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Chongming Island, the third largest island in China, has experienced dramatic shoreline changes due to erosion, river deposits, and human activities. While previous studies have shown the capacity of Landsat series images to extract shoreline dynamics, the spatial variation of shoreline dynamics and their corresponding driving factors remain unclear. Therefore, we established a method to monitor the shoreline dynamics of Chongming Island from 1984 to 2020 and to evaluate the driving factors of shoreline changes using a novel approach to Landsat image analysis. The method, based on the LISA (local indicator of spatial autocorrelation) concept, automatically extracted the shoreline from Landsat imagery. The results show that the LISA method, based on the SWIR1 band, has a high capacity for shoreline extraction in Chongming Island. By distinguishing the responses of the eastern and northern shorelines to upstream sediment loads and comprehensively analyzing the driving factors of eastern and northern dynamics, we found that: (i) although upstream sediment loads decreased dramatically, the shoreline of Chongming Island is still expanding due to human activities (i.e., reclamation and an estuary project) and sediment re-suspension from near-shore or cross-shore currents; (ii) the expansion of Chongming Island was initially due to the dynamics at the eastern shoreline, but the expansion of the eastern shoreline slowed after 2008 as upstream sedimentation slowed, less construction of cofferdams took place, and the Qingcaosha Reservoir was constructed; (iii) the northern shoreline of Chongming Island expanded rapidly after 1999, due to the merger of Xinlongsha, Xincunsha, and Chongming Island, and the transport of coastal and offshore sediments by hydrodynamic processes; and (iv) the main driving factors of eastern shoreline movement on Chongming Island are cofferdam reclamation and coastal engineering, and the changes at the northern shoreline are mainly affected by reclamation projects, offshore sediment supplies, and upstream sediment inflow. The results of this study provide theoretical fundamentals for land reclamation and future urban planning for Chongming Island. [ABSTRACT FROM AUTHOR]

High-resolution oasis maps are imperative for understanding ecological and socio-economic development of arid regions. However, due to the late establishment and relatively niche nature of the oasis discipline, there are no high-precision datasets related to oases in the world to date. To fill this gap, detailed visual interpretation of remote sensing images on Google Earth Professional or Sentinel-2 was conducted in summer 2020, and for the first time, a high-precision dataset of China's oases (abbreviation HDCO) with a resolution of 1 meter was constructed. HDCO comprises 1,466 oases with a total area of 277,375.56 km2. The kappa coefficient for this dataset validated by the field survey was 0.8686 and the AUC value for the ROC curve was 0.935. In addition, information on the geographic coordinates, climatic conditions, major landforms, and hydrological features of each oasis was added to the attribute table of the dataset. This dataset enables researchers to quantitatively monitor location and area of oases, fosters exploration of the relationship between oases and human under climate change and urbanization. [ABSTRACT FROM AUTHOR]

Monitoring shoreline movements is essential for understanding the impact of anthropogenic activities and climate change on the coastal zone dynamics. The use of remote sensing allows for large-scale spatial and temporal studies to better comprehend current trends. This study used Landsat 5 (TM), Landsat 8 (OLI), and Sentinel-2 (MSI) remote sensing images, together with the Otsu algorithm, marching squares algorithm, and tidal correction algorithm, to extract and correct the coastline positions of the east coast of Laizhou Bay in China from 1984 to 2022. The results indicate that 89.63% of the extracted shoreline segments have an error less than 30 m compared to the manually drawn coastline. The total length of the coastline increased from 166.90 km to 364.20 km, throughout the observation period, with a length change intensity (LCI) of 3.11% due to the development of coastal protection and engineering structures for human activities. The anthropization led to a decrease in the natural coastline from 83.33% to 13.89% and a continuous increase in the diversity and human use of the coastline. In particular, the index of coastline diversity (ICTD) and the index of coastline utilization degree (ICUD) increased from 0.39 to 0.79, and from 153.30 to 390.37, respectively. Over 70% of the sandy beaches experienced erosional processes. The shoreline erosion calculated using the end point rate (EPR) and the linear regression rate (LRR) is 79.54% and 85.58%, respectively. The fractal dimension of the coastline shows an increasing trend and is positively correlated with human activities. Coastline changes are primarily attributed to interventions such as land reclamation, aquaculture development, and port construction resulting in the creation of 10,000.20 hectares of new coastal areas. Finally, the use of Kalman filtering for the first time made it possible to predict that approximately 84.58% of the sandy coastline will be eroded to varying degrees by 2032. The research results can provide valuable reference for the scientific planning and rational utilization of resources on the eastern coast of Laizhou Bay. [ABSTRACT FROM AUTHOR]

