1. 25 Km – St Dv. NDVI Image 25 Km – NDVI Image Snow No Cover age No Snow 5 Channels5 Channels + St Dev NDVI Jan 16 Jan 18 Jan 17 Capabilities and Limitations of Neural Networks in Snow Cover Mapping from Passive Microwave Data 1. Introduction Snow-cover parameters are being increasingly used as input to hydrological models. Having an accurate estimation of the snow cover characteristics during the snowmelt season is indispensable for an efficient hydrological modeling and for an improved snowmelt runoff forecasts. Passive microwave remote sensing techniques have been investigated by numerous researchers using various sensors and have been demonstrated to be effective for monitoring snow pack parameters such as spatial and temporal distribution, snow water equivalent (SWE), depth, and snow condition (wet/dry state). However, the snow products derived from passive microwave sensors are usually limited by the relatively low resolution, especially when the purpose is to use this product as input for hydrological models. Moreover, the accuracy of passive-microwave-based maps is usually affected by the presence of vegetation. In this project, we used an adaptive neural network system to generate the spatial distribution of snow accumulation from multi-channel SSM/I data in the Northern Midwest of the United States. Five SSM/I channels were used in this experiment (19H, 19V, 22V, 37V, and 85V). The normalized difference vegetation index (NDVI) is used from the AVHRR to quantify the vegetation dynamic in the snow mapping process. Six snow days with high snow accumulation have been selected during the 2001/2002 winter season to train and test the neural network system. The snow depths and NDVI values have been compiled and gridded into 25 km x 25 km grid to match the final SSM/I resolution. To ensure an accurate selection of training pixels, different approaches have been tested by varying the selection criteria of snow pixels. The final results have shown the importance of these selection criterions on the neural network performance. Juan C. Arevalo ( NOAA/CREST Graduate student) jc@ce.ccny.cuny.edu, Hosni Ghedira (Assistant Professor) ghedira@ce.ccny.cuny.edu, Reza Khanbilvardi (Professor) rk@ccny.cuny.edu. 2. Study Area, Data Acquisition and Remote Sensing Data The City College of the City University of New York. Convent Avenue at 140th St, Steinman Hall, New York, NY. 10031. SSM/I Images Ground data distribution Jan23 19HJan24 19HJan25 19H Input layer2 hidden layersOutput layer Network Architecture : [5:10:10:1] 19 H 19 V 22 V 37 V 85 V Six days have been selected during the 2001/2002 winter season (01/16, 01/17, 01/18, 01/23, 01/24, and 01/25). The following illustrations show the channel 19H of SSM/I satellite with 25 km resolution for the six selected days. A total of 195 ground stations covering the study area have been identified for this experiment. The figure below shows the distribution of the ground stations over the study area (red rectangle). 4. Artificial Neural Network 5. Neural Network Approaches 8. Snow Cover Maps Original gridded data in Northern Hemisphere projection with coordinates: 119˚44’W - 99˚57’W and 49˚36’W - 34˚34’W The study area is located in the Northern Midwest of the United States within 110˚37’48’’W - 102˚02’24’’W and 48˚42’36’’N - 40˚43’48’’N.. The passive microwave data from the NOAA/NASA Pathfinder Program Special Sensor Microwave/Imager (SSM/I) Level 3 Equal Area Scalable Earth-Grid (EASE-Grid) Brightness Temperatures F13 satellite is used in both ascending and descending orbits. These images provide measurements of the brightness temperature in seven channels with different frequencies and polarizations (19 V, 19 H, 22 V, 37V, 37 H, 85 V, and 85 H). The available training data, has been divided into three subsets: The 190 training pixels have been set as follow: Learning set 90 pixels Validation set 45 pixels Test set 50 pixels The first one is the learning set, whish is used for computing and updating the network weights. The second subset is the validation set, which is used for stopping the training by monitoring the validation error during the training process. The third subset is the test set that is not used during the training process, and it is only used to assess the classification accuracy and to compare between different classifiers and different network configurations. The following color images represent two snow-cover maps for each selected day generated from the artificial neural network output. Each map contains 34 X 30 pixels with spatial resolution of 25 km. 6. Neural Network Results Highest Overall accuracy Average overall accuracy and standard deviation Average