Enhanced Map Composition and Diachronic Land Cover Classification of Landsat Data in Google Earth Engine

Vizzari, Marco; Parracciani, Cecilia; Gigante, Daniela

doi:10.1007/978-3-031-37114-1_19

Cited by 2 publications

(2 citation statements)

References 45 publications

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“…The next step would be a spatialization of the information by way of vegetation maps at a scale fine enough (1:10,000) to depict grassland-type variability; however, this would be not only a static view but also an arduous task to accomplish in huge areas, as also pointed out by Primi et al (2016) [157]. In this frame, the here-developed Google Earth Engine (GEE) application shows particular usefulness in providing near-real-time data to address conservative grassland management and demonstrates the general trend towards highly technological agriculture and robotic environmental monitoring [166][167][168][169][170][171][172][173][174][175][176]. Satellite remote sensing is invaluable in this context, offering a cost-efficient, timely, and replicable method for vegetation analysis [31,177].…”

Section: Discussionmentioning

confidence: 99%

Seminatural Grasslands: An Emblematic Challenge for Nature Conservation in Protected Areas

Gigante,

Angelucci,

Bonini

et al. 2024

Land

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Seminatural grasslands are among the most threatened habitats in Europe and worldwide, mainly due to changes in/abandonment of their traditional extensive use by grazing animals. This study aimed to develop an innovative model that integrates plant biodiversity, animal husbandry, and geo-informatics to manage and preserve seminatural grasslands in protected areas. With this objective, an integrated study was conducted on the seminatural grasslands in the hilly, montane, and (to a minimum extent) subalpine belts of the Maiella National Park, one of Europe’s most biodiversity-rich protected sites. Plant biodiversity was investigated through 141 phytosociological relevés in homogeneous areas; the pastoral value was calculated, and grasslands’ productivity was measured together with the main nutritional parameters. Uni- and multivariate statistical analyses were performed to identify the main grassland vegetation types, their indicator species and ecological–environmental characteristics, and their pastoral and nutritional values’ variability and differences. A total of 17 grassland types, most of which correspond to habitat types listed in Annex I to the 92/43/EEC Directive, were identified and characterised in terms of their biodiversity and potential animal load. To allow for near-real-time analysis of grasslands, an NDVI-based web interface running on Google Earth Engine was implemented. This integrated approach can provide decision-making support for protected-area managers seeking to develop and implement sustainable grassland management practices that ensure the long-term maintenance of their biodiversity.

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Section: Discussionmentioning

confidence: 99%

Seminatural Grasslands: An Emblematic Challenge for Nature Conservation in Protected Areas

Gigante,

Angelucci,

Bonini

et al. 2024

Land

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“…Despite this, it is essential to highlight that a significant portion of these grasslands has been degraded or somewhat altered from their balanced condition. Approximately half of the world's grassland areas have experienced degradation due to various factors such as overgrazing, inappropriate land management practices, and climate change [1,12]. In Europe, many open agroecosystems, particularly semi-natural grasslands, are at risk due to changing land use and abandonment, which threaten their biodiversity and ecosystem services [3,4,13].…”

Section: Introductionmentioning

confidence: 99%

Leveraging Google Earth Engine for a More Effective Grassland Management: A Decision Support Application Perspective

Parracciani,

Gigante,

Bonini

et al. 2024

Sensors

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Grasslands cover a substantial portion of the earth’s surface and agricultural land and is crucial for human well-being and livestock farming. Ranchers and grassland management authorities face challenges in effectively controlling herders’ grazing behavior and grassland utilization due to underdeveloped infrastructure and poor communication in pastoral areas. Cloud-based grazing management and decision support systems (DSS) are needed to address this issue, promote sustainable grassland use, and preserve their ecosystem services. These systems should enable rapid and large-scale grassland growth and utilization monitoring, providing a basis for decision-making in managing grazing and grassland areas. In this context, this study contributes to the objectives of the EU LIFE IMAGINE project, aiming to develop a Web-GIS app for conserving and monitoring Umbria’s grasslands and promoting more informed decisions for more sustainable livestock management. The app, called “Praterie” and developed in Google Earth Engine, utilizes historical Sentinel-2 satellite data and harmonic modeling of the EVI (Enhanced Vegetation Index) to estimate vegetation growth curves and maturity periods for the forthcoming vegetation cycle. The app is updated in quasi-real time and enables users to visualize estimates for the upcoming vegetation cycle, including the maximum greenness, the days remaining to the subsequent maturity period, the accuracy of the harmonic models, and the grassland greenness status in the previous 10 days. Even though future additional developments can improve the informative value of the Praterie app, this platform can contribute to optimizing livestock management and biodiversity conservation by providing timely and accurate data about grassland status and growth curves.

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Enhanced Map Composition and Diachronic Land Cover Classification of Landsat Data in Google Earth Engine

Cited by 2 publications

References 45 publications

Seminatural Grasslands: An Emblematic Challenge for Nature Conservation in Protected Areas

Seminatural Grasslands: An Emblematic Challenge for Nature Conservation in Protected Areas

Leveraging Google Earth Engine for a More Effective Grassland Management: A Decision Support Application Perspective

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