Growing Neural Gas approach for obtaining homogeneous maps by restricting the insertion of new nodes

The Growing Neural Gas model is used widely in artificial neural networks. However, its application is limited in some contexts by the proliferation of nodes in dense areas of the input space. In this study, we introduce some modifications to address this problem by imposing three restrictions on the insertion of new nodes. Each restriction aims to maintain the homogeneous values of selected criteria. One criterion is related to the square error of classification and an alternative approach is proposed for avoiding additional computational costs. Three parameters are added that allow the regulation of the restriction criteria. The resulting algorithm allows models to be obtained that suit specific needs by specifying meaningful parameters.

Detection and mapping of burnt areas from time series of MODIS-derived NDVI data in a Mediterranean region

Moderate resolution remote sensing data, as provided by MODIS, can be used to detect and map active or past wildfires from daily records of suitable combinations of reflectance bands. The objective of the present work was to develop and test simple algorithms and variations for automatic or semiautomatic detection of burnt areas from time series data of MODIS biweekly vegetation indices for a Mediterranean region. MODIS-derived NDVI 250m time series data for the Valencia region, East Spain, were subjected to a two-step process for the detection of candidate burnt areas, and the results compared with available fire event records from the Valencia Regional Government. For each pixel and date in the data series, a model was fitted to both the previous and posterior time series data. Combining drops between two consecutive points and 1-year average drops, we used discrepancies or jumps between the pre and post models to identify seed pixels, and then delimitated fire scars for each potential wildfire using an extension algorithm from the seed pixels. The resulting maps of the detected burnt areas showed a very good agreement with the perimeters registered in the database of fire records used as reference. Overall accuracies and indices of agreement were very high, and omission and commission errors were similar or lower than in previous studies that used automatic or semiautomatic fire scar detection based on remote sensing. This supports the effectiveness of the method for detecting and mapping burnt areas in the Mediterranean region.

Fine-scale estimations of bioclimatic change in the Valencia region, Spain

Recent advances in statistical downscaling have allowed the reconstruction of temperatures for the complete 1948–2011 period in a spatial resolution of 90 m and without gaps for the Valencian Community (Spain) and bordering areas. It presently enables analyses in this region, which allows the determination of recent temperature changes at subregional and local scales. The present work focuses on obtaining the thermicity index according to Rivas-Martínez, a well-known indicator of different thermotypes associated with bioclimatic horizons. The change in this index, which has happened in the region between 1948 and 2011, was calculated by generating fine-scale maps of the potential extension of different thermotypes. The results show a greater regression for the thermotypes in a finicolous position, e.g. Orotemperate, Supratemperate and Supramediterranean horizons, which herein indicate greater potential vulnerability in climate change. In the absence of, and given the need for, such fine-scale information, this work should be useful for specialized researchers to spatially limit the potentially most vulnerable biotopes to climate change.