Propagating error in land-cover-change analyses: impact of temporal dependence under increased thematic complexity

We examined the impact of temporal dependence between patterns of error in classified time-series imagery through a simulation modeling approach. This research extended the land-cover-change simulation model we previously developed to investigate: (1) the assumption of temporal independence between patterns of error in classified time-series imagery; and (2) the interaction of patterns of change and patterns of error in a post-classification change analysis. In this research, the thematic complexity of the classified land-cover maps was increased by increasing the number of simulated land-cover classes. Simulating maps with increased categorical resolution permitted the incorporation of: (1) higher-order, more complex spatial and temporal interactions between land-cover classes; and (2) patterns of error that better reproduce the complex error interactions that often occur in time-series classified imagery. The overall modeling framework was divided into two primary components: (1) generation of a map representing true change; and (2) generation of a suite of change maps that had been perturbed by specific patterns of error. All component maps in the model were produced using simulated annealing, which enabled us to create a series of map realizations with user-defined spatial and temporal patterns. Comparing the true map of change to the error-perturbed maps of change using accuracy assessment statistics showed that increasing the temporal dependence between classification errors did not improve the accuracy of resulting maps of change when the categorical scale of the land-cover classified maps was increased. The increased structural complexity within the time series of maps effectively inhibited the impact of temporal dependence. However, results demonstrated that there are interactions between patterns of error and patterns of change in a post-classification change analysis. These interactions played a major role in determining the accuracy associated with the maps of change.     

Conflation and aggregation of spatial data improve predictive models for species with limited habitats: A case of the threatened yellow-billed cuckoo in Arizona,USA

Riparian vegetation provides important wildlife habitat in the southwestern United States, but limited distributions and spatial complexity often leads to inaccurate representation in maps used to guide conservation. We test the use of data conflation and aggregation on multiple vegetation/land-cover maps to improve the accuracy of habitat models for the threatened western yellow-billed cuckoo (Coccyzus americanus occidentalis). We used species observations (n = 479) from a state-wide survey to develop habitat models from 1) three vegetation/land-cover maps produced at different geographic scales ranging from state to national, and 2) new aggregate maps defined by the spatial agreement of cover types, which were defined as high (agreement = all data sets), moderate (agreement ≥ 2), and low (no agreement required). Model accuracies, predicted habitat locations, and total area of predicted habitat varied considerably, illustrating the effects of input data quality on habitat predictions and resulting potential impacts on conservation planning. Habitat models based on aggregated and conflated data were more accurate and had higher model sensitivity than original vegetation/land-cover, but this accuracy came at the cost of reduced geographic extent of predicted habitat. Using the highest performing models, we assessed cuckoo habitat preference and distribution in Arizona and found that major watersheds containing high-probably habitat are fragmented by a wide swath of low-probability habitat. Focus on riparian restoration in these areas could provide more breeding habitat for the threatened cuckoo, offset potential future habitat losses in adjacent watershed, and increase regional connectivity for other threatened vertebrates that also use riparian corridors.     

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.     

A review of historical reconstruction methods of land use/land cover

Understanding long-term human-environment interactions requires historical reconstruction of past land-use and land-cover changes. Most reconstructions have been based primarily on consistently available and relatively standardized information from historical sources. Based on available data sources and a retrospective research, in this paper we review the approaches and methods of the digital reconstruction and analyze their advantages and possible constraints in the following aspects: (1) Historical documents contain qualitative or semi-quantitative information about past land use, which also usually include land-cover data, but preparation of archival documents is very time-consuming. (2) Historical maps and pictures offer visual and spatial quantitative land-cover information. (3) Natural archive has significant advantages as a method for reconstructing past vegetation and has its unique possibilities especially when historical records are missing or lacking, but it has great limits of rebuilding certain land-cover types. (4) Historical reconstruction models have been gradually developed from empirical models to mechanistic ones. The method does not only reconstruct the quantity of land use/cover in historical periods, but it also reproduces the spatial distribution. Yet there are still few historical land-cover datasets with high spatial resolution. (5) Reconstruction method based on multiple-source data and multidisciplinary research could build historical land-cover from multiple perspectives, complement the missing data, verify reconstruction results and thus improve reconstruction accuracy. However, there are challenges that make the method still in the exploratory stage. This method can be a long-term development goal for the historical land-cover reconstruction. Researchers should focus on rebuilding historical land-cover dataset with high spatial resolution by developing new models so that the study results could be effectively applied in simulations of climatic and ecological effects.     

