A new forest cover map of continental southeast Asia derived from SPOT‐VEGETATION satellite imagery

Question: Can recent satellite imagery of coarse spatial resolution support forest cover assessment and mapping at the regional level? Location: Continental southeast Asia. Methods: Forest cover mapping was based on digital classification of SPOT4‐VEGETATION satellite images of 1 km spatial resolution from the dry seasons 1998/1999 and 1999/2000. Following a geographical stratification, the spectral clusters were visually assigned to land cover classes. The forest classes were validated by an independent set of maps, derived from interpretation of satellite imagery of high spatial resolution (Landsat TM, 30 m). Forest area estimates from the regional forest cover map were compared to the forest figures of the FAO database. Results: The regional forest cover map displays 12 forest and land cover classes. The mapping of the region's deciduous and fragmented forest cover remained challenging. A high correlation was found between forest area estimates obtained from this map and from the Landsat TM derived maps. The regional and sub‐regional forest area estimates were close to those reported by FAO. Conclusion: SPOT4‐VEGETATION satellite imagery can be used for mapping consistently and uniformly the extent and distribution of the broad forest cover types at the regional scale. The new map can be considered as an update and improvement on existing regional forest cover maps.     

A new land-cover map of Africa for the year 2000

Aim In the framework of the Global Land Cover 2000 (GLC 2000), a land‐cover map of Africa has been produced at a spatial resolution of 1 km using data from four sensors on‐board four different Earth observing satellites. Location The map documents the location and distribution of major vegetation types and non‐vegetated land surface formations for the entire African continent plus Madagascar and the other surrounding islands. Methods The bulk of these data were acquired on a daily basis throughout the year 2000 by the VEGETATION sensor on‐board the SPOT‐4 satellite. The map of vegetation cover has been produced based upon the spectral response and the temporal profile of the vegetation cover. Digital image processing and geographical information systems techniques were employed, together with local knowledge, high resolution imagery and expert consultation, to compile a cartographic map product. Radar data and thermal sensors were also used for specific land‐cover classes. Results A total of 27 land cover categories are documented, which has more thematic classes than previously published land cover maps of Africa contain. Systematic comparison with existing land cover data and 30‐m resolution imagery from Landsat are presented, and the map is also compared with other pan‐continental land cover maps. The map and digital data base are freely available for non‐commercial uses from http://www.gvm.jrc.it/tem/africa/products.htm Main conclusions The map improves our state of knowledge of the land‐cover of Africa and presents the most spatially detailed view yet published at this scale. This first version of the map should provide an important input for regional stratification and planning purposes for natural resources, biodiversity and climate studies.     

Landsat still contributing to environmental research

Landsat data have enabled continuous global monitoring of both human-caused and other land cover disturbances since 1972. Recently degraded performance and intermittent service of the Landsat 7 and Landsat 5 sensors, respectively, have raised concerns about the condition of global Earth observation programs. However, Landsat imagery is still useful for landscape change detection and this capability should continue into the foreseeable future.     

Continental estimates of forest cover and forest cover changes in the dry ecosystems of Africa between 1990 and 2000

Aim

This study provides regional estimates of forest cover in dry African ecoregions and the changes in forest cover that occurred there between 1990 and 2000, using a systematic sample of medium‐resolution satellite imagery which was processed consistently across the continent.

Location

The study area corresponds to the dry forests and woodlands of Africa between the humid forests and the semi‐arid regions. This area covers the Sudanian and Zambezian ecoregions.

Methods

A systematic sample of 1600 Landsat satellite imagery subsets, each 20 km × 20 km in size, were analysed for two reference years: 1990 and 2000. At each sample site and for both years, dense tree cover, open tree cover, other wooded land and other vegetation cover were identified from the analysis of satellite imagery, which comprised multidate segmentation and automatic classification steps followed by visual control by national forestry experts.

Results

Land cover and land‐cover changes were estimated at continental and ecoregion scales and compared with existing pan‐continental, regional and local studies. The overall accuracy of our land‐cover maps was estimated at 87%. Between 1990 and 2000, 3.3 million hectares (Mha) of dense tree cover, 5.8 Mha of open tree cover and 8.9 Mha of other wooded land were lost, with a further 3.9 Mha degraded from dense to open tree cover. These results are substantially lower than the 34 Mha of forest loss reported in the FAO's 2010 Global Forest Resources Assessment for the same period and area.

