Using annual time-series of Landsat images to assess the effects of forest restitution in post-socialist Romania

The increasing availability of the Landsat image archive and the development of approaches to make full use of these data provide novel insights into the drivers and dynamics of land use systems change. Focusing on Romania, we asked how the drastic institutional and socio-economic transformation after the collapse of socialism in Eastern Europe affected forestry. We used an annual time series of Landsat images to investigate how three phases of forest restitution affected forest disturbances (due to both, natural events and forest management). We employed the LandTrendr (Landsat-based detection of trends in disturbance and recovery) set of change detection algorithms to perform temporal segmentation and fitting of the Landsat time series, and derived annual disturbance maps (95.72% overall accuracy) along with recovery dynamics. Our change map suggested that forest disturbances increased substantially since the collapse of socialism in 1989, with 75,000 ha of disturbed forest land (4.5% of the total studied forest area). Whereas the late socialist years were characterized by relatively low disturbance levels (12% of all detected disturbances), disturbances increased especially after each of the restitution laws were passed in 1991, 2000, and 2005 (34%, 21% and 32% respectively). Non-state ownership regimes (i.e. private owners vs. public property of local communities) and species composition of restituted forests were two important factors determining disturbance levels. The widespread disturbances we found also raise concerns about timber overexploitation in many areas of the Romanian Carpathians. Our study demonstrates the value of the temporal depth of the Landsat archive and highlights that trajectory-based change detection approaches can be highly beneficial for gaining insights on the effect of institutional shocks on land use patterns.     

Assessment of tropical forest degradation by selective logging and fire using Landsat imagery

Many studies have assessed the process of forest degradation in the Brazilian Amazon using remote sensing approaches to estimate the extent and impact by selective logging and forest fires on tropical rain forest. However, only a few have estimated the combined impacts of those anthropogenic activities. We conducted a detailed analysis of selective logging and forest fire impacts on natural forests in the southern Brazilian Amazon state of Mato Grosso, one of the key logging centers in the country. To achieve this goal a 13-year series of annual Landsat images (1992-2004) was used to test different remote sensing techniques for measuring the extent of selective logging and forest fires, and to estimate their impact and interaction with other land use types occurring in the study region. Forest canopy regeneration following these disturbances was also assessed. Field measurements and visual observations were conducted to validate remote sensing techniques. Our results indicated that the Modified Soil Adjusted Vegetation Index aerosol free (MSAVI_af) is a reliable estimator of fractional coverage under both clear sky and under smoky conditions in this study region. During the period of analysis, selective logging was responsible for disturbing the largest proportion (31%) of natural forest in the study area, immediately followed by deforestation (29%). Altogether, forest disturbances by selective logging and forest fires affected approximately 40% of the study site area. Once disturbed by selective logging activities, forests became more susceptible to fire in the study site. However, our results showed that fires may also occur in undisturbed forests. This indicates that there are further factors that may increase forest fire susceptibility in the study area. Those factors need to be better understood. Although selective logging affected the largest amount of natural forest in the study period, 35% and 28% of the observed losses of forest canopy cover were due to forest fire and selective logging combined and to forest fire only, respectively. Moreover, forest areas degraded by selective logging and forest fire is an addition to outright deforestation estimates and has yet to be accounted for by land use and land cover change assessments in tropical regions. Assuming that this observed trend of land use and land cover conversion continues, we predict that there will be no undisturbed forests remaining by 2011 in this study site. Finally, we estimated that 70% of the total forest area disturbed by logging and fire had sufficiently recovered to become undetectable using satellite data in 2004.     

Cross-border comparison of land cover and landscape pattern in Eastern Europe using a hybrid classification technique

