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1.
The increasing sophistication of classification techniques used in land use and land cover analysis has not been matched by attention to the origin and effects of land cover categories. While classifications appear unproblematic and self-evident, they carry with them their own histories, meanings and effects, which remain largely unexamined. In an effort at such scrutiny, we examine the origins of land cover categories deployed in remote sensing and conclude that categories are theory-laden metaphors and occur epistemologically prior to any clustering algorithm, no matter how sophisticated. We describe the problematic effects that the imposition of classification systems in place of in situ knowledge of the landscape can have, especially in a colonial or post-colonial context. As an alternative to imposed classification, we propose and demonstrate an empirical technique based upon a growing body of work in participatory GIS. The method compares image classifications based on local and expert knowledge, using a case study from Rajasthan, India, concluding that differing metaphors of landscape lead to divergent measures of land cover.  相似文献   

2.
Given the current lack of interoperability between global and regional land cover products, efforts are underway to link the new European global land cover map (GLOBCOVER) with the existing global land cover 2000 map (GLC2000) and European CORINE mapping initiative. Since both datasets apply different mapping standards, key for a successful implementation is a thorough understanding of the heterogeneities among both datasets. Thus, this paper provides an assessment of compatibilities and differences between the CORINE2000 and GLC2000 datasets. The comparative assessment considers inconsistencies between the thematic legends (using the UN land cover classification system-LCCS), class specific accuracies, and the spatial resolution and heterogeneity of the datasets. The results are summarized with implications for the development of the new GLOBCOVER datasets.  相似文献   

3.
利用决策树和支持向量机分类方法,基于多期Landsat MSS,TM and ETM+遥感图像和其他辅助数据,对1970s以来近40年半干旱的老哈河流域土地利用变化(land use and land cover change,LUCC)进行动态监测,并利用GIS方法对LUCC进行了定量分析和空间分布制图.结果显示,利用支持向量机分类方法对该地区1976年、1989年、1999年和2007年土地覆盖类型分类可达到较满意的效果;近40年老哈河流域土地利用变化显著,水体和草地减少,城乡用地持续扩张,耕地大幅增加,林地和未利用地大幅度波动、总体减少.LUCC主要发生在林地、草地和耕地之间,表明农、林、牧用地之间转换显著,且在各个时期的空间分布差别较大.从变化强度来看,土地利用的年综合变化率最大值渐趋增大,年均土地动态度在空间分布上差异很大,另外在各研究期赤峰市区周边动态度都很大,反映了赤峰市持续性的城市化进程.  相似文献   

4.
Landscape patterns in a region have different sizes, shapes and spatial arrangements, which contribute to the spatial heterogeneity of the landscape and are linked to the distinct behavior of thermal environments. There is a lack of research generating landscape metrics from discretized percent impervious surface area data (ISA), which can be used as an indicator of urban spatial structure and level of development, and quantitatively characterizing the spatial patterns of landscapes and land surface temperatures (LST). In this study, linear spectral mixture analysis (LSMA) is used to derive sub-pixel ISA. Continuous fractional cover thresholds are used to discretize percent ISA into different categories related to urban land cover patterns. Landscape metrics are calculated based on different ISA categories and used to quantify urban landscape patterns and LST configurations. The characteristics of LST and percent ISA are quantified by landscape metrics such as indices of patch density, aggregation, connectedness, shape and shape complexity. The urban thermal intensity is also analyzed based on percent ISA. The results indicate that landscape metrics are sensitive to the variation of pixel values of fractional ISA, and the integration of LST, LSMA. Landscape metrics provide a quantitative method for describing the spatial distribution and seasonal variation in urban thermal patterns in response to associated urban land cover patterns.  相似文献   

