Printer Friendly

CHANGES IN HABITAT SUITABILITY FOR WATERBIRDS OF THE MOMOGE NATURE RESERVE OF CHINA DURING 1990-2014.

Introduction

Over the past 50 years, humane have caused rapid and extensive declines in global biodiversity (Millennium Ecosystem Assessment 2005) despite our increased focus on nature conservation and wildlife management. Globally, 85% of threatened bird species are at risk because of habitat loss and degradation. Waterbirds, among the most symbolic ecological assets in wetlands, are good indicators of environmental changes and are natural resources of great ecological value (Reid et al. 22013). Waterbirds show high mobility and aggregation in response to the fluctuations in resources and the demands of their different life requirements (Cumming et al. 2012). Natural and semi-natural habitats are exposed to growing pressure from the intensification of agriculture, forestry and climate change (Weiers el al. 2004). The changes of habitat quality would affect the distribution, diversity and abundance of waterbirds (Sekercioglu et al. 2004). Wetlands support extensive food chains and biodiversity and are natural regulators of a number of environmental problems, such as flooding, drought and water quality (Williams 1990). Wetlands are critical habitats for a large variety of waterbirds (Ramsar 2012), and a major reason for decreasing bird populations is habitat degradation (Ronka et al. 2008). Obtaining information on habitat distribution and monitoring habitat conservation is a prominent research area (Corbane et al. 2015) an d requires both an evaluation of the current suitability of habitat and detection of habitat changes.

As the coupling of remote sensing end GIS technologies can provide quantitative and cost-effective assessment, these technologies are widely recognized for their applicability in wetland monitoring (Euliss et al. 2011) and ecological research. Remotely sensed data provide measurements and surrogates that are directly related to vegetation type and structure, biomass, and other ecosystem variables that collectively improve our understanding of habitat characteristics (Bradley et al. 2012). There are numerous examples demonstrating that the addition of remotely sensed variables improves the accuracy of habitat suitability models. Waterbirds select habitats that meet their ecological requirements of providing food and shelter, as well as abundant water and minimum disturbance. Therefore, in many studies, remotely sensed variables serve as proxies for water, food, disturbance and other attributes of habitat quality (e.g., Dong et al. 2013; Tian et al. 2008). Human presence and human-induced environmental changes also affect the suitability of habitats, and thus, land cover change serves as a proxy for habitat loss (Wang et al. 2015). Accurate assessment of habitat quality depends on a profound understanding the habitat requirements of waterbirds, followed by effective modelling of habitat suitability.

In many cases, habitats can be very dynamic, affecting animal distributions and populations, so detecting habitat dynamics can improve our understanding of habitat-abundance targets (e.g., Gardner et al. 2007; Nakamura et al. 2016). Habitats can be dynamic in response to climate changes, conservation efforts, or land use changes (Anteau 2012). Measuring habitat status and understanding habitat dynamics have become important for waterbird conservation (Liu et al. 2010). However, to our knowledge, most of the debate to date has focused on habitat quantity, while changes in habitat quality have been overlooked (Hodgson et al. 2009). Accurately assessing habitat dynamics would clearly be useful for many aspects of research, conservation and management (Anteau et al. 2014).

The West Songnen Plain has the greatest concentration of marsh wetlands in China and is noteworthy for its biodiversity (Wang et al. 2009). This area also serves as an important stopover and wintering site for migratory waterbirds. However, wetland degradation, due to climate warming and anthropogenic pressures resulting from the reclamation of farmlands, construction of water conservation projects, and petroleum and gas developments (Pan et al. 2006), has become a serious environmental problem. The Momoge Nature Reserve (MNR), which was designated as a reserve of international importance, contains a large area of natural wetland in the Western Songnen Plain and has been severely impacted by intensive human activities. In recent years, waterbird populations in the Momoge Nature Reserve have declined, primarily in response to habitat loss, fragmentation and degradation (Xiao et al. 2014). Several of these waterbird species are red-listed (Ramsar 2012). Thus, there is an urgent need to protect and restore waterbird habitat. More attention should be given to quantitatively assessing habitat suitability for waterbirds and the dynamics of habitat quality in this region. The objects of this study were: (1) to select the key ecological indicators that have prominent impacts on waterbird habitat, (2) to quantitatively assess the habitat suitability grades in the Momoge Nature Reserve, and (3) to track and analyse dynamics and spatial characteristics of habitat suitability for waterbirds during 1990-2014. We hope that these results supply accurate information about habitat suitability changes and serve as a tool for biodiversity conservation.

1. Materials and methods

1.1. Study area

The MNR (45[degrees]42' 33"-46[degrees]17' 59"N, 123[degrees]27' 09"124[degrees]4'32"E) is located in the transition zone between deserts and grasslands in the northwestern part of Jilin Province, China (Fig. 1), at an altitude ranging from 128.0-160.7 m above sea level. The total area is approximately 1440 [km.sup.2]. The reserve has a temperate continental monsoon climate, with average annual precipitation of 391.8 mm and an average annual temperature of 4.2[degrees]C (Yang et al. 2012). Winter is cold with heavy snow, while summer is hot with heavy rain. The MNR mainly draws off floodwater from the Nenjiang River, Taoer River and Erlongtao River.

The MNR was established in 1981, listed in the Chinese National Nature Reserves in 1997 and designated as internationally important during the Ramsar Convention in 2013. The MNR plays a key role in regulating the regional climate, conserving biodiversity and protecting endangered waterbird species. Thus, the MNR receives great attention from international organizations, such as the WWF (World Wildlife Fund), GEF (Global Environment Facility) and ICF (International Crane Foundation) (Pan et al. 2006). It is an inland wetland ecosystem mainly composed of lakes and marshes, serving as an important habitat for a variety of threatened migratory birds, such as the Siberian crane, hooded crane, red-crowned crane and oriental stork. There are 193 recorded bird species in the MNR (Ramsar 2012). However, human activities, such as petroleum exploitation, have led to the degradation of the waterbird habitat in recent years.