Non-current data and geographical information systems A case for data retention

Inter-urban income disparities reflect differences between individual urban localities in the average incomes of their residents. The present paper discusses different ways of visualizing such disparities on thematic maps. The approach we propose is based on the transformation of distances between individual localities and a reference city (e.g. a major population centre of a country) in proportion to the actual differences in the income levels. The general principle of such a transformation is to bring closer to the reference city places with higher incomes, while moving away localities with low income levels. Three alternative approaches to the implementation of this transformation technique are discussed. According to the ‘actual distance’ method, the spatial ‘shift’ of a locality on the map is set proportional to both the relative difference in incomes and the aerial distance between a locality and the reference city. In the ‘proportional increment’ transformation, the distance between a locality and the reference city is adjusted by a parameter whose values are proportional to income disparities between the two. Lastly, according to the ‘concentric circle’ transformation, localities with identical levels of incomes are positioned at a certain distance from the reference city, forming concentric circles around it. Both advantages and disadvantages of these transformation techniques are discussed, and the ‘proportional increment’ method is chosen as the best-performing visualization technique. The performance of this technique is demonstrated using income data for urban localities in Israel in 1991 and 1999. As analysis indicates, the proposed method helps to illustrate both the existing patterns of inter-urban income disparities and their dynamics over time.     

Knowledge discovery from soil maps using inductive learning

This paper develops a knowledge discovery procedure for extracting knowledge of soil-landscape models from a soil map. It has broad relevance to knowledge discovery from other natural resource maps. The procedure consists of four major steps: data preparation, data preprocessing, pattern extraction, and knowledge consolidation. In order to recover true expert knowledge from the error-prone soil maps, our study pays specific attention to the reduction of representation noise in soil maps. The data preprocessing step has exhibited an important role in obtaining greater accuracy. A specific method for sampling pixels based on modes of environmental histograms has proven to be effective in terms of reducing noise and constructing representative sample sets. Three inductive learning algorithms, the See5 decision tree algorithm, Naïve Bayes, and artificial neural network, are investigated for a comparison concerning learning accuracy and result comprehensibility. See5 proves to be an accurate method and produces the most comprehensible results, which are consistent with the rules (expert knowledge) used in producing the soil map. The incorporation of spatial information into the knowledge discovery process is found not only to improve the accuracy of the extracted knowledge, but also to add to the explicitness and extensiveness of the extracted soil-landscape model.     

Application of GIS Processing Techniques for Producing Mineral Prospectivity Maps—A Case Study: Mesothermal Au in the Swayze Greenstone Belt,Ontario, Canada

A Geographic Information System (GIS) is used to prepare and process digital geoscience data in a variety of ways for producing gold prospectivity maps of the Swayze greenstone belt, Ontario, Canada. Data used to produce these maps include geologic, geochemical, geophysical, and remotely sensed (Landsat). A number of modeling methods are used and are grouped into data-driven (weights of evidence, logistic regression) and knowledge-driven (index and Boolean overlay) methods. The weights of evidence (WofE) technique compares the spatial association of known gold prospects with various indicators (evidence maps) of gold mineralization, to derive a set of weights used to produce the final gold prospectivity map. Logistic regression derives statistical information from evidence maps over each known gold prospect and the coefficients derived from regression analysis are used to weight each evidence map. The gold prospectivity map produced from the index overlay process uses a weighting scheme that is derived from input by the geologist, whereas the Boolean method uses equally weighted binary evidence maps.The resultant gold prospectivity maps are somewhat different in this study as the data comprising the evidence maps were processed purposely differently for each modeling method. Several areas of high gold potential, some of which are coincident with known gold prospects, are evident on the gold prospectivity maps produced using all modeling methods. The majority of these occur in mafic rocks within high strain zones, which is typical of many Archean greenstone belts.     

