Trends of tropical deforestation in Southeast Mexico

We assessed deforestation in Southeast Mexico (a surface area of 29 000 km² in seven states) through the comparison of land use/land cover maps at a scale of 1:250 000. This facilitated mapping of the land use/land cover change (LULCC) processes and calculation of the rates of change and the change matrix for the period 1978–2000. An original method was used to assess the accuracy of the LULCC map. The verification sites were selected through a stratified random sampling and were corroborated with aerial photographs for 1978 and 2000. Error matrices were elaborated using both hard and fuzzy set approaches in order to take into account the errors related to generalization of the map in fragmented landscapes. The results showed an average annual deforestation rate of 1.1 per cent which represents an average annual loss of 190 000 ha of forest, or an estimated total reduction of 4.2 million ha over 22 years. Furthermore, deforestation processes are concentrated in some areas such as Yucatan and Chiapas states, which registered major forest conversions to grassland and slash‐burning. The overall accuracy of the LULCC map, assessed with hard and fuzzy set approaches, was 72 per cent and 88 per cent respectively.     

Disaggregating population density of the European Union with CORINE land cover

This article describes and compares six disaggregation methods used to produce a dasymetric population density grid of the European Union at a 100 m resolution. Population data were initially available at commune level. The main ancillary information source was CORINE land cover, a land cover map distributed by the European Environment Agency. Information from the Eurostat point survey, land use/cover area frame survey, was also integrated in the parameter estimation of some of the approaches tested. Accurate population data for 1 km cell grids were provided by the Statistical Offices of Austria, Denmark, Finland, the Netherlands, Northern Ireland, Estonia and Sweden. These data provided the basic reference to quantify the accuracy of each method. The best results were obtained with a modified version of the limiting variable method (Eicher, C. and Brewer, C., 2001. Dasymetric mapping and areal interpolation: implementation and evaluation. Cartography and Geographic Information Science, 28, 125–138) that could be implemented, thanks to the national reference grids. For other methods the parameters could be estimated without using the reference grids; among them a method based on logit regression gave the best results. Compared with the traditional choropleth maps that represent a homogeneous density in each commune, the accuracy improvement of the disaggregated maps ranged between 20% and 67% (between 46% and 67% for the best method).     

近5年青海省植被覆盖变化的遥感监测

This paper used five years （2001-2006） time series of MODIS NDVI images with a 1-km spatial resolution to produce a land cover map of Qinghai Province in China. A classification approach for different land cover types with special emphasis on vegetation, especially on sparse vegetation, was developed which synthesized Decision Tree Classification, Supervised Classification and Unsupervised Classification. The spatial distribution and dynamic change of vegetation cover in Qinghai from 2001 to 2006 were analyzed based on the land cover classification map and five grade elevation belts derived from Qinghai DEM. The result shows that vegetation cover in Qinghai in recent five years has been some improved and the area of vegetation was increased from 370,047 km^2 in 2001 to 374,576 km^2 in 2006. Meanwhile, vegetation cover ratio was increased by 0.63%. Vegetation cover ratio in high mountain belt is the largest （67.92%） among the five grade elevation belts in Qinghai Province. The second largest vegetation cover ratio is in middle mountain belt （61.80%）. Next, in the order of the decreasing vegetation cover ratio, the remaining grades are extreme high mountain belt （38.98%）, low mountain belt （25.55%） and flat region belt （15.46%）. The area of middle density grassland in high mountain belt is the biggest （94,003 km^2）, and vegetation cover ratio of dense grassland in middle mountain belt is the highest （32.62%）, and the increased area of dense grassland in high mountain belt is the greatest （1280 km^2）. In recent five years the conversion from sparse grass to middle density grass in high mountain belt has been the largest vegetation cover variation and the converted area is 15931 km^2.     