5.
Abstract

A methodology is presented for estimating percent coverage of impervious surface (IS) and forest cover (FC) within Landsat thematic mapper (TM) pixels of urban areas. High-resolution multi-spectral images from Quickbird (QB) play a key role in the sub-pixel mapping process by providing information on the spatial distributions of ISs and FCs at 2.4 m ground sampling intervals. Thematic classifications, also derived from the Landsat imagery, have then been employed to define relationships between 30 m Landsat-derived greenness values and percent IS and FC. By also utilizing land cover/land use classification derived from Landsat and defining unique relationships for urban sub-classes (i.e. residential, commercial/industrial, open land), confusion between impervious and fallow agricultural lands has been overcome. Test results are presented for Ottawa-Gatineau, an urban area that encompasses many aspects typical of the North American urban landscape. Multiple QB scenes have been acquired for this urban centre, thereby allowing us to undertake an in-depth study of the error budgets associated with the fractional inference process.  相似文献   

6.
MODIS土地覆盖分类的尺度不确定性研究   总被引：2，自引：0，他引：2  
以空间异质性较强的枯水期鄱阳湖为研究区,以搭载于同一卫星平台、具有同一观测时间和较高空间分辨率的ASTER数据为参照,分析研究了MODIS数据在土地覆盖分类中由空间尺度带来的不确定性。首先基于MODIS三角权重函数,建立了从ASTER到MODIS的尺度转换方法;然后对不同空间分辨率的数据进行土地覆盖分类,并基于误差矩阵和线性模型分析了MODIS土地覆盖分类结果的误差来源。结果表明,空间分辨率和光谱分辨率与成像方式这两类因素对MODIS与ASTER分类结果差异的贡献比例约为(6.6—11.2):2;MODIS像元尺度对研究区水体的分类不确定性影响较低,而对森林的不确定性影响可达63%。由此可见,在基于MODIS数据的土地覆盖分类研究中,空间尺度所产生的不确定性是比较显著的。这些研究结果对于土地覆盖分类及变化检测、尺度效应和景观生态学不确定性研究,有积极的参考意义。  相似文献   

7.
Mapping of vegetation in mountain areas based on remote sensing is obstructed by atmospheric and topographic distortions. A variety of atmospheric and topographic correction methods has been proposed to minimize atmospheric and topographic effects and should in principle lead to a better land cover classification. Only a limited number of atmospheric and topographic combinations has been tested and the effect on class accuracy and on different illumination conditions is not yet researched extensively. The purpose of this study was to evaluate the effect of coupled correction methods on land cover classification accuracy. Therefore, all combinations of three atmospheric (no atmospheric correction, dark object subtraction and correction based on transmittance functions) and five topographic corrections (no topographic correction, band ratioing, cosine correction, pixel-based Minnaert and pixel-based C-correction) were applied on two acquisitions (2009 and 2010) of a Landsat image in the Romanian Carpathian mountains. The accuracies of the fifteen resulting land cover maps were evaluated statistically based on two validation sets: a random validation set and a validation subset containing pixels present in the difference area between the uncorrected classification and one of the fourteen corrected classifications. New insights into the differences in classification accuracy were obtained. First, results showed that all corrected images resulted in higher overall classification accuracies than the uncorrected images. The highest accuracy for the full validation set was achieved after combination of an atmospheric correction based on transmittance functions and a pixel-based Minnaert topographic correction. Secondly, class accuracies of especially the coniferous and mixed forest classes were enhanced after correction. There was only a minor improvement for the other land cover classes (broadleaved forest, bare soil, grass and water). This was explained by the position of different land cover types in the landscape. Finally, coupled correction methods showed most efficient on weakly illuminated slopes. After correction, accuracies in the low illumination zone (cos β ≤ 0.65) were improved more than in the moderate and high illumination zones. Considering all results, best overall classification results were achieved after combination of the transmittance function correction with pixel-based Minnaert or pixel-based C-topographic correction. Furthermore, results of this bi-temporal study indicated that the topographic component had a higher influence on classification accuracy than the atmospheric component and that it is worthwhile to invest in both atmospheric and topographic corrections in a multi-temporal study.  相似文献   

