Mapping Tidal Flats with Landsat 8 Images and Google Earth Engine: A Case Study of the China’s Eastern Coastal Zone circa 2015

The spatiotemporal assessments for tidal flat erosion associated with urban expansion in the conterminous coastal United States from 1985 to 2015

Tidal flats are of great importance to coastal residents and environments, which are recently facing unprecedented challenges due to massive urban expansions. While some case studies have been conducted in small areas, it is yet to come a picture to examine the issue at the nationwide level. To fill this void, it is necessary to reconsider whether the analytical and statistical methods used in the previous studies are still appropriate to the larger scales, which accordingly needs to be refined and updated. Aiming at this issue, this study implemented a justification for the conterminous United States, in which nearly 70 % of the counties by the seashore with intensified tidal flats in 1985 were selected to conduct a comprehensive assessment. Based on the 156 selected counties, this paper firstly analyzed the spatiotemporal change patterns of tidal flats and urban extents from 1985 to 2015, and then combined these results and implemented a series of correlation assessments between tidal flat loss and urban expansion. As a result, we found that urban expansions in the conterminous coastal United States have not only substantially squeezed the space of tidal flats, but also significantly affected the surrounding tidal flat environments during the three decades.

Suitability evaluation of carrying capacity and utilization patterns on tidal flats of Bohai Rim in China

Tidal flats in the Bohai Rim are facing threats from human activities. Quantifying the carrying capacity and suitability of tidal flats is of great significance to the regional environment and resource management. In this study, the existing social and natural data were collected and the natural conditions of tidal flats, e.g., the distributions and utilization patterns, were investigated through remote sensing image interpretation and field investigation in the Bohai Rim. Then, a multi-index evaluation system was developed with indexes organized under the framework of the analytic hierarchy process (AHP) and the Drivers-State-Impact (DSI) framework, processed by fuzzy evaluation, and weighted by the entropy method. The studies show that the rapid expansion of industry-port-town, salt pans or aquafarms in the Bohai Rim during 1990-2020 squeezed the space of tidal flats. Despite the limitation of the declining resource condition, the carrying capacity of tidal flats in the Bohai Rim increased slightly during 2000-2018 because of the great improvement in economic and ecological conditions. We estimate 59.93% of the land resources are suitable for economic development while others are temporarily unsuitable for reclamation due to their high ecological importance. The land use data and macro-evaluation system of tidal flat utilization patterns herein can provide references for coastal resource management and ecological restoration.

Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

Remote sensing (RS) plays an important role gathering data in many critical domains (e.g., global climate change, risk assessment and vulnerability reduction of natural hazards, resilience of ecosystems, and urban planning). Retrieving, managing, and analyzing large amounts of RS imagery poses substantial challenges. Google Earth Engine (GEE) provides a scalable, cloud-based, geospatial retrieval and processing platform. GEE also provides access to the vast majority of freely available, public, multi-temporal RS data and offers free cloud-based computational power for geospatial data analysis. Artificial intelligence (AI) methods are a critical enabling technology to automating the interpretation of RS imagery, particularly on object-based domains, so the integration of AI methods into GEE represents a promising path towards operationalizing automated RS-based monitoring programs. In this article, we provide a systematic review of relevant literature to identify recent research that incorporates AI methods in GEE. We then discuss some of the major challenges of integrating GEE and AI and identify several priorities for future research. We developed an interactive web application designed to allow readers to intuitively and dynamically review the publications included in this literature review.