Over the past two decades, the location and morphology of the coastline, as well as the land use/land cover (LULC) in the Circum-Bohai region in China, have undergone significant changes due to rapid industrialization and urbanization. Analyzing the temporal and spatial variation in coastal lines and LULC can provide a meaningful basis for the rational allocation of land resources. Using Landsat TM/OLI series dates from the Google Earth Engine (GEE) platform, this study applied the Linear Superposition Water Index (LSWI) and the Otsu threshold method (OTSU) algorithm to extract and analyze the coastline of the Circum-Bohai region. Additionally, the Random Forests (RF) method was employed to extract LULC information in the coastal zone. Using the geographical detector, we further explored the influence of social and economic factors, as well as natural factors, on spatial differentiation mechanisms of LULC change in the Circum-Bohai. Our results show that between 2000 and 2020, the Circum-Bohai coastline generally expanded towards the ocean by a total of 1062.99 km. The highest rate of change occurred during 2010 to 2015, and human activities were the primary cause of most of the changes, with the exception of the Yellow River Delta, where natural factors were dominant. The main types of LULC in the study area from 2000 to 2020 were farmland and construction land. The area of farmland proportion decreased by 1.75%, while the area of construction land proportion increased from 16.73% to 29.54%. Our findings indicate that the degree of land use in the Circum-Bohai is deepening. Based on our factor detection analysis, the added value of the secondary industry was the most critical influencing factor on LULC. Furthermore, the combined effect of the added value of the secondary industry and gross domestic product (GDP) has a significant driving impact on LULC. These findings can provide reference and data support for the sustainable development and comprehensive management of land resources. The relevant departments can use these results to prompt corresponding policies for the rational allocation of land resources. [ABSTRACT FROM AUTHOR]

Accurately estimating land-use demand is essential for urban models to predict the evolution of urban spatial morphology. Due to the uncertainties inherent in socioeconomic development, the accurate forecasting of urban land-use demand remains a daunting challenge. The present study proposes a modeling framework to determine the scaling relationship between the population and urban area and simulates the spatiotemporal dynamics of land use and land cover (LULC). An allometric scaling (AS) law and a Markov (MK) chain are used to predict variations in LULC. Random forest (RF) and cellular automata (CA) serve to calibrate the transition rules of change in LULC and realize its micro-spatial allocation (MKCARF-AS). Furthermore, this research uses several shared socioeconomic pathways (SSPs) as scenario storylines. The MKCARF-AS model is used to predict changes in LULC under various SSP scenarios in Jinjiang City, China, from 2020 to 2065. The results show that the figure of merit (FoM) and the urban FoM of the MKCARF-AS model improve by 3.72% and 4.06%, respectively, compared with the MKCAANN model during the 2005–2010 simulation period. For a 6.28% discrepancy between the predicted urban land-use demand and the actual urban land-use demand over the period 2005–2010, the urban FoM degrades by 21.42%. The growth of the permanent urban population and urban area in Jinjiang City follows an allometric scaling law with an exponent of 0.933 for the period 2005–2020, and the relative residual and R2 are 0.0076 and 0.9994, respectively. From 2020 to 2065, the urban land demand estimated by the Markov model is 19.4% greater than the urban area predicted under scenario SSP5. At the township scale, the different SSP scenarios produce significantly different spatial distributions of urban expansion rates. By coupling random forest and allometric scaling, the MKCARF-AS model substantially improves the simulation of urban land use. [ABSTRACT FROM AUTHOR]