8.
A major challenge is to develop a biodiversity observation system that is cost effective and applicable in any geographic region. Measuring and reliable reporting of trends and changes in biodiversity requires amongst others detailed and accurate land cover and habitat maps in a standard and comparable way. The objective of this paper is to assess the EODHaM (EO Data for Habitat Mapping) classification results for a Dutch case study. The EODHaM system was developed within the BIO_SOS (The BIOdiversity multi-SOurce monitoring System: from Space TO Species) project and contains the decision rules for each land cover and habitat class based on spectral and height information. One of the main findings is that canopy height models, as derived from LiDAR, in combination with very high resolution satellite imagery provides a powerful input for the EODHaM system for the purpose of generic land cover and habitat mapping for any location across the globe. The assessment of the EODHaM classification results based on field data showed an overall accuracy of 74% for the land cover classes as described according to the Food and Agricultural Organization (FAO) Land Cover Classification System (LCCS) taxonomy at level 3, while the overall accuracy was lower (69.0%) for the habitat map based on the General Habitat Category (GHC) system for habitat surveillance and monitoring. A GHC habitat class is determined for each mapping unit on the basis of the composition of the individual life forms and height measurements. The classification showed very good results for forest phanerophytes (FPH) when individual life forms were analyzed in terms of their percentage coverage estimates per mapping unit from the LCCS classification and validated with field surveys. Analysis for shrubby chamaephytes (SCH) showed less accurate results, but might also be due to less accurate field estimates of percentage coverage. Overall, the EODHaM classification results encouraged us to derive the heights of all vegetated objects in the Netherlands from LiDAR data, in preparation for new habitat classifications.  相似文献   

9.
Abstract

Forest cover monitoring plays an important role in the implementation of climate change mitigation policies such as Kyoto protocol and Reducing Emissions from Deforestation and Forest Degradation (REDD). In this study, we have monitored land cover using the PALSAR (Phased Array type L-band Synthetic Aperture Radar) full polarimetric data based on incoherent target decomposition. Supervised classification technique has been applied on Cloude–Pottier decomposition, Freeman–Durden three component, and Yamaguchi four component decomposition for accurate mapping of different types of land cover classes. Based on confusion matrix derived from the predicted and defined pixels, the evergreen and sparsely deciduous forests have shown high producer's accuracy by Freeman–Durden three component and Yamaguchi four component classifications. The overall accuracy of Maximum Likelihood Classification by Yamaguchi four component is 94.1% with 0.93 kappa coefficient as compared to the 90.3% with 0.88 kappa coefficient by Freeman–Durden three component and 89.7% with 0.88 kappa coefficient by Cloude–Pottier decomposition. High accuracy of classification in a forested area using full polarimetric PALSAR data may have been because of high penetration of L-band SAR. The content of this study could be useful for the forest cover mapping during cloudy days needed for proper implementation of REDD policies in Cambodia.  相似文献   

10.
南水北调中线工程是我国大规模跨流域调水工程的一部分,开展该区域植被覆盖度变化的研究与分析,对于保护该区域的生态环境及水质具有重要意义。该文以2000年和2009年两期遥感图像为本底数据,利用基于NDVI的像元二分模型对南水北调中线水源区的植被覆盖度进行了估算,并分析了该区植被覆盖度的时空变化特征。结果表明:2000年该水源区植被覆盖度的平均值为67.5%,2009年的平均值达到72%,植被覆盖度总体呈增长趋势;植被覆盖度增幅的空间特征表现为水源区中部地区高,东西部地区相对较低;在不同植被类型中,落叶针叶林的覆盖度平均值增幅最大,草地覆盖度增幅最小;位于水源区的大多数县(市)的植被覆盖度在近十年来都有不同程度的增加,其中柞水县的植被覆盖度平均值增长幅度最大,这与国家实施退耕还林、封山育林、基本农田建设等政策有关。  相似文献   