Monitoring Sand Spit Variability Using Sentinel-2 and Google Earth Engine in a Mediterranean Estuary

Estuarine degradation is a major concern worldwide, and is rapidly increasing due to anthropogenic pressures. The Mediterranean Guadiaro estuary, located in San Roque (Cadiz, Spain), is an example of a highly modified estuary, showing severe negative effects of eutrophication episodes and beach erosion. The migration of its river mouth sand spit causes the closure of the estuary, resulting in serious water quality issues and flora and fauna mortality due to the lack of water renewal. With the aim of studying the Guadiaro estuary throughout a 4-year period (2017–2020), the Sentinel-2 A/B twin satellites of the Copernicus programme were used thanks to their 5-day and 10 m temporal and spatial resolution, respectively. Sea–land mapping was performed using the Normalized Difference Water Index (NDWI) in the Google Earth Engine (GEE) platform, selecting cloud-free Sentinel-2 Level 2A images and computing statistics. Results show a closure trend of the Guadiaro river mouth and no clear sand spit seasonal patterns. The study also reveals the potential of both Sentinel-2 and GEE for estuarine monitoring by means of an optimized processing workflow. This improvement will be useful for coastal management to ensure a continuous and detailed monitoring in the area, contributing to the development of early-warning tools, which can be helpful for supporting an ecosystem-based approach to coastal areas.

Mapping Tidal Flats of the Bohai and Yellow Seas Using Time Series Sentinel-2 Images and Google Earth Engine

Tidal flats are one of the most productive ecosystems on Earth, providing essential ecological and economical services. Because of the increasing anthropogenic interruption and sea level rise, tidal flats are under great threat. However, updated and large-scale accurate tidal flat maps around the Bohai and Yellow Seas are still relatively rare, hindering the assessment and management of tidal flats. Based on time-series Sentinel-2 imagery and Google Earth Engine (GEE), we proposed a new method for tidal flat mapping with the Normalized Difference Water Index (NDWI) extremum composite around the Bohai and Yellow Seas. Tidal flats were derived from the differences of maximum and minimum water extent composites. Overall, 3477 images acquired from 1 Oct 2020 to 31 Oct 2021 produced a tidal flat map around the Bohai and Yellow Seas with an overall accuracy of 94.55% and total area of 546,360.2 ha. The resultant tidal flat map at 10 m resolution, currently one of the most updated products around the Bohai and Yellow Seas, could facilitate the process of sustainable policy making related to tidal flats and will help reveal the processes and mechanisms of its responses to natural and human disturbance.

Long Time-Series Mapping and Change Detection of Coastal Zone Land Use Based on Google Earth Engine and Multi-Source Data Fusion

Human activities along with climate change have unsustainably changed the land use in coastal zones. This has increased demands and challenges in mapping and change detection of coastal zone land use over long-term periods. Taking the Bohai rim coastal area of China as an example, in this study we proposed a method for the long time-series mapping and change detection of coastal zone land use based on Google Earth Engine (GEE) and multi-source data fusion. To fully consider the characteristics of the coastal zone, we established a land-use function classification system, consisting of cropland, coastal aquaculture ponds (saltern), urban land, rural settlement, other construction lands, forest, grassland, seawater, inland fresh-waters, tidal flats, and unused land. We then applied the random forest algorithm, the optimal classification method using spatial morphology and temporal change logic to map the long-term annual time series and detect changes in the Bohai rim coastal area from 1987 to 2020. Validation shows an overall acceptable average accuracy of 82.30% (76.70–85.60%). Results show that cropland in this region decreased sharply from 1987 (53.97%) to 2020 (37.41%). The lost cropland was mainly transformed into rural settlements, cities, and construction land (port infrastructure). We observed a continuous increase in the reclamation with a stable increase at the beginning followed by a rapid increase from 2003 and a stable intermediate level increase from 2013. We also observed a significant increase in coastal aquaculture ponds (saltern) starting from 1995. Through this case study, we demonstrated the strength of the proposed methods for long time-series mapping and change detection for coastal zones, and these methods support the sustainable monitoring and management of the coastal zone.

