scholarly journals Study on Spectral Response and Estimation of Grassland Plants Dust Retention Based on Hyperspectral Data

2020 ◽  
Vol 12 (12) ◽  
pp. 2019
Author(s):  
Yibo Zhao ◽  
Shaogang Lei ◽  
Xingchen Yang ◽  
Chuangang Gong ◽  
Cangjiao Wang ◽  
...  
Keyword(s):  
Random Forest ◽  
Simulated Annealing Algorithm ◽  
Hyperspectral Image ◽  
Spectral Response ◽  
Hyperspectral Data ◽  
Correlation Spectroscopy ◽  
Estimation Model ◽  
Random Forest Regression ◽  
Grassland Plants ◽  
Dust Retention

Accurate monitoring of plant dust retention can provide a basis for dust pollution control and environmental protection. The aims of this study were to analyze the spectral response features of grassland plants to mining dust and to predict the spatial distribution of dust retention using hyperspectral data. The dust retention content was determined by an electronic analytical balance and a leaf area meter. The leaf reflectance spectrum was measured by a handheld hyperspectral camera, and the airborne hyperspectral data were obtained using an imaging spectrometer. We analyzed the difference between the leaf spectral before and after dust removal. The sensitive spectra of dust retention on the leaf- and the canopy-scale were determined through two-dimensional correlation spectroscopy (2DCOS). The competitive adaptive reweighted sampling (CARS) algorithm was applied to select the feature bands of canopy dust retention. The estimation model of canopy dust retention was built through random forest regression (RFR), and the dust distribution map was obtained based on the airborne hyperspectral image. The results showed that dust retention enhanced the spectral reflectance of leaves in the visible wavelength but weakened the reflectance in the near-infrared wavelength. Caused by the canopy structure and multiple scattering, a slight difference in the sensitive spectra on dust retention existed between the canopy and leaves. Similarly, the sensitive spectra of leaves and the canopy were closely related to dust and plant physiological parameters. The estimation model constructed through 2DCOS-CARS-RFR showed higher precision, compared with genetic algorithm-random forest regression (GA-RFR) and simulated annealing algorithm-random forest regression (SAA-RFR). Spatially, the amount of canopy dust increased and then decreased with increasing distance from the mining area, reaching a maximum within 300–500 m. This study not only demonstrated the importance of extracting feature bands based on the response of plant physical and chemical parameters to dust, but also laid a foundation for the rapid and non-destructive monitoring of grassland plant dust retention.

scholarly journals Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska

2021 ◽  
Vol 13 (9) ◽  
pp. 1693
Author(s):  
Anushree Badola ◽  
Santosh K. Panda ◽  
Dar A. Roberts ◽  
Christine F. Waigl ◽  
Uma S. Bhatt ◽  
...  
Keyword(s):  
Land Cover ◽  
Spectral Resolution ◽  
Hyperspectral Image ◽  
Spectral Response ◽  
Spectral Characteristics ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
High Spectral Resolution ◽  
Fuel Mapping ◽  
Sentinel 2

Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land use and land cover mapping, but they have limited utility for fuel mapping due to their coarse spectral resolution. Hyperspectral datasets have a high spectral resolution, ideal for detailed fuel mapping, but they are limited and expensive to acquire. This study simulates hyperspectral data from Sentinel-2 multispectral data using the spectral response function of the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor, and normalized ground spectra of gravel, birch, and spruce. We used the Uniform Pattern Decomposition Method (UPDM) for spectral unmixing, which is a sensor-independent method, where each pixel is expressed as the linear sum of standard reference spectra. The simulated hyperspectral data have spectral characteristics of AVIRIS-NG and the reflectance properties of Sentinel-2 data. We validated the simulated spectra by visually and statistically comparing it with real AVIRIS-NG data. We observed a high correlation between the spectra of tree classes collected from AVIRIS-NG and simulated hyperspectral data. Upon performing species level classification, we achieved a classification accuracy of 89% for the simulated hyperspectral data, which is better than the accuracy of Sentinel-2 data (77.8%). We generated a fuel map from the simulated hyperspectral image using the Random Forest classifier. Our study demonstrated that low-cost and high-quality hyperspectral data can be generated from Sentinel-2 data using UPDM for improved land cover and vegetation mapping in the boreal forest.

scholarly journals Integrating Airborne Hyperspectral, Topographic, and Soil Data for Estimating Pasture Quality Using Recursive Feature Elimination with Random Forest Regression

