Evaluating Spatial Patterns of Ecosystem Services based on a Comparative Approach on Spatial Statistics in the Central Part of Isfahan Province

Document Type : Research Paper

Authors

1 Department of Environmental Science and Engineering, Faculty of Natural Resources and Environment, Malayer University, Malayer, Iran

2 Department of Nature Engineering, Faculty of Natural Resources and Environment, Malayer University, Malayer, Iran

3 Department of Environment, Faculty of Fishery and Environment, Gorgan University of Agriculture and Natural Resource, Gorgan, Iran

4 Department of Environment, Faculty of Natural Resources, Isfahan University of Technology, Isfahan, Iran

Abstract

Considering the spatial patterns of ecosystem services plays an important role in evaluating land capacity to provide some benefits. Accordingly, the spatial variation of three ecosystem services, aesthetics value, recreation value and noise pollution reduction in the central part of Isfahan province was investigated applying statistical approaches of Local Moran’s I, and Getis-Ord Gi analysis. Then, spatial accuracy of the investigated algorithms was evaluated and compared using the Receiving Operator Characteristic method. The findings reveal that the spatial variation of the ecosystem services under study have High-cluster pattern. Based on both used approaches and for all three ecosystem services, the central part of the study area has a positive spatial correlation pattern, whereas the southern part of the study area has a negative spatial correlation pattern. Areas representing the high cluster pattern involved have the highest supply of ecosystem services in the study area, while in areas with low cluster pattern, the provision of ecosystem services is very low. Spatial accuracy evaluating of ecosystem services spatial correlation patterns indicate that returned the largest area under ROC curve of recreational value and Getis-Ord Gi analysis (0.972), whereas the lowest rate is found for the noise pollution reduction service and Local Moran’s I, (0.833). Overall, according to the results, the Getis-Ord Gi indicates higher spatial accuracy than Local Moran’s I method for three ecosystem services, and the Receiving Operator Characteristic is an appropriate index to evaluate the accuracy of the analytical approaches used in This research. The findings of this study can be applied by decision makers and land planner to identify and management spatial correlation patterns of ecosystem services.
Extended Abstract
1-Introduction
Ecological components of the ecosystem affects the total ecosystem structure through an interaction with other socio-environmental parameters form its spatial structure and instability in these components. Accordingly, the concept of ecosystem services is a tool to assess the ecosystems conditions and relates the ecosystems structure and function to human well-being. Ecosystem services are used as an appropriate tool to link knowledge, policy-making and land planning which can be identified in four categories: provisioning services, regulating services, supporting services and cultural services. For sustainable development, it is necessary to identify and investigate ecosystem services at different scales. Spatial presentation of ecosystem services supply in landscape is necessary to integrate ecosystem services in the urban and regional planning process. Therefore, mapping and quantifying the spatial distribution of ecosystem services is an important tool so that decision-makers and urban planners manage and monitor the levels of ecosystem services provision and provides a framework to identify regions including conversation value (due to the high supply of services). In recent years, spatial information systems (GIS) have been widely used to identify supply areas and assess the spatial distribution of ecosystem services. Using the capabilities of the spatial information system, we can map the spatial and temporal distribution of ecosystem services and assess the potential effects of environmental and managerial changes on the ecosystem services provision. The application of spatial statistics-based methods such as local Moran and Getis-Ord Gi analysis to evaluate spatial patterns of ecosystem services has recently been considered. However, in most studies, only one method based on spatial statistics has been used. Thus, the systematic comparison of these methods provides valuable information on how they are applied to evaluate spatial patterns of ecosystem services and provides a basis for integrating these variables into land planning.
2-Materials and Methods
The current study was conducted in the central part of Esfahan province, Iran. The area is located at 51°12′to 51°59′eastern longitudes and 32°19′to 32°56′ northern latitudes covering an area about 1181 Km2. The area includes Isfahan, Shahin-Shahr, KhomeiniShahr, Najafabad and Falavarjan townships. The annual average precipitation and temperature are 116.9 mm and 16.7 ° C, respectively.
The data used in this research are ecosystem service maps of aesthetic value, recreational value, and noise pollution reduction service, which were prepared in a GIS environment based on both effective criteria and indicators on each ecosystem service and according to the study of Abdollahi et al. (2020). In the next step, two methods of local Moran and Getis-Ord Gi analysis, the sub-branch of spatial statistics were used in the GIS environment, in order to evaluate the spatial patterns of ecosystem services. One of the basic concepts to analyze spatial statistics is the spatial contiguity of the phenomena. Various methods are applied to detect spatial contiguity and develop a weight matrix, among which, two methods of inverse distance and Contiguity edges only are used more. Finally, spatial accuracy of these approaches was calculated using Receiving Operator Characteristic (ROC).
3-Results and Discussion
The findings reveal that for both methods, the pattern of distribution of ecosystem services is the same and the central, eastern and western parts of the study area include patches with a high supply of ecosystem services, while the south part of the study area shows spots with low supply of ecosystem services. According to findings from local Moran, very high cluster patterns cover the minimum area and large part of the study area does not follow a specific spatial pattern which is not statistically significant. Based on the findings from Receiving Operator Characteristic the Getis-Ord Gi analysis has more spatial accuracy than the local Moran method for all three ecosystem services. Among ecosystem services, recreational value has the highest value under the curve for both approaches. On the other hand, regarding the fact that the values under the curve are close to one for ecosystem services in both approaches, both approaches have high spatial accuracy in order to evaluate the spatial patterns of ecosystem services.
4-Conclusion
Identifying the patterns of ecosystem services is an effective step to improve the management of the benefits obtained from nature. Overall, the Receiving Operator Characteristic (ROC) is an appropriate index to evaluate the accuracy of the analytical approaches used in this research. Along with this study, Saeidi et al (2017) reported that the ROC method is an appropriate approach to consider the different algorithms of aesthetic mapping. The results of this study can be used by decision-makers and land planners to identify and manage spatial correlation patterns of ecosystem services.
 