Sediment yield variability in Spain: a quantitative and semiqualitative analysis using reservoir sedimentation rates

An existing dataset of area-specific sediment yield (SSY) for 60 catchments in Spain that was retrieved from sediment deposition rates in reservoirs [Avendaño Salas, C., Sanz Montero, E., Rayán, C., Gómez Montaña, 1997. Sediment yield at Spanish reservoirs and its relationship with the drainage basin area. In: Proceedings of the 19th Symposium of Large Dams, Florence, 1997. ICOLD (International Committee on Large Dams), pp. 863–874] reveals that catchment area alone explains only 17% of the variability in SSY. In this study, an attempt to explain the remaining variability in SSY was made using a quantitative and a semiqualitative approach for 22 catchments. During a field survey, the 22 selected catchments were characterised by topography, vegetation cover, lithology, shape and the presence of gullies in the broad vicinity of the reservoir. This information was used to develop a factorial scoring index model that provides a fairly accurate and reliable prediction of SSY. A classical multiple regression model using climatic, topographic and land use properties derived from regional datasets could not explain as much variance as the qualitative index model, nor did it appear to be as reliable. The same conclusion could be drawn when using the CORINE soil erosion risk map of southern Europe. The low prediction capability of the multiple regression models and the CORINE soil erosion risk map could be attributed mainly to the fact that these methods do not incorporate gully erosion and that the land cover data are not a good representation of soil cover. Both variables have been shown to be of great importance during the field surveys. Future assessments of SSY could be quickly and efficiently made using the proposed factorial scoring index model. In comparison with other models, which demand more data, the index model offers an alternative prediction tool.     

Large-scale land cover mapping with the integration of multi-source information based on the Dempster–Shafer theory

Land cover type is a crucial parameter that is required for various land surface models that simulate water and carbon cycles, ecosystem dynamics, and climate change. Many land use/land cover maps used in recent years have been derived from field investigations and remote-sensing observations. However, no land cover map that is derived from a single source (such as satellite observation) properly meets the needs of land surface simulation in China. This article presents a decision-fuse method to produce a higher-accuracy land cover map by combining multi-source local data based on the Dempster–Shafer (D–S) evidence theory. A practical evidence generation scheme was used to integrate multi-source land cover classification information. The basic probability values of the input data were obtained from literature reviews and expert knowledge. A Multi-source Integrated Chinese Land Cover (MICLCover) map was generated by combining multi-source land cover/land use classification maps including a 1:1,000,000 vegetation map, a 1:100,000 land use map for the year 2000, a 1:1,000,000 swamp-wetland map, a glacier map, and a Moderate-Resolution Imaging Spectroradiometer land cover map for China in 2001 (MODIS2001). The merit of this new map is that it uses a common classification system (the International Geosphere-Biosphere Programme (IGBP) land cover classification system), and it has a unified 1 km resolution. The accuracy of the new map was validated by a hybrid procedure. The validation results show great improvement in accuracy for the MICLCover map. The local-scale visual comparison validations for three regions show that the MICLCover map provides more spatial details on land cover at the local scale compared with other popular land cover products. The improvement in accuracy is true for all classes but particularly for cropland, urban, glacier, wetland, and water body classes. Validation by comparison with the China Forestry Scientific Data Center (CFSDC)–Forest Inventory Data (FID) data shows that overall forest accuracies in five provinces increased to between 42.19% and 88.65% for our MICLCover map, while those of the MODIS2001 map increased between 27.77% and 77.89%. The validation all over China shows that the overall accuracy of the MICLCover map is 71%, which is higher than the accuracies of other land cover maps. This map therefore can be used as an important input for land surface models of China. It has the potential to improve the modeling accuracy of land surface processes as well as to support other aspects of scientific land surface investigations in China.     