Main conclusions

Our method generates the first consistent and robust estimates of forest cover and change in dry Africa with known statistical precision at continental and ecoregion scales. These results reduce the uncertainty regarding vegetation cover and its dynamics in these previously poorly studied ecosystems and provide crucial information for both science and environmental policies.     

基于RS与GIS的黄土丘陵沟壑区土地覆盖动态研究

应用遥感与GIS相结合的方法,对陕北黄土高原地区的数字影像进行人-机交互的屏幕解译,得到1990年、2000年的土地利用／覆盖数字地图,以及面积统计结果,分析表明,近10年来该地区建筑和工交用地逐年扩大,耕地面积保持稳定,果园面积显著增加,人工林地和森林覆盖率有较大幅度提高,荒野地占地比重下降,区域生态环境质量得到明显改善。     

Remote sensing imagery for resource inventories in central Africa: The importance of detailed field data

The rate of rain forest clearing throughout central Africa is of national and international interest because it affects both the region's contribution to global warming and impacts the sustainable productive capacity of its natural resource base. The size and inaccessibility of much of central Africa makes remote sensing imagery the most suitable data source for regional land cover mapping and land transformation monitoring. Present image availability is poor. Most regional studies have had to rely on coarse resolution AVHRR 1 km data that fails to detect the small-scale agricultural clearings that are the primary cause of land cover change throughout the region. This study demonstrates that higher spatial resolution Landsat MSS imagery, which comprises the most available, geographically comprehensive and longest time series dataset, is too coarse to map land cover in low population density areas typical of most of central Africa. Furthermore, this study cautions that the use of high resolution imagery without detailed collateral field data on population density and land use practices while generating superficially plausible results, will most probably produce highly inaccurate estimates of land cover and land transformation. Policies for future regional remote sensing surveys of central Africa should focus on acquisition of higher spatial, spectral, and radiometric resolution imagery and must be accompanied by detailed, systematic field data collection.     

Remote sensing of upland vegetation: the potential of high spatial resolution satellite sensors

Aim Traditional methodologies of mapping vegetation, as carried out by ecologists, consist primarily of field surveying or mapping from aerial photography. Previous applications of satellite imagery for this task (e.g. Landsat TM and SPOT HRV) have been unsuccessful, as such imagery proved to have insufficient spatial resolution for mapping vegetation. This paper reports on a study to assess the capabilities of the recently launched remote sensing satellite sensor Ikonos, with improved capabilities, for mapping and monitoring upland vegetation using traditional image classification methods. Location The location is Northumberland National Park, UK. Methods Traditional remote sensing classification methodologies were applied to the Ikonos data and the outputs compared to ground data sets. This enabled an assessment of the value of the improved spatial resolution of satellite imagery for mapping upland vegetation. Post‐classification methods were applied to remove noise and misclassified pixels and to create maps that were more in keeping with the information requirements of the NNPA for current management processes. Results The approach adopted herein for quick and inexpensive land cover mapping was found to be capable of higher accuracy than achieved with previous approaches, highlighting the benefits of remote sensing for providing land cover maps. Main conclusions Ikonos imagery proved to be a useful tool for mapping upland vegetation across large areas and at fine spatial resolution, providing accuracies comparable to traditional mapping methods of ground surveys and aerial photography.     

Autumn olive (<Emphasis Type="Italic">Elaeagnus umbellata</Emphasis>) presence and proliferation on former surface coal mines in Eastern USA

