A Neural Network Method for Land Use Change Classification, with Application to the Nile River Delta

Detecting and monitoring changes in conditions at the Earth's surface are essential for understanding human impact on the environment and for assessing the sustainability of development. In the next decade, NASA will gather high-resolution multi-spectral and multi-temporal data, which could be used for analyzing long-term changes, provided that available methods can keep pace with the accelerating flow of information. This paper introduces an automated technique for change identification, based on the ARTMAP neural network. This system overcomes some of the limitations of traditional change detection methods, and also produces a measure of confidence in classification accuracy. Landsat thematic mapper (TM) imagery of the Nile River delta provides a testbed for land use change classification methods. This dataset consists of a sequence of ten images acquired between 1984 and 1993 at various times of year. Field observations and photo interpretations have identified 358 sites as belonging to eight classes, three of which represent changes in land use over the ten-year period. Aparticular challenge posed by this database is the unequal representation of various land use categories: three classes, urban, agriculture in delta, and other, comprise 95% of pixels in labeled sites. A two-step sampling method enables unbiased training of the neural network system across sites.National Science Foundation (SBR 95-13889); Office of Naval Research (N00014-95-1-409, N00014-95-0657); Air Force Office of Scientific Research (F49620-01-1-0397, F49620-01-1-042

Fuzzy segmentation for geographic object-based image analysis

Image segmentation partitions remote sensing images into image objects before assigning them to categorical land cover classes. Current segmentation methods require users to invest considerable time and effort in the search for meaningful image objects. As an alternative method we propose 'fuzzy' segmentation that offers more flexibility in dealing with remote sensing uncertainty. In the proposed method, original bands are processed using regression techniques to output fuzzy image regions which express degrees of membership to target land cover classes. Contextual properties of fuzzy regions can be measured to indicate potential spectral confusion. A 'defuzzification' process is subsequently conducted to produce the categorical land cover classes. This method was tested using data sets of both high and medium spatial resolution. The results indicate that this approach is able to produce classification with satisfying accuracy and requires very little user interaction

Mapping Spatial Thematic Accuracy Using Indicator Kriging

Thematic maps derived from remote sensing imagery is increasingly being used in environmental and ecological modeling. Spatial information in these maps however is not free of error. Different methodologies such as error matrices are used to assess the accuracy of the spatial information. However, most of the methods commonly used for describing the accuracy assessment of thematic data fail to describe spatial differences of the accuracy across an area of interest. This thesis describes the use of indicator kriging as a geostatistical method for mapping the spatial accuracy of thematic maps. The method is illustrated by constructing accuracy maps for the forest land-cover classes in the 2001 National Land Cover Dataset (NLCD) extent covering the conterminous United States. Independent reference data collected for the accuracy assessment of the 2001 NCLD was used. This thesis also describes the use of indicator cokriging for improving the thematic accuracy of the forest land-cover classes by adding information from other land-cover classes as additional variables. Finally, probability surfaces resulted from indicator kriging and indicator cokriging will be used to generate alternate realizations of the forest land-cover class through stochastic simulation. Such realizations could serve as input parameters to spatially explicit models. Result show how thematic accuracy varies across regions and it outlines differences between land-cover estimates by NLCD and those created through indicator kriging

Let your maps be fuzzy!—Class probabilities and floristic gradients as alternatives to crisp mapping for remote sensing of vegetation

Mapping vegetation as hard classes based on remote sensing data is a frequently applied approach, even though this crisp, categorical representation is not in line with nature\u27s fuzziness. Gradual transitions in plant species composition in ecotones and faint compositional differences across different patches are thus poorly described in the resulting maps. Several concepts promise to provide better vegetation maps. These include (1) fuzzy classification (a.k.a. soft classification) that takes the probability of an image pixel\u27s class membership into account and (2) gradient mapping based on ordination, which describes plant species composition as a floristic continuum and avoids a categorical description of vegetation patterns. A systematic and comprehensive comparison of these approaches is missing to date. This paper hence gives an overview of the state of the art in fuzzy classification and gradient mapping and compares the approaches in a case study. The advantages and disadvantages of the approaches are discussed and their performance is compared to hard classification (a.k.a. crisp or boolean classification). Gradient mapping best conserves the information in the original data and does not require an a priori categorization. Fuzzy classification comes close in terms of information loss and likewise preserves the continuous nature of vegetation, however, still relying on a priori classification. The need for a priori classification may be a disadvantage or, in other cases, an advantage because it allows using categorical input data instead of the detailed vegetation records required for ordination. Both approaches support spatially explicit accuracy analyses, which further improves the usefulness of the output maps. Gradient mapping and fuzzy classification offer various advantages over hard classification, can always be transformed into a crisp map and are generally applicable to various data structures. We thus recommend the use of these approaches over hard classification for applications in ecological research

Uncertainties in land use data

This paper deals with the description and assessment of uncertainties in land use data derived from Remote Sensing observations, in the context of hydrological studies. Land use is a categorical regionalised variable reporting the main socio-economic role each location has, where the role is inferred from the pattern of occupation of land. The properties of this pattern that are relevant to hydrological processes have to be known with some accuracy in order to obtain reliable results; hence, uncertainty in land use data may lead to uncertainty in model predictions. There are two main uncertainties surrounding land use data, positional and categorical. The first one is briefly addressed and the second one is explored in more depth, including the factors that influence it. We (1) argue that the conventional method used to assess categorical uncertainty, the confusion matrix, is insufficient to propagate uncertainty through distributed hydrologic models; (2) report some alternative methods to tackle this and other insufficiencies; (3) stress the role of metadata as a more reliable means to assess the degree of distrust with which these data should be used; and (4) suggest some practical recommendations

