Land use mapping and accuracy assessment
According to the land use planning map of Zhuhai city, the characteristics of the city, the status of human activities and land use, and the types of natural ecosystems, we identified and categorized land use into 10 types: woodland, grassland, rainfed cropland, paddy fields, aquaculture areas, reservoirs and pit ponds, tidal flats, rivers and shallow water, built-up land and unutilized land (Supplemental Materials S1: Land use types and descriptions). The ecological land types include woodland, grassland, reservoirs and pit ponds, tidal flats, and rivers and shallow water. Rainfed cropland, paddy fields and aquaculture areas were not included as ecological land types because they are agricultural land mainly used for agricultural production. These land use types are greatly disturbed by humans, their ecological functions are very fragile, and they are affected by economic interests and have low ecological value. Unutilized land provides few ecological benefits and may be converted into built-up land in the short term; thus, its ecological benefits are unsustainable.
After the preprocessing and splicing of multiperiod satellite RS images, we completed object-based multiscale automatic segmentation and land use classification of the images using eCognition Developer software. Specifically, the Estimation of Scale Parameters (ESP) tool was first used to obtain the local variance parameter, which reflects the internal homogeneity of the segmentation object; then, the rate of change (ROC) of the local variance (LV) parameter was calculated37,38. When the ROC reaches its peak, the corresponding segmentation scale can be used as the optimal segmentation scale37. At the optimal segmentation scale, classification is based on the object unit using the nearest neighbor method of eCognition Developer. The nearest neighbor method is a commonly used supervised classification method that is simple and easy to understand, and it is suitable for multiclassification problems39.
Finally, based on the preliminary results data of the four stages automatically classified by eCognition Developer, obvious errors and omissions in the data of the preliminary results were revised and improved through manual visual interpretation. The final revised result data were used for the subsequent analysis of the land pattern and its changes.
This study first drew land use maps for four years: 1991, 2000, 2010, and 2018. We extracted no less than 200 regions of interest (ROIs) in each study year and compared high-resolution Google Earth images to perform a land mapping accuracy assessment. To ensure that the accuracy of each land type was reliably estimated, we confirmed that each land type had at least 10 ROIs when laying out the ROI area. Table 1 shows the land use classification accuracy for the 1991–2018 period. The overall accuracy of the land mapping for 1991, 2000, 2010, and 2018 was 93.4%, 94.1%, 91.1%, and 94.5%, respectively, and the Kappa coefficients were 0.925, 0.933, 0.890, and 0.938, respectively, meeting the research requirements.
Spatial patterns and dynamics of ecological land
From 1991 to 2018, the ecological land in Zhuhai was dominated by woodland and rivers and shallow water, and the overall area of ecological land continuously decreased (Fig. 1). In 1991, the total area of ecological land was 849.4 km2, accounting for 53.7% of Zhuhai’s urban area. In 2018, the area was reduced to 574.6 km2, accounting for only 36.3% of Zhuhai’s urban area.
The net change in ecological land in Zhuhai city, 1991–2018. The area of woodland is the largest, followed by the area of rivers and shallow water. The proportions of woodland and grassland in the total area of ecological land increased by 7.6% and 1.3%, respectively. Rivers and shallow water and tidal flats showed downward trends, decreasing by 8.7% and 1.8%, respectively. Reservoirs and pit ponds increased slightly and showed dynamic changes.
In 28 years, the amount of ecological land decreased by 32.3%, of which woodland decreased by 24.2% (129.6 km2), tidal flats decreased by 67.2% (19.3 km2), and rivers and shallow water decreased by as much as 51.8% (132.3 km2). The reduction in rivers and shallow water represented the bulk of the reduction in ecological land area (48.1%). In contrast, the area of reservoirs and pit ponds grew slightly while maintaining a steady state, increasing by 1.1 km2. Compared with 1991, the grassland area grew slightly, increasing by 5.3 km2, mainly due to the construction of golf courses and parks. Clearly, there is an order of magnitude difference between the increase and decrease in ecological land.
From the temporal perspective (Fig. 2), the change in ecological land mainly occurred in the 1991–2000 period. During this period, the reduction in ecological land was the largest (212.3 km2), mainly distributed in the contiguous area of woodland and built-up land in the central and western areas of the Doumen District and in the coastal areas of the Jinwan District and Xiangzhou District. At the same time, there was a small increase in ecological land, mainly due to the restoration and regulation of tidal flats and reservoirs and pit ponds.
