A novel cotton mapping index combining Sentinel-1 SAR and Sentinel-2 multispectral imagery

2021 ◽  
Vol 181 ◽  
pp. 148-166
Author(s):  
Lan Xun ◽  
Jiahua Zhang ◽  
Dan Cao ◽  
Shanshan Yang ◽  
Fengmei Yao
Keyword(s):  
Multispectral Imagery ◽  
Sentinel 2

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

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.

scholarly journals Feature Selection on Sentinel-2 Multispectral Imagery for Mapping a Landscape Infested by Parthenium Weed

2019 ◽  
Vol 11 (16) ◽  
pp. 1892 ◽  
Author(s):  
Zolo Kiala ◽  
Onisimo Mutanga ◽  
John Odindi ◽  
Kabir Peerbhay
Keyword(s):  
Feature Selection ◽  
Invasive Plant ◽  
Feature Selection Method ◽  
Sparse Learning ◽  
High Temporal Resolution ◽  
Multispectral Imagery ◽  
Selection Methods ◽  
Multi Temporal ◽  
Parthenium Weed ◽  
Sentinel 2

In the recent past, the volume of spatial datasets has significantly increased. This is attributed to, among other factors, higher sensor temporal resolutions of the recently launched satellites. The increased data, combined with the computation and possible derivation of a large number of indices, may lead to high multi-collinearity and redundant features that compromise the performance of classifiers. Using dimension reduction algorithms, a subset of these features can be selected, hence increasing their predictive potential. In this regard, an investigation into the application of feature selection techniques on multi-temporal multispectral datasets such as Sentinel-2 is valuable in vegetation mapping. In this study, ten feature selection methods belonging to five groups (Similarity-based, statistical-based, Sparse learning based, Information theoretical based, and wrappers methods) were compared based on f-score and data size for mapping a landscape infested by the Parthenium weed (Parthenium hysterophorus). Overall, results showed that ReliefF (a Similarity-based approach) was the best performing feature selection method as demonstrated by the high f-score values of Parthenium weed and a small size of optimal features selected. Although svm-b (a wrapper method) yielded the highest accuracies, the size of optimal subset of selected features was quite large. Results also showed that data size affects the performance of feature selection algorithms, except for statistically-based methods such as Gini-index and F-score and svm-b. Findings in this study provide a guidance on the application of feature selection methods for accurate mapping of invasive plant species in general and Parthenium weed, in particular, using new multispectral imagery with high temporal resolution.

scholarly journals Retrieval of soil salinity from Sentinel-2 multispectral imagery

2019 ◽  
Vol 52 (1) ◽  
pp. 138-154 ◽  
Author(s):  
Mohammad Mahdi Taghadosi ◽  
Mahdi Hasanlou ◽  
Kamran Eftekhari
Keyword(s):  
Soil Salinity ◽  
Multispectral Imagery ◽  
Sentinel 2

scholarly journals Mapping Recent Shoreline Changes Spanning the Lateral Collapse of Anak Krakatau Volcano, Indonesia

2020 ◽  
Vol 10 (2) ◽  
pp. 536 ◽  
Author(s):  
Alessandro Novellino ◽  
Samantha L. Engwell ◽  
Stephen Grebby ◽  
Simon Day ◽  
Michael Cassidy ◽  
...  
Keyword(s):  
Volcanic Activity ◽  
Google Earth ◽  
Multispectral Imagery ◽  
Explosive Activity ◽  
Shoreline Changes ◽  
Remote Island ◽  
History Of ◽  
Google Earth Engine ◽  
Krakatau Volcano ◽  
Sentinel 2

