AI's geographic-outreach: Mexico and Latin America

AI in Latin America is healthy and growing in at least five countries and expanding to other nations. For instance, ITESM's AI graduate programs have graduated students from Bolivia, Peru, and Ecuador, who are returning to their countries to work in universities and companies. AI is a young scientific discipline compared to other sciences. Since its creation in the mid-twentieth century by Alan Turing and various American researchers, it has grown steadily and spread across the world, including LA. This has been facilitated by the sharing of a common type of language and the same culture, but most importantly, by the great scientific challenges posed by AI objectives.     

AVHRR-derived fire frequency,distribution and area burned in Siberia

Advanced Very High Resolution Radiometer (AVHRR) data are used to produce an active-fire detection product for the fire season in 1999 and 2000 and an area burned product for 1996–2000. The distribution of fire is presented ranging from the Urals in the west to the eastern coast and from the semi-dry steppe regions in the south through the taiga in the north. A temporal and spatial pattern of fire is observed migrating from north of 40°?N latitude in April to north of 60°?N by mid-July. Fire is widespread in August, spanning the entire geographic range. In contrast to these patterns, no similar east–west migrations are discernible from these data. Peak active-fire counts are detected in early May between 50 and 55°?N latitude in both 1999 and 2000. Wildfire in Russia is highly variable, both annually and interannually, with differences in reported area burned ranging from 0.234 to 13.3?million hectares per year. Comparing Russian fire statistics to satellite-based data from this investigation and previous works, we find area burned in Russia may be commonly underestimated by an average of 213%. Underestimates of this magnitude could strongly affect emissions estimates and climate change research.     

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).     

Reconstructing long time series of burned areas in arid grasslands of southern Russia by satellite remote sensing

Fire is an important natural disturbance process in many ecosystems, but humans can irrevocably change natural fire regimes. Quantifying long-term change in fire regimes is important to understand the driving forces of changes in fire dynamics, and the implications of fire regime changes for ecosystem ecology. However, assessing fire regime changes is challenging, especially in grasslands because of high intra- and inter-annual variation of the vegetation and temporally sparse satellite data in many regions of the world. The breakdown of the Soviet Union in 1991 caused substantial socioeconomic changes and a decrease in grazing pressure in Russia's arid grasslands, but how this affected grassland fires is unknown. Our research goal was to assess annual burned area in the grasslands of southern Russia before and after the breakdown. Our study area covers 19,000 km² in the Republic of Kalmykia in southern Russia in the arid grasslands of the Caspian plains. We estimated annual burned area from 1985 to 2007 by classifying AVHRR data using decision tree algorithm, and validated the results with RESURS, Landsat and MODIS data. Our results showed a substantial increase in burned area, from almost none in the 1980s to more than 20% of the total study area burned in both 2006 and 2007. Burned area started to increase around 1998 and has continued to increase, albeit with high fluctuations among years. We suggest that it took several years after livestock numbers decreased in the beginning of the 1990s for vegetation to recover, to build up enough fuel, and to reach a threshold of connectivity that could sustain large fires. Our burned area detection algorithm was effective, and captured burned areas even with incomplete annual AVHRR data. Validation results showed 68% producer's and 56% user's accuracy. Lack of frequent AVHRR data is a common problem and our burned area detection approach may also be suitable in other parts of the world with comparable ecosystems and similar AVHRR data limitations. In our case, AVHRR data were the only satellite imagery available far enough back in time to reveal marked increases in fire regimes in southern Russia before and after the breakdown of the Soviet Union.     

The use of Meteosat and GMS imagery to detect burned areas in tropical environments

This paper describes a methodology of using data acquired by the European Meteosat and the Japanese Geostationary Meteorological Satellite (GMS) geostationary satellites to detect burned areas in different tropical environments. The methodology is based on a multiple threshold approach applied to the thermal radiance and to a spectral index specific for burned surfaces. The Simple Index for Burned Areas (SIBA), also developed in this study, makes use of the information contained in the visible and thermal InfraRed (IR) band available on the geostationary satellites, whose main advantages are the high temporal resolution and the minimal level of pre-processing required. The results obtained with Meteosat data have been evaluated comparing them with NOAA-Advanced Very High Resolution Radiometer (AVHRR) data acquired over the Central Africa forest-savannah areas. For GMS imagery, AVHRR data acquired over the woodland-savannah areas of Northern Territory in Australia have been used. Despite the very low spatial and spectral resolution of the data, accuracy assessment showed at a regional and continental scale the resulting burned area maps could be a valuable source of information for the monitoring of the fire activity and for the assessment of fire impact on tropospheric chemistry.     