1.2. Data sources

Satellite datasets from the Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM), Operational Land Imager (OLI), digital elevation model (DEM) and other ancillary data were used in this research. Landsat TM images were obtained from the United States Geological Survey (USGS) Landsat archive (http://glovis.usgs. gov/). The study area is located in the Path/Row 120/28 of the World Reference System 2 (WRS-2), and three images acquired from 25 June 1990, 6 July 2000, and 10 July 2014 were available for the study area. Satellite images from the same season were selected in order to decrease seasonal effects. Each Landsat TM image had a resolution of 30 m. DEM data were obtained from the Shuttle Radar Topography Mission (SRTM) (http://srtm.csi.cgiar.org/) with a resolution of 90 m and used to derive the slope factor. The ancillary data included road and river vector data, which were available at the AMUR-HEILONG RIVER BASIN Information Center (http://amur-heilong.net/). Prior to image classification for land cover mapping, all images were geo-rectified to topographic maps using ground control points (GCPs). The ENVI 4.8 software and ArcGIS 9.3 software were used for data processing.

1.3. Habitat suitability assessment approach

The object-oriented classification method was adopted for land cover maps, which were derived from Landsat images acquired in 1990, 2000 and 2014. To quantitatively assess habitat suitability, several indicators, including water availability, food, shelter conditions and disturbance, were selected to represent the habitat requirements of waterbirds. By numerically relating occurrence records for waterbirds to a suite of environmental variables from those locations, remote sensing-based indicators that were well correlated with field-based indicators of habitat status could be used as proxies (Strasser et al. 2014). In this paper, six indicators were identified in the analysis of habitat suitability for waterbirds, including shelter condition (land cover type and slope), food abundance (Normalized Difference Vegetation Index, NDVI), water availability (the density of rivers and NDMI), and disturbance (the density of roads). Then, an analytic hierarchy process (AHP) method was applied to determine indicator weights. In this way, an assessment matrix of indicators and their weights was established. Based on the geo-referenced database, an assessment of habitat suitability could be performed. Figure 2 presents a flow chart showing how to obtain habitat quality information using Landsat imagery with object-oriented and AHP methods.

1.3.1. Identification of habitat indicators

Among the six indicators of habitat quality, land cover was the dominant proxy variable that informed habitat or supporting ecosystem services. Land cover data were obtained from Landsat images by object-based classification in conjunction with visual interpretation. Object-based segmentation is a method that allows for mapping complex, hierarchical habitat systems (Strasser, Lang 2015). The object-based classification was performed using eCognition 8.64 software (Kartikeyan et al. 1998). Based on natural vegetation succession and human land use types, land cover maps for 1990, 2000 and 2014 were classified into seven categories: wetland, cropland, woodland, grassland, water body, built-up land and barren land. The classification accuracy was assessed using historical data during 19902000 and through field investigations during 2013-2014. The total classification accuracies of the land cover maps for 1990, 2000 and 2014 were >90%, meeting the needs of this study. NDVI was significantly correlated with vegetation biomass, primary production and greenness and also accounted for intra- and inter-annual changes in the environment (Cord, Rodder 2011). NDVI could therefore be used as a potential measure of forage for waterbirds. In this study, NDVI was a normalized transformation of the Landsat near-infrared (TM4, ETM4, OLI5) to red (TM3, ETM3, OLI4) reflectance ratio, with values between -1-1. NDVI values for 1990, 2000 and 2014 were derived from Landsat imagery.

Water availability is another important aspect for waterbirds selecting a stopover and breeding site and depends on the density of rivers at the regional level. Otherwise, the plant community also depends on soil wetness and submersibility. Thus, the moisture of the land surface becomes an important factor at the microhabitat level. The density of rivers was derived from river vector data. The normalized difference moisture index (NDMI) (Wilson, Sader 2002) is the normalization of the difference in bands of moisture between Landsat near-infrared (TM4, ETM4, OLI5) and short wave-infrared (TM5, ETM5, OLI6) reflectance ratios. The NDMI values from 1990, 2000 and 2014 were also derived from Landsat imagery. Land cover and slope reflect the safety of shelter and are crucial for waterbirds selecting a place to rest (Brotons et al. 2004). The slope angle of the study area was derived from DEM data.

Finally, wild cranes are particularly wary and will abandon their nests if disturbed by human activity or predators (Zou, Wu 2009). The density of roads has a direct effect on waterbird habitat (Hirzel et al. 2001) and was derived from the road vector data using the spatial analysis function of ArcGIS 9.3.

1.3.2. Calculation of habitat suitability index

To calculate the Habitat Suitability Index, all habitat indicators were translated into numerical data, and all data were standardized using the spatial analysis tools of ArcGIS 9.3 software. Habitat suitability was graded as poor (0-25), fair (25-50), good (50-75) and very good (75-100).

As the impact of each indicator had a different effect on habitat suitability, reliable weights had to be determined for each indicator. An AHP method was applied for the indicator weighting. AHP can be used to create decision aids that simplify and expedite the shared decision making process, with effective and feasible results (Dolan 2008). This method can combine the relationships between habitat suitability indicators (Ludwig, Ianuzzi 2006). Table 1 presents the AHP results that were used to determine the weight of each indicator of habitat suitability. Then, a linear function was adopted to calculate an overall index of habitat suitability for waterbirds.