Keywords

References

References
Abdollahi, S., Ildoromi, A., Salmanmahini, A. & Fakheran, S. (2019). Determination and Quantification of the Landscape Aesthetic Value in Central Part of Isfahan Province. Iranian Journal of Applied Ecology, 7 (4), 31-42 (In Persian).
Abdollahi, S., Ildoromi, A., Salmanmahini, A. & Fakheran, S. (2019). Identifying and Determining Key Areas for Multiple Ecosystem Services Supply in Central Part of Isfahan Province. Journal of Environmental Sciences Studies, 4 (4), 2029-2036 (In Persian).
Abdollahi, S., Ildoromi, A., Salmanmahini, A. & Fakheran, S. (2020). Determination of homogenous areas for ecosystem services supply in the central part of Isfahan province. RS & GIS for Natural Resources, 11 (1), 29-47 (In Persian).
Akbari, E., Darvishi Boloorani, A. & Neysani Samani, N. (2017). Landslide susceptibility mapping using fuzzy-analytic network process. RS & GIS for Natural Resources, 8 (3), 73-88 (In Persian).
Asadi, M. & Karami, M. (2017). Representation of Temperature Variability in Fars Province Using Spatial Statistics. Geographical Research, 32 (1), 64-75 (In Persian).
Asadolahi, Z., Salmanmahini, A., Mirkarimi, S. H. & Azimi, M. (2018). Identifying Supply Hotspots of Multiple Ecosystem Services (Case study: East of Gorgan-rud Watershed). Journal of Environmental Science and Technology, Accepted Manuscript Available Online from 24 April 2018 (In Persian).
Bachmann Vargas, P. (2013). Ecosystem services modeling as a tool for ecosystem assessment and support for decision making process in Aysén region, Chile (Northern Patagonia). Master of Science Environmental Management, Faculty of Agriculture and Nutritional Sciences, Supervisors; Prof. Dr. Felix Müller; Dr. Víctor H. Marín, Christian-Albrechts-Universität, Kiel – Germany.
Bagstad, K. J., Villa, E., Batker, D., Harrison-Cox, J., Voigt, B. & Johnson, G. W. (2014). From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments, Ecology and Society, 19 (2), 64. http://dx.doi.org/ 10.5751/ES-06523-190264.
Bagstad, K. J., Semmens, D. J., Ancona, Z. H. & Sherrouse, B. C. (2017). Evaluating alternative methods for biophysical and cultural ecosystem services hotspot mapping in natural resource planning. Landscape ecology, 32 (1), 77-97.
Bartaw, I., Hajinezhad, A., Asgary, A. & Goli, A. (2013). Pattern analysis on Residential burglary by Exploratory Spatial Data Analysis (ESDA), Case study: Zahedan city. Strategic Research on Social Problems in Iran, 2 (2), 1-23 (In Persian).
Cai, W., Gibbs, D., Zhang, L., Ferrier, G. & Cai, Y. (2017). Identifying hotspots and management of critical ecosystem services in rapidly urbanizing Yangtze River Delta Region, China. Journal of Environmental Management, 191, 258e267.
Colgan, M. S., Baldeck, C. A., Féret, J. B. & P. Asner, G. (2012). Mapping Savanna Tree Species at Ecosystem Scales Using Support Vector Machine Classification and BRDF Correction on Airborne Hyperspectral and LiDAR Data. Remote Sensing, 4, 3462-3480.
Eskandari Nodeh, M. & Khoshdelan, M. (2012). Spatial Analysis of Population Distribution and Urban Services in Anzali City Based on the TOPSIS Model. Geography and Sustainability of Environment, 2 (2), 25-44 (In Persian).
Fallah Galharei, Gh. & Asadi, M. (2018). An Assessment of Spatial-temporal Alteration of Sunshine Hours in Iran. Journal of Geography and Planning, 22 (64), 229-246 (In Persian).