Evaluation of wildfire propagation susceptibility in grasslands using burned areas and multivariate logistic regression

This research simulated wildfire propagation susceptibility based on multivariate logistic regression. Moderate Resolution Imaging Spectrometer (MODIS)-derived fuel indicators and topographic factors were the independent variables, and burnt areas served as the dependent variable. MODIS data were collected daily during the wildfire seasons of April to May and September to October from 2001 to 2007 to acquire information about live and dead fuel in the Mongolia–China grasslands. The inputs for the independent parameters for wildfire propagation susceptibility modelling were the normalized difference vegetation index (NDVI), optimized soil-adjusted vegetation index (OSAVI), moisture stress index (MSI), global vegetation moisture index (GVMI), dead fuel INDEX (DFI), elevation, slope, and aspect. Multivariate logistic regression ranking indicates that DFI, MSI, DEM, and OSAVI are the top four factors, with an overall accuracy of 80%. ‘Leave one out’ cross-validation demonstrated that the overall accuracy of the propagation susceptibility modelling ranged from 65% to 87%. Finally, the model was used to produce 10 day average wildfire propagation susceptibility maps during the wildfire seasons of 2001–2007 and to predict the location of burned areas. This research will be useful for understanding the propagation susceptibility of wildfires in grassland areas and for creating policies for preventing wildfire spread.     

Detection of burned areas from mega-fires using daily and historical MODIS surface reflectance

The detection and mapping of burned areas from wildland fires is one of the most important approaches for evaluating the impacts of fire events. In this study, a novel burned area detection algorithm for rapid response applications using Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m surface reflectance data was developed. Spectra from bands 5 and 6, the composite indices of the Normalized Burn Ratio, and the Normalized Difference Vegetation Index were employed as indicators to discover burned pixels. Historical statistical data were used to provide pre-fire baseline information. Differences in the current (post-fire) and historical (pre-fire) data were input into a support vector machine classifier, and the fire-affected pixels were detected and mapped by the support vector machine classification process. Compared with the existing MODIS level 3 monthly burned area product MCD45, the new algorithm is able to generate burned area maps on a daily basis when new data become available, which is more applicable to rapid response scenarios when major fire incidents occur. The algorithm was tested in three mega-fire cases that occurred in the continental USA. The experimental results were validated against the fire perimeter database generated by the Geospatial Multi-Agency Coordination Group and were compared with the MCD45 product. The validation results indicated that the algorithm was effective in detecting burned areas caused by mega-fires.     

Use of multitemporal SAR data for monitoring vegetation recovery of Mediterranean burned areas

Remote sensing in the optical band is a well-established tool for monitoring changes in forested areas, although it can suffer from limitations, especially where frequent cloud cover occurs. The increased availability of space-borne radar imagery offers additional means for assessing the state of forests and monitoring their dynamics. In this study, the potential of multi-temporal space-borne SAR data for monitoring vegetation recovery over burned areas next to the Mediterranean coast is investigated. In particular, the study considers a set of ERS-SAR images, C-band and VV polarization, taken over the Castel Fusano pinewood, located near Rome, Italy, devastated in summer 2000 by a fire that burned about 350 ha of the wood. Starting from the analysis of the information contained in the variations, both in burnt and unburnt areas, of the inter annual multitemporal backscattering signatures, the study presents two different approaches, one more qualitative, the other one more quantitative, for the retrieval of the biomass re-growth after the fire. In the quantitative case, the inversion procedure computes the biomass re-growth rate by means of simulations carried out with the Tor Vergata scattering model. The obtained results are satisfactory as they are in agreement with simultaneous analysis based on optical data and in-situ measurement campaigns.     

