A large‐area,spatially continuous assessment of land cover map error and its impact on downstream analyses

Land cover maps increasingly underlie research into socioeconomic and environmental patterns and processes, including global change. It is known that map errors impact our understanding of these phenomena, but quantifying these impacts is difficult because many areas lack adequate reference data. We used a highly accurate, high‐resolution map of South African cropland to assess (1) the magnitude of error in several current generation land cover maps, and (2) how these errors propagate in downstream studies. We first quantified pixel‐wise errors in the cropland classes of four widely used land cover maps at resolutions ranging from 1 to 100 km, and then calculated errors in several representative “downstream” (map‐based) analyses, including assessments of vegetative carbon stocks, evapotranspiration, crop production, and household food security. We also evaluated maps’ spatial accuracy based on how precisely they could be used to locate specific landscape features. We found that cropland maps can have substantial biases and poor accuracy at all resolutions (e.g., at 1 km resolution, up to ～45% underestimates of cropland (bias) and nearly 50% mean absolute error (MAE, describing accuracy); at 100 km, up to 15% underestimates and nearly 20% MAE). National‐scale maps derived from higher‐resolution imagery were most accurate, followed by multi‐map fusion products. Constraining mapped values to match survey statistics may be effective at minimizing bias (provided the statistics are accurate). Errors in downstream analyses could be substantially amplified or muted, depending on the values ascribed to cropland‐adjacent covers (e.g., with forest as adjacent cover, carbon map error was 200%–500% greater than in input cropland maps, but ～40% less for sparse cover types). The average locational error was 6 km (600%). These findings provide deeper insight into the causes and potential consequences of land cover map error, and suggest several recommendations for land cover map users.     

Across the grain: Multi-scale map comparison and land change assessment

Changes in the spatial distribution of land cover and land use can have significant impacts on ecological processes at multiple scales; estimating these changes provides critical data for both monitoring and understanding land-use effects on these processes. One approach to mapping landcover changes, particularly useful over longer periods of time, is comparison of existing landcover maps, (post-classification change analysis). The accuracy of these maps is often unknown and varies depending on data sources and interpretation techniques; therefore, separating change on the ground from differences attributable to sensors and methods is both critical and problematic. Through a novel map comparison method applying major axis regression at multiple spatial grains of analysis, this study partitioned accuracy into components of bias and precision in comparing maps, which aided selection of an optimal analytical grain size. Comparisons between contemporaneous maps showed the magnitude and distribution of error alone, while between-period analyses indicated both cumulative map error and change on the ground. These methods enable exploration of the nature of error and identification of differences between maps, while accounting for the imprecision and bias inherent in the source documents. Mapping landcover change delineates landscapes under recent disturbance pressure, and these measures are more effective as performance indicators for broad-scale evaluation of natural heritage policies and habitat restoration initiatives when error in the data is identified and accounted for.     

Estimating soil carbon fluxes following land-cover change: a test of some critical assumptions for a region in Costa Rica

Changes in soil carbon storage that accompany land‐cover change may have significant effects on the global carbon cycle. The objective of this work was to examine how assumptions about preconversion soil C storage and the effects of land‐cover change influence estimates of regional soil C storage. We applied three models of land‐cover change effects to two maps of preconversion soil C in a 140 000 ha area of northeastern Costa Rica. One preconversion soil C map was generated using values assigned to tropical wet forest from the literature, the second used values obtained from extensive field sampling. The first model of land‐cover change effects used values that are typically applied in global assessments, the second and third models used field data but differed in how the data were aggregated (one was based on land‐cover transitions and one was based on terrain attributes). Changes in regional soil C storage were estimated for each combination of model and preconversion soil C for three time periods defined by geo‐referenced land‐cover maps. The estimated regional soil C under forest vegetation (to 0.3 m) was higher in the map based on field data (10.03 Tg C) than in the map based on literature data (8.90 Tg C), although the range of values derived from propagating estimation errors was large (7.67–12.40 Tg C). Regional soil C storage declined through time due to forest clearing for pasture and crops. Estimated CO₂ fluxes depended more on the model of land‐cover change effects than on preconversion soil C. Cumulative soil C losses (1950–1996) under the literature model of land‐cover effects exceeded estimates based on field data by factors of 3.8–8.0. In order to better constrain regional and global‐scale assessments of carbon fluxes from soils in the tropics, future research should focus on methods for extrapolating regional‐scale constraints on soil C dynamics to larger spatial and temporal scales.     