Invasive plants threaten native plant communities. Surface coal mines in the Appalachian Mountains are among the most disturbed landscapes in North America, but information about land cover characteristics of Appalachian mined lands is lacking. The invasive shrub autumn olive (Elaeagnus umbellata) occurs on these sites and interferes with ecosystem recovery by outcompeting native trees, thus inhibiting re-establishment of the native woody-plant community. We analyzed Landsat 8 satellite imagery to describe autumn olive’s distribution on post-mined lands in southwestern Virginia within the Appalachian coalfield. Eight images from April 2013 through January 2015 served as input data. Calibration and validation data obtained from high-resolution aerial imagery were used to develop a land cover classification model that identified areas where autumn olive was a primary component of land cover. Results indicate that autumn olive cover was sufficiently dense to enable detection on approximately 12.6 % of post-mined lands within the study area. The classified map had user’s and producer’s accuracies of 85.3 and 78.6 %, respectively, for the autumn olive coverage class. Overall accuracy was assessed in reference to an independent validation dataset at 96.8 %. Autumn olive was detected more frequently on mines disturbed prior to 2003, the last year of known plantings, than on lands disturbed by more recent mining. These results indicate that autumn olive growing on reclaimed coal mines in Virginia and elsewhere in eastern USA can be mapped using Landsat 8 Operational Land Imager imagery; and that autumn olive occurrence is a significant landscape vegetation feature on former surface coal mines in the southwestern Virginia segment of the Appalachian coalfield.     

Assessing large area forest cover products derived from the same imaging source across Victoria,Australia

Forest cover products are an essential tool for land managers and policy makers. They are used at a variety of spatial scales to inform decision‐making and policy across a range of ecosystem drivers and services. This article compares three forest cover products (FCP), all of which were created using Landsat satellite imagery, but using different methodologies and covering different spatial extents that range from global to state. It also explores their use and utility across the state of Victoria, Australia. It asks the question, how interchangeable are the forest cover maps? FCP are also validated against a very high‐resolution reference data set. Overall accuracy was around 89% for the state and national FCP, and 84% for the global FCP. The global map produced the lowest estimate of total forest cover, while estimates obtained by the national and state FCP were similar across the study area. Spatially, differences, however, were apparent. The national forest cover map obtained higher estimates across most of Victoria except in the most arid region which is dominated by low open woodland. While the national and global scale forest cover maps were found to have good diagnostic ability for large area assessment and reporting, their use for land management is not optimal and can lead to gross error.     

Digital interpretation and management of land cover - a case study of Cyprus

Knowledge based methods using Geographic Information System and Remote Sensing for land cover classification and future land allocation in Cyprus are presented. Land cover was categorized combining statistical classification and expert rules, using multi-temporal Landsat TM and MSS data, and digitized maps of elevation and geology. Land needed for future infrastructure was allocated using multi-criteria evaluation. Reforestation area were identified for Pinus brutia and P. nigra comparing expert rules and multi-criteria evaluation.     

Mapping and monitoring of coastal wetlands of Çukurova Delta in the Eastern Mediterranean region

The aim of this research was to link vegetation characteristics, such as spatial and temporal distribution, and environmental variables, with land cover information derived from remotely sensed satellite images of the Eastern Mediterranean coastal wetlands of Turkey. The research method was based on (i) recording land cover characteristics by means of a vegetation indicator, and (ii) classifying and mapping coastal wetlands utilizing a Landsat Thematic Mapper (TM) image of Çukurova Deltas in Turkey. Vegetation characteristics of various habitats, such as sand dunes, salt marshes, salty plains and afforestation areas, were identified by field surveys. A Landsat TM image of 4 July 1993 was pre-processed and then classified using the Maximum Likelihood (ML) algorithm and Artificial Neural Networks (ANN). As a result of this supervised classification, the land cover types were classified with a largest accuracy of 90.2% by ANN. The classified satellite sensor imagery was linked to vegetation and bird census data, which were available through literature in a Geographical Information System (GIS) environment to determine the spatial distribution of plant and bird biodiversity in this coastal wetland. The resulting data provide an important baseline for further investigations such as monitoring, change detections and designing conservation policies in this coastal ecosystem.     

