Remote sensing image-based grassland degradation degree automatic extraction method

Abstract

The invention discloses a remote sensing image-based grassland degradation degree automatic extraction method. The method comprises the following steps that: the satellite remote sensing image data of an area to be monitored in a front time phase and a rear time phase are obtained; the images are preprocessed, abnormal wave bands are removed, and atmospheric correction is performed; band optimization is performed by using minimum noise separation transformation, and therefore, the data can be optimized, and grassland degradation information can be integrated; segmentation scales are determined, and multi-scale segmentation is performed, so that a salt and pepper effect can be eliminated; non-grassland objects are eliminated through using a NDVI index and a band threshold value, and therefore, the influence of the non-grassland objects on extraction precision is reduced; and training samples are divided, so that grassland with different degradation degrees is extracted, so that grassland degradation degree automatic extraction can be realized. With the remote sensing image-based grassland degradation degree automatic extraction method of the invention adopted, data processing amount is decreased, extraction efficiency is improved, the salt and pepper effect is eliminated, grassland degradation information can be effectively extracted, and ideal extraction precision can be achieved.

Description

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AI Technical Summary

Benefits of technology

This technology allows us to quickly identify areas where there are no longer enough green vegetations or trees that require maintenance without causing damage from overexposure during winters when snow falls down at nightfall. By doing this we aim to reduce costs associated with maintaining these crops while ensuring they last through harsh weather conditions.

Problems solved by technology

This patented describes various technical problem addressed during studying vegetal turf production: environmental factors affects how well it works over its lifetime; while decompositional environments like rain or snow may cause damage to crops. Current techniques involve manual assessment and analysis that require significant amounts of measuring equipment and subjective judgment. Additionally, these conventional approaches only provide direct feedback about changes caused by decomposition rather than understanding their impact on crop growth performance.

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