1.4. Habitat suitability change detection

Changes in habitat suitability for waterbirds were derived from images acquired during the same season in 1990, 2000 and 2014. The spatio-temporal analysis of changes in habitat grades was performed using ArcGIS 9.3 software. Spatial analysis was carried out to describe patterns of grade changes and to measure the rate of changes that occurred during 1990-2014.

In this study, the centroid coordinates approach (Li et al. 2007) was used to obtain habitat suitability patches and to spatially track the dynamics of changes in habitat quality grades. Centroid coordinates were calculated as:

[mathematical expression not reproducible], (1)

where [X.sub.c] and [Y.sub.c] are the centroid coordinates of area-weighted suitability types, [X.sub.i] and [Y.sub.i] are the centroid coordinates of i th patch, [C.sub.i] is the area of all patches in i th suitable grade, n is total numbers of all landscape patches.

To offer more details about movement process on centroids of different habitat suitability grades, the movement distance [mathematical expression not reproducible] and angle [mathematical expression not reproducible] were calculated as follows:

[mathematical expression not reproducible]; (2)

[mathematical expression not reproducible], (3)

where [mathematical expression not reproducible] are the centroid coordinates of a certain suitability type in [t.sub.i] and [t.sub.i+1] year, [t.sub.i] and [t.sub.i+1] are the beginning and terminal years.

2. Results

2.1. Indices of suitability indicators

Six suitability indicators were selected in this study. Some indicators were static, such as the density of roads and rivers, and the slope; and some indicators varied with time, such as land cover type, NDVI and NDMI, which were mapped in 1990, 2000 and 2014.

The density of roads and rivers ranged between 0-72.19 and 0-118.99, respectively (Fig. 3a, b). The distribution map of slope, ranging between 0-8.56, is presented in Figure 3c.

Land cover maps of the study area are presented in Figure 4a-c. As the maps show, the wetland area increased by 164.27 [km.sup.2] during 1990-2000 and decreased by 104.57 [km.sup.2] during 2000-2014. The area of cropland increased continuously during 1990-2014, while the area of water body declined continuously during 1990-2014. Meanwhile, the spatial distribution patterns of three main land cover types, namely, wetland, cropland and water body, changed to different extents. The NDVI values extracted from the Landsat images in 1990, 2000 and 2014 ranged between -0.76-0.76, -0.49-0.74 and -0.48-0.75, respectively, as presented in Figure 5a-c. The area with a NDVI ranging between 0-0.2 increased, and the area with a NDVI of >0.5 declined. In the eastern part of the study area, NDVI values improved, and in the western part of the MNR, NDVI values became degraded. Wetness values were derived by NDMI transformation of Landsat TM images and ranged between -0.70-0.93, -0.53-0.93 and -0.36-0.50, as presented in Figure 6a-c. The area with a wetness value ranging between -80-0 declined, and the area with a wetness value <-80 increased. For the whole study area, the degree of wetness declined during the past 25 years.

2.2. Changes in the area of suitable waterbird habitat

The integrated index of waterbird habitat suitability was calculated according to the grades of very good, good, fair, and poor (Fig. 7). Most of the habitat patches of good and very good suitability were concentrated on both sides of the rivers and around the lakes or wetlands in the Nature Reserve. Habitat patches with a fair grade had the largest area, accounting for approximately 60-70% of the total study area. Habitat with a poor grade was the second-smallest in area, accounting for approximately 10-20% of the total study area. Most of the poor habitat patches were distributed around croplands or build-up lands.

The overall changes in suitability grades for waterbird habitat during 1990-2014 are displayed in Table 2. Our results showed that the least suitable habitat area, that with a grade of poor, decreased by 45.87 [km.sup.2] (3.13%), while the area with a fair grade increased by 176.43 [km.sup.2] (12.03%). The habitat areas with good and very good grades decreased by 129.19 [km.sup.2] (8.90%) during the study period. After the national reserve was established in 1997, local governments greatly improved management and water transfer projects for the wetlands, improving wetland habitat suitability to some extent. Habitats with very good and good grades increased by 71.47 [km.sup.2] (4.88%) during 1990-2000 and decreased by 200.66 [km.sup.2] (13.78%) during 2000-2014.

2.3. Trajectories of suitable grade centroids over time

The distribution of habitat suitability of each grade showed significant spatial differences. To determine the spatial habitat movements of each grade during 1990-2014, the moving centroid of each grade was tracked for different periods of time, as displayed in Figure 8. Our results showed that the centroid with a grade of fair shifted almost imperceptibly during the past 25 years. As the habitat with a fair grade always occupies the largest proportion of the study area, movement of the centroid was not obvious. For habitats with a poor grade, the centroid shifted by 10.57 km southwest during 1990-2000 and 6.59 km southeast during 2000-2014. For habitats with a good grade, the centroid shifted by 8.08 km northeast during 1990-2000 and 4.57 km northwest during 2000-2014. For habitats with very good grade, the centroid shifted by 3.72 km northeast during 1990-2000 and 4.44 km northwest during 2000-2014. In general, the habitat centroid shifted northward for habitats with a good grade and southward for habitats with a poor grade.

