Exploring the utility of Sentinel-2 MSI and Landsat 8 OLI in burned area mapping for a heterogenous savannah landscape

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

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The Utility of the Upcoming HyspIRI’s Simulated Spectral Settings in Detecting Maize Gray Leafy Spot in Relation to Sentinel-2 MSI, VENµS, and Landsat 8 OLI Sensors

Agronomy ◽

10.3390/agronomy9120846 ◽

2019 ◽

Vol 9 (12) ◽

pp. 846

Author(s):

Mbulisi Sibanda ◽

Onisimo Mutanga ◽

Timothy Dube ◽

John Odindi ◽

Paramu L. Mafongoya

Keyword(s):

Food Security ◽

Precision Agriculture ◽

Leaf Spot ◽

Gray Leaf Spot ◽

Economic Losses ◽

Leaf Spot Disease ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Maize Crop ◽

Sentinel 2

Considering the high maize yield loses caused by incidences of disease, as well as incomprehensive monitoring initiatives in crop farming, there is a need for spatially explicit, cost-effective, and consistent approaches for monitoring, as well as for forecasting, food-crop diseases, such as maize Gray Leaf Spot. Such approaches are valuable in reducing the associated economic losses while fostering food security. In this study, we sought to investigate the utility of the forthcoming HyspIRI sensor in detecting disease progression of Maize Gray Leaf Spot infestation in relation to the Sentinel-2 MSI and Landsat 8 OLI spectral configurations simulated using proximally sensed data. Healthy, intermediate, and severe categories of maize crop infections by the Gray Leaf Spot disease were discriminated based on partial least squares–discriminant analysis (PLS-DA) algorithm. Comparatively, the results show that the HyspIRI’s simulated spectral settings slightly performed better than those of Sentinel-2 MSI, VENµS, and Landsat 8 OLI sensor. HyspIRI exhibited an overall accuracy of 0.98 compared to 0.95, 0.93, and 0.89, which were exhibited by Sentinel-2 MSI, VENµS, and Landsat 8 OLI sensor sensors, respectively. Furthermore, the results showed that the visible section, red-edge, and NIR covered by all the four sensors were the most influential spectral regions for discriminating different Maize Gray Leaf Spot infections. These findings underscore the potential value of the upcoming hyperspectral HyspIRI sensor in precision agriculture and forecasting of crop-disease epidemics, which are necessary to ensure food security.

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USABILITY, STRENGTH AND PRACTICALITY OF THE UPCOMING HYSPIRI IN DETECTING MAIZE GRAY LEAFY SPOT IN RELATION TO SENTINEL-2 MSI, VENμS AND LANDSAT 8 OLI SPECTRAL BAND-SETTINGS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w18-1015-2019 ◽

2019 ◽

Vol XLII-4/W18 ◽

pp. 1015-1022

Author(s):

M. Sibanda ◽

O. Mutanga ◽

T. Dube ◽

J. Odindi ◽

P. L. Mafongoya

Keyword(s):

Disease Progression ◽

Precision Agriculture ◽

Spectral Band ◽

Maize Yield ◽

Gray Leaf Spot ◽

Added Value ◽

Landsat 8 ◽

Landsat 8 Oli ◽

To Come ◽

Sentinel 2

Abstract. Considering the high maize yield loses that are caused by diseases incidences as well as incomprehensive monitoring initiatives in the crop farming sector of agriculture, there is a need to come up with spatially explicit, cheap, fast and consistent approaches for monitoring as well as forecasting food crop diseases, such as maize gray leaf spot. This study, therefore, we sought to investigate the usability, strength and practicality of the forthcoming HyspIRI in detecting disease progression of Maize Gray leafy spot infections in relation to the Sentinel-2 MSI, Landsat 8 OLI spectral configurations. Maize Gray leafy spot disease progression that were discriminated based on partial least squares –discriminant analysis (PLS-DA) algorithm were (i) healthy, (ii) intermediate and (ii) severely infected maize crops. Comparatively, the results show that the HyspIRI’s simulated spectral settings slightly performed better than those of Sentinel-2 MSI, VENμS and Landsat 8 OLI sensor. HyspIRI exhibited an overall accuracy of 0.98 compared to 0.95, 0.93 and 0.89 exhibited by Sentinel-2 MSI, VENμS and Landsat 8 OLI sensor sensors, respectively. Further, the results showed that the visible section the red-edge and NIR covered by all the four sensors were the most influential spectral regions for discriminating different Maize Gray leafy spot infections. These findings underscore the added value and potential scientific breakthroughs likely to be brought about by the upcoming hyperspectral HyspIRI sensor in precision agriculture and forecasting of crop disease epidemics to ensure food security.

