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植被覆盖变化是生态环境领域的核心研究内容之一,但其估算精度常受到地形效应、土壤背景、大气效应等各种因素影响。以Landsat 8 OLI为遥感数据源,基于像元二分模型,分别利用归一化差值植被指数(NDVI)、经Cosine-C校正的归一化差值植被指数(NDVI)和归一化差值山地植被指数(NDMVI)建立植被覆盖度估算模型,以评估南方丘陵区植被覆盖度的地形效应。结果表明,3种植被覆盖度估算模型均能削弱地形效应,但消除或抑制地形效应影响的能力不同。比较而言,基于NDMVI指数构建的植被覆盖度估算模型的地形效应最小,更适合地形复杂区域的植被覆盖度遥感估算;基于Cosine-C校正的NDVI植被指数构建的植被覆盖度估算模型的地形效应次之,但存在一定的过度校正现象;基于NDVI植被指数构建的植被覆盖度估算模型的地形效应最大,尤其当坡度≥10°时,阴坡植被覆盖度比阳坡明显偏低。
Vegetation cover change is one of the core research topics in the field of ecological environment. However, its estimation accuracy is often affected by various factors such as topographical effects, soil backgrounds and atmospheric effects. Landsat 8 OLI was used as remote sensing data source. Based on the pixel binary model, normalized difference vegetation index (NDVI), Cosine-C normalized difference vegetation index (NDVI) and normalized difference The Mountain Vegetation Index (NDMVI) establishes a vegetation cover estimation model to assess the topographic effects of vegetation cover in the hilly area of the South. The results show that all the three vegetation coverage estimation models can weaken the terrain effects, but have different abilities to eliminate or suppress the terrain effects. In comparison, the vegetation coverage estimation model constructed based on the NDMVI index has the least topographic effect and is more suitable for the remote sensing estimation of the vegetation coverage in the complex terrain. The topographic effect of the vegetation coverage estimation model constructed based on the Cosine-C corrected NDVI vegetation index But there is some overcorrection phenomenon. The model of vegetation coverage based on NDVI vegetation index has the largest topographic effect, especially when the slope is ≥10 °, the coverage of shady slope is obviously lower than that of sunny slope.