基于LW一CBAM的荒漠草原植被蓋度提取方法研究

中圖分類號：S812；TP751 文獻標識碼：A 文章編號：2095-5553（2025）07-0111-07

Abstract：Inorder toextractdesert grassland fractionalvegetationcoverage inrealtime，accuratelyandquickly，this paper proposedalightweightnetwork model method integrated atention mechanism（Lightweightnetwork-Convolutional Block Atention Module，LW—CBAM）based on the collected UAV hyperspectral remote sensing data.This method improved the traditional 2Dconvolution kernel to 3Ddeeplyseparableconvolution kernel，andcombined the multi-branch methodand theatention mechanismmoduletomakethe modellightweightandimprovedtheaccuracyof themodel.Inordertoobtain the optimal model，this paperoptimized the batch size and learning rateof the model.Theresultsshowed thatcompared with popular deep learning methods such as ResNet34，VGG16，MobileNetV2andMobileNetV3，LW—CBAM had a higher classification accuracy，OA was 98.97% ， Kappa coefficient was 97.94，and the model had a higher estimation accuracy for fractional vegetation coverage.The absolute error from the true value was only 0.17% .TheLW— CBAM's parameter count was reduced by over 90% compared to the other models，and its computational requirements were respectively 1.37% ， 0.74% ， 13.33% ，and 14.81% of the four other models. During the model validation stage，the estimation error of fractional vegetation coverage by LW—CBAM was below 0.3% . This model provided a feasible method for estimating fractional vegetation coverage in desert steppe and provided a basis for grassland degradation control.

Keywords：fractional vegetation coverage；hyperspectral remote sensing；deep learning；lightweight network; attention mechanism;desert steppe

0 引言

草原是我國生態系統中不可或缺的一部分[1]。我國的草地面積將近 4×10⁸hm² ，居世界第二位[2]。然而，自從20世紀60年代以來，草原退化現象在全國各地出現上升趨勢[3]，黨的二十大報告指出，推行草原森林河流湖泊濕地休養生息，提升生態系統多樣性、穩定性、持續性。全國第六次荒漠化和沙化土地監測調查結果顯示，內蒙古荒漠化土地占全國荒漠化土地面積的 23.04%^[4] ，土地沙化、次生鹽漬化嚴重，生態修復的任務艱巨[5]

草原荒漠化主要表現為植被蓋度（FVC）減少、植被種類減少、裸地面積增加等，因此， FVC 是荒漠化監測的重要指標[6。傳統的植被蓋度監測方法以地面實測為主，優點是精度高、數據可靠，但只能在小范圍內進行植被蓋度計算[7]。得益于技術的發展，衛星遙感法可以在大范圍內對植被蓋度進行反演，但其分辨率不夠高，成本也比較高昂[8]。近年來，無人機（UAV）搭載高光譜儀借助成本低、分辨率高、操作靈活等優勢迅速被大范圍應用9，可為荒漠草原植被蓋度的計算提供技術支持。

1數據采集與預處理

植被蓋度通常被定義為植被（包括枝、莖、葉）在單位面積內的垂直投影面積所占百分比[10]。張燕斌等[11]采用改進的3D—ResNet模型對荒漠草原地物進行分類，其總體分類精度為 97.73% ，為荒漠草原整體生態系統研究奠定基礎； Xu 等[12]提出了MSR—3DCNN模型用于高光譜圖像分類，并在3個開源數據集上進行試驗。深度可分離卷積（DSC）[13]降低了模型參數量，為深度學習模型的輕量化提供新思路。……