3. Discussion

3.1. Assessment of habitat suitability

The aim of our study was to determine the applicability of remotely sensed data and existing spatial data in the cost-effective assessment of habitat suitability changes for waterbirds. Most early studies applying species distribution models traditionally used spatial or climatic data only (Guisan, Zimmermann 2000). More recently, ecologists and conservation biologists have been increasingly utilizing remotely sensed indicators, such as NDVI and EVI (Enhanced Vegetation Index), in species habitat assessments. The habitat suitability assessment indicators should be selected carefully on the basis of the existing knowledge of the habitat requirements and characteristics of the species being modelled (Burnham, Anderson 2002). Waterbirds often have species-specific habitat requirements: Siberian Cranes exclusively use wetlands for nesting, feeding, and roosting; hooded cranes are found in shallow open wetlands and natural grasslands; and red-crowned cranes prefer to nest in marshes with relatively deep water and standing dead vegetation. Generally, wild cranes nest in shallow marshes with emergent herbaceous vegetation (Zou et al. 2003), and thus, areas distinguished as wetland are regarded as more suitable habitat. The six indicators selected in this study, land cover type, slope, NDVI, NDMI, and the density of rivers and roads, allowed us to calculate a habitat suitability index. Among the six indicators, NDMI has seldom been used as an indicator of habitat suitability, but it has been shown to be sensitive to water content in the vegetation and vegetation structure or cover (McDonald et al. 1998). Fiorella and Rippley (1993) reported that TM 4/5 was highly correlated with NDMI ([R.sup.2] = 0.97) and had a higher correlation than the Tasseled Cap Index. Thus, NDMI was selected as one of the indicators in this study. In addition to the indicators used here, other variables could be incorporated in the study of habitat suitability, such as climatic variables (Austin 2007), water quality, soil properties (Ronka, Hakkila 2005), water depth (Na et al. 2015), landscape and oil field distribution. Meanwhile, in order to quantify a broader range of vegetation characteristics, we could also extract summary statistics from the NDVI time series (e.g., start of season, length of growing season, and growth magnitude) (Wen et al. 2015). Moreover, the selection of all of the indicators could be applied at different spatial scales (e.g., sub-habitat level, patch level and landscape level), and multi-scale results for habitat suitability could be acquired. This multi-scale indicator selection method would make an analysis of habitat suitability more reasonable and feasible. However, although waterbirds co-exist and share common habitat elements, each species has its own particular habitat requirements. Future studies on habitat suitability would be most informative if applied to each individual species of interest. In addition, data on nest sites would be desirable for future research. The AHP method used in this research provided a simple and effective way for weighting indicators. Future research could involve structuring multiple criteria into a hierarchy, comparing alternatives of each criterion, and determining the weights of each factor (Saaty 2008).

One of the advantages of our research over previous studies that employed an HSI (Habitat Suitability Index) for waterbirds (Dong et al. 2013) was that we monitored the dynamics of habitat suitability. Habitat degradation tends to be widespread, and thus, developing methods to quantify and monitor changes in habitat quality or pressures are critical for adaptively managing protected areas (Nagendra et al. 2013). In this study, the direction and distance of changes were acquired by representing habitat shifts as vectors linking centroids from different time periods, describing details about the moving process of suitable habitats for waterbirds. Previous studies also gave more attention to habitat area, having no classification for habitat quality (Delgado, Marin 2013; Wen et al. 2015), while in this study, we made a detailed classification of the habitat area into very good, good, fair and poor grades.

In addition to applying unique methods satellite imagery from the same season was used to monitor temporal trends in habitat suitability. Multi-temporal characterizations of land cover, NDVI and wetness derived from imagery were good predictors of waterbird habitat and presented spatio-temporal characteristics in a form that was immediately usable in a geographic information system. This final result would add to evidence showing the importance of monitoring habitat dynamics. Possibly, environmental managers or researchers can use the methods described in this study to make habitat predictions as a substitute for field data when field surveys are not available. Silva et al. (2008) and Zomer et al. (2009) found that when dealing with wetland vegetation, Landsat images were useful for mapping macrophyte communities but not necessarily individual species. In the current research, high-spatial-resolution imagery (Belluco et al. 2006) and LiDAR (Zlinszky et al. 2012) have been used to map flooded and non-flooded vegetation types. Thus, further work could objectively consider using high-spatial-resolution imagery, aerial imagery and LiDAR data to perform genus-level wetland classification in order to improve the habitat suitability maps.

3.2. Changes in habitat suitability for waterbirds

Habitat loss, fragmentation and degradation are the greatest extinction threats to biodiversity (Fischer, Lindenmayer 2007). In the last few decades, over 50% of wetlands in the world have been lost, and the remaining wetlands have been degraded to different degrees because of human activities (Fraser, Keddy 2005). In that paper, it indicated that habitat recovery in the MNR occurred in the initial 10 years, but then habitat suitability became degraded during 2000-2014. In this study, we identified a 5.31% decline in the areas of very good and good habitat. The process of degradation involves gradual deterioration of habitat quality. In a degraded habitat, the number of species may decline or species may occur at lower density (Hazell et al. 2004) or have lower fitness. In the MNR, habitat degradation was associated with an increase in nearby cropland (62.34%) and a loss of water body (66.98%) during 1990-2014. Beginning in 1999, droughts lasting six years were another factor leading to the habitat degradation that occurred during 2000-2014 (Wang et al. 2009). Additionally, many hydraulic engineering projects that were built during the study period seriously damaged hydrological processes of the MNR wetlands.

Another concern for the MNR is the rich underground petroleum resources in the West Songnen Plain (Pan et al. 2006). The oilfield was listed as one of the four important oilfields that would be exploited more rapidly by the Sino-Petroleum Company in 2000. Crude oil output increased from 4 million tonnes to 260-300 million tonnes by the end of 2005 (Pan et al. 2006). Petroleum exploitation drives local economic development, but it leads to severe environmental impacts. Crude oil is toxic to water, soil and vegetation (Pan et al. 2006), with a great impact on land cover type. Petroleum exploitation led to wetland and water decline, causing habitat loss for waterbirds and imposing a great threat on the survival of waterbirds. This is consistent with the results that the habitat suitability of the study area declined from 2000-2014. To resolve the contradiction between a nature reserve and economic development, the boundaries of the core, buffer and transition ranges of the MNR were reshaped in July, 2002 (Wang et al. 2010). Meanwhile, the area of cropland surrounding the MNR has increased and shifted to the southwest in the past 25 years, which will also affect the spatial distribution of suitable habitat. As our results demonstrate, the centroid of habitat with a good grade shifted northward, while the centroid of habitat with a poor grade shifted southward. Generally, the moving process of suitable habitat reflected the impact of human activities.