遥感主体图的准确度对景观生态学研究的影响

用各种案例系统地解释了遥感数据分类误差对景观指数误差的必然影响。一方面 ,遥感数据在各种时间和空间尺度上为景观生态学研究提供必需的土地类型数据 ;另一方面 ,遥感技术的灵活性和复杂性可以产生出各种质量的土地类型数据。但景观生态学方面的用户对土地类型数据基本上是没有选择地使用 ,甚至是不知好坏地使用 ,所以景观生态学的发现和结论具有不可避免的任意性。总结了在各种情况下景观指数的变动区间 ,指出了现实较低的遥感数据的分类准确度会引起更低的景观指数的准确度 ,当进行景观变化分析时 ,这种误差的放大效应将更加明显。当前 ,人们对除面积以外的景观指数的误差仍然束手无策 ,尽可能地提高遥感数据的分类准确度是唯一力所能及的办法。     

Molecular and statistical approaches to the detection and correction of errors in genotype databases.

Errors in genotyping data have been shown to have a significant effect on the estimation of recombination fractions in high-resolution genetic maps. Previous estimates of errors in existing databases have been limited to the analysis of relatively few markers and have suggested rates in the range 0.5%-1.5%. The present study capitalizes on the fact that within the Centre d'Etude du Polymorphisme Humain (CEPH) collection of reference families, 21 individuals are members of more than one family, with separate DNA samples provided by CEPH for each appearance of these individuals. By comparing the genotypes of these individuals in each of the families in which they occur, an estimated error rate of 1.4% was calculated for all loci in the version 4.0 CEPH database. Removing those individuals who were clearly identified by CEPH as appearing in more than one family resulted in a 3.0% error rate for the remaining samples, suggesting that some error checking of the identified repeated individuals may occur prior to data submission. An error rate of 3.0% for version 4.0 data was also obtained for four chromosome 5 markers that were retyped through the entire CEPH collection. The effects of these errors on a multipoint map were significant, with a total sex-averaged length of 36.09 cM with the errors, and 19.47 cM with the errors corrected. Several statistical approaches to detect and allow for errors during linkage analysis are presented. One method, which identified families containing possible errors on the basis of the impact on the maximum lod score, showed particular promise, especially when combined with the limited retyping of the identified families. The impact of the demonstrated error rate in an established genotype database on high-resolution mapping is significant, raising the question of the overall value of incorporating such existing data into new genetic maps.     

A multipoint method for detecting genotyping errors and mutations in sibling-pair linkage data

The identification of genes contributing to complex diseases and quantitative traits requires genetic data of high fidelity, because undetected errors and mutations can profoundly affect linkage information. The recent emphasis on the use of the sibling-pair design eliminates or decreases the likelihood of detection of genotyping errors and marker mutations through apparent Mendelian incompatibilities or close double recombinants. In this article, we describe a hidden Markov method for detecting genotyping errors and mutations in multilocus linkage data. Specifically, we calculate the posterior probability of genotyping error or mutation for each sibling-pair-marker combination, conditional on all marker data and an assumed genotype-error rate. The method is designed for use with sibling-pair data when parental genotypes are unavailable. Through Monte Carlo simulation, we explore the effects of map density, marker-allele frequencies, marker position, and genotype-error rate on the accuracy of our error-detection method. In addition, we examine the impact of genotyping errors and error detection and correction on multipoint linkage information. We illustrate that even moderate error rates can result in substantial loss of linkage information, given efforts to fine-map a putative disease locus. Although simulations suggest that our method detects 相似文献   

Effect of genotyping error in model-free linkage analysis using microsatellite or single-nucleotide polymorphism marker maps

