Kevin Tansey

A New, Global, Multi-annual (2000-2007) Burnt Area Product at 1 Km Resolution

This paper reports on the development and validation of a new, global, burnt area product. Burnt areas are reported at a resolution of 1 km for seven fire years (2000 to 2007). A modified version of a Global Burnt Area (GBA) 2000 algorithm is used to compute global burnt area. The total area burnt each year (2000-2007) is estimated to be between 3.5 million km 2 and 4.5 million km(2). The total amount of vegetation burnt by cover type according to the Global Land Cover (GLC) 2000 product is reported. Validation was undertaken using 72 Landsat TM scenes was undertaken. Correlation statistics between estimated burnt areas are reported for major vegetation types. The accuracy of this new global data set depends on vegetation type.

Ten years of global burned area products from spaceborne remote sensing—A review: Analysis of user needs and recommendations for future developments

Abstract Early global estimates of carbon emissions from biomass burning were based on empirical assumptions of fire return interval in different biomes in the 1980s. Since then, significant improvements of spaceborne remote sensing sensors have resulted in an increasing number of derived products characterizing the detection of active fire or the subsequent burned area (GFED, MODIS MCD45A1, L3JRC, Globcarbon, GBS, GLOBSCAR, GBA2000). When coupled with global land cover and vegetation models allowing for spatially explicit fuel biomass estimates, the use of these products helps to yield important information about the spatial and the temporal variability of emission estimates. The availability of multi-year products (>10 years) leads to a better understanding of uncertainties in addition to increasing accuracy. We surveyed a wide range of users of global fire data products whilst also undertaking a review of the latest scientific literature. Two user groups were identified, the first being global climate and vegetation modellers and the second being regional land managers. Based on this review, we present here the current needs covering the range of end-users. We identified the increasing use of BA products since the year 2000 with an increasing use of MODIS as a reference dataset. Scientific topics using these BA products have increased in diversity and area of application, from global fire emissions (for which BA products were initially developed) to regional studies with increasing use for ecosystem management planning. There is a significant need from the atmospheric science community for low spatial resolution (gridded, 1/2 degree cell) and long time series data characterized with supplementary information concerning the accuracy in timing of the fire and reductions of omission/commission errors. There is also a strong need for precisely characterizing the perimeter and contour of the fire scar for better assimilation with land cover maps and fire intensity. Computer and earth observation facilities remain a significant gap between ideal accuracies and the realistic ones, which must be fully quantified and comprehensive for an actual use in global fire emissions or regional land management studies.

Estimating tree and stand variables in a Corsican Pine woodland from terrestrial laser scanner data.

A terrestrial laser scanner was used to take four scans of an area of trees, approximately 480 m2 in area, within a coniferous tree stand situated in Leicestershire, UK. A number of measurements were extracted from the point cloud and compared with field measurements. Automatic stem recognition was achieved for all stems except those at the edge of the study plot. From the locations of detected stems, diameter at breast height (DBH) was measured with two least-squares shape-fitting algorithms and a circular Hough transformation method; the results were then compared with field measurements. The root mean squared error (RMSE) for DBH measurement from the laser scanner was found to be in the range 0.019–0.037 m, using three measures. Stem density (1031 stems ha−1) and basal area (73 m2 ha−1) were also measured with reasonable accuracy. Estimation of tree volume was not as successful, in contradiction to previous research, as upper diameters and heights of trees could not be measured. This was probably a result of previous research being focused on low-density forest stands. This study presents an assessment of laser scanning capabilities in a forest environment with high (1000 stems ha−1) stand density, and finds automation of the analysis to yield some important tree and stand variables to be very effective.

