GIS-based health assessment of the marine ecosystem in Laizhou Bay, China

Song, Debin; Gao, Zhiqiang; Zhang, Hua; Xu, Fuxiang; Zheng, Xiaojuan; Ai, Jinquan; Hu, Xiaoke; Huang, Guosong; Zhang, Haibo

doi:10.1016/j.marpolbul.2017.08.027

Cited by 61 publications

(23 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…RS data can be used to monitor and assess the spatial heterogeneity of ecosystem health at different space-time scales [50,74]. Hence, RS and GIS technologies are used widely in EHA [20,45]. RS large-scale data can quickly assess the space-time distribution and evolution of ecosystem health in heritage sites, but RS large-scale data have certain limitations in accurately reflecting the level of ecosystem health, thus, the ground-based field sampling small-scale data were used to verify the rationality of the results of RS large-scale data [65].…”

Section: Ecosystem Health Assessment Modelmentioning

confidence: 99%

“…Ecosystem consists of multiple interactive relationships, which may be highly complex [13], and several research methods and indicators have been developed for ecosystem health assessment (EHA). In general, multicriteria-based evaluation models are used in EHA research, including the index of biotic integrity [14,15], direct measurement method (DMM) [16], ecological modelling method (EMM) [17], the pressure-state-response (PSR) model [18,19], the analytic hierarchy process model [20], and vigour, organisation, and resilience (VOR) model [10,11,21]. Among these models, the VOR is the one used most widely for EHA.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ecosystem Health Assessment of World Natural Heritage Sites Based on Remote Sensing and Field Sampling Verification: Bayanbulak as Case Study

et al. 2020

View full text Add to dashboard Cite

Monitoring the ecosystem health for world natural heritage sites is essential for protecting them and benefits the formulation of more targeted protection policies. This study used Bayanbulak world natural heritage site as a case, established a framework for assessing the ecosystem health through remote sensing based on the parameters of ecosystem vigour, organization, resilience, and services. Then, we verified the obtained results through field sampling. The results show that the ecosystem health in the overall study area had declined over time, however, the health within the property zone remained at high levels and stable. The area proportion of low health was low and primarily distributed in the buffer zone. Thus, in general, the ecosystem in the study area was healthy. Besides, the ecosystem health exhibited distinct spatial agglomeration characteristics, and the degree of agglomeration enhanced over time. In addition, the field vegetation samplings were consistent with the changes in the ecosystem health levels, therefore, the result of RS monitoring of ecosystem health were credible. Thus, this study provides a scientific basis for heritage managers to formulate suitable ecological protection policies and should aid further research on the ecological monitoring of heritage sites.

show abstract

Section: Ecosystem Health Assessment Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ecosystem Health Assessment of World Natural Heritage Sites Based on Remote Sensing and Field Sampling Verification: Bayanbulak as Case Study

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Yan et al (2016) evaluated ecosystem health in the upstream of the Liaohe River basin based on ecosystem services. Song et al (2017) used the analytic hierarchy process (AHP) to assess the ecosystem health status of marine life in Laizhou Bay. Yang, Song, and Lu (2020) used a new evaluation framework of pressure, state (vigour‐organization‐resilience‐function), and response to assess the level of land ecosystem health in Qiqihaer City.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of temporal and spatial changes of global ecosystem health

et al. 2020

View full text Add to dashboard Cite

Understanding the spatial distribution pattern and driving factors behind ecosystem health is beneficial to ecosystem management and restoration. However, the literature shows little in‐depth exploration of regional heterogeneity and the factors that influence ecosystem health on a global scale. This study assesses global ecosystem health for the period 2000–2015 based on the vigor, organization, resilience, and service (VORS) model, and it explores the factors that influence regional differences in ecosystem health. Our results show that: (a) regions with high levels of ecosystem health are distributed mainly near the Equator and within the north–south regression line (0°N–13°N, 0°S–18°S); (b) seven critical zones with high levels of ecosystem health are identified (13°N–18°S, 45°N–65°N, 35°S–56°S, 47°W–80°W, 120°W–130°W, 8°E–37°E, 92°E–157°E); and (c) average annual precipitation and soil moisture play a key role in ecosystem health globally, with correlations of 0.574 and 0.399, respectively. Socioeconomic factors act as bridges, linking and reinforcing the influence of other factors in areas with medium to low levels of ecosystem health. Our study contributes to better understanding of ecosystem health, fills gaps in global ecosystem health diagnosis, and provides reference points for management and recovery of ecosystems.

