Spatial-temporal Pattern Analysis Research Articles

Analysis of green infrastructure network (GIN) based on morphological spatial-temporal pattern analysis (MSTPA): A case study of Shijingshan district in Beijing

Abstract Rapid urbanization results in habitat fragmentation. GIN analysis provides an objective basis for the complete construction of ecological networks. Although there is currently no uniform standard for GIN analysis, this study found that the core region and the connectivity between the core became two key elements of GIN analysis. By analyzing the morphological recognition of GIN, Morphological Spatial Pattern Analysis (MSPA) is targeted in the core area and connectivity analysis, so it has become the mainstream of GIN analysis methods. At the same time, this study finds that there are commonalities in MSPA analysis methods of different scholars. However, it should be noted that GIN analysis by MSPA method in China has two main research gaps: First, it lacks attention to the space scale of urban administrative district level; Second, there is a lack of research on GIN that considers time as a factor. In order to fill these two research gaps, the Shijingshan District of Beijing was selected as the research site to conduct GIN analysis based on Morphological Spatial-Temporal Pattern Analysis (MSTPA). At the same time, the analysis method and data of this study can provide a reference for future scholars to carry out the same type of research.

STPDA: Leveraging spatial-temporal patterns for downstream analysis in spatial transcriptomic data

Spatial transcriptomics, a groundbreaking field in cellular biology, faces the challenge of effectively deciphering complex spatial-temporal gene expression patterns. Traditional data analysis methods often fail to capture the intricate nuances of this data, limiting the depth of understanding in spatial distribution and gene interactions. In response, we present Spatial-Temporal Patterns for Downstream Analysis (STPDA), a sophisticated computational framework tailored for spatial transcriptomic data analysis. STPDA leverages high-resolution mapping to bridge the gap between genomics and histopathology, offering a comprehensive perspective on the spatial dynamics of gene expression within tissues. This approach enables a view of cellular function and organization, marking a paradigm shift in our comprehension of biological systems. By employing Autoregressive Moving Average (ARMA) and Long Short-Term Memory (LSTM) models, STPDA effectively deciphers both global and local spatio-temporal dynamics in cellular environments. This integration of spatial-temporal patterns for downstream analysis offers a transformative approach to spatial transcriptomics data analysis. STPDA excels in various single-cell analytical tasks, including the identification of ligand-receptor interactions and cell type classification. Its ability to harness spatial-temporal patterns not only matches but frequently surpasses the performance of existing state-of-the-art methods. To ensure widespread usability and impact, we have encapsulated STPDA in a scalable and accessible Python package, addressing single-cell tasks through advanced spatial-temporal pattern analysis. This development promises to enhance our understanding of cellular biology, offering novel insights and therapeutic strategies, and represents a substantial advancement in the field of spatial transcriptomics.

Spatial–Temporal Pattern Analysis and Development Forecasting of Carbon Stock Based on Land Use Change Simulation: A Case Study of the Xiamen–Zhangzhou–Quanzhou Urban Agglomeration, China

The spatial–temporal distribution and evolution characteristics of carbon stock under the influence of land use changes are crucial to the scientific management of environmental resources and the optimization of land spatial layout. Taking the Xiamen–Zhangzhou–Quanzhou urban agglomeration in the southeastern coastal region of China as an example, based on seven land use types from 1990 to 2020, including cultivated land, woodland, and construction land, we quantitatively investigate the spatial–temporal patterns of carbon stock development and the spatial correlation of carbon stock distribution. Additionally, two scenarios for the development of urban and ecological priorities in 2060 are established to investigate the effects of land use changes on carbon stock. The results indicate that (1) the research area has formed a land use spatial pattern centered around urban construction in the eastern bay area, with the western forest area and coastal forest belt serving as ecological barriers. Carbon stock is influenced by land use type, and the distribution of total carbon stock exhibits a spatial aggregation phenomenon characterized by “low in the southeast, high in the north, and medium in the center”. (2) Distance of trunk and secondary roads, elevation, slope, watershed borders, population size, and gross domestic product (GDP) factors are the main drivers of the growth of land use types. The primary causes of the reduction in carbon stock are the widespread conversion of cultivated land, woodland, and grassland into construction land, as well as water and unused land. (3) In 2060, there will be a decrease of 41,712,443.35 Mg in the urban priority development scenario compared to 2020, and a decrease of 29,577,580.48 Mg in the ecological priority development scenario. The estimated carbon stock under the two scenarios varies by 12,134,862.88 Mg. The average carbon storage of Zhangpu County, Quangang County, and Jimei County is expected to rise by one level under the ecological protection scenario, indicating that the vast forest area can become a potential area to maintain carbon stock. It is crucial to encourage the coordinated development of peri-urban agroforestry and ecological barriers, as well as to establish a harmonious spatial pattern of land use and carbon stock at the scale of urban agglomerations.

Response of net water productivity to climate and edaphic moisture in wheat-maize rotation system

Comprehending the mechanisms underlying the response of water productivity to changing climate is essential for developing effective strategies that can maintain sustainable agricultural productivity and ecological stability amidst water scarcity and climate change. This study aimed to clarify the effects of climate and associated biologic and abiotic factors on the net water productivity (NWP) in the winter wheat—summer maize rotation system within the North China Plain (NCP), through integrating spatial-temporal pattern analysis, the Optimal Parameter-based Geographical Detector model, Random Forest, and Structural Equation Modeling. Despite climate warming and water scarcity, the NWP in the vast majority of rotation systems within the NCP exhibited an increasing trend between 2000 and 2019, which was anticipated to persist in the future. From 2000–2019, the impact of climate on the NWP exerted a fluctuating diminishment, besides the influence of WS. It is essential to add adaptive strategies that can cope with WS fluctuation in future management, to better tackle climate variability. Apart from direct effects, meteorological factors indirectly induced NWP variation primarily by influencing edaphic moisture dynamic and crop growth factors. In the paths underlying the effect of climate change on NWP, positive and negative effects coexisted. Edaphic moisture within the 40–100 cm layer played an important role in affecting NWP throughout rotation systems. To sustainably improve NWP, it was advisable to adopt deeper-rooting crop varieties in conjunction with moderate deficit irrigation and improving topsoil water retention capability. Overall, we incorporated soil and crop factors into the framework of climate impact research, providing a systematic elucidation for the mechanisms through which climate change affects NWP. Additionally, the conclusions were based on a continuous spatiotemporal framework that accounted for the geospatial heterogeneity of variables, thereby giving conclusions ample regional universality.

Analysis of spatial-temporal patterns and driving mechanisms of land desertification in China

Desertification is a major manifestation of land degradation in China. The monitoring and assessment of land desertification in China and the analysis of its driving mechanisms are crucial to the realization of the aspiration of “net zero land degradation” proposed by the United Nations Convention to Combat Desertification (UNCCD). An improved Mediterranean Desertification and Land Use (MEDALUS) model was applied to assess the multiyear spatial distribution of land desertification sensitivity across China in 2010, 2015, and 2020. A Principal Component Analysis (PCA) was used to evaluate the internal stability of the model. In addition, a Geographical Detector method was used to examine the driving mechanisms of desertification sensitivity in China. The results showed that extremely sensitive desertification areas were primarily concentrated within the Northwest Desert and Desertification Region, northern segment of the Qinghai-Tibet Plateau desert and desertification region, and western sector of the Inner Mongolia-Daxinganling Desert and Desertification Region. In addition, the proportion of land area showing an overall reduction in sensitivity (17.07 %) exceeded that showing an increase (16.56 %). This indicates an overall diminishing trend in sensitivity to land desertification across China. Land use intensity (LUI), drought resistance (DR), erosion protection (EP), and aridity index (AI) are consistently the most important drivers. From 2015 to 2020, the LUI emerged as the principal catalyst behind the transformation of land desertification sensitivity in China. Hence, emphasizing well-planned land use is vital for ensuring harmony between land utilization and ecological capacity. This study establishes a scientific basis for China's land desertification control strategy and serves as a quantitative analysis reference for the driving mechanisms.

Spatial-Temporal Pattern Analysis of Grassland Yield in Mongolian Plateau Based on Artificial Neural Network

Accurate and timely estimation of grass yield is crucial for understanding the ecological conditions of grasslands in the Mongolian Plateau (MP). In this study, a new artificial neural network (ANN) model was selected for grassland yield inversion after comparison with multiple linear regression, K-nearest neighbor, and random forest models. The ANN performed better than the other machine learning models. Simultaneously, we conducted an analysis to examine the spatial and temporal characteristics and trends of grass yield in the MP from 2000 to 2020. Grassland productivity decreased from north to south. Additionally, 92.64% of the grasslands exhibited an increasing trend, whereas 7.35% exhibited a decreasing trend. Grassland degradation areas were primarily located in Inner Mongolia and the central Gobi region of Mongolia. Grassland productivity was positively correlated with land surface temperature and precipitation, although the latter was less sensitive than the former in certain areas. These findings indicate that ANN model-based grass yield estimation is an effective method for grassland productivity evaluation in the MP and can be used in a larger area, such as the Eurasian Steppe.

Monitoring shifting cultivation in Laos with Landsat time series

Shifting cultivation is an agricultural practice in which plots of land are cultivated temporarily, then abandoned, and vegetation is allowed to regenerate during fallow periods. Shifting cultivation is usually associated with cutting and burning forests and thus it is an important driver of forest disturbance in the tropics. However, studies of shifting cultivation are limited, and current area estimates of shifting cultivation are highly uncertain. Although Southeast Asia is a hotspot of shifting cultivation, there are no national maps of shifting cultivation in Southeast Asia at moderate or high resolution (less than or equal to 30 m). Monitoring shifting cultivation is challenging because it is highly dynamic, small-scale and results in complex post-disturbance landscapes. In this study, we monitored shifting cultivation using Landsat time series on Google Earth Engine for the entire country of Laos from 1991 to 2020. First, CCDC-SMA (Continuous Change Detection and Classification - Spectral Mixture Analysis) was used to detect forest disturbances. Then, these disturbances were attributed by combining time series analysis, object-based image analysis (OBIA), and post-disturbance land cover classification. Forest disturbances were assigned to Shifting cultivation, New plantation, Deforestation, Severe Drought, and Subtle Disturbance annually from 1991 to 2020 at a 30-m resolution. The major forest disturbances in 1991–2020 were mapped with an overall accuracy of 85%. Shifting cultivation is mapped with a producer's accuracy of 88% and a user's accuracy of 80%. The margin of error of the sampling-based area estimates of Shifting cultivation is 5.9%. Shifting cultivation is the main land use in Laos, accounting for 32.9% ± 1.9% of Laos over the past 30 years. To study changes in shifting cultivation over time, the area of shifting cultivation was estimated at 5-year intervals between 1991 and 2020 with all margins of error <17%. Results show that the area of slash-and-burn activities in Laos increased in 2015–2020. Our study provides an effective approach for monitoring shifting cultivation, which can be potentially applied into other regions. Our results not only provide valuable information for land management in Laos, but also can support analysis of spatial-temporal patterns of shifting cultivation and estimation of carbon emissions associated with shifting cultivation for REDD+ reporting.

Seasonal Variation and Spatial-Temporal Pattern Analysis of Anthrax among Livestock in Türkiye, 2005-2019

Seasonal Variation and Spatial-Temporal Pattern Analysis of Anthrax among Livestock in Türkiye, 2005-2019

Spatial temporal pattern analysis on on-demand cab services in urban areas using GIS

As an important component of the urban public passenger transportation system, cabs / taxi offers an all-weather, convenient, comfortable, and personalized travel services for the urban residents, as well as playing a key role in the urban passenger transportation development. Cab market has gained prominence over last 2-3 years in India, in the backdrop of entry of app based aggregators which has not only disrupted auto-rickshaw and traditional taxi market but also forced automotive Original Equipment Manufacturers (OEMs) to revise their growth and marketing strategies. There is a randomness and lack of regularity of cab operation characteristics, to describe cab travel activity pattern. Inorder to plan and effectively manage on-demand cab services, and to improve better facility to public, there is a need to understand the underlying relationship between supply and demand. This study analyzed the urban cab location distribution characteristics from different temporal and spatial levels. In the time level, the difference with daily location and operation pattern (weekday versus weekends) is identified; in the space level, the cab location pattern over a space are explored. Cab coverage index and Standard deviation ellipse are the tools used in analysing the patterns over time and space frames. This research can be helpful for understanding urban cab behavior pattern, as well as the contribution to the geographical activity space analysis. GIS has been used as a supportive tool in the study

Multidimensional poverty measurement and spatial–temporal pattern analysis at county level in the arid area of Xinjiang, China

Multidimensional poverty measurement and spatial–temporal pattern analysis at county level in the arid area of Xinjiang, China

Spatial-Temporal Pattern Analysis of Land Use and Water Yield in Water Source Region of Middle Route of South-to-North Water Transfer Project Based on Google Earth Engine

The water source area of the middle route of the South-to-North Water Diversion Project is an important water conservation and ecological protection area in China. Based on remote sensing data, this paper analyzed the evolution process of land use/cover change in water source region in the past 35 years. Then, based on the InVEST model, the spatial-temporal patterns of water yield in the water source region were calculated with land use cover, meteorology and soil data as inputs. The impacts of climate factors such as precipitation and temperature and land use change on water yield were discussed, and the responses of water yield to these two changes were also discussed. The results show that from 1985 to 2020, the average water yield depth in the middle route of the South-to-North Water Diversion Project increases first and then decreases, from 615 mm in 1985 to 738 mm in 2000, and then decreases to 521 mm in 2020. The spatial heterogeneity of the water-producing capacity is obvious. The high value of the water-producing capacity is concentrated in the Daba Mountain area in the south, while the low values are concentrated in the Hanzhong Basin, Ankang Basin and the eastern plain area. The spatial pattern of water producing depth has no obvious change. The average water yield depth of forest, grassland and shrub in the region was the largest, and forest and cultivated land were the main contributors to the total water yield of the region, providing 82% and 14% of the total water yield in 2020. Precipitation has a significant effect on water yield, while land use/cover change has a small effect on water yield.

Spatial match analysis of multiple factors in the geopolitical environment of the Arctic Passage.

This study seeks to provide a basic approach to fulfill the spatial visualization of geopolitical environmental factors required for the navigation of vessels in the Arctic. Multi-dimensional geopolitical environmental factors of the Arctic Passage are analyzed and classified into geopolitics, geoeconomics, geo-military, geoculture, and laws and regulations related to geography. Their characteristics are extracted to form an attribute information table matching spatial layers. Based on the information category and basic characteristics, the spatial match method is applied and connected with the spatial layers to examine the spatial point, polyline, and polygon. According to the qualitative description, the study extracted the quantitative indicators for the following spatial–temporal pattern analysis. The standard deviational ellipse is used to analyze the spatial–temporal patterns and trends of the geopolitical environmental indicators of the Arctic Passage in the Arctic and Northeast Asia. The expansion and contraction of geoinformation coexist in the countries surrounding the Arctic Passage. The spatial–temporal changes indicate that the Arctic channel has a great economic impact on the Nordic countries and Northeast Asia, especially the coastal areas of China and Japan. The characteristic extraction and spatial match of the geopolitical environment provide integrated Arctic geoinformation inquiry and services for the diplomatic, administrative, and legal preparations required for Arctic navigation. Therefore, the geospatial analysis conducted provides scientific support and a basis for the geographical distribution and developing trends of visualization and spatial–temporal pattern in Arctic navigation. The results of this research will help decision-makers to make a comprehensive judgment on governance related to the sustainable development of the Arctic Passage.

InSAR Study of Landslides: Early Detection, Three-Dimensional, and Long-Term Surface Displacement Estimation—A Case of Xiaojiang River Basin, China

Landslides, a major natural geohazard, obstruct municipal constructions and may destroy villages and towns, at worst causing significant casualties and economic losses. Interferometric Synthetic Aperture Radar (InSAR) technique offers distinct advantages on landslide detection and monitoring. In this paper, a more systematic workflow is designed for InSAR study of landslides, in terms of three levels: (i) early detection on regional scale, (ii) three-dimensional (3D) surface displacement rates estimation on detailed scale, and (iii) time series analysis on long-term temporal scale. The proposed workflow is applied for landslide research on the Xiaojiang River Basin, China, using ascending and descending Sentinel-1 images acquired from March 2017 to May 2019. First, the landslide inventory has been mapped and updated using InSAR stacking method, supporting geohazard prevention on a regional scale. A total of 22 active landslides are identified, ranging from medium to super large scale. Compared with the existing inventory, three unrecorded landslides are newly detected by our approach, and five recorded landslides are detected significant expansion of their boundaries. Then, specific to a detected landslide, Baobao landslide, a Total Least Squares–Kalman Filter-based approach is presented. Two outcomes are provided for further spatial-temporal pattern analysis: 3D displacement rates, providing an intuitive insight on the spatial characteristics and sliding direction of landslide, which are analyzed to deep the understanding of its kinematic mechanism, and long-term time series, which contribute to deduce the dynamic evolution of landslide, presenting benefits in landslide forecasting.

Comparative analysis of spatial-temporal patterns of human metapneumovirus and respiratory syncytial virus in Africa using genetic data, 2011\u20132014

BackgroundHuman metapneumovirus (HMPV) and respiratory syncytial virus (RSV) are leading causes of viral severe acute respiratory illnesses in childhood. Both the two viruses belong to the Pneumoviridae family and show overlapping clinical, epidemiological and transmission features. However, it is unknown whether these two viruses have similar geographic spread patterns which may inform designing and evaluating their epidemic control measures.MethodsWe conducted comparative phylogenetic and phylogeographic analyses to explore the spatial-temporal patterns of HMPV and RSV across Africa using 232 HMPV and 842 RSV attachment (G) glycoprotein gene sequences obtained from 5 countries (The Gambia, Zambia, Mali, South Africa, and Kenya) between August 2011 and January 2014.ResultsPhylogeographic analyses found frequently similar patterns of spread of RSV and HMPV. Viral sequences commonly clustered by region, i.e., West Africa (Mali, Gambia), East Africa (Kenya) and Southern Africa (Zambia, South Africa), and similar genotype dominance patterns were observed between neighbouring countries. Both HMPV and RSV country epidemics were characterized by co-circulation of multiple genotypes. Sequences from different African sub-regions (East, West and Southern Africa) fell into separate clusters interspersed with sequences from other countries globally.ConclusionThe spatial clustering patterns of viral sequences and genotype dominance patterns observed in our analysis suggests strong regional links and predominant local transmission. The geographical clustering further suggests independent introduction of HMPV and RSV variants in Africa from the global pool, and local regional diversification.

Spatial-temporal pattern analysis of landscape ecological risk assessment based on land use/land cover change in Baishuijiang National nature reserve in Gansu Province, China

It is necessary to improve the ecological environment and keep ecological balance of nature reserves that have particularly important function on precious and endangered wildlife species conservation. Human activities give rise to the remarkable changes in land use/land cover (LULC) and further have an effect on the landscape and eco-environment in nature reserves. This study evaluated the landscape ecological risk based on LULC changes in Baishuijiang National Nature Reserve according to different management zones from 1986 to 2015. Results showed that the significant change characteristics of LULC were the increase of forest and the decrease of cultivated land from 1986 to 2015. The major LULC changes took place in the experimental zone, and there was 85.61 km2 cultivated land transferred to the forest. Generally, the ecological risk level was low, and the area percentages of the lowest and lower risk regions to the total area were all higher than 97% from 1986 to 2015. Moreover, the values of ecological risk were discrepant in different time, the order of that being 1986 < 2015 < 1995 < 2008. Combining the components of different ecological risk levels in every management zone, the ecological risk from low to high was the core zone, buffer zone, and experimental zone. The ecological risk tended to be aggregated in space during 1986–2008, and it was enhanced slightly from 2008 to 2015. This study focuses on Baishuijiang National Nature Reserve to provide assessment for environmental protection and ecological management in hope that the method can be applied to other national nature reserves in China.

PM2.5 Estimation and Spatial-Temporal Pattern Analysis Based on the Modified Support Vector Regression Model and the 1 km Resolution MAIAC AOD in Hubei, China

The satellite-retrieved Aerosol Optical Depth (AOD) is widely used to estimate the concentrations and analyze the spatiotemporal pattern of Particulate Matter that is less than or equal to 2.5 microns (PM2.5), also providing a way for the related research of air pollution. Many studies generated PM2.5 concentration networks with resolutions of 3 km or 10 km. However, the relatively coarse resolution of the satellite AOD products make it difficult to determine the fine-scale characteristics of PM2.5 distributions that are important for urban air quality analysis. In addition, the composition and chemical properties of PM2.5 are relatively complex and might be affected by many factors, such as meteorological and land cover type factors. In this paper, an AOD product with a 1 km spatial resolution derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, the PM2.5 measurements from ground sites and the meteorological data as the auxiliary variable, are integrated into the Modified Support Vector Regression (MSVR) model that proposed in this paper to estimate the PM2.5 concentrations and analyze the spatiotemporal pattern of PM2.5. Considering the relatively small dataset and the somewhat complex relationship between the variables, we propose a Modified Support Vector Regression (MSVR) model that based on SVR to fit and estimate the PM2.5 concentrations in Hubei province of China. In this paper, we obtained Cross Correlation Coefficient (R²) of 0.74 for the regression of independent and dependent variables, and the conventional SVR model obtained R² of 0.60 as comparison. We think our MSVR model obtained relatively good performance in spite of many complex factors that might impact the accuracy. We then utilized the optimal MSVR model to perform the PM2.5 estimating, analyze their spatiotemporal patterns, and try to explain the possible reasons for these patterns. The results showed that the PM2.5 estimations retrieved from 1 km MAIAC AOD could reflect more detailed spatial distribution characteristics of PM2.5 and have higher accuracy than that from 3 km MODIS AOD. Therefore, the proposed MSVR model can be a better method for PM2.5 estimating, especially when the dataset is relatively small.

The Analysis and Prediction of Spatial-Temporal Talent Mobility Patterns

The Analysis and Prediction of Spatial-Temporal Talent Mobility Patterns

Distribution Pattern of Red Fox (Vulpes vulpes) Dens and Spatial Relationships with Sea Turtle Nests, Recreation, and Environmental Characteristics

Although sea turtles are formidable prey as adults, their nests are highly vulnerable to terrestrial predation. Along the Southeastern coast of the United States, a primary predator of sea turtle nests is the red fox (Vulpes vulpes). Examining the relationship between fox populations and nest predation is often difficult due to coastal development. Masonboro Island, North Carolina is an undeveloped, natural, 13-km-long barrier island complex that is a component of the North Carolina National Estuarine Research Reserve (NERR). Masonboro Island consists of beaches, a dune ridge, back barrier flats, an expansive salt marsh, a lagoon, and spoil islands seaward of the Intracoastal Waterway. A field survey, which was conducted each spring from 2009 through 2012, recorded den entrance coordinates based upon recent use by foxes. Sea turtle nests were located using a similar survey methodology, which identifies viable and predated nests as well as false crawls. A series of spatial-temporal pattern analysis techniques were used to identify trends through time. The results indicated that: (1) fox den entrances and predated sea turtle nests were clustered throughout the island (p = 0.01); (2) den entrances in the northern part of the island were closer to the sea turtle nests than other locations on the island; (3) fox den entrances were positively correlated (p = 0.01) with dune height, (4) fox den entrances were located closer to the island boat access sites than expected (p = 0.01). A variety of spatial sensitivity tests were used to test the validity of the statistically significant cluster analyses. A Geographically Weighted Regression model was created to predict the location of fox dens using dune elevation, the distance to predated sea turtle nests, and the distance to boat access sites. The model accounted for 40% of the variance and had a small residual error, which indicates that the independent variables were statistically valid. Results from this project will be used by the NC NERR staff to develop management plans and to further study fox-related impacts on the island. For example, given the higher density of fox den entrances on the northern part of the island, managers may consider targeted wildlife control measures during the sea turtle nesting season to diminish predation.

Spatial-temporal pattern analysis of the vegetation coverage and geological hazards in Yanchi County based on dimidiate pixel model

Spatial-temporal pattern analysis of the vegetation coverage and geological hazards in Yanchi County based on dimidiate pixel model

Satellite Remote Sensing of Harmful Algal Blooms (HABs) and a Potential Synthesized Framework

Harmful algal blooms (HABs) are severe ecological disasters threatening aquatic systems throughout the World, which necessitate scientific efforts in detecting and monitoring them. Compared with traditional in situ point observations, satellite remote sensing is considered as a promising technique for studying HABs due to its advantages of large-scale, real-time, and long-term monitoring. The present review summarizes the suitability of current satellite data sources and different algorithms for detecting HABs. It also discusses the spatial scale issue of HABs. Based on the major problems identified from previous literature, including the unsystematic understanding of HABs, the insufficient incorporation of satellite remote sensing, and a lack of multiple oceanographic explanations of the mechanisms causing HABs, this review also attempts to provide a comprehensive understanding of the complicated mechanism of HABs impacted by multiple oceanographic factors. A potential synthesized framework can be established by combining multiple accessible satellite remote sensing approaches including visual interpretation, spectra analysis, parameters retrieval and spatial-temporal pattern analysis. This framework aims to lead to a systematic and comprehensive monitoring of HABs based on satellite remote sensing from multiple oceanographic perspectives.