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2022 ◽  
Vol 2 (1) ◽  
pp. 57-71
Author(s):  
Samara Testoni ◽  
Lorna Dawson ◽  
Vander Melo ◽  
Josiane Lopes-Mazzetto ◽  
Bruna Ramalho ◽  
...  

Most cases involving soil in criminal investigations in Brazil have focused on the chemical and mineralogical analyses of soil fractions without including the organic matter. The organic fraction contains plant-wax markers which may be useful to “fingerprint” forensic soils due to their chemical diversity, relative longevity and resistant nature. The aim of this study was to test the long- (kilometre), medium- (metre) and short- (centimetre) scale variability of plant-wax biomarkers in a forensic context in anthropised urban soils and soils developed under subtropical conditions. Two areas from the Curitiba municipality and two areas from the Colombo municipality, Paraná State, South Brazil, were selected. Soil colour analysis was carried out to obtain reflectance data over the 360–740 nm wavelength range. Furthermore, plant-wax marker compounds (n-alkanes and fatty-alcohols) were assessed by extraction and separation into different classes and an analysis of the compounds by gas chromatography (GC/MS). The compositions of the wax-marker profiles were different in samples collected side-by-side, showing sensitivity to local variations under subtropical conditions and in areas under intense human urban disturbance. Under these conditions, biomarkers may be used in real crime scenes, even on a micrometric scale of variation.


Agronomy ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 129
Author(s):  
Ivana Šestak ◽  
Paulo Pereira ◽  
Leon Josip Telak ◽  
Aleksandra Perčin ◽  
Iva Hrelja ◽  
...  

This paper aims to evaluate the ability of VNIR proximal soil spectroscopy to determine post-fire soil chemical properties and discriminate fire severity based on soil spectra. A total of 120 topsoil samples (0–3 cm) were taken from 6 ha of unburned (control (CON)) and burned areas (moderate fire severity (MS) and high fire severity (HS)) in Mediterranean Croatia within one year after the wildfire. Partial least squares regression (PLSR) and an artificial neural network (ANN) were used to build calibration models of soil pH, electrical conductivity (EC), CaCO3, plant-available phosphorus (P2O5) and potassium (K2O), soil organic carbon (SOC), exchangeable calcium (exCa), magnesium (exMg), potassium (exK), sodium (exNa), and cation exchange capacity (CEC), based on soil reflectance data. In terms of fire severity, CON samples exhibited higher average reflectance than MS and HS samples due to their lower SOC content. The PCA results pointed to the significance of the NIR part of the spectrum for extracting the variance in reflectance data and differentiation between the CON and burned area (MS and HS). DA generated 74.2% correctly classified soil spectral samples according to the fire severity. Both PLSR and ANN calibration techniques showed sensitivity to extract information from soil features based on hyperspectral reflectance, most successfully for the prediction of SOC, P2O5, exCa, exK, and CEC. This study confirms the usefulness of soil spectroscopy for fast screening and a better understanding of soil chemical properties in post-fire periods.


2021 ◽  
Vol 14 (1) ◽  
pp. 62
Author(s):  
Tristram D. L. Irvine-Fynn ◽  
Pete Bunting ◽  
Joseph M. Cook ◽  
Alun Hubbard ◽  
Nicholas E. Barrand ◽  
...  

Ice surface albedo is a primary modulator of melt and runoff, yet our understanding of how reflectance varies over time across the Greenland Ice Sheet remains poor. This is due to a disconnect between point or transect scale albedo sampling and the coarser spatial, spectral and/or temporal resolutions of available satellite products. Here, we present time-series of bare-ice surface reflectance data that span a range of length scales, from the 500 m for Moderate Resolution Imaging Spectrometer’s MOD10A1 product, to 10 m for Sentinel-2 imagery, 0.1 m spot measurements from ground-based field spectrometry, and 2.5 cm from uncrewed aerial drone imagery. Our results reveal broad similarities in seasonal patterns in bare-ice reflectance, but further analysis identifies short-term dynamics in reflectance distribution that are unique to each dataset. Using these distributions, we demonstrate that areal mean reflectance is the primary control on local ablation rates, and that the spatial distribution of specific ice types and impurities is secondary. Given the rapid changes in mean reflectance observed in the datasets presented, we propose that albedo parameterizations can be improved by (i) quantitative assessment of the representativeness of time-averaged reflectance data products, and, (ii) using temporally-resolved functions to describe the variability in impurity distribution at daily time-scales. We conclude that the regional melt model performance may not be optimally improved by increased spatial resolution and the incorporation of sub-pixel heterogeneity, but instead, should focus on the temporal dynamics of bare-ice albedo.


