Cristiano, Samanta da Costa

Portz, Luana Carla

Rockett, Gabriela Camboim

Data in Brief

English

Lume 5.8

Data in Brief 31 (2020) 105672 Contents lists available at ScienceDirect Data in Brief journal homepage: www.elsevier.com/locate/dib Data Article Beach landscape Dataset of Fernando de Noronha Island (Brazil) Luana Carla Portz a , ∗, Samanta da Costa Cristiano b , Gabriela Camboim Rockett c a Civil and Environmental Department, Universidad de la Costa, Barranquilla, Atlántico, Colombia b Programa de Pós-Graduação em Gerenciamento Costeiro, Universidade Federal do Rio Grande, RS, Brazil c Universidade Federal do Rio Grande do Sul/Campus Litoral Norte, Centro de Estudos Costeiros, Limnológicos e Marinhos (CECLIMAR), Brazil a r t i c l e i n f o Article history: Received 13 April 2020 Accepted 29 April 2020 Available online 13 May 2020 Keywords: Coastal scenarios Checklist Fuzzy logic a b s t r a c t Beach landscape Dataset of Fernando de Noronha Island (Brazil), using a checklist with 26 physical and human pa- rameters. Fernando de Noronha beaches were divided into sectors according to the landscape diversity. In total, 19 sectors were evaluated based on observations done during walks in the area, observations from viewpoints, with re- mote data. The evaluations were performed during fieldwork from 2014 (summer) and 2016 (spring). The landscape qual- ity evaluation of Fernando de Noronha was performed using the Coastal Scenery Evaluation System. This method converts qualitative-quantitative data in quantitative data by estimat- ing weights for 26 parameters (18 physical parameters – P - and 8 human-related parameters – H). The main parameters that define the landscape quality are classified from 1 (ab- sence/bad quality) to 5 (presence/excellent quality). A math- ematical model based on fuzzy logic was utilized to integrate the parameters weights in a special system for the scenarios classifications resulting in a value named D. The D-value is the indicator of the attractiveness of the evaluated place. The ∗ Corresponding author. E-mail address: lportz1@cuc.edu.co (L.C. Portz). https://doi.org/10.1016/j.dib.2020.105672 2352-3409/© 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license. ( http://creativecommons.org/licenses/by/4.0/ ) 2 L.C. Portz, S.d.C. Cristiano and G.C. Rockett / Data in Brief 31 (2020) 105672 beaches are divided into classes ranging from 1 (extremely attractive natural site) to 5 (unattractive urban areas). © 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license. ( http://creativecommons.org/licenses/by/4.0/ ) SV     1 4  o  A  t  s  t  (pecifications Table Subject Nature and Landscape Conservation Specific subject area Coastal scenarios methodology application in Fernando de Noronha Island (Brazil) Type of data Table Graph Figure How data were acquired Field trip, GNSS, data analysis and interpretation by ArcGIS program. Data format Raw Analyzed KMZ Parameters for data collection Field observation by researchers from different fields of knowledge. Two distinct periods of the year were considered. Description of data collection Data collection was made in field work observations of the landscape. Data source location Fernando de Noronha Island, Brazil 3 ° 51 ′ 13.71 ′′ S, 32 ° 25 ′ 25.63 ′′ W Data accessibility https://data.mendeley.com/datasets/42ndmsmszk/draft?a= 6bda2b9a- f6b2- 4724- 823f- 2e367501f683 Related research article Samanta da Costa Cristiano, Gabriela Camboim Rockett, Luana Carla Portz, JoséRodrigues de Souza Filho. Beach landscape management as a sustainable tourism resource in Fernando de Noronha Island (Brazil). Marine Pollution Bulletin 150 (2020) 110621. https://doi.org/10.1016/j.marpolbul.2019.110621 alue of the Data • The data can be useful for coastal management in island and protected areas. • Any researcher that deals with the Fernando de Noronha Island can benefit from thesedatasets. In addition, researchers from other fields of knowledge, decision makers and publicofficials. • The data can be useful for further researches that deal with any coastal scenarios or coastalmanagement research. • Beach landscape Dataset create new perspectives for sustainable development based on thesingularities of this touristic