Authors

Abstract

Rat-hole area and number of rat holes are indicators of the level of degradation and rat damage in grassland environments. However, rat-hole monitoring has consistently relied on manual ground surveys, leading to extremely low efficiency and accuracy. In this paper, a convolutional block attention module (CBAM) model suitable for rat-hole recognition in desert grassland monitoring, called grassland monitoring-CBAM, is proposed that comprehensively incorporates unmanned aerial vehicle hyperspectral remote-sensing technology and deep-learning methods. Validation results show that the overall accuracy and Kappa coefficient of the model were 99.35% and 98.90%, which were 3.96% and 3.35% higher, respectively, than those of the basic model. This study represents a breakthrough in the intelligent interpretation of rat holes and provides technical support for the subsequent rapid interpretation of grassland rat holes and rat damage evaluation. It also provides a solution for the fine classification and quantitative inversion of similar landscape features.

Abstract in Chinese

鼠洞面积和鼠洞数是监测和评价草原退化分级及草原鼠危害等级的双重指标。然而鼠洞监测一直沿用人工地面勘察，效率和精度极低。本研究综合运用无人机高光谱遥感技术和深度学习方法，首次提出了一种适用于荒漠草原监测中鼠洞识别的卷积块注意力模块（CBAM）模型（GM-CBAM）。经过精度验证，模型的总体精度和Kappa系数分别为99.35%、98.90%，相较于基础模型分别提高了3.96%、3.35%，解决了小样本、高冗余及混合像元导致的识别精度低、泛化能力差等难题，实现了荒漠草原景观下鼠洞的高精度识别。本研究不仅在鼠洞的智能解译方法上有所突破，为后续快速解译草原鼠洞及鼠害等级评价等研究提供了技术支持，也为其他相似景观地物的精细分类和定量反演提供了解决思路。