土地利用/覆被历史重建方法(英文)

Understanding long-term human-environment interactions requires historical reconstruction of past land-use and land-cover changes. Most reconstructions have been based primarily on consistently available and relatively standardized information from historical sources. Based on available data sources and a retrospective research, in this paper we review the approaches and methods of the digital reconstruction and analyze their advantages and possible constraints in the following aspects:(1) Historical documents contain qualitative or semi-quantitative information about past land use, which also usually include land-cover data, but preparation of archival documents is very time-consuming.(2) Historical maps and pictures offer visual and spatial quantitative land-cover information.(3) Natural archive has significant advantages as a method for reconstructing past vegetation and has its unique possibilities especially when historical records are missing or lacking, but it has great limits of rebuilding certain land-cover types.(4) Historical reconstruction models have been gradually developed from empirical models to mechanistic ones. The method does not only reconstruct the quantity of land use/cover in historical periods, but it also reproduces the spatial distribution. Yet there are still few historical land-cover datasets with high spatial resolution.(5) Reconstruction method based on multiple-source data and multidisciplinary research could build historical land-cover from multiple perspectives, complement the missing data, verify reconstruction results and thus improve reconstruction accuracy. However, there are challenges that make the method still in the exploratory stage. This method can be a long-term development goal for the historical land-cover reconstruction. Researchers should focus on rebuilding historical land-cover dataset with high spatial resolution by developing new models so that the study results could be effectively applied in simulations of climatic and ecological effects.     

Use of the weighted Kappa coefficient in classification error assessment of thematic maps

Abstract

The weighted Kappa coefficient is applied to the comparison of thematic maps. Weighted Kappa is a useful measure of accuracy when the map classes are ordered, or when the relative seriousness of the different possible errors may vary. The calculation and interpretation of weighted Kappa are demonstrated by two examples from forest surveys. First, the accuracy of thematic site quality maps classified according to an ordinal scale is assessed. Error matrices are derived from map overlays, and two different sets of agreement weights are used for the calculation. Weighted Kappa ranges from 0.34 to 0.55, but it does not differ significantly between two separate areas. Secondly, weighted Kappa is calculated for a tree species cover classified according to a nominal scale. Weights reflecting the economic loss for the forest owner due to erroneous data are used for the computation. The value of weighted Kappa is 0.56.     

Structuring image data within a multi-media information system

Abstract

This paper describes a generic structure model for large numbers of spatially-related images held within a multi-media information system. Applications vary from multi-scale map tilings underlying geographical information system user interfaces to sets of spatially-related visual data held in image form, such as ‘structured walks’ and sets of views with finder maps. The model enables an abstract structure to be soft-configured onto any indexed collection of images and related to standard databases of textual and graphical information at various levels. This is achieved by defining a number of customized ‘image structures’ and providing techniques for their manipulation. Key features of the model are defined in VDM-SL, a formal specification language being standardized by the International Organization for Standardization. The formal definitions are accompanied by explanations in English and by examples. Prototype implementations are described. A glossary of symbols, terms and expressions is given in the Appendix.     

Identifying strengths and limitations of pan-European forest cover maps through spatial comparison

Detailed and harmonized information on spatial forest distribution is an essential input for forest-related environmental assessments, in particular, for biomass and growing stock modeling. In the last years, several mapping approaches have been developed in order to provide such information for Europe in a harmonized way. Each of these maps exhibits particular properties and varies in accuracy. Yet, they are often used in parallel for different modeling purposes. A detailed spatial comparison seemed necessary in order to provide information on the advantages and limitations of each of these forest cover maps in order to facilitate their selection for modeling purposes.

This article confronts the high-resolution forest cover map recently developed by the Joint Research Centre for the year 2000 (FMAP2000) with previously existing maps for the same time period: the CORINE Land Cover 2000 (CLC2000) and the Calibrated European Forest Map 1996 (CEFM1996). The spatial comparison of these three maps was carried out based on forest proportion maps of 1 km derived from the original maps. To characterize differences according to biogeographic regions, two criteria were used: detail of thematic content within each map and local spatial agreement.