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Comparative Evaluation of the Spectral and Spatial Consistency of Sentinel-2 and Landsat-8 OLI Data for Igneada Longos Forest

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8020056 ◽

2019 ◽

Vol 8 (2) ◽

pp. 56 ◽

Cited By ~ 9

Author(s):

Maliheh Arekhi ◽

Cigdem Goksel ◽

Fusun Balik Sanli ◽

Gizem Senel

Keyword(s):

Vegetation Index ◽

Data Retrieval ◽

Forest Monitoring ◽

Landsat 8 ◽

Spring Season ◽

Landsat 8 Oli ◽

Atmospheric Effect ◽

Spatial Consistency ◽

The Mean ◽

Sentinel 2

This study aims to test the spectral and spatial consistency of Sentinel-2 and Landsat-8 OLI data for the potential of monitoring longos forests for four seasons in Igneada, Turkey. Vegetation indices, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI), were generated for the study area in addition to the five corresponding bands of Sentinel-2 and Landsat-8 OLI Images. Although the spectral consistency of the data was interpreted by cross-calibration analysis using the Pearson correlation coefficient, spatial consistency was evaluated by descriptive statistical analysis of investigated variables. In general, the highest correlation values were achieved for the images that were acquired in the spring season for almost all investigated variables. In the spring season, among the investigated variables, the Red band (B4), NDVI and EVI have the largest correlation coefficients of 0.94, 0.92 and 0.91, respectively. Regarding the spatial consistency, the mean and standard deviation values of all variables were consistent for all seasons except for the mean value of the NDVI for the fall season. As a result, if there is no atmospheric effect or data retrieval/acquisition error, either Landsat-8 or Sentinel-2 can be used as a combination or to provide the continuity data in longos monitoring applications. This study contributes to longos forest monitoring science in terms of remote sensing data analysis.

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Assessment of Burned Area Mapping Methods for Smoke Covered Sentinel-2 Data

2020 13th International Conference on Communications (COMM) ◽

10.1109/comm48946.2020.9141999 ◽

2020 ◽

Cited By ~ 1

Author(s):

Alexandru-Cosmin Grivei ◽

Corina Vaduva ◽

Mihai Datcu

Keyword(s):

Burned Area ◽

Burned Area Mapping ◽

Sentinel 2

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Estimation of Chlorophyll-a in Northern Coastal Bay of Bengal Using Landsat-8 OLI and Sentinel-2 MSI Sensors

Frontiers in Marine Science ◽

10.3389/fmars.2019.00598 ◽

2019 ◽

Vol 6 ◽

Cited By ~ 5

Author(s):

Shukla Poddar ◽

Neethu Chacko ◽

Debadatta Swain

Keyword(s):

Chlorophyll A ◽

Bay Of Bengal ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Coastal Bay ◽

Sentinel 2

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Comparison of Sentinel-2 and Landsat-8 OLI satellite images vs. high spatial resolution images (MIVIS and WorldView-2) for mapping Posidonia oceanica meadows

Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2018 ◽

10.1117/12.2326798 ◽

2018 ◽

Cited By ~ 3

Author(s):

Pasquale Lanera ◽

Luigi Dattola ◽

Francesco Sante Rende ◽

Rossella Di Mento ◽

Rocco Dominici ◽

...

