Mangrove forests, situated in the intertidal zone of tropical and sub-tropical coastal regions, are one of the most productive ecosystems on Earth. Their distinct marine and terrestrial characteristics [1
] provide significant ecological services and functions in terms of coastal water purification, biodiversity conservation, shoreline stabilization, storm protection and fishery harvest [4
]. Moreover, as an important component of blue carbon [7
], mangrove forests can sequester carbon in aboveground biomass [8
], below-ground biomass [9
], and in sediments [11
], which is vital for the equilibrium of the global carbon cycle. Despite an abundant range of economic and ecological values, a third of mangrove forests worldwide have been lost in the last fifty years because of rapid urban growth, increasing population pressure, aquaculture expansion, and other impacts caused by anthropogenic disturbances and climate change [13
]. However, there is an emerging demand for conservation and restoration efforts. To make appropriate decisions and polices, the spatio-temporal extent of mangrove forests needs to be inventoried and monitored, and the driving factors of change need to be identified [17
Mangrove forests are difficult to monitor because of their inaccessibility and large area, which makes field observation problematic. A considerable number of remote sensing monitoring and mapping studies have been conducted, from the local to the global scale, over the past two decades [19
]. Various remote sensing-based methodologies, either exclusively or in combination, have been applied to monitor the extent of change in mangrove forests. Traditional mapping approaches, including visual interpretation and on-screen digitization, have been used to map mangrove forests. Applying visual interpretation as an auxiliary support improves the accuracy of classification results obtained by supervised and unsupervised classification methodologies [21
]. A variety of classification methods have been investigated and compared to enhance the effectiveness of spectral discrimination for mangrove forests, such as the support vector machine [22
], artificial neural networks [23
], maximum likelihood classifier [24
], machine learning [26
], and the iterative self-organizing data analysis technique (ISODATA) algorithm [27
]. Nevertheless, due to “salt-and-pepper” effects, pixel-based classification methods frequently generate erroneous classifications of pixels [28
]. In contrast, the object-oriented classification method segments an image into groups of contiguous and homogeneous pixels (image objects) as the mapping unit [29
], which can reduce the “within-class” spectral variation and effectively overcome the salt-and-pepper effect. In addition, these classification methods consider not only the spectral properties of the objects, but also their texture, shape, and geometric features in the classification process, and as a result, more accurate and effective performances are obtained than with pixel-based approaches [30
Image segmentation is deemed to be a critical prerequisite for object-oriented classification, because its quality largely affects the final performance of geo-object recognition. Understanding how to effectively determine the optimal segmentation scale is crucial to the improvement of segmentation quality [33
]. To date, visual inspection has mainly been used to assess the accuracy of segmentation results. For example, Liu et al. (2017) determined the advisable segmentation scale of Google Earth images based on multiple tests and visual analysis [31
]. Yet, visual inspection, as a qualitative approach, cannot provide a quantitative evaluation and may suffer from subjectivity, since different people are likely to have different opinions on which segmentation result is better [33
]. Quantitative evaluation approaches can score a range of segmentation results. However, there are few studies focused on optimal segmentation scale selection in terms of monitoring and mapping for mangrove forests.
Given the importance of mangrove forests, as well as the limitations of segmentation scale evaluation and selection methods in mangrove forest mapping, the objectives of this study were to: (1) Apply a systematic optimal segmentation scale selection method to assist in object-oriented image classification; (2) monitor the dynamics of mangrove forest extent during the period 1990–2017, and transitions between mangrove forests and other land cover types; (3) analyze the influence of anthropogenic activities, climate change, and plant invasion on the spatio-temporal changes of mangrove forests; and (4) propose more feasible conservation policies and local management plans for mangrove forests. We used the mangrove forests in Quanzhou Bay, Fujian Province, China, as the study site. An optimal segmentation scale model based on object-oriented classification was applied to determine the best segmentation scale. We combined image segmentation with a decision tree and visual interpretation to produce land cover maps. The mangrove forest dynamics and conversion to and from other land cover types were described using annual land change rate (ALCR), centroid migration, and overlay analysis.
4.1. Mangrove Forest Losses Associated by Human Activities and Possible Environmental Threats
, as invasive plant species, were first introduced to China’s coastal provinces from the Atlantic Coast of the U.S. for coastal environment promotion in 1976 [46
]. However, due to their rapid growth, high productivity and strong adaptability, introduced Spartina
expanded rapidly in tidal zones and now pose a tremendous threat to native biodiversity. Once Spartina
have invaded the mangrove forest gaps, they inhibit the germination and growth of mangrove seedlings by competing for space, light and nutrients [47
]. Our results found that the propagation of mangrove forests was suppressed where they grew in or around Spartina
). Conversion to Spartina
was the primary contributor of mangrove forest losses, especially from 1997 to 2005 (Table 5
). Previous studies have also shown Spartina
expansion increased the pressure on survival and breeding of mangrove forests [48
], which is consistent with our findings. Moreover, in other coastal zones of China, for instance, Shankou of Guangxi province, Qi’ao island, and Shenzhen Bay, Spartina
invasion has become one of the most serious threats to mangrove forest growth [41
can become established in mudflats, easily resulting in the loss of suitable habitats which are available for mangrove forest reproduction [31
]. Our results also showed that the Spartina
area increased by 7.84 km2
with an ALCR of 100.13% from 1990 to 2017, with mudflats as the priority areas of encroachment (Table 4
, Figure 4
, Figure 5
and Figure 6
). Without effective systematic intervention, we posit that Spartina
would gradually cover the entire intertidal mudflat and become an irrevocable threat to mangrove ecosystems. Figure 8
a,b illustrates the inhibition of mangrove seedling growth by Spartina
and its expansion in mudflats, respectively.