Concerning thematic content, CLC2000 displayed a surfeit of non-forested areas at the cost of low forest proportions, while FMAP2000 showed a more balanced distribution likely to preserve more detail in forest spatial pattern. Good spatial agreement was found for CLC2000 and FMAP2000 within about 70% of the study area, while only 50% agreement was found when compared with CEFM1996. The largest spatial differences between all maps were found in the Alpine and Mediterranean regions. Reasons for these might be different input data and classification techniques and, in particular, the calibration of CEFM1996 to reported national statistics.     

A metaheuristic approach for efficient and effective sketch-to-metric map alignment

Sketching as a natural mode for human communication and creative processes presents opportunities for improving human–computer interaction in geospatial information systems. However, to use a sketch map as user input, it must be localized within the underlying spatial data set of the information system, the base metric map. This can be achieved by a matching process called qualitative map alignment in which qualitative spatial representations of the two input maps are used to establish correspondences between each sketched object and one or more objects in the metric map. The challenge is that, to the best of our knowledge, no method for matching qualitative spatial representations suggested so far is applicable in realistic scenarios due to excessively long runtimes, incorrect algorithm design or the inability to use more than one spatial aspect at a time. We address these challenges with a metaheuristic algorithm which uses novel data structures to match qualitative spatial representations of a pair of maps. We present the design, data structures and performance evaluation of the algorithm using real-world sketch and metric maps as well as on synthetic data. Our algorithm is novel in two main aspects. Firstly, it employs a novel system of matrices known as local compatibility matrices, which facilitate the computation of estimates for the future size of a partial alignment and allow several types of constraints to be used at the same time. Secondly, the heuristic it computes has a higher accuracy than the state-of-the-art heuristic for this task, yet requires less computation. Our algorithm is also a general method for matching labelled graphs, a special case of which is the one involving complete graphs whose edges are labelled with spatial relations. The results of our evaluation demonstrate practical runtime performance and high solution quality.     

Comparison of thematic maps using symbolic entropy

Comparison of thematic maps is an important task in a number of disciplines. Map comparison has traditionally been conducted using cell-by-cell agreement indicators. More recently, other methods have been proposed that take into account not only spatially coincident cells in two maps, but also their surroundings or the spatial structure of their differences. The objective of this article is to propose a framework for map comparison that considers (1) the patterns of spatial association in two maps, in other words, the map elements in their surroundings; (2) the equivalence of those patterns; and (3) the independence of patterns between maps. Two new statistics for the spatial analysis of qualitative data are introduced that are based on the symbolic entropy of the maps. As well, all inferential elements to conduct hypothesis testing are developed. The framework is illustrated using real and simulated maps.     

Multi-faceted land cover and land use change analyses in the Yellow River Basin based on dense Landsat time series: Exemplary analysis in mining,agriculture, forest,and urban areas

The Yellow River Basin is one of China's most dynamic regions, where past and recent anthropogenic land use activities and polices have had a remarkably impact on the basin's surface. Over the past decades, the rapid socio-economic development has increased the pressure on the prevailing water and land resources with various repercussions on the environment and society. Counteracting ecological degradation in the basin, large-scale conservation and restoration plans have been initiated to expand vegetation coverage on deteriorated land, simultaneously fostering rural sustainable agriculture production. In this context, we derived precise spatial thematic products from long-term satellite time-series about high-frequency temporal dynamics. This information, available in a consistent and repeatable fashion is rare and relevant for many regional and local stakeholders and must be monitored annually to capture the rapid rate of change. Such information serves as a valuable base for decision-making processes. In this study, we used all the archived Landsat images between 2000 and 2015 (4520 scenes) to computed annually the spatially continuous spectral-temporal and textual metrics based on dense Landsat time-series to derive annual maps showing the most prominent land-cover change types related to mining, agriculture, forestry, and urbanization in four sub-regions spread over the Yellow River Basin. These novel land cover/use products provide new insights into recent regional and local dynamics. For final classification, we employed random forest classifiers for each thematic focus-region, trained and tested based on a stable-pixels data set. The resulting maps achieved high accuracies and show afforestation on the Loess Plateau and urbanization as the most prominent drivers of land use/cover dynamics. Agricultural land remained stable, showing local small-scale dynamics. Our study highlights the great potential of using consistent spectral-temporal metrics derived from dense Landsat time-series data together with a stable pixels reference set, allowing for local and regional land surface dynamics mapping at high spatial resolution and the prediction of implications of future change for effective and sustainable basin management.     