Tidal flats provide ecosystem services to billions of people worldwide; however, their changing status is largely unknown. Several challenges in the fine extraction of tidal flats using remote sensing techniques, including tide-level and water-edge line changes, exist at present, especially regarding the spatial and temporal distribution of mangroves. This study proposed a tidal flats extraction method using a combination of threshold segmentation and tidal-level correction, considering the influence of mangrove changes. We extracted the spatial distribution of tidal flats in Beibu Gulf, Southwest China, from 1987 to 2021 using time-series Landsat and Sentinel-2 images, and further analyzed the dynamic variation characteristics of the total tidal flats, each coastal segment, and the range of erosion and silting. To quantitatively investigate the interaction between tidal flats and mangroves, this study established a regression model based on multi-temporal tidal flats and mangrove data. The results indicated that the overall accuracy of the tidal flat extraction results was 93.9%, and the kappa coefficient was 0.82. The total area of tidal flats in Beibu Gulf decreased by 130 km2 from 1987 to 2021, with an average annual change of −3.7 km2/a. In addition, a negative correlation between the tidal flat change area and mangrove change area in Shankou, Maowei Sea, and Pearl Bay was observed, with correlation coefficients of −0.28, −0.30 and −0.64, respectively. These results demonstrate that the distribution of tidal flats provides a good environment and expansion space for the rapid growth of mangroves. These results can provide references for tidal flats' resource conservation, ecological health assessment, and vegetation changes in coastal wetlands in China and other countries in Southeast Asia. [ABSTRACT FROM AUTHOR]

The Shapotou National Nature Reserve in the Ningxia Hui Autonomous Region is a typical arid region in China. There is an exceptionally serious problem of surface water resource conservation, and dynamic monitoring of surface water with the help of water indices can help to elucidate its change patterns and impact mechanisms. Here, we analysed the characteristics of interannual variation in surface water area in the study area from 1992–2021. The correlation coefficients of the surface water area in the previous year and the contemporaneous water bodies of the Yellow River with the total surface water area (TSWA) were calculated. The results show the following: ① In terms of the classification accuracy of the two methods, water indices and support vector machine classification, water indices are more suitable for water body extraction in the study area. In particular, the three water indices, NDWI, MNDWI and AWEIsh, were more effective, with average overall accuracies of 90.38%, 90.33% and 90.36% over the 30-year period, respectively. ② From the TSWA extraction results from the last 30 years, the TSWA showed an increasing trend with an increase of 368.28 hm2. Among the areas, Tenggeli Lake contributed the most to the increase in TSWA. ③ The highest correlation between the TSWA and the previous year's TSWA was 0.89, indicating that the better way to protect the water body is to maintain water surface stability year-round. The surface water area of the Yellow River and TSWA also showed a strong correlation, indicating that the rational use of Yellow River water is also an important direction for the future conservation of water resources in the study area. [ABSTRACT FROM AUTHOR]

Deep learning-based semantic segmentation technology is widely applied in remote sensing and has achieved excellent performance in remote sensing image target extraction. Greenhouses play an important role in the development of agriculture in China. However, the rapid expansion of greenhouses has had a series of impacts on the environment. Therefore, the extraction of large-scale greenhouses is crucial for the sustainable development of agriculture and environmental governance. It is difficult for existing methods to acquire precise boundaries. Therefore, we propose a spatial convolutional long short-term memory structure, which can fully consider the spatial continuity of ground objects. We use multitask learning to improve the network's ability to extract image boundaries and promote convergence through auxiliary loss. We propose a superpixel optimization module to optimize the main-branch results of network semantic segmentation using more precise boundaries obtained by advanced superpixel segmentation techniques. Compared with other mainstream methods, our proposed structure can better consider spatial information and obtain more accurate results. We chose Shandong Province, China, as the study area and used Gaofen-1 satellite remote sensing images to create a new greenhouse dataset. Our method achieved an F1 score of 77%, a significant improvement over mainstream semantic segmentation networks, and it could extract greenhouse results with more precise boundaries. We also completed large-scale greenhouse mapping for Shandong Province, and the results show that our proposed modules have great potential in greenhouse extraction. [ABSTRACT FROM AUTHOR]

River dynamics and sediment budget play a crucial role in shaping geomorphic variability of river channels and deltaic environments. Basin-scale human activities, including dam construction, induce alterations in river flow and sediment dynamics in the downstream channels and to the delta, and quantification of sediment source shift along downstream fluvial-deltaic systems is often uncertain. This study analyzed the river regime changes and sediment dynamics of a typical sediment-laden fluvial-deltaic system—the lower Yellow River (LYR) and the Yellow River Delta (YRD) —to assess the integrated effects of dam impoundment and dam-based river regulation schemes on downstream hydrogeomorphic transition processes. The Xiaolangdi (XLD) Reservoir, which was completed in 2000 with a total storage of 12.7 km3, is the final reservoir located in the middle Yellow River and plays an important role in flood control and energy supply. Following the full operation of XLD Reservoir, the relationship between water and sediment in the LYR became more balanced, with a drastic decline of sediment input and seasonal migrations of floodwaters. The interannual variability of water levels at downstream hydrological stations indicated a geomorphic transition in the LYR from net deposition to erosion state. The building of the XLD Reservoir caused a downstream shift of river-originated sediment source and 48% of the total sediment delivered to the YRD was derived from the LYR. However, the reduced sediment delivery since 2000 has still triggered net land loss regarding the YRD system, with a strong spatial variability which is dominated by the reduced accretion at the active delta front and erosion at the abandoned river mouth and coastal engineering zone. Compared with other environmental factors, the construction of upstream dams contributed the most to the decline of downstream sediment delivery over the past decades. The challenge for sustainable sediment management is the gradual decline of scouring efficiency as the riverbed sediment is coarsening. Our study suggests that future river regulation strategies should consider the geomorphic sustainability of both the LYR and the YRD system. [ABSTRACT FROM AUTHOR]

