Change Vector Analysis to Monitor the Changes in Fuzzy Shorelines

Abstract

Abstract:Mapping of shorelines and monitoring of their changes is challenging due to the large variation in shoreline position related to seasonal and tidal patterns. This study focused on a flood-prone area in the north of Java. We show the possibility of using fuzzy-crisp objects to derive shoreline positions as the transition zone between the classes water and non-water. Fuzzy c-means classification (FCM) was used to estimate the membership of pixels to these classes. A transition zone between the classes represents the shoreline, and its spatial extent was estimated using fuzzy-crisp objects. In change vector analysis (CVA) applied to water membership of successive shorelines, a change category was defined if the change magnitude between two years, T 1 and T 2 , differed from zero, while zero magnitude corresponded to no-change category. Over several years, overall change magnitude and change directions of the shoreline allowed us to identify the trend of the fluctuating shoreline and the uncertainty distribution. The fuzzy error matrix (FERM) showed overall accuracies between 0.84 and 0.91. Multi-year patterns of water membership changes could indicate coastal processes such as: (a) high change direction and high change magnitude with a consistent positive direction probably corresponding to land subsidence and coastal inundation, while a consistent negative direction probably indicates a success in a shoreline protection scheme; (b) low change direction and high change magnitude indicating an abrupt change which may result from spring tides, extreme waves and winds; (c) high change direction and low change magnitude which could be due to cyclical tides and coastal processes; and (d) low change direction and low change magnitude probably indicating an undisturbed environment, such as changes in water turbidity or changes in soil moisture. The proposed method provided a way to analyze changes of shorelines as fuzzy objects and could be well-suited to apply to coastal areas around the globe.

Comparing Fuzzy Sets and Random Sets to Model the Uncertainty of Fuzzy Shorelines

“…To model a shoreline using fuzzy sets, we need to adopt other concepts to quantify the extensional uncertainty of the shoreline, such as α-cut, shoreline as a margin, and fuzzy-crisp object [4,5,28], whereas a random sets approach through its covering function and statistical parameters directly quantifies the extensional uncertainty of shoreline without resorting to other concepts.…”

“…Since 1990s the productive fishponds and rice fields have been submerged and abandoned as swamp areas [50,51] leading to shoreline changes [4]. The area is prone to frequent inundations at high tide [52,53].…”

“…Several methods have been proposed, for example, using manual digitization [7], spectral indices extraction such as water and vegetation indices [8], active contour segmentation [6], band ratios [9], and image classification [10,11]. Most of these methods are based on hard classifications, and only a few considered soft classifications in the context of shoreline mapping [4,5,12]. A hard classifier allocates a pixel to one class only based on the highest similarity.…”

“…A series of remote sensing images can be used, for example, for mapping the dynamics of wet grassland and vegetation patches [1], mapping depth and water quality [2], coastal erosion [3], and in particular shoreline mapping [4][5][6]. Instantaneous shoreline locations extracted from remote sensing images have become popular since mapping shorelines using ground survey and photogrammetry is costly.…”

Multitemporal image analysis for monitoring fuzzy shorelines

“…Remote sensing offers a practical and economical means for coastal research. A series of remote sensing images can be used, for example, for mapping the dynamics of wet grassland and vegetation patches (Zhao et al, 2011a), mapping depth and water quality (Jay and Guillaume, 2014), coastal erosion (Hereher, 2011), and in particular shoreline mapping (Dewi et al, 2017a;Dewi et al, 2016;Sukcharoenpong et al, 2016). Instantaneous shoreline locations extracted from remote sensing images have become popular since mapping shorelines using ground survey and photogrammetry is costly.…”