Vegetation density extraction is influenced by the characteristics of satellite images, vegetation type, classification algorithm, and region, but there is little information about multispectral imaging (MSI). Studying the compatibility of the information content of sensors to replace sensors in areas where there is no easy access to their data is necessary for remote sensing (RS) studies. This study aims to assess the suitability of MSI from Sentinel-2 and Spot-5 satellites for generating pasture density maps. The Middle Kashkan watershed in the Lorestan Province of Iran was the study area. Geometric correction of the images was performed using ground control points (GCP) and the area's digital elevation model, achieving an accuracy of 0.21 pixels or better. Supervised classification techniques including parallelogram, minimal distance, maximum likelihood, and artificial neural network (ANN) algorithms were applied to the primary MSI of each satellite, as well as the integrated image of Spot-5 and the resulting pasture density map. Three density categories were considered: 5–25%, 25–50%, and over 50%. To validate the accuracy of the classification, a ground truth map of the region was created by interpreting a referenced official digital orthophotomosaic image at a scale of 1:40,000. Comparative analysis of the classified images revealed that the Sentinel-2 image with PCA-2-8 band composition and ANN classification algorithm yielded superior results, with an overall accuracy of 65.72% and a kappa coefficient of 0.08, compared to the Spot-5 image with PCA-3-1 band composition and the ANN classification algorithm. Spot-5 satellite images demonstrated greater effectiveness in generating pasture cover maps across the three density categories. These findings suggest that satellite images with suitable spatial and spectral resolution can be effectively utilized for generating accurate pasture density maps and monitoring long-term pasture preservation, particularly in regions characterized by high aerial photography altitudes in pasture areas. This approach holds the potential for effective pasture management and conservation efforts on a global scale.

植被密度提取受卫星图像特征、植被类型、分类算法和区域的影响，但关于多光谱成像（MSI）的信息很少。研究传感器信息内容的兼容性，以在无法轻松访问其数据的地区替换传感器，对于遥感（RS）研究来说是必要的。本研究旨在评估 Sentinel-2 和 Spot-5 卫星的 MSI 用于生成牧场密度图的适用性。伊朗洛雷斯坦省的中卡什坎流域是研究区域。使用地面控制点 (GCP) 和该区域的数字高程模型对图像进行几何校正，达到 0.21 像素或更好的精度。将包括平行四边形、最小距离、最大似然和人工神经网络 (ANN) 算法在内的监督分类技术应用于每颗卫星的主要 MSI、Spot-5 的集成图像和由此产生的牧场密度图。考虑了三个密度类别：5-25%、25-50% 和 50% 以上。为了验证分类的准确性，通过解释比例为 1:40,000 的参考官方数字正射马赛克图像，创建了该区域的地面实况地图。对分类图像的比较分析表明，与 Spot-5 图像相比，采用 PCA-2-8 波段组成和 ANN 分类算法的 Sentinel-2 图像取得了优异的结果，总体准确率为 65.72%，kappa 系数为 0.08具有 PCA-3-1 波段组成和 ANN 分类算法。 Spot-5 卫星图像显示在生成三个密度类别的牧场覆盖图方面具有更大的效率。 这些发现表明，具有适当空间和光谱分辨率的卫星图像可以有效地用于生成准确的牧场密度图和监测长期牧场保护，特别是在牧场地区航空摄影高度较高的地区。这种方法具有在全球范围内进行有效牧场管理和保护工作的潜力。