Combining spatial data with survey data improves predictions of boundaries between settlements

Studies of land-use change often require the combination of socioeconomic survey data with spatially continuous maps of land-cover change. One approach is to define maps of land ownership, assuming that all land-use change can be attributed to the owners or managers of each parcel of land. Unfortunately, records of administrative boundaries between towns and villages are commonly unavailable in developing countries and prohibitively costly or time consuming to map for individual projects. However, point locations of the settlements themselves can be obtained easily from existing maps or remotely-sensed imagery. In this paper we compare three methods – circular buffers, unweighted Voronoi polygons (sometimes referred to as Thiessen polygons) and multiplicatively weighted Voronoi polygons – for estimating boundaries between villages in an agricultural landscape in West Africa. The benefits and limitations of each approach are discussed, and their accuracy assessed using 98 independently collected GPS coordinates of village boundaries. We present a novel method for generating and optimising weights for multiplicatively weighted Voronoi polygons using survey data of village sizes from a subset of villages. By using both spatial information and survey data from villages, we show that multiplicatively weighted Voronoi polygons outperform other methods of predicting village boundaries, and increase the correlation coefficient between surveyed village area and mapped areas from 0.18 to 0.68 compared with more commonly used unweighted Voronoi polygons. Our method of weighting Voronoi polygons can be implemented with data and software commonly available to researchers and non-governmental organisations.     

土地利用/土地覆被变化研究:寻求新的综合途径

土地利用/土地覆被变化是很复杂的现象,参与该项目的研究人员要避免“瞎子摸大象”那样的片面性,必须寻求新的综合研究途径。为此,不能简单地沿袭传统土地利用研究的思路和方法,需要不断提出新的研究论题;对土地利用变化驱动力必须有一种普遍的、综合的认识;需要将多个案例研究联结为一个可代表区域空间异质性的网络,需要作多空间尺度的研究,从而将地方尺度和区域尺度的土地覆被动态联系起来;需要发展新的研究方法,并将从农户调查到遥感数据的各种信息综合起来;尤其需要形成关于土地利用/土地覆被变化的综合科学理论框架     

Research Article. Modelling inside GIS: Part 1. Model structures,exploratory spatial data analysis and aggregation

Abstract

This is the first of two papers elaborating a framework for embedding urban models within GIS. This framework is based upon using the display capabilities of GIS as the user interface to the conventional modelling process, beginning with data selection and analysis, moving to model specification and calibration, and thence to prediction. In this paper, we outline how various stages in this process based on purpose-built software outside the system, are accessed and operated through the GIS. We first deal with display based on thematic maps, surfaces, graphs and linked windows, standard to any data from whatever source, be it observations, model estimates or predictions. We then describe how various datasets are selected, how the spatial system can be partitioned or aggregated, and how rudimentary exploratory spatial data analysis enables scatterplots to be associated with thematic maps. We illustrate all these functions and operations using the proprietary GIS ARC-INFO applied to population data at the tract level in the Buffalo region. In the second part of the paper, various residential location models are outlined and the full modelling framework is assembled and demonstrated.     

闽南丘陵区典型小流域空间框架数据采集与应用

综合利用Photoshop、Geoway、MapInfo、ArcView等软件提供的处理技术,通过对扫描得到的地形、土地利用和水土流失等要素图进行图像剪切、拼接、配准、采样、跟踪、调整以及属性表的维护等一系列处理步骤,建立了大比例尺小流域地理空间框架数据集,提供了小流域空间框架数据采集与应用的技术流程与方法,提出应用各种软件进行大比例尺空间框架数据处理过程中应当注意的各种细节问题.最后,以闽南丘陵地区草子坝小流域为例,利用ArcView的3D Analyst、Spatial Analyst、Geoprocessing等扩展模块以及MapInfo SQL查询功能,对流域空间框架数据进行处理,得到了用于比较精细空间尺度上表达流域三维地形模型、坡度、坡向、河流垂直剖面、晕渲,不同高程上的土地利用类型等专题应用结果.