2018 ◽  
Vol 10 (7) ◽  
pp. 1117 ◽  
Author(s):  
Rajasheker Pullanagari ◽  
Gabor Kereszturi ◽  
Ian Yule
Keyword(s):  
Random Forest ◽  
Hyperspectral Imaging ◽  
Hyperspectral Data ◽  
Environmental Data ◽  
Recursive Feature Elimination ◽  
Slope Aspect ◽  
Random Forest Regression ◽  
Hill Country ◽  
Pasture Quality ◽  
Quality Maps

Accurate and efficient monitoring of pasture quality on hill country farm systems is crucial for pasture management and optimizing production. Hyperspectral imaging is a promising tool for mapping a wide range of biophysical and biochemical properties of vegetation from leaf to canopy scale. In this study, the potential of high spatial resolution and airborne hyperspectral imaging for predicting crude protein (CP) and metabolizable energy (ME) in heterogeneous hill country farm was investigated. Regression models were developed between measured pasture quality values and hyperspectral data using random forest regression (RF). The results proved that pasture quality could be predicted with hyperspectral data alone; however, accuracy was improved after combining the hyperspectral data with environmental data (elevation, slope angle, slope aspect, and soil type) where the prediction accuracy for CP was R2CV (cross-validated coefficient of determination) = 0.70, RMSECV (cross-validated root mean square error) = 2.06%, RPDCV (cross-validated ratio to prediction deviation) = 1.82 and ME: R2CV = 0.75, RMSECV = 0.65 MJ/kg DM, RPDCV = 2.11. Interestingly, the accuracy was further out-performed by considering important hyperspectral and environmental variables using RF combined with recursive feature elimination (RFE) (CP: R2CV = 0.80, RMSECV = 1.68%, RPDCV = 2.23; ME: R2CV = 0.78, RMSECV = 0.61 MJ/kg DM, RPDCV = 2.19). Similar performance trends were noticed with validation data. Utilizing the best model, spatial pasture quality maps were created across the farm. Overall, this study showed the potential of airborne hyperspectral data for producing accurate pasture quality maps, which will help farm managers to optimize decisions to improve environmental and economic benefits.

scholarly journals Active Semi-Supervised Random Forest for Hyperspectral Image Classification

2019 ◽  
Vol 11 (24) ◽  
pp. 2974 ◽  
Author(s):  
Youqiang Zhang ◽  
Guo Cao ◽  
Xuesong Li ◽  
Bisheng Wang ◽  
Peng Fu
Keyword(s):  
Random Forest ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Data Sets ◽  
Great Success ◽  
Full Potential ◽  
Hyperspectral Image Classification ◽  
Unified Framework ◽  
Proposed Model

Random forest (RF) has obtained great success in hyperspectral image (HSI) classification. However, RF cannot leverage its full potential in the case of limited labeled samples. To address this issue, we propose a unified framework that embeds active learning (AL) and semi-supervised learning (SSL) into RF (ASSRF). Our aim is to utilize AL and SSL simultaneously to improve the performance of RF. The objective of the proposed method is to use a small number of manually labeled samples to train classifiers with relative high classification accuracy. To achieve this goal, a new query function is designed to query the most informative samples for manual labeling, and a new pseudolabeling strategy is introduced to select some samples for pseudolabeling. Compared with other AL- and SSL-based methods, the proposed method has several advantages. First, ASSRF utilizes the spatial information to construct a query function for AL, which can select more informative samples. Second, in addition to providing more labeled samples for SSL, the proposed pseudolabeling method avoids bias caused by AL-labeled samples. Finally, the proposed model retains the advantages of RF. To demonstrate the effectiveness of ASSRF, we conducted experiments on three real hyperspectral data sets. The experimental results have shown that our proposed method outperforms other state-of-the-art methods.

scholarly journals Remote Sensing of Lake Sediment Core Particle Size Using Hyperspectral Image Analysis

2020 ◽  
Vol 12 (23) ◽  
pp. 3850
Author(s):  
Hamid Ghanbari ◽  
Olivier Jacques ◽  
Marc-Élie Adaïmé ◽  
Irene Gregory-Eaves ◽  
Dermot Antoniades
Keyword(s):  
Particle Size ◽  
Random Forest ◽  
Lake Sediment ◽  
Hyperspectral Image ◽  
Sediment Cores ◽  
Model Fitting ◽  
Principal Component Regression ◽  
Principal Component ◽  
Support Vector ◽  
Random Forest Regression

