采用广义加法模型整合数字高程模型和遥感数据进行植被分布预测

 为了采用广义加法模型整合数字高程模型和遥感数据进行植被分布的预测, 并探索耦合环境变量和遥感数据作为预测变量是否能够有效地提高植被分布预测的精度, 选择海拔、坡度、至黄河最近距离、至海岸线最近距离, 以及从SPOT5遥感影像中提取的光谱变量作为预测变量, 采用广义加法模型整合环境变量和光谱变量, 建立植被分布预测模型。研究设置3种建模情景(以环境变量作为预测变量, 以光谱变量作为预测变量, 综合使用环境变量与光谱变量作为预测变量)对黄河三角洲的优势植被类型的分布进行了预测, 并对预测结果采用偏差分析、受试者工作特征曲线和野外采样点对比等3种方法进行了验证。结果表明: (1)基于广义加法模型的植被分布预测方法具有一定的实用性, 可以较为准确地预测植被的分布; 盖度较高的植被类型预测精度较高, 盖度较低的植被类型预测精度较低, 植物群落结构的特点是出现这些差异的主要原因; 综合使用环境变量和光谱变量作为预测变量的模型, 预测精度高于单独以环境变量或者光谱变量作为预测变量的模型。(2)环境变量、光谱变量大多被选入模型, 二者均对植被分布预测有重要的作用; 同一预测变量在不同植被类型的预测模型中的贡献不同, 这与植被的光谱、环境特征差异有关; 同一预测变量在不同的建模情景下对模型的贡献不同, 环境变量与光谱变量的耦合效应可能是导致预测变量对模型的贡献出现变化的原因。

Applying generalized additive model to integrate digital elevation model and remotely sensed data to predict the vegetation distribution

Aims Our objective was to integrate vegetation survey data, remotely sensed data and environmental data based on generalized additive model to map vegetation and investigate whether coupling remotely sensed data and environmental data could improve the performance of generalized additive model.
Methods Altitude, slope, nearest distance to coastline, nearest distance to the Yellow River and spectral variables were selected as predictive variables. Generalized additive model was employed to describe the relation between the vegetation and predictive variables. Deviance (D²) was employed to test the goodness of curve fitting, and a probability map of each vegetation type produced by the fitted generalized additive model was assessed by cross-validation of receiver operating characteristic (ROC). Vegetation type of each grid cell was determined according to the maps of probability. We validated the final predicted vegetation type map by comparing the predicted vegetation type map with vegetation survey data.
Important findings Results indicated that integrating field sampling, digital elevation model and remotely sensed data based on generalized additive model was feasible to map vegetation. Coupling environmental variables and spectral variables improved the performance of generalized additive model. Prediction accuracy varied according to the plant community type, and plant community with dense cover was better predicted than sparse plant community. The selected generalized additive models for each vegetation type indicated that both environmental variables and spectral variables were important factors for predicting vegetation distribution. Drop contribution calculation indicated that the contribution of the same predictive variable for different vegetation types was different. This can be explained by the different spectral and environmental characteristics of vegetation types. The contribution of each predictive variable for the same vegetation type varied according to the modeling scene. This might be explained by the coupling effects of environmental variables and spectral variables.