Errors while genotyping are inevitable and can reduce the power to detect linkage. However, does genotyping error have the same impact on linkage results for single-nucleotide polymorphism (SNP) and microsatellite (MS) marker maps? To evaluate this question we detected genotyping errors that are consistent with Mendelian inheritance using large changes in multipoint identity-by-descent sharing in neighboring markers. Only a small fraction of Mendelian consistent errors were detectable (e.g., 18% of MS and 2.4% of SNP genotyping errors). More SNP genotyping errors are Mendelian consistent compared to MS genotyping errors, so genotyping error may have a greater impact on linkage results using SNP marker maps. We also evaluated the effect of genotyping error on the power and type I error rate using simulated nuclear families with missing parents under 0, 0.14, and 2.8% genotyping error rates. In the presence of genotyping error, we found that the power to detect a true linkage signal was greater for SNP (75%) than MS (67%) marker maps, although there were also slightly more false-positive signals using SNP marker maps (5 compared with 3 for MS). Finally, we evaluated the usefulness of accounting for genotyping error in the SNP data using a likelihood-based approach, which restores some of the power that is lost when genotyping error is introduced.     

A retrospective analysis of land cover change using a polygon shape index

Aim This study tests the hypothesis that the propensity of land cover patches to change is related to their shape and geometric complexity. Location The analysis is based on a 1000‐km² area of the Cairngorms in Scotland, incorporating part of Speyside and the high plateau area within the Grampian Mountains. Methods A combined dataset was created by intersecting 1964 land cover data (derived from archive aerial photography) and 1988 land cover data (from the Land Cover of Scotland dataset). A shape index was calculated for each land cover polygon inside a GIS. Information on land cover change was analysed with reference to land cover class and the polygon shape index using a regression analysis. Results For upland seminatural land cover classes, subject to low levels of management, change is related to polygon shape, such that the more complex patches were found to be more susceptible to change. This relationship breaks down where classes are more intensively managed or have been aggregated into mosaic classes. Conclusions Propensity to change was related to shape index for seminatural land cover classes. This implies that at least some landscape processes, such as anthropogenic disturbance of seminatural land covers, can be linked to ecological theory via measurements of spatial pattern. The study also highlighted some of the cartographic issues involved in estimating changes between land cover classes: there are advantages in replacing the ‘cartographic paradigm’ of comparing two derived datasets (in this case land cover maps) with direct comparison of the digital data — air photographs or satellite imagery. Such a direct approach avoids the compounding of errors introduced by the approximation of each successive air photo as a thematic map.     

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.     

Quantifying spatiotemporal patterns concerning land change in Changsha,China

Changsha has undergone speedy socio-economic development, rapid modification of industrial structure, and acceleration of urbanization, which has influenced land cover change during the most recent three decades. Policies have aimed to conserve total agricultural area, but it is not clear how successful these policies have been. Our purpose is to characterize and interpret spatiotemporal patterns of land change with respect to the policy to maintain agricultural area in Changsha, China. Maps at 1990, 2000, and 2010 show four land categories: Built, Forest, Crop and Other. We compute change components and apply Intensity Analysis to compare the land changes during two time intervals: 1990–2000 and 2000–2010. We also compare the central region to the peripheral region during 1990–2010. The maps show that Changsha’s land change accelerated from 1990–2000 to 2000–2010. Change was more intensive in the central region than in the peripheral region. Crop and Forest experienced net decreases while Built experienced net increase during both time intervals and in both regions. Built’s gain targeted Crop and avoided Forest during both time intervals and in both regions. The central region’s largest change component is quantity change, due to Built’s net gain. The peripheral region’s largest change component is exchange, due to simultaneous transitions from Forest to Crop and from Crop to Forest. According to these data, policies have not maintained the quantity of Crop, as the peripheral region has not gained Crop sufficiently to compensate for Crop’s loss from the central region.     

What is the Value of a Good Map? An Example Using High Spatial Resolution Imagery to Aid Riparian Restoration

Riparian areas contain structurally diverse habitats that are challenging to monitor routinely and accurately over broad areas. As the structural variability within riparian areas is often indiscernible using moderate-scale satellite imagery, new mapping techniques are needed. We used high spatial resolution satellite imagery from the QuickBird satellite to map harvested and intact forests in coastal British Columbia, Canada. We distinguished forest structural classes used in riparian restoration planning, each with different restoration costs. To assess the accuracy of high spatial resolution imagery relative to coarser imagery, we coarsened the pixel resolution of the image, repeated the classifications, and compared results. Accuracy assessments produced individual class accuracies ranging from 70 to 90% for most classes; whilst accuracies obtained using coarser scale imagery were lower. We also examined the implications of map error on riparian restoration budgets derived from our classified maps. To do so, we modified the confusion matrix to create a cost error matrix quantifying costs associated with misclassification. High spatial resolution satellite imagery can be useful for riparian mapping; however, errors in restoration budgets attributable to misclassification error can be significant, even when using highly accurate maps. As the spatial resolution of imagery increases, it will be used more routinely in ecosystem ecology. Thus, our ability to evaluate map accuracy in practical, meaningful ways must develop further. The cost error matrix is one method that can be adapted for conservation and planning decisions in many ecosystems.     