2021 ◽  
Author(s):  
Hongye Cao ◽  
Ling Han ◽  
Liangzhi Li

Abstract Remote sensing dynamic monitoring methods often benefit from a dense time series of observations. To enhance these time series, it is sometimes necessary to integrate data from multiple satellite systems. For more than 40 years, Landsat has provided the longest time record of space-based land surface observations, and the successful launch of the Landsat-8 Operational Land Imager (OLI) sensor in 2013 continues this tradition. However, the 16-day observation period of Landsat images has challenged the ability to measure subtle and transient changes like never before. The European Space Agency (ESA) launched the Sentinel-2A satellite in 2015. The satellite carries a Multispectral Instrument (MSI) sensor that provides a 10-20m spatial resolution data source providing an opportunity to complement the Landsat data record. The collection of Sentinel-2A MSI, Landsat-7 ETM+, and Landsat-8 OLI data provide multispectral global coverage from 10m to 30m with further reduced data revisit intervals. There are many differences between sensor data that need to be taken into account to use these data together reliably. The purpose of this study is to evaluate the potential of integrating surface reflectance data from Landsat-7, Landsat-8 and Sentinel-2 archived in the Google Earth Engine (GEE) cloud platform. To test and quantify the differences between these sensors, hundreds of thousands of surface reflectance data from sensor pairs were collected over China. In this study, some differences in the surface reflectance of the sensor pairs were identified, based upon which a cross-sensor conversion model was proposed, i.e., a suitable adjustment equation was fitted using an ordinary least squares (OLS) linear regression method to convert the Sentinel-2 reflectance values closer to the Landsat-7 or Landsat-8 values. The regression results show that the Sentinel MSI data are spectrally comparable to both types of Landsat image data, just as the Landsat sensors are comparable to each other. The root mean square error (RMSE) values between MSI and Landsat spectral values before coordinating the sensors ranged from 0.014 to 0.037, and the RMSE values between OLI and ETM + ranged from 0.019 to 0.039. After coordination, RMSE values between MSI and Landsat spectral values ranged from 0.011 to 0.026, and RMSD values between OLI and ETM + ranged from 0.013 to 0.034. The fitted adjustment equations were also compared to the HLS (Harmonized Landsat-8 Sentinel-2) global fitted equations (Sentinel-2 to Landsat-8) published by the National Aeronautics and Space Administration (NASA) and were found to be significantly different, increasing the likelihood that such adjustments would need to be fitted on a regional basis. This study believes that despite the differences in these datasets, it appears feasible to integrate these datasets by applying a linear regression correction between the bands.


2021 ◽  
Vol 35 (6) ◽  
pp. 926-942
Author(s):  
Ling Sun ◽  
Hong Qiu ◽  
Ronghua Wu ◽  
Jing Wang ◽  
Liyang Zhang ◽  
...  

Agriculture ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1179
Author(s):  
Jia Quan Goh ◽  
Abdul Rashid Mohamed Shariff ◽  
Nazmi Mat Nawi

The quality of palm oil depends on the maturity level of the oil palm fresh fruit bunch (FFB). This research applied an optical spectrometer to collect the reflectance data of 96 FFB from unripe, ripe, and overripe classes for the maturity level classification. The spectrometer scanned the FFB from different parts, including apical, front equatorial, front basil, back equatorial, and back basil. Principal component analysis was carried out to extract principal components from the reflectance data of each of the parts. The extracted principal components were used in an ANOVA test, which found that the reflectance data of the front equatorial showed statistically significant differences between the three maturity groups. Then, the collected reflectance data was subjected to machine learning training and testing by using the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). The front equatorial achieved the highest accuracy, of 90.6%, by using SVM as classifiers; thus, it was proven to be the most optimal part of FFB that can be utilized for maturity classification. Next, the front equatorial dataset was divided into UV (180–400 nm), blue (450–490 nm), green (500–570 nm), red (630–700 nm), and NIR (800–1100 nm) regions for classification testing. The UV bands showed a 91.7% accuracy. After this, representative bands of 365, 460, 523, 590, 623, 660, 735, and 850 nm were extracted from the front equatorial dataset for further classification testing. The 660 nm band achieved an 89.6% accuracy using KNN as a classifier. Composite models were built from the representative bands. The combination of 365, 460, 735, and 850 nm had the highest accuracy in this research, which was 93.8% with the use of SVM. In conclusion, these research findings showed that the front equatorial has the better ability for maturity classification, whereas the composite model with only four bands has the best accuracy. These findings are useful to the industry for future oil palm FFB classification research.