Ecological land gains and losses in Zhuhai city, 1991–2018. (a,c,e) show an increase in ecological land; (b,d,f) show a decrease in ecological land. The decrease in ecological land is obviously higher than the increase, and there is an increase in the degree of patch fragmentation. The reduced patches are mostly marginal woodland and river and shallow water areas. The boundaries of the map come from the Zhuhai Natural Resources Bureau. The drawing of the map was completed with the support of ArcGIS 10.7 software.
Since 2000, ecological environmental protection and construction work have gradually been taken more seriously, and the State Council of China promulgated the “National Ecological Environmental Protection Program”. Local governments at all levels have gradually strengthened their awareness of ecological environmental protection. The occupation of ecological land by urban development has rapidly decreased, while the area of new ecological land formed by ecological protection and ecological restoration has gradually and steadily increased. From 2000 to 2010, the ecological land in Zhuhai decreased by 130.1 km2 and increased by 53.6 km2, with a net reduction of 76.5 km2. From 2010 to 2018, the decrease and increase in ecological land were similar, and the net reduction in area was only 18.6 km2; thus, the spatial distribution and quantity of ecological land in Zhuhai city was approximately stable (Fig. 3).
Losses and gains in ecological land area in Zhuhai city, 1991–2018. Green indicates an increase in ecological land, and red indicates a decrease in ecological land. From 1991 to 2000, the net reduction in ecological land was 177.9 km2. From 2000 to 2010, the net reduction in ecological land was 76.5 km2. From 2010 to 2018, the net reduction in ecological land was 18.6 km2.
In the 28-year monitoring period of this paper, the reduction in ecological land in the first 10 years (1991–2000) was 0.99 times that in the subsequent 18 years (2000–2018). The total amount of ecological land added in the subsequent 18 years (2000–2018) was 3.5 times that of the first 10 years (1991–2000).
Landscape characteristics
At the landscape level (Table 2), the edge density (ED) of ecological land in the study area is significantly lower than that of nonecological land. The ED exhibited a pattern of first increasing, then decreasing, and subsequently slightly increasing (with values of 33.6 in 1991, 37.7 in 2000, 31.8 in 2010, and 34.7 in 2018). The patch density (PD), landscape shape index (LSI), and largest patch index (LPI) had the same trend as that of the ED. These changes indicate that over time, the landscape of ecological land began to experience an increase in fragmentation and a decrease in regularity and continuity; then, the landscape was reintegrated into a more regular and continuous pattern.
In addition, from 1991 to 2018, the contagion index (CONTAG) of all land in Zhuhai city fluctuated slightly at approximately 55%, and the degree of landscape pattern aggregation did not change much. However, the CONTAG of ecological land was approximately 70%, which was significantly higher than that of nonecological land; this result indicates that the CONTAG and connectivity of ecological land were higher than those of nonecological land. Shannon’s diversity index (SHDI) and Shannon’s evenness index (SHEI) did not change much in the time series, indicating that the landscape diversity of Zhuhai city has basically been stable over the past 28 years. However, compared with 1991, the SHDI and SHEI decreased slightly, indicating that the ecological landscape diversity and uniformity decreased in the study area, while the landscape heterogeneity increased.
At the class level (Table 3), the PD and the area-weighted mean contiguity index (CONTIG_AM) of woodland remained basically unchanged, the LSI increased from 19.99 to 21.7, and the LPI decreased from 9.6 to 3.9. These changes were caused by the following processes: the expansion of built-up land, the preferential occupation of marginal forestland by built-up land, the reduction in the dominance of the landscape type, and the increasing complexity of the original geometry. However, woodland mainly exists in a continuous form, and these encroachment behaviors have little effect on the number, spatial connectivity or proximity of woodland patches.
The PD and LSI of grassland showed downward trends, while the LPI and CONTIG_AM showed upward trends. This result is closely related to the increase in grassland in the study area. The increased grassland caused the number of patches to increase slightly, improving the superiority of the landscape. The construction of artificial grassland is more regular in the shape of grass patches, and the connectivity is enhanced between landscape units.
In addition, the PD, LSI and LPI of tidal flats showed downward trends, indicating that the development and utilization of tidal flat reclamation were strengthened, the number decreased, and the shape tended to be regular. The landscape characteristics of reservoirs and pit ponds and rivers and shallow water were basically the same: the LSI showed an upward trend, indicating that the patches were seriously disturbed by human activities, the large patches experienced continuous fragmentation, and the landscape type shapes were complicated. In contrast, the LPI showed a downward trend, indicating that activities such as sea filling led to a continuous decrease in sea area.
