Landscape ecology considers vegetation as a mosaic of patches with unique landform, species composition and disturbance gradient (Ravan et al., 1998). Johst and Huth (2005) such that succession after disturbance generates a mosaic of patches in different successional stages. However, the effects of disturbance on patches may show up in the form of increase distance from other patches and their connectivity. Spatial and temporal understanding of a landscape is required to aid research and management of these characteristic patches. In characterizing a landscape to identify disturbance, GIS has proven effective in landscape ecology by mapping disturbance zones in ecosystems, quantifying their impact on biodiversity and detecting land cover changes over a period of time. The landscapemetrics, based on the geometric properties of the landscape elements, are indicators that are widely used to measure several aspects of the landscape structure and spatial pattern, and their variation in space and time (Li et al., 2005). These metrics have been used in landscape monitoring, including landscape changes (Lausch and Herzog, 2002; Peng et al., 2010; Petrov and Sugumaran, 2009; Rocchini et al., 2006), assessing impacts of management decisions and human activities (Geri et al., 2010; Lin et al., 2010; Narumalani et al., 2004; Proulx and Fahrig, 2010), supporting decisions on landscape and conservation planning (Leitão and Ahern, 2002; Sundell-Turner and Rodewald, 2008), and analyzing landscape and habitat fragmentation (Hargis et al., 1998; Zeng and Wu, 2005). Landscapemetrics derived from land use and land cover maps have been used to quantify environmental change in arid and semi-arid regions (Kepner et al., 2000; Jia et al., 2004). Spatial heterogeneity has been shown to be one of the most reliable indicators of desertification (Schlesinger et al., 1990). In this research, landscapemetrics and spatial heterogeneity indicators are combined to model desertification changes in Ain-e-khosh region, Iran.
half times, during last five decades, but urban India has grown nearly five times. At one among the country of low level of urbanization. Same growth pattern has been seen in the built- up areas of the study area. Due to the development in road and rail network, high value of land at the center of the city, opment of new industrial and recreational zones and population explosion, the rate of -urban fringe. The unprecedented growth and urban sprawl are often unnoticed by the planners, as they are unable to visualize this type of growth patterns. Characterizing and understanding the changing patterns of urban growth is critical, given that urbanization continues to be one of the major global environmental changes in foreseeable future (Rashed, 2008). Landscapemetrics is one of imperative methods for understanding the structure, function and dynamics of landscapes and has a pivotal role to play in finding those solutions and navigating a sustainable urban 2006; Jelinski et al., 2000). The development of ensing and geographic information techniques provides data source and powerful spatial analysis methods for the research on landscapemetrics. Fragstats is a spatial pattern analysis program, used to evaluate the changes of landscape rea based on the output result of the
Dealing with vegetation restoration, most recent studies have focused on landscapemetrics, as these are easy to calculate indicators of ecosystem health (see for example Mahiny, 2007). Larsen and Harvey (2010) predict distinct classes of landscape pattern, process, and restoration potential in shallow aquatic ecosystems. In this connection, Jones et al. (2001) successfully used landscapemetrics to model water quality at the watershed scale. Wang et al. (2005) studied long term effects of land use change on non-point source pollution of a river and offered measures to restore water quality. Novotny et al. (2005) offer a multilayered schema for restoration of water quality through landscapemetrics. Uuemaa et al. (2005) demonstrate that there are relationships between landscapemetrics and water quality data in their study area in Estonia. Xiao and Ji (2007) affirm that landscape characteristics including proportion, edge density and contagion in mine waste- located watersheds could account for as much as 77% of the variation of water quality indicators. Goetz and Fiske (2008) evaluate the relationship between diversity and abundance of stream biota to landscapes in the mid-Atlantic USA. Roberts and Prince (2010) show that landscapemetrics can be used to approximate the amount of total nitrogen (TP) and total phosphorous (TP) in their study site.
While previous studies emphasised the issue on spatial scale or resolution , this present study underscored the importance of segregating the area of interest into relevant regions. The use of three floodplain regions (upper, middle, and lower) has provided us more meaningful landscapemetrics compared with the total floodplain approach. Furthermore, it is important not to rely on a single landscape index to generate conclusions from a study. We found that the combination of the indices “number of patches”, “mean patch size”, “mean nearest-neighbour distance” and “mean proximity index” constitute a minimum requirement for inundation area mapping and analysis.