ODS2: a multiplatform software application for creating integrated physical and genetic maps

A contig map is a physical map that shows the native order of a library of overlapping genomic clones. One common method for creating such maps involves using hybridization to detect clone overlaps. False- positive and false-negative hybridization errors, the presence of chimeric clones, and gaps in library coverage lead to ambiguity and error in the clone order. Genomes with good genetic maps, such as Neurospora crassa, provide a means for reducing ambiguities and errors when constructing contig maps if clones can be anchored with genetic markers to the genetic map. A software application called ODS2 for creating contig maps based on clone-clone hybridization data is presented. This application is also designed to exploit partial ordering information provided by anchorage of clones to a genetic map. This information, along with clone-clone hybridization data, is used by a clone ordering algorithm and is represented graphically, allowing users to interactively align physical and genetic maps. ODS2 has a graphical user interface and is implemented entirely in Java, so it runs on multiple platforms. Other features include the flexibility of storing data in a local file or relational database and the ability to create full or minimum tiling contig maps.     

A Practical and Automated Approach to Large Area Forest Disturbance Mapping with Remote Sensing

In this paper, I describe a set of procedures that automate forest disturbance mapping using a pair of Landsat images. The approach is built on the traditional pair-wise change detection method, but is designed to extract training data without user interaction and uses a robust classification algorithm capable of handling incorrectly labeled training data. The steps in this procedure include: i) creating masks for water, non-forested areas, clouds, and cloud shadows; ii) identifying training pixels whose value is above or below a threshold defined by the number of standard deviations from the mean value of the histograms generated from local windows in the short-wave infrared (SWIR) difference image; iii) filtering the original training data through a number of classification algorithms using an n-fold cross validation to eliminate mislabeled training samples; and finally, iv) mapping forest disturbance using a supervised classification algorithm. When applied to 17 Landsat footprints across the U.S. at five-year intervals between 1985 and 2010, the proposed approach produced forest disturbance maps with 80 to 95% overall accuracy, comparable to those obtained from traditional approaches to forest change detection. The primary sources of mis-classification errors included inaccurate identification of forests (errors of commission), issues related to the land/water mask, and clouds and cloud shadows missed during image screening. The approach requires images from the peak growing season, at least for the deciduous forest sites, and cannot readily distinguish forest harvest from natural disturbances or other types of land cover change. The accuracy of detecting forest disturbance diminishes with the number of years between the images that make up the image pair. Nevertheless, the relatively high accuracies, little or no user input needed for processing, speed of map production, and simplicity of the approach make the new method especially practical for forest cover change analysis over very large regions.     

Mapping global cropland and field size

A new 1 km global IIASA‐IFPRI cropland percentage map for the baseline year 2005 has been developed which integrates a number of individual cropland maps at global to regional to national scales. The individual map products include existing global land cover maps such as GlobCover 2005 and MODIS v.5, regional maps such as AFRICOVER and national maps from mapping agencies and other organizations. The different products are ranked at the national level using crowdsourced data from Geo‐Wiki to create a map that reflects the likelihood of cropland. Calibration with national and subnational crop statistics was then undertaken to distribute the cropland within each country and subnational unit. The new IIASA‐IFPRI cropland product has been validated using very high‐resolution satellite imagery via Geo‐Wiki and has an overall accuracy of 82.4%. It has also been compared with the EarthStat cropland product and shows a lower root mean square error on an independent data set collected from Geo‐Wiki. The first ever global field size map was produced at the same resolution as the IIASA‐IFPRI cropland map based on interpolation of field size data collected via a Geo‐Wiki crowdsourcing campaign. A validation exercise of the global field size map revealed satisfactory agreement with control data, particularly given the relatively modest size of the field size data set used to create the map. Both are critical inputs to global agricultural monitoring in the frame of GEOGLAM and will serve the global land modelling and integrated assessment community, in particular for improving land use models that require baseline cropland information. These products are freely available for downloading from the http://cropland.geo-wiki.org website.     