Eastern Europe has experienced drastic changes in political and economic conditions following the breakdown of the Soviet Union. Furthermore, these changes often differ among neighboring countries. This offers unique possibilities to assess the relative importance of broad-scale political and socioeconomic factors on land cover and landscape pattern. Our question was how much land cover differed in the Polish, the Slovak, and the Ukrainian Carpathian Mountains and to what extent these differences can be related to dissimilarities in societal, economic, and political conditions. We used a hybrid classification technique, combining advantages from supervised and unsupervised methods, to derive a land cover map from three Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images from 2000. Results showed marked differences in land cover between the three countries. Forest cover and composition was different for the three countries, for example Slovakia and Poland had about 20% more forest cover at higher elevations than Ukraine. Broadleaved forest dominated in Slovakia while high percentages of conifers were found in Poland and Ukraine. Agriculture was most abundant in Slovakia where the lowest level of agricultural fragmentation was found (22% core area compared to less than 5% in Poland and Ukraine). Post-socialist land change was greatest in Ukraine, were we found high agricultural fragmentation and widespread early-successional shrublands indicating extensive land abandonment. Concerning forests, differences can largely be explained by socialist forest management. The abundance and pattern of arable land and grassland can be explained by two factors: land tenure in socialist times and economic transition since 1990. These results suggest that broad-scale socioeconomic and political factors are of major significance for land cover patterns in Eastern Europe, and possibly elsewhere.     

Mapping and monitoring conifer mortality using remote sensing in the Lake Tahoe Basin

A prolonged drought in the western United States has resulted in alarming levels of mortality in conifer forests. Satellite remote sensing holds the potential for mapping and monitoring the effects of such environmental changes over large geographic areas in a timely manner. Results from the application of a forest canopy reflectance model using multitemporal Landsat TM imagery and field measurements, indicate conifer mortality can be effectively mapped and inventoried. The test area for this project is the Lake Tahoe Basin Management Unit in the Sierra Nevada of California. The Landsat TM images are from the summers of 1988 and 1991. The Li-Strahler canopy model estimates several forest stand parameters, including tree size and canopy cover for each conifer stand, from reflectance values in satellite imagery. The difference in cover estimates between the dates forms the basis for stratifying stands into mortality classes, which are used as both themes in a map and the basis of the field sampling design. Field measurements from 61 stands collected in the summer of 1992 indicate 15 % of the original timber volume in the true fir zone died between 1988 and 1992. The resulting low standard error of 11 % for this estimate indicates the utility of these mortality classes for detecting areas of high mortality. Also, the patterns in the estimated mean timber volume loss for each class follow the expected trends. The results of this project are immediately useful for fire hazard management, by providing both estimates of the degree of overall mortality and maps showing its location. They also indicate current remote sensing technology may be useful for monitoring the changes in vegetation that are expected to result from climate change.     

Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia

Boreal forests are a critical component of the global carbon cycle, and timely monitoring allows for assessing forest cover change and its impacts on carbon dynamics. Earth observation data sets are an important source of information that allow for systematic monitoring of the entire biome. Landsat imagery, provided free of charge by the USGS Center for Earth Resources Observation and Science (EROS) enable consistent and timely forest cover updates. However, irregular image acquisition within parts of the boreal biome coupled with an absence of atmospherically corrected data hamper regional-scale monitoring efforts using Landsat imagery. A method of boreal forest cover and change mapping using Landsat imagery has been developed and tested within European Russia between circa year 2000 and 2005. The approach employs a multi-year compositing methodology adapted for incomplete annual data availability, within-region variation in growing season length and frequent cloud cover. Relative radiometric normalization and cloud/shadow data screening algorithms were employed to create seamless image composites with remaining cloud/shadow contamination of less than 0.5% of the total composite area. Supervised classification tree algorithms were applied to the time-sequential image composites to characterize forest cover and gross forest loss over the study period. Forest cover results when compared to independently-derived samples of Landsat data have high agreement (overall accuracy of 89%, Kappa of 0.78), and conform with official forest cover statistics of the Russian government. Gross forest cover loss regional-scale mapping results are comparable with individual Landsat image pair change detection (overall accuracy of 98%, Kappa of 0.71). The gross forest cover loss within European Russia 2000-2005 is estimated to be 2210 thousand hectares, and constitutes a 1.5% reduction of year 2000 forest cover. At the regional scale, the highest proportional forest cover loss is estimated for the most populated regions (Leningradskaya and Moskovskaya Oblast). Our results highlight the forest cover depletion around large industrial cities as the hotspot of forest cover change in European Russia.     

