Carmen Quintano

Spectral unmixing

Satellite imagery is formed by finite digital numbers representing a specific location of ground surface in which each matrix element is denominated as a picture element or pixel. The pixels represent the sensor measurements of spectral radiance. The radiance recorded in the satellite images is then an integrated sum of the radiances of all targets within the instantaneous field of view (IFOV) of the sensors. Therefore, the radiation detected is caused by a mixture of several different materials within the image pixels. For this reason, spectral unmixing has been used as a technique for analysing the mixture of components in remotely sensed images for almost 30 years. Different spectral unmixing approaches have been described in the literature. In recent years, many authors have proposed more complex models that permit obtaining a higher accuracy and use less computing time. Although the most widely used method consists of employing a single set of endmembers (typically three or four) on the whole image and using a constrained least squares method to perform the unmixing linearly, every different algorithm has its own merits and no single approach is optimal and applicable to all cases. Additionally, the number of applications using unmixing techniques is increasing. Spectral unmixing techniques are used mainly for providing information to monitor different natural resources (agricultural, forest, geological, etc.) and environmental problems (erosion, deforestation, plagues and disease, forest fires, etc.). This article is a comprehensive exploration of all of the major unmixing approaches and their applications.

Mapping burned areas in Mediterranean countries using spectral mixture analysis from a uni‐temporal perspective

The main aim of this study was to evaluate the usefulness of spectral mixture analysis (SMA) for mapping forest areas burned by fires in the Mediterranean area using low and medium spatial resolution satellite sensor data. A methodology requiring only one single post‐fire image was used to carry out the study (uni‐temporal techniques). This methodology is based on the contextual classification of the fraction images obtained after applying SMA to the original post‐fire image. The results showed that the proposed method, using only one image acquired post‐fire, could accurately identify the burned surface area (Kappa coefficient>0.8). The spatial resolution of the satellite images had practically no influence on the accuracy of the burned area estimate but did affect the possibility of detecting areas inside the perimeter of the burned area which were only slightly damaged.

Improving satellite image classification by using fractional type convolution filtering.

This letter shows how conventional methods for satellite image classification can be improved by applying some filtering algorithms as a pre-classifying step. We will introduce a filtering scheme based on convolution equations of fractional type. The use of this kind of filter as a pre-classification step will be illustrated by classifying MODerate-resolution Imaging Spectroradiometer (MODIS) data to map burned areas in Mediterranean countries. The methodology we propose improved the estimations obtained by merely classifying the post-fire images (i.e. without filtering) in the study areas considered.

A spectral unmixing approach for mapping burned areas in Mediterranean countries

The principal aim of this Letter is to evaluate the usefulness of Spectral Mixture Analysis (SMA) for estimating the area burned by forest fires in Mediterranean countries using National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) unitemporal data. The results show that the method, using an image acquired just after the fire occurrence, is capable of discriminating burned area accurately (Kappa coefficient >0.76).

Forecast of NDVI in coniferous areas using temporal ARIMA analysis and climatic data at a regional scale

Important issues such as the prediction of drought, fire risk and forest disease are based on analysis of forest vegetation response. A method of forecasting the short-term response of forest vegetation on the basis of an autoregressive integrated moving average (ARIMA) analysis was designed in this study. We used 10-day maximum value composite (MVC) bands of the Normalized Difference Vegetation Index (NDVI) obtained from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data from 1993 to 1997. Using the theory of stochastic processes (Box–Jenkins), the MVC-NDVI series was analysed and a seasonal ARIMA (SARIMA) model was developed for forecasting NDVI in the following 10-day periods. The SARIMA model identified a moving-average regular term with a 10-day lag and an autoregressive 37 10-day period seasonal term with a one-season (1-year) component. The study also demonstrated a slight relationship between the NDVI and the precipitation level in some species of conifers by using climatic time series and the analysis of dynamic models and allowed us to elaborate an image of the immediate future NDVI for the study area (Castile and Leon, Spain).

Pattern validation for MODIS image mining of burned area objects

An image mining method was applied to Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data to estimate the area burned by forest fires occurring in Galicia (Spain) between 4 August and 15 August 2006. Five different inputs were considered: post-fire near-infrared reflectance (NIR) band, post-fire Normalized Difference Vegetation Index (NDVI) image, pre-fire and post-fire NDVI difference image and 4-μm and 11-μm thermal bands. The proposed image mining method consists of three steps: a pre-classification step, applying kernel smoothing, based on the fast Fourier transform (FFT), a modelling step applying Gaussian distributions on individual grid cells with deviating values, and a thresholding step classifying the model into burned and unburned classes. Polygons collected in the field with Global Positioning System (GPS) measurements from a helicopter permitted validation of the burned area estimation. A Z-test based on the κ statistic compared the accuracy of this estimation with the accuracies achieved by traditional methods based both on spectral changes in reflectance after the fire and active fire detection. The results showed a significant improvement (7.5%) in the accuracy of the burned area estimation after kernel smoothing. Burned area estimation based on the smoothed difference image between pre-fire and post-fire NDVI image had the highest accuracy (κ = 0.72). We conclude that the image mining algorithm successfully extracted burned area objects and that extraction was best when smoothing was applied prior to classification. Image mining methods based on using the κ statistic thus provide a valuable validation procedure when selecting the optimal MODIS input image for estimating burned area objects.

