Quality Assessment of Ecological Environment Based on Google Earth Engine: A Case Study of the Zhoushan Islands

With the development of society, the impact of human activities on the ecological environment is becoming increasingly intense, so the dynamic monitoring of the status of the ecological environment is of great significance to the management and protection of urban ecology. As an objective and rapid ecological quality monitoring and evaluation technique, the remote sensing based ecological index (RSEI) has been widely used in the field of ecological research. Free available Landsat series data has the character of a long time series and high spatial resolution provides the possibility to conduct large-scale and long-term monitoring of ecological environment quality. Compared with traditional methods, the Google Earth Engine (GEE) platform can save a lot of time and energy in the data acquisition and preprocessing steps. To monitor the quality of the ecological environment in Zhoushan from 2000 to 2020, the GEE platform was used for cloud computing to obtain the RSEI, which can reflect the quality of the ecological environment. The results show that (1) from 2000 to 2020, the average RSEI value in Zhoushan Islands decreased from 0.748 to 0.681, indicating that the overall ecological environment exhibited a degradation trend. (2) From 2000 to 2020, the change in the area of each ecological environment level indicates that the quality of the ecological environment in Zhoushan Islands exhibited a degradation trend. The proportion of the area with an excellent eco-environment grade decreased by 13.54%, and the proportion of the area with poor and fair eco-environment grades increased by 3.43%.

Introduction

The environment is not only a basic condition for human existence and development but also an important cornerstone of sustainable social and economic development (Chen et al., 2020a,b; Jia H. et al., 2021; Nourani et al., 2021). Under global climate change and the intensification of human activites, many ecological problems have arisen, which have a significant impact on the ecosystems on which human beings depend for survival, resulting in the continuous decline of the restoration ability of the ecosystem (Pekel et al., 2016; Hussain and Khan, 2020; Alqadhi et al., 2021; Chen et al., 2021a,b). Therefore, developing a method for the dynamic monitoring and evaluation of the quality of the ecological environment has become an important issue in ecological research.

For the past few years, scholars have been exploring quantitative methods of evaluating the regional environment. Thus far, promulgated by China’s Ministry of Environmental Protection, the eco-environment index (EI) (Chiabai et al., 2018), which is based on ecological environment evaluation specification, and the remote sensing based ecological index (RSEI) proposed by Xu et al. (2018) are the most widely used to evaluate the regional environment (Hossain and Hashim, 2019; Bonney and He, 2021; Boori et al., 2021; Fu et al., 2021; Jia M. et al., 2021). The RSEI model has the advantages of easy parameter acquisition and a wide evaluation range, and it makes up for the index acquisition and analysis deficiencies of the EI so it is widely used in the evaluation of the quality of the ecological environment (Berberoglu and Akin, 2009; Chiabai et al., 2018; Xu et al., 2018; Eveleth et al., 2021; Firozjaei et al., 2021a,b). However, when applied to a large area, complex data processing and index calculation become a problem with the RSEI that cannot be ignored (Wen et al., 2019; Duan et al., 2021; Sun et al., 2021).

With the development of remote sensing technology, it is widely used in land use and cover change, forestry resource survey, and ecological environment monitoring (Yang et al., 2018; Chen et al., 2022). The Landsat series data meet the requirements of ecological environment quality monitoring due to its long time series and high spatial resolution (Yang et al., 2022). In recent years, the long time series, large spatial scale, fast, and accurate calculation of remote sensing data impose higher requirements for software and hardware (Chen et al., 2020a,b; Xiong et al., 2021). However, the Google Earth Engine (GEE) platform is a special tool for the batch processing of satellite image data, and it can quickly process a large number of images (Yang et al., 2020; Fan et al., 2021; Firozjaei et al., 2021b; Jia H. et al., 2021; Nietupski et al., 2021). Compared with traditional methods, it can save a lot of time and energy in the data acquisition and preprocessing steps. Thanks to the powerful computing ability and cloud storage features of the GEE platform, environmental monitoring studies based on this platform have been continuously carried out in recent years (Fan et al., 2021; Jia H. et al., 2021; Jia M. et al., 2021; Murayama et al., 2021).