Monitoring forest changes in the southwestern United States using multitemporal Landsat data

Landsat time series data sets were acquired for the Santa Fe National Forest in New Mexico. This area includes the San Pedro Parks Wilderness area, which was designated as an official wilderness in 1964. Eight autumnal Landsat Thematic Mapper (TM) scenes acquired from 1988 to 2006 were analyzed to determine whether significant changes have occurred throughout the region during the past 18 years and, if so, to assess whether the changes are long-term and gradual or short-term and abrupt. It was found that, starting in about 1995, many of the conifer stands within the Wilderness area showed consistently gradual and marked increases in the Shortwave Infrared/Near Infrared Index. These trends generally imply decreases in canopy greenness or increases in mortality. Other high-elevation conifer forests located outside of the Wilderness area showed similar spectral trends, indicating that changes are potentially widespread. The spatial patterns of forest damage as inferred from the image analyses were very similar to the general patterns of insect defoliation damage mapped via aerial sketch mapping by the United States Department of Agriculture Forest Service Forest Health Monitoring Program. A field visit indicated that zones of spectral change are associated with high levels of forest damage and mortality, likely caused by a combination of insects and drought. The study demonstrates the effectiveness of using historical Landsat data for providing objective and consistent long-term assessments of the gradual ecosystem changes that are occurring within the western United States.     

Comparison and assessment of coarse resolution land cover maps for Northern Eurasia

Information on land cover at global and continental scales is critical for addressing a range of ecological, socioeconomic and policy questions. Global land cover maps have evolved rapidly in the last decade, but efforts to evaluate map uncertainties have been limited, especially in remote areas like Northern Eurasia. Northern Eurasia comprises a particularly diverse region covering a wide range of climate zones and ecosystems: from arctic deserts, tundra, boreal forest, and wetlands, to semi-arid steppes and the deserts of Central Asia. In this study, we assessed four of the most recent global land cover datasets: GLC-2000, GLOBCOVER, and the MODIS Collection 4 and Collection 5 Land Cover Product using cross-comparison analyses and Landsat-based reference maps distributed throughout the region. A consistent comparison of these maps was challenging because of disparities in class definitions, thematic detail, and spatial resolution. We found that the choice of sampling unit significantly influenced accuracy estimates, which indicates that comparisons of reported global map accuracies might be misleading. To minimize classification ambiguities, we devised a generalized legend based on dominant life form types (LFT) (tree, shrub, and herbaceous vegetation, barren land and water). LFT served as a necessary common denominator in the analyzed map legends, but significantly decreased the thematic detail. We found significant differences in the spatial representation of LFT's between global maps with high spatial agreement (above 0.8) concentrated in the forest belt of Northern Eurasia and low agreement (below 0.5) concentrated in the northern taiga-tundra zone, and the southern dry lands. Total pixel-level agreement between global maps and six test sites was moderate to fair (overall agreement: 0.67-0.74, Kappa: 0.41-0.52) and increased by 0.09-0.45 when only homogenous land cover types were analyzed. Low map accuracies at our tundra test site confirmed regional disagreements and difficulties of current global maps in accurately mapping shrub and herbaceous vegetation types at the biome borders of Northern Eurasia. In comparison, tree dominated vegetation classes in the forest belt of the region were accurately mapped, but were slightly overestimated (10%-20%), in all maps. Low agreement of global maps in the northern and southern vegetation transition zones of Northern Eurasia is likely to have important implications for global change research, as those areas are vulnerable to both climate and socio-economic changes.     

Use of a dark object concept and support vector machines to automate forest cover change analysis

An automated method was developed for mapping forest cover change using satellite remote sensing data sets. This multi-temporal classification method consists of a training data automation (TDA) procedure and uses the advanced support vector machines (SVM) algorithm. The TDA procedure automatically generates training data using input satellite images and existing land cover products. The derived high quality training data allow the SVM to produce reliable forest cover change products. This approach was tested in 19 study areas selected from major forest biomes across the globe. In each area a forest cover change map was produced using a pair of Landsat images acquired around 1990 and 2000. High resolution IKONOS images and independently developed reference data sets were available for evaluating the derived change products in 7 of those areas. The overall accuracy values were over 90% for 5 areas, and were 89.4% and 89.6% for the remaining two areas. The user's and producer's accuracies of the forest loss class were over 80% for all 7 study areas, demonstrating that this method is especially effective for mapping major disturbances with low commission errors. IKONOS images were also available in the remaining 12 study areas but they were either located in non-forest areas or in forest areas that did not experience forest cover change between 1990 and 2000. For those areas the IKONOS images were used to assist visual interpretation of the Landsat images in assessing the derived change products. This visual assessment revealed that for most of those areas the derived change products likely were as reliable as those in the 7 areas where accuracy assessment was conducted. The results also suggest that images acquired during leaf-off seasons should not be used in forest cover change analysis in areas where deciduous forests exist. Being highly automatic and with demonstrated capability to produce reliable change products, the TDA-SVM method should be especially useful for quantifying forest cover change over large areas.     

