Forest ecosystem monitoring system and method based on space-time big data

By constructing a forest ecosystem monitoring system based on spatiotemporal big data, the problems of data spatiotemporal continuity discontinuity and early warning lag in forest ecosystem monitoring have been solved. This has enabled high-precision, dynamic, and comprehensive assessment of the state of forest ecosystems and early disturbance detection, providing a scientific basis for decision-making.

CN121542677AInactive Publication Date: 2026-02-17甘肃白龙江插岗梁省级自然保护区管护中心
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Patent Information

Application Number
CN202610062311.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-17
Publication Date
2026-02-17
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Existing forest ecosystem monitoring technologies have significant limitations in terms of data spatiotemporal continuity discontinuity, state identification bias, and early warning lag, making it difficult to achieve high-precision and timely comprehensive assessment.

Method used

A forest ecosystem monitoring system based on spatiotemporal big data is constructed, including modules for multi-source heterogeneous data acquisition and preprocessing, spatiotemporal feature extraction embedded in ecological process mechanisms, adaptive dynamic state characterization and evolution, early perception and causal tracing of abnormal events, and comprehensive evaluation of multi-dimensional ecological functions. Deep spatiotemporal neural networks and graph neural networks are used to achieve a unified spatiotemporal benchmark for data, feature extraction, and early perception and causal tracing of abnormal events.

Benefits of technology

It enables continuous and high-precision dynamic characterization of the state of forest ecosystems, allowing for early detection of sudden disturbances and tracing of their driving factors. It provides comprehensive and quantitative ecosystem health assessments and graded early warnings, supporting scientific decision-making.

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Abstract

The invention discloses a forest ecosystem monitoring system and method based on space-time big data, and relates to the technical field of data processing systems. The system comprises a multi-source heterogeneous data acquisition and preprocessing module, an ecological process mechanism embedded spatio-temporal feature extraction module, a self-adaptive dynamic state characterization and evolution module, an abnormal event early perception and causal traceability module and a multi-dimensional ecological function comprehensive evaluation and early warning module. The method comprises the following steps: collecting and processing multi-source heterogeneous spatio-temporal data to generate a multi-source spatio-temporal data set; extracting a multi-dimensional spatio-temporal feature vector; constructing a dynamic forest state characterization model, and generating a forest state index map; executing abnormal event early perception and causal traceability analysis; and evaluating the health state of the forest ecosystem and generating early warning information. According to the method, the characterization capability of the complex ecological process is improved, more accurate and coherent dynamic description of the ecological system state is realized, and a reliable basis is provided for ecological management and decision making.
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