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Home PE&RS Journals In Press Peer Reviewed Articles

PE&RS Journals

In Press Peer Reviewed Articles

As a convenience to ASPRS members, in-press peer reviewed articles approved for publication in forthcoming issues of PE&RS have been made available for members of the society.

August 2014 Issue

Improved Capability in Stone Pine Forest Mapping and Management in Lebanon Using Hyperspectral CHRIS-Proba Data Relative to Landsat ETM+ 

Mohamad Awad, Ihab Jomaa, and Fatima Arab

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The Stone Pine (pinus pinea) is native to the Mediterranean region and has been used for their edible pine nuts since prehistoric times. They are widespread in horticultural cultivation as ornamental trees and planted in gardens and parks around the world. Economically speaking, the Stone Pine is very important for the agriculture sector, for tourism, and for the health sector. In this research, a pilot area located in Mount Lebanon is compared for changes in the Stone Pine cover between the years of 1962 and 2012. The comparison is based on processing a hyperspectral image provided by the European Space Agency (ESA) and a Landsat ETM+ image as well as topographic maps. Several issues related to the use of CHRIS-Proba hyperspectral images have been investigated and analyzed. The results established that hyperspectral data: (a) is 30 percent or more accurate and efficient when compared with multispectral data, and (b) helps determine precise extent of the Stone Pine cover.


Combining Hyperspectral and Lidar Data for Vegetation Mapping in the Florida Everglades

Caiyun Zhang

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This study explored a combination of hyperspectral and lidar systems for vegetation mapping in the Florida Everglades. A framework was designed to integrate two remotely sensed datasets and four data processing techniques. Lidar elevation and intensity features were extracted from the original point cloud data to avoid the errors and uncertainties in the raster-based lidar methods. Lidar significantly increased the classification accuracy compared with the application of hyperspectral data alone. Three lidar-derived features (elevation, intensity, and topography) had the same contributions in the classification. A synergy of hyperspectral imagery with all lidar-derived features achieved the best result with an overall accuracy of 86 percent and a Kappa value of 0.82 based on an ensemble analysis of three machine learning classifiers. Ensemble analysis did not significantly increase the classification accuracy, but it provided a complementary uncertainty map for the final classified map. The study shows the promise of the synergy of hyperspectral and lidar systems for mapping complex wetlands.


Hyperspectral Optical, Thermal, and Microwave L-Band Observations For Soil Moisture Retrieval at Very High Spatial Resolution

Nilda Sánchez, Maria Piles, José Martínez-Fernández, Mercè Vall-llossera, Luca Pipia, Adriano Camps, Albert Aguasca, Fernando Pérez-Aragüés, and Carlos M. Herrero-Jiménez

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The results of an experiment conducted in Spain over the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS) are presented. The observations included airborne observations from hyperspectral optical, thermal, and microwave sensors coinciding with intensive field measurements. The hyperspectral optical and thermal datasets were first analyzed and processed to select the best hyperspectral features to be included in the soil moisture retrieval procedure. A linear model linking the selected hyperspectral features to the microwave observations and the in situ soil moisture is proposed. The application of this model resulted in soil moisture estimates that agree with in situ measurements (correlation coefficient: R >0.76, root mean squared differences: RMSD <0.07 m3m-3). The hyperspectral dataset strengthened the link between optical, thermal and microwave L-band observations with soil moisture, and provided a spatial framework to disaggregate soil moisture at very high spatial resolution (3.5 m), useful in hydrological modeling and precision agriculture.


Biomass Modeling of Four Leading World Crops Using Hyperspectral Narrowbands in Support of HyspIRI Mission

Michael Marshall and Prasad Thenkabail

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New satellite missions are expected to record high spectral resolution information globally and consistently for the first time, so it is important to identify modeling techniques that take advantage of these new data. In this paper, we estimate biomass for four major crops using ground-based hyperspectral narrowbands. The spectra and their derivatives are evaluated using three modeling techniques: two-band hyperspectral vegetation indices (HVIs), multiple band-HVIs (MB-HVIs) developed from Sequential Search Methods (SSM), and MB-HVIs developed from Principal Component Regression. Overall, the two-band HVIs and MB-HVIs developed from SSMs using first derivative transformed spectra in the visible blue and green and NIR explained more biomass variability and had lower error than the other approaches or transformations; however a better search criterion needs to be developed in order to reflect the true ability of the two-band HVI approach. Short-Wave Infrared 1 (1000 to 1700 nm) proved less effective, but still important in the final models.


Hyperspectral Data Dimensionality Reduction and the Impact of Multi-seasonal Hyperion EO-1 Imagery on Classification Accuracies of Tropical Forest Species

Manjit Saini, Binal Christian, Nikita Joshi, Dhaval Vyas, Prashanth Marpu, and N.S.R Krishnayya

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Synchronizing hyperspectral data acquisition with phenological changes in a tropical forest can generate comprehensive information for their effective management. The present study was performed to identify a suitable dimensionality reduction method for better classification and to evaluate the impact of seasonality on classification accuracy of tropical forest cover. EO-1 Hyperion images were acquired for three different seasons (summer (April), monsoon (October), and winter (January)). Spectral signatures of pure patches of Teak, Bamboo, and mixed species covers are significantly different across the three seasons indicating distinctive phenology of each cover. Kernel Principal Component Analysis (k-PCA) is more suitable for dimensionality reduction for these covers. The three vegetation covers classified using images of three seasons achieved the best classification accuracies using k-PCA with maximum likelihood classifier for the monsoon season with overall accuracies of 83 to 100 percent for single species, 74 to 81 percent for two species, and 72 percent for three species respectively.


Automated Hyperspectral Vegetation Index Retrieval from Multiple Correlation Matrices with HyperCor

Helge Aasen, Martin Leon Gnyp, Yuxin Miao, and Georg Bareth

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Hyperspectral vegetation indices have shown high potential for characterizing, classifying, monitoring, and modeling of vegetation and agricultural crops. Correlation matrices from hyperspectral vegetation indices and plant growth parameters help select important wavelength domains and identify redundant bands.
We introduce the software HyperCor for automated pre-processing of narrowband hyperspectral field data and computation of correlation matrices. In addition, we propose a multi-correlation matrix strategy which combines multiple correlation matrices from different datasets and uses more information from each matrix.
We apply this method to a large multi-temporal spectral library to derive vegetation indices and related regression models for rice biomass detection in the tillering, stem elongation, heading and across all growth stages. The models are calibrated with data from three consecutive years and validated with two other years. The results reveal that the multi-correlation matrix strategy can improve the model performance by 10 to 62 percent, depending on the growth stage.


Automated Class Labeling Of ClassifiedLandsat TM Imagery Using a Hyperion-Generated Hyperspectral Library

Ilia Parshakov, Craig Coburn, and Karl Staenz

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Image classification remains dependent on user intervention for class label assignment. Whether that effort takes place in advance of or post classification is immaterial. This paper explores a novel approach to automating the assignment of class labels using a normalized spectral distance measure and a hyperspectral library. The technique resulted in an automatically labeled agricultural map with an overall classification accuracy of 51 percent, outperforming the manual labeling (40 percent to 45 percent accuracy, depending on the analyst performing the labeling) and the Spectral Angle Mapper classifier (39 percent), and was comparable to, or lower than, the classification accuracy of a Maximum Likelihood supervised technique (53 percent to 63 percent) depending on the analyst. The newly developed class-labeling algorithm provided better results for the majority of targets while having similar performance to manual labeling on targets that are particularly difficult to differentiate in a purely spectral manner.


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