Multi-temporal Vegetation Index Research Articles

Fine-resolution remote-sensing and modelling of Himalayan catchment sustainability

A number of studies have reported on environmental degradation in the Nepal Himalaya as a result of large-scale deforestation and the associated agricultural extension. In contrast to many previous regional scale studies, we consider land cover and its environmental impact on an individual catchment-scale, using fine-resolution Quickbird data and a soil erosion model. First, using a detailed land cover map generated from Quickbird imagery, we establish basic relationships between land cover, dwelling density and topographic variability, which exist in a typical mid-elevation Nepalese catchment, the Pokhare Khola. These data suggest a strongly subsistence type of household economy based predominantly on terraced arable farming. We demonstrate using multitemporal vegetation indices that this farmland has existed in the region since the late 1980s, and that widespread deforestation has not taken place since then, possibly as a result of specific forest conservation policies of the government coupled with efforts by local communities. Further, through the use of soil erosion modelling we demonstrate the role that the terraced farming practices can play in reducing runoff and hence soil nutrient loss, thereby enabling restoration of vegetation in the previously deforested land terrains. Finally, by combining this information with regional land cover data, we show that the findings of this research can be scaled up to draw conclusions about environmental degradation across the Nepal Himalayan region.

Comparison of SAR and optical sensor data for monitoring of rice plant around Hiroshima

Abstract Through the research on the application of the multi-temporal data acquired with Landsat-5 TM, SPOT-2 HRV, SPOT-4 HRVIR and RADARSAT-1 SAR to monitoring of rice field following features are clarified. Vegetation indices NDVI (Normalized Difference Vegetation Index) and EVI (Extended Vegetation Index) derived from different optical sensors TM, HVR, HRVIR indicate nearly same characteristics suggesting validity of calibration of respective sensors. It was found that by combined use of multi-temporal vegetation indices NDVI and EVI together with SAR (Synthetic Aperture Radar) data it was found that rice planted area can be estimated with the error less than 2%.

A Gaussian adaptive resonance theory neural network classification algorithm applied to supervised land cover mapping using multitemporal vegetation index data

Neural network classifiers have been shown to provide supervised classification results that significantly improve on traditional classification algorithms such as the Bayesian (maximum likelihood [ML]) classifier. While the predominant neural network architecture has been the feedforward multilayer perceptron known as backpropagation. Adaptive resonance theory (ART) neural networks offer advantages to the classification of optical remote sensing data for vegetation and land cover mapping. A significant advantage is that it does not require prior specification of the neural net structure, creating as many internal nodes as are needed to represent the calibration (training) data. The Gaussian ARTMAP classification algorithm bases the probability that input training samples belong to specific classes on the parameters of its Gaussian distributions: the means, standard deviations, and a priori probabilities. The performance of the Gaussian ARTMAP classification algorithm in terms of classification accuracy using independent validation data indicated was over 70% accurate when applied to an annual series of monthly 1-km advanced very high resolution radiometer (AVHRR) satellite normalized difference vegetation index (NDVI) data. The accuracies were comparable to those of fuzzy ARTMAP and a univariate decision tree, and significantly higher than a Bayesian classification algorithm. Algorithm testing is based on calibration and validation data developed and applied to Central America to map the International Geosphere-Biosphere Programme (IGBP) land cover classification system.