Quantum Machine Learning Algorithm for Iron, Gold, and Copper Detection in Optical Remote Sensing Data

Abstract The main way to understand variability of any spatial data using remote sensing is calculating spectral indices. For now, some difficulties have receiving water surface temperature due to specific properties for satellite sensors and low spatial resolution. The main sources of receiving salinity data are remote sensing data from ESA SMOS, NASA Aquarius and SMAP satellites. Using different machine learning algorithms, we can get models or equations, representing dependency between studied environmental variable and different spectral channels of remote monitoring data. After receiving and collecting remote sensing data in database this system uses machine learning algorithms to find dependency between collected field data and different spectral bands of the remote sensing data. Our goal was to form an analytical system based on remote sensors and machine learning algorithm to analyse, predict and evaluate water ecosystems for fisheries and environmental protection.

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Detection of Crop Hail Damage with a Machine Learning Algorithm Using Time Series of Remote Sensing Data

Agronomy ◽

10.3390/agronomy11102078 ◽

2021 ◽

Vol 11 (10) ◽

pp. 2078

Author(s):

Leandro Sosa ◽

Ana Justel ◽

Íñigo Molina

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Time Series ◽

Vegetation Index ◽

Learning Algorithm ◽

Remote Sensing Data ◽

Machine Learning Techniques ◽

Sensing Data ◽

Hail Damage ◽

Efficient Detection

Hailstorms usually result in total crop loss. After a hailstorm, the affected field is inspected by an insurance claims adjuster to assess yield loss. Assessment accuracy depends largely on in situ detection of homogeneous damage sectors within the field, using visual techniques. This paper presents an algorithm for the automatic detection of homogeneous hail damage through the application of unsupervised machine learning techniques to vegetation indices calculated from remote sensing data. Five microwave and five spectral indices were evaluated before and after a hailstorm in zones with different degrees of damage. Dual Polarization SAR Vegetation Index and Normalized Pigment Chlorophyll Ratio Index were the most sensitive to hail-induced changes. The time series and rates of change of these indices were used as input variables in the K-means method for clustering pixels into homogeneous damage zones. Validation of the algorithm with data from 91 soybean, wheat, and corn plots showed that in 87.01% of cases there was significant evidence of differences in average damage between zones determined by the algorithm within the plot. Thus, the algorithm presented in this paper allowed efficient detection of homogeneous hail damage zones, which is expected to improve accuracy and transparency in the characterization of hailstorm events.

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Rapid Evaluation and Validation Method of Above Ground Forest Biomass Estimation Using Optical Remote Sensing in Tundi Reserved Forest Area, India

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10010029 ◽

2021 ◽

Vol 10 (1) ◽

pp. 29

Author(s):

Praveen Kumar ◽

Akhouri P. Krishna ◽

Thorkild M. Rasmussen ◽

Mahendra K. Pal

Keyword(s):

Remote Sensing ◽

Vegetation Cover ◽

Remote Sensing Data ◽

Test Site ◽

Forest Area ◽

Relational Model ◽

Biomass Estimation ◽

Optical Remote Sensing ◽

Ground Sample ◽

Sensing Data

Optical remote sensing data are freely available on a global scale. However, the satellite image processing and analysis for quick, accurate, and precise forest above ground biomass (AGB) evaluation are still challenging and difficult. This paper is aimed to develop a novel method for precise, accurate, and quick evaluation of the forest AGB from optical remote sensing data. Typically, the ground forest AGB was calculated using an empirical model from ground data for biophysical parameters such as tree density, height, and diameter at breast height (DBH) collected from the field at different elevation strata. The ground fraction of vegetation cover (FVC) in each ground sample location was calculated. Then, the fraction of vegetation cover (FVC) from optical remote sensing imagery was calculated. In the first stage of method implementation, the relation model between the ground FVC and ground forest AGB was developed. In the second stage, the relational model was established between image FVC and ground FVC. Finally, both models were fused to derive the relational model between image FVC and forest AGB. The validation of the developed method was demonstrated utilizing Sentinel-2 imagery as test data and the Tundi reserved forest area located in the Dhanbad district of Jharkhand state in eastern India was used as the test site. The result from the developed model was ground validated and also compared with the result from a previously developed crown projected area (CPA)-based forest AGB estimation approach. The results from the developed approach demonstrated superior capabilities in precision compared to the CPA-based method. The average forest AGB estimation of the test site obtained by this approach revealed 463 tons per hectare, which matches the previous estimate from this test site.

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Some Peculiarities of Arable Soil Organic Matter Detection Using Optical Remote Sensing Data

Remote Sensing ◽

10.3390/rs13122313 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2313

Author(s):

Elena Prudnikova ◽

Igor Savin

Keyword(s):

Remote Sensing ◽

Organic Matter ◽

Soil Organic Matter ◽

Spectral Reflectance ◽

Remote Sensing Data ◽

Soil Surface ◽

Optical Remote Sensing ◽

Test Field ◽

Sensing Data ◽

Sentinel 2

Optical remote sensing only provides information about the very thin surface layer of soil. Rainfall splash alters soil surface properties and its spectral reflectance. We analyzed the impact of rainfall on the success of soil organic matter (SOM) content (% by mass) detection and mapping based on optical remote sensing data. The subject of the study was the arable soils of a test field located in the Tula region (Russia), their spectral reflectance, and Sentinel-2 data. Our research demonstrated that rainfall negatively affects the accuracy of SOM predictions based on Sentinel-2 data. Depending on the average precipitation per day, the R2cv of models varied from 0.67 to 0.72, RMSEcv from 0.64 to 1.1% and RPIQ from 1.4 to 2.3. The incorporation of information on the soil surface state in the model resulted in an increase in accuracy of SOM content detection based on Sentinel-2 data: the R2cv of the models increased up to 0.78 to 0.84, the RMSEcv decreased to 0.61 to 0.71%, and the RPIQ increased to 2.1 to 2.4. Further studies are necessary to identify how the SOM content and composition of the soil surface change under the influence of rainfall for other soils, and to determine the relationships between rainfall-induced SOM changes and soil surface spectral reflectance.

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Mapping Allochemical Limestone Formations in Hazara, Pakistan Using Google Cloud Architecture: Application of Machine-Learning Algorithms on Multispectral Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10020058 ◽

2021 ◽

Vol 10 (2) ◽

pp. 58

Author(s):

Muhammad Fawad Akbar Khan ◽

Khan Muhammad ◽

Shahid Bashir ◽

Shahab Ud Din ◽

Muhammad Hanif

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Learning Algorithms ◽

Remote Sensing Data ◽

Kappa Coefficient ◽

Machine Learning Algorithms ◽

Landsat 8 ◽

Sensing Data ◽

Fossiliferous Limestone

Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area.

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