Immediate remote sensing of sudden earth’s surface anomalies and its geographical significance
Year: 2025
Type: article
Abstract: Sudden earth’s surface anomalies caused by natural and anthropogenic factors pose significant threats to ecological sustainability and the safety of human life and property, highlighting the urgent need for their immediate monitoring and early warning. Satellite remote sensing is the most effective means for large-scale earth’s surface anomaly detection. However, constrained by traditional observation paradigms, satellite payload limitations, and other physical factors, current remote sensing detection faces two major challenges: “inability to observe quickly” and “inability to observe effectively”. To solve these problems, we have researched immediate remote sensing detection of sudden earth’s surface anomalies. Its core concept is to deploy the entire detection process on satellites, enabling on-orbit immediate detection of earth’s surface anomalies based on a single image through the integrated “positioning, navigation, timing, remote sensing, communication (PNTRC)” intelligent constellation and edge computing technologies. Subsequently, the detection results are transmitted directly to the subscriber mobile terminal through the BeiDou Navigation Satellite System (BDS). The immediate remote sensing of sudden earth’s surface anomalies emphasizes the continuous capture and immediate feedback of geographic processes, overcoming the longstanding reliance of traditional geography on “slow variables”. Its significance lies not only in the improvement of data acquisition efficiency but also in promoting the transformation of geography from a “descriptive science” to a “predictive science”.
Source: Geography and sustainability
Authors: Qiao Wang, Haishuo Wei
Institution: Beijing Normal University
Language: English
Topic: Remote Sensing and Land Use
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,700
Award: G2131151903
OpenAlex ID: W4410110207