resource – the landscape. • The data investigate extensively the effect of tourism on changes in the coastal scenery, in-cluding natural and anthropogenic parameters. . Data Description The dataset consists in the classification of Fernando de Noronha’s beaches in classes (1-), according to the Coastal Scenarios. The classification is in Fig. 1 . The highest diversityf classes is in the APA/Mar de Dentro area (Fernando de Noronha Environmental Protectionrea-APA). On the other hand, in the PARNAMAR/Mar de Fora area (Fernando de Noronha Na-ional Marine Park-PARNAMAR), there is only one beach in class 3 (mostly natural areas, withome landscape parameters that stands out), and all the other beaches are in classes 1 (ex-remely attractive natural sites) and 2 (natural, attractive areas with high landscape value sites) Fig. 1 and Table 1 ). L.C. Portz, S.d.C. Cristiano and G.C. Rockett / Data in Brief 31 (2020) 105672 3 Fig. 1. Location of the Fernando de Noronha island beaches (Brazil) analyzed and specific sectors of APA and PARNAMAR areas. (Spatial data sources: Basemap: ESRI; APA/PARNAMAR delimitation Shapefile: ICMBio). Tabla 1 Summary of the results, average D-value (classes) (spring and summer). ID Beach/Bay Sector D-values D-values summer Class summer D-values spring Class spring 1 Porto/Pier Beach APA 0.35 0,36 4 0.35 4 2 Porto/natural Beach APA 0.77 0,76 2 0.78 2 3 Biboca Beach APA 1.09 1,09 1 1.09 1 4 Cachorro Beach APA 0.43 0.50 3 0.37 4 5 Meio Beach APA 0.76 0.75 2 0.78 2 6 Conceição Beach APA 0.63 0.57 3 0.70 2 7 Boldró Beach APA 0.77 0.76 2 0.79 2 8 Americano Beach APA 1.12 1.12 1 1.13 1 9 Bode Beach APA 1.01 0.98 1 1.04 1 10 Cacimba do Padre APA 0.84 0.79 2 0.89 1 11 Porcos Bay PARNA 1.03 1.02 1 1.05 1 12 Sancho Bay PARNA 1.20 1.20 1 1.20 1 13 Capim-Açú PARNA 0.74 0.74 2 0.74 2 14 Leão Beach PARNA 1.15 1.15 1 1.15 1 15 Sueste Bay PARNA 0.71 0.71 2 0.71 2 16 Abreus PARNA 0.79 0.79 2 0.79 2 17 Atalaia/sand Beach PARNA 1.10 1.10 1 1.10 1 18 Atalaia/rocky Beach PARNA 0.99 0.99 1 0.99 1 19 Caieira Beach PARNA 0.47 0.48 3 0.46 3      The D value (attractiveness indicator) is presented for summer and spring periods ( Table 1 ). Histograms provide a visual summary of the physical and human parameters obtainedthrough the application of the checklist and are useful for immediate evaluation of high andlow ranking attributes. The values defined for each parameter (physical and human) are shownin Fig. 2 (beaches without seasonal variations). Seasonal changes in the parameters can be seenin the histogram ( Figs. 3 , 4 and 5 ). As an example, Cacimba do Padre Beach changes from4 L.C. Portz, S.d.C. Cristiano and G.C. Rockett / Data in Brief 31 (2020) 105672     0 1 2 3 4 5ScoressretemaraPAbreus Assessment HistogramPhysical Parameters Human Parameters0 1 2 3 4 5ScoressretemaraPAmericano Beach Assessment HistogramPhysical Parameters Human Parameters0 1 2 3 4 5ScoressretemaraPAtalaia/sand Beach Assessment HistogramPhysical Parameters Human Parameters0 1 2 3 4 5ScoressretemaraPAtalaia/rocky Beach Assessment HistogramPhysical Parameters Human ParametersFig. 2. Histogram with variations in the attributes’ values of categories 1 to 5 (to see these categories look at Table 1 ). L.C. Portz, S.d.C. Cristiano and G.C. Rockett / Data in Brief 31 (2020) 105672 5     0 1 2 3 4 5ScoressretemaraPBiboca Beach Assessment HistogramPhysical Parameters Human Parameters0 1 2 3 4 5ScoressretemaraPBode Beach Assessment HistogramPhysical Parameters Human Parameters0 1 2 3 4 5ScoressretemaraPBoldró Beach Assessment HistogramPhysical Parameters Human Parameters0 1 2 3 4 5ScoressretemaraPCaieira Beach Assessment HistogramPhysical Parameters Human ParametersFig. 2. Continued     class 1 in the spring season (D-value = 0.89) to class 2 in the summer season (D-value = 0.79)( Table 1 ) ( Fig. 3 ). Attribute values 4 or 5 produces a high scenic value (high rating). The predominance of val-ues 4 and 5 in phisical and anthropogenic parameters in the histogram can be observed for LeãoBeach and Sancho Bay ( Fig. 3 ). On the other hand, attribute values 1 or 2 produces a high scenic6 L.C. Portz, S.d.C. Cristiano and G.C. Rockett / Data in Brief 31 (2020) 105672     0 1 2 3 4 5ScoressretemaraPCapim-Açú Assessment HistogramPhysical Parameters Human Parameters0 1 2 3 4 5ScoressretemaraPLeão Beach Assessment HistogramPhysical Parameters Human Parameters0 1 2 3 4 5ScoressretemaraPMeio Beach Assessment HistogramPhysical Parameters Human Parameters0 1 2 3 4 5ScoressretemaraPPorcos Bay Assessment HistogramPhysical Parameters Human ParametersFig. 