11.
基于空间连续数据的小流域景观格局破碎化研究   总被引：1，自引：0，他引：1  
基于空间连续数据，采用局部空间关联指标（LISA）——局部Moran指数（Local Moran Index, LMI），通过探测小流域内景观均质性和异质性的变化情况来反映景观格局破碎化的变化过程。作为一种空间明确的景观格局研究方法，LMI能够发现流域景观格局变化过程中的热点地区，并分析其与流域土地利用变化之间的联系，明确了土地利用变化是引起小流域景观格局变化的最主要的驱动因素。研究表明，基于空间连续数据的局部空间关联指标方法可以作为传统景观格局变化研究方法的有益补充。  相似文献   

12.
Natural and semi-natural landscape cover is heterogeneous. Ideally, mapping land cover requires an approach that represents both gradients and land covers spatiotemporal variability. These aspects can be visualized and depicted by applying a new spatio-temporal analysis based Landscape Heterogeneity Mapping (LaHMa) method to natural and semi-natural landscapes. Using MODIS NDVI 16-day imagery (February 2000–July 2009) for Crete, a 65-cluster image was selected from ISODATA classification results using the separability values of the divergence statistics. The 65 clusters appropriately generalize the spatial and temporal variability in land cover. Using classified outputs from 10 to 65 clusters, the frequency of pixels identified as boundaries of homogeneous land cover classes was translated into the form of a landscape heterogeneity map, which was then validated using field data. The results show that the heterogeneity map had moderate correlation (R2 = 0.60 and 0.63 in two transects) with the sum of differences between neighbouring transect pixels in all land cover components. In general, the study found this new approach (LaHMa) to be suitable for mapping landscape heterogeneity in the natural and semi-natural landscape of Crete, Greece. The new method appears to be of potential use for informing gradient analyses in landscape ecological studies.  相似文献   

13.
The Phase 1 Survey is the most comprehensive and widely used national level map of semi-natural habitats in Wales. However, the survey was based largely on field survey and was conducted over several decades, before being completed in 1997. Given that resources for a repeat survey were limited, this study has used an object-orientated rule-based classification implemented within eCognition of multi-temporal satellite sensor data acquired between 2003 and 2006 to map semi-natural habitats and agricultural land across Wales, thereby allowing a progressive update of the Phase 1 Survey. The classification of objects to Phase 1 habitat classes was undertaken in two steps; firstly the landscape of Wales was divided into objects using orthorectified SPOT-5 High Resolution Geometric (HRG) reflectance data (10 m spatial resolution) and Land Parcel Information System (LPIS) boundaries. A rule-base was then developed to progressively discriminate and map the distribution of 105 sub-habitats across Wales based on time-series of SPOT HRG, Terra-1 Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Indian Remote Sensing Satellite (IRS) LISS-3 data, derived datasets (e.g., vegetation indices, fractional images) and ancillary information (e.g., topography). The rules coupled knowledge of ecology and the information content of these remote sensing data using a combination of thresholds, Boolean operations and fuzzy membership functions. A second rule-base was then developed to translate the more detailed sub-habitat classification to Phase 1 habitat classes. Indicative accuracies of the revised Phase 1 mapping, based on comparisons with the later Phase 2 survey (for selected habitats), were >80% overall and typically between 70% and 90% for many classes. Through this exercise, Wales has become the first country in Europe to produce a national map of habitats (as opposed to land cover) through object-orientated classification of satellite sensor data. Furthermore, the approach can be adapted to allow continual monitoring of the extent and condition of habitats and agricultural land.  相似文献   