The Zhoushan Islands are a typical archipelago region in China. As the first prefecture-level city established in the form of several islands, the city of Zhoushan has a relatively special geographic location. As it is naturally formed land areas surrounded by seawater and that remain above the water surface at high tide. Islands are very vulnerable to extreme weather or natural disasters, and thus, more attention should be paid to the protection of their ecological environment (Gernez et al., 2021; He et al., 2021; Murayama et al., 2021). Due to the intensification of human activities in recent years, the development of these islands has become more intense. Therefore, the monitoring of the ecological environment in the Zhoushan Islands is particularly important. Using the GEE platform, Landsat Thematic Mapper/Operational Land Imager (TM/OLI) image data were acquired in this study, and the RSEI model was used to carry out ecological environmental quality monitoring in Zhoushan from 2000 to 2020. The results of this study provide a reference for the formulation of policies and measures related to ecological restoration and protection and play an important role in the sustainable development of the ecological environment in this region.

Materials and Methods

Study Area

The Zhoushan Islands as shown in Figure 1 is located along the coast of Zhejiang Province. As the gateway to the Yangtze River valley, the developed Yangtze River Delta region gives this region significant resource advantages (Chen et al., 2021a,2022). The Zhoushan Islands is the first prefecture-level city established in the form of an archipelago in China (Chen et al., 2021c). It consists of 1,390 islands with an area of over 500 m² (Wang et al., 2021a,b).

FIGURE 1

Figure 1. Location of the study area.

Data Source

The Landsat data were obtained from the United States Geological Survey (USGS) and were integrated on the GEE platform with a spatial resolution of 30 m. In this study, the surface reflectance (SR) datasets obtained using the Landsat 5 TM sensors in 2000, 2005, and 2010 were used. The SR datasets of the Landsat 8 OLI/Thermal Infrared Sensor sensors obtained in 2015 and 2020 were selected using the GEE platform. The obtained images are all images synthesized from the median of summer images in each year and they have been preprocessed, including radiometric correction, atmospheric correction, and geometric precision correction. Then, the Landsat cloud mask algorithm (Xiong et al., 2021) was applied in the GEE platform to conduct cloud removal from the obtained data. The data used in this study are listed in Table 1.

TABLE 1

Table 1. Description of data used by the study.

Methods

Based on the calculation formula for each component of the Landsat image proposed by Xu et al. (2018), in this study, remote sensing monitoring of the quality of the ecological environment was carried out using the GEE platform (Zhao et al., 2017). First, the Landsat satellite remote sensing data were acquired using the GEE platform, and cloud removal was conducted. Second, the normalized difference vegetation index (NDVI), humidity component (Wet), the normalized difference building-up and soil index (NDBSI), and land surface temperature (LST) were inverted to obtain the greenness, humidity, dryness, and heat indexes, and the results were subjected to standardized processing (Xu et al., 2018). Third, we conducted principal component analysis (PCA) and obtained PC1, which was used for the construction of the RSEI. Finally, the ecological environmental quality of the study area was classified, and the characteristics of the changes in the ecological quality in Zhoushan over the past 20 years were analyzed.

(1) Cloud Mask Processing

In this study, Landsat_SR image data were selected with an interval of 5 years from 2000 to 2020 in the GEE. The cloud content of the Landsat_SR image in the GEE was marked in the “CLOUD_COVER” field (Foga et al., 2017). This field was used to screen all images with cloud contents of less than 50% in the study area. The mask function established according to cloud shadow and cloud attribute fields contained in the quality evaluation band “pixel_qa” in the Landsat_SR dataset image removed the cloud-containing area in each image.

(2) Calculation and Normalization of the Component Index

Xu used four important indexes of the environment as evaluation indexes of the eco-environment to construct the RSEI, namely, the greenness, humidity, heat, and dryness (Xu et al., 2018). In remote sensing, they are defined as the vegetation index, soil index, moisture component, and land surface temperature, respectively.

$$RSEI=f(NDVI,Wet,LST,NDBSI),(1)$$

where NDVI is the normalized difference vegetation index, Wet is the wet component of the tasseled cap transformation, LST is the land surface temperature, and NDBSI is the normalized difference built-up and soil index. f indicates that the Remote sensing based ecological index can be expressed as a function of these four indicators.