Grêt-regamey, A., Altwegg, J., Sirén, E. A., Van Strien, M. J. & Weibel, B. (2017). Integrating ecosystem services into spatial planning—A spatialdecision support tool. Landscape and Urban Planning, 165, 206-219.
Guerra, C. A., Maes, J., Geijzendorffer, I. & Metzger, M. J. (2016). An assessment of soil erosion prevention by vegetation in Mediterranean Europe: Current trends of ecosystem service provision. Ecological Indicators, 60, 213-222.
Han, R., Feng, C., Xu, N. & Guo, L. (2020). Spatial heterogeneous relationship between ecosystem services and human disturbances: A case study in Chuandong, China. Science of the Total Environment, 721:137818. DOI: 10.1016/j.scitotenv.2020.137818.
Jana, M. & Sar, N. (2016). Modeling of hotspot detection using cluster outlier analysis and Getis-Ord Gi* statistic of educational development in upper-primary level, India. Modeling Earth Systems and Environment, 2, 60. https://doi.org/10.1007/s40808-016-0122-x.
Korpilo, S., Jalkanen, J., Virtanen, T. & Lehvavirta, S. (2018). Where are the hotspots and coldspots of landscape values, visitor use and biodiversity in an urban forest? PLoS ONE, 13 (9), pone.0203611 e0203611. https://doi.org/10.1371/journal.
Law, E. A., Bryan, B. A., Meijaard, E., Mallawaarachchi, T., Struebig, M. & Wilson, K. A. (2015). Ecosystem services from a degraded peatland of Central Kalimantan: implications for policy, planning, and management. Ecological Application, 25, 70-87
Li, Y., Zhang, L, Yann J., Wang, P., Hu, N., Cheng, W. & Fu, B. (2017). Mapping the hotspots and coldspots of ecosystem services in conservation priority setting. Journal of Geographical Sciences, 27 (6), 681-696.
Management and Planning Organization of Esfahan Province. (2016). Statistical Yearbook of Esfahan Province, 180 pages (In Persian).
Moshtaghinezhad, F., Aghamohammadi, H. & Behzadi, S. (2017). Developing an Analytical Model based on Spatial Statistics for Analyzing Rainfall in the Catchment Area of Lake Urmia. Journal of Geomatics and Technology, 7 (2), 139-151 (In Persian).
Nadian, M., Mirzaei, R.& Soltani Mohammadi, S. (2018). Application of Moran'sI Autocorrelation in Spatial-Temporal Analysis of PM2.5 Pollutant (A case Study: Tehran City). Journal of Environmental Health Engineering, 5 (3), 197-213 (In Persian).
Nemec, K. T. & Raudsepp-Hearne, C. (2013). The use of geographic information systems to map and assess ecosystem services, Biodiversity and Conservation, 22 (1), 1-15.
Nino, K., Mamo, Y., Mengesha, G. & Kibret, K. S. (2017). GIS based ecotourism potential assessment in Munessa Shashemene Concession Forest and its surrounding area, Ethiopia. Applied Geography, 82, 48-58. https://doi.org/10.1016/j.apgeog.2017.02.010.
Orsi, F., Ciolli, M., Primmer, E., Varumo, L. & Geneletti, D. (2020). Mapping hotspots and bundles of forest ecosystem services across the European Union. Land Use Policy, 99, 104840
Petter, M., Mooney, Sh., Maynard, S. M., Davidson, A., Cox, M. & Horosak, I. (2013). A Methodology to Map Ecosystem Functions to Support Ecosystem Services Assessments. Ecology and Society, 18 (1), 1-36
Pirali, A., Hedayati, A., Pourmanafi, S., Beyraghdar Kashkooli, O.& Ghorbani, R. (2019). Investigation of changes of spatial autocorrelation patterns of chlorophyll-a in Choghakhor International wetland using hot spots index (Gi*) and remote sensing, Iran Science Fisheries Journal, 28 (1), 155-164. (In Persian)
Pontius, R. G. J. & Schneider, L. C. (2001). Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA, Agriculture. Ecosystems and Environment, 85, 239-248.