show abstract

“…From the city level, there were 16 cities with 10 or more elite hospitals, including 4 municipalities directly under the Central Government, Beijing, Shanghai, Tianjin, and Chongqing, and 12 provincial capital cities. Among them, Beijing, Shanghai and Guangzhou had the largest number of elite hospitals, 30, 24, and 20 respectively, followed by Tianjin (17), Wuhan (17), Xi'an (16), Hangzhou (13), and Taiyuan (13) , Nanchang (13), Nanjing (12), Chongqing (11), Chengdu (11), Guiyang (11), Changchun (11), Harbin (10), Fuzhou (10); those with 5 to 10 elite hospitals There are 17 cities in total, namely Xining (9), Shenyang (9), Foshan (9), Shijiazhuang (8), Nanning (8), Jinan (7), Dongguan (7), Dalian (7), Shenzhen ( 6) ), Changsha (6), Lanzhou (6), Hohhot (6), Urumqi (6), Zhengzhou (5), Wuxi (5), Xiamen (5), Jilin (5).…”

Section: Spatial Distribution Of Elite Hospitals In Chinamentioning

confidence: 99%

“…Elite hospitals are one of the important indicators reflecting high-quality medical resources in the region, and their spatial distribution is directly related to the fairness of health resource allocation. At present, China is pursuing the reform of the medical system [4][5][6][7] . Taking China as an example to study the space allocation of elites is of great significance for achieving the goal of health equity and protecting citizens' right to health, and it can also provide reference for other countries.…”

mentioning

confidence: 99%

Research on the Spatial Pattern and Spatial Heterogeneity of Chinese Elite Hospitals

Shi¹,

Wang²,

Zheng³

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: Facing the problems of unreasonable allocation of medical resources and health inequity, the layout of elite hospitals in China deserves further attention. This paper explores the unbalanced allocation of high-quality resources and its influencing factors in the development of China's health system , so as to provide reference for the rational distribution of high-quality medical resources in China and other countries, from the perspective of space.Methods: This study investigated 706 elite hospitals in 31 provinces, cities and autonomous regions in 2017. In this paper, geographic weighted regression (GWR), spatial lag model and spatial error model are used to study the influencing factors and spatial heterogeneity of spatial pattern.Results: Spatial auto-correlation of elite hospitals in China is of great significance and the results of OLS regression showed that city level, number of medical schools, urban population and resident population were its significant variables. Further, its spatial agglomeration phenomenon were confirmed through SEM and SLM model. Among them, the city level is the most important factor affecting the spatial distribution of elite hospitals in China. The influence intensity of urban level gradually weakened from northeast to southwest. And medical colleges, whose degree of influence is gradually increasing from northeast to southwest, is the second influence factor. There is a weak relationship between the distribution of elite hospitals and population size,which indicates other factors' important impact.Conclusion: China's elite hospitals are unevenly distributed and have obvious spatial heterogeneity. Therefore, we suggest that we should pay attention to the spatial governance of high-quality medical resources, attract medical elites in the region, increase investment in medical education in the scarce areas of elite hospitals and develop tele-medicine service. Through these means, we can solve the problem of unbalanced spatial distribution of medical resources and the demand of areas lacking high-quality medical resources.

show abstract

GIS-based health assessment of the marine ecosystem in Laizhou Bay, China

Cited by 61 publications

References 14 publications

Ecosystem Health Assessment of World Natural Heritage Sites Based on Remote Sensing and Field Sampling Verification: Bayanbulak as Case Study

Ecosystem Health Assessment of World Natural Heritage Sites Based on Remote Sensing and Field Sampling Verification: Bayanbulak as Case Study

Evaluation of temporal and spatial changes of global ecosystem health

Research on the Spatial Pattern and Spatial Heterogeneity of Chinese Elite Hospitals

Contact Info

Product

Resources

About