Kappa coefficient and its standard deviation AVHRR Image The AVHRR data was acquired from the Distributed Active Archive Center (DAAC) located at Goddard Space Flight Center, NASA. Jan16 19HJan17 19HJan18 19H 5TbNDVIS-NDVI Threshold 0.4 StDv Threshold 6 5TbNDVIS-NDVIStDv Run % Approach 4. Five channels,Tb + standard deviation of NDVI. Threshold 0.6 Jan 23 Jan 24 Jan 25 Ground snow map 3. Data Acquisition: Vegetation The input layer size may vary depending on the approach used; from 5 to 7 neurons. Threshold % Graph shows the pattern and difference from the truth data; snow/no-snow and the ANN result 01 Threshold ANN Truth data Actual snow depth from the truth data and the ANN result Snow - Snow Non snow – Non snow Overall accuracy Kappa coefficient Effect of the decision threshold on classification Truth data – snow depth [in] Threshold ANN During the simulation process, a continuous range from zero to one will be produced by the output neuron. We have introduced a threshold value (between 0 and 1) to decide if the pixel will be classified as snow or no- snow pixel. The optimal threshold value cannot be identified with certainty without measuring its effect on the overall accuracy of the neural network classification. In this project, the threshold value has been varied from 0.2 to 0.8. The effect of the decision threshold on classification accuracy of each class is illustrated in the following figure: The original 8 Km NDVI values have been gridded into a regular grid of 25 Km over the study area. The standard deviation values have been measured during the gridding process to quantify the vegetation homogeneity for each pixel. 7. Confusion matrices 5 Channels + NDVI5 Channels 5 Channels+St Dev. NDVI 5 Channels+NDVI+St Dev. NDVI As a part of the assessment of the capabilities and limitations of neural networks for snow mapping, the neural network output has been evaluated with a confusion matrix that was computed for each approach. The overall accuracy and Kappa coefficient were measured. The following matrices correspond to the net giving the highest accuracy out of 100 runs. Source of Data: Ground data from National Oceanic and Atmospheric Administration, NOAA SSM/I data from DMSP SSM/I Pathfinder daily EASE-Grid brightness temperatures, January 2002. Boulder, CO: National Snow and Ice Data Center, NSIDC (Armstrong, R.L., K.W. Knowles, M.J. Brodzik and M.A. Hardman. 1994, updated current year). The Normalized Difference Vegetation Index (NDVI) has been derived from the visible and the near-infrared channels of NOAA-AVHRR Sensor over our study area. 8-Km, 10-days composite NDVI image, January 21-31 1994. Only pixels with ground stations inside their boundaries are considered for the training and validation of Neural Network. A total of 165 pixels satisfy this criterion. Results obtained for the four approaches (number of input channels): 1. the 5 brightness temperature (Tb) channels; 2. the 5 Tb plus NDVI; 3. the 5 Tb plus NDVI and standard deviation, and 4. the 5 Tb plus the standard deviation of NDVI. A total of 100 runs of the neural network for each approach have been performed. The following graphs show for each approach the average accuracy and the Kappa coefficient for the 100-runs and their corresponding standard deviation. These results have shown that the addition of the NDVI standard deviation (homogeneity factor) improves the snow identification accuracy. The graph below shows the accuracy variation of 100 neural network trained with different initial configurations. Threshold 0.6 for the approach 4, which yields the net with the highest accuracy. That net was used to simulate the corresponding snow maps. The overall accuracy varies between 60 and 80 %, having a fairly stable pattern. However, some high and low peaks have been observed; 86 % and 52% being the highest and lowest accuracy. The Kappa coefficient, which assess the agreement in the classification between the snow and non-snow pixels, has a very irregular pattern. For each vector of five brightness temperatures presented to the input layer, a value equal to one will be assigned in the output layer if the presented vector correspond to a snow pixel. Otherwise, a value equal to zero will be assigned to the corresponding vector. The 5 channels maps (first column) represent the simulation generated by simulating the trained neural network in the approach 1, (threshold = 0.4). The other maps (2 nd column) represent the simulation results of neural network trained with the approach 4 by using 5 SSM/I channels plus the standard deviation of the NDVI as input. For this configuration, the best performance was obtained with a threshold equal to 0.6.