Because each indicator has a different unit and value range, the four indicators need to be normalized separately using the following equation:

$$NIi=\left(Ii-Imin\right)\mathrm{/}\left(Imax-Imin\right),(2)$$

where NI_i is the result of the normalization of the indicators, I_i is the ith pixel value; I_min is the minimum value, and I_max is the maximum value (Yi et al., 2018).

(3) Calculation of the Remote Sensing Based Ecological Index

The principal component transformation was used to construct the RSEI. The main information for the four indicators was mainly concentrated in the first principal component (PC1), which enables the RSEI to comprehensively reflect the information about the four indicators. PCA is a multi-dimensional data compression technique. This method rotates the coordinate axis vertically and concentrates the information about multiple variables into a few feature components through linear transformation (Yi et al., 2018; Turpie et al., 2021). This method can avoid the deviation of subjective factors during the weight assignment process, which makes the RSEI more objective and reliable.

To make a large value of PC1 represent good ecological conditions, the first principal component of the function of these four indicators can be further subtracted from 1 to obtain the initial ecological index RSEI₀, and the formula is as follows:

$$RSEI0=1-PC1\left(f(NDVI,Wet,LST,NDBSI)\right).(3)$$

where RSEI₀ is normalized to facilitate the measurement and comparison of the indicators as follows:

$$RSEIf=\left(RSEI0-RSEI0\mathrm{\_}\min \right)\mathrm{/}\left(RSEI0\mathrm{\_}\max -RSEI0\mathrm{\_}\min \right).(4)$$

The obtained RSEI_f value is within the range of [0–1]. The closer RSEI is to 1, the better the quality of the eco-environment of the region (Xu et al., 2018; Sekovski et al., 2020).

Results and Analysis

Results of Principal Component Analysis

The first to fourth principal component analysis index values can be expressed as PC1, PC2, PC3, and PC4, respectively, as shown in Table 2.

TABLE 2

Table 2. The results of the principal component analysis.

Table 2 shows the results of the principal component analysis from 2000 to 2020 in the study area. It can be seen from Table 2 that (1) The contribution rates of the first principal component from 2000 to 2020 all exceeded 70%. The contribution rates of PC1 in 2000, 2005, 2010, 2015, and 2020 were 71.81, 75.88, 77.71, 72.74, and 82.68%, respectively. (2) Compared with the other components, the first principal component contained more than 70% of the characteristic information about each indicator. Therefore, it can integrate the information about each indicator better, representing the characteristics of the regional ecological environment, and it can be used to establish the Remote sensing based ecological index.

Table 3 shows that the quality of the ecological environment in Zhoushan was generally good from 2000 to 2020, and the mean value of the Remote sensing based ecological index initially decreased and then slowly increased. The mean value of the Remote sensing based ecological index decreased from 0.748 in 2000 to 0.668 in 2010, and then, it remained basically unchanged until 2015 (0.666). Finally, it increased to 0.681 in 2020. This indicates that the eco-environmental quality in Zhoushan exhibited a slowly increasing trend after decreasing. During the study period, the standard deviation of the Remote sensing based ecological index was low, indicating a high degree of data concentration and that the research results are reliable.

TABLE 3

Table 3. Mean values of normalized ecological environment factors in Zhoushan.

Analysis of Temporal Changes in the Quality of the Ecological Environment

To better analyze the quality of the ecological environment in Zhoushan, according to the Technical Specifications for the Assessment of the Ecological and Environmental Conditions issued in 2015 (HJ/T192-2006) (Firozjaei et al., 2021a), the ecological and environmental quality was divided into the following five grades with a 0.2 interval: poor [0–0.2], fair [0.2–0.4], moderate [0.4–0.6], good [0.6–0.8], and excellent [0.8–1]. The results are presented in Table 4 and Figure 2.

TABLE 4

Table 4. Statistics of ecological quality grade and area of Zhoushan.

FIGURE 2

Figure 2. Spatial distribution of RSEI in Zhoushan Islands during 2000–2020.

