Deep learning-based fusion of Landsat-8 and Sentinel-2 images for a harmonized surface reflectance product

Shao, Zhenfeng; Cai, Jiajun; Fu, Ping; Hu, Leiqiu; Liu, Tao

doi:10.1016/j.rse.2019.111425

Cited by 180 publications

(104 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The method was found to be very simple to compute and easy to implement with a high accuracy; however, it needs more testing particularly in a more complex heterogeneous landscape to help differentiate more land-cover classes [71]. It may be useful to test the method on fused data of continuous reflectance for the evaluation of urban hydrological systems as explored by [72,73]. In addition, future work involves how the index can be integrated into Google Earth engine platform so the issue of data availability, preprocessing, and cloud removal can be dealt with within a single platform.…”

Section: Discussionmentioning

confidence: 99%

Impact Quantification of Decentralization in Urban Growth by Extracting Impervious Surfaces Using ISEI in Model Maker

et al. 2020

View full text Add to dashboard Cite

Decentralization problems in Africa have caused some infrastructure disparity between country capitals and distant districts. In Ghana, less public investment has created a gap between implementation results and theoretical benefits. Spectral indices are a good approach to extracting impervious surfaces, which is a good method of measuring urbanization. These are restricted by complexity, sensor limitation, threshold values, and high computational time. In this study, we measure the urbanization dynamics of Wa District in Ghana by applying a proposed method of impervious surface extraction index (ISEI), to evaluate the decentralization policy using Landsat images from 1984–2018 and a single S2A data. Comparing our proposed method with five other existing indexes, ISEI provided good discriminated results between target feature and background, with pixel values ranging between 0 and +1. Other indexes produced negative values. ISEI accuracy varied from 84.62–94.00% while existing indexes varied from 73.85–90.00%. Our results also showed increased impervious surface areas of 83.26 km2, which is about 7.72% of total area while the average annual urban growth was recorded as 4.42%. These figures proved that the quantification of decentralization is very positive. The study provides a foundation for urban environment research in the context of decentralization policy.

show abstract

Section: Discussionmentioning

confidence: 99%

Impact Quantification of Decentralization in Urban Growth by Extracting Impervious Surfaces Using ISEI in Model Maker

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In addition, two reflectance wavelength bands have been added: the shorter wavelength blue band (0.43-0.45) and shortwave infrared SWIR band (1.36-1.39). The former improves the chlorophyll sensitivity while the latter enables cloud cirrus detection [49,50]. The acquired Landsat 8-OLI (level 2) imageries were from December 2013 to December 2018 during the monsoon period with lesser rainfall and minimal cloud cover.…”

Section: Landsat 8-olimentioning

confidence: 99%

Spatio-Temporal Analysis of Oil Spill Impact and Recovery Pattern of Coastal Vegetation and Wetland Using Multispectral Satellite Landsat 8-OLI Imagery and Machine Learning Models

et al. 2020

View full text Add to dashboard Cite

Oil spills are a global phenomenon with impacts that cut across socio-economic, health, and environmental dimensions of the coastal ecosystem. However, comprehensive assessment of oil spill impacts and selection of appropriate remediation approaches have been restricted due to reliance on laboratory experiments which offer limited area coverage and classification accuracy. Thus, this study utilizes multispectral Landsat 8-OLI remote sensing imagery and machine learning models to assess the impacts of oil spills on coastal vegetation and wetland and monitor the recovery pattern of polluted vegetation and wetland in a coastal city. The spatial extent of polluted areas was also precisely quantified for effective management of the coastal ecosystem. Using Johor, a coastal city in Malaysia as a case study, a total of 49 oil spill (ground truth) locations, 54 non-oil-spill locations and Landsat 8-OLI data were utilized for the study. The ground truth points were divided into 70% training and 30% validation parts for the classification of polluted vegetation and wetland. Sixteen different indices that have been used to monitor vegetation and wetland stress in literature were adopted for impact and recovery analysis. To eliminate similarities in spectral appearance of oil-spill-affected vegetation, wetland and other elements like burnt and dead vegetation, Support Vector Machine (SVM) and Random Forest (RF) machine learning models were used for the classification of polluted and nonpolluted vegetation and wetlands. Model optimization was performed using a random search method to improve the models’ performance, and accuracy assessments confirmed the effectiveness of the two machine learning models to identify, classify and quantify the area extent of oil pollution on coastal vegetation and wetland. Considering the harmonic mean (F1), overall accuracy (OA), User’s accuracy (UA), and producers’ accuracy (PA), both models have high accuracies. However, the RF outperformed the SVM with F1, OA, PA and UA values of 95.32%, 96.80%, 98.82% and 95.11%, respectively, while the SVM recorded accuracy values of F1 (80.83%), OA (92.87%), PA (95.18%) and UA (93.81%), respectively, highlighting 1205.98 hectares of polluted vegetation and 1205.98 hectares of polluted wetland. Analysis of the vegetation indices revealed that spilled oil had a significant impact on the vegetation and wetland, although steady recovery was observed between 2015-2018. This study concludes that Chlorophyll Vegetation Index, Modified Difference Water Index, Normalized Difference Vegetation Index and Green Chlorophyll Index vegetation indices are more sensitive for impact and recovery assessment of both vegetation and wetland, in addition to Modified Normalized Difference Vegetation Index for wetlands. Thus, remote sensing and Machine Learning models are essential tools capable of providing accurate information for coastal oil spill impact assessment and recovery analysis for appropriate remediation initiatives.