A geospatial approach to monitoring impervious surfaces in watersheds using Landsat data (the Mondego Basin,Portugal as a case study)

The urbanization of watersheds is a highly dynamic global phenomenon that must be monitored. With consequences for the environment, the population, and the economy, accurate products at adequate spatial and temporal resolutions are required and demanded by the science community and stakeholders alike. To address these needs, a new Impervious Surface Area (ISA) product was created for a Portuguese Watershed (Mondego river) from Landsat data (a combination of leaf-on multispectral bands, derived products, and NDVI time series), using Regression Tree Models (RTM). The product provides 30-m spatial resolution ISA estimates (0–100%) with a Mean Average Error (MAE) of 1.6% and Root Mean Square Error (RMSE) of 5.5%.A strategy to update the baseline product was tested in earlier imagery (2001 and 2007) for a subset of the watershed. Instead of updating the baseline product, the strategy seeks to identify stable training samples and remove those where change was detected in a time series of Change Vector Analysis (CVA). The stable samples were then used to create new ISA models using RTM. The updated maps were similar to the original product in terms of accuracy metrics (MAE: 2001: 2.6%; 2007:3.6%).The products and methodology offer a new perspective on the urban development of the watershed, at a scale previously unavailable. It can also be replicated elsewhere at a low cost, leveraging the growing Landsat data archive, and provide timely information on relevant land cover metrics to the scientific community and stakeholders.     

Classifying the Neotropical savannas of Belize using remote sensing and ground survey

Aim This paper evaluates a method of combining data from GPS ground survey with classifications of medium spatial resolution LANDSAT imagery to distinguish variations within Neotropical savannas and to characterize the boundaries between savanna areas and the associated gallery forests, seasonally dry forests and wetland communities. Location Rio Bravo Conservation Area, Orange Walk District, Belize, Central America. Methods Dry season LANDSAT data for 10 April 1993 and 9 March 2001 covering a conservation area of 240,000 acres (97,459 ha), were rectified to sub‐pixel accuracy using ground control points positioned by GPS ground survey. The 1993 image was used to assess the accuracy with which the boundaries between the savanna matrix and gallery forests, high forests, wetlands and water bodies could be discriminated. The image was classified by a maximum likelihood (ML) classifier and the shapes and areas of forest and wetland classes were compared with an interpretation of these land cover types from 1 : 24,000 aerial photography, mapped at 1 : 50,000 scale in 1993. The 2001 image was used to assess whether different subtypes of savanna could be distinguished from LANDSAT data. This required the creation of a reference (‘ground truth’) data set for testing classifications of the image. One hundred and sixty sample patches (650 ha, distributed over an area of 7000 ha) of ten sub‐types of savanna vegetation and associates identified using a physiognomic classification scheme, were delineated on the ground by GPS and divided into two subsets for training and testing. Continuous classifications of LANDSAT data covering the savannas were developed that estimated potential contributions from up to five sub‐types of land cover (grassland, wetland, pine woodland, gallery forest and palmetto). The accuracy of each classification was assessed by comparison against ground data. An ML classification was also produced for the 2001 image using the same areas for training. This allowed a comparison of the relative accuracy of both continuous and Boolean ML methods for classifying savanna areas. Results The boundary between savannas and evergreen forests, gallery forests and open water in the study region could be delineated by the ML classifier to within 2 pixels (60 m) using LANDSAT imagery. However, the constituent sub‐types within the savanna were poorly discriminated. Whilst the shape and extent of closed canopy forest, gallery forest, wetlands and water bodies agreed closely with the distributions interpreted from aerial photography, classes such as ‘open pine savanna’ or ‘grassland’ were only 45–65% accurate when tested against ground data. A continuous classification, estimating the proportions of three savanna vegetation subtypes (grassland, marshland and woodland) present in each pixel, correctly classified more of the ground data for these cover types than the comparable ML result. Proportional mixtures of the land cover estimated by the continuous classifier also compared realistically with the vegetation formations observed along ground transects. Main conclusions By using GPS, a ground survey of vegetation cover was accurately matched to remotely sensed imagery and the accuracy of delineating boundaries and classifying areas of savanna was assessed directly. This showed that ML classification techniques can reliably delineate the boundaries of savannas, but continuous classifiers more accurately and realistically represent the distribution of the subtypes comprising savanna land cover. By combining these ground survey and image classification methods, medium spatial resolution satellite sensor data can provide an affordable means for land managers to assess the nature, extent and distribution of savanna formations. Over time, using the archives of LANDSAT (and SPOT) data together with marker sites surveyed in the field, quantitative changes in the extents and boundaries of savannas in response to both natural (e.g. fire, hurricane and drought) and anthropogenic (e.g. cutting and disturbance) factors can be assessed.     