Evaluating Landsat ETM+ emissivity-enhanced spectral indices for burn severity discrimination in Mediterranean forest ecosystems

Fires are a yearly recurring phenomenon in Mediterranean forest ecosystems. Accurate classification of burn severity is fundamental for the rehabilitation planning of affected areas. This work shows how conventional remote sensing methods for burn severity assessment may be improved by using land surface emissivity (LSE) to enhance standard spectral indices. We considered a large wildfire in August 2012 in north western Spain. The composite burn index (CBI) was measured in 111 field plots and grouped into three burn severity levels. Evaluation of the relationship between Landsat 7 Enhanced Thematic Mapper LSE-enhanced spectral indices and CBI was performed by correlation analysis, regression models, and one-way analysis of variance. The result was a 16.22% overall improvement in adjusted coefficient of determination over the standard spectral indices. Our results demonstrate the potential of LSE for improving mapping of burn severity. Future research, however, is needed to evaluate the performance of the proposed spectral indices in other fire regimes and ecosystems.

Remote Sensing Applied to the Study of Fire Regime Attributes and Their Influence on Post-Fire Greenness Recovery in Pine Ecosystems

We aimed to analyze the relationship between fire regime attributes and the post-fire greenness recovery of fire-prone pine ecosystems over the short (2-year) and medium (5-year) term after a large wildfire, using both a single and a combined fire regime attribute approach. We characterized the spatial (fire size), temporal (number of fires, fire recurrence, and return interval), and magnitude (burn severity of the last fire) fire regime attributes throughout a 40-year period with a long-time series of Landsat imagery and ancillary data. The burn severity of the last fire was measured by the dNBR (difference of the Normalized Burn Ratio) spectral index, and classified according to the ground reference values of the CBI (Composite Burn Index). Post-fire greenness recovery was obtained through the difference of the NDVI (Normalized Difference Vegetation Index) between pre- and post-fire Landsat scenes. The relationship between fire regime attributes (single attributes: fire recurrence, fire return interval, and burn severity; combined attributes: fire recurrence-burn severity and fire return interval-burn severity) and post-fire greenness recovery was evaluated using linear models. The results indicated that all the single and combined attributes significantly affected greenness recovery. The single attribute approach showed that high recurrence, short return interval and low severity situations had the highest vegetation greenness recovery. The combined attribute approach allowed us to identify a wider variety of post-fire greenness recovery situations than the single attribute one. Over the short term, high recurrence as well as short return interval scenarios showed the best post-fire greenness recovery independently of burn severity, while over the medium term, high recurrence combined with low severity was the most recovered scenario. This novel combined attribute approach (temporal plus magnitude) could be of great value to forest managers in the development of post-fire restoration strategies to promote vegetation recovery in fire-prone pine ecosystems in the Mediterranean Basin under complex fire regime scenarios.

Linear fractional-based filter as a pre-classifier to map burned areas in Mediterranean countries

Wildfires in Southern Europe burn thousands of square kilometres every year, causing extensive economic and ecological losses. Accurate mapping of fire-burned areas is a decisive factor in guiding forest management decisions. In this work a linear fractional-based filter is considered as a pre-classifier for burned area estimation from Moderate Resolution Imaging Spectroradiometer (MODIS) accurate satellite imagery. Three vegetation indices (normalized difference vegetation index, enhanced vegetation index, and global environment monitoring index) and three spectral indices specifically designed for burned area identification (normalized burnt ratio, burned area index, and burned area adapted for MODIS) have been used as inputs for the proposed filter. The filtered images were classified and the accuracy of the burned area estimates was computed by kappa () statistic. Burned area perimeters measured on the ground by global positioning system were used as reference truth. A linear Gaussian pre-classification filter was used as reference to check the burned area estimates accuracy improvements. When using the proposed fractional-type filter, an accurate estimation () of areas burned by forest fires in the four study areas located in Central Spain was achieved. The results showed that the non-local filter, used as a pre-classifier, allowed higher accuracy than the same inputs both without filtering and with the Gaussian filter ( index increased up to 30% with statistical significance). McNemar test confirmed that such accuracy improvement had statistical significance, and a statistical separability test showed that filtering increased the inter-class distance, helping to improve the latter classification. Burned areas in Central Spain were accurately mapped when the linear fractional-based filter proposed here was used as a pre-classifier.

CCD CBERS and ASTER data in dasometric characterization of Pinus radiata D. Don (North-western Spain)

A Chinese-Brazilian Earth Resources Satellite (CBERS) and an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scenes coupled with ancillary georeferenced data and field survey were employed to examine the potential of the remote sensing data in stand basal area, volume and aboveground biomass assessment over large areas of Pinus radiata D. Don plantations in Northwestern Spain. Statistical analysis proved that the near infrared band and the shade fraction image showed significant correlation coefficients with all stand variables considered. Predictive models were accordingly selected and utilized to undertake the spatial distribution of stand variables in radiata stands delimited by the National Forestry Map. The study reinforces the potentiality of remote sensing techniques in a cost-effective assessment of forest systems.