Modeling Words for Qualitative Distance Based on Interval Type-2 Fuzzy Sets

Modeling qualitative distance words is important for natural language understanding, scene reconstruction and many decision support systems (DSSs) based on a geographic information system (GIS). However, it is difficult to establish the relationship between qualitative distance words and quantitative distance for special applications since the meanings of these words are influenced by both subjective and objective factors. Some existing methods are reviewed, and the Hao–Mendel approach (HMA) is improved to model qualitative distance words for four travel modes by using interval type-2 fuzzy sets (IT2 FSs), aiming at addressing the individual and interpersonal uncertainty among qualitative distance words. The area of the footprint of uncertainty (FOU), fuzziness (entropy), and variance are adopted to measure the uncertainties of qualitative distance words. The experimental results show that the improved HMA algorithm is better than the original HMA algorithm and can be used in spatial information retrieval and GIS-based DSSs

An Enhanced IT2FCM* Algorithm Integrating Spectral Indices and Spatial Information for Multi-Spectral Remote Sensing Image Clustering

Interval type-2 fuzzy c-means (IT2FCM) clustering methods for remote-sensing data classification are based on interval type-2 fuzzy sets and can effectively handle uncertainty of membership grade. However, most of these methods neglect the spatial information when they are used in image clustering. The spatial information and spectral indices are useful in remote-sensing data classification. Thus, determining how to integrate them into IT2FCM to improve the quality and accuracy of the classification is very important. This paper proposes an enhanced IT2FCM* (EnIT2FCM*) algorithm by combining spatial information and spectral indices for remote-sensing data classification. First, the new comprehensive spatial information is defined as the combination of the local spatial distance and attribute distance or membership-grade distance. Then, a novel distance metric is proposed by combining this new spatial information and the selected spectral indices; these selected spectral indices are treated as another dataset in this distance metric. To test the effectiveness of the EnIT2FCM* algorithm, four typical validity indices along with the confusion matrix and kappa coefficient are used. The experimental results show that the spatial information definition proposed here is effective, and some spectral indices and their combinations improve the performance of the EnIT2FCM*. Thus, the selection of suitable spectral indices is crucial, and the combination of soil adjusted vegetation index (SAVI) and the Automated Water Extraction Index (AWEIsh) is the best choice of spectral indices for this method

Fuzzy Geospatial Object-Based Membership Function Downscaling

The area-to-point kriging method (ATPK) is an important technology of downscaling without auxiliary information in remote sensing. However, it uses a constant semivariogram to downscale geospatial variables, which ignores the spatial heterogeneity between the geospatial objects. To deal with this kind of heterogeneity, this study proposes a fuzzy geospatial object-based ATPK method, which mainly consists of three steps: the extraction of fuzzy geospatial objects, the estimation of semivariograms for each object, and the downscaling of each object by ATPK with the corresponding semivariogram. Two groups of membership functions acquired from Worldview-2 and Sentinel-2 are used to test the proposed approach. Six classic downscaling algorithms are compared, and the results of two experiments show a better performance than the classical methods

Hyperspectral Image Classification for Land Cover Based on an Improved Interval Type-II Fuzzy C-Means Approach

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Remote Sensing of Spatiotemporal Changes in Wetland Geomorphology Based on Type 2 Fuzzy Sets: A Case Study of Beidagang Wetland from 1975 to 2015

Few studies have considered the spatiotemporal changes in wetland land cover based on type 2 fuzzy sets using long-term series of remotely sensed data. This paper presents an improved interval type 2 fuzzy c-means (IT2FCM*) approach to analyse the spatial and temporal changes in the geomorphology of the Beidagang wetland in North China from 1975 to 2015 based on long-term Landsat data. Unlike traditional type 1 fuzzy c-means methods, the IT2FCM* algorithm based on interval type-2 fuzzy set has an ability to better handle the spectral uncertainty. Four indexes were adopted to validate the separability of classes with the IT2FCM* algorithm. These four validity indexes showed that IT2FCM* obtained better results than traditional methods. Additionally, the accuracy of the classification results was assessed based on the confusion matrix and kappa coefficient, which were high for the analysis of wetland landscape changes. Based on the analysis of separability of classes with the IT2FCM* algorithm using four validity indexes, the classification results, and the membership value images, the long-term series of satellite datasets were processed using the IT2FCM* method, and the study area was classified into six classes. Because water resources and vegetation are two key wetland components, the water resource dynamics and vegetation dynamics, based on the normalized difference vegetation index (NDVI), were analysed in detail according to the spatiotemporal classification results. The results show that the changes in vegetation types have historically been associated with water resource variations and that water resources play an important role in the evolution of vegetation types

Ice storm damage to oak forests in subtropical China

Ice storms, as important sources of frequent and injurious disturbances, drive forest dynamics in the Northern Hemisphere. However, stand-level differential vulnerability to ice storms and the associated factors that predispose forest stands remain unclear. This is particularly concerning in the subtropics where the frequency of ice storms is predicted to increase with global warming. Here we assessed how the impact on three forest stands (early and late secondary-growth forests, and old-growth forests) differed after an extreme ice storm during 20–21 March 2022, and identified the abiotic and biotic factors that determine the damage intensity in the Shennongjia World Natural Heritage Site, a biodiversity conservation hotspot in central China. We found a stand-specific ‘mid-domain effect’ where the late secondary-growth forest sustained the most severe damage, the early secondary-growth forest sustained the least, and the old-growth forest suffered an intermediate amount. ‘Crown broken’ was the most severe damage type across all three forest stands, although the proportion of ‘branch broken’ was also high in the old-growth forest. Topography played a significant role in determining the vulnerability of the early secondary-growth forest to severe ice storms whereas the forest structure and composition were important factors in explaining the damage rates in the old-growth forest, although they differed among the damage categories. In contrast, topography, forest structure and composition generally explain the intensity of damage in the late secondary-growth forests. Our results highlight that, in subtropical forests, the intensity of damage caused by severe ice storms and related determining factors are stand-level dependent. We also suggest exploring potential management strategies (e.g., slow-growing hardwood species that can resist storms should be the main species for reforestation in early secondary-growth forests) to mitigate the risk of future severe ice storms, as well as other wind-related climatic extremes