及时准确地了解草原产草量的时空配置状况,对于科学合理地利用、管理草地,保证畜牧业生产持续稳定发展、改善生态环境等具有重要的意义。本文利用2005年的MODIS数据和同期野外实测的668个样方产草量数据,分析了5种植被指数和草地生物量之间的相关关系。研究表明:(1)分区模型优于不分区模型,在分区基础上建模更能反映产草量的实际情况;(2)通过线性、非线性模型和BP神经网络模型的对比,得出BP神经网络模型在拟合精度上优于线性和非线性模型,是最适宜监测北方农牧交错带草原产草量的模型;(3)5种植被指数中,NDVI和SAVI与草地生物量之间的拟合精度最高,是研究区最适宜使用的植被指数。
杨秀春, 徐 斌, 朱晓华, 陶伟国, 刘天科
. 北方农牧交错带草原产草量遥感监测模型[J]. 地理研究, 2007
, 26(2)
: 213
-222
.
DOI: 10.11821/yj2007020001
There is an ecotone connecting farming region and pasturing region for northern agro-grazing ecotone. Its ecological function consists of conserving water sources, checking the wind and fixing the shifting sand, purifying air and maintaining biodiversity.Grassland is not only one of the important ecosystems, but also a background vegetation. Over the past decades, human activities have caused great land cover changes, such as desertification, grassland degradation, and sandy. Therefore, accurate and timely monitoring grassland is of critical importance for utilizing and administering grassland, developing pasturage and improving ecological environment. Using MODIS remote sensing data for the year 2005 and the ground measured grass yield of the corresponding period, linear regression model,non-linear regression models and BP neural network model were respectively established, to express the regression relationships between ground truth data and vegetation indices in this paper. Some conclusions are drawn as follows: (1) Regional models are better than whole-area general models. It is reasonable for the four grassland areas, and the regional models can better describe grass production.(2) Models based on BP neural network are better than linear regression models and non-linear regression models in fitness accuracy. Its decision coefficient increases by more than 3%, and the highest is 6.92%. Moreover, by precision validating, we find its root mean square error and relative errors are smaller, the models precision increases by more than 2.5%, and the maximum increases 23.22%. It is obvious that models based on BP neural network are most suitable for monitoring grass production of northern agro-grazing ecotone, and it can meet the need of estimating of grass production in northern agro-grazing ecotone.(3) The suitable vegetation indices for monitoring grass production of northern agro-grazing ecotone are NDVI and SAVI.(4) With the accumulation of the temporal scales data, further studies may focus on input data for BP neural network model. For example, input data may adopt soil moisture index and temperature and precipitation, and so on, which may further increase precision of models, and approach actual grass production for monitoring results.
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