Special Issue "Spatial Environmental Analysis, Informatics, Planning, Restoration, and Management"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (28 February 2018)

Special Issue Editor

Guest Editor
Prof. Dr. Jason K. Levy

Disaster Preparedness and Emergency Management, University of Hawaii, Kapolei, HI 96707, USA
Website | E-Mail
Interests: disaster risk governance; sustainable hazard mitigation; emergency management decision making; natural–technologic (na-tech) crises; health-related emergencies; fluvial and marine disasters; global climate change

Special Issue Information

Dear Colleagues,

Spatial analysis is essential for environmental science, planning, restoration and management. Environmental changes over space and time can now be addressed with a plethora of novel and powerful techniques and modelling approaches. A robust and integrated approach to dealing with environmental spatial problems can help to better model and understand spatio-temporal physical environmental processes. This Special Issue encourages a number of timely and valuable geospatial methods and computational tools, such as environmental remote sensing, machine learning and environmental informatics. This Special Issue explores the development and application of these advanced geospatial tools, including spatial Multiple Criteria Analysis (MCA) for monitoring, analyzing, and predicting to study terrestrial and aquatic ecosystems. Possible topics include the use of modern spatial analytical methods to integrate heterogeneous environmental data sources in analyses and models. Related research includes the incorporation of “big” data from spatial databases, sensor nets and other ground based networks. Recent advances in spatial environmental analysis also include machine learning, geospatial information, crowdsourcing tools from Location Based Services (LBS), Citizen Censors and Volunteered Geographic Information (VGI).

This Special Issue is devoted to all aspects of spatial science related to Environmental Analysis, Restoration Planning and Management including:

• terrestrial and aquatic mapping
• climate variability and change
• multiple Criteria Analysis and geographic information systems,
• environmental statistics
• landscape patterns and ecology
• big data and machine learning
• vegetation mapping
• habitat management
• land use change
• soil Degradation
• remote sensing and Location Based Services (LBS)
• species distribution modelling
• Volunteering Geographic Information (VGI)

Prof. Dr. Jason K. Levy
Guest Editor

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Published Papers (11 papers)