Author(s):  
Douglas Stefanello Facco ◽  
Laurindo Antonio Guasselli ◽  
Luis Fernando Chimelo Ruiz ◽  
João Paulo Delapasse Simioni ◽  
Daiane Gerhardt Dick

Water quality and the useful life of reservoirs and dams are influenced by the entry of suspended solids, in addition to reducing theirtransparency and storage capacity. It is primary to monitor and analyses its space-time dynamics. Thus, the objective of this work isto characterize the dynamics of the Itaipu Reservoir waters from turbidity, rainfall and spectral reflectance data. To characterize thedynamics, the reservoir was divided into 18 aquatic compartments between upstream and downstream, using precipitation data fromthe TRMM sensor and Landsat 8 images in different precipitation situations. NDWI, MNDWI and NDTI water spectral indexes werecalculated from Landsat 8 images. The results showed high correlation between the NDTI index and the turbidity (R² = 0.91). Then theNDTI images were reclassified into low, medium and high turbidity. A strong correlation between turbidity and 4 Band correspondingto the spectral range of red (R² = 0.94) was also obtained. The precipitation has a determinant influence, being the Paraná River, in theperiods of greater precipitation, the main agent in sediment transport. The space-time dynamics showed that the lateral compartmentsof the reservoir have less influence on sediment transport. In this sense, our analysis brought new elements to understand the turbidityvariation in these Itaipu Reservoir compartments, as well as the spectral reflectance dynamics in the space-time characterization relatedto turbidity.


2021 ◽  
Vol 2 ◽  
Author(s):  
Xiangnan Ni ◽  
Yuri Knyazikhin ◽  
Yuanheng Sun ◽  
Xiaojun She ◽  
Wei Guo ◽  
...  

In vegetation canopies cross-shading between finite dimensional leaves leads to a peak in reflectance in the retro-illumination direction. This effect is called the hot spot in optical remote sensing. The hotspot region in reflectance of vegetated surfaces represents the most information-rich directions in the angular distribution of canopy reflected radiation. This paper presents a new approach for generating hot spot signatures of equatorial forests from synergistic analyses of multiangle observations from the Multiangle Imaging SpectroRadiometer (MISR) on Terra platform and near backscattering reflectance data from the Earth Polychromatic Imaging Camera (EPIC) onboard NOAA’s Deep Space Climate Observatory (DSCOVR). A canopy radiation model parameterized in terms of canopy spectral invariants underlies the theoretical basis for joining Terra MISR and DSCOVR EPIC data. The proposed model can accurately reproduce both MISR angular signatures acquired at 10:30 local solar time and diurnal courses of EPIC reflectance (NRMSE < 9%, R2 > 0.8). Analyses of time series of the hot spot signature suggest its ability to unambiguously detect seasonal changes of equatorial forests.


2021 ◽  
Author(s):  
Dwaipayan Deb ◽  
Pavan Chakraborty

Abstract Surfaces of solid solar system objects are covered by layers of particulate materials called regolith originated from their surface bedrock. They preserve important information about surface geological processes. Often regolith is composed of more than one type of particle in terms of composition, maturity, size, etc. Experiments and theoretical works are being carried out to constrain the result of mixing and extract the abundance of compositional end-members from regolith spectra. In this work we have studied, photometric light scattering from simulated surfaces made of two different materials – one is highly bright quartz particles ≈ 80µm and the other moderately bright sandstone particles ≈ 250µm. The samples were mixed with varying proportions and investigated at normal illumination conditions to avoid the shadowing effect. Said combinations may resemble ice mixed regolith on various solar system objects and therefore important for in situ observations. We find that the combinations show a linear trend in the corresponding reflectance data in terms of their mixing proportion and some interesting facts come out when compared to previous studies.


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