In recent years, many researches have done much work on environmental protection (Ilgurel et al. 2016; Zhu et al. 2017; Li, Zhao 2017), and the results from this study could give some perspectives on environmental protection through assessing the present status of habitat quality and monitoring the changes in habitat quality for waterbirds. Considering the changing impact on waterbirds habitat, the ecological planning, design and construction of nature reserves is needed to meet the environmental requirement.

Conclusions

Landsat images and vector data, object-oriented segmentation, AHP and a calculation of centroid movement were used to assess the dynamics of habitat suitability for waterbirds in the MNR. These approaches showed that remote sensing and GIS can provide applicable information for spatial analysis methods, and thus be useful tools in assessing habitat quality and monitoring changes. Our results indicated that the habitat with a grade of poor decreased in area by 45.87 [km.sup.2] (3.13%), habitat with a fair grade increased in area by 176.43 [km.sup.2] (12.03%), and habitat with good and very good grades decreased in area by 129.19 [km.sup.2] (8.90%) from 1990-2014. The habitat suitability of the study area improved during 1990-2000, but habitat suitability declined during 2000-2014. As a whole, the centroid of habitat with a poor grade moved to the south and the centroid habitat with a good grade moved to the north during 1990-2014. For the habitat with a poor grade, the centroid shifted by 10.57 km to the southwest during 1990-2000 and 6.59 km to the southeast during 2000-2014. For the habitat with a good grade, the centroid shifted by 8.08 km to the northeast during 19902000 and 4.57 km to the northwest during 2000-2014. In conclusion, the approaches used here have great potential in modelling habitat suitability in highly heterogeneous and dynamic environments and may be used to help track habitat suitability dynamics in protected areas in other regions. Our findings provide up-to-date spatial information for biodiversity conservation in the MNR.

https://doi.org/10.38436/16486897.2017.1316982

Acknowledgements

This study was jointly supported by the National Key Research and Development Project (No. 2016YFA0602301) and the National Natural Science Foundation of China (Nos. 41371403, 41401502 and 41671219).

Disclosure statement

We do not have any competing financial, professional, or personal interests from other parties.

References

Anteau, M. J. 2012. Do interactions of land use and climate affect productivity of waterbirds and prairie-pothole wetlands?, Wetlands 32: 1-9. https://doi.org/10.1007/s13157-011-0206-3

Anteau, M. J.; Wiltermuth, M. T.; Sherfy, M. H.; Shaffer, T. L. 2014. Measuring and predicting abundance and dynamics of habitat for piping plovers on a large reservoir, Ecological Modelling 272: 16-27. https://doi.org/10.1016/j.ecolmodel.2013.08.020

Austin, M. 2007. Species distribution models and ecological theory: a critical assessment and some possible new approaches, Ecological Modelling 200(1): 1-19. https://doi.org/10.1016/j.ecolmodel.2006.07.005

Belluco, E.; Camuffo, M.; Ferrari, S.; Modenese, L. 2006. Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing, Remote Sensing of Environment 105: 54-67. https://doi.org/10.1016/j.rse.2006.06.006

Bradley, B. A.; Olsson, A. D.; Wang, O.; Dickson, B. G. 2012. Species detection vs. habitat suitability: are we biasing habitat suitability models with remotely sensed data?, Ecological Modelling 244: 57-64. https://doi.org/10.1016/j.ecolmodel.2012.06.019

Brotons, L.; Thuiller, W.; Araujo, M. B.; Hirzel, A. H. 2004. Presence-absence versus presence-only modelling methods for predicting bird habitat suitability, Ecography 27: 437-448. https://doi.org/10.1111/j.0906-7590.2004.03764.x

Burnham, K. P.; Anderson, D. R. 2002. Model selection and multimodel inference: a practical information-theoretic approach. 2nd ed. New York: Springer-Verlag.

Corbane, C.; Lang, S.; Pipkins, K.; Alleaume, S. 2015. Remote sensing for mapping natural habitats and their conservation status-New opportunities and challenges, International Journal of Applied Earth Observation and Geoinformation 37: 7-16. https://doi.org/10.1016/j.jag.2014.11.005

Cord, A.; Rodder, D. 2011. Inclusion of habitat availability in species distribution models through multi-temporal remote-sensing data?, Ecological Applications 21: 3285-3298. https://doi.org/10.1890/11-0114.!