Hyperspectral imaging has recently emerged in the geosciences as a technology that provides rapid, accurate, and high-resolution information from lake sediment cores. Here we introduce a new methodology to infer particle size distribution, an insightful proxy that tracks past changes in aquatic ecosystems and their catchments, from laboratory hyperspectral images of lake sediment cores. The proposed methodology includes data preparation, spectral preprocessing and transformation, variable selection, and model fitting. We evaluated random forest regression and other commonly used statistical methods to find the best model for particle size determination. We tested the performance of combinations of spectral transformation techniques, including absorbance, continuum removal, and first and second derivatives of the reflectance and absorbance, along with different regression models including partial least squares, multiple linear regression, principal component regression, and support vector regression, and evaluated the resulting root mean square error (RMSE), R-squared, and mean relative error (MRE). Our results show that a random forest regression model built on spectra absorbance significantly outperforms all other models. The new workflow demonstrated herein represents a much-improved method for generating inferences from hyperspectral imagery, which opens many new opportunities for advancing the study of sediment archives.

scholarly journals Water Turbidity Retrieval Based on UAV Hyperspectral Remote Sensing

Water ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 128
Author(s):  
Mengying Cui ◽  
Yonghua Sun ◽  
Chen Huang ◽  
Mengjun Li
Keyword(s):  
Hyperspectral Image ◽  
Spectral Response ◽  
Field Investigation ◽  
Hyperspectral Data ◽  
Liaoning Province ◽  
Water Quality Parameter ◽  
Retrieval Models ◽  
Band Ratio ◽  
Water Turbidity ◽  
Aquaculture Ponds

The water components affecting turbidity are complex and changeable, and the spectral response mechanism of each water quality parameter is different. Therefore, this study mainly aimed at the turbidity monitoring by unmanned aerial vehicle (UAV) hyperspectral technology, and establishes a set of turbidity retrieval models through the artificial control experiment, and verifies the model’s accuracy through UAV flight and water sample data in the same period. The results of this experiment can also be extended to different inland waters for turbidity retrieval. Retrieval of turbidity values of small inland water bodies can provide support for the study of the degree of water pollution. We collected the images and data of aquaculture ponds and irrigation ditches in Dawa District, Panjin City, Liaoning Province. Twenty-nine standard turbidity solutions with different concentration gradients (concentration from 0 to 360 NTU—the abbreviation of Nephelometric Turbidity Unit, which stands for scattered turbidity.) were established through manual control and we simultaneously collected hyperspectral data from the spectral values of standard solutions. The sensitive band to turbidity was obtained after analyzing the spectral information. We established four kinds of retrieval, including the single band, band ratio, normalized ratio, and the partial least squares (PLS) models. We selected the two models with the highest R2 for accuracy verification. The band ratio model and PLS model had the highest accuracy, and R2 was, respectively, 0.65 and 0.72. The hyperspectral image data obtained by UAV were combined with the PLS model, which had the highest R2 to estimate the spatial distribution of water turbidity. The turbidity of the water areas in the study area was 5–300 NTU, and most of which are 5–80 NTU. It shows that the PLS models can retrieve the turbidity with high accuracy of aquaculture ponds, irrigation canals, and reservoirs in Dawa District of Panjin City, Liaoning Province. The experimental results are consistent with the conclusions of the field investigation.

scholarly journals Cartografía de calizas con datos hiperespectrales AISA Eagle II en una zona montañosa con vegetación densa: cómo orientar geológicamente la corrección atmosférica

2018 ◽  
pp. 125
Author(s):  
J. Buzzi ◽  
E. Costa ◽  
A. Riaza ◽  
O. Fernández ◽  
D. García-Sellés ◽  
...  
Keyword(s):  
Iron Oxide ◽  
Wavelength Range ◽  
Hyperspectral Image ◽  
Spectral Response ◽  
Atmospheric Correction ◽  
Hyperspectral Data ◽  
Rock Outcrops ◽  
Visible Wavelength ◽  
Visible Wavelength Range ◽  
Geological Units

<p>Carbonated rocks are crucial targets for oil exploration, outcropping often in large areas with minimum spectral differences among geological units. The typical carbonate spectral absorptions in 2200 nm and 2300 nm, are excluded from the wavelength range of AISA Eagle II. AISA Eagle II hyperspectral data are processed in flight lines of 1024 swath pixels in the visible to near-infrared wavelength range (400 to 970 nm). The flight has a spatial resolution of 1 m and records a total of 128 channels with a spectral resolution of 4,8 nm. The area of study is a carbonate rocky mountain densely vegetated, covered by variably dense trees and bushes. Masking vegetation cover and shade effects is prior to any geological analysis using hyperspectral image processing. Carbonate units occur in mountain slopes, with small areas of ridges of rock outcrops and wide fans of loose material. The background soil of different geological units differ spectrally only by overall reflectance. Instead, limestone rocky outcrops display spectral responses with smooth typical iron oxide absorptions that distinguish them apart from loose boulders of limestone. Trying to enhance spectral differences in the visible wavelength range among carbonate geological units, an atmospheric correction using field spectra from geologically selected targets in a limestone quarry was performed. This way, it was possible to map apart lithologically similar detrital units dominated by carbonate in a river plain. The limy river bottom displays spectra with a straight line in the visible wavelength range due to abundant organic matter and small grain size. The spectra of the upper terraces record spectral absorption features related to iron oxide contents similar to the rock outcrops in ridges of mountains. The use of field spectra from geologically selected targets improves the mapping capability of hyperspectral imagery in areas with geological units with a homogeneous spectral response.</p>