Lake bottom topography is a basic parameter that reflects the elevation of all lake bottom geographical locations. In this study, a novel method was proposed for mapping lake bottom topography by combining the water occurrence map from the Global Surface Water (GSW) dataset with long-term measured water levels. This method took advantage of the following feature: the rapid change in water level of a lake's dynamic inundation area leads to a different water occurrence frequency and, therefore, put forward the concept of lake water level frequency, which refers to the frequency at which the water level is higher than or equal to a specified elevation. As water occurs more frequently in lake bottoms with lower elevations and less frequently in lake bottoms with higher elevations, we assume that lake water level frequency is identical to the water occurrence frequency over a long time. The water level frequency curve of all the measured water level data was generated through the P-III distribution function, and the elevation values from the water level frequency curve were assigned to pixels with the same frequency in the water occurrence map in order to generate the lake bottom topographic map. A case study was conducted on Poyang Lake in China to demonstrate the performance of the method. The derived bottom topographic map of Poyang Lake was verified by four measured sections. The results showed that the proposed method was feasible and could well reflect the bottom topography of Poyang Lake. The absolute error was mostly less than 0.5 m, the mean relative error was 7.4%, and the root mean square error was 0.99 m. The proposed method enriches the mapping means of lake bottom topography and has the potential to become a useful tool with a broad application prospect. [ABSTRACT FROM AUTHOR]

The coastline is an indicator line for human exploitation in the coastal zone. With the acceleration of human development and climate change, changes in the coastal zone are more active than ever. Quickly extracting the coastline and monitoring its changes in real time is of great significance for enacting the development and utilisation planning of coastal zones. In this research, we developed an automatic-coastline-extraction technique based on edge detection and object-oriented in complex situations. Meanwhile, on the basis of Landsat TM images, it realised the extraction of the mainland China coastline. Accuracy comparison showed that the matching commission and omission errors between the extracted and reference coastlines within two-pixel radii were 8% and 5%, respectively. The Overall quality reached as high as 87%, achieving an overall mapping accuracy of 1:250,000. We also constructed the index system of coastline exploitation and utilisation to analyse changes of the continental coastline from 2000 to 2015. It was found that natural and economic conditions affected coastline changes, in which the sea-expansion trend of northern China was more concentrated on a larger scale and with a higher load in coastline development and utilisation, while the southern region of China was relatively stable. [ABSTRACT FROM AUTHOR]

This study presented an intuitive approach to derive large‐scale tidal flat's Digital Elevation Model (DEM). We first developed an automated method for accurately extracting the waterline from Synthetic Aperture Radar images acquired in Subei Sandbanks along the Yellow Sea coast of China between 2015 and 2020 based on deep convolutional neural networks. The statistical results show this method has appreciable accuracy for efficient waterline extraction even under complex imaging conditions with a mean recall and precision of 0.90 and 0.80, respectively. Then the pixel‐level extracted waterlines are calibrated with a global tide model to construct the large‐scale tidal flat's DEM in the study region. The comparison against in situ topographic data shows an error of 29 cm, demonstrating the usefulness of monitoring the morpho‐sedimentary evolution in intertidal areas. Furthermore, the Subei Sandbanks remained stable from 2015 to 2020, while the coastal region changed drastically due to human activities. Plain Language Summary: Monitoring the geomorphological evolution of tidal flats is critical for coastal development and wetland conservation. Compared to traditional surveying approaches, the waterline method can provide topographic maps more frequently with a lower cost and higher coverage. However, both the signal returned from the sea surface and the exposed tidal flats on radar images change drastically under different sea conditions. Benefiting from the development of deep learning, especially the deep convolutional neural networks, waterlines of tidal flats on complex Synthetic Aperture Radar images can now be automatically and accurately extracted. Furthermore, with a calibrated ocean tide model that can make the tidal evaluation instantly, we develop an automatic approach to topographic map the large‐scale tidal flats, which may provide timely information for stakeholders related to coastal management and ecosystem protection. Key Points: A deep‐learning‐based technique was developed to extract waterline from synthetic aperture radar imagesA combination of the deep‐learning model and tidal analysis is used for automatic topographic mapping of large‐scale tidal flatsMulti‐year digital elevation model series in the study region are obtained with morpho‐sedimentary evolution analysis [ABSTRACT FROM AUTHOR]