Simultaneous detection of burned areas of multiple fires in the tropics using multisensor remote-sensing data

Fires associated with recurrent El Niño events have caused severe damage to tropical peat swamp forests. Accurate quantitative information about the frequency and distribution of the burned areas is imperative to fire management but is lacking in the tropics. This article examines a novel method based on principal component analysis (PCA) of the normalized difference water index (NDWI) from multisensor data for simultaneously detecting areas burned due to multiple El Niño–related fires. The principal components of multitemporal NDWI (NDWI-PCs) were able to capture the areas burned in the 1998 and 2003 El Niño fires in NDWI-PC3 and 2, respectively. The proposed method facilitates the reduction of dimensionality in detecting the burned areas. From 22 image bands, the proposed method was able to accurately detect the burned areas of multiple fires with only three NDWI-PCs. The proposed method also shows superior performance to unsupervised classifications of the principal components of combined image bands, multitemporal NDWI, NDWI differencing and post-classification comparison methods. The results show that the 1998 El Niño fire was devastating especially to intact peat swamp forest. For degraded peat swamp forest, there was an increase in the burned area from 1998 to 2003. The proposed method offers the retrieval of accurate and reliable quantitative information on the frequency and spatial distribution of burned areas of multiple fires in the tropics. This method is also applicable to the detection of changes in general as well as the detection of vegetation changes.     

Temporal and spatial variability in biomass burned areas across the USA derived from the GOES fire product

Burned area is a critical input to the algorithms of biomass burning emissions and understanding variability in fire activity due to climate change but it is difficult to estimate. This study presents a robust algorithm to reconstruct the patterns in burned areas across Contiguous United States (CONUS) in diurnal, seasonal, and interannual scales from 2000-2006. Specifically, burned areas in individual fire pixels are empirically calculated using diurnal variations in instantaneous fire sizes from the Geostationary Operational Environmental Satellites (GOES) WF_ABBA (Wildfire Automated Biomass Burning Algorithm) fire product. GOES burned areas exhibit diurnal variability with a temporal scale of half hours. The cumulative burned area during 9:00-16:00 local solar time accounts for 65%-81% of the total daily burned area. The diurnal variability is strongest in croplands compared to shrublands, grasslands, savannas, and forests. Analysis on a seasonal scale indicates that over 56% of burning occurs during summer (June-August). On average, the total annual burned area during the last seven years is 2.12 × 10⁴ ± 0.41 × 10⁴ km². The algorithm developed in this study can be applied to obtain burned area from the detections of GOES active fires at near real time, which can greatly improve the estimates of biomass burning emissions needed for predicting air quality.     

A quantitative comparison of methods for classifying burned areas with LISS‐III imagery

Environmental agencies frequently require tools for quick assessments of areas affected by large fires. Remote sensing techniques have been reported as efficient tools to evaluate the effects of fire. However, there exist few quantitative comparisons about the performance of the diverse methods. This study quantitatively evaluated the accuracy of five different techniques, a field survey and four satellite‐based techniques, in order to quickly classify a large forest fire that occurred in 1998 in Solsonès (north‐east Spain) by means of an IRS LISS‐III image. Three pure classes were determined: burned area, unburned vegetation, and bare soil; along with a non‐pure class that we called mixed area. These selected techniques were included into a tree classifier to investigate their partial contribution to the final classification. The most accurate methods when focusing on pure classes were those directly related to the spectral characteristics of the pixel: Reflectance Data and Spectral Unmixing (82% of overall accuracy), versus the poorer performances of Vegetation Indices (70%), Textural measures (72%) and the field survey (68.6%). Since no image processing technique was applied to the Raw Reflectance Data, it can be considered the most cost‐effective method, and the tree classifier reinforces its importance. The results of this study reveal that time consuming and expensive methods are not necessarily the most accurate, especially when potentially easily distinguishable classes are involved.     