show abstract

“…Spatial resolution and the quality of information contained in remote sensing images have a great influence on AGB estimation. Aggregation of remote sensing images can increase the resolution of remote sensing data as well as the spatial resolution and available spectral information, for example in panchromatic and multispectral images or different types of remote sensing images [74], [75], and thus the accuracy of AGB prediction can be increased. A better method of quantifying the effects of landscape heterogeneity on spatial scaling for AGB prediction is desirable.…”

Section: B Problems and Handlingmentioning

confidence: 99%

Spatial Scaling of Forest Aboveground Biomass Using Multi-Source Remote Sensing Data

Wang

Jiao

2020

IEEE Access

View full text Add to dashboard Cite

Accurate estimation of aboveground forest biomass (AGB) at a large scale is important in global carbon cycle, forest productivity, and climate change. Coarse resolution remote sensing data of long time series are often used to estimate large scale AGB, but the result is inaccurate due to the scaling effect caused by nonlinearity in data representation and the existence of mixed pixels containing different forest types and land uses. Improvement in the accuracy of AGB estimated from coarse resolution remote sensing data is urgently needed. Research on spatial scaling of AGB is still lacking, therefore, this paper proposed an approach based on structural analysis of mixed pixels and the Random Forest model (SMPRF) to increase the accuracy of AGB estimated from coarse resolution data. MODIS and SPOT 5 data were used to create forest biomass distribution maps of the study area at two scales. The scaling effect on estimating forest biomass based on remote sensing was analyzed by comparing data from these two datasets. SMPRF, which included a correction factor for the scaling effect on AGB estimated from coarse resolution MODIS data, was used to create a model that scaled from the fine resolution data (SPOT 5) to the coarse resolution data (MODIS). The results showed that the accuracy of AGB estimated from MODIS data was increased using this method. The Pearson correlation coefficient (r) for data verification increased from 0.63 to 0.89 and the root mean squared error decreased from 51.6 Mg•ha-1 to 26.8 Mg•ha-1. The difference tests showed that the changes were extremely significant (p = 0). Thus, SMPRF can significantly improve the accuracy of large scale AGB estimation based on coarse resolution remote sensing data and the feasibility of applying the method proposed in this study to related fields is verified.

show abstract

Deep learning-based fusion of Landsat-8 and Sentinel-2 images for a harmonized surface reflectance product

Cited by 180 publications

References 43 publications

Impact Quantification of Decentralization in Urban Growth by Extracting Impervious Surfaces Using ISEI in Model Maker

Impact Quantification of Decentralization in Urban Growth by Extracting Impervious Surfaces Using ISEI in Model Maker

Spatio-Temporal Analysis of Oil Spill Impact and Recovery Pattern of Coastal Vegetation and Wetland Using Multispectral Satellite Landsat 8-OLI Imagery and Machine Learning Models

Spatial Scaling of Forest Aboveground Biomass Using Multi-Source Remote Sensing Data

Contact Info

Product

Resources

About