Mapping Aquaculture Areas with Multi-Source Spectral and Texture Features: A Case Study in the Pearl River Basin (Guangdong), China

Aquaculture has grown rapidly in the field of food industry in recent years; however, it brought many environmental problems, such as water pollution and reclamations of lakes and coastal wetland areas. Thus, the evaluation and management of aquaculture industry are needed, in which accurate aquaculture mapping is an essential prerequisite. Due to the difference between inland and marine aquaculture areas and the difficulty in processing large amounts of remote sensing images, the accurate mapping of different aquaculture types is still challenging. In this study, a novel approach based on multi-source spectral and texture features was proposed to map simultaneously inland and marine aquaculture areas. Time series optical Sentinel-2 images were first employed to derive spectral indices for obtaining texture features. The backscattering and texture features derived from the synthetic aperture radar (SAR) images of Sentinel-1A were then used to distinguish aquaculture areas from other geographical entities. Finally, a supervised Random Forest classifier was applied for large scale aquaculture area mapping. To address the low efficiency in processing large amounts of remote sensing images, the proposed approach was implemented on the Google Earth Engine (GEE) platform. A case study in the Pearl River Basin (Guangdong Province) of China showed that the proposed approach obtained aquaculture map with an overall accuracy of 89.5%, and the implementation of proposed approach on GEE platform greatly improved the efficiency for large scale aquaculture area mapping. The derived aquaculture map may support decision-making services for the sustainable development of aquaculture areas and ecological protection in the study area, and the proposed approach holds great potential for mapping aquacultures on both national and global scales.

Mangrove Forest Landcover Changes in Coastal Vietnam: A Case Study from 1973 to 2020 in Thanh Hoa and Nghe An Provinces

Mangrove forests can ameliorate the impacts of typhoons and storms, but their extent is threatened by coastal development. The northern coast of Vietnam is especially vulnerable as typhoons frequently hit it during the monsoon season. However, temporal change information in mangrove cover distribution in this region is incomplete. Therefore, this study was undertaken to detect change in the spatial distribution of mangroves in Thanh Hoa and Nghe An provinces and identify reasons for the cover change. Landsat satellite images from 1973 to 2020 were analyzed using the NDVI method combined with visual interpretation to detect mangrove area change. Six LULC classes were categorized: mangrove forest, other forests, aquaculture, other land use, mudflat, and water. The mangrove cover in Nghe An province was estimated to be 66.5 ha in 1973 and increased to 323.0 ha in 2020. Mangrove cover in Thanh Hoa province was 366.1 ha in 1973, decreased to 61.7 ha in 1995, and rose to 791.1 ha in 2020. Aquaculture was the main reason for the loss of mangroves in both provinces. Overall, the percentage of mangrove loss from aquaculture was 42.5% for Nghe An province and 60.1% for Thanh Hoa province. Mangrove restoration efforts have contributed significantly to mangrove cover, with more than 1300 ha being planted by 2020. This study reveals that improving mangrove restoration success remains a challenge for these provinces, and further refinement of engineering techniques is needed to improve restoration outcomes.

Integrating a Three-Level GIS Framework and a Graph Model to Track, Represent, and Analyze the Dynamic Activities of Tidal Flats

Tidal flats (non-vegetated area) are soft-sediment habitats that are alternately submerged and exposed to the air by changeable tidal levels. The tidal flat dynamics research mainly utilizes the cell-level comparisons between the consecutive snapshots, but the in-depth study requires more detailed information of the dynamic activities. To better track, represent, and analyze tidal flats’ dynamic activities, this study proposes an integrated approach of a three-level Geographic Information Science (GIS) framework and a graph model. In the three-level GIS framework, the adjacent cells are assembled as the objects, and the objects on different time steps are linked as lifecycles by tracking the predecessor–successor relationships. Furthermore, eleven events are defined to describe the dynamic activities throughout the lifecycles. The graph model provides a better way to represent the lifecycles, and graph operators are utilized to facilitate the event analysis. The integrated approach is applied to tidal flats’ dynamic activities in the southwest tip of Florida Peninsula from 1984 to 2018. The results suggest that the integrated approach provides an effective way to track, represent, and analyze the dynamic activities of tidal flats, and it offers a novel perspective to examine other dynamic geographic phenomena with large spatiotemporal scales.