The role of topographic correction in mapping recently burned Mediterranean forest areas from LANDSAT TM images

Operational use of remote sensing as a tool for post‐fire, Mediterranean forest management has been limited by problems of classification accuracy arising from confusion of burned and non‐burned areas. Frequently, this occurs as a result of slope illumination and shadowing effects caused by the complex topography encountered in many forested areas. Cloud shadows can also be a problem. The aim of this work was to investigate how image classification results could be improved by removing the illumination effects of topography from satellite images. This was achieved by applying supervised classification to both uncorrected and topographically corrected LANDSAT TM data for a site on the Greek island of Thasos. The classification methodology included atmospheric and geometric correction, field‐based training, seperability/contingency analysis and maximum likelihood processing. The classification scheme was determined on the basis of consultation with the Greek Forest Service. Overlay of the resulting class maps enabled comparison of the total burned area and its spatial extent using the two different approaches to processing. The results of each approach were compared with the forest perimeter map generated by the Forest Service using traditional survey methods. Accuracy assessment and error analysis clearly indicated that the removal of the topographic effect from the satellite image before its classification resulted in more accurate mapping of the burned area. It is concluded that operational use of satellite remote sensing for forest fire management depends on accurate, robust, widely available and proven techniques. Topographic correction should now be regarded as an essential element of any classification methodology which will be used for operational, post‐fire management of forests in complex Mediterranean landscapes.     

Three-dimensional distribution of cloud types over the USA and surrounding areas observed by CloudSat

The vertical and horizontal distributions of the cloud types across different seasons and over the contiguous USA and surrounding areas are studied. The study is performed by collecting two years (2007 and 2008) of data from the CloudSat 2B-CLDCLASS product that uses effective radar reflectivity factor Ze, the presence of precipitation and ancillary data such as surface topography and the model-predicted temperature profile to classify clouds into seven distinct types. Considerable seasonal variations of the horizontal distribution of the cloud-type fractions are observed in the study area among different seasons and for both daytime and night-time CloudSat observations. It was found that during spring and summer, deep convective (Dc) clouds are observed much more frequently during night‐time than during daytime over both the land and ocean. For the studied area and during daytime, low clouds were more frequent (up to ?50%) over the land and less frequent over the ocean compared with night-time observations. Analysis of the vertical distribution of cloud layers reveals that the fraction of cloudy scenes with two or more distinct cloud layers is the highest (up to 30%) over the northwest corner of the USA and the southwest corner of Canada and the nearby oceans. The southwest corner of the USA and the nearby east Pacific Ocean appeared to have the lowest fraction (<0.05%) of cloudy scenes with two or more distinct cloud layers. Over the land, approximately 18% of the total cloudy scenes are classified as two-layer clouds, whereas over the ocean, two-layer clouds are less frequent and range from 13% to 17% with a stronger seasonal dependency. Only about 2–3% of the total cloudy scenes are classified as multilayer clouds, with three or more distinct layers over both the land and ocean. The vertical distribution of cloud-top heights over both the land and ocean shows two distinct peaks. Over the land, the lower peak, at around 2 km, is almost independent of season, whereas the higher peak is seasonally dependent and varies between ?8 km (during winter) and ?11 km (during summer). Over the ocean, the lower peak is also observed near 2 km (or less), whereas the higher peak ranges approximately from 11 km (during winter) to 12 km (during summer).     

Analysis of the relationship between spatial pattern and spectral detectability of areas burned in southern Africa using satellite data

Fires in Africa affect atmospheric emissions and carbon sequestration, landscape patterns, and regional and global climatic conditions. Studies of these effects require accurate estimation of the extent of measurable fire events. The goal of this study was to assess the influence of burned area spatial patterns on the spectral detectability of burned areas. Six Landsat‐7 ETM+ images from the southern Africa were used for burned area mapping and spatial pattern analysis, while contemporaneous MODIS 500 m spatial resolution images were used to measure the spectral detectability of burned areas. Using a 15 by 15 km sample quadrats analysis, we showed that above a burned area proportion threshold of approximately 0.5 the spectral detectability of burned areas increase due to the decrease in the number of mixed pixels. This was spatially related to the coalescence of burned patches and the decrease in the total burned area perimeter. Simple burned area shapes were found at the Botswana site, where the absence of tree cover and the presence of bright surfaces (soil and dry grass) enhanced the spectral contrast of the burned surfaces, thus enabling better estimates of burned area extent. At the Zambia and Congo sites, landscape fragmentation due to human activity and the presence of a tree vegetation layer, respectively, contribute to the presence of small burned area patches, which may remain undetectable using moderate spatial resolution satellite imagery, leading to less accurate burned area extent estimates.     