Physical mapping with automatic capture of hybridization data

MOTIVATION: Contig maps are a type of physical map that show the native order of a set of overlapping genomic clones. Overlaps between clones can be detected by finding common sequences using a number of experimental protocols including hybridization of probes. All current mapping algorithms of which we are aware require that hybridizations be scored using a fixed number of discrete values (typically 0/1 or high/medium/low). When hybridization data is captured automatically using digital equipment, this provides the opportunity for hybridization intensities to be used in map construction. More fine-grained distinctions in the levels of hybridization may be exploited by algorithms to generate more accurate physical maps. RESULTS: We describe an approach to creating contig maps that uses measured hybridization intensities instead of data scored with a fixed number of discrete values. We describe and compare four algorithms for creating physical maps with hybridization intensities. Simulations using measured intensities sampled from actual data on Aspergillus nidulans indicate that using hybridization intensities rather than data that is automatically scored with respect to threshold values may yield more accurate physical maps.     

Systematic detection of errors in genetic linkage data.

Construction of dense genetic linkage maps is hampered, in practice, by the occurrence of laboratory typing errors. Even relatively low error rates cause substantial map expansion and interfere with the determination of correct genetic order. Here, we describe a systematic method for overcoming these difficulties, based on incorporating the possibility of error into the usual likelihood model for linkage analysis. Using this approach, it is possible to construct genetic maps allowing for error and to identify the typings most likely to be in error. The method has been implemented for F2 intercrosses between two inbred strains, a situation relevant to the construction of genetic maps in experimental organisms. Tests involving both simulated and real data are presented, showing that the method detects the vast majority of errors.     

Vegetation Cover,Tidal Amplitude and Land Area Predict Short-Term Marsh Vulnerability in Coastal Louisiana

The loss of coastal marshes is a topic of great concern, because these habitats provide tangible ecosystem services and are at risk from sea-level rise and human activities. In recent years, a significant effort has gone into understanding and modeling the relationships between the biological and physical factors that contribute to marsh stability. Simulation-based process models suggest that marsh stability is the product of a complex feedback between sediment supply, flooding regime and vegetation response, resulting in elevation gains sufficient to match the combination of relative sea-level rise and losses from erosion. However, there have been few direct, empirical tests of these models, because long-term datasets that have captured sufficient numbers of marsh loss events in the context of a rigorous monitoring program are rare. We use a multi-year dataset collected by the Coastwide Reference Monitoring System that includes transitions of monitored vegetation plots to open water to build and test a predictive model of near-term marsh vulnerability. We found that despite the conclusions of previous process models, elevation change had no ability to predict the transition of vegetated marsh to open water. However, we found that the processes that drive elevation change were significant predictors of transitions. Specifically, vegetation cover in prior year, land area in the surrounding 1 km² (an estimate of marsh fragmentation) and the interaction of tidal amplitude and position in tidal frame were all significant factors predicting marsh loss. This suggests that (1) elevation change is likely better a predictor of marsh loss at timescales longer than we consider in this study and (2) the significant predictive factors affect marsh vulnerability through pathways other than elevation change, such as resistance to erosion. In addition, we found that, while sensitivity of marsh vulnerability to the predictive factors varied spatially across coastal Louisiana, vegetation cover in prior year was the best single predictor of subsequent loss in most sites followed by changes in percent land and tidal amplitude. The model’s predicted land loss rates correlated well with land loss rates derived from satellite data, although agreement was spatially variable. These results indicate (1) monitoring the loss of small-scale vegetation plots can inform patterns of land loss at larger scales, (2) the drivers of land loss vary spatially across coastal Louisiana, and (3) relatively simple models have potential as highly informative tools for bioassessment, directing future research and management planning.     