Cumming, G. S.; Paxton, M.; King, J.; Beuster, H. 2012. Foraging guild membership explains variation in waterbird responses to the hydrological regime of an arid-region flood-pulse river in Namibia, Freshwater Biology 57: 1202-1213. https://doi.org/10.1111/j.1365-2427.2012.02789.x

Delgado, L. E.; Marin, V. H. 2013. Interannual changes in the habitat area of the black-necked swan, cygnus melancoryphus, in the carlos anwandter sanctuary, southern Chile: a remote sensing approach, Wetlands 33: 91-99. https://doi.org/10.1007/s13157-012-0354-0

Dolan, J. G. 2008. Shared decision-making-transferring research into practice: the Analytic Hierarchy Process (AHP), Patient Education Counseling 73: 418-425. https://doi.org/10.1016Zj.pec.2008.07.032

Dong, Z. Y.; Wang, Z. M.; Liu, D. W.; Li, L. 2013. Assessment of habitat suitability for waterbirds in the West Songnen Plain, China, using remote sensing and GIS, Ecological Engineering 55: 94-100. https://doi.org/10.1016/j.ecoleng.2013.02.006

Euliss, N. H.; Smith, L. M.; Liu, S.; Duffy, W. G. 2011. Integrating estimates of ecosystem services from conservation programs and practices into models for decision makers, Ecological Applications 21: S128-S134. https://doi.org/10.1890/09-0285.1

Fiorella, M.; Rippley, W. J. 1993. Analysis of conifer forest regeneration using Landsat thematic mapper data, Photogrammetric Engineering and Remote Sensing 59(9): 1383-1388.

Fischer, J.; Lindenmayer, D. B. 2007. Landscape modification and habitat fragmentation: a synthesis, Global Ecology and Biogeography 16: 265-280. https://doi.org/10.1111/j.1466-8238.2007.00287.x

Fraser, L. H.; Keddy, P. A. 2005. The world's largest wetlands: ecology and conservation. Cambridge: Cambridge University Press. https://doi.org/10.1017/CB09780511542091

Gardner, T. A.; Barlow, J.; Peres, C. A. 2007. Paradox, presumption and pitfalls in conservation biology: the importance of habitat change for amphibians and reptiles, Biological Conservation I38: I66-I79. https://doi.org/10.1016/j.biocon.2007.04.017

Guisan, A.; Zimmermann, N. E. 2000. Predictive habitat distribution models in ecology, Ecological Modelling 135: 147-186. https://doi.org/10.1016/S0304-3800(00)00354-9

Hazell, D.; Hero, J. M.; Lindenmayer, D.; Cunningham, R. 2004. A comparison of constructed and natural habitat for frog conservation in an Australian agricultural landscape, Biological Conservation 119: 61-71. https://doi.org/10.1016/j.biocon.2003.10.022

Hirzel, A. H.; Helfer, V.; Metral, F. 2001. Assessing habitat-suitability models with a virtual species, Ecological Modelling 145: 111-121. https://doi.org/10.1016/S0304-3800(01)00396-9

Hodgson, J. A.; Moilanen, A.; Thomas, C. D. 2009. Metapopulation responses on patch connectivity and quality are masked by successional habitat dynamics, Ecology 90: 1608-1619. https://doi.org/10.1890/08-1227.1

Ilgurel, N.; Akdag, N. Y.; Akdag, A. 2016. Evaluation of noise exposure before and after noise barriers, a simulation study in Istanbul, Journal of Environmental Engineering and Landscape Management 24: 293-302. https://doi.org/10.3846/16486897.2016.1184671

Kartikeyan, B.; Sarkar, A.; Majumdar, K. L. 1998. A segmentation approach to classification of remote sensing imager, International Journal of Remote Sensing 19: 1695-1709. https://doi.org/10.1080/014311698215199

Li, C.; Zhao, J. 2017. Assessment of future urban growth impact on landscape pattern using cellular automata model: a case study of Xuzhou city, China, Journal of Environmental Engineering and Landscape Management 25: 23-38. https://doi.org/10.3846/16486897.2016.1187620.

Li, X. Y.; Wang, Z. M.; Song, K. S.; Zhang, B. 2007. Assessment for salinized wasteland expansion and land use change using GIS and remote sensing in the west part of Northeast China, Environment Monitoring and Assessment 131: 421-437. https://doi.org/10.1007/s10661-006-9487-z

Liu, C. Y.; Sun, Y.; Liu, L. F.; Zhang, S. Q. 2010. 3S-based wetland habitat information management system in Songnen Plain, China, UNEP/GEF Siberian Crane Wetland Project-Project Completion Workshop Proceedings: 1-10.

Ludwig, D. F.; Ianuzzi, T. J. 2006. Habitat equivalency in urban estuaries: an analytical hierarchy process for planning ecological restoration, Urban Ecosystems 9: 265-290. https://doi.org/10.1007/s11252-006-0007-2

McDonald, A. J.; Genmell, F. M.; Lewis, P. E. 1998. Investigation of the utility of spectral indices for determining information on coniferous forests, Remote Sensing of the Environment 66: 250-272. https://doi.org/10.1016/S0034-4257(98)00057-1

Millennium Ecosystem Assessment. 2005. Ecosystems and Human Well-Being: Synthesis. Berlin: Springer.

Na, X. D.; Zang, S.; Zhang, Y. H.; Li, W. 2015. Assessing breeding habitat suitability for the endangered red-crowned (Grus Japonensis) based on multi-source remote sensing data, Wetlands 35: 955-967. https://doi.org/10.1007/s13157-015-0686-7

Nagendra, H.; Lucas, R.; Honrado, J. P.; Jongman, R. H. G. 2013. Remote sensing for conservation monitoring: Assessing protected areas, habitat extent, habitat condition, species diversity, and threats, Ecological Indicators 33: 45-59. https://doi.org/10.1016/j.ecolind.2012.09.014

Nakamura, M.; Masuda, R.; Tsukamoto, K.; Otake, T. 2016. Narrowed temperature adaptability in non-natal osmotic environments of two euryhaline wanderers, dace and black porgy: implications for seasonal habitat changes, Fisheries Science 82: 261-268. https://doi.org/10.1007/s12562-015-0956-6

Pan, X. L.; Zhang, D. Y.; Quan, L. 2006. Interactive factors leading to dying-off Carex tato in Momoge wetland polluted by crude oil, Western Jilin, China, Chemosphere 65: 1772-1777. https://doi.org/10.1016/j.chemosphere.2006.04.063

Ramsar. 2012. The Ramsar Convention on Wetlands [online], [cited 8 July 2012]. Available from Internet: http://www.ramsar.org/

Reid, J. R. W.; Colloff, M. J.; Arthur, A. D.; McGinness, H. M. 2013. Influence of catchment condition and water resource development on waterbird assemblages in the Murray-Darling Basin, Australia, Biological Conservation 165: 25-34. https://doi.org/10.1016/j.biocon.2013.05.009

Ronka, M. T. H.; Hakkila, K. 2005. Environmental changes and population trends of breeding waterfowl in northern Baltic Sea, Annales Zoologici Fennm 42: 587-602.

