Satellite-based Machine Learning modelling of Ecosystem Services indicators: A review and meta-analysis

Applied Geography, Год журнала: 2024, Номер 165, С. 103249 - 103249

Опубликована: Март 11, 2024

Satellite-based Machine Learning (ML) modelling has emerged as a powerful tool to understand and quantify spatial relationships between landscape dynamics, biophysical variables natural stocks. Ecosystem Services indicators (ESi) provide qualitative quantitative information aiding the assessment of ecosystems' status. Through systematic meta-analysis following PRISMA guidelines, studies from one decade (2012–2022) were analyzed synthesized. The results indicated that Random Forest most frequently utilized ML algorithm, while Landsat missions stood out primary source Satellite Earth Observation (SEO) data. Nonetheless, authors favoured Sentinel-2 due its superior spatial, spectral, temporal resolution. While 30% examined focused on proxies climate regulation services, assessments stocks such biomass, water, food production, raw materials also applied. Meta-analysis illustrated utilization classification regression tasks in estimating measurements extent conditions findings underscored connections established methods their replication. This study offers current perspectives existing satellite-based approaches, contributing ongoing efforts employ artificial intelligence for unveiling potential SEO data technologies ESi.

Язык: Английский

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Interoperability for ecosystem service assessments: Why, how, who, and for whom?

Ecosystem Services, Год журнала: 2025, Номер 72, С. 101705 - 101705

Опубликована: Март 4, 2025

Язык: Английский

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Towards Responsible Urban Geospatial AI: Insights From the White and Grey Literatures

Journal of Geovisualization and Spatial Analysis, Год журнала: 2024, Номер 8(2)

Опубликована: Июнь 26, 2024

Abstract Artificial intelligence (AI) has increasingly been integrated into various domains, significantly impacting geospatial applications. Machine learning (ML) and computer vision (CV) are critical in urban decision-making. However, AI implementation faces unique challenges. Academic literature on responsible largely focuses general principles, with limited emphasis the domain. This important gap scholarly work could hinder effective integration Our study employs a multi-method approach, including systematic academic review, word frequency analysis insights from grey literature, to examine potential challenges propose strategies for (GeoAI) integration. We identify range of practices relevant complexities using planning its implementation. The review provides comprehensive actionable framework adoption domain, offering roadmap researchers practitioners. It highlights ways optimise benefits while minimising negative consequences, contributing sustainability equity.

Язык: Английский

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Predicting land cover driven ecosystem service value using artificial neural network model

Remote Sensing Applications Society and Environment, Год журнала: 2024, Номер 34, С. 101180 - 101180

Опубликована: Март 5, 2024

Язык: Английский

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Tree selection for a virtual urban park: Comparing aided and unaided decision-making to support public engagement in greenspace design

Urban forestry & urban greening, Год журнала: 2024, Номер 99, С. 128447 - 128447

Опубликована: Июль 20, 2024

Язык: Английский

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Integrating crowdsourced data in the built environment studies: A systematic review

Journal of Environmental Management, Год журнала: 2025, Номер 373, С. 123936 - 123936

Опубликована: Янв. 1, 2025

Язык: Английский

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A review of the role of nature-based solutions in mitigating food insecurity in Africa

Green Technologies and Sustainability, Год журнала: 2025, Номер unknown, С. 100173 - 100173

Опубликована: Янв. 1, 2025

Язык: Английский

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Urban Ecosystem Services: Agroecology, Green Spaces, and Environmental Quality for Sustainable Futures

Land, Год журнала: 2025, Номер 14(2), С. 288 - 288

Опубликована: Янв. 30, 2025

The cycle of population growth, rural-to-urban migration, and subsequent urban overbuilding poses a significant threat to both human health the ecosystems [...]

Язык: Английский

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Assessing recreationists’ preferences of the landscape and species using crowdsourced images and machine learning

Landscape and Urban Planning, Год журнала: 2025, Номер 257, С. 105315 - 105315

Опубликована: Фев. 9, 2025

Язык: Английский

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Using large language models to investigate cultural ecosystem services perceptions: A few-shot and prompt method

Landscape and Urban Planning, Год журнала: 2025, Номер 258, С. 105323 - 105323

Опубликована: Фев. 24, 2025

Язык: Английский

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