PreprintReviewVersion 1This version is not peer-reviewed
Integrating Remote Sensing, GIS, AI, and Machine Learning for Natural Resource Management: Comparative Analysis of Tools and the Critical Role of In‐Situ Validation Data
Version 1
: Received: 29 September 2024 / Approved: 30 September 2024 / Online: 1 October 2024 (08:10:13 CEST)
How to cite:
Sharma, S.; Beslity, J. O.; Rustad, L.; Shelby, L. J.; Manos, P. T.; Khanal, P.; Reinmann, A. B.; Khanal, C. Integrating Remote Sensing, GIS, AI, and Machine Learning for Natural Resource Management: Comparative Analysis of Tools and the Critical Role of In‐Situ Validation Data. Preprints2024, 2024092466. https://doi.org/10.20944/preprints202409.2466.v1
Sharma, S.; Beslity, J. O.; Rustad, L.; Shelby, L. J.; Manos, P. T.; Khanal, P.; Reinmann, A. B.; Khanal, C. Integrating Remote Sensing, GIS, AI, and Machine Learning for Natural Resource Management: Comparative Analysis of Tools and the Critical Role of In‐Situ Validation Data. Preprints 2024, 2024092466. https://doi.org/10.20944/preprints202409.2466.v1
Sharma, S.; Beslity, J. O.; Rustad, L.; Shelby, L. J.; Manos, P. T.; Khanal, P.; Reinmann, A. B.; Khanal, C. Integrating Remote Sensing, GIS, AI, and Machine Learning for Natural Resource Management: Comparative Analysis of Tools and the Critical Role of In‐Situ Validation Data. Preprints2024, 2024092466. https://doi.org/10.20944/preprints202409.2466.v1
APA Style
Sharma, S., Beslity, J. O., Rustad, L., Shelby, L. J., Manos, P. T., Khanal, P., Reinmann, A. B., & Khanal, C. (2024). Integrating Remote Sensing, GIS, AI, and Machine Learning for Natural Resource Management: Comparative Analysis of Tools and the Critical Role of In‐Situ Validation Data. Preprints. https://doi.org/10.20944/preprints202409.2466.v1
Chicago/Turabian Style
Sharma, S., Andrew B. Reinmann and Churamani Khanal. 2024 "Integrating Remote Sensing, GIS, AI, and Machine Learning for Natural Resource Management: Comparative Analysis of Tools and the Critical Role of In‐Situ Validation Data" Preprints. https://doi.org/10.20944/preprints202409.2466.v1
Abstract
Remote sensing (RS) and Geographic Information Systems (GIS) provide significant opportunities for monitoring and managing natural resources across various temporal, spectral, and spatial resolutions. There is a critical need for natural resource managers to understand the expanding capabilities of image sources, analysis techniques, and in-situ validation methods. This article reviews a range of image analysis tools applicable to natural resource management, including agriculture, water, forests, soil, and natural hazards, and compares their functionalities. Our study highlights that Google Earth Engine (GEE) is favored for wide-area analysis due to its extensive coverage and free access. Global Mapper excels in 3D and light detection and ranging (LIDAR) data, environment for visualizing images (ENVI) specializes in multi- and hyperspectral image processing, ERDAS IMAGINE is optimal for radar data, and eCognition is used for object-based image analysis. The article emphasizes the importance of in-situ validation data, which provides essential ground truth information to calibrate and validate RS models, thereby enhancing accuracy. Understanding these tools and integrating them with in-situ validation techniques enables natural resource managers to improve their monitoring and decision-making processes, facilitating effective collaboration with RS researchers.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
