Jose L. Casanova

Automatic mapping of surfaces affected by forest fires in Spain using AVHRR NDVI composite image data

Abstract In this work, we describe the statistical techniques used to analyze images from the National Oceanic and Atmospheric Administrations advanced very high resolution radiometer for the calculation and mapping of surfaces affected by large forest fires in Spain in 1993 and 1994. Maximum value normalized difference vegetation index (NDVI) composites (MVCs) were generated for every ten-day period over the two years of the study. Two techniques, one regression analysis and the other differencing, were applied to the NDVI-MVCs both before and after each fire event to determine detection thresholds of change and to delineate and objectively evaluate the burned surfaces. The comparison between the single-fires burned areas predicted by the techniques and that provided by the Spanish Forestry Service (ground based) showed that the regression algorithm was more reliable, giving rise to virtually no bias (−0.9%) and a root mean square error (RMS) of 20.3%, both calculated as a percentage of the mean burned area of the whole sample. The technique of differencing provided worse results with a 3.2% bias and a 23.5% RMS error. Likewise, a comparison between. the perimeters of the large fires supplied by official data (GPS-based) and those obtained by the regression method confirmed the validity of the technique not only for calculating fire size, but also for mapping of large forest fires.

Land use mapping methodology using remote sensing for the regional planning directives in Segovia, Spain

This study presents the methodology followed in the land use mapping at scale 1:50,000 for the Functional Area of Segovia, Spain. The study is included in the regional planning directives for Segovia, and makes a comprehensive use of the remote sensing techniques developed during the second half of the 1990s. The methodological precedents in this respect are analyzed, mainly the CORINE land cover project, together with the data sources and the techniques that were used to obtain the thematic information searched for. The less known methods are described and a complete methodological sequence of the work is offered. The main characteristics of the methodology are the high degree of automation, objectivity, possibility of direct contrasting and its capacity for quick updating. Diverse data sources have been used, such as cartographic vector information and satellite imagery. LANDSAT-TM and IRS-1D Pan were used as well as aerial oblique photography. All information was integrated into a Geographic Information System (GIS; named SIGIM-TD). Data fusion methods were also widely used to improve the spatial resolution of the images. A neural network was generated to provide an appropriate classification method. Results of the neural network-based classification are shown and a classification-fieldwork correlation of 0.887 was obtained, in contrast with coefficients of 0.334, 0.432, 0.234 and 0.678 achieved through other techniques. Graphic results of the work are presented together with the data sources used for the map elaboration. Finally, a discussion on the advances that these techniques represent is done.

Forest Fires And Remote Sensing

The use of remote sensing techniques for the study of forest fires is a subject that started already several years ago and whose possibilities have been increasing as new sensors were incorporated into earth observation international programmes and new goals were reached based on the improved techniques that have been introduced. Three main topics can be distinguished, in which remote sensing provides results that can be applied directly to the subject of forest fires: risk of fire spreading, detection of hot-spots and establishment of fire thermal parameters and, finally, cartography of affected areas. In the last years, other two important topics are getting increasing interest; the first one is the estimation of severity, related to the post-fire phase, and the other one is the atmospheric impact of fire emissions.

Detecting Areas Vulnerable to Sand Encroachment Using Remote Sensing and GIS Techniques in Nouakchott, Mauritania

Sand dune advances poses a major threat to inhabitants and local authorities in the area of Nouakchott, Mauritania. Despite efforts to control dune mobility, accurate and adequate local studies are still needed to tackle sand encroachment. We have developed a Sand Dune Encroachment Vulnerability Index (SDEVI) to assess Nouakchott’s vulnerability to sand dune encroachment. Said index is based on the geo-physical characteristics of the area (wind direction and intensity, slope and surface height, land use, vegetation or soil properties) with Geographic Information System (GIS) techniques that can support local authorities and decision-makers in implementing preventive measures or reducing impact on the population and urban infrastructures. In order to validate this new index, we use two remote sensing approaches: optical-Sentinel 2 and Synthetic Aperture Radar (SAR)–Sentinel 1 data. Results show that the greatest vulnerability is located in the north-eastern part of Nouakchott, where local conditions favor the advance of sand in the city, although medium to high values are also found in the eastern part. Optical images enabled us to distinguish desert sand using the ratio between near infrared/blue bands, and SAR Coherence Change Detection (CCD) imagery was used to assess the degree of stability of those sand bodies. The nature of the SDEVI index allows us to currently assess which areas are vulnerable to sand encroachment since we use long data records. Nevertheless, optical and SAR remote sensing allow sand evolution to be monitored on a near real-time basis.

Discriminación de bosques de Araucaria araucana en el Parque Nacional Conguillío, centro-sur de Chile, mediante datos Landsat TM

Los bosques de Araucaria araucana poseen gran relevancia ecologica, sin embargo su distribucion espacial es poco conocida. Solo han sido clasificados a escala pequena, utilizando fotos e imagenes de satelite procesados con metodos convencionales. El presente estudio tuvo como objetivo discriminar y caracterizar tipos de bosques de A. araucana en el Parque Nacional Conguillio, localizados en el centro-sur de Chile, mediante datos derivados del satelite Landsat-5 TM y sistemas de informacion geografica. El indice de vegetacion de diferencia normalizada (NDVI) se relaciono satisfactoriamente con las variables cobertura de copa y el diametro a la altura del pecho, por esta razon, se incorporaron valores de este indice al proceso de clasificacion. A partir del modelo digital elevacion y el NDVI se minimizo el efecto provocado por la sombra. Se discriminaron siete tipos de bosques, entre densos y semidensos-abiertos, y de acuerdo con las especies acompanantes. La fiabilidad global de la clasificacion fue de 83,8 %. La mayor fiabilidad para el productor fue para el bosque de mediana densidad de copa de A. araucana-Nothofagus dombeyi (B2) (87,5 %) y para el consumidor, para los bosques de alta densidad de copa de A. araucana-N. dombeyi (B1) y tambien los de mediana densidad (B2) (93 %). Se concluye que incorporando valores NDVI y datos provenientes del modelo digital de elevacion al proceso de clasificacion satelital, es posible discriminar bosques de araucaria con fiabilidad satisfactoria en areas de relieve abrupto, informacion muy util para manejar estos ecosistemas boscosos.

A Very Quick Neural Network Algorithm for Cloud Detection

Abstract A very quick neural network algorithm for cloud detection, based on a neural network, is developed. Cloud detection is speeded up through the use of the Class Assigning Space (CAS). The CAS is a classified Radiance Space (RS), which has been built using the trained neural network. The CAS is used to assign a class for each pixel in the image instead of using the ANN to treat every single pixel. The detection time is approximately one second. Channel 1 and channel 5 of NOAA‐AVHRR images have been used. The Supervised ART‐II artificial neural network has been employed. The system performance has been tested with different training sets and different input data.

GIS In Quantitative Geographical Analysis

The aim of this paper is to explain and discuss the role of GIS in quantitative geographical analysis. Special interest has been placed in offering a complete scheme of quantitative geographical analysis and GIS tools to develop this within the field of locational analysis as well as showing a theoretical framework for this kind of study. This theoretical framework comes from a mathematical conceptualisation of geographical space, and provides the appropriate numerical tools in each case. GIS-Remote Sensing-Statistical Methods relationships are studied, as is the probabilistic approach to some geographical problems, considering the connection with GIS. Finally, some GIS requirements in relation to geographical analysis are laid out, with some considerations on the prospects in the near future.