14.
以黄上高原泾河流域为例,首先利用遥感植被指数和气候干燥度指数之间的回归模型,模拟出了潜在植被指数,在此基础上通过遥感监督分类方法得出泾河流域现生植被分布格局和潜在植被的分布格局,并利用转换矩阵方法得出了植被退化的空间态势.结果表明:泾河流域最主要的潜在植被类型是针阔叶疏林(32.44%)、阔叶落叶林(31.28%)和中牛灌丛(23.71%).与现生植被相比较,有25.08%的阔叶落叶林潜在分布区被开垦为农作物,13.32%退化为针阔叶疏林,13.04%退化为中生灌丛,14.22%变化为旱生灌丛,仅有25.90%的面积保持了阔叶落叶林植被;针阔叶疏林分布区主要退化为农作物(26.01%)、旱生灌丛(20.99%)、草甸(17.12%);中生灌丛主要退化为草甸(30.29%)和温带草原(43.21%).植被的退化以流域中部、南部的黄土残塬沟壑区退化最为严重,其次为流域北部的黄土丘陵区,而流域东部的子午岭山区和流域西部的六盘山区植被退化相对较轻.  相似文献   

15.
Characterizing and quantifying distributions of shrubland ecosystem components is one of the major challenges for monitoring shrubland vegetation cover change across the United States. A new approach has been developed to quantify shrubland components as fractional products within National Land Cover Database (NLCD). This approach uses remote sensing data and regression tree models to estimate the fractional cover of shrubland ecosystem components. The approach consists of three major steps: field data collection, high resolution estimates of shrubland ecosystem components using WorldView-2 imagery, and coarse resolution estimates of these components across larger areas using Landsat imagery. This research seeks to explore this method to quantify shrubland ecosystem components as continuous fields in regions that contain wide-ranging shrubland ecosystems. Fractional cover of four shrubland ecosystem components, including bare ground, herbaceous, litter, and shrub, as well as shrub heights, were delineated in three ecological regions in Arizona, Florida, and Texas. Results show that estimates for most components have relatively small normalized root mean square errors and significant correlations with validation data in both Arizona and Texas. The distribution patterns of shrub height also show relatively high accuracies in these two areas. The fractional cover estimates of shrubland components, except for litter, are not well represented in the Florida site. The research results suggest that this method provides good potential to effectively characterize shrubland ecosystem conditions over perennial shrubland although it is less effective in transitional shrubland. The fractional cover of shrub components as continuous elements could offer valuable information to quantify biomass and help improve thematic land cover classification in arid and semiarid areas.  相似文献   

16.
Land degradation is believed to be one of the most severe and widespread environmental problems. In South Africa, large areas of land have been identified as degraded, as shown by the lower vegetation cover. One of the major causes of grassland degradation is change in plant species composition that leads to presence of unpalatable grass species. Some grass species have been successfully used as indicators of different levels of grassland degradation in the country. This paper, therefore explores the possibility of mapping grassland degradation in Cathedral Peak, South Africa, using indicators of grass species and edaphic factors. Multispectral SPOT 5 data were used to produce a grassland degradation map based on the spatial distribution of decreaser (Themeda triandra) and increaser (Hyparrhenia hirta) species. To improve mapping accuracy, soil samples were collected from each species site and analysed for nutrient content. A t-test and machine learning random forest classification algorithm were applied for variable selection and classification using SPOT 5 data and edaphic variables. Results indicated that the decreaser and increaser grass species can be mapped with modest accuracy using SPOT 5 data (overall accuracy of 75.30%, quantity disagreement = 2 and allocation disagreement = 23). The classification accuracy was improved to 88.60%, 1 and 11 for overall accuracy, quantity and allocation disagreements, respectively, when SPOT 5 bands and edaphic factors were combined. The study demonstrated that an approach based on the integration of multispectral data and edaphic variables, which increased the overall classification accuracy by about 13%, is a suitable when adopting remote sensing to monitor grassland degradation.  相似文献   

