Jennifer Viezzer’s research while affiliated with Federal University of Paraná and other places

The urban forest generates diverse ecosystem services for people. Among the components of the urban forest, public garden squares are different for having a smaller area but a greater quantity and better distribution across cities, acting as small forests that provide benefits. However, these benefits may not be shared by the entire population, due to the variability in the vegetation cover of the squares and the inequality concerning areas with better or worse socioeconomic conditions. Therefore, we investigated the relationship between the squares and the mean monthly income in the 40 clustered neighborhoods of Curitiba, aiming at supporting urban planning to maintain equal distribution of benefits to the population or reduce inequalities, if any. Thus, quantitative and qualitative data from the squares were compared to the mean monthly income through descriptive and correlation statistical analysis and spatial data visualization. Results indicate that neighborhoods with lower income have fewer squares concerning the neighborhood total area and fewer tree species in total and per square. Neighborhoods with higher incomes have better indicators of quantity and quality, in general, except for the mean square area and permeability. We concluded that the benefits are not equally distributed to the entire population of Curitiba, with a loss of access to the benefits generated by the squares and their ecosystem services for the population with worse socioeconomic conditions.

Street trees offer numerous ecosystem benefits when well managed by the municipal administration. However, quantifying these benefits is complex, which contributes to the negligence and undervaluation of afforestation by municipal managers and the population as a whole. Thus, the valorization of urban afforestation emerges as a strategic awareness tool, capable of translating the importance of each individual tree within the urban green infrastructure for citizens and municipal managers, in addition to assisting in the process of choosing the best guidelines and practices for urban forest management. When applied to street tree planting, these methods fail to consider the most important aspect of a tree in the urban environment: its canopy. Thus, this research aimed to propose a new method for the monetary valuation of street trees, focusing on the benefits of the canopy and applying this method in a case study. Tree species, condition, location, biometric value and implementation cost were considered.

Áreas verdes urbanas (AVU) são espaços livres de edificações, com predominância de cobertura vegetal, que geram serviços ecossistêmicos importantes nas cidades, como a melhoria microclimática, a segurança hídrica, e a saúde física e mental dos seus habitantes. Geralmente, as AVU são consideradas como um só componente em estudos sobre a floresta urbana, embora possam ter coberturas do solo e qualidade da vegetação diferentes entre si. Esta pesquisa buscou caracterizar tipologias de áreas verdes usando o índice de vegetação por diferença normalizada (NDVI) para investigar se existe distinção entre elas. Para isso, Curitiba foi selecionada como área de estudo, tendo 18 tipologias de áreas verdes. O NDVI do município, das áreas verdes em conjunto, e de cada tipologia foi calculado com base em imagem LANDSAT-8 e classificado em água, não-vegetação, vegetação gramínea e ou arbustiva, vegetação arbórea esparsa, ou vegetação arbórea densa. Uma análise estatística descritiva e um teste t de igualdade de médias também foram realizados. Os resultados demonstraram diferenças entre a maioria das tipologias, com alta amplitude entre valores mínimos, máximos e médios, estes diferentes entre si na maioria dos casos (72,50%). A proporção ocupada por cada classe de NDVI variou bastante entre as tipologias, principalmente para vegetação gramínea e ou arbustiva (0,00% a 72,22%) e arbórea esparsa (5,26% a 80,00%). Por isso, destaca-se a importância de se conhecer e qualificar a vegetação de diferentes áreas verdes em estudos futuros.

Recognition of landscape configuration is essential to define priority sites most likely to succeed in forest restoration. The research aimed to reduce uncertainties regarding the feasibility of ecological restoration processes by applying landscape metrics to support priority areas for restoration in three watersheds located in the northwest region of the state of Paraná, Brazil. A temporal analysis of the landscape was performed for the years 1985, 1996, 2007, and 2018. Seven land use and land coverage categories were proposed and seven landscape metrics were calculated to assess the configuration using the Fragstats® software, and an assessment of restoration success using GoFor® software. The studied area was divided into four priority levels: low, medium, high, and very high probability of restoration success. The landscape matrix was composed of pastures and agriculture, which corresponded to almost 90% of the area. There has been a considerable increase in forest coverage over the years due to commercial plantations and the recovery of Permanent Preservation Areas (APP). Approximately 12.6 percent of the landscape has the potential to be restored if restoration efforts are allocated to areas with the highest potential for restoration success.