2. Continued v  I  Palue (high rating), indicating the unfavorable impact on physical and / or human parameters.n this assessment, it is generally the human parameters that reduce the assessment, such asorto/Pier Beach ( Fig. 2 ). L.C. Portz, S.d.C. Cristiano and G.C. Rockett / Data in Brief 31 (2020) 105672 7     0 1 2 3 4 5ScoressretemaraPPorto/Natural Beach Assessment HistogramPhysical Parameters Human Parameters0 1 2 3 4 5ScoressretemaraPPorto/Pier Beach Assessment HistogramPhysical Parameters Human Parameters0 1 2 3 4 5ScoressretemaraPSancho Bay Assessment HistogramPhysical Parameters Human Parameters0 1 2 3 4 5ScoressretemaraPSueste Bay Assessment HistogramPhysical Parameters Human ParametersFig. 2. Continued     2. Experimental Design, Materials, and Methods The methodology used to perform the beach landscape quality evaluation of Fernando deNoronha island beaches (Brazil) was the Coastal Scenery Evaluation System [1–3] . This methodconsists of estimating weights for 26 parameters (18 physical parameters – P - and 8 human-related parameters – H) and converting qualitative-quantitative data in quantitative data. Ac-8 L.C. Portz, S.d.C. Cristiano and G.C. Rockett / Data in Brief 31 (2020) 105672   0 1 2 3 4 5ScoressretemaraPCacimba do Padre - spring Assessment HistogramPhysical Parameters Human Parameters0 1 2 3 4 5ScoressretemaraPCacimba do Padre - summer Assessment HistogramPhysical Parameters Human ParametersFig. 3. Histogram with variations in the attribute’s values of categories 1 to 5 (to see these categories look at Table 1 ) for Cacimba do Padre Beach (Spring and Summer). L.C. Portz, S.d.C. Cristiano and G.C. Rockett / Data in Brief 31 (2020) 105672 9   0 1 2 3 4 5ScoressretemaraPConceição Beach - spring Assessment HistogramPhysical Parameters Human Parameters0 1 2 3 4 5ScoressretemaraPConceição Beach - summer Assessment HistogramPhysical Parameters Human ParametersFig. 4. Histogram with variations in the attribute’s values of categories 1 to 5 (to see these categories look at Table 1 ) for Conceição Beach (Spring and Summer). 10 L.C. Portz, S.d.C. Cristiano and G.C. Rockett / Data in Brief 31 (2020) 105672   0 1 2 3 4 5ScoressretemaraPCachorro Beach - spring Assessment HistogramPhysical Parameters Human Parameters0 1 2 3 4 5ScoressretemaraPCachorro Beach - summer Assessment HistogramPhysical Parameters Human ParametersFig. 5. Histogram with variations in the attribute’s values of categories 1 to 5 (to see these categories look at Table 1 ) for Cachorro Beach (Spring and Summer). L.C. Portz, S.d.C. Cristiano and G.C. Rockett / Data in Brief 31 (2020) 105672 11 Table 2 Classification of Beach landscape quality, according to the D-value obtained from the Coastal Scenery Evaluation System [1] . D-Value Beach Landscape Quality Class Class Description D > 0.85 1 extremely attractive natural site; 0.85 > D ≥ 0.65 2 natural, attractive areas with high landscape value sites; 0.65 > D ≥ 0.4 3 mostly natural areas with some landscape values highlighted; 0.4 > D ≥0 4 urban areas, mainly unattractive, with few landscapes’ values highlighted; D < 0 5 unattractive urban areas, with intense development and low landscape values.                             cording to the Evaluation System, the 26 parameters were considered essential for an attractivecoastal landscape and are shown in Table 1 . The parameters are weighted from 1 to 5, where:1 refer to the item’s “absence or bad quality” and 5 refers to the item’s “presence or excellentquality”. For the evaluation of Fernando de Noronha island beaches, the beaches were divided in 19homogeneous landscape sectors, (shown in Fig. 1 ). Some beaches, due to its and landscape vari-ation (heterogeneity), were divided into more than one sector (e.g. Atalaia/rocky beach and Ata-laia/sandy beach; Porto/Pier Beach and Porto/Natural Beach). From the total beach sectors, 10 ofthem are inside the APA area and 9 