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Proposing an early-warning system for optimal management of protected areas (Case study: Darmiyan protected area, Eastern Iran)
Accepted Manuscript Title: Proposing an Early-Warning System for Optimal Management of Protected Areas (Case Study: Darmiyan Protected Area, Eastern Iran) Authors: Meysam Bahrami Nejad, Behzad Rayegani, Ali Jahani, Bagher Nezami PII: DOI: Reference:
S1617-1381(18)30130-4 https://doi.org/10.1016/j.jnc.2018.08.013 JNC 25661
To appear in: Received date: Revised date: Accepted date:
12-4-2018 27-8-2018 29-8-2018
Please cite this article as: Bahrami Nejad M, Rayegani B, Jahani A, Nezami B, Proposing an Early-Warning System for Optimal Management of Protected Areas (Case Study: Darmiyan Protected Area, Eastern Iran), Journal for Nature Conservation (2018), https://doi.org/10.1016/j.jnc.2018.08.013 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Proposing an Early-Warning System for Optimal Management of Protected Areas (Case Study: Darmiyan Protected Area, Eastern Iran) Meysam Bahrami Nejad1, Behzad Rayegani1*, Ali Jahani1, Bagher Nezami1 1 College of Environment, Department of Environment, Karaj, Iran
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* Corresponding Author: Email; [email protected]
Abstract
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An early-warning system is a general idea that can act as a functional and inexpensive tool to ease the access to the global strategic goals of protection and sustainable development. Before the crisis happens, this system can provide effective information by using known resources, which creates an awareness of probable dangers and necessary actions. This study proposes an optimal method to cover the shortages in protected-area management. The proposed early-warning system is based on the pressure-state-response (P-S-R) approach and ecological security index. Twelve environmental indicators in three different criteria (P=4, S=5, R=3) were chosen and the ecological security index of the protected area was generated. Based on the ecological security index status in the study area, statistical analysis, and expert opinions, three indicators (precipitation, vegetation covering status, and soil brightness) were chosen as the main and final indicators, to be used in the early-warning system. Eventually, with the calculation of the thirty-year average of the mentioned indicators in the area under study, the confidence interval for each of these indicators with a confidence factor of 95% was achieved. According to the results of this research, some parts of the south-west and east of the area under study were in warning status, which requires special management decisions. Based on the field visit and expert review in these parts, there was an obvious breakdown in the selected indicators. In fact, we’ve prevented the ecological disturbance in the ecosystem which plays an important role in preserving species of international importance by using technologies. We’ve made a shortcut to achieve the managerial goals in less developed countries in further studies in this field; the importance of each early-warning system indicator can be determined, so warning regions can be divided into more categories. This research can be applied in other climates, and the result can be compared to this paper, but the choice of general and effective indicators remains the most important part of the approach reported here.
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Keywords: Early-warning system; Darmiyan protected area; ecological security; PSR model
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Introduction
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Sea and land protected areas are the cornerstones of biodiversity protection. These regions consist of national parks, protected landscapes, and numerous types of reserves (Leverington et al., 2010). Some significant roles of such regions are biodiversity conservation, protection of cultural heritage, maintenance of vital ecosystem service, and socio-economic advantages (Nicholas, 2010). In order to utilize, manage, and maintain these roles, societies have invested an enormous amount of money, land, and manpower. Moreover, in the second half of the last century, most countries have increased their protected areas in terms of number and size as a core strategy to protect biodiversity and the environment (Leverington et al., 2010). Furthermore, the world's protected areas cover more than 14% of the terrestrial surface in 2016 (Abman, 2018). Therefore, with this strategy, developing and implementing management plans require more time, cost and manpower. In this research, following the appropriate ways in order to solve the problems above, approaches for better management were given considerable attention by the international research community (Xiuping et al., 2000). The ES assessment comprised three goals: 1) identifying the stability in the relationship between ecosystems and their elements; 2) distinguishing the capacity of ecosystems for sustainable health and integrity under different risks and pressures; and, 3) indicating how to eliminate the problems of an ecosystem by its elements (Yu et al., 2014). To assess the ES, the indicators play the most significant role. Various ecosystems, in different dimensions of space-time, require their specific index systems (Zhao et al., 2006). The earlywarning system1 is deployed for in-time and correct implementation of the ES concept, identifying the “early warning” signs, and ecological or environmental degradations of protected areas and maintenance of sustainable development; the concept of ecological security2 has been developed. This concept was first proposed by the United States. The EWS facilitates the systematic collection of information, which could shed light on the reasons and dynamics of natural hazards ( Li et al., 2013). EWS would reduce natural hazard risks; it works by using scientific knowledge, monitoring and giving consideration to the factors that affect disaster severity and frequency (Lumbroso, 2018). The system was first discussed by Ansoff in 1975, and then the concept was developed by Nikander in 2002 (Ansoff, 1975; Nikander, 2002)
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So far, efforts have been made to use the EWS in various studies such as describing the biological dynamics, environmental elements involving ecological security (Barlindhaug, et al., 2007; Hockey & Curtis, 2009; Tegler et al., 2001), reducing the risk of landslides (Piciullo et al., 2018), preventing the loss of resources in environmental calamities (Iverson & Prasad, 2007), mitigating flood damage (Badji & Dautrebande, 1997) and air quality early-warning system for cities in China (Xu et al., 2016). Research in China, proposed an early-warning method to ensure the ecological security of landscape throughout every stage of urban planning (Li et al., 2010). 1 2
Early-Warning System= EWS Ecological Security= ES
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Due to the EWS efficiency in different ecological fields, the use of this system appears to be productive for maintenance and mobility of environmental elements in protected areas.
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This study is based on a research on RS_GIS and PSR (Pressure-Status-Responses) model, and about the ES status in the Darmiyan protected area3, which is located in the eastern part of Iran. In developing countries such as Iran, less time and cost are spent on management and monitoring of protected areas, so management of protected areas is an important issue in such countries. The major goal of this study is to present EWS for in-time and appropriate monitoring, maintaining the mobility of relationships and ecosystem health of protected areas. The DPA plays an important and specific role in maintaining the natural landscape of the area and also Urial Wild sheep (Ovis orientalis), which could become good capitals for the area in the future. In the International Union for Conservation of Nature (IUCN), Urial wild sheep has listed as a vulnerable (VU) species (Hijmans et al., 2005). This EWS will be a useful and inexpensive tool to maintain sustainable development in the area, to facilitate access to the major global strategic goals of protection, and to help the national and regional policies.
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DDPA with 7931 km2 of land area is located in the eastern part of Iran (Fig. 1). This mountainous region has a semi-arid climate and its average temperature is about 29 °C, and its annual precipitation is approximately 170 mm. Its average height is 1950 m, highest at 2870 m inside the watershed, and lowest at 1619 m outside the watershed. Crop and livestock farming are the major economic activities of the people. It has a rich diversity of plants and animals, and is an international habitat (Iran and Afghanistan) for Urial wild sheep (Ovis orientalis). Furthermore, it is considered the most prominent habitat of this species in the eastern part of Iran. Some of the threats and conflicts necessitating the implementation of an effective monitoring system are the recent droughts, the destruction and seizure of lands by residents, wasteful grazing, poaching, the 120day Sistan winds, road construction, and installed telecommunication towers and satellites.
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Study Area
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Darmiyan protected area= DPA
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Fig. 1. Location of the study area.
Materials The information regarding land cover (in 2000, and 2014), vegetation covering, and soil brightness (in 10 periods of time within the last 28 years) were obtained from the images of TM and OLI sensors of Landsat satellite, which was extracted during the months of April, July, and September.
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The images of Landsat and GDEM-Aster were obtained from the U.S. Geological Survey Agency (“EarthExplorer,” n.d.) and necessary corrections were performed on them by remote sensing. The land surface temperature (LST) data were extracted from MODIS sensor with MOD11 code (“LAADS DAAC,” n.d.). Furthermore, precipitation data of the area, statistical information regarding wildlife census, presence points of Urial wild sheep (Ovis orientalis) during the springs, summers, and winters in the area (obtained by sightings and GPS), the amount of governmental financing in the region, the types and numbers of tools and facilities used for protection of the area, were obtained from the meteorology, and environment organizations of South Khorasan. Average precipitation maps in the area were provided for 10 periods of time within the last 30 years. According to the research requirements, the obtained data was converted into maps with a spatial resolution of 30×30 m for years of study. Each 30 m cell acts as a homogeneous unit for each indicator.
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In the last two decades, considerable changes in climate, population, transportation, and occupation have occurred in the eastern part of South Khorasan province. The study area was announced as a protected area in 2007, which previously was just a region where hunting was prohibited. Thus the evaluation of changes in years, 2000, and 2014 could investigate the ecological security status of the region before the protection and some years after that. Methods
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Index system for Ecological Security
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Considering that the EWS has been used for the in-time implementation of ES concept; therefore, EWS indicators must have a direct relationship with the index system which was selected for assessing the ES in the study area. EWS has four main elements: the risk knowledge; the monitoring and predicting; disseminating information; and, response (Grasso, 2007). On the other hand, ecological indicators could be used for assessing the status of the environment, monitoring the status all the time and showing signs of rapid change in the environment. They can also be used to diagnose the cause of environmental problems (Brooks et al., 1998). With regard to the main elements of an EWS and ecological indicator’s features, the necessity of the indicator presence in all elements of an EWS is felt. The index system of Pressure_ State_ Response (P_ S_ R) is based on the causality and in terms of selection indicator is such that settles the main needs of EWS like risk knowledge, predicting and response. This indexing system also converts the concept of ES that is a general and 4
macroscopic concept into a measurable and smaller one (Yu et al., 2014). So we used PSR to assess the ecological security concept in DPA in alignment with the EWS concept.
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Considering the situations of topographical, climatic, socio-economic, and conflicts in the study area, 12 indicators in the aforementioned factors were proposed to evaluate the ES of the study area (Table. 1). In the pressure factor, indicators show the pressure on the environment through human activities and environmental changes (directly and indirectly). The pressure factor (B1) includes the indicators: the average annual temperature (C11); annual precipitation (C12); fragmentation (C13); and, the poacher presence risk (C14). The indicators of the state factor (B2) [ distance from the human-made area (C21), distance from farms (C22), vegetation cover status (C23), soil brightness (C24), the finite growth rate of wildlife (C25)] shows an overview of the environment status. The indicators of response (B3) [the increment rate of government financing in the region (C31), the incompatibility percentage of the region with protection use (C32), utilization and development of technology in protection (C33)] combined with unique and interconnected activities that are for protection and preservation of the environment and natural resources. We used the mentioned indicators to assess the ecological security of DPA. The procedure for EWS in DPA is shown in Fig 2.