Figure 2 and Table 4 show the changes in the areas of the various eco-environmental grades in Zhoushan from 2000 to 2020. The results show that (1) from 2000 to 2020, the area with an excellent eco-environmental grade in Zhoushan decreased by 13.54%, and the area with poor and fair eco-environmental grades increased by 3.43%, so the overall quality of the eco-environment exhibited a degradation trend. (2) In 2000 and 2005, the quality of the eco-environment in Zhoushan remained basically stable, the overall quality of the eco-environment was mainly excellent, accounting for about 50% of the total area. In 2000 and 2005, the area with a good eco-environment grade accounted for 28.16 and 28.64%, respectively. The area with moderate eco-environmental quality increased from 11.97% in 2000 to 15.26% in 2005. In 2005, the area with poor and fair eco-environment grades accounted for 10.77%, an increase of 40.41 km² compared with 2000. (3) In 2010, the overall eco-environmental quality was predominantly excellent and good, accounting for 34.82 and 33.81% of the total area, respectively. Compared with 2005, the area with an excellent grade decreased by 132.21 km². In addition, the area with poor and fair eco-environment grades accounted for 15.27%, an increase of 56.68 km² compared with 2005. This shows that from 2005 to 2010, the eco-environment exhibited a degradation trend. (4) In 2015, the area with poor and fair eco-environment grades decreased by 8.22%, indicating a significant increase in the eco-environmental quality compared with 2010. However, the area with an excellent eco-environment grade decreased by 107.68 km² from 2010 to 2015. (5) In 2020, the eco-environmental quality was predominantly excellent. Compared with 2010 and 2015, the area with an excellent eco-environment grade was significantly higher in 2020, accounting for 38.77%, while the area with a poor eco-environmental grade was significantly smaller. This shows that there was a trend of improvement in the ecological environment from 2010 to 2020.

Conclusion

In this study, the RSEI for Zhoushan from 2000 to 2020 was analyzed using the GEE platform. These results provide a decision-making basis for the sustainable development of the ecological environment in Zhoushan. The main conclusions are as follows.

(1) The mean value of the RSEI in Zhoushan initially decreased and then slowly increased from 2000 to 2020. The mean value of the RSEI decreased from 0.748 in 2000 to 0.668 in 2010, then increased to 0.681 in 2020.

(2) It can be concluded that the overall ecological environment in the region exhibited a degradation trend during the study period. The area with an excellent eco-environment grade decreased by 13.54%, and the area with poor and fair eco-environment grades increased by 3.43% from 2000 to 2020.

However, this study also needs to be extended in the following aspects. (1) The temporal and spatial evolution of the RSEI in the key areas of the Zhoushan Islands greatly affected by human activities should be analyzed to better reveal the changes in the RSEI in Zhoushan. (2) In order to improve the monitoring model of the quality of the ecological environment in Zhoushan, other indexes reflecting the quality of the ecological environment can be selected to add to the analysis in the future.

Data Availability Statement

The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author Contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.

Funding

This work was supported by the National Key Research and Development Program of China (No. 2018YFC1503204-04) and the National Natural Science Foundation of China (Grant No. 42171311), the Training Program of Excellent Master Thesis of Zhejiang Ocean University, and the Project of Beijing VMinFull Limited (VMF2021RS).

Conflict of Interest

BL was employed by Beijing VMinFull Limited.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

References

Alqadhi, S., Mallick, J., Balha, A., Bindajam, A., Singh, C. K., and Hoa, P. V. (2021). Spatial and decadal prediction of land use/land cover using multi-layer perceptron-neural network (MLP-NN) algorithm for a semi-arid region of Asir. Saud. Arab. Earth Sci. Inform. 14, 1547–1562. doi: 10.1007/s12145-021-00633-2

CrossRef Full Text | Google Scholar

Berberoglu, S., and Akin, A. (2009). Assessing different remote sensing techniques to detect land use/cover changes in the eastern Mediterranean. Int. J. Appl. Earth Observ. Geoinform. 11, 46–53. doi: 10.1016/j.jag.2008.06.002

CrossRef Full Text | Google Scholar

Bonney, M. T., and He, Y. (2021). Temporal connections between long-term Landsat time-series and tree-rings in an urban–rural temperate forest. Int. J. Appl. Earth Observ. Geoinform. 103:102523. doi: 10.1016/j.jag.2021.102523

CrossRef Full Text | Google Scholar

Boori, M. S., Choudhary, K., Paringer, R., and Kupriyanov, A. (2021). Spatiotemporal ecological vulnerability analysis with statistical correlation based on satellite remote sensing in Samara, Russia. J. Environ. Manage. 285:112138. doi: 10.1016/j.jenvman.2021.112138