Ronka, M.; Tolvanen, H.; Lehikoinen, E.; Numers, M. V. 2008. Breeding habitat preferences of 15 bird species on southwestern Finnish archipelago coast: Applicability of digital spatial data archives to habitat assessment, Biological Conservation 141: 402-416. https://doi.org/10.1016/j.biocon.2007.10.010

Saaty, T. L. 2008. Decision making with the analytic hierarchy process, International Journal of Services Sciences 1: 83-98. https://doi.org/10.1504/IJSSCI.2008.017590

Sekercioglu, C. H.; Daily, G. C.; Ehrlich, P. R. 2004. Ecosystem consequences of bird declines, Proceedings of the National Academy of Sciences of the United States of America 101: 18042-18047. https://doi.org/10.1073/pnas.0408049101

Silva, T. S. F.; Costa, M. P. F.; Melack, J. M.; Novo, E. M. L. M. 2008. Remote sensing of aquatic vegetation: theory and applications, Environmental Monitoring and Assessment 140: 131-145. https://doi.org/10.1007/s10661-007-9855-3

Strasser, T.; Lang, S. 2015. Object-based class modelling for multi-scale riparian forest habitat mapping, International Journal of Applied Earth Observation and Geoinformation 37: 29-37. https://doi.org/10.1016/j.jag.2014.10.002

Strasser, T.; Lang, S.; Riedler, B.; Pernkopf, L. 2014. Multiscale object feature library for habitat quality monitoring in riparian forests, IEEE Geoscience and Remote Sensing Letters 11: 559-563. https://doi.org/10.1109/LGRS.2013.2278335

Tian, B.; Zhou, Y. X.; Zhang, L. Q.; Yuan, L. 2008. Analyzing the habitat suitability for migratory birds at the Chongming Dongtan Nature Reserve in Shanghai, China, Estuarine, Coastal and Shelf Science 80: 296-302. https://doi.org/10.1016Zj.ecss.2008.08.014

Wang, X. Y.; Feng, J.; Zhao, J. 2010. Effects of crude oil residuals on soil chemical properties in oil sites, Momoge Wetland, China, Environmental Monitoring and Assessment 161: 271-280. https://doi.org/10.1007/s10661-008-0744-1

Wang, Z. M.; Mao, D. H.; Li, L.; Jia, M. M. 2015. Quantifying changes in multiple ecosystem services during 1992-2012 in the Sanjiang Plain of China, Stience of the Total Environment 514: 119-130. https://doi.org/10.1016/j.scitotenv.2015.01.007

Wang, Z. M.; Song, K. S.; Zhang, B.; Liu, D. W. 2009. Shrinkage and fragmentation of grasslands in the West Songnen Plain, China, Agriculture, Ecosystems & Environment 12: 315-324. https://doi.org/10.1016/j.agee.2008.10.009

Weiers, S.; Bock, M.; Wissen, M.; Rossner, G. 2004. Mapping and indicator approaches for the assessment of habitats at different scales using remote sensing and GIS methods, Landscape and Urban Planning 67: 43-65. https://doi.org/10.1016/S0169-2046(03)00028-8

Wen, L.; Saintilan, N.; Yang, X. H.; Hunter, S. 2015. MODIS NDVI based metrics improve habitat suitability modelling in fragmented patchy floodplains, Remote Sensing Applications: Society and Environment 1: 85-97. https://doi.org/10.1016/j.rsase.2015.08.001

Williams M. (Ed.). 1990. In Wetlands: a threatened landscape. Oxford: Blackwell Press.

Wilson, E. H.; Sader, S. A. 2002. Detection of forest harvest type using multiple dates of Landsat TM imagery, Remote Sensing of Environment 80: 385-396. https://doi.org/10.1016/S0034-4257(01)00318-2

Xiao, H. Y.; Zhang, M. X.; Xiao, R. 2014. Dynamic evaluation of main ecological service functions in Momoge wetlands, Wetland Science 4: 451-458.

Yang, F. Y.; Lu, X. G.; Lou, Y. J.; Lou, X. N. 2012. Fish diversity in Momoge National Nature Reserve, Wetland Science 2: 214-222.

Zhu, C. Y.; Ji, P.; Li, S. H. 2017. Effects of urban green belts on the air temperature, humidity and air quality, Journal of Environmental Engineering and Landscape Management 25: 39-55. https://doi.org/10.3846/16486897.2016.1194276

Zlinszky, A.; Mucke, W.; Lehner, H.; Briese, C. 2012. Categorizing wetland vegetation by airborne laser scanning on Lake Balaton and Kis-Balaton, Hungary, Remote Sensing 4: 1617-1650. https://doi.org/10.3390/rs4061617

Zomer, R. J.; Trabucco, A.; Usin, S. L. 2009. Building spectral libraries for wetlands land cover classification and hyperspectral remote sensing, Journal of Environmental Management 90: 2170-2177. https://doi.org/10.1016/j.jenvman.2007.06.028

Zou, H. F.; Wu, Q. M. 2009. Internal distribution pattern of the nests and home ranges of red-crowned cranes in Zhalong Nature Reserve, Acta Ecologica Sinica 29: 1710-1718.