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Research

Open AccessArticle
A Knowledge-Informed and Pareto-Based Artificial Bee Colony Optimization Algorithm for Multi-Objective Land-Use Allocation
ISPRS Int. J. Geo-Inf. 2018, 7(2), 63; https://doi.org/10.3390/ijgi7020063
Received: 18 December 2017 / Revised: 4 February 2018 / Accepted: 6 February 2018 / Published: 12 February 2018
Cited by 3 | PDF Full-text (24593 KB) | HTML Full-text | XML Full-text
Abstract
Land-use allocation is of great significance in urban development. This type of allocation is usually considered to be a complex multi-objective spatial optimization problem, whose optimized result is a set of Pareto-optimal solutions (Pareto front) reflecting different tradeoffs in several objectives. However, obtaining [...] Read more.
Land-use allocation is of great significance in urban development. This type of allocation is usually considered to be a complex multi-objective spatial optimization problem, whose optimized result is a set of Pareto-optimal solutions (Pareto front) reflecting different tradeoffs in several objectives. However, obtaining a Pareto front is a challenging task, and the Pareto front obtained by state-of-the-art algorithms is still not sufficient. To achieve better Pareto solutions, taking the grid-representative land-use allocation problem with two objectives as an example, an artificial bee colony optimization algorithm for multi-objective land-use allocation (ABC-MOLA) is proposed. In this algorithm, the traditional ABC’s search direction guiding scheme and solution maintaining process are modified. In addition, a knowledge-informed neighborhood search strategy, which utilizes the auxiliary knowledge of natural geography and spatial structures to facilitate the neighborhood spatial search around each solution, is developed to further improve the Pareto front’s quality. A series of comparison experiments (a simulated experiment with small data volume and a real-world data experiment for a large area) shows that all the Pareto fronts obtained by ABC-MOLA totally dominate the Pareto fronts by other algorithms, which demonstrates ABC-MOLA’s effectiveness in achieving Pareto fronts of high quality. Full article
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Open AccessArticle
A Case Study of the Forced Invariance Approach for Soil Salinity Estimation in Vegetation-Covered Terrain Using Airborne Hyperspectral Imagery
ISPRS Int. J. Geo-Inf. 2018, 7(2), 48; https://doi.org/10.3390/ijgi7020048
Received: 20 October 2017 / Revised: 29 January 2018 / Accepted: 1 February 2018 / Published: 4 February 2018
Cited by 1 | PDF Full-text (6249 KB) | HTML Full-text | XML Full-text
Abstract
Soil spectroscopy is a promising technique for soil analysis, and has been successfully utilized in the laboratory. When it comes to space, the presence of vegetation significantly affects the performance of imaging spectroscopy or hyperspectral imaging on the retrieval of topsoil properties. The [...] Read more.
Soil spectroscopy is a promising technique for soil analysis, and has been successfully utilized in the laboratory. When it comes to space, the presence of vegetation significantly affects the performance of imaging spectroscopy or hyperspectral imaging on the retrieval of topsoil properties. The Forced Invariance Approach has been proven able to effectively suppress the vegetation contribution to the mixed image pixel. It takes advantage of scene statistics and requires no specific a priori knowledge of the referenced spectra. However, the approach is still mainly limited to lithological mapping. In this case study, the objective was to test the performance of the Forced Invariance Approach to improve the estimation accuracy of soil salinity for an agricultural area located in the semi-arid region of Northwest China using airborne hyperspectral data. The ground truth data was obtained from an eco-hydrological wireless sensing network. The relationship between Normalized Difference Vegetation Index (NDVI) and soil salinity is discussed. The results demonstrate that the Forced Invariance Approach is able to improve the retrieval accuracy of soil salinity at a depth of 10 cm, as indicated by a higher value for the coefficient of determination (R2). Consequently, the vegetation suppression method has the potential to improve quantitative estimation of soil properties with multivariate statistical methods. Full article
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Open AccessArticle
The Ordered Capacitated Multi-Objective Location-Allocation Problem for Fire Stations Using Spatial Optimization
ISPRS Int. J. Geo-Inf. 2018, 7(2), 44; https://doi.org/10.3390/ijgi7020044
Received: 16 December 2017 / Revised: 26 January 2018 / Accepted: 28 January 2018 / Published: 31 January 2018
Cited by 3 | PDF Full-text (2122 KB) | HTML Full-text | XML Full-text
Abstract
Determining the positions of facilities, and allocating demands to them, is a vitally important problem. Location-allocation problems are optimization NP-hard procedures. This article evaluates the ordered capacitated multi-objective location-allocation problem for fire stations, using simulated annealing and a genetic algorithm, with goals such [...] Read more.