Table 1
Criterion level (P)
Pressure (B1)
ESI
CR=0.02
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State (B2)
0.371
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Ecological Security Index
Weight of P
CR= 0.01
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Index
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Index system of ecological security assessment for DPA.
CR=0.03
0.332
weight of I 0.226 0.430 0.215 0.129
Final weight (Wp*WI) 0.083846 0.15953 0.079765 0.047859
Source of data
C21 C22 C23 C24 C25
0.407 0.157 0.167 0.189 0.080
0.120879 0.046629 0.049599 0.056133 0.02376
Landsat data Landsat data Landsat data Landsat data Statistical data
CR=0.01
C31 C32 C33
Modis Data Statistical data Landsat data Statistical data
0.458 0.152056 Statistical data 0.416 0.138112 Landsat data 0.126 0.041832 Statistical data In this table B1 indicates pressure factor, B2 indicates state factor and B3 indicates response factor. C11, C12, C13, and C14 are indicators of pressure factor. C21, C22, C23, C24, and C25 are indicators of state factor. Also, C31, C32, and C33 are indicators
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Response (B3)
0.297
Indicator level (I) C11 C12 C13 C14
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of response factor. CR is Consistency rate for each factor.
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Selection the Index system (ESI)
Calculation indicators
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Selection Indicators for each factor
Weighting calculation with AHP
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Normalization of indicator
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Calculation ESI with WLC
Result ESI in 2014
Statistical analysis and the opinions of experts
Choose EWS indicators
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Result ESI in 2000
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Determining the scope of reliability of indicators
Result EWS for DPA in 2014
Fig. 2. The flowchart of EWS for Darmiyan protected area
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Measurement of Ecological Security Index All the indicators were measured on a scientific basis for the study years, and their raster maps were extracted in 30*30 pixels. Some of these indicators are explained as follows. Fragmentation indicator
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Landscape fragmentation has negative effects on biodiversity and it is a process where habitats, become smaller and more isolated (Montis et al., 2017). After providing the land use map of the study area during the study years by using unsupervised classification, the fragmentation indicator was calculated in different parts of the region. At the outset, the standard sampling blocks of 1Km ×1Km were created, thereafter fragmentation indicator was calculated by the following formula (Li et al., 2010). FLi=N𝑖 ⁄TA𝑖
1)
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Where FLi is fragmentation indicator for a given sampling block i. Ni is the number of patches for all land usage types in a given sampling block i, TAi is the total area of the sampling block i.
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Vegetation covering status indicator
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Many indexes are in pursuit of defining vegetation covering status, and normalized-difference vegetation index4 is one of the most prevalent indexes being applied in various fields (Wang et al., 2014). The NDVI index was obtained for a 28-year period by using 10 images of the Landsat satellite of April, and TM measures (1986, 1987, 1990, 1991, 1993, 1998, 2000, 2009, 2011) and OLI (2014). Due to the use of two measures with different sensitivity range bands, the acquired data was normalized at the outset according to: 2)
NDVIn =
NVDI − µ⁄ 𝛿
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Where NDVIn indicates the normalized NDVI, µ indicates the average NVDI obtained from each image, and δ is the standard deviation of NVDI for the corresponding image. Vegetation covering status was calculated by using of NVDI data for years of study as mentioned below (Wang et al., 2014): 3)
F= [(NDVIn –NDVImin)] / [(NDVImax – NDVImin)]
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Where F indicates vegetation covering status, NVDImin is the least value of NVDI without vegetation covering, and NVDImax is the maximum value of NVDI when the vegetation covering is in its maximum status. Soil brightness indicator
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normalized-difference vegetation index =NDVI
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The amount of interrill-rill erosion of jungle fields and rangelands, which are least manipulated by humans has a direct relationship with soil brightness (Saadat et al., 2014). Given that more than 80% of our study area consists of rangelands, soil brightness is employed as the indicator of interrill-rill erosion in the study area. In the first instance, the soil brightness was obtained by the conversion algorithm of Tasseled Cap for 10 images of Landsat satellite of April, and TM measures (1986, 1987, 1990, 1991, 1993, 1998, 2000, 2009, 2011) and OLI (2014). Similar to the indicator of vegetation covering, due to the use of two measures with different sensitivity range bands, the acquired data was normalized at the outset according to the following formula: Bn =
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Bright − µ ⁄ δ
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Where Bn represents the map of normalized soil brightness, µ is the average soil brightness acquired from each image, and δ is the standard deviation of the corresponding image. Soil brightness indicator was calculated through brightness data for years of study by the following formula: Bs= [(Bn – Bright min)] / [(Bright max - Bright min) ]
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Finite rate of population growth
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Where Bs represents the amount of soil brightness, Brightmin, and Brightmax are the minimum and maximum amount of soil brightness, respectively. As soil brightness increases, the interrill-rill erosion increases as well (Saadat et al., 2014).
er = 𝑁𝑡+1 /𝑁𝑡
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The focal species of DPA is Urial wild sheep which reside in hillsides of the region. In this study, the finite rate of population growth of this species is chosen as a representative to express the status of habitats in the region and also wildlife status of DPA. The finite rate of population growth is obtained by the ratio of the two censuses (Sinclair et al., 2006):
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In this formula, eʳ represents the finite rate of population growth, Nt+1 indicates a population size of the intended year and Nt is the population size of the previous year. Utilization and development of technology in protection
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Technology plays the role of a bridge in the scenario of realizing goals. Effective devices in the protection of the study area, which can be referred to as technology, are identified and their weight and significance in protection are determined by an analytic hierarchy process (AHP). In this method, the opinions of 15 experts were used. Using the following formula, the amount of technology utilization in each year was determined: TP = ∑ni=1 𝑣𝑖 ∗ Wi
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Where Tp indicates the amount of technology utilization in protection, Vi is the number of the ith device, and Wi is the weight of the ith device, and n represents the variety of the utilized devices. Standardization of data
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The effective indicators for ES in the study area have different dimensions. In order to compare them and determine the ESI of the region, certain dimensions must be omitted (Yu et al., 2014). In most of the studies, the methods of normalization and standardization are used to omit the dimension (Wang et al., 2014; Yu et al., 2014; Zhao et al., 2006). In the standardization of indicators and measures, we divided them into two groups of positive and negative ones. The indicators and measures, which positively affect the ES as their value increases are grouped as the positive ones and those with the negative effect as their value increases are grouped as negative ones. For standardizing the positive indicators and measures, Formula 9 is deployed, which is based on the linear-fuzzy conversion formula (Ye, Ma, & Dong, 2011): 𝑃𝑖 =
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𝑋𝑖 − Xmin ⁄X max − X min
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For standardizing the negative indicators and measures, Formula 10 is applied: 𝑃𝑖 =
X max − 𝑋𝑖 ⁄X max − X min
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In these formulas, Xi represents the real amount of the indicator or measure, Xmax, and Xmin are the maximum and minimum measured value of the indicator or measure, respectively.
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In this study, the analytic hierarchy process (AHP) is applied to give weight to the criteria and indicators. In the AHP approach, opinions of 15 experts of Tehran Department of Environment and College of Environment, Karaj was used in order to score the criteria and indicators. Consistency rate (CR) in the level of criteria is 0.01, and for indicators of factors of Pressure, State and Reaction are 0.02, 0.03, and 0.01, respectively. All of them are below 0.1, hence consistency is confirmed (Table. 1). Assessing Ecological Security Index
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The multi-criteria evaluation5 method is the decision-making analysis and is applied in the evaluating sustainability of land use ( Li et al., 2010). The ESI for each of the 30 m pixels of the area under study was determined by the MCE method utilizing Weighted Linear Combination 6 in Formula 11 ( Li et al., 2013): ESI = ∑12 i=1 𝐴𝑖 ∗ Wi
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multi-criteria evaluation =MCE Weighted Linear Combination= WLC
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In this formula, Ai indicates the standardized amount of indicator i, Wi is the weight of indicator i. According to the classification of Natural Breaks in the ESI of the year 2014 and the experts’ opinion, DPA was classified into 5 levels with different sensitivities (very high sensitivity, high sensitivity, medium sensitivity, low sensitivity, very low sensitivity) (Table. 2). Early warning system
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First, we must answer the question of how environmental indicators could help predict the future? A wide variety of environmental indicators has been used recently. Environmental indicators reflect the trends in the environment and reveal environment policy targets. The main function of these indicators is communication. In fact, they should be able to show the exchanges regarding the issue they address. For example, our body temperature is a clear example of indicators which we use regularly. Like our body temperature, environmental indicators provide information on the phenomena that are critical for the environmental qualities. Indicators always simplify a complex reality. They select according to the desired goal and focus on specific aspects. Actually environmental indicators disclose the growing trend of environmental problems to policymakers in order to apply the necessary precautionary measures (Gabrielsen & Bosch, 2003).
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The structure and function of an EWS depend on the type of risk and threats and also on the social, economic and political status in the region. EWSs can be different from a simple system to a very complex one and are designed for the multi-hazard one. The important point here is to fit EWS with social structure, region structure, dangers and concerns in the region. Expected features in an early-warning system include: having the ability to create a risk map; being capable of covering more than one hazard; and, giving useful information (Glantz, 2003). Generally, the concept of EWS is a broad concept and can be applied to most regions in which development and environmental manipulation had resulted in the disorder of the environment (Haji-kazemi et al., 2015). Indicators can prognosticate the changes in environmental parameters (Clarke, 1995). However, the indicators used in the EWS firstly should be specific to the region where the EWS has been used. Secondly, these indicators should have some features to show that the EWS is more efficient. The most significant features are: A) being sensitive to the environmental changes; B) providing accurate and precise information on the ecological security status of the region; C) the constant availability of information on the system monitoring demands; and, D) the capability of being economically measured in terms of time, cost and manpower.