A method to compare and improve land cover datasets: Application to the GLC-2000 and MODIS land cover products

This paper presents a methodology for the comparison of different land cover datasets and illustrates how this can be extended to create a hybrid land cover product. The datasets used in this paper are the GLC-2000 and MODIS land cover products. The methodology addresses: 1) the harmonization of legend classes from different global land cover datasets and 2) the uncertainty associated with the classification of the images. The first part of the methodology involves mapping the spatial disagreement between the two land cover products using a combination of fuzzy logic and expert knowledge. Hotspots of disagreement between the land cover datasets are then identified to determine areas where other sources of data such as TM/ETM images or detailed regional and national maps can be used in the creation of a hybrid land cover dataset

The applications of neural network in mapping, modeling and change detection using remotely sensed data

Thesis (Ph.D.)--Boston UniversityAdvances in remote sensing and associated capabilities are expected to proceed in a number of ways in the era of the Earth Observing System (EOS). More complex multitemporal, multi-source data sets will become available, requiring more sophisticated analysis methods. This research explores the applications of artificial neural networks in land-cover mapping, forward and inverse canopy modeling and change detection. For land-cover mapping a multi-layer feed-forward neural network produced 89% classification accuracy using a single band of multi-angle data from the Advanced Solidstate Array Spectroradiometer (ASAS). The principal results include the following: directional radiance measurements contain much useful information for discrimination among land-cover classes; the combination of multi-angle and multi-spectral data improves the overall classification accuracy compared with a single multi-angle band; and neural networks can successfully learn class discrimination from directional data or multi-domain data. Forward canopy modeling shows that a multi-layer feed-forward neural network is able to predict the bidirectional reflectance distribution function (BRDF) of different canopy sites with 90% accuracy. Analysis of the signal captured by the network indicates that the canopy structural parameters, and illumination and viewing geometry, are essential for predicting the BRDF of vegetated surfaces. The inverse neural network model shows that the R2 between the network-predicted canopy parameters and the actual canopy parameters is 0.85 for canopy density and 0.75 for both the crown shape and the height parameters. [TRUNCATED

Urban land cover change detection analysis and modeling spatio-temporal Growth dynamics using Remote Sensing and GIS Techniques: A case study of Dhaka, Bangladesh

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Dhaka, the capital of Bangladesh, has undergone radical changes in its physical form, not only in its vast territorial expansion, but also through internal physical transformations over the last decades. In the process of urbanization, the physical characteristic of Dhaka is gradually changing as open spaces have been transformed into building areas, low land and water bodies into reclaimed builtup lands etc. This new urban fabric should be analyzed to understand the changes that have led to its creation. The primary objective of this research is to predict and analyze the future urban growth of Dhaka City. Another objective is to quantify and investigate the characteristics of urban land cover changes (1989-2009) using the Landsat satellite images of 1989, 1999 and 2009. Dhaka City Corporation (DCC) and its surrounding impact areas have been selected as the study area. A fisher supervised classification method has been applied to prepare the base maps with five land cover classes. To observe the change detection, different spatial metrics have been used for quantitative analysis. Moreover, some postclassification change detection techniques have also been implemented. Then it is found that the ‘builtup area’ land cover type is increasing in high rate over the years. The major contributors to this change are ‘fallow land’ and ‘water body’ land cover types. In the next stage, three different models have been implemented to simulate the land cover map of Dhaka city of 2009. These are named as ‘Stochastic Markov (St_Markov)’ Model, ‘Cellular Automata Markov (CA_Markov)’ Model and ‘Multi Layer Perceptron Markov (MLP_Markov)’ Model. Then the best-fitted model has been selected based on various Kappa statistics values and also by implementing other model validation techniques. This is how the ‘Multi Layer Perceptron Markov (MLP_Markov)’ Model has been qualified as the most suitable model for this research. Later, using the MLP_Markov model, the land cover map of 2019 has been predicted. The MLP_Markov model shows that 58% of the total study area will be converted into builtup area cover type in 2019. The interpretation of depicting the future scenario in quantitative accounts, as demonstrated in this research, will be of great value to the urban planners and decision makers, for the future planning of modern Dhaka City

Using mixed objects in the training of object-based image classifications

Image classification for thematic mapping is a very common application in remote sensing, which is sometimes realized through object-based image analysis. In these analyses, it is common for some of the objects to be mixed in their class composition and thus violate the commonly made assumption of object purity that is implicit in a conventional object-based image analysis. Mixed objects can be a problem throughout a classification analysis, but are particularly challenging in the training stage as they can result in degraded training statistics and act to reduce mapping accuracy. In this paper the potential of using mixed objects in training object-based image classifications is evaluated. Remotely sensed data were submitted to a series of segmentation analyses from which a range of under- to over-segmented outputs were intentionally produced. Training objects were then selected from the segmentation outputs, resulting in training data sets that varied in terms of size (i.e. number of objects) and proportion of mixed objects. These training data sets were then used with an artificial neural network and a generalized linear model, which can accommodate objects of mixed composition, to produce a series of land cover maps. The use of training statistics estimated based on both pure and mixed objects often increased classification accuracy by around 25% when compared with accuracies obtained from the use of only pure objects in training. So rather than the mixed objects being a problem, they can be an asset in classification and facilitate land cover mapping from remote sensing. It is, therefore, desirable to recognize the nature of the objects and possibly accommodate mixed objects directly in training. The results obtained here may also have implications for the common practice of seeking an optimal segmentation output, and also act to challenge the widespread view that object-based classification is superior to pixel-based classification