Aquaculture development is another factor that has contributed to the losses of mangrove forest in the QBEWNR. Over the period 1990–2017, aquaculture ponds increased significantly by 3.57 km2
) and most were established in mudflat areas (Figure 4
and Figure 6
). Diseases caused by aquaculture have a negative effect on mangrove forests [52
]. The untreated water discharged by aquaculture can bring more pressures against the nutrient load in water bodies. Once the self-purification capacity of water bodies has been exceeded, the health of mangrove forests faces serious threats [53
]. In addition, suitable habitat for mangrove forests was encroached upon by constructing aquaculture ponds in mudflats. Consequently, the losses of mangrove forests were due to aquaculture expansion to some extent. In previous studies, researchers have indicated that of the one-third of mangrove forests that have disappeared worldwide in the last 30 years, 35% was lost to aquaculture; this figure may reach 60% by 2030 [54
]. Figure 8
c,d displays aquaculture ponds located adjacent to mangrove forests and aquaculture development in mudflats, respectively.
Due to their location in low coastal elevation areas, mangrove forests are particularly vulnerable to sea-level rise [56
]. Previous research on sea-level change has reported that the sea-level of the Fujian coastal zone increased at an average rate of 2 mm/y during the last half century [59
]. In response to rising sea-levels, mangrove forests are more likely to migrate landward [30
]. However, such a landward migration and establishment has been obstructed by artificial seawalls in the study area (Figure 9
), which meant that mangrove forests could only expand seaward. In addition, the roots of mangrove forests should be exposed to the air at a certain time, otherwise they would not be able to complete their own physiological processes and propagation [58
]. As a result of sea-level rise, lower elevation areas would be inundated and waterlogged longer, which means mangrove forests would not meet the time required for mangrove root respiration, so mangrove forests cannot easily establish and propagate seaward.
As shown in Figure 10
, the annual average temperature and annual precipitation both increased in the QBEWNR from 1990 to 2017. Although a warmer and wetter climate would favor the establishment of mangrove forests [63
], such climate change would promote sea-level rise, leading directly to the space loss of mangrove forest expansion seaward. Therefore, it is evident that, in the context of global warming, the potential for major coastal change aggravates the survival risk and pressures of seaward succession for mangrove forests. Overall, as for the study area, climate change and sea-level rise would potentially have adverse effects on mangrove forests.
4.2. Positive Effects of Reforestation Projects and Spartina Control
One notable caveat to these results is that the area of mangrove forests in the QBEWNR was only 0.09 km2
in 1990, had doubled to 0.18 km2
in 1997, and then declined to 0.16 km2
in 2005. These areas represent a limited distribution. Since 2005, mangrove forests have increased rapidly (Figure 5
and Figure 6
). Under the ecological restoration schemes for coastal wetlands in Quanzhou Bay, various national and local ecological conservation projects have been carried out since 2000, including “National Wetland Conservation and Restoration”, “Marine Environment Ecological Restoration Project in Quanzhou Bay”, and “Mangrove Forest Restoration Project in Luoyang River” [64
]. One of the common objectives for these ecological engineering projects is to cultivate and replant mangrove forests (i.e., reforestation projects) to increase mangrove forest area. According to the previous report [65
], the afforestation area of mangrove forests was approximately 200 ha until 2015, indicating an 80%‒85% increase in mangrove forests because of the reforestation projects. Studies focusing on the mangrove forest dynamics in China from 1973 to 2015 had indicated that protection and reforestation actions had supported mangrove forest restoration greatly, which is consistent with our results [30
]. Figure 8
e,f demonstrates a monument of mangrove forest reforestation project and the mangrove seedling cultivation base, respectively.
Although awareness of the need to protect mangrove forests has increased, the threats of Spartina
invasion and the importance of Spartina
control were recognized relatively late in China [30
]. This probably explains why reforestation engineering was implemented in 2000, but the mangrove forests continued to decrease between 1997 and 2005 (Table 4
). Since 2007, both national and local agencies have promoted the implementation and enforcement of Spartina
control projects in the QBEWNR [67
]. Up to 2017, the area of Spartina
converted to mangrove forests had increased to 53.42 ha, including 7.20 ha from 2005 to 2010 and 46.22 ha from 2010 to 2017 (Table 5
). Meanwhile, the annual expansion rate of Spartina
had declined from 20.66% at the beginning of the same period, to 3.64% at the end. Moreover, the centroid distance between Spartina
and mangrove forests has increased (Figure 7
). Therefore, it can be inferred that, due to the management and control of Spartina
, the disturbance caused by Spartina
to mangrove forests has been mitigated to some degree. Figure 8
g,h show the specific implementation scenario and subsequent effects of Spartina
As mentioned above, whether through mangrove forest reforestation projects or Spartina control, the performance of conservation activities plays a substantial role in the existence and expansion of mangrove forests, which to a large extent neutralizes or offsets the losses of mangrove forests caused by negative factors.