of them are inside the PARNAMAR area ( Fig. 1 ). The evaluation of each beach sector and checklist filling was performed (i) in fieldwork -using landscape observations during walks in the beaches and/or from viewpoints, and (ii) indata remote-check using Google Earth imagery. Due to seasonal variability, landscape evaluationswere performed during spring and during summer - summer fieldwork performed in 2014 andspring fieldwork performed in 2016. The professionals involved in the field evaluations werefrom the areas of biosciences and geosciences/geography. After each parameter’s evaluation (checklist), data processing was performed, in order to in-tegrate the parameters weights in a special system for the scenarios classifications. A graphi-cal summary of the investigated sceneries were obtained/generated from the weighted averagesand association degrees and histograms [4] . Beach scenery is better when most of the parame-ters scores “5” (which result in a right-leaning association degree curve), and in the same waythe potential status of the scenic assessment are indicated from the weighted average – themore parameters scoring “5”, the better the coastal scenery. For this integration, a mathematicalmodel based on fuzzy logic is used and the result obtained from this model is a value named D(D-value), which is the indicator of the attractiveness of the evaluated beach. According to theMethod [1] , there are five possible beach classes, according to the D-value obtained ( Table 2 ). Class 1 beaches are extremely attractive natural sites, with a D-value > 0.85. Class 2 beachesare natural, attractive areas with high landscaping value site and a D-value between 0.65 and085. Class 3 beaches are mostly natural areas with some landscaping value highlighted, andD-value between 0.65 and 0.40. Class 4 beaches are urban areas, mainly unattractive, withfew landscaping values highlighted, and D-value between zero and 0.40. Class 5 beaches areunattractive urban areas, with intense development and low landscaping value, and D-value be-low zero. The data obtained in this study are available as georreferenced files (.kmz) CRediT authorship contribution statement Luana Carla Portz: Conceptualization, Formal analysis, Investigation, Methodology, Visualiza- tion, Writing - original draft, Writing - review & editing. Samanta da Costa Cristiano: Concep-tualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Visualization, 12 L.C. Portz, S.d.C. Cristiano and G.C. Rockett / Data in Brief 31 (2020) 105672 W  t  -A 6  tC tR[  [  [  [riting - original draft, Writing - review & editing. Gabriela Camboim Rockett: Conceptualiza-ion, Data curation, Formal analysis, Investigation, Methodology, Resources, Visualization, Writing original draft, Writing - review & editing. cknowledgments This research was authorized by the Instituto Chico Mendes da Biodiversidade (number 42493-), which provided accommodation and free access to the beaches for field surveys. We thankhe Administração do Distrito Estadual de Fernando de Noronha for the logistical support. onflict of Interest The authors declare that they have no known competing financial interests or personal rela-ionships that could have appeared to influence the work reported in this paper. eferences 1] A. Ergin, E. Karaesmen, B. Uçar, A Quantitative Study for Evaluation of Coastal Scenery, Journal of Coastal Research277 (2011) 1065–1075 https://doi.org/10.2112/jcoastres- d- 09- 0 0 093.1 . 2] A . Ergin, A .T. Williams, A . Micallef, Coastal Scenery: Appreciation and Evaluation, Journal of Coastal Research 224(2006) 958–964 https://doi.org/10.2112/04-0351.1 . 3] A. Ergin, E. Karaesmen, A. Micallef, A.T. Williams, A new methodology for evaluating coastal scenery: Fuzzy logicsystems, Area 36 (2004) 367–386 https://doi.org/10.1111/j.0 0 04-0894.20 04.0 0238.x . 4] A .T. Williams, A . Khattabi, Beach scenery at Nador Province, Morocco, Journal of Coastal Conservation 19 (2015) 743–755 https://doi.org/10.1007/s11852-015-0393-9 . 

Beach landscape dataset of Fernando de Noronha Island (Brazil)

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Beach landscape dataset of Fernando de Noronha Island (Brazil)

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