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In choosing the indicators for a warning system in the study area, all of the above features were taken into consideration. Statistic relations were used in two steps to apply feature (B). In the first step, indicators which provide us with relatively precise information on ES of the region were separated by the analysis of decomposition into main factors. In the second step, in order to decrease the indicators to the lowest number in the intended warning system, multi-variable regression was applied, and the indicators which had the most correlation with ES of the area were chosen. Finally, by calculating the 28-year average of the chosen indicators for EWS of DPA, the assurance distance for each of these indicators was acquired with the utilization of Rule 12 with 95% assurance ratio (Table. 3) (Mirza Ghaderi et al., 2010): 10
P( 𝑥̅ − 2𝛿𝑥̅ < 𝜇 < 𝑥̅ + 2𝛿𝑥̅ )= 0.954
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In this relation, X̅ represents the 28-year average of chosen indicators to propose EWS in each pixel, δ is the standard deviation of average pixels, and µ stands for an average of all average pixels of the study area.
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Results Ecological security in Darmiyan protected area Using Equation 11, ESI is calculated for each pixel of the study area for the years 2000, and 2014; and the average value of this index was 0.315 in 2000 and 0.518 in 2014. According to Table 2, in 2000, this protected area was in the first level with very high sensitivity and was on the third level in 2014 with a medium sensitivity, which shows improvement in the ES for 2014 in comparison to 2000. According to this result and given that hunting was prohibited in the study area since 2000, this progress indicates the importance of protecting ecological areas. After confirming the importance of conservation, the importance of EWS in helping to manage such areas is further enhanced. Checking the variance plot (Fig. 3) of the standard value of the effective indicators in ESI showed that except for three indicators of distance from human-made areas (C21), distance from farms (C22), and the incompatibility percentage of the region with protection use (C32), the other
ESI
Representation state
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Table 2 The ESI classification for ecosystem sensitivity in the study area
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0 - 0.431
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0.431 – 0.513
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0.283 %
42.49 %
0.513 – 0.573
Medium sensitivity
53.49 %
0.573 – 0.769
Low sensitivity
3.73 %
0.769 - 1
Very low sensitivity
0%
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high sensitivity
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very high sensitivity
Percent of each category in DPA
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Characteristic description Rangelands are lost and erosion is at a high level. Ecosystem structure is mutilated. There is no hope to see important species. Grazing has affected the area. Rangelands are poor. Diversity is low. Ecosystem structure damage is relatively serious. Grazing is destroying the area. Erosion is starting. Rangeland is normal. Ecosystem structure change partly. Diversity is lower that perennial average. There is no destruction because of grazing or there is no grazing. Erosion is very low. Ecosystem structure has relative integrity. Diversity is good. Cover vegetation is good. There is no erosion. Diversity is very good. Ecosystem structure has integrity. Cover vegetation is very good.
2000
2014
VALUE
1.5 1 0.5 0 C11
C12
C13
C14
C21
C22
C23
C24
C25
INDICATOR
C31
C32
C33
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Fig. 3. Change of indicators and ESI from 2000 to 2014
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indicators held a better status in 2014 in comparison to 2000. These results represent the powerful impact of protection and management in the region. However, the decrease in the standardized value of the three mentioned indicators indicates the lack of attention to the exceptions of the PA and the increasing trend of land tenure in the area under study. In fact, management methods have not been applied correctly and at the right time; an in-time decision will be possible with an earlywarning system.
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In Figure 4, the sensitivity classification map of DPA is shown. According to Table 2, relatively 95% of the study area is in medium and high sensitivity. The sensitivity level areas are in direct relation to the distance from the borders and increasing of the height. In this figure, the dispersal points of Urial wild sheep are also visible. As shown in Fig. 4., this species has chosen to live in the medium and low sensitive areas, which are hill areas with high ecological security.
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An early warning system in Darmiyan protected area
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Based on the defined features for suitable indicators of an EWS, the statistical analysis, and the expert's opinions, eventually, three indicators of precipitation (C12), vegetation covering status (C23), and soil brightness (C24) were chosen as appropriate indicators to be used in the EWS of DPA. When we use the warning system to monitor the study area in pixels where the standardized value of indicators of this system (Table. 3) are in a warning status, these pixels are in the warning or red status, which can be considered for the managers (Fig. 5). In fact, this system operates as an appropriate tool to optimize management by classifying managed areas. In addition, the EWS prevents animals and plant’s habitat destruction by predicting degraded ecosystems. This approach will help protect these areas into the future, in countries with a poor economic status, by reducing the number of areas that need direct monitoring and attention. Table 3 The status of EWS indicators in the study area INDICATOR
RELIABILITY INTERVAL
NORMAL STATUS
WARNING STATUS
COVER VEGETATION BRIGHT SOIL TOTAL ANNUAL PRECIPITATION
0.455 < F < 0.558 0.382 < BS < 0.489 0.231 < R < 0.455
F > 0.455 BS > 0.382 R > 0.231
F < 0.455 BS < 0.382 R < 0.231
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Fig. 4. The Sensitivity classification map in 2014
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Monitoring DPA
if
if
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Cover vegetation > 0.455
Cover vegetation < 0.455
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Bright soil < 0.382
Bright soil > 0.382
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&
Total annual precipitation > 0.231
Total annual precipitation < 0.231
Warning status
Normal status
Fig. 5. It shows how we used EWS indicators for monitoring Darmiyan protected areas
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Early warning system usage in Darmiyan protected area
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The proposed EWS was used in the study area in 2014. The patches where the system was found in the danger area, were extracted based on the indicator information in 2014. Due to the fragile characteristics of these patches, EWS selected them with a 100 m buffer. The accurate operation of the proposed warning system was examined by field visit (Fig. 6). The system was very precise; the visited sites were in poor condition. According to Fig. 6., all areas at risk are in the margins of the study area. It might be one of the reasons for the reluctance of the Urial wild sheep to be present in these areas. DPA and its surrounding areas have a bad economic status because of the successive droughts and the lack of oil and gas resources available within the region. On the other hand, the occupations of the majority of the people in this area are agriculture and livestock, and the percentage of the rural population is more than that of other parts of the country, and all of these factors create more pressure on the natural ecosystems in this region. According to the above-mentioned issues, with the inappropriate economic status on one hand and the ecological pressures on the other, it is difficult to manage such areas. The DPA manager can significantly reduce the area of the regions that need to be directly monitored, (economically practical) by using the EWS. Managers can also take the identified section out of risk by using case studies and work experiences; managers will benefit if they know the environment
Fig .6. Red status points in Darmiyan protected area in 2014
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Table 4 ESI in different land use in DPA
2014
min max mean
Total mean
Farm 0.38 0.20 0.28
Human made 0.36 0.20 0.26
Rangeland 0.41 0.20 0.31
Arid land 0.41 0.19 0.30
0.57 0.38 0.48 0.38
0.53 0.38 0.46 0.36
0.61 0.38 0.52 0.415
0.60 0.39 0.51 0.405
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2000
min max mean
better (Forleo & Palmieri, 2018). In addition, he or she can see the effects of decisions, which have been taken in previous years by re-using of the EWS in the next year. Discussion
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The change in the ESI of different land use classes from 2000 to 2014 is shown in Table 4. The average ESI values for all land use types were higher in 2014 than in 2000. The greatest ecological security average belongs to the rangeland land use. According to the land-use change during the study years (20% increase in rangeland) and the ESI change, it is inferred that preventing the rangeland degradation for reasons such as overgrazing is one of the prominent factors, which has affected the ESI of this area.
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Given that 80% of the area consists of rangelands, the indicators of vegetation covering and soil brightness have more effects on the ecological security of the area, thus the management
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activities such as livestock control in the region, the industrial livestock promotion and education on the correct agricultural methods are recommended.
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In DPA, the location change in the ESI in a specific year has a very close relation with the land shape. As we advance to heights from flat areas, the ecological security index increases. Climate changes and Ecological Security System
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During the 14 study years, the temperature changes were very slight while the precipitation had much more changes. The precipitation had a 20 mm rise in 2014 compared to 2000. During these 14 years, the ecological security of the area had a 0.2 increase. Thus areas with a climate such as that of DPA, their main climatic restriction is precipitation, and also its effect is more than temperature; this information could be practical in choosing the appropriate indicators for these areas. The Sensitivity classification and land use 15
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The areas of land uses are determined at different sensitivity levels in Fig 7. According to this graph, at level 4 (low sensitivity), the percentage of the presence of human-made land use area is zero. This is probably the main reason why these regions are in this level. In this graph, as we move from the first sensitivity level to the higher levels, the area of human-made, farm, and arid land uses to reduce and rangeland increases; therefore, there is a straight relationship between the sensitivity reduction in the area and the area reduction of incompatible land uses. Moreover, Range land Farm the area of each land use in a particular region affects the progress of indicator selection for ESI Human made Arid land and EWS in that region. According to land use, which an indicator originates from, the effect of 100% that indicator on the ecosystem is higher if the land use area is being extended and 90% consequently, the indicator frequency increases as well.
70%
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The Management of Darmiyan protected area and Early warning system
presence percentage of land use
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Based on what has been mentioned and considering that the Urial wild sheep lives in rangeland with medium and low sensitivity levels (level 3 and 4), this species has avoided residential areas and agricultural land. In fact, it does not compete with domestic animals. Another reason for the presence of Urial wild sheep at these levels is that these areas have higher elevations than other areas. These habitats are important for lambing. Moreover, it seems that the selection of this kind of habitat is related to the species ecology and Urial wild sheep could escape in these the steep slopes easily (Maleki et al., 2010).
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Sensitivity levels Establishment of the EWS in DPA, by fundamental indicators and remote-sensing Fig .7. Presence percentage of land use in sensitivity levels tools, with the least-expensive possible method and without using much. Manpower and field work, help assess vulnerable areas. Most of these areas were located in exits of watersheds and
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regions with a high erosion rate. In fact, the prominent reasons for the destruction of these areas were: cutting bushes; overgrazing; land seizure; and, turning them into farms.
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Information gained from this system of assessing the crucial management weaknesses of the area includes the lack of attention to exceptions in the region and the lack of environmental monitoring stations in the south and east borders. Clarifying the managerial weaknesses is useful for codifying comprehensive, purposive, and directed managerial plans for important ecological regions. On the other hand, sustainable ecological security increased biodiversity and maintaining sustainable development are the result of ecosystem improvement. These results will raise the level of social and economic welfare of the people in this region.
