Reforest or perish: ecosystem services provided by riparian vegetation to improve water quality in an urban reservoir (São Paulo, Brazil)

Adas, Mariana; Hardt, Elisa; Miraglia, Simone Georges El Khouri; Semensatto, Décio

doi:10.18472/sustdeb.v11n1.2020.28152

Cited by 4 publications

(2 citation statements)

References 32 publications

(59 reference statements)

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“…The results obtained in that study demonstrated the potential of creating riparian UGSs to rehabilitate polluted rivers. A study conducted on ecosystem services in a riparian protected area (RPA) in São Paulo estimated that 5917.5 ha of the reforested RPA could reduce the cost of water treatment by USD 181,774 per 1000 m 3 [46]. Additionally, Figure 5 shows a cluster of districts with higher population densities and larger impervious areas (roads and concrete pavements) and built spaces.…”

Section: Interaction Of Urban Green Spaces and Intra-urban Elementsmentioning

confidence: 99%

Section: Interaction Of Urban Green Spaces and Intra-urban Elementsmentioning

confidence: 99%

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Using a Novel Green Index to Support Ecosystem Services in a Megacity

Sant’Anna Neto,

Lana,

Lucas

et al. 2023

Forests

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We present a novel and efficient approach that enables the evaluation of environmental quality in cities worldwide using high-resolution satellite imagery, based on a new green index (GI) through multivariate analysis, to compare the proportion of urban green spaces (UGSs) with built and impervious surfaces. High-resolution images were used to perform a supervised classification of 25 districts in the city of São Paulo, Brazil. Only 11 districts showed higher urban forests, green spaces, green index, and green vs. built values, and impervious surface proportions with lower impervious and built spaces. On the other hand, the remaining districts had higher population densities and unfavorable conditions for urban ecosystem development. In some cases, urban green spaces were three-times smaller than the built and impervious surfaces, and none of the districts attained a high green quality index (0.75 to 1). Artificial intelligence techniques improved the precise identification of land cover, particularly vegetation, such as trees, shrubs, and grasses. The development of a novel green index, using multivariate statistical analysis, enhanced positive interactions among soil cover classes, emphasizing priority areas for enhancing environmental quality. Most of them should be prioritized by decision makers due to the low environmental quality, as identified by the low green index and worse ecosystem services, well-being, and health outcomes. The method can be employed in many other cities to enhance urban ecosystem quality, well-being, and health. The green index and supervised classification can characterize pastures, degraded forest fragments, and guide forest restoration techniques in diverse landscapes.

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Section: Interaction Of Urban Green Spaces and Intra-urban Elementsmentioning

confidence: 99%

Section: Interaction Of Urban Green Spaces and Intra-urban Elementsmentioning

confidence: 99%