Land-cover classification using radar and optical images: a case study in Central Mexico

Land-cover studies based on optical remote sensing in regions which exhibit disorderly urban growth and quick-use conversion of farmland to non-farm usage face problems due to inaccurate discrimination of cover types and hence inaccurate extent estimations. The use of data in the visible and infrared areas of the electromagnetic spectrum for classifying crop types has been extensively explored, concluding that data acquisitions must be made during critical crop development periods. This raises a concern in Central Mexico where such periods coincide with important cloud coverage and where good estimates of the extent of agricultural areas and of particular crops are keenly sought by government agencies for planning purposes. Due to the interest in accurate and updated maps for this area, repeated studies have been carried out over a number of years by the National Institute of Research for Forestry, Agriculture and Livestock for the Ministry of Agriculture of Mexico. Taking into consideration the difficulties of acquiring and analysing data derived from optical sensors, the objective of this study was to assess the advantages of combining synthetic aperture radar (SAR) and optical remote sensing in producing more accurate maps. The study area covers 15 634 ha and is located in Central Mexico in a region where agricultural plots of varied sizes and forms are interspersed with rapid urbanization spaces. We investigated alternative supervised classification schemes combining the Radarsat-1 C-band with Landsat Enhanced Thematic Mapper Plus (ETM+) bands to estimate land cover distributions and assess the quality of results with field data. Then, we set forth and evaluated a methodology which applies data fusion of selected Landsat ETM+?bands and the C-radar band. The separation and similarities for vegetated and non-vegetated cover types depends on whether the selected agricultural crops are annual or perennial, and on whether there are bare soils present. This knowledge for the particular study area influenced the selection of dates for image take and analysis. Partial fused and non-fused land-cover maps were assessed for accuracy and were combined to obtain a final map. The results demonstrate that the combined utilization of optical and radar imagery yields useful land cover information and improved classification accuracy over those obtained using either type of image on its own.     

时空数据库中时空查询语言的设计与实现

给出了时空数据查询的表示方法及时空数据查询的意义。在此基础上，提出了一种时空查询语言的设计及其在关系数据库中的实现方法。     

Mapping burned areas and burn severity patterns in SW Australian eucalypt forest using remotely-sensed changes in leaf area index

Remote sensing is the most practical method available to managers of fire-prone forests for quantifying and mapping fire impacts. Differenced Normalised Burn Ratio (ΔNBR) is among the most widely used spectral indices for the mapping of burn severity but is difficult to interpret in terms of fire-related changes in key biophysical attributes and processes. We propose to quantify burn severity as a change in the leaf area index (ΔLAI) of a stand. LAI is a key biophysical attribute of forests, and is central to understanding their water and carbon cycles. Previous studies have suggested that changes in canopy LAI may be a major contributor to ΔNBR and to the composite burn index (CBI) that is frequently used in combination with the NBR to assess burn severity on the ground. We applied remotely-sensed ΔLAI to map burn severity in jarrah (Eucalyptus marginata) forest in south-western Australia burnt during the January 2005 Perth Hills wildfires. Ground-based digital photography was used to measure LAI in typical stands representing the full range of canopy densities present in the study area as well as variation in the time since the last fire. Regression models for the prediction of LAI were developed using NBR, the Normalised Difference Vegetation Index (NDVI) or the Simple Ratio (SR) as the independent variable. All three LAI models had equally high coefficients of determination (R²: 0.87) and small root mean squared errors (RMSE: 0.27–0.28). ΔLAI was calculated as the difference between pre- and post-fire LAI, predicted using imagery from January 2004 and February 2005, respectively. The area affected by the January 2005 fire and the burn severity patterns within that area were mapped using ΔLAI and ΔNBR. Landscape patterns of burn severity obtained from differencing pre- and post-fire LAI were similar to those mapped by ΔNBR. We conclude that fire-affected areas and burn severity patterns in the northern jarrah forest can be objectively mapped using remotely-sensed changes in LAI, while offering the important advantage over NBR of being readily interpretable in the wider context of ecological forest management.