GISD30: global 30-m impervious surface dynamic dataset from 1985 to 2020 using time-series Landsat imagery on the Google Earth Engine platform

Abstract. Accurately mapping impervious surface dynamics has great scientific significance and application value for urban sustainable development research, anthropogenic carbon emission assessment and global ecological environment modeling. In this study, a novel and accurate global 30 m impervious surface dynamic dataset (GISD30) for 1985 to 2020 was produced using the spectral generalization method and time-series Landsat imagery, on the Google Earth Engine cloud-computing platform. Firstly, the global training samples and corresponding reflectance spectra were automatically derived from prior global 30 m land-cover products after employing the multitemporal compositing method and relative radiometric normalization. Then, spatiotemporal adaptive classification models, trained with the migrated reflectance spectra of impervious surfaces from 2020 and pervious surface samples in the same epoch for each 5° × 5° geographical tile, were applied to map the impervious surface in each period. Furthermore, a spatiotemporal consistency correction method was presented to minimize the effects of independent classification errors and improve the spatiotemporal consistency of impervious surface dynamics. Our global 30 m impervious surface dynamic model achieved an overall accuracy of 91.5 % and a kappa coefficient of 0.866 using 18,540 global time-series validation samples. Cross-comparisons with four existing global 30 m impervious surface products further indicated that our GISD30 dynamic product achieved the best performance in capturing the spatial distributions and spatiotemporal dynamics of impervious surfaces in various impervious landscapes. The statistical results indicated that the global impervious surface has doubled in the past 35 years, from 5.116 × 105 km2 in 1985 to 10.871 × 105 km2 in 2020, and Asia saw the largest increase in impervious surface area compared to other continents, with a total increase of 2.946 × 105 km2. Therefore, it was concluded that our global 30 m impervious surface dynamic dataset is an accurate and promising product, and could provide vital support in monitoring regional or global urbanization as well as in related applications. The global 30 m impervious surface dynamic dataset from 1985 to 2020 generated in this paper is free to access at http://doi.org/10.5281/zenodo.5220816 (Liu et al., 2021b).

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A Scheme for the Long-Term Monitoring of Impervious−Relevant Land Disturbances Using High Frequency Landsat Archives and the Google Earth Engine

Remote Sensing ◽

10.3390/rs11161891 ◽

2019 ◽

Vol 11 (16) ◽

pp. 1891 ◽

Cited By ~ 3

Author(s):

Hanzeyu Xu ◽

Yuchun Wei ◽

Chong Liu ◽

Xiao Li ◽

Hong Fang

Keyword(s):

High Frequency ◽

Impervious Surface ◽

Google Earth ◽

Impervious Surfaces ◽

Surface Dynamics ◽

Google Earth Engine ◽

Long Term Monitoring ◽

Disturbance And Recovery ◽

Term Monitoring

Impervious surfaces are commonly acknowledged as major components of human settlements. The expansion of impervious surfaces could lead to a series of human−dominated environmental and ecological issues. Tracing impervious surface dynamics at a finer temporal−spatial scale is a critical way to better understand the increasingly human-dominated system of Earth. In this study, we put forward a new scheme to conduct long-term monitoring of impervious−relevant land disturbances using high frequency Landsat archives and the Google Earth Engine (GEE). First, the developed region was identified using a classification-based approach. Then, the GEE-version LandTrendr (Landsat-based detection of Trends in Disturbance and Recovery) was used to detect land disturbances, characterizing the conversion from vegetation to impervious surfaces. Finally, the actual disturbance areas within the developed regions were derived and quantitatively evaluated. A case study was conducted to detect impervious surface dynamics in Nanjing, China, from 1988 to 2018. Results show that our scheme can efficiently monitor impervious surface dynamics at yearly intervals with good accuracy. The overall accuracy (OA) of the classification results for 1988 and 2018 are 95.86% and 94.14%. Based on temporal−spatial accuracy assessments of the final detection result, the temporal accuracy is 90.75%, and the average detection time deviation is −1.28 a. The OA, precision, and recall of the sampling inspection, respectively, are 84.34%, 85.43%, and 96.37%. This scheme provides new insights into capturing the expansion of impervious−relevant land disturbances with high frequency Landsat archives in an efficient way.

