Pedro A. Hernandez-Leal

Synergy of GIS and Remote Sensing Data in Forest Fire Danger Modeling

Forest fires constitute an important problem for the environment degradation. In this paper, we propose a Dynamic Fire Risk Index (DFRI) that takes into account different static and dynamic factors of risk for fire occurrence. Variables like insolation hours, vegetation cover, altitude, slope, proximity to main roads, and fire statistics have been used to develop a Static Fire Risk Index (SFRI) using a logistic regression model. Using satellite data to derive water stress of forest, a new dynamic index is defined weighting the static index with the actual value of water stress indicators. This methodology has been previously tested for some fires in the Canary Islands (Spain), and, in this case, we prove its usefulness using both NOAA-AVHRR and Terra-MODIS sensors data. As test sites, two different fires that took place in September 2005 on La Palma Island and August 2007 on Tenerife Island (Canary Islands, Spain) have been considered in order to validate the suitability of these tools for a regional scale application, in an area where multiple microclimates are present mainly due to its steep orography and the trade winds.

Pixel and object-based classification approaches for mapping forest fuel types in Tenerife Island from ASTER data

Four classification algorithms have been assessed and compared with mapped forest fuel types from Terra-ASTER sensor images in a representative area of Tenerife Island (Canary Islands, Spain). A BEHAVE fuel-type map from 2002, together with field data also obtained in 2002 during the Third Spanish National Forest Inventory, was used as reference data. The BEHAVE fuel types of the reference dataset were first converted into the Fire Behaviour Fuel Types described by Scott and Burgan, taking into account the vegetation of the study area. Then, three pixel-based algorithms (Maximum Likelihood, Neural Network and Support Vector Machine) and an Object-Based Image Analysis were applied to classify the Scott and Burgan fire behaviour fuel types from an ASTER image from 3 March 2003. The performance of the algorithms tested was assessed and compared in terms of quantity disagreement and allocation disagreement. Within the pixel-based classifications, the best results were obtained from the Support Vector Machine algorithm, which showed an overall accuracy of 83%; 14% of disagreement was due to allocation and 3% to quantity disagreement. The Object-Based Image Analysis approach produced the most accurate maps, with an overall accuracy of 95%; 4% disagreement was due to allocation and 1% to quantity disagreement. The object-based classification achieved thus an overall accuracy of 12% above the best results obtained for the pixel-based algorithms tested. The incorporation of context information to the object-based classification allowed better identification of fuel types with similar spectral behaviour.

Fusion of WorldView-2 and LiDAR Data to Map Fuel Types in the Canary Islands

Wildland fires are one of the factors causing the deepest disturbances on the natural environment and severely threatening many ecosystems, as well as economic welfare and public health. Having accurate and up-to-date fuel type maps is essential to properly manage wildland fire risk areas. This research aims to assess the viability of combining Geographic Object-Based Image Analysis (GEOBIA) and the fusion of a WorldView-2 (WV2) image and low density Light Detection and Ranging (LiDAR) data in order to produce fuel type maps within an area of complex orography and vegetation distribution located in the island of Tenerife (Spain). Independent GEOBIAs were applied to four datasets to create four fuel type maps according to the Prometheus classification. The following fusion methods were compared: Image Stack (IS), Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF), as well as the WV2 image alone. Accuracy assessment of the maps was conducted by comparison against the fuel types assessed in the field. Besides global agreement, disagreement measures due to allocation and quantity were estimated, both globally and by fuel type. This made it possible to better understand the nature of disagreements linked to each map. The global agreement of the obtained maps varied from 76.23% to 85.43%. Maps obtained through data fusion reached a significantly higher global agreement than the map derived from the WV2 image alone. By integrating LiDAR information with the GEOBIAs, global agreement improvements by over 10% were attained in all cases. No significant differences in global agreement were found among the three classifications performed on WV2 and LiDAR fusion data (IS, PCA, MNF). These study’s findings show the validity of the combined use of GEOBIA, high-spatial resolution multispectral data and low density LiDAR data in order to generate fuel type maps in the Canary Islands.

