Gains and losses in ecosystem service values (ESV) in coastal zones in Zhejiang Province during rapid urbanization were analyzed in terms of land-use changes. Decision-making on coastal development based on ESV estimation is significant for the sustainable utilization of coastal resource. In this study, coastal land-use changes in Zhejiang Province during rapid urbanization were discussed based on remote-sensing derived land-use maps created in the years 1990, 2000 and 2010. The ESV changes in coastal zones in Zhejiang Province from 1990 to 2010 were estimated by using the established ESV estimation model. The analysis results demonstrate the following: (1) with the continuous acceleration of urbanization, land-use types in coastal zones in Zhejiang Province changed significantly from 1990 to 2010, demonstrated by considerable growth of urban construction land and reduction of forest land and farmland; (2) in the study period, the total ESV in coastal zones in Zhejiang Province continuously decreased in value from RMB 35.278 billion to 29.964 billion, a reduction of 15.06%; (3) in terms of the spatial distribution of ESV, the ESVs in coastal zones in Zhejiang Province were generally converted from a higher ESV to a lower ESV; (4) estimates of ESV for the three years 1990, 2000 and 2010 appear to be relatively stable; and (5) land-use intensity in coastal zones in Zhejiang Province continuously increased during the 20 years. The spatial distribution of land-use intensity was consistent with that of the ESV change rate. Disordered land-use changes from forestland and farmland to urban construction land was a major cause of ESV loss.

Spatiotemporal analysis of coastline land utilization is important for strengthening coastline resource management and promoting sustainable development in coastal zones. In this study, basic spatiotemporal characteristics of coastline land utilization in Zhejiang Province, China, were analyzed using remote sensing (RS) and GIS techniques. For this case, Landsat Thematic Mapper and Operational Land Imager remote-sensing image data acquired from 1990 to 2015 were collected and processed. Several indices and metrics used for quantifying and analyzing the coastline utilization in Zhejiang Province were defined and calculated through processing remote sensing data and using GIS tools. They included the intensity index of coastline change, fractal dimension of the coastline, subjectivity of coastline utilization, and the integrated coastline utilization index. The analysis results demonstrated that (1) significant coastline changes took place in Zhejiang Province, and the coastline moved seaward, while the total coastline length decreased continuously; (2) the average fractal dimension of the coastlines was 1.0922, and it was relatively stable before 2000, but declined continuously after 2000, reduced to 1.086 in 2015; (3) the artificialization index of the coastlines kept rising; (4) there were single, binary, and other multi-land use types in different coastline sections in the study area; and (5) the coastline land use intensity increased continuously from 0.25 to 0.38, and the coastline in the Xiangshan Harbour had the highest land use intensity index, reaching 0.53 in 2015, throughout the study period, and in the study area.

Monitoring storm-induced dramatic beach morphology changes and long-term beach evolution provides crucial data for coastal management. Beach-profile measurement using total station has been conducted along the coast of west-central Florida over the last decade. This paper reviews several case studies of beach morphology changes based on total-station survey along this coast. The advantage of flexible and low-cost total-station surveys is discussed in comparison to LIDAR (light detection and ranging) method. In an attempt to introduce total-station survey from a practical prospective, measurement of cross-shore beach profile in various scenarios are discussed, including: (1) establishing a beach profile line with known instrument and benchmark locations; (2) surveying multiple beach profiles with one instrument setup; (3) implementation of coordinate rotation to convert local system to real-earth system. Total-station survey is a highly effective and accurate method in documenting beach profile changes along low-energy coasts.

Hyperspectral remote sensing imagery contains much more information in the spectral domain than does multispectral imagery. The consecutive and abundant spectral signals provide a great potential for classification and anomaly detection. In this study, two real hyperspectral data sets were used for anomaly detection. One data set was an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data covering the post-attack World Trade Center (WTC) and anomalies are fire spots. The other data set called SpecTIR contained fabric panels as anomalies compared to their background. Existing anomaly detection algorithms including the Reed–Xiaoli detector (RXD), the blocked adaptive computation efficient outlier nominator (BACON), the random selection based anomaly detector (RSAD), the weighted-RXD (W-RXD), and the probabilistic anomaly detector (PAD) are reviewed here. The RXD generally sets strict assumptions to the background, which cannot be met in many scenarios, while BACON, RSAD, and W-RXD employ strategies to optimize the estimation of background information. The PAD firstly estimates both background information and anomaly information and then uses the information to conduct anomaly detection. Here, the BACON, RSAD, W-RXD, and PAD outperformed the RXD in terms of detection accuracy, and W-RXD and PAD required less time than BACON and RSAD.

This article proposed a novel spectral-spatial classification framework for hyperspectral image (HSI) through combining collaborative representation (CR) and maximum margin projection (MMP). First, class-dependent CR classifier (CDCRC) is used on HSI classification to fully make use of self-information contained in each class. Second, the MMP is included into the framework to discover local manifold structure. Combined with CDCRC, it formed the classifier named CDCRC based on MMP (CMCRC), which aims to reduce band redundancy. Finally, a comprehensive spectral-spatial classifier, called union of CMCRC, is proposed to optimize the classification map through integrating cumulative probability of residuals instead of applying strong constraints to maintain the spatial consistency. Experimental results on three real hyperspectral datasets demonstrate the effectiveness and practicality of the proposed methods over other related models for HSI classification tasks.

Sparse representation (SR)-based models have been widely applied for hyperspectral image classification. In our previously established constraint representation (CR) model, we exploited the underlying significance of the sparse coefficient and proposed the participation degree (PD) to represent the contribution of the training sample in representing the testing pixel. However, the spatial variants of the original residual error-driven frameworks often suffer the obstacles to optimization due to the strong constraints. In this paper, based on the object-based image classification (OBIC) framework, we firstly propose a spectral–spatial classification method, called superpixel-level constraint representation (SPCR). Firstly, it uses the PD in respect to the sparse coefficient from CR model. Then, transforming the individual PD to a united activity degree (UAD)-driven mechanism via a spatial constraint generated by the superpixel segmentation algorithm. The final classification is determined based on the UAD-driven mechanism. Considering that the SPCR is susceptible to the segmentation scale, an improved multiscale superpixel-level constraint representation (MSPCR) is further proposed through the decision fusion process of SPCR at different scales. The SPCR method is firstly performed at each scale, and the final category of the testing pixel is determined by the maximum number of the predicated labels among the classification results at each scale. Experimental results on four real hyperspectral datasets including a GF-5 satellite data verified the efficiency and practicability of the two proposed methods.