Rahimi, L., Malekmohammadi, B. & Yavari, A. (2019). The Ecosystem Services Assessment of Wetlands Based on the Classification of Hydrological-Ecological Structures and Functions (Case Study: Shadegan Wetland). Geography and Sustainability of Environment, 30, 51-72 (In Persian).
Raudsepp-Hearne, C., Peterson, G. D. & Bennett, E. M. (2010). Ecosystem service bundles for analyzing tradeoffs in diverse landscapes. In: Proceedings of the National Academy of Sciences, 11, 5242-5247.
Saeidi, S., Mohammadzadeh, M., Salmanmahini, A. & Mirkarimi, S. H. (2016). Application of Logistic Regression in Landscape Aesthetic Quality Modelling (Case study: Ziarat watershed of Golestan Province). Journal of Environmental Studies (JES), 42 (2), 427-439 (In Persian).
Saeidi, S., Mohammadzadeh, M., Salmanmahini, A. & Mirkarimi, S. H. (2017). Performance evaluation of multiple methods for landscape aesthetic suitability mapping: A comparative study between Multi-Criteria Evaluation, Logistic Regression and Multilayer Perceptron neural network. Land use policy, 67, 1-12.
Sakieh, Y., Salmanmahiny, A., Mirkarimi, S. H. & Saeidi, S. (2017). Measuring the relationships between landscape aesthetics suitability and spatial patterns of urbanized lands: an informed modelling framework for developing urban growth scenarios. Geocarto International, 32 (8), 1-21.
Schroter, M. & Remme, R. P. (2016). Spatial prioritization for conserving ecosystem services: comparing hotspots with heuristic optimization. Landscape Ecology, 31, 431-450.
Siroosi, H., Heshmati, Gh. & Salmanmahiny, A. (2019). Can empirically based model results be fed into mathematical models? MCE for neural network and logistic regression in tourism landscape planning. Environment, Development and Sustainability, 22, 3701-3722
Suryabhagavan, K. V., Tamirat, H. & Balakrishinan, M. (2015). Multi criteria evaluation in identifcation of potential ecotourism sites in Hawassa town and its surroundings, Ethiopia. Journal of Geomatics, 1 (9), 86-92.
Teimoori, A., Seyedian, S. M., Rouhani, H.& Ahmadi, R. (2018). Ecosystem Sustainability Assessment Using IUCN (Case Study: Dorook Basin). Geography and Sustainability of Environment, 7 (4), 81-93 (In Persian).
Torkashvand, M. (2017). Detection of Heat Islands over Arak City Based on Spatial Autocorrelation Analysis. Quarterly Journal of Environmental Based Territorial Planning, 9 (35), 123-148 (In Persian).
Trabucchi, M., O'Farrell, P. J., Notivol, E. & Comin, A. (2014). Mapping ecological processes and ecosystem services for prioritizing restoration efforts in a semi-arid Mediterranean river basin. Environmental Management, 53 (6), 1132-1145.
Van Renterghem, T. (2018). Improving the noise reduction by green roofs due to solar panels and substrate shaping. Building Acoustics, 25 (3), 219-232.
Wang, Z. B. & Fang, C. L. (2016). Spatial-temporal characteristics and determinants of PM 2.5 in the Bohai Rim Urban Agglomeration. Chemosphere. 148, 148-62.
Wu, J., Feng, Z., Gao, Y. & Peng, J. (2013). Hotspot and relationship identification in multiple landscape services: A case study on an area with intensive human activities. Ecological Indicators, 29, 529-537.
Yuan, Y., Cave, M.& Zhang, C. (2018). Using Local Moran’s, I to identify contamination hotspots of rare earth elements in urban soils of London. Applied Geochemistry, 88, 167-178.

Files

Share

How to cite

Statistics