气象台站环境的卫星遥感调查与评估-以安徽代表气象站为例

气象观测环境的保护与代表性在天气预报和气候变化研究中具有重要意义。利用多时相的高分辨率陆地卫星资料,结合GIS技术,提取了气象台站3 km缓冲区内的土地利用类型,并将其分为四大类:耕地、植被、水体和城建用地。选取了安徽省6个气象站作为代表,分析了其周边土地类型的时空变化特征,并应用于气象站点环境代表性的评价研究。结果表明,基于卫星遥感的气象站点缓冲区内土地利用类型时空演变能很好地应用于气象站的环境代表性调查与评估,且效果良好。遥感结果显示安徽省国家气候基准站的环境代表性较好,而受到城市化影响严重的合肥国家基本站环境代表性较差。此外,上述卫星遥感的气象站环境分析方法也可以有效地应用于城市热岛研究中参考站点的选取与判别。     

Use of satellite remote sensing in Malaysian forestry and its potential

The use of satellite remote sensing in Malaysian forestry and its potential are discussed under three headings (1) Application of satellite remote sensing in Malaysian forestry; (2) Current efforts in remote sensing research application; (3) Potentials of remote sensing techniques in monitoring logging operations and forest change; and remote sensing as a tool in rehabilitation and reforestation. It is concluded that there is a high potential of satellite remote sensing application in Malaysia, especially with the Landsat and SPOT data supported with aerial photographs. This is due to its fast delivery of relevant, timely and accurate information needed for sustainable forestry and a sound management decision.     

Assessment of forest disturbances by selective logging and forest fires in the Brazilian Amazon using Landsat data

The rapid environmental changes occurring in the Brazilian Amazon due to widespread deforestation have attracted the attention of the scientific community for several decades. A topic of particular interest involves the assessment of the combined impacts of selective logging and forest fires. Forest disturbances by selective logging and forest fires may vary in scale, from local to global changes, mostly related to the increase of carbon dioxide released into the atmosphere. Selective logging activities and forest fires have been reported by several studies as important agents of land-use and land-cover changes. Previous studies have focused on selective logging, but forest fires on a large scale in tropical regions have yet to be properly addressed. This study involved a more comprehensive investigation of temporal and basin-wide changes of forest disturbances by selective logging and forest fires using remotely sensed data acquired in 1992, 1996, and 1999. Landsat imagery and remote-sensing techniques for detecting burned forests and estimating forest canopy cover were applied. We also conducted rigorous ground measurements and observations to validate remote-sensing techniques and to assess canopy-cover impacts by selective logging and forest fires in three different states in the Brazilian Amazon. The results of this study showed a substantial increase in total forested areas impacted by selective logging and forest fires from approximately 11,800 to 35,600 km² in 1992 and 1999, respectively. Selective logging was responsible for 60.4% of this forest disturbance in the studied period. Approximately 33% and 7% of forest disturbances detected in the same period were due to impacts of forest fires only and selective logging and forest fires combined, respectively. Most of the degraded forests (～90%) were detected in the states of Mato Grosso and Pará. Our estimates indicated that approximately 5467, 7618, and 17437 km² were new areas of selective logging and/or forest fires in 1992, 1996, and 1999, respectively. Protected areas seemed to be very effective in constraining these types of forest degradation. Approximately 2.4% and 1.3% of the total detected selectively logged and burned forests, respectively, were geographically located within protected areas. We observed, however, an increasing trend for these anthropogenic activities to occur within the limits of protected areas from 1992 to 1999. Although forest fires impacted the least area of tropical forests in the study region, new areas of burned forests detected in 1996 and 1999 were responsible for the greatest impact on canopy cover, with an estimated canopy loss of 18.8% when compared to undisturbed forests. Selective logging and forest fires combined impacted even more those forest canopies, with an estimated canopy loss of 27.5%. Selectively logged forest only showed the least impact on canopy cover, with an estimated canopy loss of 5%. Finally, we observed that forest canopy cover impacted by selective logging activities can recover faster (up to 3 years) from impact when compared to those forests disturbed by fires (up to 5 years) in the Amazon region.     