The objective of this study was to evaluate forest cover change and forest degradation in Nyungwe-Kibira Park, a natural reserve straddling Rwanda and Burundi from 1986 to 2015. Landsat TM, ETM+ and 8OLI images of 30 m spatial resolution were used as primary datasets. Geographic Information System (GIS) techniques were used for forest cover mapping and landscapemetrics were calculated by using FRAGSTATS software. Classification and change analysis of forest cover type and landscape patterns analysis were carried out. In addition, to analyze the correlated external disturbances, the buffer zone of 5 Km was delineated outside the boundary of Nyungwe-Kibira Park. The results revealed that in among 5 land cover classes considered within the Park, the dominant one was dense forest class covering over 70% of the entire Park area while in the buffer zone cultivated and open land domi- nated at over 90% between the years 1986 and 2015. Change detection highlighted that within Nyungwe-Kibira forest, approximately 0.27% (4.97 Km 2 ) of forest cover
Of the landscapemetrics included in this study, our results indicate strongest support for the importance of the greenspace splitting index and the proportion of water cover. A large greenspace splitting index, which results from green land covers being split into many patches with an even size distribution, is associated with higher levels of poor health. The splitting index is high along the river corridors to the north-west and north-east of the city centre, where greenspace was largely replaced by heavy industry in the past. It is also high in the city centre and areas to its immediate west, where population densities are highest, leaving little residual greenspace between residential developments. A large splitting index has previously been reported to be related to higher levels of urban noise (Han et al., 2018; Sakieh et al., 2017), but has not been tested in relation to any other mechanisms of beneit to human health. Fig. 4a shows the distribution of this metric across the study area. A low proportion of water cover (Fig. 4b) is associated with greater levels of self-reported bad health in an LSOA. The spatial dis- tribution of this metric is partially dependent on topography, with natural rivers and ponds/lakes contributing, but it is notably lower on average in the city centre, where culverting, covering and illing of water bodies to make space for development is more common. Previous research has found positive relationships between water in landscapes and emotional, restoration and recreational beneits, and the presence of water plays a signiicant role in landscape preferences (Völker and Kistemann, 2011). Water cover was also positively associated with aesthetic preferences and biodiversity in previous landscape metric studies (Beninde et al., 2015; Franco et al., 2003; Palmer, 2004).
Abstract—Biosphere reserve is designed as an international model for exchange knowledge and experiences on sustainable development innovations across national and continental borders. To provide baseline information for future planning and management on biodiversity and environmental conservation this research investigated and evaluated the changes on landscape pattern in the Sakaerat Biosphere Reserve (SBR) of Thailand from 1980 to 2010. Multi-temporal remote sensing, geographic information system, and landscapemetrics were applied to classify and analyze changes on landscape types and patterns. SBR landscape was classified into 6 landscape types and then four aspects of landscapemetrics were applied to measure SBR landscape structure. The results showed that the natural forest landscape was the major landscape type, followed by the agriculture and the disturbed forest landscapes. Landscapes change occurred mostly in the disturbed forest, forest plantation and the urban landscapes. For landscapemetrics measurement, it was found that the SBR landscape pattern variations occurred in increasing of fragmentation and diversity whereas decreasing occurred in core area and shape complexity at landscape level. Concurrently, at class level the indices indicated distinctively the trend of fragmentation, isolation, aggregation and extent of core area in the urban, forest plantation, agriculture, and the disturbed forest class.
We conducted a linear regression analysis with bobwhite density as the dependent variable and 5 landscapemetrics and presence or absence of a CP33 border as predictor variables (PROC REG; SAS Institute, Cary NC). All landscapemetrics were standardized using a z-score transformation to improve normality of the data (Osborne and Waters 2002). We used a global model to calculate standardized parameter estimates (coefficients) for all predictor variables. We determined statistical significance of predictor variables by calculating 95% confidence intervals for each coefficient. We considered a variable
After connected components labeling and gaussian smoothing technique finished. In this paper, we compute all object that get from the past processing step by shape metrics (one of the landscapemetrics for measuring spatial object complexity) is used . Sample result that get from computing shape score was shown in Figure 3. Geometrical characteristics of the roads are captured and differentiated from other spatial objects in the given image. Other geometry metrics can also be used such as rectangular degree, aspect ratio, etc. More information on other landscapemetrics can be found in , .
