially explicit changes of forestland in Taiwan Province from 1910 to 2010. Journal of Geographical Sciences, 32(3): 441-457.
https://doi.org/10.1007/s11442-022-1956-y
Cited in this article [1] Abstract

Reconstructing long-term changes of forest cover (FC) can provide reliable underlying data for carbon source/sink accounting and simulation of the effects of land use on the climate and environment. Historical maps contain a wealth of forest related information and can provide first-hand data for studying the changes in FC over a long time period. Taking the reconstruction of FC in Taiwan Province from 1910-2010 as the research object, we used map extraction and mining methods to extract forest distribution information from historical forest thematic survey maps, topographic and land cover maps, and reconstructed the spatiotemporal patterns of FC in Taiwan from 1910-2010. The results show that: first, the relative bias of the FC area extracted from the historical maps of Taiwan was below 8%, meaning the FC information contained in maps is highly accurate. Second, the FC of Taiwan has generally declined in the past 100 years. From 1910-2010, the forest area declined from 2.62×106 ha to 2.47×106 ha, with relatively obvious forest reduction having occurred. In stages, the forest area of Taiwan decreased by 26.39×104 ha from 1910 to 1950; and increased by 10.53×104 ha during the period 1950-2010. Forest reduction was obvious during the Japanese occupation period, while forest increase was remarkable during the Kuomintang period. Third, during the study period, the total area of patches maintained as forests was 2.17×106 ha with little change in the overall pattern, and forests were mainly distributed in the mountain regions. The loss of forest mainly occurred in the plains, with expanding forest areas mainly in the mountain regions at high elevations and on steep slopes. Fourth, land clearing for agriculture during the Japanese occupation period has been the important driver of forest reduction in Taiwan over the past century. After retreated to Taiwan, the Kuomintang government introduced effective and remarkable reforms that led to effective restoration of forest vegetation in many areas where forests had previously disappeared.
[34]
Yu H, Lim W X, 2014. The Guangxi Beibu Gulf Economic Zone and China’s Maritime Silk Road in the 21st century. East Asian Policy, 6(3): 16-29.
https://doi.org/10.1142/S1793930514000233
https://www.worldscientific.com/doi/abs/10.1142/S1793930514000233
Cited in this article [1]
[35]
Yue H, Liu Y, Li Y et al., 2019. Eco-environmental quality assessment in China’s 35 major cities based on remote sensing ecological index. IEEE Access, 7: 51295-51311.
https://doi.org/10.1109/ACCESS.2019.2911627
https://ieeexplore.ieee.org/document/8692417/
Cited in this article [1]
[36]
Zhou W, Ming D P, Lv X W et al., 2020. SO-CNN based urban functional zone fine division with VHR remote sensing image. Remote Sensing of Environment, 236: 111458.
https://doi.org/10.1016/j.rse.2019.111458
https://linkinghub.elsevier.com/retrieve/pii/S0034425719304778
Cited in this article [1]

Funding

Guangxi Natural Science Foundation(2020GXNSFAA297176)
National Natural Science Foundation of China(U21A2022)
National Natural Science Foundation of China(42101369)
Youth Teacher Scientific Research Ability Improvement Project of Guangxi(2021KY0393)
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