Performance of change detection using remotely sensed data and evidential fusion: comparison of three cases of application

The detection of changes affecting continental surfaces has important applications in hydrological, meteorological and climatic modelling. Using remote sensing data, numerous change indices have already been proposed. Previous work showed the interest of combining several of these to improve change detection performance, using the Dempster–Shafer evidence theory framework. This study analyses the performance of different change indices and their combination in different cases of application: forest logging either in pine forest or in mixed forest, and winter vegetation cover of fields in intensive farming areas, in comparison to the forest fire case presented in previous work. The interest of indices derived from Information Theory, some of which are original, is shown.     

Comparing annual MODIS and PRODES forest cover change data for advancing monitoring of Brazilian forest cover

Annual forest cover loss indicator maps for the humid tropics from 2000 to 2005 derived from time-series 500 m data from the MODerate Resolution Imaging Spectroradiometer (MODIS) were compared with annual deforestation data from the PRODES (Amazon Deforestation Monitoring Project) data set produced by the Brazilian National Institute for Space Research (INPE). The annual PRODES data were used to calibrate the MODIS annual change indicator data in estimating forest loss for Brazil. Results indicate that MODIS data may be useful in providing a first estimate of national forest cover change on an annual basis for Brazil. When directly compared with PRODES change at the MODIS grid scale for all years of the analysis, MODIS change indicator maps accounted for 75% of the PRODES change. This ratio was used to scale the MODIS change indicators to the PRODES area estimates. A sliding threshold of percent PRODES forest and 2000 to 2005 deforestation classes per MODIS grid cell was used to match the scaled MODIS to the official PRODES change estimates, and then to differentiate MODIS change within various sub-areas of the PRODES analysis. Results indicate significant change outside of the PRODES-defined intact forest class. Total scaled MODIS change area within the PRODES historical deforestation and forest area of study is 120% of the official PRODES estimate. Results emphasize the importance of synoptic monitoring of all forest change dynamics, including the cover dynamics of intact humid forest, regrowth, plantations, and cerrado tree cover assemblages. Results also indicate that operational MODIS-only forest cover loss algorithms may be useful in providing near-real time areal estimates of annual change within the Amazon Basin.     

基于MODIS数据的森林覆盖变化监测方法研究

自然灾害、森林采伐以及其它人为活动等都将导致森林覆盖的变化。遥感提供了大尺度范围监测森林覆盖变化的能力, 但要获得森林覆盖变化的信息, 则需要有效地从遥感影像中提取变化信息的技术方法。研究在借鉴美国宇航局(NASA ) 生产MODIS 土地覆盖变化(LCC) 产品所采用的算法基础上, 利用MODIS 数据, 分别采用红光- 近红外法、共生纹理矩阵法和基于相似度的变化检测方法对我国东北林区的森林覆盖变化监测方法进行了研究, 并用近年发生在黑龙江的重(特)大森林火灾为实例进行了验证。     

Mapping land cover change in a reindeer herding area of the Russian Arctic using Landsat TM and ETM+ imagery and indigenous knowledge

Much of Russia north of the treeline is grazed by reindeer, and this grazing has materially altered the vegetation cover in many places. Monitoring vegetation change in these remote but ecologically sensitive regions is an important task for which satellite remote sensing is well suited. Further difficulties are imposed by the highly dynamic nature of arctic phenology, and by the difficulty of obtaining accurate official data on land cover in arctic Russia even where such data exist. We have approached the problem in a novel fashion by combining a conventional multispectral analysis of satellite imagery with data on current and historical land use gathered by the techniques of social anthropology, using a study site in the Nenets Autonomous Okrug (NAO). A Landsat-7 ETM+ image from the year 2000 was used to generate a current land cover classification. A Landsat-5 TM image was used to generate a land-cover classification for 1988, taking due account of phenological differences and between the two dates. A cautious comparison of these two classifications, again taking account of possible effects of phenological differences, shows that much of the study area has already undergone a notable transformation to grass-dominated tundra, almost certainly as a result of heavy grazing by reindeer. The grazing pattern is quite heterogeneous, and may have reached unsustainable levels in some areas. Finally, we suggest that this situation is unlikely to be unique to our study area and may well be widespread throughout the Eurasian tundra zone, particularly in the west.     