Application of airborne LiDAR to mapping seismogenic faults in forested mountainous terrain, southeastern Alps, Slovenia

Results are presented of the first airborne LiDAR survey ever flown in Europe for the purpose of mapping the surface expression of earthquake-prone faults. Detailed topographic images derived from LiDAR data of the Idrija and Ravne strike-slip faults in NW Slovenia reveal geomorphological and structural features that shed light on the overall architecture and kinematic history of both fault systems. The 1998 Mw = 5.6, and 2004 Mw = 5.2 Ravne Fault earthquakes and the historically devastating 1511 M = 6.8 Idrija earthquake indicate that both systems pose a serious seismic hazard in the region. Because both fault systems occur within forested terrain, a tree removal algorithm was applied to the data; the resulting images reveal surface scarps and tectonic landforms in unprecedented detail. Importantly, two sites were discovered to be potentially suitable for fault trenching and palaeo-seismological analysis. This study highlights the potential contribution of LiDAR surveying in both low-relief valley terrain and high-relief mountainous terrain to a regional seismic hazard assessment programme. Geoscientists working in other tectonically active regions of the world where earthquake-prone faults are obscured by forest cover would also benefit from LiDAR maps that have been processed to remove the canopy return and reveal the forest floor topography.

Integrating building footprints and LiDAR elevation data to classify roof structures and visualise buildings

Abstract Three-dimensional urban models are increasingly needed for applications as varied as urban planning and design, microclimate investigation and tourism. Light Detection And Ranging (LiDAR) data are considered to be highly suitable for the three-dimensional reconstruction of urban features such as buildings. Ongoing research is determining how best to integrate LiDAR elevation data with already available vector-based data. This paper reports research on combining building footprints and LiDAR to visualise an urban area (Portbury near Bristol, England) with an emphasis on representing buildings in a GIS environment. The main emphasis here is on retaining a vector model that is suitable for representing regular man-made structures. A major difference between this and earlier work is that before visualisation, this work classifies roof types of buildings as either flat or pitched. We compared LiDAR data at three point densities in terms of successful building type detection and visualisation: 1 (low), 16 (medium) and 40 (high) points per m 2 . There are important data acquisition cost issues at each of these resolutions. High density LiDAR yielded the highest overall accuracy of building type detection and proved useful for identifying roof features, yet lower densities proved more useful for revealing overall roof morphology.

Tropical peatland fires in Southeast Asia

Extensive tropical peatlands are located in the Malaysian and Indonesian lowlands, particularly in Borneo, Sumatra, West Papua, and Peninsular Malaysia. In an undisturbed condition, these peatlands make a significant contribution to terrestrial carbon storage, both in terms of their aboveground biomass (peat swamp forest) and thick deposits of peat. Occasional forest fires, including peatland fires, have occurred in Southeast Asia over several millennia but, in recent years, they have become a more regular feature. The most severe fires have been linked with the El Nino phase of ENSO which causes extended periods of drought, particularly across the peatland areas of southern Sumatra and southern Kalimantan. During the last 20 years, rapid land use change, exacerbated by climatic variability, has led to an increase in fire frequency, as the remaining peat swamp forests come under pressure from increased illegal logging, development for plantations and agriculture-based settlement, and, where economic development has failed, land abandonment. A case study of fire occurrence in Borneo illustrates that peat swamp forests are much more prone to fire than any other forest type, largely as a result of the high pressure being put on these last remaining forested lands. From studies in central Kalimantan (southern Borneo), we demonstrate the relationships between peat drainage, vegetation change, and increased fire frequency, including the role that peat combustion and subsidence play in an increased incidence of surface flooding. Tropical peatland fires, and the changes in vegetation that they bring about, have significant impacts on the atmosphere, the carbon cycle, and various ecosystem services; they also cause wide-ranging social and economic impacts. Fires on peatlands usually affect both the surface vegetation and the underlying peat layer and, as a result, they release much larger amounts of C02 into the atmosphere than forest fires on mineral soils. In 1997, peatland fires in Indonesia resulted in the release of between 0.81 Gt and 2.57Gt of carbon into the atmosphere, equivalent to 13% to 40% of mean annual global carbon emissions from fossil fuels, and over the last ten years a conservative estimate of total carbon emissions from peatland fires in Southeast Asia is of the order of 2Gt to 3Gt. Future climate changes may place further pressure on the tropical peatland ecosystem and are likely to lead to enhanced carbon emissions from both peat degradation and fire.