Ecological quality evaluation
Ecological quality is used to characterize the conditions of the ecosystem; the ecosystem is disturbed by human activities and land use change, and the ability to provide services is also affected40. The value of ecosystem services is an important comprehensive indicator reflecting ecological quality, and the ecological service value of ecological land is higher than that of nonecological land41. Based on the ecosystem service value coefficient proposed by Xie et al.28, we normalized the coefficient value to 0–1 and used the equivalent area and the average equivalent area, which were used to evaluate the ecological service quality of ecological land.
The transformation matrix of ecological land and nonecological land shows the following (Table 4): the probability of ecological land being transformed into nonecological land in the periods 1991–2000, 2000–2010 and 2010–2018 was 25.0%, 19.4% and 14.3%, respectively. The contributions of ecological land to nonecological land were 23.3%, 13.2% and 8.4%, respectively. The transformation of ecological land to nonecological land showed a weakening trend after 2000, and the ecological quality showed improvement.
From 1991 to 2018, the equivalent area of ecological land continued to decrease, but the downward trend gradually stabilized after 2000 (Fig. 4). In 1991, the equivalent area of regional ecological land was 849.4 km2, and in 2000, it was 673.2 km2, indicating a significant decrease in the equivalent area, with a reduction of 20.7%. In 2010, the equivalent area of ecological land further dropped to 600.2 km2, a reduction of 10.8%, although the decrease was significantly smaller than that in the previous period. In 2018, the equivalent area was 574.6 km2, representing a reduction of only 4.3%.
Dynamic changes in ecological land quality in Zhuhai city, 1991–2018. From 1991 to 2018, the equivalent area of ecological land in Zhuhai city showed a downward trend, with a decrease of 274.8 km2, i.e., 32.3%. The average equivalent area index showed an upward trend, with an increase of 0.11, i.e., 9.3%.
As shown in Fig. 4, the average equivalent area of ecological land showed a continuous upward trend. Specifically, the average equivalent area was 1.14 in 1991, 1.22 in 2000, 1.24 in 2010, and 1.25 in 2018. This result shows that although the ecological land area decreased, the quality of the ecological land gradually improved. In reality, this pattern was manifested as follows: the area of grasslands and reservoirs and pit ponds gradually increased, the degree of landscape fragmentation weakened, and the landscape dominance became more obvious. In addition, these land types have relatively high ecosystem service values among all land types.
Changes in the center of gravity of ecological land
From 1991 to 2018, the center of gravity of ecological land shifted to the northeast, and the center of gravity of built-up land shifted to the southwest (Fig. 5).
Changes in the center of gravity of ecological land and built-up land in Zhuhai city, 1991–2018. From 1991 to 2018, the center of gravity of ecological land in Zhuhai moved to the northeast by 1346 m. The center of gravity of built-up land moved in the opposite direction, moving 7254 m to the southwest. The boundaries of the map come from the Zhuhai Natural Resources Bureau, and the base map in the main map is the China Online Community Basemap in ArcGIS. The drawing of this map was completed with the support of ArcGIS 10.7 software.
From 1991 to 2000, the center of gravity of ecological land moved 404 m to the east and 409 m to the north, and the overall movement was 578 m to the northeast. From 2000 to 2010, the center of gravity of ecological land moved 24 m to the east and 355 m to the north, and the overall movement trend was northward. From 2010 to 2018, the center of gravity of ecological land moved 273 m to the east and 236 m to the north, and the overall movement was 473 m to the northeast. In these three periods, the center of gravity of built-up land moved to the southwest by 2871 m, 3983 m and 424 m. The urban expansion and internal construction mainly experienced a rapid and then slow evolution from the northeast to the southwest.
From the spatial distribution of all ecological land types, the center of gravity of woodland moved to the southeast (0.68 km) from 1991 to 2018. This movement occurred because human construction activities such as deforestation, urban expansion, and infrastructure construction were prominent in the western and northern parts of Zhuhai during the 1991–2000 period. The movement of the center of gravity of grassland to the east and south was highly related to the construction of golf courses, such as the Zhuxiandong Golf Club in the Xiangzhou District, the Dananshan Cuihu Golf Course in Jinding Town, a golf club in the Jinwan District, and Zhuhai Stadium in the Xiangzhou District. The center of gravity of reservoirs and pit ponds moved southward (2.9 km); the center of gravity of tidal flats moved eastward (5.8 km); and the center of gravity of rivers and shallow water moved northward (3.5 km). These changes were closely related to the reclamation engineering carried out by Zhuhai city in recent years.
Modeling the ecological land change process
Changes in urban ecological land are mainly due to the expansion of the outer edge of cities and the oppression of urban internal land development. Therefore, we selected four indicators of natural geography and regional development that might reflect changes in urban expansion and urban construction: elevation, slope, distance from built-up land, and growth rate of built-up land.