Rapid Land Cover Classification Using Landsat Time Series based on the Google Earth Engine

Accurate maps of land cover at high spatial resolution are fundamental to many researchs on carbon cycle, climate change monitoring and soil degradation. Google Earth Engine is a cloud-based platform that makes it easy to access high-performance computing resources for processing very large geospatial datasets. It offer opportunities for generating land cover maps designed to meet the increasingly detailed information needs for science,monitoring, and reporting.In this study, we classified the land cover types in Shanxi using Landsat time series data based on the Google Earth Engine Platform. We selected 1 580 sample points be visual interpretation of the original fine spatial resolution images along with Google Earth historical images over six different cover types. We defined training data by randomly sampling 60% of the sample points. The remaining 40% was used for validation. We generated two diffirent types of Landsat composite: (1) one based on median values which is used as the input image for single-date classification; (2)one based on percentile values which is used as input images for time series classification. Random forest classification was performed with two different types of Landsat composites. Random forest classification was performed with two different types of Landsat composites.We visually compared the single-date based to the time series based cover maps of 1990, 2000, 2010 and 2017 in five local areas, and we future compared the results of time series to other products. We aslo performed an accuracy assessment on the land cover classification products. The results shown: (1) The results of time series classification had an overall accuracy of 84%~94%. The time series results improved overall accuracy by 5%~10% compared to single-date results; (2) The result of time series achieves the classification accuracy of products such as CNLUCC, GlobeLand30 and FROM-GLC.The following conclusions were drawn: (1) Cloud computing and archived Landsat data in the GEE has many advantages for land cover classification at a large geographic scale, such as s strong timeliness, short time cycle and low cost; (2) The statistics metrics from Landsat time series is a viable means for discrimination of land cover types, which is particularly useful for the time series classification.     

影像的土地覆被快速分类

精确的土地覆盖信息是进行碳循环、气候变化监测、土壤退化等相关科学研究的基础。随着云计算技术的不断成熟,一些高效算法与平台被不断提出,用来充分挖掘遥感数据所包含的海量信息。基于Google Earth Engine(GEE)云平台,利用随机森林监督分类法对1990、2000、2010、2017年的山西省土地覆被进行了分类。参考Google Earth高清影像选择的1580个样本点,对分类结果进行了验证;同时将分类结果与CNLUCC、GlobeLand30、FROM-GLC等现有土地覆被分类产品进行比较。验证和对比发现时间序列分类结果的总体精度达到86%~94%,比同期单时相分类总体精度提高了5%~10%;本文时间序列结果达到了CNLUCC、GlobeLand30、FROM-GLC等产品的分类精度。结果表明:①在快速准确土地覆被分类方面,时间序列影像与云平台结合,显示出时效性强、时间周期短、成本低等优势;②时间序列百分位数指标能有效地区分不同土地覆被类型的物候差别,在进行土地覆被分类方面显示出简单、易用、高效等特点。该方法对于深入研究大区域尺度的土地覆被变化过程具有重要的参考价值。     

Mapping canopy damage from understory fires in Amazon forests using annual time series of Landsat and MODIS data

Understory fires in Amazon forests alter forest structure, species composition, and the likelihood of future disturbance. The annual extent of fire-damaged forest in Amazonia remains uncertain due to difficulties in separating burning from other types of forest damage in satellite data. We developed a new approach, the Burn Damage and Recovery (BDR) algorithm, to identify fire-related canopy damages using spatial and spectral information from multi-year time series of satellite data. The BDR approach identifies understory fires in intact and logged Amazon forests based on the reduction and recovery of live canopy cover in the years following fire damages and the size and shape of individual understory burn scars. The BDR algorithm was applied to time series of Landsat (1997-2004) and MODIS (2000-2005) data covering one Landsat scene (path/row 226/068) in southern Amazonia and the results were compared to field observations, image-derived burn scars, and independent data on selective logging and deforestation. Landsat resolution was essential for detection of burn scars < 50 ha, yet these small burns contributed only 12% of all burned forest detected during 1997-2002. MODIS data were suitable for mapping medium (50-500 ha) and large (> 500 ha) burn scars that accounted for the majority of all fire-damaged forests in this study. Therefore, moderate resolution satellite data may be suitable to provide estimates of the extent of fire-damaged Amazon forest at a regional scale. In the study region, Landsat-based understory fire damages in 1999 (1508 km²) were an order of magnitude higher than during the 1997-1998 El Niño event (124 km² and 39 km², respectively), suggesting a different link between climate and understory fires than previously reported for other Amazon regions. The results in this study illustrate the potential to address critical questions concerning climate and fire risk in Amazon forests by applying the BDR algorithm over larger areas and longer image time series.     