基于知识库的像斑光谱向量相似度土地覆盖变化检测方法

土地利用/覆盖变化检测是国内外全球化进程研究的重要内容,选择适当的变化检测方法对西北地区土地利用/覆盖变化进行研究在"生态十年项目"中具有重要的意义。选择西北地区具有典型代表性的TM轨道号134033区域作为变化检测方法验证的试验区,采用2005和2010年两期Landsat TM影像,在e Cognition Developer 8.64软件支持下,采用基于像斑的光谱特征特征向量相似度方法进行变化检测,并利用2010年土地覆盖数据作为先验知识库对变化区域分类,提取土地利用/覆盖变化信息,并对变化结果进行定量分析。结果表明,采用基于像斑的光谱特征特征向量相似度方法对于试验区的土地利用/覆盖变化制图具有检测快速、检测精度高等优点,适合试验区以及整个西北地区的土地利用/覆盖变化的检测。最终采用该方法以及分类后比较法获得了西北地区2000—2010年近10年的土地利用/覆盖分类图。     

Monitoring trends in the extent of major floods in the lower reach of Songkhram River Basin, Northeastern Thailand

This study analyzes trends in the extent of major floods in the lower reach of Songkhram River Basin, one of the most important areas for aquatic biological production in the Lower Mekong River Basin. We first classified the time series Landsat imagery acquired at a 2-year interval between 2000 and 2006 using the unsupervised classification method. We then analyzed the extent of major floods through the image matrix analysis on seasonal land cover map pairs. Additionally, we estimated flood volumes and discharge rates for each time series. We finally analyzed the extent of land uses that were affected by major flood events. Accuracy assessment showed that the extent of major floods was accurately mapped. Analysis of trends of major floods revealed that there was a considerable variation in the extent through 2006, with the flood decreasing since 2002. Analysis of flood risk areas based on the 2000–2004 data showed that about 3.04% of the study areas was at high risk of being flooded. Between 2000 and 2006, about 5.5% of the study area that are classified as agriculture and built-up land uses was affected by major floods. Our estimates on flood volumes and discharge rates are consistent with the data from other studies. Overall findings suggest that accurate mapping of major floods and flood risk areas using space and time dependent data can be important for developing protocols for flash flood early warning and flood risk management and mitigation. Output GIS maps and data combined with crude assumptions about water flow will serve as the baseline data to estimate the amount of water that flows in and out of the basin. Further research should focus on integration of social science research to evaluate the socio-economic impacts of major floods and identifying coping strategies of affected communities.     

Identifying and quantifying uncertainty and spatial disagreement in the comparison of Global Land Cover for different applications

This paper provides a methodology for comparing global land cover maps that allows for differences in legend definitions between products to be taken into account. The legends of the two maps are first reconciled by creating a legend lookup table that shows how the legends map onto one another. Where there is overlap, the specific definitions for each legend class are used to calculate the degree of overlap between legend classes. In this way, one‐to‐many mappings are accounted for unlike in most methods where the legend definitions are often forced into place. Another advantage over previous map comparison methods is that application‐specific requirements are captured using expert input, whereby the user rates the importance of disagreement between different legend classes based on the needs of the application. This user‐defined matrix in conjunction with the degree of overlap between legend classes is applied on a pixel‐by‐pixel basis to create maps of spatial disagreement and uncertainty. The user can then highlight the areas of highest thematic uncertainty and disagreement between the different land cover maps allowing for areas that require further detailed examination to be readily identified. It would also be possible for several users to input their knowledge into the process, leading to a potentially more robust comparison of land cover products. The methodology of map comparison is illustrated using different land cover products including Global Land Cover 2000 (GLC‐2000) and the MODIS land cover data set. Two diverse applications are provided including the estimation of global forest cover and monitoring of agricultural land. In the case of global forest cover, an example was provided for Columbia, which showed that the MODIS land cover map overestimates forest cover in comparison with the GLC‐2000. The agricultural example, on the other hand, served to illustrate that for Sudan, MODIS tends to underestimate crop areas while GLC‐2000 overestimates them.     