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, C., Chen, H., Liao, W., Sui, X., Wang, L., Chen, J., et al. (2020a). Dynamic monitoring and analysis of land-use and land-cover change using Landsat multitemporal data in the Zhoushan archipelago. China. IEEE Access 8, 210360–210369. doi: 10.1109/ACCESS.2020.3036128

CrossRef Full Text | Google Scholar

Chen, C., He, X., Lu, Y., and Chu, Y. (2020b). Application of Landsat time-series data in island ecological environment monitoring: a case study of Zhoushan Islands, China. J. Coast. Res. 108, 193–199. doi: 10.2112/JCR-SI108-038.1

CrossRef Full Text | Google Scholar

Chen, C., Liang, J., Xie, F., Hu, Z., Sun, W., Yang, G., et al. (2022). Temporal and spatial variation of coastline using remote sensing images for Zhoushan archipelago, China. Int. J. Appl. Earth Observ. Geoinform. 107:102711. doi: 10.1016/j.jag.2022.102711

CrossRef Full Text | Google Scholar

Chen, C., Wang, L., Chen, J., Liu, Z., Liu, Y., and Chu, Y. (2021a). A seamless economical feature extraction method using Landsat time series data. Earth Sci. Inform. 14, 321–332. doi: 10.1007/s12145-020-00564-4

CrossRef Full Text | Google Scholar

Chen, C., Wang, L., Zhang, Z., Lu, C., Chen, H., and Chen, J. (2021b). Construction and application of quality evaluation index system for remote-sensing image fusion. J. Appl. Remote Sensing 16:012006. doi: 10.1117/1.JRS.16.012006

CrossRef Full Text | Google Scholar

Chen, H., Chen, C., Zhang, Z., Lu, C., Wang, L., He, X., et al. (2021c). Changes of the spatial and temporal characteristics of land-use landscape patterns using multi-temporal Landsat satellite data: a case study of Zhoushan Island, China. Ocean Coast. Manage. 213:105842. doi: 10.1016/j.ocecoaman.2021.105842

CrossRef Full Text | Google Scholar

Chiabai, A., Quiroga, S., Martinez-Juarez, P., Higgins, S., and Taylor, T. (2018). The nexus between climate change, ecosystem services and human health: towards a conceptual framework. Sci. Total Environ. 635, 1191–1204. doi: 10.1016/j.scitotenv.2018.03.323

PubMed Abstract | CrossRef Full Text | Google Scholar

Duan, Y., Tian, B., Li, X., Liu, D., Sengupta, D., Wang, Y., et al. (2021). Tracking changes in aquaculture ponds on the China coast using 30 years of Landsat images. Int. J. Appl. Earth Observ. Geoinform. 102:102383. doi: 10.1016/j.jag.2021.102383

CrossRef Full Text | Google Scholar

Eveleth, R., Glover, D. M., Long, M. C., Lima, I. D., Chase, A. P., and Doney, S. C. (2021). Assessing the Skill of a High-Resolution Marine Biophysical Model Using Geostatistical Analysis of Mesoscale Ocean Chlorophyll Variability From Field Observations and Remote Sensing. Front. Mar. Sci. 8:612764. doi: 10.3389/fmars.2021.612764

CrossRef Full Text | Google Scholar

Fan, X., Nie, G., Xia, C., and Zhou, J. (2021). Estimation of pixel-level seismic vulnerability of the building environment based on mid-resolution optical remote sensing images. Int. J. Appl. Earth Observ. Geoinform. 101:102339. doi: 10.1016/j.jag.2021.102339

CrossRef Full Text | Google Scholar

Firozjaei, M. K., Fathololoumi, S., Kiavarz, M., Biswas, A., Homaee, M., and Alavipanah, S. K. (2021a). Land Surface Ecological Status Composition Index (LSESCI): a novel remote sensing-based technique for modeling land surface ecological status. Ecol. Indicat. 123:107375. doi: 10.1016/j.ecolind.2021.107375