鲁西北地区土地现实生产力调查与估算

以鲁西北地区县级1∶5万土壤类型图图斑作为基础评价单元,根据农户现有的投入水平,采用“调查法”模拟分析农业土地资源的现实生产力,并与统计资料和“机制法”模型模拟结果进行对比分析。研究结果表明,现实投入水平下鲁西北地区夏玉米单产略高于冬小麦单产,对冬小麦的投入从整体上来看相当于中等投入,但又存在着区域差异。对夏玉米的投入具有南北向的差异,由南到北投入水平逐渐降低。鲁西北地区土地现实生产力以宁津县最高,庆云县最低,与“机制法”模型模拟结果比较,各县市之间土地生产潜力差距较大。由此可以说明,各县市土壤质量本身对土地年生产力的大小起着重要作用,进一步证明“调查法”能充分反映土壤在土地综合性质中的主要作用。     

Hydrologic variation with land use across the contiguous United States: Geomorphic and ecological consequences for stream ecosystems

Using daily discharge data from the USGS, we analyzed how hydrologic regimes vary with land use in four large hydrologic regions that span a gradient of natural land cover and precipitation across the continental United States. In each region we identified small streams (contributing area < 282 km²) that have continuous daily streamflow data. Using a national database, we characterized the composition of land cover of the watersheds in terms of aggregate measures of agriculture, urbanization, and least disturbed (“natural”). We calculated hydrologic alteration using 10 ecologically-relevant hydrologic metrics that describe magnitude, frequency, and duration of flow for 158 watersheds within the Southeast (SE), Central (CE), Pacific Northwest (NW), and Southwest (SW) hydrologic regions of the United States. Within each watershed, we calculated percent cover for agriculture, urbanized land, and least disturbed land to elucidate how components of the natural flow regime inherent to a hydrologic region is modified by different types and proportions of land cover. We also evaluated how dams in these regions altered the hydrologic regimes of the 43 streams that have pre- and post-dam daily streamflow data. In an analysis of flow alteration along gradients of increasing proportion of the three land cover types, we found many regional differences in hydrologic responses. In response to increasing urban land cover, peak flows increased (SE and CE), minimum flows increased (CE) or decreased (NW), duration of near-bankfull flows declined (SE, NW) and flow variability increased (SE, CE, and NW). Responses to increasing agricultural land cover were less pronounced, as minimum flows decreased (CE), near-bankfull flow durations increased (SE and SW), and flow variability declined (CE). In a second analysis, for three of the regions, we compared the difference between least disturbed watersheds and those having either > 15% urban and > 25% agricultural land cover. Relative to natural land cover in each region, urbanization either increased (SE and NW) or decreased (SW) peak flows, decreased minimum flows (SE, NW, and SW), decreased durations of near-bankfull flows (SE, NW, and SW), and increased flow variability (SE, NW, and SW). Agriculture had similar effects except in the SE, where near-bankfull flow durations increased. Overall, urbanization appeared to induce greater hydrologic responses than similar proportions of agricultural land cover in watersheds. Finally, the effects of dams on hydrologic variation were largely consistent across regions, with a decrease in peak flows, an increase in minimum flows, an increase in near-bankfull flow durations, and a decrease in flow variability. We use this analysis to evaluate the relative degree to which land use has altered flow regimes across regions in the US with naturally varying climate and natural land cover, and we discuss the geomorphic and ecological implications of such flow modification. We end with a consideration of what elements will ultimately be required to conduct a more comprehensive national assessment of the hydrologic responses of streams to land cover types and dams. These include improved tools for modeling hydrologic metrics in ungauged watersheds, incorporation of high-resolution geospatial data to map geomorphic and hydrologic drivers of stream response to different types of land cover, and analysis of scale dependence in the distribution of land-use impacts, including mixed land uses. Finally, ecological and geomorphic responses to human alteration of land cover will have to be calibrated to the regional hydroclimatological, geologic, and historical context in which the streams occur, in order to determine the degree to which stream responses are region-specific versus geographically independent and broadly transferable.     