Efficiency of a global algorithm for retrieving SST from satellite data in a subtropical region

Abstract A global split-window algorithm for the retrieval of sea surface temperature from AVHRR has been developed using a radiative transfer code. In order to analyze the performance of the equation obtained which is applied to the Canary Islands zone, we have simulated a set of satellite measurements. The purpose was to classify the errors determined by the global algorithm, depending on the atmospheric situation at each moment. In the final results we give an explanation of the inefficient performance of global algorithm in comparison with a regional algorithm.

Evaluation of Surface Temperature and Emissivity Derived from ASTER Data: A Case Study Using Ground-Based Measurements at a Volcanic Site

Abstract The land surface temperature (LST) and emissivity (LSE) derived from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were evaluated in a low spectral contrast volcanic site at an altitude of 2000 m on the island of Tenerife, Spain. The test site is almost flat, thermally homogeneous, and without vegetation cover or variation in its surface composition. ASTER data correspond to six scenes, under both day- and nighttime conditions during 2008. This case study analyzes the impacts of the sources of inaccuracies using the temperature–emissivity separation (TES) algorithm. Uncertainties associated with inaccurate atmospheric correction were minimized by means of local soundings and the climate advantages of the area. Concurrent ground-based radiometric measurements were performed for LST, and laboratory and field measurements for LSE, to obtain reference values. The TES evaluation showed a good level of agreement in the emissivity derived for ASTER bands 13 and 14 [root-mea...

Use of TOMS data to correct the saharan dust effects on SST retrievals from satellite

Abstract High loads of atmospheric aerosols introduce large errors in satellite-derived sea surface temperature (SST) retrievals. Airborne plumes of desert dust from North Africa are the most evident and persistent and cover large areas of the tropical Atlantic Ocean. We propose a methodology to correct for Saharan dust effects on sea surface temperatures derived from the AVHRR sensor. This method links SST errors in AVHRR estimates with concentrations of absorbing atmospheric aerosols as estimated by the Earth Probe Total Ozone Mapping Spectrometer (TOMS) Aerosol Index. Errors in the SST algorithm increase with increasing aerosol index estimates. To avoid these errors a correction term based on the TOMS aerosol index is incorporated to the AVHRR SST algorithm.

Aerosol phase function modeling in Saharan dust invasions using the ratio AVHRR/NOAA Ch1/Ch2

Abstract Some authors have pointed out that the mineral dust could have an important effect in the atmosphere radiative properties in oceanic regions where this component is the principal aerosol constituent (mainly in regions close to arid zones). The radiative characterization of this component is a global scale problem, so it is necessary to use satellites techniques. One of the most useful parameter to study the dynamic and radiative properties of the mineral dust is the aerosol optical depth (AOD). To minimize the possible error sources, in order to give this parameter, it is necessary to work with a realistic aerosol phase function. We propose a Henyey-Greenstein type phase function calculated only by the ratio AVHRR/NOAA Ch1/Ch2, whose parameters have been optimized to situations of mineral dust invasions. This proposed phase function has been obtained by radiometric measurements of ground-based instruments.

Relation between TOMS-AI and air masses in the Northeast Atlantic free troposphere