Determining the positions of facilities, and allocating demands to them, is a vitally important problem. Location-allocation problems are optimization NP-hard procedures. This article evaluates the ordered capacitated multi-objective location-allocation problem for fire stations, using simulated annealing and a genetic algorithm, with goals such as minimizing the distance and time as well as maximizing the coverage. After tuning the parameters of the algorithms using sensitivity analysis, they were used separately to process data for Region 11, Tehran. The results showed that the genetic algorithm was more efficient than simulated annealing, and therefore, the genetic algorithm was used in later steps. Next, we increased the number of stations. Results showed that the model can successfully provide seven optimal locations and allocate high demands (280,000) to stations in a discrete space in a GIS, assuming that the stations’ capacities are known. Following this, we used a weighting program so that in each repetition, we could allot weights to each target randomly. Finally, by repeating the model over 10 independent executions, a set of solutions with the least sum and the highest number of non-dominated solutions was selected from among many non-dominated solutions as the best set of optimal solutions. Full article
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Open AccessArticle
Assessment of Sustainable Livelihood and Geographic Detection of Settlement Sites in Ethnically Contiguous Poverty-Stricken Areas in the Aba Prefecture, China
ISPRS Int. J. Geo-Inf. 2018, 7(1), 16; https://doi.org/10.3390/ijgi7010016
Received: 18 August 2017 / Revised: 20 November 2017 / Accepted: 29 December 2017 / Published: 5 January 2018
Cited by 1 | PDF Full-text (7207 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The Chinese government aims to deal with poverty by 2020 for people living in ethnic and rural regions, including mountainous ethnic regions with the highest concentration of poverty and chronic poverty. Based on a sustainable livelihood Framework, five capitals and 33 evaluation indices [...] Read more.
The Chinese government aims to deal with poverty by 2020 for people living in ethnic and rural regions, including mountainous ethnic regions with the highest concentration of poverty and chronic poverty. Based on a sustainable livelihood Framework, five capitals and 33 evaluation indices of livelihood were built, and 13 counties’ resources of the Aba Tibetan and Qiang Autonomous Prefecture were compared in order to calculate the degree of poverty. Topographic factors index of settlement sites (TFIS) were constructed by eight topographic factors, and diagnoses of the dominant factors of differentiation of 2699 settlements were calculated by using the geographical detector model to establish the poverty alleviation policies and models for different regions. The results showed that the livelihood capital evaluation indices were different (0.56–1.88), and natural capitals (mean value 1.56) had obvious advantages, but physical (mean value 0.56), financial (mean value 0.78), and human capital were lower (mean value 0.93), limiting the rate of transforming the ecological resources advantage into the economy. In the TFIS, the settlement points indicate topographic factors of natural breakpoint classification superposition, including elevation, slope, relief amplitude, surface incision, variance coefficient in elevation, surface roughness, distance to roads, and distance to rivers. These are within the 8–34 range, and their power determinant value to TFIS are 0.02, 0.70, 0.77, 0.76, 0.51, 0.66, 0.06, and 0.09. Livelihood capital evaluation indices and TFIS classification one (8–14) are positively correlated, and negative correlation (22–26 and 27–34) is at the 0.05 level. The county's poverty alleviation measures and development under different livelihood indices and TFIS indicate that the ecotourism industry has become the inevitable choice for promoting rapid and coordinated development of economy, society, and the environment in ethnic regions. Full article
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Open AccessArticle
Comparison and Evolution of Extreme Rainfall-Induced Landslides in Taiwan
ISPRS Int. J. Geo-Inf. 2017, 6(11), 367; https://doi.org/10.3390/ijgi6110367
Received: 12 September 2017 / Revised: 5 November 2017 / Accepted: 13 November 2017 / Published: 16 November 2017
Cited by 1 | PDF Full-text (20325 KB) | HTML Full-text | XML Full-text
Abstract
This study analyzed the characteristics of, and locations prone to, extreme rainfall-induced landslides in three watersheds in Taiwan, as well as the long-term evolution of landslides in the Laonong River watershed (LRW), based on multiannual landslide inventories during 2003–2014. Extreme rainfall-induced landslides were [...] Read more.