Global greenhouse gas implications of land conversion to biofuel crop cultivation in arid and semi-arid lands – Lessons learned from Jatropha

Biofuels are considered as a climate-friendly energy alternative. However, their environmental sustainability is increasingly debated because of land competition with food production, negative carbon balances and impacts on biodiversity. Arid and semi-arid lands have been proposed as a more sustainable alternative without such impacts. In that context this paper evaluates the carbon balance of potential land conversion to Jatropha cultivation, biofuel production and use in arid and semi-arid areas. This evaluation includes the calculation of carbon debt created by these land conversions and calculation of the minimum Jatropha yield necessary to repay the respective carbon debts within 15 or 30 years.The carbon debts caused by conversion of arid and semi-arid lands to Jatropha vary largely as a function of the biomass carbon stocks of the land use types in these regions. Based on global ecosystem carbon mapping, cultivated lands and marginal areas (sparse shrubs, herbaceous and bare areas) show to have similar biomass carbon stocks (on average 4–8 t C ha⁻¹) and together cover a total of 1.79 billion ha. Conversion of these lands might not cause a carbon debt, but still might have a negative impact on other sustainability dimensions (e.g. biodiversity or socio-economics). Jatropha establishment in shrubland (0.75 billion ha) would cause a carbon debt of 24–28 t C ha⁻¹ on average (repayable within 30 year with yield of 3.5–3.9 t seed ha⁻¹ yr⁻¹). Land use change in the 1.15 billion ha of forested area under arid and semi-arid climates could cause a carbon debt between 70 and 118 t C ha⁻¹. This debt requires 8.6–13.9 t seed production ha⁻¹ yr⁻¹ for repayment within 30 years. If repayment is required within 15 years, the necessary minimum yields almost double. Considering that 5 t seed ha⁻¹ yr⁻¹ is the current maximum Jatropha yield, conversion of forests cannot be repaid within one human generation. Repayment of carbon debt from shrubland conversions in 30 years is challenging, but feasible. Repayment in 15 year is currently not attainable.Based on this analysis the paper discusses the carbon mitigation potential of biofuels in arid and semi-arid environments.     

中国传统农区1820年耕地数据网格化方法

Recent studies have demonstrated the importance of LUCC change with climate and ecosystem simulation, but the result could only be determined precisely if a high-resolution underlying land cover map is used. While the efforts based satellites have provided a good baseline for present land cover, what the next advancement in the research about LUCC change required is the development of reconstruction of historical LUCC change, especially spatially-explicit historical dataset. Being different from other similar studies, this study is based on the analysis of historical land use patterns in the traditional cultivated region of China. Taking no account of the less important factors, altitude, slope and population patterns are selected as the major drivers of reclamation in ancient China, and used to design the HCGM (Historical Cropland Gridding Model, at a 60 km×60 km resolution), which is an empirical model for allocating the historical cropland inventory data spatially to grid cells in each political unit. Then we use this model to reconstruct cropland distribution of the study area in 1820, and verify the result by prefectural cropland data of 1820, which is from the historical documents. The statistical analyzing result shows that the model can simulate the patterns of the cropland distribution in the historical period in the traditional cultivated region efficiently. Foundation: National Natural Science Foundation of China, No.40471007; Innovation Knowledge Project of CAS, No.KZCX2-YW-315 Author: Lin Shanshan (1982–), Master, specialized in historical environmental change.     