Keyword(s):

Spatial Resolution ◽

Satellite Images ◽

High Spatial Resolution ◽

Posidonia Oceanica ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Sentinel 2

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Multispectral Image Super-Resolution Burned-Area Mapping Based on Space-Temperature Information

Remote Sensing ◽

10.3390/rs11222695 ◽

2019 ◽

Vol 11 (22) ◽

pp. 2695

Author(s):

Peng Wang ◽

Lei Zhang ◽

Gong Zhang ◽

Benzhou Jin ◽

Henry Leung

Keyword(s):

Objective Function ◽

Multispectral Imaging ◽

Accurate Determination ◽

Super Resolution ◽

Burned Area ◽

Landsat 8 ◽

Random Walker ◽

Burned Areas ◽

Burned Area Mapping ◽

Super Resolution Mapping

Multispectral imaging (MI) provides important information for burned-area mapping. Due to the severe conditions of burned areas and the limitations of sensors, the resolution of collected multispectral images is sometimes very rough, hindering the accurate determination of burned areas. Super-resolution mapping (SRM) has been proposed for mapping burned areas in rough images to solve this problem, allowing super-resolution burned-area mapping (SRBAM). However, the existing SRBAM methods do not use sufficiently accurate space information and detailed temperature information. To improve the mapping accuracy of burned areas, an improved SRBAM method utilizing space–temperature information (STI) is proposed here. STI contains two elements, a space element and a temperature element. We utilized the random-walker algorithm (RWA) to characterize the space element, which encompassed accurate object space information, while the temperature element with rich temperature information was derived by calculating the normalized burn ratio (NBR). The two elements were then merged to produce an objective function with space–temperature information. The particle swarm optimization algorithm (PSOA) was employed to handle the objective function and derive the burned-area mapping results. The dataset of the Landsat-8 Operational Land Imager (OLI) from Denali National Park, Alaska, was used for testing and showed that the STI method is superior to the traditional SRBAM method.

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Mapping Winter Wheat with Combinations of Temporally Aggregated Sentinel-2 and Landsat-8 Data in Shandong Province, China

Remote Sensing ◽

10.3390/rs12122065 ◽

2020 ◽

Vol 12 (12) ◽

pp. 2065 ◽

Cited By ~ 1

Author(s):

Feng Xu ◽

Zhaofu Li ◽

Shuyu Zhang ◽

Naitao Huang ◽

Zongyao Quan ◽

...

Keyword(s):

Winter Wheat ◽

Google Earth ◽

Shandong Province ◽

Sensor Data ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Multi Temporal ◽

Wheat Development ◽

Development Phases ◽

Sentinel 2

Winter wheat is one of the major cereal crops in China. The spatial distribution of winter wheat planting areas is closely related to food security; however, mapping winter wheat with time-series finer spatial resolution satellite images across large areas is challenging. This paper explores the potential of combining temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data available via the Google Earth Engine (GEE) platform for mapping winter wheat in Shandong Province, China. First, six phenological median composites of Landsat-8 OLI and Sentinel-2 MSI reflectance measures were generated by a temporal aggregation technique according to the winter wheat phenological calendar, which covered seedling, tillering, over-wintering, reviving, jointing-heading and maturing phases, respectively. Then, Random Forest (RF) classifier was used to classify multi-temporal composites but also mono-temporal winter wheat development phases and mono-sensor data. The results showed that winter wheat could be classified with an overall accuracy of 93.4% and F1 measure (the harmonic mean of producer’s and user’s accuracy) of 0.97 with temporally aggregated Landsat-8 and Sentinel-2 data were combined. As our results also revealed, it was always good to classify multi-temporal images compared to mono-temporal imagery (the overall accuracy dropped from 93.4% to as low as 76.4%). It was also good to classify Landsat-8 OLI and Sentinel-2 MSI imagery combined instead of classifying them individually. The analysis showed among the mono-temporal winter wheat development phases that the maturing phase’s and reviving phase’s data were more important than the data for other mono-temporal winter wheat development phases. In sum, this study confirmed the importance of using temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data combined and identified key winter wheat development phases for accurate winter wheat classification. These results can be useful to benefit on freely available optical satellite data (Landsat-8 OLI and Sentinel-2 MSI) and prioritize key winter wheat development phases for accurate mapping winter wheat planting areas across China and elsewhere.

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