This study analyzed the characteristics of, and locations prone to, extreme rainfall-induced landslides in three watersheds in Taiwan, as well as the long-term evolution of landslides in the Laonong River watershed (LRW), based on multiannual landslide inventories during 2003–2014. Extreme rainfall-induced landslides were centralized beside sinuous or meandering reaches, especially those with large sediment deposition. Landslide-prone strata during extreme rainfall events were sandstone and siltstone. Large-scale landslides were likely to occur when the maximum 6-h accumulated rainfall exceeded 420 mm. All of the large-scale landslides induced by short-duration and high-intensity rainfall developed from historical small-scale landslides beside the sinuous or meandering reaches or in the source area of rivers. However, most of the large-scale landslides induced by long-duration and high-intensity rainfall were new but were still located beside sinuous or meandering reaches or near the source. The frequency density of landslides under long-duration and high-intensity rainfall was larger by one order than those under short-duration rainfall, and the β values in the landslide frequency density-area analysis ranged from 1.22 to 1.348. The number of downslope landslides was three times larger than those of midslope and upslope landslides. The extreme rainfall-induced landslides occurred in the erosion gullies upstream of the watersheds, whereas those beside rivers were downstream. Analysis of the long-term evolution of landslides in the LRW showed that the geological setting, sinuousness of reaches, and sediment yield volume determined their location and evolution. Small-scale landslides constituted 71.9–96.2% of the total cases from 2003 to 2014, and were more easily induced after Typhoon Morakot (2009). The frequency density of landslides after Morakot was greater by one order than before, with 61% to 68% of total landslides located in the downslope. Small-scale landslides not beside the rivers disappeared within four years, whereas those beside rivers or located in the source areas either developed into large-scale landslides or slowly disappeared. Large-scale landslides caused by Morakot were either combined from several historical small-scale landslides in the river source areas or located beside the sinuous or meandering reaches. The probabilities of landslide recurrence in the LRW during the next 5, 10, and 20 years were determined to be 7.26%, 9.16%, and 10.48%, respectively, and those beside the rivers were 10.47%, 13.33%, and 15.41%, respectively. Full article
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Open AccessArticle
Ground Deformation Detection Using China’s ZY-3 Stereo Imagery in an Opencast Mining Area
ISPRS Int. J. Geo-Inf. 2017, 6(11), 361; https://doi.org/10.3390/ijgi6110361
Received: 25 September 2017 / Revised: 6 November 2017 / Accepted: 13 November 2017 / Published: 16 November 2017
Cited by 1 | PDF Full-text (22937 KB) | HTML Full-text | XML Full-text
Abstract
Detection and extraction of mining-induced ground deformation can be used to understand the deformation process and space distribution and to estimate the deformation laws and trends. This study focuses on the application of ground deformation detection and extraction combined with digital surface model [...] Read more.
Detection and extraction of mining-induced ground deformation can be used to understand the deformation process and space distribution and to estimate the deformation laws and trends. This study focuses on the application of ground deformation detection and extraction combined with digital surface model (DSM), derived from China’s ZiYuan-3 (ZY-3) satellite stereo imagery and the advanced spaceborne thermal emission and reflection radiometer global digital elevation model (ASTER GDEM) data. A district covering 200 km2 around the west open-pit mine in Fushun of Liaoning Province, a city located in Northeast China, is chosen as the study area. Regional overall deformation, typical region deformation, and topographical profile deformation are extracted to analyze the distribution and the link between the regional ground deformations. The results show that the mean elevation has already increased by 3.12 m from 2010 to 2015; 71.18% of this area is deformed, and 22.72% of this area has an elevation variation of more than 10 m. Four districts of rising elevation and three districts of descending elevation are extracted. They are deformed with distinct elevation and volume changes. The total area with distinct rising elevation (>15 m) is about 8.44 km2, and the change in volume is 2.47 × 108 m3. However, the total area with distinct descending elevation (<−10 m) is about 6.12 km2, and the change in volume is 2.01 × 108 m3. Moreover, the deformation in the local mining area has expanded to the surrounding areas. Experiments in the mining area demonstrate that ground deformation, especially acute deformation such as large fractures or landslides, can be monitored using DSMs derived from ZY-3 satellite stereo images. Full article
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Open AccessArticle
Exploring the Role of the Spatial Characteristics of Visible and Near-Infrared Reflectance in Predicting Soil Organic Carbon Density
ISPRS Int. J. Geo-Inf. 2017, 6(10), 308; https://doi.org/10.3390/ijgi6100308
Received: 26 August 2017 / Revised: 26 September 2017 / Accepted: 16 October 2017 / Published: 18 October 2017
Cited by 2 | PDF Full-text (2665 KB) | HTML Full-text | XML Full-text
Abstract