Small-area estimation of land cover statistics by post-stratification of a national area frame survey

The objective of this paper is to examine a method for estimation of land cover statistics for local environments from available area frame surveys of larger, surrounding areas. The method is a simple version of the small-area estimation methodology. The starting point is a national area frame survey of land cover. This survey is post-stratified using a coarse land cover map based on topographic maps and segmentation of satellite images. The approach is to describe the land cover composition of each stratum and subsequently use the results to calculate land cover statistics for a smaller area where the relative distribution of the strata is known. The method was applied to a mountain environment in Gausdal in Eastern Norway and the result was compared to reference data from a complete in situ land cover map of the study area. The overall correlation (Pearson’s rho) between the observed and the estimated land cover figures was r = 0.95. The method does not produce a map of the target area and the estimation error was large for a few of the land cover classes. The overall conclusion is, however, that the method is applicable when the objective is to produce land cover statistics and the interest is the general composition of land cover classes - not the precise estimate of each class. The method will be applied in outfield pasture management in Norway, where it offers a cost-efficient way to screen the management units and identify local areas with a land cover composition suitable for grazing. The limited resources available for in situ land cover mapping can then be allocated efficiently to in-depth studies of the areas with the highest grazing potential. It is also expected that the method can be used to compile land cover statistics for other purposes as well, provided that the motivation is to describe the overall land cover composition and not to provide exact estimates for the individual land cover classes.     

Utilizing discriminant function analysis with a geographical information system to model ecological succession spatially

Abstract

A raster/quadtree geographical information system (GIS) was established For a 932 ha natural area in central Missouri. The GIS contained map layers depicting vegetative cover from 1939 to 1982, soils, topographic aspect and distance from a forest seed source. Discriminant function analysis was used to quantify and describe ecological succession on the area during this period. The calibration of discriminant functions is discussed, as are the statistical and spatial validations of the model. It was concluded that this approach provides a useful technique to examine long-term temporal phenomena in a spatial context.     

A population density grid for Spain

This article describes a high-resolution land cover data set for Spain and its application to dasymetric population mapping (at census tract level). Eventually, this vector layer is transformed into a grid format. The work parallels the effort of the Joint Research Centre (JRC) of the European Commission, in collaboration with Eurostat and the European Environment Agency (EEA), in building a population density grid for the whole of Europe, combining CORINE Land Cover with population data per commune. We solve many of the problems due to the low resolution of CORINE Land Cover, which are especially visible with Spanish data. An accuracy assessment is carried out from a simple aggregation of georeferenced point population data for the region of Madrid. The bottom-up grid constructed in this way is compared to our top-down grid. We show a great improvement over what has been reported from commune data and CORINE Land Cover, but the improvements seem to come entirely from the higher resolution data sets and not from the statistical modeling in the downscaling exercise. This highlights the importance of providing the research community with more detailed land cover data sets, as well as more detailed population data. The dasymetric grid is available free of charge from the authors upon request.     

Land cover classification by an artificial neural network with ancillary information

Abstract

Remote sensing is an important source of land cover data required by many GIS users. Land cover data are typically derived from remotely–sensed data through the application of a conventional statistical classification. Such classification techniques are not, however, always appropriate, particularly as they may make untenable assumptions about the data and their output is hard, comprising only the code of the most likely class of membership. Whilst some deviation from the assumptions may be tolerated and a fuzzy output may be derived, making more information on class membership properties available, alternative classification procedures are sometimes required. Artificial neural networks are an attractive alternative to the statistical classifiers and here one is used to derive a fuzzy classification output from a remotely–sensed data set that may be post–processed with ancillary data available in a GIS to increase the accuracy with which land cover may be mapped. With the aid ancillary information on soil type and prior knowledge of class occurrence the accuracy of an artificial neural network classification was increased by 29–93 to 77–37 per cent. An artificial neural network can therefore be used generate a fuzzy classification output that may be used with other data sets in a GIS, which may not have been available to the producer of the classification, to increase the accuracy with which land cover may be classified.     