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Data gathering is a time–consuming process which effects on data collection negatively (Rimando et al., 2015). Moreover, processing and analysis of the data collected can also be timeconsuming. Therefore, if we consider all the time it takes for data collection, analysis and interpretation of results, speeding up each stage is one of the most important needs for a quick response to environmental hazards. By using EWS decision-makers can get adequate information about the region at the right time. In
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order to reduce the time required for data mining, this approach has selected basic information by considering the ecological, climatic, and topographical conditions of the study area. EWS also uses data, which can be gathered easily with modern technologies, and it does not require extensive fieldwork.
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Conclusion
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This research is based on the indicators and the environmental effects on them and also remotesensing studies. In this study, we tried to achieve results by using discrete ecosystem components. We tried to minimize the cost of monitoring an ecosystem, so satellite images and modern technologies have been employed. The method which has been used in this paper will help the speed of decision making and recovery operations of regions that are not in good status by identifying them in a simple and fast way. This study will provide the basis for creating alternative management methods in a region and will reveal the effects of this change. DPA with a semi-arid climate was selected to indicate how EWS was used in a management area. The results showed that some parts of the marginal areas in the east and south of the region which are also the watershed outlet are at risk. Field visit and the indicators’ statuses which were selected for EWS showed that vegetation cover in these areas is very poor and with the environment positive feedback, the amount of erosion has increased, so these areas will decline. Considering the density of the villages in the east and south of the study area, it seems that the main cause for this is excessive grazing livestock in the region. DPA managers can greatly overcome this problem by adopting the managerial decisions such as granting to promote the industrial livestock. This research contributes to the protection of an ecosystem that is considered as an international species
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(Urial wild sheep) habitat and also introduces the ecological security indicators in a region with a semi-arid climate.
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Research about EWS could be carried out in other important and natural areas. The significant point in this approach is the selection of the indicators based on the ecological conditions of each area. This research was done by the minimum number of indicators for EWS, hence more investigation can be conducted with more indicators, and their results can be compared with this research’s result. In further studies in this field, the importance of each EWS indicator can be determined, and the warning regions can be divided into three categories: in danger; pre-warning; and, warning status. Other researchers should choose EWS indicators with at least overlap in terms of information to examine the areas in all dimensions. We believe this research can be an appropriate starting point to discuss the use of EWS in key areas and inaccessible areas in different parts of the world.
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Acknowledgment We would like to thank Mr. Mostaqim, who provided data. Special thanks to Saber Feizie and Morteza Roshankar who helped us in editing this paper. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. References
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Abman, R. (2018). Rule of Law and Avoided Deforestation from Protected Areas ☆. Ecological Economics, 146(November 2017), 282–289. https://doi.org/10.1016/j.ecolecon.2017.11.004 Ansoff, H. I. (1975). Managing Strategic Surprise by Response to Weak Signals. California Management Review, XVIII(2), 21–33. https://doi.org/citeulike-article-id:1109593 Badji, M., & Dautrebande, S. (1997). Characterization of flood inundated areas and delineation of poor drainage soil using ERS-1 SAR imagery. Hydrological Processes, 11(10), 1441–1450. https://doi.org/10.1002/(SICI)10991085(199708)11:10<1441::AID-HYP527>3.0.CO;2-Y Barlindhaug, S., Holm-olsen, I. M., & Tòmmervik, H. (2007). Monitoring Archaeological Sites in a Changing Landscape ^ Using Multitemporal Satellite Remote Sensing as an “ Early Warning ” Method for Detecting Regrowth Processes y, 244(May), 231–244. https://doi.org/10.1002/arp Brooks, R. P., O’Connell, T. J., Wardrop, D. H., & Jackson, L. E. (1998). Towards a regional index of biological integrity: The example of forested riparian ecosystems. Environmental Monitoring and Assessment, 51(1–2), 131–143. https://doi.org/10.1023/A:1005962613904 Clarke, G. M. (1995). Relationships Between Developmental Stability and Fitness: Application for Conservation Biology. Conservation Biology, 9(1), 18–24. https://doi.org/10.1046/j.1523-1739.1995.09010018.x EarthExplorer. (n.d.). Retrieved April 17, 2015, from https://earthexplorer.usgs.gov/ Forleo, M. B., & Palmieri, N. (2018). A framework for assessing the relational accessibility of protected areas. Journal of Cleaner Production, 194, 594–606. https://doi.org/10.1016/j.jclepro.2018.05.149 Gabrielsen, P., & Bosch, P. (2003). Environmental Indicators: Typology and Use in Reporting. Eea, (August), 1–20. Glantz, M. (2003). Usable science 8: early warning systems: do’s and don’ts. Report of Workshop, 80301(October 2003), 0–76. Retrieved from http://www.academia.edu/download/30495281/glantz2003.pdf Grasso, V. F. (2007). Early Warning Systems A State of the Art Analysis and Future Directions. Haji-kazemi, S., Andersen, B., Eleftheriadis, R., & Capellan, A. (2015). The Early Warning Procedure in an International Context. Procedia - Social and Behavioral Sciences, 194(1877), 85–95. https://doi.org/10.1016/j.sbspro.2015.06.122 Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, G., & Jarvis, A. (2005). VERY HIGH RESOLUTION INTERPOLATED CLIMATE SURFACES FOR GLOBAL LAND AREAS, 1978, 1965–1978. https://doi.org/10.1002/joc.1276 Hockey, P. A. R., & Curtis, O. E. (2009). Use of basic biological information for rapid prediction of the response of species to habitat loss. Conservation Biology, 23(1), 64–71. https://doi.org/10.1111/j.1523-1739.2008.01028.x
18
A
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EP
TE
D
M
A
N
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SC RI PT
Iverson, L. R., & Prasad, A. M. (2007). Using landscape analysis to assess and model tsunami damage in Aceh province, Sumatra. Landscape Ecology, 22(3), 323–331. https://doi.org/10.1007/s10980-006-9062-6 LAADS DAAC. (n.d.). Retrieved July 8, 2015, from https://ladsweb.modaps.eosdis.nasa.gov/ Leverington, F., Costa, K. L., Courrau, J., Pavese, H., Nolte, C., Marr, M., … Hockings, M. (2010). Management effectiveness evaluation in protected areas – a global study, 87. https://doi.org/10.1007/s00267-010-9564-5 Li, X., Lao, C., Liu, Y., Liu, X., Chen, Y., Li, S., … He, Z. (2013). Early warning of illegal development for protected areas by integrating cellular automata with neural networks. Journal of Environmental Management, 130, 106– 116. https://doi.org/10.1016/j.jenvman.2013.08.055 Li, Y., Sun, X., Zhu, X., & Cao, H. (2010). An early warning method of landscape ecological security in rapid urbanizing coastal areas and its application in Xiamen, China. Ecological Modelling, 221(19), 2251–2260. https://doi.org/10.1016/j.ecolmodel.2010.04.016 Lumbroso, D. (2018). International Journal of Disaster Risk Reduction How can policy makers in sub-Saharan Africa make early warning systems more e ff ective ? The case of Uganda. International Journal of Disaster Risk Reduction, 27(July 2017), 530–540. https://doi.org/10.1016/j.ijdrr.2017.11.017 Maleki, S., Hemami, M.-R., & Salman Mahiny, A. (2010, January 1). Determining habitat suitability of Ovis orientalis isphahanica in Mooteh Wildlife Refuge using ENFA. In Journal of Natural Environment (Vol. 63, pp. 1–13). Montis, A. De, Martín, B., Ortega, E., Ledda, A., & Serra, V. (2017). Land Use Policy Landscape fragmentation in Mediterranean Europe : A comparative approach. Land Use Policy, 64, 83–94. https://doi.org/10.1016/j.landusepol.2017.02.028 Nicholas, Lord. (2010). Arguments for Protection PACT 2020 : Protected Areas and Climate Turnaround. Evaluation. https://doi.org/10.1017/S0030605311001608 Nikander, I. O. (2002). Early warnings : a phenomenon in project management. Helsinki University of technology. Piciullo, L., Calvello, M., & Cepeda, J. M. (2018). Earth-Science Reviews Territorial early warning systems for rainfall-induced landslides. Earth-Science Reviews, 179(April 2017), 228–247. https://doi.org/10.1016/j.earscirev.2018.02.013 Rimando, M., Brace, A. M., & Parr, T. L. (2015). Data Collection Challenges and Recommendations for Early Career Researchers Data Collection Challenges and Recommendations for Early Career, 20(12), 2025–2036. Saadat, H., Adamowski, J., Tayefi, V., Namdar, M., Sharifi, F., & Ale-Ebrahim, S. (2014). A new approach for regional scale interrill and rill erosion intensity mapping using brightness index assessments from medium resolution satellite images. Catena, 113, 306–313. https://doi.org/10.1016/j.catena.2013.08.012 Sinclair, A. R. E., Fryxell, J. M., & Caughley, G. (2006). Wildlife Ecology, Conservation, and Management. Management. Tegler, B., Sharp, M., & Johnson, M. A. (2001). Ecological monitoring and assessment network's proposed core monitoring variables: An early warning of environmental change. Environ. Monit. Assess., 67(1–2), 29– 56. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.00035075092&partnerID=40&md5=fdf2a7670c7620e7f07fc7d883982890 Wang, H., Long, H., Li, X., & Yu, F. (2014). Evaluation of changes in ecological security in China’s Qinghai Lake Basin from 2000 to 2013 and the relationship to land use and climate change. Environmental Earth Sciences, 72(2), 341–354. https://doi.org/10.1007/s12665-013-2955-1 Xiuping, Z., Shaofeng, C., & Qingwen, Q. (2000). a Resarch on the Assessment of Regional Ecological Security. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B6b. Beijing 2008. Xu, Y., Yang, W., & Wang, J. (2016). Air quality early-warning system for cities in China. Atmospheric Environment. https://doi.org/10.1016/j.atmosenv.2016.10.046 Ye, H., Ma, Y., & Dong, L. (2011). Land ecological security assessment for bai autonomous prefecture of dali based using PSR model-with data in 2009 as case. Energy Procedia, 5, 2172–2177. https://doi.org/10.1016/j.egypro.2011.03.375 Yu, G., Zhang, S., Yu, Q., Fan, Y., Zeng, Q., Wu, L., … Zhao, P. (2014). Assessing ecological security at the watershed scale based on RS/GIS: A case study from the Hanjiang River Basin. Stochastic Environmental Research and Risk Assessment, 28(2), 307–318. https://doi.org/10.1007/s00477-013-0750-x Zhao, Y. Z., Zou, X. Y., Cheng, H., Jia, H. K., Wu, Y. Q., Wang, G. Y., … Gao, S. Y. (2006). Assessing the ecological security of the Tibetan plateau: Methodology and a case study for Lhaze County. Journal of Environmental Management, 80(2), 120–131. https://doi.org/10.1016/j.jenvman.2005.08.019
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S1617-1381(18)30130-4 https://doi.org/10.1016/j.jnc.2018.08.013 JNC 25661
To appear in: Received date: Revised date: Accepted date:
12-4-2018 27-8-2018 29-8-2018
Please cite this article as: Bahrami Nejad M, Rayegani B, Jahani A, Nezami B, Proposing an Early-Warning System for Optimal Management of Protected Areas (Case Study: Darmiyan Protected Area, Eastern Iran), Journal for Nature Conservation (2018), https://doi.org/10.1016/j.jnc.2018.08.013 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Proposing an Early-Warning System for Optimal Management of Protected Areas (Case Study: Darmiyan Protected Area, Eastern Iran) Meysam Bahrami Nejad1, Behzad Rayegani1*, Ali Jahani1, Bagher Nezami1 1 College of Environment, Department of Environment, Karaj, Iran
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* Corresponding Author: Email; [email protected]
Abstract
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An early-warning system is a general idea that can act as a functional and inexpensive tool to ease the access to the global strategic goals of protection and sustainable development. Before the crisis happens, this system can provide effective information by using known resources, which creates an awareness of probable dangers and necessary actions. This study proposes an optimal method to cover the shortages in protected-area management. The proposed early-warning system is based on the pressure-state-response (P-S-R) approach and ecological security index. Twelve environmental indicators in three different criteria (P=4, S=5, R=3) were chosen and the ecological security index of the protected area was generated. Based on the ecological security index status in the study area, statistical analysis, and expert opinions, three indicators (precipitation, vegetation covering status, and soil brightness) were chosen as the main and final indicators, to be used in the early-warning system. Eventually, with the calculation of the thirty-year average of the mentioned indicators in the area under study, the confidence interval for each of these indicators with a confidence factor of 95% was achieved. According to the results of this research, some parts of the south-west and east of the area under study were in warning status, which requires special management decisions. Based on the field visit and expert review in these parts, there was an obvious breakdown in the selected indicators. In fact, we’ve prevented the ecological disturbance in the ecosystem which plays an important role in preserving species of international importance by using technologies. We’ve made a shortcut to achieve the managerial goals in less developed countries in further studies in this field; the importance of each early-warning system indicator can be determined, so warning regions can be divided into more categories. This research can be applied in other climates, and the result can be compared to this paper, but the choice of general and effective indicators remains the most important part of the approach reported here.