Soil organic carbon stock plays a key role in the global carbon cycle and the precision agriculture. Visible and near-infrared reflectance spectroscopy (VNIRS) can directly reflect the internal physical construction and chemical substances of soil. The partial least squares regression (PLSR) is a [...] Read more.
Soil organic carbon stock plays a key role in the global carbon cycle and the precision agriculture. Visible and near-infrared reflectance spectroscopy (VNIRS) can directly reflect the internal physical construction and chemical substances of soil. The partial least squares regression (PLSR) is a classical and highly commonly used model in constructing soil spectral models and predicting soil properties. Nevertheless, using PLSR alone may not consider soil as characterized by strong spatial heterogeneity and dependence. However, considering the spatial characteristics of soil can offer valuable spatial information to guarantee the prediction accuracy of soil spectral models. Thus, this study aims to construct a rapid and accurate soil spectral model in predicting soil organic carbon density (SOCD) with the aid of the spatial autocorrelation of soil spectral reflectance. A total of 231 topsoil samples (0–30 cm) were collected from the Jianghan Plain, Wuhan, China. The spectral reflectance (350–2500 nm) was used as auxiliary variable. A geographically-weighted regression (GWR) model was used to evaluate the potential improvement of SOCD prediction when the spatial information of the spectral features was considered. Results showed that: (1) The principal components extracted from PLSR have a strong relationship with the regression coefficients at the average sampling distance (300 m) based on the Moran’s I values. (2) The eigenvectors of the principal components exhibited strong relationships with the absorption spectral features, and the regression coefficients of GWR varied with the geographical locations. (3) GWR displayed a higher accuracy than that of PLSR in predicting the SOCD by VNIRS. This study aimed to help people realize the importance of the spatial characteristics of soil properties and their spectra. This work also introduced guidelines for the application of GWR in predicting soil properties by VNIRS. Full article
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Open AccessArticle
Assessing Spatial Accessibility of Public and Private Residential Aged Care Facilities: A Case Study in Wuhan, Central China
ISPRS Int. J. Geo-Inf. 2017, 6(10), 304; https://doi.org/10.3390/ijgi6100304
Received: 4 September 2017 / Revised: 13 October 2017 / Accepted: 13 October 2017 / Published: 16 October 2017
Cited by 3 | PDF Full-text (2409 KB) | HTML Full-text | XML Full-text
Abstract
In the increasingly serious aging China, aged service is the provision of one of the most urgent and important public services to citizens, and private facilities has become an important service force with the aged service market opening in China. This study aims [...] Read more.
In the increasingly serious aging China, aged service is the provision of one of the most urgent and important public services to citizens, and private facilities has become an important service force with the aged service market opening in China. This study aims to explore the spatial variation in the accessibility of residential aged care facilities (RACFs) and compared the service capacity of public RACFs and private RACFs. It facilitates RACFs to be allocated rationally in the future and achieve the equalization of aged services. A village-level analysis of spatial access to public and private RACFs by the multi-catchment sizes Gaussian two-step floating catchment area (MCSG2SFCA) method was conducted through a case study in Wuhan City in Central China. The major results are as follows: (1) the accessibility of RACFs in urban areas is better than that in rural areas; (2) the public RACFs still dominate aged care services but the role of private RACFs is important as well; (3) in developed urban areas, the accessibility to private RACFs surpasses that of public ones, and the situation is opposite in rural areas; (4) the capacity of aged care services in Wuhan is not high, meanwhile there is remarkable regional disparity. The accessibility of RACFs in Wuhan is not satisfactory, and there is a significant gap between urban and rural areas. The private RACFs have significantly improved the urban capacity of aged care services, but the role in rural areas is still very weak. Full article
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Open AccessArticle
Mapping Forest Species in the Central Middle Atlas of Morocco (Azrou Forest) through Remote Sensing Techniques
ISPRS Int. J. Geo-Inf. 2017, 6(9), 275; https://doi.org/10.3390/ijgi6090275
Received: 26 July 2017 / Revised: 12 August 2017 / Accepted: 29 August 2017 / Published: 3 September 2017
Cited by 4 | PDF Full-text (1870 KB) | HTML Full-text | XML Full-text
Abstract
The studies of forest ecosystems from remotely-sensed data are of great interest to researchers because of ecosystem services provided by this ecosystem, including protection of soils and vegetation, climate stabilization, and regulation of the hydrological cycle. In this context, our study focused on [...] Read more.