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Advancing the Mapping of Mangrove Forests at National-Scale Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine: A Case Study in China

Remote Sensing ◽

10.3390/rs12193120 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3120

Author(s):

Luojia Hu ◽

Nan Xu ◽

Jian Liang ◽

Zhichao Li ◽

Luzhen Chen ◽

...

Keyword(s):

Time Series ◽

High Resolution ◽

Mangrove Forest ◽

Time Series Data ◽

Landsat Imagery ◽

Google Earth ◽

Series Data ◽

National Scale ◽

Google Earth Engine ◽

Sentinel 2

A high resolution mangrove map (e.g., 10-m), including mangrove patches with small size, is urgently needed for mangrove protection and ecosystem function estimation, because more small mangrove patches have disappeared with influence of human disturbance and sea-level rise. However, recent national-scale mangrove forest maps are mainly derived from 30-m Landsat imagery, and their spatial resolution is relatively coarse to accurately characterize the extent of mangroves, especially those with small size. Now, Sentinel imagery with 10-m resolution provides an opportunity for generating high-resolution mangrove maps containing these small mangrove patches. Here, we used spectral/backscatter-temporal variability metrics (quantiles) derived from Sentinel-1 SAR (Synthetic Aperture Radar) and/or Sentinel-2 MSI (Multispectral Instrument) time-series imagery as input features of random forest to classify mangroves in China. We found that Sentinel-2 (F1-Score of 0.895) is more effective than Sentinel-1 (F1-score of 0.88) in mangrove extraction, and a combination of SAR and MSI imagery can get the best accuracy (F1-score of 0.94). The 10-m mangrove map was derived by combining SAR and MSI data, which identified 20003 ha mangroves in China, and the area of small mangrove patches (<1 ha) is 1741 ha, occupying 8.7% of the whole mangrove area. At the province level, Guangdong has the largest area (819 ha) of small mangrove patches, and in Fujian, the percentage of small mangrove patches is the highest (11.4%). A comparison with existing 30-m mangrove products showed noticeable disagreement, indicating the necessity for generating mangrove extent product with 10-m resolution. This study demonstrates the significant potential of using Sentinel-1 and Sentinel-2 images to produce an accurate and high-resolution mangrove forest map with Google Earth Engine (GEE). The mangrove forest map is expected to provide critical information to conservation managers, scientists, and other stakeholders in monitoring the dynamics of the mangrove forest.

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A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: a case study in Newfoundland

GIScience & Remote Sensing ◽

10.1080/15481603.2020.1846948 ◽

2020 ◽

Vol 57 (8) ◽

pp. 1102-1124

Author(s):

M. Mahdianpari ◽

H. Jafarzadeh ◽

J. E. Granger ◽

F. Mohammadimanesh ◽

B. Brisco ◽

...

Keyword(s):

Time Series ◽

Large Scale ◽

Landsat Imagery ◽

Google Earth ◽

Scale Change ◽

Large Scale Change ◽

Google Earth Engine ◽

Change Monitoring

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Development of a global 30-m impervious surface map using multi-source and multi-temporal remote sensing datasets with the Google Earth Engine platform

10.5194/essd-2019-200 ◽

2020 ◽

Author(s):

Xiao Zhang ◽

Liangyun Liu ◽

Changshan Wu ◽

Xidong Chen ◽

Yuan Gao ◽

...