A Classification Scheme for Sediments and Habitats on Exposed Intertidal Flats with Multi-Frequency Polarimetric SAR

We developed an extension of a previously proposed classification scheme that is based upon Freeman–Durden and Cloude–Pottier decompositions of polarimetric Synthetic Aperture Radar (SAR) data, along with a Double-Bounce Eigenvalue Relative Difference (DERD) parameter, and a Random Forest (RF) classifier. The extension was done, firstly, by using dual-copolarization SAR data acquired at shorter wavelengths (C- and X-band, in addition to the previously used L-band) and, secondly, by adding indicators derived from the (polarimetric) Kennaugh elements. The performance of the newly developed classification scheme, herein abbreviated as FCDK-RF, was tested using SAR data of exposed intertidal flats. We demonstrate that the FCDK-RF scheme is capable of distinguishing between different sediment types, namely mud and sand, at high spatial accuracies. Moreover, the classification scheme shows good potential in the detection of bivalve beds on the exposed flats. Our results show that the developed FCDK-RF scheme can be applied for the mapping of sediments and habitats in the Wadden Sea on the German North Sea coast using multi-frequency and multi-polarization SAR from ALOS-2 (L-band), Radarsat-2 (C-band) and TerraSAR-X (X-band).

The pattern, change and driven factors of vegetation cover in the Qin Mountains region

The Qin Mountains region is one of the most important climatic boundaries that divide the North and South of China. This study investigates vegetation covers changes across the Qin Mountains region over the past three decades based on the Landsat-derived Normalized Difference Vegetation Index (NDVI), which were extracted from the Google Earth Engine (GEE). Our results show that the NDVI across the Qin Mountains have increased from 0.624 to 0.776 with annual change rates of 0.0053/a over the past 32 years. Besides, its abrupt point occurred in 2006 and the change rates after this point increased by 0.0094/a (R² = 0.8159, p < 0.01) (2006-2018), which is higher than that in 1987-1999 and 1999-2006. The mean NDVI have changed in different elevation ranges. The NDVI in the areas below 3300 m increased, such increased is especially most obviously in the cropland. Most of the forest and grassland locate above 3300 m with higher increased rate. Before 2006, the temperature and reference evapotranspiration (PET) were the important driven factors of NDVI change below 3300 m. After afforestation, human activities become important factors that influenced NDVI changes in the low elevation area, but hydro-climatic factors still play an important role in NDVI increase in the higher elevations area.

Recent Evolution of Coastal Tidal Flats and the Impacts of Intensified Human Activities in the Modern Radial Sand Ridges, East China

The coastal tidal flats of the modern Radial Sand Ridges (RSRs) are typical silt-muddy tidal flats in Central Jiangsu Province. These tidal flats play a critical role in coastline protection and biodiversity conservation, and against storm surges, but have recently been displaying drastic changes in geomorphic dynamics because of human activities. However, a comprehensive understanding of spatiotemporal changes in tidal flats in RSRs remains lacking. Hence, we employed a novel remote sensing method by obtaining the instantaneous high/low tide line positions from over 112 scenes of Landsat satellite images of the study area from 1975 to 2017, which were used to track the recent evolution of the coastal tidal flats in the modern RSRs over the past four decades. We found that the shoreline of the tidal flats showed an advanced seaward trend, and the waterline of the tidal flat presented a gradual process during different periods. The total tidal flat area in the study area showed an obviously decreasing trend overall, and approximately 992 km2 of the tidal flat was lost. We also found that the coastal tidal flats in the modern RSRs were generally undergoing erosion in the low tidal flats, especially in the Northern Swing and Southern Swing areas, while the high tidal flats showed a slowed accretionary change. Land reclamation was the main factor affecting the reduction in the tidal flat area, as the reclamation area has increased by 1300 km2, with an average of 35.14 km2/year. In addition, the erosion of the tidal flats was associated with a reduced sediment supply. Our findings will provide useful information for local managers and researchers to support future environmental management because increasing demand for land and rising sea levels are expected in the future.