17.
This paper presents novel techniques to estimate the uncertainty in extrapolations of spatially-explicit land-change simulation models. We illustrate the concept by mapping a historic landscape based on: 1) tabular data concerning the quantity in each land cover category at a distant point in time at the stratum level, 2) empirical maps from more recent points in time at the grid cell level, and 3) a simulation model that extrapolates land-cover change at the grid cell level. This paper focuses on the method to show uncertainty explicitly in the map of the simulated landscape at the distant point in time. The method requires that validation of the land-cover change model be quantified at the grid-cell level by Kappa for location (Klocation). The validation statistic is used to estimate the certainty in the extrapolation to a point in time where an empirical map does not exist. As an example, we reconstruct the 1951 landscape of the Ipswich River Watershed in Massachusetts, USA. The technique creates a map of 1951 simulated forest with an overall estimated accuracy of 0.91, with an estimated users accuracy ranging from 0.95 to 0.84. We anticipate that this method will become popular, because tabular information concerning land cover at coarse stratum-level scales is abundant, while digital maps of the specific location of land cover are needed at a finer spatial resolution. The method is a key to link non-spatial models with spatially-explicit models.  相似文献   

18.
Many data fusion methods are available, but it is poorly understood which fusion method is suitable for integrating Landsat Thematic Mapper (TM) and radar data for land cover classification. This research explores the integration of Landsat TM and radar images (i.e., ALOS PALSAR L-band and RADARSAT-2 C-band) for land cover classification in a moist tropical region of the Brazilian Amazon. Different data fusion methods—principal component analysis (PCA), wavelet-merging technique (Wavelet), high-pass filter resolution-merging (HPF), and normalized multiplication (NMM)—were explored. Land cover classification was conducted with maximum likelihood classification based on different scenarios. This research indicates that individual radar data yield much poorer land cover classifications than TM data, and PALSAR L-band data perform relatively better than RADARSAT-2 C-band data. Compared to the TM data, the Wavelet multisensor fusion improved overall classification by 3.3%-5.7%, HPF performed similarly, but PCA and NMM reduced overall classification accuracy by 5.1%-6.1% and 7.6%-12.7%, respectively. Different polarization options, such as HH and HV, work similarly when used in data fusion. This research underscores the importance of selecting a suitable data fusion method that can preserve spectral fidelity while improving spatial resolution.  相似文献   

19.
Abstract

A classification method was developed for mapping land cover in NE Costa Rica at a regional scale for spatial input to a biogeochemical model (CENTURY). To distinguish heterogeneous cover types, unsupervised classifications of Landsat Thematic Mapper data were combined with ancillary and derived data in an iterative process. Spectral classes corresponding to ground control types were segregated into a storage raster while ambiguous pixels were passed through a set of rules to the next stage of processing. Feature sets were used at each step to help sort spectral classes into land cover classes. The process enabled different feature sets to be used for different types while recognizing that spectral classification alone was not sufficient for separating cover types that were defined by heterogeneity. Spectral data included the TM reflective bands, principal components and the NDVI. Ancillary data included GIS coverages of swamp extents, banana plantation boundaries and river courses. Derived data included neighborhood variety and majority measures that captured texture. The final map depicts 18 land cover types and captures the general patterns found in the region. Some confusion still exists between closely related types such as pasture with different amounts of tree cover.  相似文献   

20.
In this paper, we present a two-stage method for mapping habitats using Earth observation (EO) data in three Alpine sites in South Tyrol, Italy. The first stage of the method was the classification of land cover types using multi-temporal RapidEye images and support vector machines (SVMs). The second stage involved reclassification of the land cover types to habitat types following a rule-based spatial kernel. The highest accuracies in land cover classification were 95.1% overall accuracy, 0.94 kappa coefficient and 4.9% overall disagreement. These accuracies were obtained when the combination of images with topographic parameters and homogeneity texture was used. The habitat classification accuracies were rather moderate due to the broadly defined rules and possible inaccuracies in the reference map. Overall, our proposed methodology could be implemented to map cost-effectively alpine habitats over large areas and could be easily adapted to map other types of habitats.  相似文献   

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