Keyword(s):

Remote Sensing ◽

Random Forest ◽

Human Activity ◽

Impervious Surface ◽

Google Earth ◽

Impervious Surfaces ◽

Sar Images ◽

Training Samples ◽

Multi Temporal ◽

Google Earth Engine

Abstract. The amount of impervious surface is an important indicator in the monitoring of the intensity of human activity and environmental change. The use of remote sensing techniques is the only means of accurately carrying out global mapping of impervious surfaces covering large areas. Optical imagery can capture surface reflectance characteristics, while synthetic aperture radar (SAR) images can be used to provide information on the structure and dielectric properties of surface materials. In addition, night-time light (NTL) imagery can detect the intensity of human activity and thus provide important a priori probabilities of the occurrence of impervious surfaces. In this study, we aimed to generate an accurate global impervious surface map at a resolution of 30-m for 2015 by combining Landsat-8 OLI optical images, Sentinel-1 SAR images and VIIRS NTL images based on the Google Earth Engine (GEE) platform. First, the global impervious and non-impervious training samples were automatically derived by combining the GlobeLand30 land-cover product with VIIRS NTL and MODIS enhanced vegetation index (EVI) imagery. Then, based on global training samples and multi-source and multi-temporal imagery, a random forest classifier was trained and used to generate corresponding impervious surface maps for each 5°×5° cell of a geographical grid. Finally, a global impervious surface map, produced by mosaicking numerous 5°×5° regional maps, was validated by interpretation samples and then compared with three existing impervious products (GlobeLand30, FROM_GLC and NUACI). The results indicated that the global impervious surface map produced using the proposed multi-source, multi-temporal random forest classification (MSMT_RF) method was the most accurate of the maps, having an overall accuracy of 96.6 % and kappa coefficient of 0.903 as against 92.5 % and 0.769 for FROM_GLC, 91.1 % and 0.717 for GlobeLand30, and 87.43 % and 0.585 for NUACI. Therefore, it is concluded that a global 30-m impervious surface map can accurately and efficiently be generated by the proposed MSMT_RF method based on the GEE platform. The global impervious surface map generated in this paper are available at https://doi.org/10.5281/zenodo.3505079 (Zhang et al., 2019).

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Mapping Three Decades of Changes in the Brazilian Savanna Native Vegetation Using Landsat Data Processed in the Google Earth Engine Platform

Remote Sensing ◽

10.3390/rs12060924 ◽

2020 ◽

Vol 12 (6) ◽

pp. 924 ◽

Cited By ~ 9

Author(s):

Ane Alencar ◽

Julia Z. Shimbo ◽

Felipe Lenti ◽

Camila Balzani Marques ◽

Bárbara Zimbres ◽

...

Keyword(s):

Time Series ◽

Average Rate ◽

Landsat Imagery ◽

Google Earth ◽

Native Vegetation ◽

Brazilian Cerrado ◽

Statistical Decision ◽

Machine Learning Classification ◽

Cerrado Vegetation ◽

Google Earth Engine

Widespread in the subtropics and tropics of the Southern Hemisphere, savannas are highly heterogeneous and seasonal natural vegetation types, which makes change detection (natural vs. anthropogenic) a challenging task. The Brazilian Cerrado represents the largest savanna in South America, and the most threatened biome in Brazil owing to agricultural expansion. To assess the native Cerrado vegetation (NV) areas most susceptible to natural and anthropogenic change over time, we classified 33 years (1985–2017) of Landsat imagery available in the Google Earth Engine (GEE) platform. The classification strategy used combined empirical and statistical decision trees to generate reference maps for machine learning classification and a novel annual dataset of the predominant Cerrado NV types (forest, savanna, and grassland). We obtained annual NV maps with an average overall accuracy ranging from 87% (at level 1 NV classification) to 71% over the time series, distinguishing the three main NV types. This time series was then used to generate probability maps for each NV class. The native vegetation in the Cerrado biome declined at an average rate of 0.5% per year (748,687 ha yr−1), mostly affecting forests and savannas. From 1985 to 2017, 24.7 million hectares of NV were lost, and now only 55% of the NV original distribution remains. Of the remnant NV in 2017 (112.6 million hectares), 65% has been stable over the years, while 12% changed among NV types, and 23% was converted to other land uses but is now in some level of secondary NV. Our results were fundamental in indicating areas with higher rates of change in a long time series in the Brazilian Cerrado and to highlight the challenges of mapping distinct NV types in a highly seasonal and heterogeneous savanna biome.