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Keywords: Early-warning system; Darmiyan protected area; ecological security; PSR model
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Introduction
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Sea and land protected areas are the cornerstones of biodiversity protection. These regions consist of national parks, protected landscapes, and numerous types of reserves (Leverington et al., 2010). Some significant roles of such regions are biodiversity conservation, protection of cultural heritage, maintenance of vital ecosystem service, and socio-economic advantages (Nicholas, 2010). In order to utilize, manage, and maintain these roles, societies have invested an enormous amount of money, land, and manpower. Moreover, in the second half of the last century, most countries have increased their protected areas in terms of number and size as a core strategy to protect biodiversity and the environment (Leverington et al., 2010). Furthermore, the world's protected areas cover more than 14% of the terrestrial surface in 2016 (Abman, 2018). Therefore, with this strategy, developing and implementing management plans require more time, cost and manpower. In this research, following the appropriate ways in order to solve the problems above, approaches for better management were given considerable attention by the international research community (Xiuping et al., 2000). The ES assessment comprised three goals: 1) identifying the stability in the relationship between ecosystems and their elements; 2) distinguishing the capacity of ecosystems for sustainable health and integrity under different risks and pressures; and, 3) indicating how to eliminate the problems of an ecosystem by its elements (Yu et al., 2014). To assess the ES, the indicators play the most significant role. Various ecosystems, in different dimensions of space-time, require their specific index systems (Zhao et al., 2006). The earlywarning system1 is deployed for in-time and correct implementation of the ES concept, identifying the “early warning” signs, and ecological or environmental degradations of protected areas and maintenance of sustainable development; the concept of ecological security2 has been developed. This concept was first proposed by the United States. The EWS facilitates the systematic collection of information, which could shed light on the reasons and dynamics of natural hazards ( Li et al., 2013). EWS would reduce natural hazard risks; it works by using scientific knowledge, monitoring and giving consideration to the factors that affect disaster severity and frequency (Lumbroso, 2018). The system was first discussed by Ansoff in 1975, and then the concept was developed by Nikander in 2002 (Ansoff, 1975; Nikander, 2002)
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So far, efforts have been made to use the EWS in various studies such as describing the biological dynamics, environmental elements involving ecological security (Barlindhaug, et al., 2007; Hockey & Curtis, 2009; Tegler et al., 2001), reducing the risk of landslides (Piciullo et al., 2018), preventing the loss of resources in environmental calamities (Iverson & Prasad, 2007), mitigating flood damage (Badji & Dautrebande, 1997) and air quality early-warning system for cities in China (Xu et al., 2016). Research in China, proposed an early-warning method to ensure the ecological security of landscape throughout every stage of urban planning (Li et al., 2010). 1 2
Early-Warning System= EWS Ecological Security= ES
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Due to the EWS efficiency in different ecological fields, the use of this system appears to be productive for maintenance and mobility of environmental elements in protected areas.
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This study is based on a research on RS_GIS and PSR (Pressure-Status-Responses) model, and about the ES status in the Darmiyan protected area3, which is located in the eastern part of Iran. In developing countries such as Iran, less time and cost are spent on management and monitoring of protected areas, so management of protected areas is an important issue in such countries. The major goal of this study is to present EWS for in-time and appropriate monitoring, maintaining the mobility of relationships and ecosystem health of protected areas. The DPA plays an important and specific role in maintaining the natural landscape of the area and also Urial Wild sheep (Ovis orientalis), which could become good capitals for the area in the future. In the International Union for Conservation of Nature (IUCN), Urial wild sheep has listed as a vulnerable (VU) species (Hijmans et al., 2005). This EWS will be a useful and inexpensive tool to maintain sustainable development in the area, to facilitate access to the major global strategic goals of protection, and to help the national and regional policies.
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DDPA with 7931 km2 of land area is located in the eastern part of Iran (Fig. 1). This mountainous region has a semi-arid climate and its average temperature is about 29 °C, and its annual precipitation is approximately 170 mm. Its average height is 1950 m, highest at 2870 m inside the watershed, and lowest at 1619 m outside the watershed. Crop and livestock farming are the major economic activities of the people. It has a rich diversity of plants and animals, and is an international habitat (Iran and Afghanistan) for Urial wild sheep (Ovis orientalis). Furthermore, it is considered the most prominent habitat of this species in the eastern part of Iran. Some of the threats and conflicts necessitating the implementation of an effective monitoring system are the recent droughts, the destruction and seizure of lands by residents, wasteful grazing, poaching, the 120day Sistan winds, road construction, and installed telecommunication towers and satellites.
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Fig. 1. Location of the study area.
Materials The information regarding land cover (in 2000, and 2014), vegetation covering, and soil brightness (in 10 periods of time within the last 28 years) were obtained from the images of TM and OLI sensors of Landsat satellite, which was extracted during the months of April, July, and September.
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The images of Landsat and GDEM-Aster were obtained from the U.S. Geological Survey Agency (“EarthExplorer,” n.d.) and necessary corrections were performed on them by remote sensing. The land surface temperature (LST) data were extracted from MODIS sensor with MOD11 code (“LAADS DAAC,” n.d.). Furthermore, precipitation data of the area, statistical information regarding wildlife census, presence points of Urial wild sheep (Ovis orientalis) during the springs, summers, and winters in the area (obtained by sightings and GPS), the amount of governmental financing in the region, the types and numbers of tools and facilities used for protection of the area, were obtained from the meteorology, and environment organizations of South Khorasan. Average precipitation maps in the area were provided for 10 periods of time within the last 30 years. According to the research requirements, the obtained data was converted into maps with a spatial resolution of 30×30 m for years of study. Each 30 m cell acts as a homogeneous unit for each indicator.
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In the last two decades, considerable changes in climate, population, transportation, and occupation have occurred in the eastern part of South Khorasan province. The study area was announced as a protected area in 2007, which previously was just a region where hunting was prohibited. Thus the evaluation of changes in years, 2000, and 2014 could investigate the ecological security status of the region before the protection and some years after that. Methods
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Considering that the EWS has been used for the in-time implementation of ES concept; therefore, EWS indicators must have a direct relationship with the index system which was selected for assessing the ES in the study area. EWS has four main elements: the risk knowledge; the monitoring and predicting; disseminating information; and, response (Grasso, 2007). On the other hand, ecological indicators could be used for assessing the status of the environment, monitoring the status all the time and showing signs of rapid change in the environment. They can also be used to diagnose the cause of environmental problems (Brooks et al., 1998). With regard to the main elements of an EWS and ecological indicator’s features, the necessity of the indicator presence in all elements of an EWS is felt. The index system of Pressure_ State_ Response (P_ S_ R) is based on the causality and in terms of selection indicator is such that settles the main needs of EWS like risk knowledge, predicting and response. This indexing system also converts the concept of ES that is a general and 4
macroscopic concept into a measurable and smaller one (Yu et al., 2014). So we used PSR to assess the ecological security concept in DPA in alignment with the EWS concept.
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Considering the situations of topographical, climatic, socio-economic, and conflicts in the study area, 12 indicators in the aforementioned factors were proposed to evaluate the ES of the study area (Table. 1). In the pressure factor, indicators show the pressure on the environment through human activities and environmental changes (directly and indirectly). The pressure factor (B1) includes the indicators: the average annual temperature (C11); annual precipitation (C12); fragmentation (C13); and, the poacher presence risk (C14). The indicators of the state factor (B2) [ distance from the human-made area (C21), distance from farms (C22), vegetation cover status (C23), soil brightness (C24), the finite growth rate of wildlife (C25)] shows an overview of the environment status. The indicators of response (B3) [the increment rate of government financing in the region (C31), the incompatibility percentage of the region with protection use (C32), utilization and development of technology in protection (C33)] combined with unique and interconnected activities that are for protection and preservation of the environment and natural resources. We used the mentioned indicators to assess the ecological security of DPA. The procedure for EWS in DPA is shown in Fig 2.