AVHRR-based mapping of fires in Russia: New products for fire management and carbon cycle studies

A new database of fire activity in Russia derived from 1-km resolution remote sensing imagery is presented and discussed. The procedure used to generate this burned-area product is described, including active-fire detection and burn-scar mapping approaches. Fire detection makes use of a probabilistic procedure using image data from the United States National Oceanic and Atmospheric Administration's (NOAA) advanced very high resolution radiometer (AVHRR) system. Using the combination of AVHRR data collected at the Krasnoyarsk, Russia, high-resolution picture transmission (HRPT) receiving station, and data from the NOAA Satellite Active Archive (SAA), fire maps are being created for all of Russia for 1995 to 1997 and all of Eastern Russia (east of the Ural Mountains) for 1995 to 2002. This mapping effort has resulted in the most complete set of historic fire maps available for Russia. An initial validation indicates that the burned-area estimates are conservative because the approaches do not detect smaller fires, and, in many cases, fire areas are slightly underestimated. Analyses using the fire database showed that an average of 7.7×10⁶ ha yr⁻¹ of fire occurred in Eastern Russia between 1996 and 2002 and that fire was widely dispersed in different regions. The satellite-based burned-area estimates area were two to five times greater than those contained in official government burned-area statistics. The data show that there is significant interannual variability in area burned, ranging between a low of 1.5×10⁶ ha in 1997 to a high of 12.1×10⁶ ha in 2002. Seasonal patterns of fire are similar to patterns seen in the North American boreal region, with large-fire seasons experiencing more late-season burning (in August and September) than during low-fire years. There was a distinct zonal distribution of fires in Russia; 65% of the area burned occurred in the taiga zone, which includes southern, middle, and northern taiga subzones, 20% in the steppe and forest steppe zones, 12% in the mixed forest zone, and 3% in the tundra and forest-tundra zones. Lands classified as forest experienced 55% of all burned area, while crops and pastures, swamps and bogs, and grass and shrubs land cover categories experienced 13% to 15% each. Finally, the utility of the products is discussed in the context of fire management and carbon cycling.     

Land cover characterization and mapping of continental Southeast Asia using multi-resolution satellite sensor data

Land use/land cover change, particularly that of tropical deforestation and forest degradation, has been occurring at an unprecedented rate and scale in Southeast Asia. The rapid rate of economic development, demographics and poverty are believed to be the underlying forces responsible for the change. Accurate and up-to-date information to support the above statement is, however, not available. The available data, if any, are outdated and are not comparable for various technical reasons. Time series analysis of land cover change and the identification of the driving forces responsible for these changes are needed for the sustainable management of natural resources and also for projecting future land cover trajectories. We analysed the multi-temporal and multi-seasonal NOAA Advanced Very High Resolution Radiometer (AVHRR) satellite data of 1985/86 and 1992 to (1) prepare historical land cover maps and (2) to identify areas undergoing major land cover transformations (called ‘hot spots’). The identified ‘hot spot’ areas were investigated in detail using high-resolution satellite sensor data such as Landsat and SPOT supplemented by intensive field surveys. Shifting cultivation, intensification of agricultural activities and change of cropping patterns, and conversion of forest to agricultural land were found to be the principal reasons for land use/land cover change in the Oudomxay province of Lao PDR, the Mekong Delta of Vietnam and the Loei province of Thailand, respectively. Moreover, typical land use/land cover change patterns of the ‘hot spot’ areas were also examined. In addition, we developed an operational methodology for land use/land cover change analysis at the national level with the help of national remote sensing institutions.