The wetland of Liao River estuary (LRE) in northeastern China is one of the best-preserved wetlands across the globe. However, it is extremely vulnerable to hydrological changes as well as other disturbances, particularly upstream river discharges and the reclamation from anthropogenic activities. In this study, a 3D hydrodynamic model was used to reproduce the flow patterns of the LRE and to explore the variation in salinity under different scenarios. Furthermore, the impact of river discharge and shoreline changes on the salinity distribution in the LRE was quantitatively analyzed and discussed through several simulation experiments. The model reasonably reconstructed the spatiotemporal variability and distribution of salinity in the Liao River estuary and the wetlands across intertidal areas. Increases in runoff were demonstrated to significantly decrease the mean salinity values of the estuary, with changes in salinity negatively correlated to the longitudinal distance from the estuary mouth. Moreover, the shoreline change caused by the construction of Panjin Port was observed to have an obvious influence on the salinity distribution of the LRE, particularly in the lower reaches of the Pink Beach wetland. Comparisons of the Liao River estuary residual flow fields under different shorelines revealed that the establishment of the port resulted in the diffusion of the runoff from the Daliao River due to the obstruction of the port body, which enhanced the tidal mixing effect and consequently weakened the dilution effect of freshwater entering the Pink Beach wetland. Overall, the current study should be helpful for offering a greater understanding of Suaeda heteroptera vegetation degradation in the LRE, China, and also provides a new perspective in investigating the degradation mechanism in other estuarine wetlands. [ABSTRACT FROM AUTHOR]

Introduction: Large-scale disordered coastal aquaculture development causes severe environmental problems. However, quantitative assessments of spatiotemporal dynamics and driving factors for coastal aquaculture are essential and urgent for coastal aquaculture sustainability.Outcomes:Using remote sensing products and geographic information science, we investigated the long-term landscape changes due to coastal aquaculture, and explored its underlying driving factors in the Ningde coastal region, Southeastern China. Results show that coastal aquaculture area increased from 90.65 km2 in 2003 to 96.08 km2 in 2016, and its structure underwent tremendous changes. The area of artificial shrimp ponds increased by 496.15% and the area of farmland ponds decreased by 25.81% between 2003 and 2016. In addition, we revealed that from 2003 to 2016, the change trends of the coastal aquaculture area and the entire Ningde coastal region were consistent, and became more fragmented and dispersive. Furthermore, regression results indicate that the growth and attenuation of coastal aquaculture areas were significantly affected by the initial coastal aquaculture area in 2003. Discussion and Conclusion:To sustainably manage the coastal ecosystems, we provide several recommendations (e.g., a coupled human and natural systems approach to understanding human-nature interactions, integrated assessment, and systematical spatial planning and monitoring) for future research and management. [ABSTRACT FROM AUTHOR]

Automated coastline extraction from optical satellites is fundamental to coastal mapping, and sea-land segmentation is the core technology of coastline extraction. Deep convolutional neural networks (DCNNs) have performed well in semantic segmentation in recent years. However, sea-land segmentation using deep learning techniques remains a challenging task, due to the lack of a benchmark dataset and the difficulty of deciding which semantic segmentation model to use. We present a comparative framework of sea-land segmentation to Landsat-8 OLI imagery via semantic segmentation in deep learning techniques. Three issues are investigated: (1) constructing a sea-land benchmark dataset using Landsat-8 Operational Land Imager (OLI) imagery consisting of 18,000 km2 of coastline around China; (2) evaluating the feasibility and performance of sea-land segmentation by comparing the accuracy assessment, time complexity, spatial complexity and stability of state-of-the-art DCNNs methods; (3) choosing the most suitable semantic segmentation model for sea-land segmentation in accordance with Akaike information criterion (AIC) and Bayesian information criterion (BIC) model selection. Results show that the average test accuracy achieves over 99% accuracy, and the mean Intersection over Unions (mean IoU) is above 92%. These findings demonstrate that the Fully Convolutional DenseNet (FC-DenseNet) performs better than other state-of-the-art methods in sea-land segmentation, based on both AIC and BIC. Considering training time efficiency, DeeplabV3+ performs better for sea-land segmentation. The sea-land segmentation benchmark dataset is available at: . [ABSTRACT FROM AUTHOR]