STEJSKALOVÁ DAGMAR, KARÁSEK PETR, TLAPÁKOVÁ LENKA, PODHRÁZSKÁ JANA: Landscapemetrics as a tool for evaluation of landscape structure, a case study of Hubenov region, Czech Republic. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2013, LXI, No. 1, pp. 193–203 The present paper contains the evaluation of rural landscape development in model territories by means of landscape structure analysis. Based on the computed values of landscape ecology indexes, development and typical and speciﬁ c features of analysed territories are interpreted in deﬁ ned time horizons. The territories diﬀ er in the intensity of their use, natural conditions and diﬀ erent social requirements. Two territories have intensive agriculture in diﬀ erent natural conditions, the third model territory is situated in the protective zone of a water resource, and the fourth model territory is a protected area. For all localities, the studied baseline period was that of the Stable Cadastre mapping (1825–1839), followed by the sixties of the 20 th century and the present time. The method of processing
It is important to discuss the results of this study in the light of desakota which continues to raise interest in Asian urban land use change studies (Sui and Zheng 2001; Firman 2009; Wu 2009). For most of these studies the emergence of desakota is identified with areas of high population density. This situation is in contrast with what is obtained in Iskandar Malaysia, where the population is low and is 174 persons/km 2 (IRDA 2012). This situation of rapid urban growth and landscape change in low-density population area presents an in- teresting area for further studies on how capital influx affects people’s experiences with landscapes including their rights to use landscape resources. This is important because in some desakota regions people are dispos- sessed of lands by the powerful investors (Ortega 2012). Sometimes the effects of land development by investors increase siltation and sedimentation of rivers upon which local communities earn livelihoods through fish- ing (Joeman 2011). In other words, the issue of land- scape change and investment influx in developing countries could be critically understood through socio- ecological prism.
Bernhard Hohmann et al.  have proposed a new scheme for cooperative transmission in ad-hoc networks, which achieves full diversity. Their scheme is a time slotted distributed protocol; they have utilized this protocol to enhance transmissions in ad-hoc networks. They have achieved their objective by employing the well-known cooperative virtual multiple input multiple output (MIMO) technique. Further, their protocol is evaluated by considering bit error rate (BER), signal to noise ratio (SNR) and delay as target metrics.
Nonetheless, achieving insight into the depth of the strength of the relationships between factors in a study becomes cumbersome without a uniform metric. Therefore, converting metrics into a uniform format becomes pertinent before a meta-analysis can be conducted. In this study, we adopted the Pearson’s correlation co-efficient as effect size index representing the empirical strength of the relationship between each pair of the UTAUT construct. We followed the approach described by Lipsey & Wilson (2001) and Ma & Liu (2004), for each of the pair of the UTAUT construct: performance expectancy (PE), effort expectancy (EE), social influence (SI), faciliatating condition (FC), behavioural intention (BI) and use behaviour (UB) the effect size was computed such that it is simply a correlation coefficient (r) if reported, otherwise a conversion is made using equation (1) if other metrics such as t-value was reported. This procedure by Rosenthal (1984) and has been widely adopted by several studies (See Szymansky& Henard, 2001; Ma and Liu 2004). The effect sizes of variables in each study were computed to access prediction effect towards behavioural intention and use behaviour. Effect sizes reported by authors were not recalculated but were used directly. The computed outcome of effect values were computed into excel spreadsheet. Generally, about 96% of the effect sizes were calculated using the means and the mean and standard deviation spreadsheet. 4% of the effect sizes were calculated using the F or t test spreadsheet.
This paper found that there is no specific law putting ISPs liable for end users’ security. Although, there are existing laws related to copyright, defamation, privacy, and similar crimes. Examples of these laws are; in the United States, the Communications Decency Act (CDA) and the Digital Millennium Copyright Act (DMCA) were thus passed respectively in 1996 and in 1998, while the Electronic Commerce Directive (e-commerce Directive) in Europe was adopted in 2000 . However, the laws did not put the ISPs liable for the users illegal activities or end users’ internet security instead were more of providing immunity for Internet service providers, which have created some controversies in legal system in these two continents. Farano said, "Over the past ten years, the potential liability of online service providers for third party content has raised one of the most spirited and fascinating debates in the legal arena, putting right holders, service providers and Internet users at loggerheads. In the United States and in Europe, lawmakers have endeavored to resolve this tension by enacting, more than ten years ago, a set of essentially consistent regulations – most notably the U.S. D.M.C.A. and the EU E-commerce Directive – aimed at fostering the growth of the digital economy, while not hampering the protection of IP rights in the digital environment. However, courts in Europe and in the United States are facing increasing difficulties in interpreting these regulations and adapting them to a new economic and technical landscape that involves unprecedented levels of online piracy and new kinds of online intermediaries. As a result, courts in Europe and in the United States have reached contrasting conclusions and have failed to offer consistent guidelines in an increasingly global market” .