Unmixing-Based Fusion of Hyperspatial and Hyperspectral Airborne Imagery for Early Detection of Vegetation Stress

"© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.” Upon publication, authors are asked to include either a link to the abstract of the published article in IEEE Xplore®, or the article’s Digital Object Identifier (DOI).Many applications require a timely acquisition of high spatial and spectral resolution remote sensing data. This is often not achievable since spaceborne remote sensing instruments face a tradeoff between spatial and spectral resolution, while airborne sensors mounted on a manned aircraft are too expensive to acquire a high temporal resolution. This gap between information needs and data availability inspires research on using Remotely Piloted Aircraft Systems (RPAS) to capture the desired high spectral and spatial information, furthermore providing temporal flexibility. Present hyperspectral imagers on board lightweight RPAS are still rare, due to the operational complexity, sensor weight, and instability. This paper looks into the use of a hyperspectral-hyperspatial fusion technique for an improved biophysical parameter retrieval and physiological assessment in agricultural crops. First, a biophysical parameter extraction study is performed on a simulated citrus orchard. Subsequently, the unmixing-based fusion is applied on a real test case in commercial citrus orchards with discontinuous canopies, in which a more efficient and accurate estimation of water stress is achieved by fusing thermal hyperspatial and hyperspectral (APEX) imagery. Narrowband reflectance indices that have proven their effectiveness as previsual indicators of water stress, such as the Photochemical Reflectance Index (PRI), show a significant increase in tree water-stress detection when applied on the fused dataset compared to the original hyperspectral APEX dataset (R-2 = 0.62, p 0.1). Maximal R-2 values of 0.93 and 0.86 are obtained by a linear relationship between the vegetation index and the resp., water and chlorophyll, parameter content maps.This work was supported in part by the Belgian Science Policy Office in the frame of the Stereo II program (Hypermix project-SR/00/141), in part by the project Chameleon of the Flemish Agency for Innovation by Science and Technology (IWT), and in part by the Spanish Ministry of Science and Education (MEC) for the projects AGL2012-40053-C03-01 and CONSOLIDER RIDECO (CSD2006-67). The European Facility for Airborne Research EUFAR (www.eufar.net) funded the flight campaign (Transnational Access Project 'Hyper-Stress'). The work of D. S. Intrigliolo was supported by the Spanish Ministry of Economy and Competitiveness program "Ramon y Cajal."Delalieux, S.; Zarco-Tejada, PJ.; Tits, L.; Jiménez Bello, MÁ.; Intrigliolo Molina, DS.; Somers, B. (2014). Unmixing-Based Fusion of Hyperspatial and Hyperspectral Airborne Imagery for Early Detection of Vegetation Stress. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 7(6):2571-2582. https://doi.org/10.1109/JSTARS.2014.2330352S257125827

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensin

Manifold learning based spectral unmixing of hyperspectral remote sensing data

Nonlinear mixing effects inherent in hyperspectral data are not properly represented in linear spectral unmixing models. Although direct nonlinear unmixing models provide capability to capture nonlinear phenomena, they are difficult to formulate and the results are not always generalizable. Manifold learning based spectral unmixing accommodates nonlinearity in the data in the feature extraction stage followed by linear mixing, thereby incorporating some characteristics of nonlinearity while retaining advantages of linear unmixing approaches. Since endmember selection is critical to successful spectral unmixing, it is important to select proper endmembers from the manifold space. However, excessive computational burden hinders development of manifolds for large-scale remote sensing datasets. This dissertation addresses issues related to high computational overhead requirements of manifold learning for developing representative manifolds for the spectral unmixing task. Manifold approximations using landmarks are popular for mitigating the computational complexity of manifold learning. A new computationally effective landmark selection method that exploits spatial redundancy in the imagery is proposed. A robust, less costly landmark set with low spectral and spatial redundancy is successfully incorporated with a hybrid manifold which shares properties of both global and local manifolds. While landmark methods reduce computational demand, the resulting manifolds may not represent subtle features of the manifold adequately. Active learning heuristics are introduced to increase the number of landmarks, with the goal of developing more representative manifolds for spectral unmixing. By communicating between the landmark set and the query criteria relative to spectral unmixing, more representative and stable manifolds with less spectrally and spatially redundant landmarks are developed. A new ranking method based on the pixels with locally high spectral variability within image subsets and convex-geometry finds a solution more quickly and precisely. Experiments were conducted to evaluate the proposed methods using the AVIRIS Cuprite hyperspectral reference dataset. A case study of manifold learning based spectral unmixing in agricultural areas is included in the dissertation.Remotely sensed data collected by airborne or spaceborne sensors are utilized to quantify crop residue cover over an extensive area. Although remote sensing indices are popular for characterizing residue amounts, they are not effective with noisy Hyperion data because the effect of residual striping artifacts is amplified in ratios involving band differences. In this case study, spectral unmixing techniques are investigated for estimating crop residue as an alternative approach to empirical models developed using band based indices. The spectral unmixing techniques, and especially the manifold learning approaches, provide more robust, lower RMSE estimates for crop residue cover than the hyperspectral index based method for Hyperion data