Two commonly used definitions of coastline are compared and analyzed in this article. To build a more perfect digital tide-coordinated coastline model in China, we should first solve the problems of the existing definition and unclear algorithms of China's coastline. On the basis of defining the coastline in China as the intersection of coastal land and the mean high water spring (MHWS), it should be further revised, and defined either as an intersection of coastal land and the MHWS or as a mean higher high water level (MHHWL) considering the diversity of tidal characteristics in China Sea areas. Since MHWS is related to the extreme of the lunar phase and MHHWL is related to the extreme of the angle of the lunar declination elevation, the algorithms to obtain them are more complicated than that of mean high water (MHW) used in the United States. We should consider the specific law of tidal movement in different tide types of the predominantly semidiurnal or diurnal sea areas in China when studying the precise algorithms for MHWS and MHHWL. Several experiments show that the difference between the two types of calculations of coastal height, such as water level observations and tidal predictions, is within centimeter level. The research results of this article have a certain reference value for countries with similar coastline features as in China. [ABSTRACT FROM AUTHOR]

Human activities such as the rapid development of marine aquaculture in the central Jiangsu coast have had a marked impact on the tidal flat morphology. This research focuses on characterizing the spatial expansion of laver cultivation and its influence on the sedimentary evolution of tidal flats in the central Jiangsu coast. First, seasonal digital elevation models (DEMs) were established using 160 satellite images with medium resolution. Then, laver aquaculture regions were extracted from 50 time-series satellite images to calculate the area and analyze the spatial distribution and expansion of these areas. Finally, seasonal and intra-annual sedimentary evolution patterns of both aquaculture and non-aquaculture regions were determined using the constructed DEMs. Our results show that aquaculture regions have gradually expanded to the north and peripheral domains of the entire sand ridge since 1999 and by 2013, the seaward margins of each sandbank developed into dense cultivation regions. Additionally, the aquaculture regions increased from 11.99 km2 to 295.28 km2. The seasonal sedimentary evolution patterns indicate that deposition occurs during the winter and erosion during the summer. Thus, the aquaculture regions experience deposition in certain elevation intervals during the laver growing period and in the non-growing period, alluvial elevation intervals in the aquaculture regions are eroded and erosive ones are deposited in order to maintain the balance between scouring and silting. The sedimentary evolution of each sandbank is heterogeneous due to their different locations and the difference in sediment transport. The intra-annual evolution pattern is characterized by deposition in the high tidal flats and erosion in low ones. Hydrodynamic conditions and laver cultivation dominate partial sedimentary evolution, which gradually shapes the beach surface. [ABSTRACT FROM AUTHOR]

The evolution of estuarine islands is potentially controlled by sediment discharge, tidal currents, sea level rise, and intensive human activities. An understanding of the spatial and temporal changes of estuarine islands is needed for environmental change monitoring and assessment in estuarine and coastal areas. Such information can also help us better understand how estuarine islands respond to sea level rise in the context of global warming. The temporal changes of two estuarine islands in Shanghai near the Yangtze River Estuary were obtained using Landsat TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper) images from 1987 to 2016 on an annual scale. First, a composite image was generated by using the multi-temporal Landsat images for each year. Then, a modified normalized difference water index (MNDWI) was applied to the annual estuarine island maps using a threshold segmentation method. Finally, we obtained the temporal changes of the estuarine islands in Shanghai during the period 1987-2016. The results suggest that (1) Landsat TM/ETM+ images can be used for estuarine island mapping and change detection; (2) the two estuarine islands have expanded significantly during the past three decades; (3) human activities are the main driving factor that caused the expansion of the estuarine islands; and (4) the sea level can also partly explain the change in the estuarine islands. This study demonstrates that Landsat data are useful for determining the annual variations in the land area of two estuarine islands in Shanghai during the past 30 years. In the future, other factors and their contributions to estuarine island changes should be further investigated. [ABSTRACT FROM AUTHOR]