Table 1
Criterion level (P)
Pressure (B1)
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weight of I 0.226 0.430 0.215 0.129
Final weight (Wp*WI) 0.083846 0.15953 0.079765 0.047859
Source of data
C21 C22 C23 C24 C25
0.407 0.157 0.167 0.189 0.080
0.120879 0.046629 0.049599 0.056133 0.02376
Landsat data Landsat data Landsat data Landsat data Statistical data
CR=0.01
C31 C32 C33
Modis Data Statistical data Landsat data Statistical data
0.458 0.152056 Statistical data 0.416 0.138112 Landsat data 0.126 0.041832 Statistical data In this table B1 indicates pressure factor, B2 indicates state factor and B3 indicates response factor. C11, C12, C13, and C14 are indicators of pressure factor. C21, C22, C23, C24, and C25 are indicators of state factor. Also, C31, C32, and C33 are indicators
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Indicator level (I) C11 C12 C13 C14
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of response factor. CR is Consistency rate for each factor.
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Selection the Index system (ESI)
Calculation indicators
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Selection Indicators for each factor
Weighting calculation with AHP
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Result ESI in 2014
Statistical analysis and the opinions of experts
Choose EWS indicators
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Result ESI in 2000
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Determining the scope of reliability of indicators
Result EWS for DPA in 2014
Fig. 2. The flowchart of EWS for Darmiyan protected area
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Measurement of Ecological Security Index All the indicators were measured on a scientific basis for the study years, and their raster maps were extracted in 30*30 pixels. Some of these indicators are explained as follows. Fragmentation indicator
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Landscape fragmentation has negative effects on biodiversity and it is a process where habitats, become smaller and more isolated (Montis et al., 2017). After providing the land use map of the study area during the study years by using unsupervised classification, the fragmentation indicator was calculated in different parts of the region. At the outset, the standard sampling blocks of 1Km ×1Km were created, thereafter fragmentation indicator was calculated by the following formula (Li et al., 2010). FLi=N𝑖 ⁄TA𝑖
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Where FLi is fragmentation indicator for a given sampling block i. Ni is the number of patches for all land usage types in a given sampling block i, TAi is the total area of the sampling block i.
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Vegetation covering status indicator
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Many indexes are in pursuit of defining vegetation covering status, and normalized-difference vegetation index4 is one of the most prevalent indexes being applied in various fields (Wang et al., 2014). The NDVI index was obtained for a 28-year period by using 10 images of the Landsat satellite of April, and TM measures (1986, 1987, 1990, 1991, 1993, 1998, 2000, 2009, 2011) and OLI (2014). Due to the use of two measures with different sensitivity range bands, the acquired data was normalized at the outset according to: 2)
NDVIn =
NVDI − µ⁄ 𝛿
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Where NDVIn indicates the normalized NDVI, µ indicates the average NVDI obtained from each image, and δ is the standard deviation of NVDI for the corresponding image. Vegetation covering status was calculated by using of NVDI data for years of study as mentioned below (Wang et al., 2014): 3)
F= [(NDVIn –NDVImin)] / [(NDVImax – NDVImin)]
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Where F indicates vegetation covering status, NVDImin is the least value of NVDI without vegetation covering, and NVDImax is the maximum value of NVDI when the vegetation covering is in its maximum status. Soil brightness indicator
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normalized-difference vegetation index =NDVI
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The amount of interrill-rill erosion of jungle fields and rangelands, which are least manipulated by humans has a direct relationship with soil brightness (Saadat et al., 2014). Given that more than 80% of our study area consists of rangelands, soil brightness is employed as the indicator of interrill-rill erosion in the study area. In the first instance, the soil brightness was obtained by the conversion algorithm of Tasseled Cap for 10 images of Landsat satellite of April, and TM measures (1986, 1987, 1990, 1991, 1993, 1998, 2000, 2009, 2011) and OLI (2014). Similar to the indicator of vegetation covering, due to the use of two measures with different sensitivity range bands, the acquired data was normalized at the outset according to the following formula: Bn =
4)
Bright − µ ⁄ δ
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Where Bn represents the map of normalized soil brightness, µ is the average soil brightness acquired from each image, and δ is the standard deviation of the corresponding image. Soil brightness indicator was calculated through brightness data for years of study by the following formula: Bs= [(Bn – Bright min)] / [(Bright max - Bright min) ]
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Finite rate of population growth
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Where Bs represents the amount of soil brightness, Brightmin, and Brightmax are the minimum and maximum amount of soil brightness, respectively. As soil brightness increases, the interrill-rill erosion increases as well (Saadat et al., 2014).
er = 𝑁𝑡+1 /𝑁𝑡
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6)
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The focal species of DPA is Urial wild sheep which reside in hillsides of the region. In this study, the finite rate of population growth of this species is chosen as a representative to express the status of habitats in the region and also wildlife status of DPA. The finite rate of population growth is obtained by the ratio of the two censuses (Sinclair et al., 2006):
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In this formula, eʳ represents the finite rate of population growth, Nt+1 indicates a population size of the intended year and Nt is the population size of the previous year. Utilization and development of technology in protection
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Technology plays the role of a bridge in the scenario of realizing goals. Effective devices in the protection of the study area, which can be referred to as technology, are identified and their weight and significance in protection are determined by an analytic hierarchy process (AHP). In this method, the opinions of 15 experts were used. Using the following formula, the amount of technology utilization in each year was determined: TP = ∑ni=1 𝑣𝑖 ∗ Wi
7)
8
Where Tp indicates the amount of technology utilization in protection, Vi is the number of the ith device, and Wi is the weight of the ith device, and n represents the variety of the utilized devices. Standardization of data
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The effective indicators for ES in the study area have different dimensions. In order to compare them and determine the ESI of the region, certain dimensions must be omitted (Yu et al., 2014). In most of the studies, the methods of normalization and standardization are used to omit the dimension (Wang et al., 2014; Yu et al., 2014; Zhao et al., 2006). In the standardization of indicators and measures, we divided them into two groups of positive and negative ones. The indicators and measures, which positively affect the ES as their value increases are grouped as the positive ones and those with the negative effect as their value increases are grouped as negative ones. For standardizing the positive indicators and measures, Formula 9 is deployed, which is based on the linear-fuzzy conversion formula (Ye, Ma, & Dong, 2011): 𝑃𝑖 =
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8)
𝑋𝑖 − Xmin ⁄X max − X min
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For standardizing the negative indicators and measures, Formula 10 is applied: 𝑃𝑖 =
X max − 𝑋𝑖 ⁄X max − X min
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9)
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In these formulas, Xi represents the real amount of the indicator or measure, Xmax, and Xmin are the maximum and minimum measured value of the indicator or measure, respectively.
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In this study, the analytic hierarchy process (AHP) is applied to give weight to the criteria and indicators. In the AHP approach, opinions of 15 experts of Tehran Department of Environment and College of Environment, Karaj was used in order to score the criteria and indicators. Consistency rate (CR) in the level of criteria is 0.01, and for indicators of factors of Pressure, State and Reaction are 0.02, 0.03, and 0.01, respectively. All of them are below 0.1, hence consistency is confirmed (Table. 1). Assessing Ecological Security Index
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The multi-criteria evaluation5 method is the decision-making analysis and is applied in the evaluating sustainability of land use ( Li et al., 2010). The ESI for each of the 30 m pixels of the area under study was determined by the MCE method utilizing Weighted Linear Combination 6 in Formula 11 ( Li et al., 2013): ESI = ∑12 i=1 𝐴𝑖 ∗ Wi
10)
5 6
multi-criteria evaluation =MCE Weighted Linear Combination= WLC
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In this formula, Ai indicates the standardized amount of indicator i, Wi is the weight of indicator i. According to the classification of Natural Breaks in the ESI of the year 2014 and the experts’ opinion, DPA was classified into 5 levels with different sensitivities (very high sensitivity, high sensitivity, medium sensitivity, low sensitivity, very low sensitivity) (Table. 2). Early warning system
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First, we must answer the question of how environmental indicators could help predict the future? A wide variety of environmental indicators has been used recently. Environmental indicators reflect the trends in the environment and reveal environment policy targets. The main function of these indicators is communication. In fact, they should be able to show the exchanges regarding the issue they address. For example, our body temperature is a clear example of indicators which we use regularly. Like our body temperature, environmental indicators provide information on the phenomena that are critical for the environmental qualities. Indicators always simplify a complex reality. They select according to the desired goal and focus on specific aspects. Actually environmental indicators disclose the growing trend of environmental problems to policymakers in order to apply the necessary precautionary measures (Gabrielsen & Bosch, 2003).
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The structure and function of an EWS depend on the type of risk and threats and also on the social, economic and political status in the region. EWSs can be different from a simple system to a very complex one and are designed for the multi-hazard one. The important point here is to fit EWS with social structure, region structure, dangers and concerns in the region. Expected features in an early-warning system include: having the ability to create a risk map; being capable of covering more than one hazard; and, giving useful information (Glantz, 2003). Generally, the concept of EWS is a broad concept and can be applied to most regions in which development and environmental manipulation had resulted in the disorder of the environment (Haji-kazemi et al., 2015). Indicators can prognosticate the changes in environmental parameters (Clarke, 1995). However, the indicators used in the EWS firstly should be specific to the region where the EWS has been used. Secondly, these indicators should have some features to show that the EWS is more efficient. The most significant features are: A) being sensitive to the environmental changes; B) providing accurate and precise information on the ecological security status of the region; C) the constant availability of information on the system monitoring demands; and, D) the capability of being economically measured in terms of time, cost and manpower.
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In choosing the indicators for a warning system in the study area, all of the above features were taken into consideration. Statistic relations were used in two steps to apply feature (B). In the first step, indicators which provide us with relatively precise information on ES of the region were separated by the analysis of decomposition into main factors. In the second step, in order to decrease the indicators to the lowest number in the intended warning system, multi-variable regression was applied, and the indicators which had the most correlation with ES of the area were chosen. Finally, by calculating the 28-year average of the chosen indicators for EWS of DPA, the assurance distance for each of these indicators was acquired with the utilization of Rule 12 with 95% assurance ratio (Table. 3) (Mirza Ghaderi et al., 2010): 10
P( 𝑥̅ − 2𝛿𝑥̅ < 𝜇 < 𝑥̅ + 2𝛿𝑥̅ )= 0.954
11)
In this relation, X̅ represents the 28-year average of chosen indicators to propose EWS in each pixel, δ is the standard deviation of average pixels, and µ stands for an average of all average pixels of the study area.
