Author
Francesca Bottalico
Bio: Francesca Bottalico is an academic researcher from University of Florence. The author has contributed to research in topics: Forest management & Ecosystem services. The author has an hindex of 13, co-authored 41 publications receiving 1077 citations.
Papers
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TL;DR: In this paper, the authors implemented a new approach, Multiscale Mapping of Ecosystem Services (MIMOSE), to assess ecosystem services in Mediterranean forests located in a mountainous region of Italy.
Abstract: In recent decades, Mediterranean landscapes have been affected by human-induced drivers, such as land use and climate change. Forest ecosystems and landscapes have been particularly affected in mountainous regions due to limited management and stewardship, especially in remote areas. Therefore, there is a need to set up new strategies to enhance ecosystem services in forested areas which, in turn, will benefit local communities and economies. In this study, we implemented a new approach—Multiscale Mapping of Ecosystem Services (MIMOSE)—to assess ecosystem services in Mediterranean forests located in a mountainous region of Italy. We spatially assessed timber provision and carbon sequestration according to three forest management strategies: business-as-usual, maximizing economic values, and prioritizing conservation. Sustainable strategies for forest planning were identified at the landscape scale. We found that (i) timber provision is a conflicting service, especially when adaptation strategies are promoted; (ii) the most balanced set of forest ecosystem services is achieved through prioritizing conservation; and (iii) the ecosystem services availability is enhanced by optimizing the spatial allocation of different management strategies. Our approach is suitable to support landscape planning for balancing forest ecosystem potentialities while respecting local community needs and promoting sustainable development goals in the Mediterranean area.
599 citations
TL;DR: In this paper, the authors investigated the potential performance of air pollution removal by the green infrastructures and urban forests in the city of Florence, central Italy, with a focus on the two most detrimental pollutants for human health.
78 citations
TL;DR: A spatially explicit method based on a multi-scale approach (MiMoSe-Multiscale Mapping of ecoSystem services) to assess the current and future potential of a given forest area to provide ES, contributing to the ongoing debate about trade-offs and synergies between carbon sequestration and wood production benefits associated with socio-ecological systems.
76 citations
TL;DR: In this article, the authors assess the potential of integrating ALS in forest mensuration and inventory using laser scanning technologies and evaluate the potential for integrating ALS with existing methods for forest management.
Abstract: The development of laser scanning technologies has gradually modified methods for forest mensuration and inventory. The main objective of this study is to assess the potential of integrating ALS an...
69 citations
TL;DR: Evaluating the performance of the S2-MSI imagery for estimating the growing stock volume of forest ecosystems found that S2 worked better than Landsat in 37.5% of the cases and in 62.5%.
69 citations
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TL;DR: In this paper, the authors comprehensively analyse the diversity within and between the three concepts of Circular Economy, Green Economy and Bioeconomy, and find that, for what concerns environmental sustainability, Green economy acts as an ‘umbrella’ concept, including elements from Circular economy and bioeconomy concepts, as well as additional ideas, e.g. nature-based solutions.
614 citations
TL;DR: In this paper, the authors implemented a new approach, Multiscale Mapping of Ecosystem Services (MIMOSE), to assess ecosystem services in Mediterranean forests located in a mountainous region of Italy.
Abstract: In recent decades, Mediterranean landscapes have been affected by human-induced drivers, such as land use and climate change. Forest ecosystems and landscapes have been particularly affected in mountainous regions due to limited management and stewardship, especially in remote areas. Therefore, there is a need to set up new strategies to enhance ecosystem services in forested areas which, in turn, will benefit local communities and economies. In this study, we implemented a new approach—Multiscale Mapping of Ecosystem Services (MIMOSE)—to assess ecosystem services in Mediterranean forests located in a mountainous region of Italy. We spatially assessed timber provision and carbon sequestration according to three forest management strategies: business-as-usual, maximizing economic values, and prioritizing conservation. Sustainable strategies for forest planning were identified at the landscape scale. We found that (i) timber provision is a conflicting service, especially when adaptation strategies are promoted; (ii) the most balanced set of forest ecosystem services is achieved through prioritizing conservation; and (iii) the ecosystem services availability is enhanced by optimizing the spatial allocation of different management strategies. Our approach is suitable to support landscape planning for balancing forest ecosystem potentialities while respecting local community needs and promoting sustainable development goals in the Mediterranean area.
599 citations
TL;DR: This paper develops, and statistically validates a model for understanding the user perceptions on BT adoption, based on the integration of three adoption theories- technology acceptance model (TAM), technology readiness index (TRI), and the theory of planned behaviour (TPB).
Abstract: Blockchain technology (BT) is expected to bring a revolutionary paradigm shift in the manner the transactions are carried in the supply chains. BT provides better visibility and transparency by rem...
480 citations
TL;DR: Sentinel-2 has a positive impact on land cover/use monitoring, specifically in monitoring of crop, forests, urban areas, and water resources and the literature shows that the use of Sentinel-2 data produces high accuracies with machine-learning classifiers such as support vector machine (SVM) and Random forest (RF).
Abstract: The advancement in satellite remote sensing technology has revolutionised the approaches to monitoring the Earth’s surface. The development of the Copernicus Programme by the European Space Agency (ESA) and the European Union (EU) has contributed to the effective monitoring of the Earth’s surface by producing the Sentinel-2 multispectral products. Sentinel-2 satellites are the second constellation of the ESA Sentinel missions and carry onboard multispectral scanners. The primary objective of the Sentinel-2 mission is to provide high resolution satellite data for land cover/use monitoring, climate change and disaster monitoring, as well as complementing the other satellite missions such as Landsat. Since the launch of Sentinel-2 multispectral instruments in 2015, there have been many studies on land cover/use classification which use Sentinel-2 images. However, no review studies have been dedicated to the application of ESA Sentinel-2 land cover/use monitoring. Therefore, this review focuses on two aspects: (1) assessing the contribution of ESA Sentinel-2 to land cover/use classification, and (2) exploring the performance of Sentinel-2 data in different applications (e.g., forest, urban area and natural hazard monitoring). The present review shows that Sentinel-2 has a positive impact on land cover/use monitoring, specifically in monitoring of crop, forests, urban areas, and water resources. The contemporary high adoption and application of Sentinel-2 can be attributed to the higher spatial resolution (10 m) than other medium spatial resolution images, the high temporal resolution of 5 days and the availability of the red-edge bands with multiple applications. The ability to integrate Sentinel-2 data with other remotely sensed data, as part of data analysis, improves the overall accuracy (OA) when working with Sentinel-2 images. The free access policy drives the increasing use of Sentinel-2 data, especially in developing countries where financial resources for the acquisition of remotely sensed data are limited. The literature also shows that the use of Sentinel-2 data produces high accuracies (>80%) with machine-learning classifiers such as support vector machine (SVM) and Random forest (RF). However, other classifiers such as maximum likelihood analysis are also common. Although Sentinel-2 offers many opportunities for land cover/use classification, there are challenges which include mismatching with Landsat OLI-8 data, a lack of thermal bands, and the differences in spatial resolution among the bands of Sentinel-2. Sentinel-2 data show promise and have the potential to contribute significantly towards land cover/use monitoring.
234 citations