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Automatically Monitoring Impervious Surfaces Using Spectral Generalization and Time Series Landsat Imagery from 1985 to 2020 in the Yangtze River Delta

Journal of Remote Sensing ◽

10.34133/2021/9873816 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Xiao Zhang ◽

Liangyun Liu ◽

Xidong Chen ◽

Yuan Gao ◽

Mihang Jiang

Keyword(s):

Yangtze River ◽

Landsat Imagery ◽

Impervious Surface ◽

Yangtze River Delta ◽

Surface Reflectance ◽

Impervious Surfaces ◽

The Yangtze River ◽

Surface Dynamics ◽

Training Samples ◽

The Yangtze River Delta

Accurately monitoring the spatiotemporal dynamics of impervious surfaces is very important for understanding the process of urbanization. However, the complicated makeup and spectral heterogeneity of impervious surfaces create difficulties for impervious surface monitoring. In this study, we propose an automatic method to capture the spatiotemporal expansion of impervious surfaces using spectral generalization and time series Landsat imagery. First, the multitemporal compositing and relative radiometric normalization methods were used to extract phenological information and ensure spectral consistency between reference imagery and monitored imagery. Second, we automatically derived training samples from the prior MSMT_IS30-2020 impervious surface products and migrated the surface reflectance of impervious surfaces in the reference period of 2020 to other periods (1985–2015). Third, the random forest classification method, trained using the migrated surface reflectance of impervious surfaces and pervious surface training samples at each period, was employed to extract temporally independent impervious surfaces. Further, a temporal consistency check method was applied to ensure the consistency and reliability of the monitoring results. According to qualitative and quantitative validation results, the method achieved an overall accuracy of 90.9% and kappa coefficient of 0.859 in identifying the spatiotemporal expansion of impervious surfaces and performed better in capturing the impervious surface dynamics when compared with other impervious surface datasets. Lastly, our results indicate that a rapid increase of impervious surfaces was observed in the Yangtze River Delta, and the area of impervious surfaces in 2000 and 2020 was 1.86 times and 4.76 times that of 1985, respectively. Therefore, it could be concluded that the proposed method offered a novel perspective for providing timely and accurate impervious surface dynamics.

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Development of a global 30 m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform

Earth System Science Data ◽

10.5194/essd-12-1625-2020 ◽

2020 ◽

Vol 12 (3) ◽

pp. 1625-1648 ◽

Cited By ~ 4

Author(s):

Xiao Zhang ◽

Liangyun Liu ◽

Changshan Wu ◽

Xidong Chen ◽

Yuan Gao ◽

...

Keyword(s):