Mapping coastal wetlands of China using time series Landsat images in 2018 and Google Earth Engine

Coastal wetlands, composed of coastal vegetation and non-vegetated tidal flats, play critical roles in biodiversity conservation, food production, and the global economy. Coastal wetlands in China are changing quickly due to land reclamation from sea, aquaculture, industrialization, and urbanization. However, accurate and updated maps of coastal wetlands (including vegetation and tidal flats) in China are unavailable, and the detailed spatial distribution of coastal wetlands are unknown. Here, we developed a new pixel- and phenology-based algorithm to identify and map coastal wetlands in China for 2018 using time series Landsat imagery (2,798 ETM+/OLI images) and the Google Earth Engine (GEE). The resultant map had a very high overall accuracy (98%). There were 7,474.6 km2 of coastal wetlands in China in 2018, which included 5,379.8 km2 of tidal flats, 1,856.4 km2 of deciduous wetlands, and 238.3 km2 of evergreen wetlands. Jiangsu Province had the largest area of coastal wetlands in China, followed by Shandong, Fujian, and Zhejiang Provinces. Our study demonstrates the high potential of time series Landsat images, pixel- and phenology-based algorithm, and GEE for mapping coastal wetlands at large scales. The resultant coastal wetland maps at 30-m spatial resolution serve as the most current dataset for sustainable management, ecological assessments, and conservation of coastal wetlands in China.

Remote Sensing Applications in Monitoring of Protected Areas

Protected areas (PAs) have been established worldwide for achieving long-term goals in the conservation of nature with the associated ecosystem services and cultural values. Globally, 15% of the world’s terrestrial lands and inland waters, excluding Antarctica, are designated as PAs. About 4.12% of the global ocean and 10.2% of coastal and marine areas under national jurisdiction are set as marine protected areas (MPAs). Protected lands and waters serve as the fundamental building blocks of virtually all national and international conservation strategies, supported by governments and international institutions. Some of the PAs are the only places that contain undisturbed landscape, seascape and ecosystems on the planet Earth. With intensified impacts from climate and environmental change, PAs have become more important to serve as indicators of ecosystem status and functions. Earth’s remaining wilderness areas are becoming increasingly important buffers against changing conditions. The development of remote sensing platforms and sensors and the improvement in science and technology provide crucial support for the monitoring and management of PAs across the world. In this editorial paper, we reviewed research developments using state-of-the-art remote sensing technologies, discussed the challenges of remote sensing applications in the inventory, monitoring, management and governance of PAs and summarized the highlights of the articles published in this Special Issue.

Rapid Loss of Tidal Flats in the Yangtze River Delta since 1974

As the home to national nature reserves and a Ramsar wetland, the tidal flats of the Yangtze River Delta are of great significance for ecological security, at both the local and global scales. However, a comprehensive understanding of the spatiotemporal conditions of the tidal flats in the Yangtze River Delta remains lacking. Here, we propose using remote sensing to obtain a detailed spatiotemporal profile of the tidal flats, using all available Landsat images from 1974 to 2018 with the help of the Google Earth Engine cloud platform. In addition, reclamation data were manually extracted from time series Landsat images for the same period. We found that approximately 40.0% (34.9-43.1%) of the tidal flats in the study area have been lost since 1980, the year in which the tidal flat area was maximal. The change in the tidal flat areas was consistent with the change in the riverine sediment supply. We also found that the cumulative reclamation areas totaled 816.6 km2 and 431.9 km2 in the Yangtze estuary zone and along the Jiangsu coast, respectively, between 1974 and 2018. Because of reclamation, some areas (e.g., the Hengsha eastern shoal and Pudong bank), which used to be quite rich, have lost most of their tidal flats. Currently, almost 70% of the remaining tidal flats are located in the shrinking branch (North Branch) and the two National Nature Reserves (Chongming Dongtan and Jiuduansha) in the Yangtze estuary zone. Consequently, the large-scale loss of tidal flats observed was primarily associated with reduced sediment supply and land reclamation at the time scale of the study. Because increasing demand for land and rising sea levels are expected in the future, immediate steps should be taken to prevent the further deterioration of this valuable ecosystem.

Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

The forest stock volume (FSV) is one of the key indicators in forestry resource assessments on local, regional, and national scales. To date, scaling up in situ plot-scale measurements across landscapes is still a great challenge in the estimation of FSVs. In this study, Sentinel-2 imagery, the Google Earth Engine (GEE) cloud computing platform, three base station joint differential positioning technology (TBSJDPT), and three algorithms were used to build an FSV model for forests located in Hunan Province, southern China. The GEE cloud computing platform was used to extract the imagery variables from the Sentinel-2 imagery pixels. The TBSJDPT was put forward and used to provide high-precision positions of the sample plot data. The random forests (RF), support vector regression (SVR), and multiple linear regression (MLR) algorithms were used to estimate the FSV. For each pixel, 24 variables were extracted from the Sentinel-2 images taken in 2017 and 2018. The RF model performed the best in both the training phase (i.e., R2 = 0.91, RMSE = 35.13 m3 ha−1, n = 321) and in the test phase (i.e., R2 = 0.58, RMSE = 65.03 m3 ha−1, and n = 138). This model was followed by the SVR model (R2 = 0.54, RMSE = 65.60 m3 ha−1, n = 321 in training; R2 = 0.54, RMSE = 66.00 m3 ha−1, n = 138 in testing), which was slightly better than the MLR model (R2 = 0.38, RMSE = 75.74 m3 ha−1, and n = 321 in training; R2 = 0.49, RMSE = 70.22 m3 ha−1, and n = 138 in testing) in both the training phase and test phase. The best predictive band was Red-Edge 1 (B5), which performed well both in the machine learning methods and in the MLR method. The Blue band (B2), Green band (B3), Red band (B4), SWIR2 band (B12), and vegetation indices (TCW, NDVI_B5, and TCB) were used in the machine learning models, and only one vegetation index (MSI) was used in the MLR model. We mapped the FSV distribution in Hunan Province (3.50 × 108 m3) based on the RF model; it reached a total accuracy of 63.87% compared with the official forest report in 2017 (5.48 × 108 m3). The results from this study will help develop and improve satellite-based methods to estimate FSVs on local, regional and national scales.

Impact of Fractional Calculus on Correlation Coefficient between Available Potassium and Spectrum Data in Ground Hyperspectral and Landsat 8 Image

As the level of potassium can interfere with the normal circulation process of biosphere materials, the available potassium is an important index to measure the ability of soil to supply potassium to crops. There are rarely studies on the inversion of available potassium content using ground hyperspectral remote sensing and Landsat 8 multispectral satellite data. Pretreatment of saline soil field hyperspectral data based on fractional differential has rarely been reported, and the corresponding relationship between spectrum and available potassium content has not yet been reported. Because traditional integer-order differential preprocessing methods ignore important spectral information at fractional-order, it is easy to reduce the accuracy of inversion model. This paper explores spectral preprocessing effect based on Grünwald–Letnikov fractional differential (order interval is 0.2) between zero-order and second-order. Field spectra of saline soil were collected in Fukang City of Xinjiang. The maximum absolute of correlation coefficient between ground hyperspectral reflectance and available potassium content for five mathematical transformations appears in the fractional-order. We also studied the tendency of correlation coefficient under different fractional-order based on seven bands corresponding to the Landsat 8 image. We found that fractional derivative can significantly improve the correlation, and the maximum absolute of correlation coefficient under five spectral transformations is in Band 2, which is 0.715766 for the band at 467 nm. This study deeply mined the potential information of spectra and made up for the gap of fractional differential for field hyperspectral data, providing a new perspective for field hyperspectral technology to monitor the content of soil available potassium.