Digital analysis of multispectral airborne imagery of coral reefs

 The digital airborne sensor, CASI (Compact Airborne Spectrographic Imager) has considerable potential for mapping marine habitats. Here we present an account of one of the first coral reef applications. The CASI was flown over reefs of the Turks and Caicos Islands (British West Indies) and set to view 1 m pixels in 8 spectral bands. In addition, reef habitats were sampled in situ by visual assessment of percent cover in 1 m quadrats. Seagrass standing crop was assessed using a calibrated visual scale. Benthic habitats were classified using hierarchical cluster and similarity percentage analyses of the field survey data. Two levels of habitat discrimination were assessed: a coarse level (corals, algae, sand, seagrass) and a fine level which included nine reef habitats. Overall accuracies of CASI-derived habitat maps were 89% and 81% for coarse and fine levels of habitat discrimination, respectively. Accuracies were greatest once CASI data had been processed to compensate for variations in depth and edited to take account of generic patterns of reef distribution. These overall accuracies were significantly (P<0.001) better than those obtained from satellite imagery of the same site (Landsat MSS, Landsat TM, SPOT XS, SPOT Pan, merged Landsat TM/SPOT Pan). Results from CASI were also significantly better than those from interpretation of 1:10 000 colour aerial photographs of reefs in Anguilla (Sheppard et al. 1995). However, the studies may not have been entirely comparable due to a disparity in the areas mapped. Accepted: 26 May 1997     

Numeric classification as an aid to spectral mapping of vegetation communities

This study demonstrates a vegetation mapping methodology that relates the reflectance information contained in multispectral imagery to traditionally accepted ecological classifications. Key elements of the approach used are (a) the use of cover rather than density or presence/absence to quantify the vegetation, (b) the inclusion of physical components as well as vegetation cover to describe and classify field sites, (c) development of an objective land cover classification from this quantitative data, (d) use of the field sample sites as training areas for the spectral classification, and (e) the use of a discriminant function to effectively tie the two classifications together. Land cover over 39000 ha of Australian chenopod shrubland was classified into nine groups using agglomerative hierarchical clustering, a discriminant function developed to relate cover and spectral classes, and the vegetation mapped using a maximum likelihood classification of multi-date Landsat TM imagery. The accuracy of the mapping was assessed with an independent set of field samples and by comparison with a map of land systems previously interpreted from aerial photography. Overall agreement between the digital classification and the land system map was good. The units that have been mapped are those derived from numeric vegetation classification, demonstrating that accepted ecological methods and sound image analysis can be successfully combined.     

Evidence of incipient forest transition in southern Mexico

Case studies of land use change have suggested that deforestation across Southern Mexico is accelerating. However, forest transition theory predicts that trajectories of change can be modified by economic factors, leading to spatial and temporal heterogeneity in rates of change that may take the form of the Environmental Kuznets Curve (EKC). This study aimed to assess the evidence regarding potential forest transition in Southern Mexico by classifying regional forest cover change using Landsat imagery from 1990 through to 2006. Patterns of forest cover change were found to be complex and non-linear. When rates of forest loss were averaged over 342 municipalities using mixed-effects modelling the results showed a significant (p<0.001) overall reduction of the mean rate of forest loss from 0.85% per year in the 1990-2000 period to 0.67% in the 2000-2006 period. The overall regional annual rate of deforestation has fallen from 0.33% to 0.28% from the 1990s to 2000s. A high proportion of the spatial variability in forest cover change cannot be explained statistically. However analysis using spline based general additive models detected underlying relationships between forest cover and income or population density of a form consistent with the EKC. The incipient forest transition has not, as yet, resulted in widespread reforestation. Forest recovery remains below 0.20% per year. Reforestation is mostly the result of passive processes associated with reductions in the intensity of land use. Deforestation continues to occur at high rates in some focal areas. A transition could be accelerated if there were a broader recognition among policy makers that the regional rate of forest loss has now begun to fall. The changing trajectory provides an opportunity to actively restore forest cover through stimulating afforestation and stimulating more sustainable land use practices. The results have clear implications for policy aimed at carbon sequestration through reducing deforestation and enhancing forest growth.