Since the late 1990s, there are growing studies on the development of cellular automata (CA) as a simulation tool for assisting urban and regional planning in China. Rapid urban development is the main reason that this country has become one of the best places to test the methodology of CA and analyze the effectiveness of using these models. This paper attempts to summarize the experiences and issues of using CA to solve various environmental and planning problems in China. The analysis is based on the literature review using the search engines of ISI Web of Science and Google Scholar. These experiences could be important for those who want to apply CA in developing countries. For example, which environmental and ecological problems can be solved by using this bottom-up approach? What are the data inputs to these models and how can they be calibrated? Our analyses indicate that CA have the great potential to support land-use planning and policy analysis for fast-growing regions. Some specific features of using CA in China are also identified in the literature review, including delineation of urban growth boundary, prevention of illegal development and formulating zoning schemes. The CA studies in this fast-growing country provided valuable experiences for other developing countries to solve a series of simulation and planning problems by using this bottom-up approach. [ABSTRACT FROM PUBLISHER]

The normalized difference water indices (NDWIs) were successfully used in map land surface water mapping (LSWM) from Landsat series multispectral images. This paper evaluates the potential of the recent Landsat satellite (Landsat-8) Operational Land Imager (OLI) multispectral images for LSWM using three NDWI models. We tested the accuracy and robustness of the three OLI NDWI models in the Yangtze River Basin and the Huaihe River Basin in China. The results demonstrate that the three OLI NDWI models achieve an overall accuracy of more than 95%, a kappa coefficient of 0.89 and a producer’s accuracy of 95% for LSWM. The results also demonstrate that the NDWI model using the green band (Band 3) and the SWIR1 band (Band 6) (referred to as NDWIO6,3) of the OLI sensor has a higher LSWM accuracy than the other two NDWI models. [ABSTRACT FROM AUTHOR]

This paper explores the geometric performance of integration of aerial and QuickBird images. Different integration scenarios with different bias compensation schemes in the image space were studied, and the results showed that the introduction of the aerial images can improve the geopositioning accuracy of the QuickBird images to close to the aerial pixel level. In addition, methods of correcting biases in the object space were tested, and the results revealed the disadvantage of the bias compensation in the object space. An experiment was conducted for mapping ground objects such as inland rivers, buildings, and roads using the proposed integration method. [ABSTRACT FROM AUTHOR]

In China, landslides are abundant, widespread, and regular, destroying villages and agriculture and sometimes posing a threat to people's lives. The question of how to rapidly detect and attain landslide data is a significant topic of research, yet traditional measurement using medium-resolution remote sensing data is problematic. Object-oriented categorization is utilized in this research to extract landside data from high-resolution GF-1 and Sentinel-2 data. Data preprocessing begins with orthophoto correction, image matching, and data fusion, followed by band enhancement, which comprises band synthesis, principal component analysis, and filtering, and finally landside extraction using an object-oriented technique. The impact of geology, lithology, rainfall, and human activities on the occurrence of landslides in the study area is explored utilizing DEM data, visualization tools, remote sensing interpretation map, and other associated data. The studies are conducted in Shuicheng County, Guizhou Province, China, with a segmentation scale of 25 pixels and 14 classification feature parameters. Following that, the landslide mass is extracted and categorization findings of nearby characteristics are acquired. Finally, the destructiveness of the landslide is determined by comparing the results of object-oriented classification before and after the landslide. With a Kappa coefficient of 0.76 and a landslide extraction accuracy of 79.8%, the overall classification accuracy is 87%. Combined with the geological structure, rock lithology, spatial location, landslide occurrence process, elevation of the study area, precipitation and the impact of human activities, the causes of the landslide are discussed and analyzed. The early warning of other unknown landslides can be obtained by analyzing the features of the aforementioned components. [ABSTRACT FROM AUTHOR]

Previous studies mostly focus on an individual delta, or deltas at a global scale, to explore dam effects on deltaic hydrological alteration and morphological evolution, while comparative studies on selected similar deltas remain scarce. In this study, we compare the alteration of river discharge and sediment load, as well as the associated deltaic area and shoreline, of two deltas, namely, the Volta River Delta in Ghana and the Yellow River Delta in China, which are subject to similar forcings and mainstem dam influences. The results show that the sediment loads of the Volta River Delta and Yellow River Delta have decreased abruptly and gradually, respectively, to ~10% of the pre-dam level, presumably due to differences in reservoir capacity and upstream dam location. Sediment decline has led to a decrease of the fluvial dominance ratio, which has also been affected by the river mouth location and shoreline orientation. As a consequence, the area of the Volta River Delta has shifted to a new quasi-equilibrium, whereas the Yellow River Delta has kept prograding. This comparative study provides references for understanding the future evolution of similar deltas around the world. [ABSTRACT FROM AUTHOR]