The studies of forest ecosystems from remotely-sensed data are of great interest to researchers because of ecosystem services provided by this ecosystem, including protection of soils and vegetation, climate stabilization, and regulation of the hydrological cycle. In this context, our study focused on the use of a spectral angle mapper (SAM) classification method for mapping species in the Azrou Forest, Central Middle Atlas, Morocco. A Sentinel-2A image combined with ground reference data were used in this research. Four classes (holm oak, cedar forest, bare soil, and others-unclassified) were selected; they represent, respectively, 27, 11, 24, and 38% of the study area. The overall accuracy of classification was estimated to be around 99.72%. This work explored the potential of the SAM classification combined with Sentinel-2A data for mapping land cover in the Azrou Forest ecosystem. Full article
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Open AccessArticle
Improving Identification of Areas for Ecological Restoration for Conservation by Integrating USLE and MCDA in a GIS-Environment: A Pilot Study in a Priority Region Northern Mexico
ISPRS Int. J. Geo-Inf. 2017, 6(9), 262; https://doi.org/10.3390/ijgi6090262
Received: 20 July 2017 / Revised: 17 August 2017 / Accepted: 20 August 2017 / Published: 25 August 2017
Cited by 1 | PDF Full-text (3912 KB) | HTML Full-text | XML Full-text
Abstract
Nature conservation is critical for securing an adequate supplying of environmental services to humans. Paradoxically, financial resources for conservation are normally scarce and, forest ecosystem restoration activities are expensive. So, a careful and detailed planning is vital for optimizing economic funds when ecosystems [...] Read more.
Nature conservation is critical for securing an adequate supplying of environmental services to humans. Paradoxically, financial resources for conservation are normally scarce and, forest ecosystem restoration activities are expensive. So, a careful and detailed planning is vital for optimizing economic funds when ecosystems restoration practices are implemented. In this work, we developed a methodology to find physically-degraded sites in order to determine both, urgency and feasibility to carry out ecological forest restoration activities in the Priority Region for Conservation Xilitla in the state of San Luis Potosí (Mexico). Both, Universal Soil Loss Equation (USLE) and Multi-Criteria Decision Analysis (MCDA) were integrated together by using climatic, soil, remotely-sensed, and proximity data at a 30 m spatial resolution. The results indicated that, more than 80% of the bare soil land in the protected area is under several conditions that lead to feasible ecosystem restoration. This methodology can be further applied to know about the spatial location of soil degraded sites when planning forest restoration practices in natural protected areas. Full article
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Open AccessFeature PaperArticle
Spatiotemporal Assessment of Littoral Waterbirds for Establishing Ecological Indicators of Mediterranean Coastal Lagoons
ISPRS Int. J. Geo-Inf. 2017, 6(8), 256; https://doi.org/10.3390/ijgi6080256
Received: 24 July 2017 / Revised: 7 August 2017 / Accepted: 18 August 2017 / Published: 19 August 2017
Cited by 1 | PDF Full-text (720 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Waterbirds are vital indicators of anthropogenic influence on the ecological status of Mediterranean coastal lagoons. Our study relates temporal waterbird data to key environmental gradients at catchment scale that have a structural or functional influence on littoral waterbird assemblages at different scales. During [...] Read more.
Waterbirds are vital indicators of anthropogenic influence on the ecological status of Mediterranean coastal lagoons. Our study relates temporal waterbird data to key environmental gradients at catchment scale that have a structural or functional influence on littoral waterbird assemblages at different scales. During two full-year cycles and two additional wintering seasons, the nearshore waterbird assemblages of the Mar Menor coastal lagoon (Murcia Region, SE Spain) were monitored monthly. Several biological indicator variables were related to the anthropogenic environmental gradient in the catchment area. Results showed that there was a strong dependence of waterbird assemblages on the distance to shore, emphasizing the importance of the first 100-m band, in which many species relevant to conservation converge on food resources. Well-preserved shoreline tracts therefore had a clear positive effect on community richness and diversity values, and were correlated with the occurrence of some species. These results clearly support the need for effective protection and restoration measures of such littoral habitats. Specific responses to local disturbing processes were nested within habitat and landscape preferences, supporting the value of aquatic birds as integrative ecological signals in semi-enclosed coastal systems. Moreover, waterbird-based indicators responded positively to environmental improvements both qualitatively and quantitatively. Full article
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