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Results Ecological security in Darmiyan protected area Using Equation 11, ESI is calculated for each pixel of the study area for the years 2000, and 2014; and the average value of this index was 0.315 in 2000 and 0.518 in 2014. According to Table 2, in 2000, this protected area was in the first level with very high sensitivity and was on the third level in 2014 with a medium sensitivity, which shows improvement in the ES for 2014 in comparison to 2000. According to this result and given that hunting was prohibited in the study area since 2000, this progress indicates the importance of protecting ecological areas. After confirming the importance of conservation, the importance of EWS in helping to manage such areas is further enhanced. Checking the variance plot (Fig. 3) of the standard value of the effective indicators in ESI showed that except for three indicators of distance from human-made areas (C21), distance from farms (C22), and the incompatibility percentage of the region with protection use (C32), the other
ESI
Representation state
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category
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Table 2 The ESI classification for ecosystem sensitivity in the study area
1
0 - 0.431
2
0.431 – 0.513
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0.283 %
42.49 %
0.513 – 0.573
Medium sensitivity
53.49 %
0.573 – 0.769
Low sensitivity
3.73 %
0.769 - 1
Very low sensitivity
0%
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4
high sensitivity
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3
very high sensitivity
Percent of each category in DPA
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Characteristic description Rangelands are lost and erosion is at a high level. Ecosystem structure is mutilated. There is no hope to see important species. Grazing has affected the area. Rangelands are poor. Diversity is low. Ecosystem structure damage is relatively serious. Grazing is destroying the area. Erosion is starting. Rangeland is normal. Ecosystem structure change partly. Diversity is lower that perennial average. There is no destruction because of grazing or there is no grazing. Erosion is very low. Ecosystem structure has relative integrity. Diversity is good. Cover vegetation is good. There is no erosion. Diversity is very good. Ecosystem structure has integrity. Cover vegetation is very good.
2000
2014
VALUE
1.5 1 0.5 0 C11
C12
C13
C14
C21
C22
C23
C24
C25
INDICATOR
C31
C32
C33
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ESI
Fig. 3. Change of indicators and ESI from 2000 to 2014
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indicators held a better status in 2014 in comparison to 2000. These results represent the powerful impact of protection and management in the region. However, the decrease in the standardized value of the three mentioned indicators indicates the lack of attention to the exceptions of the PA and the increasing trend of land tenure in the area under study. In fact, management methods have not been applied correctly and at the right time; an in-time decision will be possible with an earlywarning system.
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In Figure 4, the sensitivity classification map of DPA is shown. According to Table 2, relatively 95% of the study area is in medium and high sensitivity. The sensitivity level areas are in direct relation to the distance from the borders and increasing of the height. In this figure, the dispersal points of Urial wild sheep are also visible. As shown in Fig. 4., this species has chosen to live in the medium and low sensitive areas, which are hill areas with high ecological security.
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An early warning system in Darmiyan protected area
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Based on the defined features for suitable indicators of an EWS, the statistical analysis, and the expert's opinions, eventually, three indicators of precipitation (C12), vegetation covering status (C23), and soil brightness (C24) were chosen as appropriate indicators to be used in the EWS of DPA. When we use the warning system to monitor the study area in pixels where the standardized value of indicators of this system (Table. 3) are in a warning status, these pixels are in the warning or red status, which can be considered for the managers (Fig. 5). In fact, this system operates as an appropriate tool to optimize management by classifying managed areas. In addition, the EWS prevents animals and plant’s habitat destruction by predicting degraded ecosystems. This approach will help protect these areas into the future, in countries with a poor economic status, by reducing the number of areas that need direct monitoring and attention. Table 3 The status of EWS indicators in the study area INDICATOR
RELIABILITY INTERVAL
NORMAL STATUS
WARNING STATUS
COVER VEGETATION BRIGHT SOIL TOTAL ANNUAL PRECIPITATION
0.455 < F < 0.558 0.382 < BS < 0.489 0.231 < R < 0.455
F > 0.455 BS > 0.382 R > 0.231
F < 0.455 BS < 0.382 R < 0.231
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Fig. 4. The Sensitivity classification map in 2014
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Monitoring DPA
if
if
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Cover vegetation > 0.455
Cover vegetation < 0.455
&
&
Bright soil < 0.382
Bright soil > 0.382
&
&
Total annual precipitation > 0.231
Total annual precipitation < 0.231
Warning status
Normal status
Fig. 5. It shows how we used EWS indicators for monitoring Darmiyan protected areas
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Early warning system usage in Darmiyan protected area
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The proposed EWS was used in the study area in 2014. The patches where the system was found in the danger area, were extracted based on the indicator information in 2014. Due to the fragile characteristics of these patches, EWS selected them with a 100 m buffer. The accurate operation of the proposed warning system was examined by field visit (Fig. 6). The system was very precise; the visited sites were in poor condition. According to Fig. 6., all areas at risk are in the margins of the study area. It might be one of the reasons for the reluctance of the Urial wild sheep to be present in these areas. DPA and its surrounding areas have a bad economic status because of the successive droughts and the lack of oil and gas resources available within the region. On the other hand, the occupations of the majority of the people in this area are agriculture and livestock, and the percentage of the rural population is more than that of other parts of the country, and all of these factors create more pressure on the natural ecosystems in this region. According to the above-mentioned issues, with the inappropriate economic status on one hand and the ecological pressures on the other, it is difficult to manage such areas. The DPA manager can significantly reduce the area of the regions that need to be directly monitored, (economically practical) by using the EWS. Managers can also take the identified section out of risk by using case studies and work experiences; managers will benefit if they know the environment
Fig .6. Red status points in Darmiyan protected area in 2014
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Table 4 ESI in different land use in DPA
2014
min max mean
Total mean
Farm 0.38 0.20 0.28
Human made 0.36 0.20 0.26
Rangeland 0.41 0.20 0.31
Arid land 0.41 0.19 0.30
0.57 0.38 0.48 0.38
0.53 0.38 0.46 0.36
0.61 0.38 0.52 0.415
0.60 0.39 0.51 0.405
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2000
min max mean
better (Forleo & Palmieri, 2018). In addition, he or she can see the effects of decisions, which have been taken in previous years by re-using of the EWS in the next year. Discussion
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Land use, land shape, and Ecological Security System
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The change in the ESI of different land use classes from 2000 to 2014 is shown in Table 4. The average ESI values for all land use types were higher in 2014 than in 2000. The greatest ecological security average belongs to the rangeland land use. According to the land-use change during the study years (20% increase in rangeland) and the ESI change, it is inferred that preventing the rangeland degradation for reasons such as overgrazing is one of the prominent factors, which has affected the ESI of this area.
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Given that 80% of the area consists of rangelands, the indicators of vegetation covering and soil brightness have more effects on the ecological security of the area, thus the management
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activities such as livestock control in the region, the industrial livestock promotion and education on the correct agricultural methods are recommended.
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In DPA, the location change in the ESI in a specific year has a very close relation with the land shape. As we advance to heights from flat areas, the ecological security index increases. Climate changes and Ecological Security System
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During the 14 study years, the temperature changes were very slight while the precipitation had much more changes. The precipitation had a 20 mm rise in 2014 compared to 2000. During these 14 years, the ecological security of the area had a 0.2 increase. Thus areas with a climate such as that of DPA, their main climatic restriction is precipitation, and also its effect is more than temperature; this information could be practical in choosing the appropriate indicators for these areas. The Sensitivity classification and land use 15
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The areas of land uses are determined at different sensitivity levels in Fig 7. According to this graph, at level 4 (low sensitivity), the percentage of the presence of human-made land use area is zero. This is probably the main reason why these regions are in this level. In this graph, as we move from the first sensitivity level to the higher levels, the area of human-made, farm, and arid land uses to reduce and rangeland increases; therefore, there is a straight relationship between the sensitivity reduction in the area and the area reduction of incompatible land uses. Moreover, Range land Farm the area of each land use in a particular region affects the progress of indicator selection for ESI Human made Arid land and EWS in that region. According to land use, which an indicator originates from, the effect of 100% that indicator on the ecosystem is higher if the land use area is being extended and 90% consequently, the indicator frequency increases as well.
70%
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The Management of Darmiyan protected area and Early warning system
presence percentage of land use
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Based on what has been mentioned and considering that the Urial wild sheep lives in rangeland with medium and low sensitivity levels (level 3 and 4), this species has avoided residential areas and agricultural land. In fact, it does not compete with domestic animals. Another reason for the presence of Urial wild sheep at these levels is that these areas have higher elevations than other areas. These habitats are important for lambing. Moreover, it seems that the selection of this kind of habitat is related to the species ecology and Urial wild sheep could escape in these the steep slopes easily (Maleki et al., 2010).
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The Sensitivity classification and Urial wild sheep
60%
50%
40%
30%
20%
10%
0% 1
2
3
4
Sensitivity levels Establishment of the EWS in DPA, by fundamental indicators and remote-sensing Fig .7. Presence percentage of land use in sensitivity levels tools, with the least-expensive possible method and without using much. Manpower and field work, help assess vulnerable areas. Most of these areas were located in exits of watersheds and
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regions with a high erosion rate. In fact, the prominent reasons for the destruction of these areas were: cutting bushes; overgrazing; land seizure; and, turning them into farms.
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Information gained from this system of assessing the crucial management weaknesses of the area includes the lack of attention to exceptions in the region and the lack of environmental monitoring stations in the south and east borders. Clarifying the managerial weaknesses is useful for codifying comprehensive, purposive, and directed managerial plans for important ecological regions. On the other hand, sustainable ecological security increased biodiversity and maintaining sustainable development are the result of ecosystem improvement. These results will raise the level of social and economic welfare of the people in this region.
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Data gathering is a time–consuming process which effects on data collection negatively (Rimando et al., 2015). Moreover, processing and analysis of the data collected can also be timeconsuming. Therefore, if we consider all the time it takes for data collection, analysis and interpretation of results, speeding up each stage is one of the most important needs for a quick response to environmental hazards. By using EWS decision-makers can get adequate information about the region at the right time. In
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order to reduce the time required for data mining, this approach has selected basic information by considering the ecological, climatic, and topographical conditions of the study area. EWS also uses data, which can be gathered easily with modern technologies, and it does not require extensive fieldwork.