Effect of repeated fires on land-cover change on peatland in southern Central Kalimantan, Indonesia, from 1973 to 2005

Fire plays an increasingly important role in deforestation and degradation of carbon-dense tropical peatlands in South-east Asia. In this study, analysis of a time-series of satellite images for the period 1973–2005 showed that repeated, extensive fires, following drainage and selective logging, played an important role in land-cover dynamics and forest loss in the peatlands of Central Kalimantan, Indonesia. A study of peatlands in the former Mega Rice Project area revealed a rising trend in the rate of deforestation and identified fire as the principal factor influencing subsequent vegetation succession. A step change in fire regime was identified, with an increase in burned area and fire frequency following peatland drainage. During the 23-year pre-Mega Rice Project period (1973–1996), peat swamp forest was the most extensive land-cover class and fires were of relatively limited extent, with very few repeated fires. During the 9-year post-Mega Rice Project period (1997–2005), there was a 72% fire-related loss in area of peat swamp forest, with most converted to non-woody vegetation, dominated by ferns or mosaics of trees and non-woody vegetation, rather than cultivated land.

Relationship between MODIS fire hot spot count and burned area in a degraded tropical peat swamp forest in Central Kalimantan, Indonesia

A number of space-borne sensors observe radiant energy at thermal wavelengths.Thermal anomaly data, otherwise known as hotspot data, have been shown to beparticularly correlated with the occurrence of active fires (a fire normally with a flamingcomponent and/or smoldering component). Because of a lack of high-quality burnedarea data, recent studies have used hotspot data as a proxy for burned area whencalculating gas emissions or atmospheric pollutants as a result of biomass burning. Weargue that the relationship between hotspot data and burned area is spatially variable andstrongly dependent on the vegetation type and function. In this article, we explore therelationship between hotspot data and burned area for a region of degraded and partiallyaltered tropical peat swamp forest in southern Kalimantan, Indonesia. MODIS thermalanomaly (MOD14A1) data were used, alongside disaster monitoring constellation (DMC)and Landsat TM data that were used to derive the burnt area, to calculate a figureindicating the average burned area per hotspot (A

Accuracy assessment of a large-scale forest cover map of central Siberia from synthetic aperture radar

Russias boreal forests host 11% of the worlds live forest biomass. They play a critical role in Russias economy and in stabilizing the global climate. The boreal forests of central and western Siberia represent the largest unbroken tracts of forest in the world. The European Commission funded SIBERIA project aimed at producing a forest map covering an area of 1.2 million square kilometres. Three synthetic aperture radars (SAR) on board the European remote sensing satellites ERS-1 and ERS-2 and the Japanese Earth resources satellite JERS-1 were used to collect remote sensing data. Radar is the only sensor capable of penetrating cloud cover and imaging at night. An adaptive, model-based, contextual classification to derive ranked total growing stock volume classes suitable for large-scale mapping is described. The accuracy assessment of the Siberian forest cover map is presented. The weighted coefficient of agreement κw is calculated to quantify the agreement between the classified map and the reference data. First, the classified map is compared with Russian forest inventory data (κw = 0.72). The inherent uncertainty in the forest inventory data is simulated by allowing for fuzziness. The effect of uncertainty on the unweighted coefficient of agreement κ is stronger than that on the weighted coefficient of agreement κw. Second, the map is compared with a more reliable, independent posterior ground survey by Russian forestry experts (κw = 0.94). The follow-on project SIBERIA-II started in January 2002 and is striving to develop multisensor concepts for greenhouse gas accounting (www.siberia2.uni-jena.de).

An algorithm for mapping burnt areas in Australia using SPOT-VEGETATION data

An algorithm has been developed to map burnt areas over the Australian continent using SPOT-VEGETATION (VGT) S1 satellite images. The algorithm is composed of a set of thresholds applied to each pixels value of the VGT spectral channels, two spectral indices and their temporal difference. The threshold values have been derived by means of a supervised classification methodology based on the classification and regression trees algorithm. A procedure has also been developed specifically for preprocessing the daily S1 images for burnt area mapping purposes. The final product is composed of ten-day and monthly burnt area maps over Australia for the full year 2000.