CrossRef Full Text | Google Scholar

Firozjaei, M. K., Sedighi, A., Firozjaei, H. K., Kiavarz, M., Homaee, M., Arsanjani, J. J., et al. (2021b). A historical and future impact assessment of mining activities on surface biophysical characteristics change: a remote sensing-based approach. Ecol. Indicat. 122:107264. doi: 10.1016/j.ecolind.2020.107264

CrossRef Full Text | Google Scholar

Foga, S., Scaramuzza, P. L., Guo, S., Zhu, Z., Dilley, R. D., Beckmann, T., et al. (2017). Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing Environ. 194, 379–390. doi: 10.1016/j.rse.2017.03.026

CrossRef Full Text | Google Scholar

Fu, T., Zhang, L., Yuan, X., Chen, B., and Yan, M. (2021). Spatio-temporal patterns and sustainable development of coastal aquaculture in Hainan Island, China: 30 Years of evidence from remote sensing. Ocean Coast. Manage. 214:105897. doi: 10.1016/j.ocecoaman.2021.105897

CrossRef Full Text | Google Scholar

Gernez, P., Palmer, S. C., Thomas, Y., and Forster, R. (2021). Remote sensing for aquaculture. Front. Mar. Sci. 7:1258. doi: 10.3389/fmars.2020.638156

CrossRef Full Text | Google Scholar

He, X., Chen, C., Zhang, Z., Hu, H., Tan, A., and Xing, Z. (2021). Temporal and spatial characteristics of harmful algal blooms in the offshore waters, China during 1990 to 2019. J. Appl. Remote Sensing 16:012004. doi: 10.1117/1.JRS.16.012004

CrossRef Full Text | Google Scholar

Hossain, M. S., and Hashim, M. (2019). Potential of Earth Observation (EO) technologies for seagrass ecosystem service assessments. Int. J. Appl. Earth Observ. Geoinform. 77, 15–29. doi: 10.1016/j.jag.2018.12.009

CrossRef Full Text | Google Scholar

Hussain, D., and Khan, A. A. (2020). Machine learning techniques for monthly river flow forecasting of Hunza River. Pakistan. Earth Sci. Inform. 13, 939–949. doi: 10.1007/s12145-020-00450-z

CrossRef Full Text | Google Scholar

Jia, H., Yan, C., and Xing, X. (2021). Evaluation of Eco-Environmental Quality in Qaidam Basin Based on the Ecological Index (MRSEI) and GEE. Remote Sensing 13:4543. doi: 10.3390/rs13224543

CrossRef Full Text | Google Scholar

Jia, M., Wang, Z., Mao, D., Ren, C., Wang, C., and Wang, Y. (2021). Rapid, robust, and automated mapping of tidal flats in China using time series Sentinel-2 images and Google Earth Engine. Remote Sensing Environ. 255:112285. doi: 10.1016/j.rse.2021.112285

CrossRef Full Text | Google Scholar

Murayama, Y., Simwanda, M., and Ranagalage, M. (2021). Spatiotemporal Analysis of Urbanization Using GIS and Remote Sensing in Developing Countries. Sustainability 13:3681. doi: 10.3390/su13073681

CrossRef Full Text | Google Scholar

Nietupski, T. C., Kennedy, R. E., Temesgen, H., and Kerns, B. K. (2021). Spatiotemporal image fusion in Google Earth Engine for annual estimates of land surface phenology in a heterogenous landscape. Int. J. Appl. Earth Observ. Geoinform. 99:102323. doi: 10.1016/j.jag.2021.102323

CrossRef Full Text | Google Scholar

Nourani, V., Foroumandi, E., Sharghi, E., and Dąbrowska, D. (2021). Ecological-environmental quality estimation using remote sensing and combined artificial intelligence techniques. J. Hydroinform. 23, 47–65. doi: 10.2166/hydro.2020.048

CrossRef Full Text | Google Scholar

Pekel, J. F., Cottam, A., Gorelick, N., and Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422. doi: 10.1038/nature20584

PubMed Abstract | CrossRef Full Text | Google Scholar

Sekovski, I., Del Río, L., and Armaroli, C. (2020). Development of a coastal vulnerability index using analytical hierarchy process and application to Ravenna province (Italy). Ocean Coast. Manage. 183:104982. doi: 10.1016/j.ocecoaman.2019.104982