Incorporating DEM Uncertainty in Coastal Inundation Mapping

Coastal managers require reliable spatial data on the extent and timing of potential coastal inundation, particularly in a changing climate. Most sea level rise (SLR) vulnerability assessments are undertaken using the easily implemented bathtub approach, where areas adjacent to the sea and below a given elevation are mapped using a deterministic line dividing potentially inundated from dry areas. This method only requires elevation data usually in the form of a digital elevation model (DEM). However, inherent errors in the DEM and spatial analysis of the bathtub model propagate into the inundation mapping. The aim of this study was to assess the impacts of spatially variable and spatially correlated elevation errors in high-spatial resolution DEMs for mapping coastal inundation. Elevation errors were best modelled using regression-kriging. This geostatistical model takes the spatial correlation in elevation errors into account, which has a significant impact on analyses that include spatial interactions, such as inundation modelling. The spatial variability of elevation errors was partially explained by land cover and terrain variables. Elevation errors were simulated using sequential Gaussian simulation, a Monte Carlo probabilistic approach. 1,000 error simulations were added to the original DEM and reclassified using a hydrologically correct bathtub method. The probability of inundation to a scenario combining a 1 in 100 year storm event over a 1 m SLR was calculated by counting the proportion of times from the 1,000 simulations that a location was inundated. This probabilistic approach can be used in a risk-aversive decision making process by planning for scenarios with different probabilities of occurrence. For example, results showed that when considering a 1% probability exceedance, the inundated area was approximately 11% larger than mapped using the deterministic bathtub approach. The probabilistic approach provides visually intuitive maps that convey uncertainties inherent to spatial data and analysis.     

A protocol for assessment and integration of vegetation maps,with an application to spatial data sets from south‐eastern Australia

Abstract Maps are important tools in natural resource management. Often, there may be multiple maps that represent the same resource, which have been constructed using very different philosophies and methods, at different scales, for different dates and areas. In such cases, conservation planners and other natural resource managers are faced with a choice of map that will best serve their decision making. However, the best available information for a given purpose is often a combination of data from a number of different source maps. In this paper we present a protocol for assessing and integrating multiple maps of vegetation for a particular area of interest. The protocol commences with a consideration of management or policy context and technical issues to determine the basic specifications for the map. It then defines and assesses a set of measurable attributes, representing the concepts of theme, accuracy, precision and currency, for all candidate maps available for compilation. The resulting ranks for accuracy, precision and currency are used to compute a suitability index, which is used to assemble a composite map from the most suitable candidate maps. The final step in the protocol is to display spatial patterns in thematic consistency, accuracy, precision and currency for the composite map. We demonstrate the application of the protocol by constructing a map that discriminates structurally intact native vegetation from cleared land for the whole of New South Wales, south‐eastern Australia. The source data include 46 maps that cover various parts of the region at various scales and which were made at different dates using different methods. The protocol is an explicit and systematic method to evaluate the strengths and weaknesses of alternative data sets. It implements spatial integration in a way that promotes overall accuracy, precision and currency of map data. It also promotes transparent reporting of map limitations, to help map users accommodate risks of map errors in their decision making, and to inform priorities for future survey and mapping.     

三大沿海城市群滨海湿地的陆源人类活动影响模式

随着陆源人类活动对滨海湿地的干扰日益强烈,研究滨海湿地陆源人类活动影响规律对于滨海湿地资源管理和保护具有重要的现实意义.以我国三大沿海城市群(环渤海湾、长江三角洲、珠江三角洲)滨海湿地为研究对象,分别选取围海造地(直接因子)和流域污染物(营养盐、非点源无机污染物)输入(间接因子)表征1990-2000年间滨海湿地的陆源人类活动影响强度及规律.研究发现,虽然围海造地仍然以农业用地为主(＞50％),但城市建设用地比例随人口城市化率增高而增加,以珠江三角洲城市群最高;在环渤海湾和珠江三角洲城市群区域超过60％的滨海湿地面积受到高或中等污染物输入强度影响,长江三角洲城市群区域则有44.7％的滨海湿地面积处于中等污染物输入强度及以上.通过叠加围海造地和污染物输入双重陆源人类活动的综合影响强度表明,珠江三角洲城市群平均强度指数达到0.79,且处于中等及以上影响强度区域面积比例达到78％,均高于其他两个城市群滨海湿地区域.空间分析结果表明,陆源人类活动对滨海湿地的强影响区域多集中分布于河流人海口、较为发达城市滨海区以及海岸线附近.