With the support of SPSS software, the equation of the transformation probability of ecological land to nonecological land in Zhuhai can be obtained through the binary logistic regression analysis module. Specifically, this equation is expressed as follows (see Supplemental Materials S2: Parameter of the driving factors for modeling):
$$P = 1 – frac{1}{{{1 + }e^{{{ – }left( {{0}{text{.069}} times {text{A } + text{ 0}}{.033} times {text{B } + text{ 0}}{.473} times {text{C } – text{ 1}}{.079} times {text{D } – text{ 0}}{.963} times {text{E } – text{ 0}}{.853}} right)}} }}$$
(1)
where A is the slope; B is the elevation; C is the distance from built-up land; and D and E are the built-up land growth rates of categories 4 and 5, respectively. The squared maximum likelihood of the numerical values (− 2 log-likelihood) of the model was 18,155.4, and the value of the χ2(5) comprehensive test statistic was 7871.2 (p < 0.001), which was significantly higher than the critical test value of 20.5. This result shows that the model constructed based on the above training data has superior precision and can be used for future prediction simulations.
To eliminate the influence of the distribution deviation of the training samples, we performed model accuracy verification. We randomly selected 10,000 ecological land sample points from the land use map for 1991 and observed their changes in 2018; we then tested the simulation accuracy of the model. The results show (Table 5) that there are 7853 sample points with the same simulation results as the actual observations. Thus, the model simulation accuracy is 78.6%, indicating that the model has high accuracy and robustness.
Transformation probability of ecological land
Based on the above model, elevation, slope, distance from built-up land (based on 2018 data) and growth rate of built-up land (based on 2010–2018 change data) are used as input variables to predict the future transformation probability of ecological land to nonecological land in Zhuhai city (Fig. 6).
The distribution of the transformation probability of ecological land in Zhuhai city in the future. In Zhuhai, the average transformation probability of ecological land is 0.176; that of forestland is the lowest, at 0.097; that of grassland is 0.257; that of tidal flats is 0.342; that of reservoirs and pit ponds is 0.354; and that of rivers and shallow water is 0.380. The administrative boundary in Fig. 6 comes from the Zhuhai Municipal Bureau of Natural Resources. The goal of the map is to illustrate the probability of the ecological land transformation process. The natural breakpoints method was used to divide the transformation probability of ecological land into 5 levels: a transformation probability less than or equal to 0.1 is defined as low, a transformation probability greater than 0.1 and less than or equal to 0.3 is defined as relatively low, a transformation probability greater than 0.3 and less than or equal to 0.45 is defined as medium, a transition probability greater than 0.45 and less than or equal to 0.65 is defined as relatively high, and a transition probability greater than 0.65 is defined as high. The gray areas in the figure indicate nonecological land.
The simulation results show that the average transformation probability of ecological land in Zhuhai is 0.176. The transformation probabilities of tidal flats, reservoirs and pit ponds, rivers and shallow water and grassland (0.342, 0.354, 0.380, and 0.257, respectively) are higher than the average transformation probability of ecological land in the study area, while the transformation probability of woodland is 0.097, which is significantly lower than the average transformation probability of regional ecological land. The reasons for these patterns are as follows: tidal flats, reservoirs and pit ponds and grassland are distributed in flat areas with low elevations, meaning that it is easier to prioritize their development and utilization. In addition, because Zhuhai is a coastal city, urban builders have a strong impulse to reclaim land, which is likely to result in the transformation of ecological land in the form of tidal flats and rivers and shallow water to built-up land. On the other hand, woodland in the study area is mostly in areas with relatively steep slopes and high elevations, mainly including Huangyang Mountain, the Fenghuang Mountain Nature Reserve, Jianfeng Mountain and Lanlang Mountain Forest Park. These woodlands are less likely to be further developed and utilized.
In terms of spatial distribution, ecological land in the Jinwan District has the highest transformation probability, followed by that in the Xiangzhou District. The ecological land in the Doumen District, an important agricultural product protection and ecological agricultural development zone in Zhuhai, is mostly unsuitable for woodland development. Therefore, the ecological land in this district has the lowest transformation probability, and there is less pressure affecting ecological land protection. The Jinwan District and Xiangzhou District are zones in Zhuhai that gather high-end industries and business services, and there is a strong demand for land for regional development. In addition, these two districts are coastal and riverside areas; thus, city planners and builders have a strong impulse to expand urban built-up land by means of sea reclamation and river reconstruction. In these two districts, the pressure on regional ecological land protection is enormous, and the need to apply new technologies and methods to save land and increase ecological land is the strongest.
Source: Ecology - nature.com