Landscape ‘Contagion’ in Raster and Vector Environments

Abstract

Raster land cover data has been used to construct adjacency matrices by scanning the pixel edges and calculating proportions according to the land cover types joined. Like joins (e.g., forest to forest) are most common because pixel size is typically much smaller than the average patch size. The dominance of like joins (the matrix diagonal) of the adjacency matrix has led to one interpretation of the matrix as landscape contagion (the tendency of land cover to cluster into a few, large patches). Construction of the adjacency matrix in a vector environment results in a different measurement. In a vector environment, land cover patches are not sub–divided into pixels. Therefore, the main diagonal of the adjacency matrix is zero. Construction of an adjacency matrix in a vector environment estimates the evenness of distribution of edge types, not contagion. The same edge type evenness metric is estimated in a raster environment by ignoring the matrix diagonal and re-scaling the off-diagonal elements so that they sum to one (1).     

Broad scale forest cover reconstruction from historical topographic maps

Land cover change is one of the major contributors to global change, but long-term, broad-scale, detailed and spatially explicit assessments of land cover change are largely missing, although the availability of historical maps in digital formats is increasing. The problem often lies in efficiency of analyses of historical maps for large areas. Our goal was to assess different methods to reconstruct land cover and land use from historical maps to identify a time-efficient and reliable method for broad-scale land cover change analysis. We compared two independent forest cover reconstruction methods: first, regular point sampling, and second, wall-to-wall mapping, and tested both methods for the Polish Carpathians (20,000 km²) for the 1860s, 1930s and 1970s. We compared the two methods in terms of their reliability for forest change analysis, relative to sampling error, point location and landscape context including local forest cover, area of the spatial reference unit and forest edge-to-core ratio. Our results showed that the point-based analysis overestimated forest cover in comparison to wall-to-wall mapping by 1–3%, depending on the mapping period. The reasons for the differences were mainly the backdating approach and map generalisation rather than the point grid position or sampling error. When we compared forest cover trajectories over time, we found that the point-based reconstruction captured forest cover dynamics with a comparable accuracy to the wall-to-wall mapping. More broadly, our assessment showed that historical maps can provide valuable data on long-term land cover trends, and that point-based sampling can be an efficient and accurate way to assess forest area and change trends. We suggest that our point-based approach could allow land cover mapping across much of Europe starting in the 1800s. Our findings are important because they suggest that land cover change, a key component of global change, can be assessed over large areas much further back in time than it is commonly done. This would allow to truly understand path dependencies, land use legacies, and historical drivers of land cover change.     

The consequences of land-cover changes on soil erosion distribution in Slovakia

Soil erosion is a complex process determined by mutual interaction of numerous factors. The aim of erosion research at regional scales is a general evaluation of the landscape susceptibility to soil erosion by water, taking into account the main factors influencing this process. One of the key factors influencing the susceptibility of a region to soil erosion is land cover. Natural as well as human-induced changes of landscape may result in both the diminishment and acceleration of soil erosion. Recent studies of land-cover changes indicate that during the last decade more than 4.11% of Slovak territory has changed. The objective of this study is to assess the influence of land-cover and crop rotation changes over the 1990–2000 period on the intensity and spatial pattern of soil erosion in Slovakia. The assessment is based on principles defined in the Universal Soil Loss Equation (USLE) modified for application at regional scale and the use of the CORINE land cover (CLC) databases for 1990 and 2000. The C factor for arable land has been refined using statistical data on the mean crop rotation and the acreage of particular agricultural crops in the districts of Slovakia. The L factor has been calculated using sample areas with parcels identified by LANDSAT TM data. The results indicate that the land-cover and crop rotation changes had a significant influence on soil erosion pattern predominately in the hilly and mountainous parts of Slovakia. The pattern of soil erosion changes exhibits high spatial variation with overall slightly decreased soil erosion risks. These changes are associated with ongoing land ownership changes, changing structure of crops, deforestation and afforestation.     

A MAP OF URBAN PLACES,U.S.S.R., 1970

Abstract

Figures from the 1970 Soviet Census provide an abundance of information on population changes in the U.S.S.R. Of special note is the process of urbanization and a noticeable concentration of urban growth in the hitherto largely rural zones of western Russia. A large map of urban places in 1970, designed primarily for classroom use, accompanies the text (see inside back cover). The map utilizes a special stippled-sphere technique to allow a three-dimensional effect without half-tone reproduction.     

