Emerging frontiers in ecosystem and biodiversity monitoring using remote sensing and geographic information systems
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| Publicado en: | Global Journal of Environmental Science and Management vol. 11, no. 4 (Autumn 2025), p. 1791-1819 |
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| Autor principal: | |
| Otros Autores: | , , , , , , |
| Publicado: |
Solid Waste Engineering and Management Association, Faculty of Environment, University of Tehran
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 001 | 3290412271 | ||
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| 022 | |a 2383-3572 | ||
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| 024 | 7 | |a 10.22034/gjesm.2025.04.23 |2 doi | |
| 035 | |a 3290412271 | ||
| 045 | 2 | |b d20251001 |b d20251231 | |
| 100 | 1 | |a Zhidebayeva, A |u Department of Ecology and Geology, Faculty of Engineering, Yessenov University, Aktau 130000, Kazakhstan | |
| 245 | 1 | |a Emerging frontiers in ecosystem and biodiversity monitoring using remote sensing and geographic information systems | |
| 260 | |b Solid Waste Engineering and Management Association, Faculty of Environment, University of Tehran |c Autumn 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The accelerating biodiversity crisis, driven by climate change and intensifying anthropogenic pressures, demands accurate, scalable, and dynamic tools to monitor ecosystem health and biological diversity. Remote sensing and geographic information systems have long been pivotal in observing environmental conditions and measuring biodiversity, nonetheless, the fast-paced development of sensing technologies, analytical approaches, and computational power is greatly transforming their purpose in conservation science. This study provides a comprehensive synthesis of next-generation applications of remote sensing and geographic information systems in biodiversity and ecosystem monitoring. The study aimed to gather recent developments in the use of remote sensing and geographic information systems for biodiversity and ecosystem monitoring, thoroughly evaluate existing methods, recognize enduring challenges, and recommend innovative, technology-driven pathways for improving ecological assessments and conservation planning. A notable transition is taking place from standard land cover mapping towards assessing ecological functions, evaluating habitat quality, and detecting environmental changes in near real-time. Innovative technologies, including hyperspectral imaging, drone-based sensing, radar interferometry, threedimensional laser scanning, and small satellite constellations, are combined with sophisticated computational methods, featuring machine learning, deep learning, spatiotemporal data fusion, and cloud-based geo-processing. These developments are transforming applications ranging from automated species distribution modelling and ecosystem service mapping to structural-functional landscape phenotyping, habitat connectivity assessment, and predictive early-warning systems for biodiversity loss. The merging of datasets with differing resolutions, timeframes, and sensors is promoting the establishment of broad ecological intelligence, which contributes to adaptive conservation strategies and evidence-based environmental governance. Despite these advances, several challenges remain, including algorithmic bias, the harmonization of heterogeneous datasets, limited direct biodiversity proxies, and disparities in access to emerging technologies. Ethical considerations along with the integration of community-driven monitoring frameworks, are essential for ensuring that technological advancements are in harmony with global sustainability goals. Anticipating the future, the integration of sophisticated sensing technologies, artificial intelligence, and cloud computing platforms presents remarkable opportunities to transform biodiversity monitoring and conservation planning. By enabling predictive, adaptive, and near real-time decisionmaking, these innovations are reshaping strategies for environmental management and the development of resilient socio-ecological systems in the context of rapid global change. | |
| 653 | |a Geographical distribution | ||
| 653 | |a Warning systems | ||
| 653 | |a Conservation | ||
| 653 | |a Spatiotemporal data | ||
| 653 | |a Early warning systems | ||
| 653 | |a Ecosystem services | ||
| 653 | |a Mapping | ||
| 653 | |a Computer applications | ||
| 653 | |a Data integration | ||
| 653 | |a Ecological function | ||
| 653 | |a Satellite constellations | ||
| 653 | |a Geographic information systems | ||
| 653 | |a Machine learning | ||
| 653 | |a Evaluation | ||
| 653 | |a Remote sensing | ||
| 653 | |a Environmental management | ||
| 653 | |a Datasets | ||
| 653 | |a Environmental changes | ||
| 653 | |a Environmental quality | ||
| 653 | |a Biodiversity loss | ||
| 653 | |a Interferometry | ||
| 653 | |a Ecosystems | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Biodiversity | ||
| 653 | |a Real time | ||
| 653 | |a Climate change | ||
| 653 | |a Small satellites | ||
| 653 | |a Environmental conditions | ||
| 653 | |a Remote monitoring | ||
| 653 | |a Radar imaging | ||
| 653 | |a Environmental governance | ||
| 653 | |a Land cover | ||
| 653 | |a Deep learning | ||
| 653 | |a Social-ecological systems | ||
| 653 | |a Cloud computing | ||
| 653 | |a Structure-function relationships | ||
| 653 | |a Hyperspectral imaging | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Syrlybekkyzy, S |u Department of Ecology and Geology, Faculty of Engineering, Yessenov University, Aktau 130000, Kazakhstan | |
| 700 | 1 | |a Taizhanova, L |u Department of Ecology and Geology, Faculty of Engineering, Yessenov University, Aktau 130000, Kazakhstan | |
| 700 | 1 | |a Koibakova, S |u Department of Ecology and Geology, Faculty of Engineering, Yessenov University, Aktau 130000, Kazakhstan | |
| 700 | 1 | |a Altybaeva, Z |u Department of Ecology and Geology, Faculty of Engineering, Yessenov University, Aktau 130000, Kazakhstan | |
| 700 | 1 | |a Koishina, A | |
| 700 | 1 | |a Seidaliyeva, L | |
| 700 | 1 | |a Mkilima, T | |
| 773 | 0 | |t Global Journal of Environmental Science and Management |g vol. 11, no. 4 (Autumn 2025), p. 1791-1819 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3290412271/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3290412271/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |