Dynamic Monitoring and Analysis of Land-Use and Land-Cover Change Using Landsat Multitemporal Data in the Zhoushan Archipelago, China

Chen, Chao; Chen, Huixin; Liao, Weimin; Sui, Xinxin; Wang, Liyan; Chen, Jianyu; Chu, Yanli

doi:10.1109/access.2020.3036128

Cited by 24 publications

(7 citation statements)

References 26 publications

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“…Earth observing system data and information system (EOSDIS) and National Environmental Satellite Data and Information Service (NESDIDS) maintain environmental data for land, ocean, and atmosphere applications collected by several satellites, aircraft, and field measurements. Remote sensing data is also used to study the spatial and temporal characteristics of earth's landscape which can be used to understand the effects of human activities, population growth, and natural and biological factors (Chen et al 2020a;2021). Studies like Chen et al (2022) have developed methods for extracting coastline information using remote sensing images to study the evolution of the coastlines.…”

Section: Satellites and Aircraftsmentioning

confidence: 99%

The Gulf of Mexico in trouble: Big data solutions to climate change science

Sunkara

McKenna²,

Kar³

et al. 2023

Front. Mar. Sci.

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The latest technological advancements in the development and production of sensors have led to their increased usage in marine science, thus expanding data volume and rates within the field. The extensive data collection efforts to monitor and maintain the health of marine environments supports the efforts in data driven learning, which can help policy makers in making effective decisions. Machine learning techniques show a lot of promise for improving the quality and scope of marine research by detecting implicit patterns and hidden trends, especially in big datasets that are difficult to analyze with traditional methods. Machine learning is extensively used on marine science data collected in various regions, but it has not been applied in a significant way to data generated in the Gulf of Mexico (GOM). Machine learning methods using ocean science data are showing encouraging results and thus are drawing interest from data science researchers and marine scientists to further the research. The purpose of this paper is to review the existing approaches in studying GOM data, the state of the art in machine learning techniques as applied to the GOM, and propose solutions to GOM data problems. We review several issues faced by marine environments in GOM in addition to climate change and its effects. We also present machine learning techniques and methods used elsewhere to address similar problems and propose applications to problems in the GOM. We find that Harmful Algal Blooms (HABs), hypoxia, and sea-level rises have not received as much attention as other climate change problems and within the machine learning literature, the impacts on estuaries and coastal systems, as well as oyster mortality (also major problems for the GOM) have been understudied – we identify those as important areas for improvement. We anticipate this manuscript will act as a baseline for data science researchers and marine scientists to solve problems in the GOM collaboratively and/or independently.

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Section: Satellites and Aircraftsmentioning

confidence: 99%

The Gulf of Mexico in trouble: Big data solutions to climate change science

Sunkara

McKenna²,

Kar³

et al. 2023

Front. Mar. Sci.

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“…In summary, many studies have focused on the evaluation of the ecological environments of large-and medium-sized cities, while studies of island ecological environments are relatively scarce [57][58][59][60]. An island is a land area surrounded by water on all sides that is located above the water level during high tide periods [61,62]. Due to their unique characteristics, the ecological environments of islands are extremely sensitive to the dual impacts of high-intensity human activities and global change [63][64][65].…”

Section: Introductionmentioning

confidence: 99%

Mapping of Ecological Environment Based on Google Earth Engine Cloud Computing Platform and Landsat Long-Term Data: A Case Study of the Zhoushan Archipelago

Chen,

Wang,

Yang

et al. 2023

Remote Sensing

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In recent years, with the rapid advancement of China’s urbanization, the contradiction between urban development and the ecological environment has become increasingly prominent, and the urban ecological system now faces severe challenges. In this study, we proposed an ecological index-based approach to monitor and evaluate the ecological environment using a Google Earth Engine cloud-based platform and Landsat time series. Firstly, a long-term series of Landsat images was obtained to construct and calculate the remote sensing-based ecological index (RSEI). Then, the Theil–Sen median estimation and the Mann–Kendall test were used to evaluate the trend and significance of the RSEI time series and combined with the Hurst index to predict the future development trend of the ecological environment in the study area. Finally, the coefficient of variation method was used to determine the temporal stability of the ecological environment. Taking Zhoushan Archipelago, China, as the study area, we mapped the distribution of the ecological environment using a spatial resolution of 30 m and evaluated the ecological environment from 1985 to 2020. The results show that (1) from 1985 to 2020, the average RSEI in the Zhoushan Archipelago decreased from 0.7719 to 0.5817, increasing at a rate of −24.64%. (2) The changes in the areas of each level of ecological environmental quality show that the ecological environment in the Zhoushan Archipelago generally exhibited a decreasing trend. During the study period, the proportion of the areas with excellent ecological environmental quality decreased by 38.83%, while the proportion of areas with poor and relatively poor ecological environmental quality increased by 20.03%. (3) Based on the overall change trend, the degradation in the ecological environment in the Zhoushan Archipelago was greater than the improvement, with the degradation area accounting for 84.35% of the total area, the improvement area accounting for 12.61% of the total area, and the stable area accounting for 3.05% of the total area. (4) From the perspective of the sustainability of the changes, in 86.61% of the study area, the RSEI exhibited positive sustainability, indicating that the sustainability of the RSEI was relatively strong. (5) The coefficient of variation in the RSEI was concentrated in the range of 0–0.40, having an average value of 0.1627 and a standard deviation of 0.1467, indicating that the RSEI values in the Zhoushan Archipelago during the study period were concentrated, the interannual fluctuations of the data were small, and the time series was relatively stable. The results of this study provide theoretical methods and a decision-making basis for the dynamic monitoring and regional governance of the ecological environment in island areas.

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“…As a result, remote sensing and satellite imagery are seen as essential for monitoring and managing landscapes (H. Chen et al., 2021). Land cover mapping and analysis of spatio‐temporal changes and disaster detection through the use of remote sensing and satellite imagery has been a great success worldwide (Assoma et al., 2021; C. Chen et al., 2020, 2022; H. Chen et al., 2021; Solly et al., 2021; Sun et al., 2021; Tsayem Demaze, 2008).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Land Use Change in the N'ZI Watershed of Côte d'Ivoire Using Landsat Satellite Images

YAO

Kouakou²,

Kouassi³

et al. 2023

Earth and Space Science

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The forest resource of Côte d'Ivoire was estimated at 16 million hectares in 1900, the country's forest cover was only 7,850,864 ha in 1986 and 3,401,146 ha in 2015(BNETD & Tera, 2016. The country lost a total of 78% of its crop cover. Due to the expansion of agriculture which occupies about 62% of the Ivorian territory (BNETD & Tera, 2016), forest space has fallen to over 2 million hectares today (Assoma et al., 2021;Koné et al., 2021).Clear forests and forested savannahs experienced a decline of more than 14.95%, a decrease of 1.93% in dense forests and shrubby and/or grassy savannas of less than 15.15% were estimated using Landsat satellite images between 2000 and 1990 on the N'ZI catchment area using the Isodata classification method (A. M. Kouassi, 2010). Forests are composed of secondary degraded forests around the river N'ZI in particular (17% degraded forest, 10% clear forest and 3% gallery forest) (Noho et al., 2018). The N'ZI river basin constitutes a feeding zone between the savannah and forest zones. This basin was the cocoa and coffee loop from 1960 to 1980 (Yao et al., 2022). The demographic explosion of the study area from 900,000 to 3,030,381 inhabitants (INS, 2014), constituted an anthropogenic and land cause created a great pressure on resources, including land degradation resulting in the abandonment of land to other sources of income.In the search for information leading to the abandonment of land to other more productive regions, remote sensing and geographical information systems (GIS) have been chosen to monitor the evolution of land use of N'ZI watershed to inform public opinion. Updating land use and monitoring the evolution of vegetation cover are of great importance for economic development in our regions. As a result, remote sensing and satellite imagery are seen as essential for monitoring and managing landscapes (H. Chen et al., 2021). Land cover mapping and analysis of spatio-temporal changes and disaster detection through the use of remote sensing and satellite imagery has been a great success worldwide (

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Dynamic Monitoring and Analysis of Land-Use and Land-Cover Change Using Landsat Multitemporal Data in the Zhoushan Archipelago, China

Cited by 24 publications

References 26 publications

The Gulf of Mexico in trouble: Big data solutions to climate change science

The Gulf of Mexico in trouble: Big data solutions to climate change science

Mapping of Ecological Environment Based on Google Earth Engine Cloud Computing Platform and Landsat Long-Term Data: A Case Study of the Zhoushan Archipelago

Analysis of Land Use Change in the N'ZI Watershed of Côte d'Ivoire Using Landsat Satellite Images

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