A Summary of Recent Advances in the Literature on Machine Learning Techniques for Remote Sensing of Groundwater Dependent Ecosystems (GDEs) from Space

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(8), P. 1460 - 1460

Published: April 19, 2025

While groundwater-dependent ecosystems (GDEs) occupy only a small portion of the Earth’s surface, they hold significant ecological value by providing essential ecosystem services such as habitat for flora and fauna, carbon sequestration, erosion control. However, GDE functionality is increasingly threatened human activities, rainfall variability, climate change. To address these challenges, various methods have been developed to assess, monitor, understand GDEs, aiding sustainable decision-making conservation policy implementation. Among these, remote sensing advanced machine learning (ML) techniques emerged key tools improving evaluation dryland GDEs. This study provides comprehensive overview progress made in applying ML algorithms assess monitor It begins with systematic literature review following PRISMA framework, followed an analysis temporal geographic trends applications research. Additionally, it explores different their across types. The paper also discusses challenges mapping GDEs proposes mitigation strategies. Despite promise studies, field remains its early stages, most research concentrated China, USA, Germany. enable high-quality classification at local global scales, model performance highly dependent on data availability quality. Overall, findings underscore growing importance potential geospatial approaches generating spatially explicit information Future should focus enhancing models through hybrid transformative techniques, well fostering interdisciplinary collaboration between ecologists computer scientists improve development result interpretability. insights presented this will help guide future efforts contribute improved management

Language: Английский

AI and Environmental Stewardship

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 567 - 582

Published: Feb. 28, 2025

AI offers significant opportunities to reshape industries and corporate practices, addressing pressing societal issues like ecological sustainability. The decline of ecosystems climate-related challenges require innovative solutions. This chapter posits that can foster culturally relevant organizational structures personal behaviors reduce energy resource consumption. Research indicates plays a crucial role in advancing sustainability across various sectors by enhancing efficiency urban forestry management. Utilizing neural networks the internet things (IoT) transforms processes while minimizing impacts. However, AI's high consumption, ethical dilemmas, inadequate infrastructure pose substantial challenges. Effective implementation initiatives necessitates collaboration with regulatory frameworks. proposes intelligent strategies for environmental protection through climate forecasting pollution reduction.

Language: Английский