Shoreline changes are of great importance for evaluating the interaction between humans and ecosystems in coastal areas. They serve as a useful metric for assessing the ecological costs of socioeconomic developmental activities along the coast. In this paper, we present an assessment of shoreline changes along the eastern coast of mainland China from ~1990 to 2019 by applying a novel method recently developed by us. This method which we call the Nearest Distance Method (NDM) is used to make a detailed assessment of shorelines delineated from Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) images. The results indicate a dramatic decline in natural shorelines that correspond to the rapid increase in the construction of artificial shorelines, driven by China's economic growth. Of the entire coast of mainland China, the biggest change occurred along the Bohai Sea, where artificial shorelines expanded from 42.4% in ~1990 to 81.5% in 2019. Over this period, this study indicates that China lost > 60% of its biological shorelines, a trend that is especially worrisome because these include areas that were once biologically diverse and extremely rich. As anticipated, shoreline losses were greatest where regions of low economic value had been transformed to areas of higher economic value. Overall, this influence of human activities on shorelines in China is unprecedented. The repercussions of these changes on ecosystems, and the susceptibility of new shoreline developments to population growth and sea-level rise, need to be assessed urgently before additional changes are effected. [ABSTRACT FROM AUTHOR]

The spaceborne synthetic aperture radar (SAR) is quite powerful in worldwide ocean observation, especially for ship monitoring, as a hot topic in ocean surveillance. The launched Gaofen-3 (GF3) satellite of China can provide C-band and multi-polarization SAR data, and one of its scientific applications is ocean ship detection. Compared with the single polarization system, polarimetric systems can be used for more effective ship detection. In this paper, a generalized extreme value (GEV)-based constant false alarm rate (CFAR) detector is proposed for ship detection in the ocean by using the reflection symmetry metric of dual-polarization. The reflection symmetry property shows big differences between the metallic targets at sea and the sea surface. In addition, the GEV statistical model is employed for reflection symmetry statistical distribution, which fits the reflection symmetry probability density function (pdf) well. Five dual-polarimetric GF3 stripmap ocean data sets are introduced in the paper, to show the contrast in enhancement by using reflection symmetry and to investigate the GEV model fit to the reflection symmetry metric. Additionally, with the detection experiments on the real GF3 datasets, the effectiveness and efficiency of the GEV model for reflection symmetry and the model-based ocean ship detector are verified. [ABSTRACT FROM AUTHOR]

Increasing demand for land resources at the coast has exerted immense pressure on vulnerable environments. Population and economic growth in coastal cities have combined to produce a scarcity of suitable space for development, the response to which has frequently been the reclamation of land from the sea, most prominently in China. Urbanization is a key driver of such changes and a detailed investigation of coastal land reclamation at the city scale is required. This study analyzed remote sensing imagery for the period 1990 to 2018 to explore the trajectories of coastal land reclamation in nine major urban agglomerations across the three largest deltas in China using the JRC Global Surface Water (Yearly Water Classification History, v1.1) (GSW) dataset on the Google Earth Engine platform. The results are considered in the context of major national policy reforms over the last three decades. The analysis reveals that total land reclaimed among nine selected cities had exceeded 2800 km2 since 1984, 82% of which occurred after 2000, a year following the enactment of China's agricultural 'red line' policy. Shanghai exhibited the greatest overall area of land extension, followed by Ningbo and Tianjin, especially in the period following the privatization of property rights in 2004. In analyzing annual trends, we identified the developmental stages of a typical coastal reclamation project and how these vary between cities. Scrutiny of the results revealed voids in nighttime light satellite data (2014–2018) in some localities. Although these voids appeared to be characterized by construction, they were occupied by vacant buildings, and were therefore examples of so-called "ghost cities." In China, as elsewhere, continual land reclamation needs to be considered in relation to, inter alia, sea level rise and land subsidence that pose significant challenges to the vision of sustainable urban development in these three deltaic megacities. [ABSTRACT FROM AUTHOR]