Remote Sensing ◽

Random Forest ◽

Human Activity ◽

Impervious Surface ◽

Google Earth ◽

Landsat 8 ◽

Impervious Surfaces ◽

Sar Images ◽

Training Samples ◽

Google Earth Engine

Abstract. The amount of impervious surface is an important indicator in the monitoring of the intensity of human activity and environmental change. The use of remote sensing techniques is the only means of accurately carrying out global mapping of impervious surfaces covering large areas. Optical imagery can capture surface reflectance characteristics, while synthetic-aperture radar (SAR) images can be used to provide information on the structure and dielectric properties of surface materials. In addition, nighttime light (NTL) imagery can detect the intensity of human activity and thus provide important a priori probabilities of the occurrence of impervious surfaces. In this study, we aimed to generate an accurate global impervious surface map at a resolution of 30 m for 2015 by combining Landsat 8 Operational Land Image (OLI) optical images, Sentinel-1 SAR images and Visible Infrared Imaging Radiometer Suite (VIIRS) NTL images based on the Google Earth Engine (GEE) platform. First, the global impervious and nonimpervious training samples were automatically derived by combining the GlobeLand30 land-cover product with VIIRS NTL and MODIS enhanced vegetation index (EVI) imagery. Then, the local adaptive random forest classifiers, allowing for a regional adjustment of the classification parameters to take into account the regional characteristics, were trained and used to generate regional impervious surface maps for each 5∘×5∘ geographical grid using local training samples and multisource and multitemporal imagery. Finally, a global impervious surface map, produced by mosaicking numerous 5∘×5∘ regional maps, was validated by interpretation samples and then compared with five existing impervious products (GlobeLand30, FROM-GLC, NUACI, HBASE and GHSL). The results indicated that the global impervious surface map produced using the proposed multisource, multitemporal random forest classification (MSMT_RF) method was the most accurate of the maps, having an overall accuracy of 95.1 % and kappa coefficient (one of the most commonly used statistics to test interrater reliability; Olofsson et al., 2014) of 0.898 as against 85.6 % and 0.695 for NUACI, 89.6 % and 0.780 for FROM-GLC, 90.3 % and 0.794 for GHSL, 88.4 % and 0.753 for GlobeLand30, and 88.0 % and 0.745 for HBASE using all 15 regional validation data. Therefore, it is concluded that a global 30 m impervious surface map can accurately and efficiently be generated by the proposed MSMT_RF method based on the GEE platform. The global impervious surface map generated in this paper is available at https://doi.org/10.5281/zenodo.3505079 (Zhang and Liu, 2019).

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Spatiotemporal Continuous Impervious Surface Mapping by Fusion of Landsat Time Series Data and Google Earth Imagery

Remote Sensing ◽

10.3390/rs13122409 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2409

Author(s):

Rui Chen ◽

Xiaodong Li ◽

Yihang Zhang ◽

Pu Zhou ◽

Yalan Wang ◽

...

Keyword(s):

Remote Sensing ◽

Time Series ◽

Urban Areas ◽

Impervious Surface ◽

Google Earth ◽

Temporal Consistency ◽

Impervious Surfaces ◽

Consistency Check ◽

Surface Mapping ◽

Fraction Images

The monitoring of impervious surfaces in urban areas using remote sensing with fine spatial and temporal resolutions is crucial for monitoring urban development and environmental changes in urban areas. Spatiotemporal super-resolution mapping (STSRM) fuses fine-spatial-coarse-temporal remote sensing data with coarse-spatial-fine-temporal data, allowing for urban impervious surface mapping at both fine-spatial and fine-temporal resolutions. The STSRM involves two main steps: unmixing the coarse-spatial-fine-temporal remote sensing data to class fraction images, and downscaling the fraction images to sub-pixel land cover maps. Yet, challenges exist in each step when applying STSRM in mapping impervious surfaces. First, the impervious surfaces have high spectral variability (i.e., high intra-class and low inter-class variability), which impacts the accurate extraction of sub-pixel scale impervious surface fractions. Second, downscaling the fraction images to sub-pixel land cover maps is an ill-posed problem and would bring great uncertainty and error in the predictions. This paper proposed a new Spatiotemporal Continuous Impervious Surface Mapping (STCISM) method to deal with these challenges in fusing Landsat and Google Earth imagery. The STCISM used the Multiple Endmember Spectral Mixture Analysis and the Fisher Discriminant Analysis to minimize the within-class variability and maximize the between-class variability to reduce the spectral unmixing uncertainty. In addition, the STCISM adopted a new temporal consistency check model to incorporate temporal contextual information to reduce the uncertainty in the time-series impervious surface prediction maps. Unlike the traditional temporal consistency check model that assumed the impervious-to-pervious conversion is unlikely to happen, the new model allowed the bidirectional conversions between pervious and impervious surfaces. The temporal consistency check was used as a post-procession method to correct the errors in the prediction maps. The proposed STCISM method was used to predict time-series impervious surface maps at 5 m resolution of Google Earth image at the Landsat frequency. The results showed that the proposed STCISM outperformed the STSRM model without using the temporal consistency check and the STSRM model using the temporal consistency check based on the unidirectional pervious-to-impervious surface conversion rule.

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