CrossRef Full Text | Google Scholar

Sun, W., Liu, K., Ren, G., Liu, W., Yang, G., Meng, X., et al. (2021). A simple and effective spectral-spatial method for mapping large-scale coastal wetlands using China ZY1-02D satellite hyperspectral images. Int. J. Appl. Earth Observ. Geoinform. 104:102572. doi: 10.1016/j.jag.2021.102572

CrossRef Full Text | Google Scholar

Turpie, K. R., Ackleson, S. G., Byrd, K. B., and Moisan, T. A. (2021). Science and Applications of Coastal Remote Sensing. Front. Mar. Sci. 8:641029. doi: 10.3389/fmars.2021.641029

CrossRef Full Text | Google Scholar

Wang, L., Chen, C., Xie, F., Hu, Z. J., Zhang, Z., Chen, H. X., et al. (2021a). Estimation of the value of regional ecosystem services of an archipelago using satellite remote sensing technology: a case study of Zhoushan Archipelago, China. Int. J. Appl. Earth Observ. Geoinform. 105:102616. doi: 10.1016/j.jag.2021.102616

CrossRef Full Text | Google Scholar

Wang, L., Chen, C., Zhang, Z., Gan, W., Yu, J., and Chen, H. (2021b). Approach for estimation of ecosystem services value using multitemporal remote sensing images. J. Appl. Remote Sensing 16:012010. doi: 10.1117/1.JRS.16.012010

CrossRef Full Text | Google Scholar

Wen, X., Ming, Y., Gao, Y., and Hu, X. (2019). Dynamic monitoring and analysis of ecological quality of pingtan comprehensive experimental zone, a new type of sea island city, based on RSEI. Sustainability 12:21. doi: 10.3390/su12010021

CrossRef Full Text | Google Scholar

Xiong, Y., Xu, W., Lu, N., Huang, S., Wu, C., Wang, L., et al. (2021). Assessment of spatial–temporal changes of ecological environment quality based on RSEI and GEE: a case study in Erhai Lake Basin. Yunnan province, China. Ecol. Indicat. 125:107518. doi: 10.1016/j.ecolind.2021.107518

CrossRef Full Text | Google Scholar

Xu, H., Wang, M., Shi, T., Guan, H., Fang, C., and Lin, Z. (2018). Prediction of ecological effects of potential population and impervious surface increases using a remote sensing based ecological index (RSEI). Ecol. Indicat. 93, 730–740. doi: 10.1016/j.ecolind.2018.05.055

CrossRef Full Text | Google Scholar

Yang, X., Qin, Q., Grussenmeyer, P., and Koehl, M. (2018). Urban surface water body detection with suppressed built-up noise based on water indices from Sentinel-2 MSI imagery. Remote Sensing Environ. 219, 259–270. doi: 10.1016/j.rse.2018.09.016

CrossRef Full Text | Google Scholar

Yang, X., Qin, Q., Yésou, H., Ledauphin, T., Koehl, M., Grussenmeyer, P., et al. (2020). Monthly estimation of the surface water extent in France at a 10-m resolution using Sentinel-2 data. Remote Sensing Environ. 244:111803. doi: 10.1016/j.rse.2020.111803

CrossRef Full Text | Google Scholar

Yang, X., Zhu, Z., Qiu, S., Kroeger, K., Zhu, Z., and Covington, S. (2022). Detection and characterization of coastal tidal wetland change in the northeastern US using Landsat time series. Remote Sensing Environ. 276:113047. doi: 10.1016/j.rse.2022.113047

CrossRef Full Text | Google Scholar

Yi, L., Chen, J., Jin, Z., Quan, Y., Han, P., Guan, S., et al. (2018). Impacts of human activities on coastal ecological environment during the rapid urbanization process in Shenzhen. Chin. Ocean Coast. Manage. 154, 121–132. doi: 10.1016/j.ocecoaman.2018.01.005

CrossRef Full Text | Google Scholar

Zhao, X., Wang, P., Chen, C., Jiang, T., Yu, Z., and Guo, B. (2017). Waterbody information extraction from remote-sensing images after disasters based on spectral information and characteristic knowledge. Int. J. Remote Sensing 38, 1404–1422. doi: 10.1080/01431161.2016.1278284

CrossRef Full Text | Google Scholar