We use satellite imagery to investigate the shoreline changes associated with volcanic activity in 2018–2019 at Anak Krakatau, Indonesia, spanning a major lateral collapse and period of regrowth through explosive activity. The shoreline changes have been analyzed and validated through the adaptation of an existing methodology based on Sentinel-2 multispectral imagery and developed on Google Earth Engine. This work tests the results of this method in a highly dynamic volcanic environment and validates them with manually digitized shorelines. The analysis shows that the size of the Anak Krakatau Island increased from 2.84 km2 to 3.19 km2 during 15 May 2018–1 November 2019 despite the loss of area in the 22 December 2018 lateral collapse. The lateral collapse reduced the island area to ~1.5 km2 but this was followed by a rapid increase in area in the first two months of 2019, reaching up to 3.27 km2. This was followed by a period of little change as volcanic activity declined and then by a net decrease from May 2019 to 1 November 2019 that resulted from erosion on the SW side of the island. This history of post-collapse eruptive regrowth and coastal erosion derived from the shoreline changes illuminates the potential for satellite-based automated shoreline mapping to provide databases for monitoring remote island volcanoes.

scholarly journals Evaluation of Grass Quality under Different Soil Management Scenarios Using Remote Sensing Techniques

2019 ◽  
Vol 11 (15) ◽  
pp. 1835 ◽  
Author(s):  
Mohammad Sadegh Askari ◽  
Timothy McCarthy ◽  
Aidan Magee ◽  
Darren J. Murphy
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Quality Indicators ◽  
Prediction Models ◽  
Unmanned Aircraft ◽  
Multispectral Imagery ◽  
European Climate ◽  
Grass Biomass ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Hyperspectral and multispectral imagery have been demonstrated to have a considerable potential for near real-time monitoring and mapping of grass quality indicators. The objective of this study was to evaluate the efficiency of remote sensing techniques for quantification of aboveground grass biomass (BM) and crude protein (CP) in a temperate European climate such as Ireland. The experiment was conducted on 64 plots and 53 paddocks with varying quantities of nitrogen applied. Hyperspectral imagery (HSI) and multispectral imagery (MSI) were analyzed to develop the prediction models. The MSI data used in this study were captured using an unmanned aircraft vehicle (UAV) and the satellite Sentinel-2, while the HSI data were obtained using a handheld hyperspectral camera. The prediction models were developed using partial least squares regression (PLSR) and stepwise multi-linear regression (MLR). Eventually, the spatial distribution of grass biomass over plots and paddocks was mapped to assess the within-field variability of grass quality metrics. An excellent accuracy was achieved for the prediction of BM and CP using HSI (RPD > 2.5 and R2 > 0.8), and a good accuracy was obtained via MSI-UAV (2 < RPD < 2.5 and R2 > 0.7) for the grass quality indicators. The accuracy of the models calculated using MSI-Sentinel-2 was reasonable for BM prediction and insufficient for CP estimation. The red-edge range of the wavelengths showed the maximum impact on the predictability of grass BM, and the NIR range had the greatest influence on the estimation of grass CP. Both the PLSR and MLR techniques were found to be sufficiently robust for spectral modelling of aboveground BM and CP. The PLSR yielded a slightly better model than MLR. This study suggested that remote sensing techniques can be used as a rapid and reliable approach for near real-time quantitative assessment of fresh grass quality under a temperate European climate.

scholarly journals New Spectral Index for Detecting Wheat Yellow Rust Using Sentinel-2 Multispectral Imagery

Sensors ◽  
2018 ◽  
Vol 18 (3) ◽  
pp. 868 ◽  
Author(s):  
Qiong Zheng ◽  
Wenjiang Huang ◽  
Ximin Cui ◽  
Yue Shi ◽  
Linyi Liu
Keyword(s):  
Spectral Index ◽  
Yellow Rust ◽  
Multispectral Imagery ◽  
Sentinel 2

scholarly journals Ship-Iceberg Discrimination in Sentinel-2 Multispectral Imagery by Supervised Classification

2017 ◽  
Vol 9 (11) ◽  
pp. 1156 ◽  
Author(s):  
Peder Heiselberg ◽  
Henning Heiselberg
Keyword(s):  
Supervised Classification ◽  
Multispectral Imagery ◽  
Sentinel 2
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