A method for determining rice planting areas considering the phenological characteristics of multi-phase high-resolution remote sensing images

Xiaopan Wang; Xia Long; Zezhong Ma

doi:10.1117/12.3007678

30 August 2023 A method for determining rice planting areas considering the phenological characteristics of multi-phase high-resolution remote sensing images

Xiaopan Wang, Xia Long, Zezhong Ma

Author Affiliations +

Proceedings Volume 12797, Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023); 127972L (2023) https://doi.org/10.1117/12.3007678
Event: 2nd International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023), 2023, Qingdao, China

Abstract

In this paper, we propose a methodology for extracting rice planting areas using high-resolution remote sensing images, while also considering vegetation phenology. This method utilizes the typical spectral and texture features of rice during the early planting, filling, and post-harvest phases. Combined with vegetation index and texture variance methods, this approach enables the assessment of the favorable attributes of rice during the early planting phase, as well as the distinctive characteristics of rice during the filling and post-harvest phases. Our objective is to integrate remote sensing extraction findings with management data to improve recognition accuracy within the spatial constraints of natural resource surveys and monitoring. The accuracy of extracting rice planting areas has been determined to be 95.1% through field operation verification.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Xiaopan Wang, Xia Long, and Zezhong Ma "A method for determining rice planting areas considering the phenological characteristics of multi-phase high-resolution remote sensing images", Proc. SPIE 12797, Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023), 127972L (30 August 2023); https://doi.org/10.1117/12.3007678

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KEYWORDS

Remote sensing

Vegetation

Image fusion

Feature extraction

Phenology

Spatial resolution

Satellites

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