Abstract One of the most useful technique to evaluate the presence of atmospheric aerosol is based on the aerosol index (AI) calculated from the Total Ozone Mapping Spectrometer (TOMS) data. For this methodology, positive values of AI are related with UV-absorbing aerosols, mainly dust, smoke and volcanic aerosols, and negative values of AI are associated with non-absorbing aerosols. Using the aerosol index (AI) provided by TOMS and isentropic backwards trajectories, we have investigated the relation between the aerosol load of the air masses and the Al values. The back trajectories have been classified in eight categories attending to the contribution of the main aerosols sources, taking into account the geographical regions, the residence time in these sectors and the altitude of the air mass during its evolution towards the Izana GAW station (IZO, 28.3 °N 16.5 °W, 2367m asl), Canary Islands. The categories are: African, Maritimes, Atlantic Free Troposphere, European, Maritimes-European, Maritimes-African, European-African, and European-African-Maritimes. Thus this station is characterized mainly by clean air masses with the origin in the middle troposphere and air masses from Africa with an annual frequency of 36% and 10% respectively of the total cases study. Each point of these back trajectories has been associated with the corresponding AI value, obtaining statistical information about the relation between AI values and expected aerosol type at the station. In this Atlantic region, the most important aerosol events are associated with Saharan dust outbreaks. For these episodes we have investigated the influence of the altitude of the air masses that reach IZO station, on the AI value. The main difference is the time that these air masses spend above 2km over the African continent. For air masses associated with Al positive values greater than 0.4 this time is around the 40%, whereas for air masses associated with AI values smaller than 0.4 is around 20%. 0 2003 COSPAR.

Radiative transfer modeling in the UV-VIS region with the presence of Saharan mineral desert aerosols

Abstract Recent studies have shown that the mineral dust is an important radiative forcing agent although the role played by this atmospheric compound in the UV-VIS radiative transfer into the atmosphere is still uncertain. To study how these particles scatter the solar radiation it is necessary to accurately know the aerosol phase function. The goodness of different phase functions with the presence of mineral dust have been tested comparing the simulated backscattered radiances in the UV-VIS region using real phase functions and different standard phase function approximations. The real ones have been calculated by the aerosol optical depth data measured with a ground-based solar photometer at Tenerife (28.5°N, 16.3°W) during Saharan dust invasions. From the comparison of these results we conclude that when a unique phase function is used in the radiative transfer model the one term Henyey-Greenstein (OTHG) is the worst approximation whereas the two parameter Henyey-Greenstein (TTHGL) is the best one, with an error of less than ±10%.

Evaluation of a Bayesian Algorithm to Detect Burned Areas in the Canary Islands’ Dry Woodlands and Forests Ecoregion Using MODIS Data

Burned Area (BA) is deemed as a primary variable to understand the Earth’s climate system. Satellite remote sensing data have allowed for the development of various burned area detection algorithms that have been globally applied to and assessed in diverse ecosystems, ranging from tropical to boreal. In this paper, we present a Bayesian algorithm (BY-MODIS) that detects burned areas in a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2002 to 2012 of the Canary Islands’ dry woodlands and forests ecoregion (Spain). Based on daily image products MODIS, MOD09GQ (250 m), and MOD11A1 (1 km), the surface spectral reflectance and the land surface temperature, respectively, 10 day composites were built using the maximum temperature criterion. Variables used in BY-MODIS were the Global Environment Monitoring Index (GEMI) and Burn Boreal Forest Index (BBFI), alongside the NIR spectral band, all of which refer to the previous year and the year the fire took place in. Reference polygons for the 14 fires exceeding 100 hectares and identified within the period under analysis were developed using both post-fire LANDSAT images and official information from the forest fires national database by the Ministry of Agriculture and Fisheries, Food and Environment of Spain (MAPAMA). The results obtained by BY-MODIS can be compared to those by official burned area products, MCD45A1 and MCD64A1. Despite that the best overall results correspond to MCD64A1, BY-MODIS proved to be an alternative for burned area mapping in the Canary Islands, a region with a great topographic complexity and diverse types of ecosystems. The total burned area detected by the BY-MODIS classifier was 64.9% of the MAPAMA reference data, and 78.6% according to data obtained from the LANDSAT images, with the lowest average commission error (11%) out of the three products and a correlation (R2) of 0.82. The Bayesian algorithm—originally developed to detect burned areas in North American boreal forests using AVHRR archival data Long-Term Data Record—can be successfully applied to a lower latitude forest ecosystem totally different from the boreal ecosystem and using daily time series of satellite images from MODIS with a 250 m spatial resolution, as long as a set of training areas adequately characterising the dynamics of the forest canopy affected by the fire is defined.