Retrieval of vegetation understory information fusing Hyperion and panchromatic QuickBird data in the method of Neural Network

Vegetation cover is of great significance in understanding climate change process due to its vital role in controlling water and carbon cycles. The properties of vegetation´s surfaces are usually estimated by remotely sensed data through regression models or physical-based models, which simulates the interactions of solar radiation with the vegetation medium. In real domain, the spectral responses measured by the sensor in forested area are strongly influenced by the different understory natural conditions that limit the possibility of applying both retrieval methods to predict overstory vegetation parameters. Understory information is therefore needed for estimating trees´ parameters; moreover from a biodiversity point of view and perspective of forest management, understory represents a critical component of forest ecosystem that needs a better characterization. An experiment has been conducted using hyperion and panchromatic QuickBird data to explore the status of different vegetation´s understory under a sparse forest in the Longmenhe Nature Reserve, China. Understory vegetation information of study area is classified into five classes. The novel aspect of the method is the integration of spectral (hyperspectral) domain fusion and spatial domain fusion techniques within a multi-layer perceptron artificial neural network model. Real data from the experiment on a limited ground as well as hyperion and QuickBird data are used as input dataset. A nonlinear artificial neural network achieved a classification accuracy of 80% despite the presence of co-occurring mid-story and understory vegetation. The achieved results show that this method is able to identify the different vegetation information under the tree canopy. Our studies suggest that it is necessary to incorporate the geographic and vegetation community prior information to further improve the accuracy in order to monitor understory vegetation.