O Objetivo do Desenvolvimento Sustentável 11.7 visa fornecer acesso universal a espaços públicos verdes, o que pode ser alcançado por um sistema bem planejado de áreas verdes (AV). Este estudo buscou, portanto, identificar lacunas na distribuição de AV relacionada a variáveis demográficas e socioeconômicas. Curitiba foi selecionada como área de estudo por ser internacionalmente conhecida por suas 1.182 AV. Dez variáveis sobre população, renda e AV foram calculadas com base em dados espaciais e divididas em 40 bairros agrupados. Foram realizadas análises estatísticas descritivas bem como correlações de Pearson, Spearman e Kendall. Os principais resultados são apresentados em valores mínimos-máximos por bairro (com valor médio entre parênteses): quantidade 12 – 113 (31,07); área média 0,00 – 0,66 km² (0,03); área total 0,02 – 7,87 km² (0,67); quantidade por área do bairro 0,35 – 12,44 AV/km² (3,98); área do bairro ocupada por AV 0,30 – 42,44% (5,03%); população por quantidade de AV 442,17 – 2.789,26 habitantes/AV (1.564,97); e índice de AV 0,65 – 269,56 m²/habitante (16,77). Correlações fortes foram encontradas principalmente para renda per capita entre a quantidade de AV por área do bairro (positiva) e a população por quantidade de AV (inversa). Os resultados mostram grande amplitude e diferenças relevantes entre bairros que podem auxiliar planejadores urbanos.

Urban areas are highly vulnerable to a variety of flood types, including river floods. Mapping vulnerable areas of a watershed is a key measure to develop an assessment of restoration strategies to protect people from flooding, but it is difficult to pinpoint the best place to start. Studies about flooding in urban areas and priority areas for restoration have been conducted separately; however, the interface between these themes has been less explored. Therefore, this paper presents two methods to develop flood susceptibility maps, as well as priority restoration areas: Areas of Variable Inflow (AVI) and the most Hydrologically Sensitive Areas (HSA+). The AVI calculation was developed using the Topographic Wetness Index (TWI). The HSA + calculation was based on criteria that are among the most used in flood studies, combined using the Analytic Hierarchy Process (AHP). All AVI and HSA+ within Riparian Preservation Areas (RPA) and without vegetation cover were considered priority areas for restoration. AVI occupied 16.6759 km² (18.63%), with 2.1172 km² as a priority (3.76% of the RPA). HSA + included 12.4863 km² (13.95%), with 3.3603 km² as a priority (5.97% of the RPA). Most AVI and HSA+ were located outside the RPA, which indicates the necessity for additional conservation on the watershed, even though restoration within RPA must be the priority. Both methods may be applied in any watershed, but we indicate that HSA + provides better results since it considers a wider range of criteria. Since flooding events are rising due to climate change, prioritizing restoration efforts becomes indispensable.

The objective of this study was to evaluate both the landscape’s visual and sound quality influence on propagating noise pollution in the Botanical Garden and the São Lourenço Park in Curitiba, Brazil. Data were collected in three transects (A, B, and C), representing different vegetation densities. Landscape visual quality was characterized through a direct method for valuing landscape elements. The visual quality was classified as Good, Average, or Bad. We used two INSTRUTHERM DEC-470 decibel meters with 1.5-decibel precision for the sound analysis. The landscapes evaluated at site C showed variation in the landscape visual quality from 1 to 21.57, with the best visual and noise attenuation qualities. Correlation analysis between the landscape visual quality and the amount of blocked noise showed a strong correlation coefficient (0.65; p = 0.0001). Landscapes with better visual quality resulted in a greater amount of attenuated noise, mainly due to the presence of vegetation.

The objective was to evaluate the influence of noise pollution on both visual and sound landscapes at the Botanical Garden and the São Lourenço Park in Curitiba, Brazil. Data were collected in three transects (A, B, and C), representing different vegetation densities. Landscape visual quality was characterized through a direct method for valuing landscape elements. The visual quality was classified as Good, Mean, or Bad. For the sound analysis, two INSTRUTHERM decibel meters, model DEC-470, with 1.5-decibel precision, were used. The landscapes evaluated at site C showed variation in the quality of the landscape from 1 to 21.57, with the best visual and noise attenuation qualities. Correlation analysis between the visual quality of the landscape and the amount of blocked noise showed a strong correlation coefficient (0.65; p = 0.0001). Landscapes with better visual quality result in a greater amount of attenuated noise, mainly due to the presence of vegetation.