Integrated Remote Sensing System for Climate Change Mitigation

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Table of Contents

Introduction
Main body
Conclusion
References

Introduction

Climate change presents a complicated global problem that requires a thorough understanding of society’s impact on the climate and the internal mechanisms in the process of climate change. In the past, climate change studies primarily focused on developing knowledge about climate change through history. Therefore, the main task for humanity at this stage is to create systems that will allow the tracking of climate changes in real time and promptly eliminate their causes. Furthermore, it is crucial to outline the requirements for the conceptual system to ensure that the system optimally addresses the global issue.

Main body

The human population continues to grow, and a large portion of the population is concentrated in urban areas. Remote sensing systems can be used to analyze the impact of urban regions on climate change. According to Milesi and Churkina (2020), implementing geostationary sensors can provide the necessary information about the impact of cities’ environment on climate change. Furthermore, the study conducted by Sirmacek and Vinuesa (2022) suggests that the integration of artificial intelligence (AI) can provide an opportunity to measure climate adaptations and predict their outcomes automatically. Thus, the requirements for the conceptual system include geostationary sensors and AI integration possibilities.

Next, the analysis of microclimate changes can be used to predict possible responses to climate change. The research conducted by Zellweger et al. (2019) determined that Airborne Light Detection and Ranging (LiDAR) scanning system provides data required for assessing microclimate changes. Thus, the technology allows for predicting possible ecological outcomes from climate change. Lastly, the article by Aznarez et al. (2021) explains how the Soil and Water Assessment Tool (SWAT) can be used with remote sensing data to analyze how climate change will affect the hydrological ecosystem. Therefore, the conceptual remote sensing system requirements also include implementing LiDAR and SWAT technologies for more detailed predictions of climate change outcomes.

Conclusion

In conclusion, the summary explained how specific system requirements would help address the global climate change problem. The summary defined that the main goal in the fight against climate change is to assess human activity’s impact on the climate. Lastly, the summary explored how remote sensing systems can help eliminate potential factors causing climate change with detailed forecasting through the integration of AI and such technologies as LiDAR and SWAT.

References

Arnanez, C., Jimeno-Saez, P., López-Ballesteros, A., Pacheco, J. P., & Senent-Aparicio, J. (2021). Analyzing the impact of climate change on hydrological ecosystem services in Laguna del Sauce (Uruguay) using the SWAT model and remote sensing data. Remote Sensing, 13(10), 1-23. Web.

Milesi, C., & Churkina, G. (2020). Measuring and monitoring urban impacts on climate change from space. Remote Sensing, 12(21), 1-21. Web.

Sirmacek, B., & Vinuesa, R. (2022). Remote sensing and AI for building climate adaptation applications. ArXiv, 21(7), 1-32. Web.

Zellweger, F., De Frenne, P., Lenoir, J., Rocchini, D., & Coomes, D. (2019). Advances in microclimate ecology arising from remote sensing. Trends in Ecology & Evolution, 34(4), 327-341. Web.

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Integrated Remote Sensing System for Climate Change Mitigation

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