Application of the MODIS global supervised classification model to vegetation and land cover mapping of Central America
Application of the MODIS global supervised classification model to vegetation and land cover mapping of Central America
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DOI：
10.1080/014311600210100
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发表时间：
2000-04-15
影响因子：
3.4
通讯作者：
Strahler, A
中科院分区：
文献类型：
--
作者：
Muchoney, D;Borak, J;Strahler, A
虽然使用遥感数据绘制植被和土地覆盖图在地方尺度上有着丰富的应用历史，但直到最近，这种能力才发展到允许在区域、大陆和全球尺度上应用分类模型。为全球开发支持监督和非监督分类模型的综合训练、测试和验证站点网络充满了规模、站点的生物气候代表性、辅助地图和高空间分辨率遥感数据的可用性、景观异质性和植被变异性带来的问题。陆地生态系统参数化系统 (STEP) 是一种用于表征场地生物物理、植被和景观参数的模型，用于算法训练、测试和验证，旨在支持监督土地覆盖制图。该系统在中美洲应用，采用基于 428 个地点的两种分类系统。结果表明：（1）可以在区域尺度上高效生成站点数据； (2) 使用人工神经网络和决策树分类算法实现监督模型在区域层面是可行的，分类准确率可达 75-88%； (3)STEP位点参数模型可有效生成多个分类系统，从而支持全球表面生物物理参数的开发。
While mapping vegetation and land cover using remotely sensed data has a rich history of application at local scales, it is only recently that the capability has evolved to allow the application of classification models at regional, continental and global scales. The development of a comprehensive training, testing and validation site network for the globe to support supervised and unsupervised classification models is fraught with problems imposed by scale, bioclimatic representativeness of the sites, availability of ancillary map and high spatial resolution remote sensing data, landscape heterogeneity, and vegetation variability. The System for Terrestrial Ecosystem Parameterization (STEP)-a model for characterizing site biophysical, vegetation and landscape parameters to be used for algorithm training and testing and validation-has been developed to support supervised land cover mapping. This system was applied in Central America using two classification systems based on 428 sites. The results indicate that: (1) it is possible to generate site data efficiently at the regional scale; (2) implementation of a supervised model using artificial neural network and decision tree classification algorithms is feasible at the regional level with classification accuracies of 75-88%; and (3) the STEP site parameter model is effective for generating multiple classification systems and thus supporting the development of global surface biophysical parameters.