Comparison of input data with different spatial resolution in landscape pattern analysis – A case study from northern Latvia

A suitable spatial scale needs to be selected in geographical and landscape ecological research, and this requires great consideration as different scales have profound effect on derived landscape spatial patterns. Numerous studies have investigated the effects of different scales on landscape metrics using simulated patterns, but few have been conducted to compare different data sources with variable scale for regional- and landscape-scale assessments. Possibly this has occurred because researchers have been prone to use the best available source, a well-known standard, and easiest to use. This study was conducted to assess the impact of input data resolution on values of landscape pattern metrics in four landscapes at scales 1:10 000, 1:50 000 and 1:100 000. The aim was to determine the applicability of three data sources for thematic models in landscape pattern analyses in the Eastern Baltic region. We found that the utility of CORINE Land Cover data for comprehensive structural assessment in mosaic-type landscapes was very limited, as the level of cartographic generalization excluded many small and linear landscape structure elements with potentially high importance for landscape functioning, such as habitat continuity. We also found that actual area harvested using clearcuts was considerably higher than shown in CORINE data, due to clearcuts size being much smaller than the minimum mapping unit. In the light of this, we suggest using data with spatial resolution corresponding to a cartographic scale of at least 1:50 000, in cases when spatial patches have size up to 25 ha.     

The Norwegian area frame survey of land cover and outfield land resources

There is a growing demand for reliable information about land cover and land resources. The Norwegian area frame survey of land cover and outfield land resources (AR18X18) is a response to this demand. AR18X18 provides unbiased land cover and land resource statistics and constitutes a baseline for studying changes in outfield land resources in Norway and a framework for a national land resource accounting system for the outfields. The area frame survey uses a systematic sampling technique with 0.9 km² sample plots at 18 km intervals. A complete wall-to-wall land cover map of an entire plot surveyed is obtained in situ by a team of fieldworkers equipped with aerial photographs. The use of sample plots with extended coverage (0.9 km²) ensures that the survey also deals with local variation, thus strengthening the estimates well beyond simple point sampling. The article documents the methodology used in the survey, followed by a discussion of issues raised by the choice of methodology. These issues include the problem of calculating uncertainty and a confidence interval for the estimates, the focus on common rather than rare land cover categories, and the prospect of downscaling the results in order to obtain statistics for subnational regions.     

Integration of multi-source remote sensing data for land cover change detection

The objective of this study is to develop a methodology to integrate multi-source remote sensing data into a homogeneous time series of land cover maps in order to carry out change detection. We developed a method to increase the comparability between land cover maps coming from panchromatic aerial photographs and SPOT XS (multi-spectral) data by equalizing their levels of thematic content and spatial details. The methodology was based on the hypotheses that: (1) map generalization can improve the integration of data for change detection purpose, and (2) the spatial structure of a land cover map, as measured by a set of landscape metrics, is an indicator of the level of generalization of that map. Firstly, the methodology for data integration was developed by using land cover maps generated from near-synchronous data. Results revealed that, by controlling successively the parameters that influence the level of map generalization, the percentage of agreement between the near-synchronous land cover maps can be increased from 42% to 93%. The computation of five landscape metrics for a set of generalized land cover maps and for the target map allowed us to optimize the level of generalization by measuring the similarity in landscape pattern of the maps. The optimum level of generalization of the land cover map obtained from the aerial photographs for comparison with a land cover map derived from SPOT XS data was found at a resolution of 41m for two generalization levels of the thematic content. The spatial structure of a land cover map, as measured by a set of landscape metrics, is thus a good indicator of the level of generalization of this map. Secondly, the method was applied by integrating a land cover map obtained from aerial photographs of 1954 with a land cover map obtained from a SPOT XS image of 1992.