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Conclusion
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This research is based on the indicators and the environmental effects on them and also remotesensing studies. In this study, we tried to achieve results by using discrete ecosystem components. We tried to minimize the cost of monitoring an ecosystem, so satellite images and modern technologies have been employed. The method which has been used in this paper will help the speed of decision making and recovery operations of regions that are not in good status by identifying them in a simple and fast way. This study will provide the basis for creating alternative management methods in a region and will reveal the effects of this change. DPA with a semi-arid climate was selected to indicate how EWS was used in a management area. The results showed that some parts of the marginal areas in the east and south of the region which are also the watershed outlet are at risk. Field visit and the indicators’ statuses which were selected for EWS showed that vegetation cover in these areas is very poor and with the environment positive feedback, the amount of erosion has increased, so these areas will decline. Considering the density of the villages in the east and south of the study area, it seems that the main cause for this is excessive grazing livestock in the region. DPA managers can greatly overcome this problem by adopting the managerial decisions such as granting to promote the industrial livestock. This research contributes to the protection of an ecosystem that is considered as an international species
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(Urial wild sheep) habitat and also introduces the ecological security indicators in a region with a semi-arid climate.
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Research about EWS could be carried out in other important and natural areas. The significant point in this approach is the selection of the indicators based on the ecological conditions of each area. This research was done by the minimum number of indicators for EWS, hence more investigation can be conducted with more indicators, and their results can be compared with this research’s result. In further studies in this field, the importance of each EWS indicator can be determined, and the warning regions can be divided into three categories: in danger; pre-warning; and, warning status. Other researchers should choose EWS indicators with at least overlap in terms of information to examine the areas in all dimensions. We believe this research can be an appropriate starting point to discuss the use of EWS in key areas and inaccessible areas in different parts of the world.
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Acknowledgment We would like to thank Mr. Mostaqim, who provided data. Special thanks to Saber Feizie and Morteza Roshankar who helped us in editing this paper. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. References
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Abman, R. (2018). Rule of Law and Avoided Deforestation from Protected Areas ☆. Ecological Economics, 146(November 2017), 282–289. https://doi.org/10.1016/j.ecolecon.2017.11.004 Ansoff, H. I. (1975). Managing Strategic Surprise by Response to Weak Signals. California Management Review, XVIII(2), 21–33. https://doi.org/citeulike-article-id:1109593 Badji, M., & Dautrebande, S. (1997). Characterization of flood inundated areas and delineation of poor drainage soil using ERS-1 SAR imagery. Hydrological Processes, 11(10), 1441–1450. https://doi.org/10.1002/(SICI)10991085(199708)11:10<1441::AID-HYP527>3.0.CO;2-Y Barlindhaug, S., Holm-olsen, I. M., & Tòmmervik, H. (2007). Monitoring Archaeological Sites in a Changing Landscape ^ Using Multitemporal Satellite Remote Sensing as an “ Early Warning ” Method for Detecting Regrowth Processes y, 244(May), 231–244. https://doi.org/10.1002/arp Brooks, R. P., O’Connell, T. J., Wardrop, D. H., & Jackson, L. E. (1998). Towards a regional index of biological integrity: The example of forested riparian ecosystems. Environmental Monitoring and Assessment, 51(1–2), 131–143. https://doi.org/10.1023/A:1005962613904 Clarke, G. M. (1995). Relationships Between Developmental Stability and Fitness: Application for Conservation Biology. Conservation Biology, 9(1), 18–24. https://doi.org/10.1046/j.1523-1739.1995.09010018.x EarthExplorer. (n.d.). Retrieved April 17, 2015, from https://earthexplorer.usgs.gov/ Forleo, M. B., & Palmieri, N. (2018). A framework for assessing the relational accessibility of protected areas. Journal of Cleaner Production, 194, 594–606. https://doi.org/10.1016/j.jclepro.2018.05.149 Gabrielsen, P., & Bosch, P. (2003). Environmental Indicators: Typology and Use in Reporting. Eea, (August), 1–20. Glantz, M. (2003). Usable science 8: early warning systems: do’s and don’ts. Report of Workshop, 80301(October 2003), 0–76. Retrieved from http://www.academia.edu/download/30495281/glantz2003.pdf Grasso, V. F. (2007). Early Warning Systems A State of the Art Analysis and Future Directions. Haji-kazemi, S., Andersen, B., Eleftheriadis, R., & Capellan, A. (2015). The Early Warning Procedure in an International Context. Procedia - Social and Behavioral Sciences, 194(1877), 85–95. https://doi.org/10.1016/j.sbspro.2015.06.122 Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, G., & Jarvis, A. (2005). VERY HIGH RESOLUTION INTERPOLATED CLIMATE SURFACES FOR GLOBAL LAND AREAS, 1978, 1965–1978. https://doi.org/10.1002/joc.1276 Hockey, P. A. R., & Curtis, O. E. (2009). Use of basic biological information for rapid prediction of the response of species to habitat loss. Conservation Biology, 23(1), 64–71. https://doi.org/10.1111/j.1523-1739.2008.01028.x
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Iverson, L. R., & Prasad, A. M. (2007). Using landscape analysis to assess and model tsunami damage in Aceh province, Sumatra. Landscape Ecology, 22(3), 323–331. https://doi.org/10.1007/s10980-006-9062-6 LAADS DAAC. (n.d.). Retrieved July 8, 2015, from https://ladsweb.modaps.eosdis.nasa.gov/ Leverington, F., Costa, K. L., Courrau, J., Pavese, H., Nolte, C., Marr, M., … Hockings, M. (2010). Management effectiveness evaluation in protected areas – a global study, 87. https://doi.org/10.1007/s00267-010-9564-5 Li, X., Lao, C., Liu, Y., Liu, X., Chen, Y., Li, S., … He, Z. (2013). Early warning of illegal development for protected areas by integrating cellular automata with neural networks. Journal of Environmental Management, 130, 106– 116. https://doi.org/10.1016/j.jenvman.2013.08.055 Li, Y., Sun, X., Zhu, X., & Cao, H. (2010). An early warning method of landscape ecological security in rapid urbanizing coastal areas and its application in Xiamen, China. Ecological Modelling, 221(19), 2251–2260. https://doi.org/10.1016/j.ecolmodel.2010.04.016 Lumbroso, D. (2018). International Journal of Disaster Risk Reduction How can policy makers in sub-Saharan Africa make early warning systems more e ff ective ? The case of Uganda. International Journal of Disaster Risk Reduction, 27(July 2017), 530–540. https://doi.org/10.1016/j.ijdrr.2017.11.017 Maleki, S., Hemami, M.-R., & Salman Mahiny, A. (2010, January 1). Determining habitat suitability of Ovis orientalis isphahanica in Mooteh Wildlife Refuge using ENFA. In Journal of Natural Environment (Vol. 63, pp. 1–13). Montis, A. De, Martín, B., Ortega, E., Ledda, A., & Serra, V. (2017). Land Use Policy Landscape fragmentation in Mediterranean Europe : A comparative approach. Land Use Policy, 64, 83–94. https://doi.org/10.1016/j.landusepol.2017.02.028 Nicholas, Lord. (2010). Arguments for Protection PACT 2020 : Protected Areas and Climate Turnaround. Evaluation. https://doi.org/10.1017/S0030605311001608 Nikander, I. O. (2002). Early warnings : a phenomenon in project management. Helsinki University of technology. Piciullo, L., Calvello, M., & Cepeda, J. M. (2018). Earth-Science Reviews Territorial early warning systems for rainfall-induced landslides. Earth-Science Reviews, 179(April 2017), 228–247. https://doi.org/10.1016/j.earscirev.2018.02.013 Rimando, M., Brace, A. M., & Parr, T. L. (2015). Data Collection Challenges and Recommendations for Early Career Researchers Data Collection Challenges and Recommendations for Early Career, 20(12), 2025–2036. Saadat, H., Adamowski, J., Tayefi, V., Namdar, M., Sharifi, F., & Ale-Ebrahim, S. (2014). A new approach for regional scale interrill and rill erosion intensity mapping using brightness index assessments from medium resolution satellite images. Catena, 113, 306–313. https://doi.org/10.1016/j.catena.2013.08.012 Sinclair, A. R. E., Fryxell, J. M., & Caughley, G. (2006). Wildlife Ecology, Conservation, and Management. Management. Tegler, B., Sharp, M., & Johnson, M. A. (2001). Ecological monitoring and assessment network's proposed core monitoring variables: An early warning of environmental change. Environ. Monit. Assess., 67(1–2), 29– 56. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.00035075092&partnerID=40&md5=fdf2a7670c7620e7f07fc7d883982890 Wang, H., Long, H., Li, X., & Yu, F. (2014). Evaluation of changes in ecological security in China’s Qinghai Lake Basin from 2000 to 2013 and the relationship to land use and climate change. Environmental Earth Sciences, 72(2), 341–354. https://doi.org/10.1007/s12665-013-2955-1 Xiuping, Z., Shaofeng, C., & Qingwen, Q. (2000). a Resarch on the Assessment of Regional Ecological Security. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B6b. Beijing 2008. Xu, Y., Yang, W., & Wang, J. (2016). Air quality early-warning system for cities in China. Atmospheric Environment. https://doi.org/10.1016/j.atmosenv.2016.10.046 Ye, H., Ma, Y., & Dong, L. (2011). Land ecological security assessment for bai autonomous prefecture of dali based using PSR model-with data in 2009 as case. Energy Procedia, 5, 2172–2177. https://doi.org/10.1016/j.egypro.2011.03.375 Yu, G., Zhang, S., Yu, Q., Fan, Y., Zeng, Q., Wu, L., … Zhao, P. (2014). Assessing ecological security at the watershed scale based on RS/GIS: A case study from the Hanjiang River Basin. Stochastic Environmental Research and Risk Assessment, 28(2), 307–318. https://doi.org/10.1007/s00477-013-0750-x Zhao, Y. Z., Zou, X. Y., Cheng, H., Jia, H. K., Wu, Y. Q., Wang, G. Y., … Gao, S. Y. (2006). Assessing the ecological security of the Tibetan plateau: Methodology and a case study for Lhaze County. Journal of Environmental Management, 80(2), 120–131. https://doi.org/10.1016/j.jenvman.2005.08.019
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