Estimating nitrogen contents of apple leaves based on hyperspectral parameter models at different phenophases

2016 ◽  
Vol 6 (2) ◽  
pp. 942-952
Author(s):  
Xicun ZHU ◽  
Zhuoyuan WANG ◽  
Lulu GAO ◽  
Gengxing ZHAO ◽  
Ling WANG
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Shoot Growth ◽  
Hyperspectral Data ◽  
Estimation Model ◽  
Apple Leaves ◽  
Bp Neural Network Model ◽  
Nitrogen Contents

The objective of the paper is to explore the best phenophase for estimating the nitrogen contents of apple leaves, to establish the best estimation model of the hyperspectral data at different phenophases. It is to improve the apple trees precise fertilization and production management. The experiments were done in 20 orchards in the field, measured hyperspectral data and nitrogen contents of apple leaves at three phenophases in two years, which were shoot growth phenophase, spring shoots pause growth phenophase, autumn shoots pause growth phenophase. The study analyzed the nitrogen contents of apple leaves with its original spectral and first derivative, screened sensitive wavelengths of each phenophase. The hyperspectral parameters were built with the sensitive wavelengths. Multiple stepwise regressions, partial least squares and BP neural network model were adopted in the study. The results showed that 551 nm, 716 nm, 530 nm, 703 nm; 543 nm, 705 nm, 699 nm, 756 nm and 545 nm, 702 nm, 695 nm, 746 nm were sensitive wavelengths of three phenophases. R551+R716, R551*R716, FDR530+FDR703, FDR530*FDR703; R543+R705, R543*R705, FDR699+FDR756, FDR699*FDR756and R545+R702, R545*R702, FDR695+FDR746, FDR695*FDR746 were the best hyperspectral parameters of each phenophase. Of all the estimation models, the estimated effect of shoot growth phenophase was better than other two phenophases, so shoot growth phenophase was the best phenophase to estimate the nitrogen contents of apple leaves based on hyperspectral models. In the three models, the 4-3-1 BP neural network model of shoot growth phenophase was the best estimation model. The R2 of estimated value and measured value was 0.6307, RE% was 23.37, RMSE was 0.6274.

scholarly journals Cluster-Wise Weighted NMF for Hyperspectral Images Unmixing with Imbalanced Data

2021 ◽  
Vol 13 (2) ◽  
pp. 268
Author(s):  
Xiaochen Lv ◽  
Wenhong Wang ◽  
Hongfu Liu
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Imbalanced Data ◽  
Reconstruction Error ◽  
Hyperspectral Data ◽  
Weight Matrix ◽  
Hyperspectral Images ◽  
Mixed Data ◽  
Sparsity Constraints ◽  
Additional Constraints

Hyperspectral unmixing is an important technique for analyzing remote sensing images which aims to obtain a collection of endmembers and their corresponding abundances. In recent years, non-negative matrix factorization (NMF) has received extensive attention due to its good adaptability for mixed data with different degrees. The majority of existing NMF-based unmixing methods are developed by incorporating additional constraints into the standard NMF based on the spectral and spatial information of hyperspectral images. However, they neglect to exploit the nature of imbalanced pixels included in the data, which may cause the pixels mixed with imbalanced endmembers to be ignored, and thus the imbalanced endmembers generally cannot be accurately estimated due to the statistical property of NMF. To exploit the information of imbalanced samples in hyperspectral data during the unmixing procedure, in this paper, a cluster-wise weighted NMF (CW-NMF) method for the unmixing of hyperspectral images with imbalanced data is proposed. Specifically, based on the result of clustering conducted on the hyperspectral image, we construct a weight matrix and introduce it into the model of standard NMF. The proposed weight matrix can provide an appropriate weight value to the reconstruction error between each original pixel and the reconstructed pixel in the unmixing procedure. In this way, the adverse effect of imbalanced samples on the statistical accuracy of NMF is expected to be reduced by assigning larger weight values to the pixels concerning imbalanced endmembers and giving smaller weight values to the pixels mixed by majority endmembers. Besides, we extend the proposed CW-NMF by introducing the sparsity constraints of abundance and graph-based regularization, respectively. The experimental results on both synthetic and real hyperspectral data have been reported, and the effectiveness of our proposed methods has been demonstrated by comparing them with several state-of-the-art methods.

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