4.3. Suggestions for Conserving and Managing Mangrove Forests
Monitoring and dynamic change analysis for mangrove forests are critical for their conservation and management, and to help with formulating and implementing government policies [68
]. The results from this study on the spatio-temporal change of mangrove forests and conversion between mangrove forests and other land cover types (Figure 4
, Figure 5
and Figure 6
, Table 4
and Table 5
) can be used as a guideline for conserving and managing mangrove forests.
First, the implementation of artificial afforestation projects should be continued to increase the area of mangrove forests. Establishing a mangrove forest monitoring system is also indispensable and would allow for the elimination and control of insect pests and effective feedback on all aspects of mangrove forests. Meanwhile, cutting mangrove forests indiscriminately and disposing of rubbish within mangrove forests must be prohibited. Second, prevention and control of Spartina should be further studied, and more feasible measures should be taken to impede the expansion of Spartina in mudflats, with the aim of restoring suitable habitat for mangrove forests. For the zones in which Spartina has been eradicated, breeding conditions should be monitored regularly. Third, strict limitations on aquaculture development should be implemented in the reserve, and the pollution problems caused by aquaculture must be contained. Fourth, in focusing on the important ecological value of mangrove forests, local managers and conservationists should reinforce mangrove forest conservation by teaching local residents about the conservation implications and by disseminating information about the severe threats faced by mangrove forests.
4.4. Advantages and Uncertainties of the Methods for Mangrove Forest Monitoring
So far, the primary segmentation scale determination approach is based on visual analysis, which is subjective and cannot effectively avoid over-segmentation and under-segmentation errors [70
]. The optimal segmentation scale model used in our study not only gave a quantitative result, which is based on objective evaluation, but also kept better boundary consistency between the segmented image objects and real land cover types (Figure 3
). Moreover, in the visual interpretation process, the manual modification of objects obtained using the optimal segmentation scale model was less than the objects generated through visual analysis determining the best scale, which improved classification efficiency and reduced the workload of interpreters.
Classification accuracy can be improved by using the spectral difference among different land cover types effectively [71
]. In this study, the spectral discrimination of mangrove forests, Spartina
, and mudflats derived from seasonal change was given a full comparison and analysis, and we found that they were more accurately distinguished than in previous research [21
]. Therefore, multi-seasonal images appear to be feasible and desirable data sources for monitoring mangrove forests located in subtropical areas. In tropical zones, owing to very small phenological differences, it may be difficult to discern spectral distinction between mangrove forests and Spartina
. Accordingly, multi-seasonal images may not be applicable for mapping mangrove forests growing in tropical regions.
Our analysis focused on mangrove forest dynamics over a long time series (from 1990 to 2017). This is significant for maintaining the spatial coherence of classification maps derived from various sensor images. Due to the spatial uncertainties that arose from the scanning system, wavelength setting, over-pass time, and angular effect, unbiased maps were difficult to produce. In addition, several uncertainties and limitations also existed in the segmentation parameter selection. Besides the segmentation scale, the shape and compactness variables, which respectively balance spectral homogeneity with the shape of the objects and the compactness with smoothness, are essential for controlling the clustering decision process of image objects. Although the optimal segmentation scale was determined objectively, the selection of other segmentation factors chosen by referencing previous research was subjective.
Considering the importance of tidal information for mangrove forest monitoring and the spectral differences among mangrove forests, mudflats, and Spartina, several multi-seasonal Landsat images acquired during low tide were selected as the basic data sources. Combining an optimal segmentation scale model based on object-oriented classification, centroid migration calculations and spatial analysis, we discerned dynamic changes in the mangrove forest and their influencing factors in the QBEWNR from 1990 to 2017. Our results showed that there were some advantages for the approaches used in this study for mangrove forest monitoring, since the classification accuracy and efficiency of land cover map improved. Mangrove forests significantly expanded during the period of 1990–2017: the total area increased by 2.48 km2, with a dynamic degree of 102.06%. Most of the expanding mangrove forests transitioned from mudflats and Spartina. Mangrove forest changes were influenced by many factors. Environmental threats, including climate change and sea-level rise, Spartina invasion and aquaculture development exerted negative effects, while reforestation projects and Spartina control had a positive effect. In this study, the role of the latter was greater than the former. We demonstrated that conservation activities benefit the existence and expansion of mangrove forests. These conclusions can be used as a guide for governments and conservationists to make policies and effectively protect and monitor mangrove forests.