Zou, H. F.; Wu, Q. M.; Ma, J. Z. 2003. The nest-site selection of red-crowned crane in Zhalong Nature Reserve after burning and irrigating, Journal of Northeast Normal University 35: 54-59.

Yue ZHANG. PhD student, lecturer of College of Resources and Environment, Jilin Agricultural University. Doctor of Cartography and geographic information system. Publications: author of over 10 scientific publications, participant of five international conferences. Research interests: wetland vegetation ecology.

Zongming WANG. PhD, Professor of Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences. Publications: author of over 30 scientific publications. Research interests: remote sensing and ecosystem services assessment of wetlands, and regional environmental changes.

Chunying REN. PhD, Associate professor of Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences. More than 20 scientific publications. Research interests: GIS technology, wetland landscape change analysis, remote sensing of resource and environment.

Hao YU. PhD student of Northeast Institute of Geography Agroecology, Chinese Academy of Sciences. Research interests: Resource and environment remote sensing, and land degradation and restoration.

Zhangyu DONG. PhD, lecturer of Hefei University of Technology. Doctor of Cartography and geographic information system. More than 30 scientific publications. Research interests: wetland restoration modelling.

Chunyan LU. PhD, lecturer of Computer and Information Sciences College, Fujian Agriculture and Forestry University. Doctor of Cartography and geographic information system. More than 20 scientific publications. Research interests: GIS technology, wetland landscape change analysis, remote sensing information processing.

Dehua MAO. PhD, Assistant Professor of Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences. Publications: author of over 20 scientific publications. Research interests: remote sensing of wetland, landscape pattern and ecosystem services, vegetation net primary productivity and modelling.

Yue ZHANG (a,b,c), Zongming WANG (a), Chunyang REN (a), Hao YU (a), Zhangyu DONG (a), Chunyan LU (a), Dehua MAO (a)

(a) Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, 1330102 Changchun, China

(b) College of Resources and Environment, Jilin Agricultural University, 130118 Changchun, China

(c) University of Chinese Academy of Sciences, 100049 Beijing, China

Submitted 21 May 2016; accepted 05 Apr. 2017

Corresponding author: Zongming Wang

E-mail: zongmingwang@iga.ac.cn

Caption: Fig. 1. Location of the Momoge Nature Reserve (Landsat 8 OLI 543 data (07/10/2013))

Caption: Fig. 2. Flow chart of the waterbird habitat suitability assessment in MNR

Caption: Fig. 3. Static waterbird habitat suitability indicators of the Momoge Nature Reserve: (a) density of roads, (b) density of rivers, and (c) slope

Caption: Fig. 4. Land cover maps for the Momoge Nature Reserve in 1990 (a), 2000 (b) and 2014 (c)

Caption: Fig. 5. NDVI maps for the Momoge Nature Reserve in 1990 (a), 2000 (b) and 2014 (c)

Caption: Fig. 6. NDMI maps for the Momoge Nature Reserve in 1990 (a), 2000 (b) and 2014 (c)

Caption: Fig. 7. Waterbird habitat suitability at the Momoge Nature Reserve in 1990 (a), 2000 (b) and 2014 (c)

Caption: Fig. 8. The centroid trajectories of different habitat grades during 1990-2014: (a) poor suitability, (b) fair suitability, (c) good suitability, and (d) very good suitability
Table 1. The indicator weights for waterbird habitat suitability
determined using an AHP method

                    Criterion level             Index level
Objective
level           Indicators     Weights    Indicators    Weights

Waterbird         Water          0.45     Density of      0.34
Habitat          abundance                  rivers
Suitability                                  NDMI         0.11
Index
in the             Food          0.32        NDVI          --
Momoge
Nature           Shelter         0.15     Land cover      0.12
Reserve          condition                   type
                                             Slope        0.03

                Disturbance      0.08     Density of       --
                                             roads

Table 2. Changes in habitat area and proportion of habitat with
different suitability grades

Grades                  1990                           2000

                 Area        Percentage        Area        Percentage
             ([km.sup.2])        (%)       ([km.sup.2])        (%)

Poor            279.06          19.04         186.85          12.75
Fair            853.49          58.22         874.24          59.63
Good            326.27          22.26         379.78          25.91
Very good        6.78           0.48           24.74          1.71

Grades                   2014

                 Area        Percentage
             ([km.sup.2])        (%)

Poor            233.19          15.91
Fair            1029.92         70.25
Good            198.64          13.55
Very good        5.22           0.29


Please Note: Illustration(s) are not available due to copyright restrictions.
COPYRIGHT 2017 Vilnius Gediminas Technical University
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2017 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Zhang, Yue; Wang, Zongming; Ren, Chunyang; Yu, Hao; Dong, Zhangyu; Lu, Chunyan; Mao, Dehua
Publication:Journal of Environmental Engineering and Landscape Management
Date:Dec 1, 2017
Words:7217
Previous Article:FORECASTING SURFACE WATER LEVEL FLUCTUATIONS OF LAKE SERWY (NORTHEASTERN POLAND) BY ARTIFICIAL PLEURAL NETWORKS AND MULTIPLE LINEAR REGRESSION.
Next Article:NUMERICAL MODELING OF UNSATURATED LAYERED SOIL FOR RAINFALL-INDUCED SHALLOW LANDSLIDES.

Terms of use | Privacy policy | Copyright © 2021 Farlex, Inc. | Feedback | For webmasters |