A Developed Algorithm for Automating the Multiple Bands Multiple Endmember Selection of Hyperion data Applied on Central of Cairo, Egypt

This study attempts to provide an answer regarding the utility of Hyperion imagery in mapping urban settings in developed countries. The authors present a novel method for extracting quantitative land cover information at the sub-pixel level from hyperspectral or Hyperion imagery. The proposed method is based on the multiple endmember spectral mixture (MESMA) proposed by Roberts et al. (1998b), but extends it to handle the high-dimensional pixels characterizing hyperspectral images. The proposed method utilizes a multiband multiple endmember spectral mixture analysis (Multiband MESMA) model that allows for both spectral bands and endmembers to vary on a per-pixel basis across a hyperspectral image. The goal is to select an optimal subset of spectral bands that maximizes spectral separability among a candidate set of endmembers for a given pixel, and accordingly to minimize spectral confusion among modeled endmembers and increase the accuracy and physical representativeness of derived fractions for that pixel. The authors develop a tool to automate this method and test its utility in a case study using a Hyperion image of Central Cairo, Egypt. The EO-1 Hyperion hyperspectral sensor is the only source of hyperspectral data currently available for Cairo, unlike cities in Europe and North America, where multiple sources of such data generally exist. The study scene represents a very heterogeneous landscape and has an ecological footprint of a complex range of interrelated socioeconomic, environmental and urban dynamics. The results of this study show that Hyperion data, with its rich spectral information, can help address some of the limitations in automated mapping that are reported by previous studies. For this, proper bands and endmembers are selected and used within a multiple endmember, with a multiple-band SMA process to determine the best Root Mean Square Error (RMSE) and abundance percentages. This results in a better mapping of land cover extricated from hyperspectral imagery (Hyperion). Keywords: Spectral Mixture Analysis, Hyperspectral Data, Hyperion Data, Cairo, Egyp

Spatiotemporal dynamics of stress factors in wheat analysed by multisensoral remote sensing and geostatistics

Plant stresses, in particular fungal diseases, basically show a high variability in space and time with respect to their impact on the host. Recent ‘Precision Agriculture’ techniques allow for a spatially and temporally adjusted pest control that might reduce the amount of cost-intensive and ecologically harmful agrochemicals. Conventional stress detection techniques such as random monitoring do not meet demands of such optimally placed management actions. The prerequisite is a profound knowledge about the controlled phenomena as well as their accurate sensor-based detection. Therefore, the present study focused on spatiotemporal dynamics of stress factors in wheat, Europe’s main crop. Primarily, the spatiotemporal characteristics of the fungal diseases, powdery mildew (Blumeria graminis) and leaf rust (Puccinia recondita), were analysed by remote sensing techniques and geo-statistics on leaf and field scale. Basically, there are two different approaches to sensor-based detection of crop stresses: near-range sensors and airborne-/satellite-borne sensors. In order to assess the potential of both approaches, various experiments in field and laboratory were carried out with the use of multiple sensors operated at different scales. Besides the spatial dimension of crop stresses, all studies focussed on the temporal dimension of these phenomena, since this is the key question for an operational use of these techniques. In addition, a comparison between multispectral and hyperspectral data gave an indication of their suitability for this purpose. The results exhibit very high spatiotemporal dynamics for both fungal diseases. However, powdery mildew and leaf rust showed different characteristics, with leaf rust showing a more systematic temporal progress. The physiological behaviours of the phenomena, which are strongly influenced by various environmental factors, define the optimal disease detection date as well as the temporal resolution required for sensor-based disease detection. Due to the high spatiotemporal dynamics of the investigated diseases, a general recommendation of optimal detection periods can not be given, but critical periods are highlighted for each pathogen. The results indicate that multispectral remote sensing data with high spatial resolution shows a high potential for quantifying crop vigour by using spectral mixture analyses. Simulated endmembers for the identification of stressed wheat areas were utilized, whereby promising results could be achieved. However, due to the low spectral resolution of these data, a discrimination of stress factors or early disease detection is not possible. Hyperspectral data was therefore used to point out the potential of early detection of crop diseases, which is a crucial and restrictive factor for Precision Agriculture applications. In a laboratory experiment, leaf rust infections could be detected by hyperspectral data five days after inoculation. In a field experiment with respect to early stress detection, it could be demonstrated that hyperspectral data outperformed multispectral data. High accuracy for the detection of powdery mildew infections in the field was thereby achieved. Due to the fact that typical spatiotemporal characteristics for each pathogen were found, there is a high potential for decision support systems, considering all variables that affect the disease progress. Besides the further analysis of hyperspectral data for disease detection, the development of a decision support system is the subject of the upcoming last period of the Research Training Group 722

Hyperspectral Imaging from Ground Based Mobile Platforms and Applications in Precision Agriculture

This thesis focuses on the use of line scanning hyperspectral sensors on mobile ground based platforms and applying them to agricultural applications. First this work deals with the geometric and radiometric calibration and correction of acquired hyperspectral data. When operating at low altitudes, changing lighting conditions are common and inevitable, complicating the retrieval of a surface's reflectance, which is solely a function of its physical structure and chemical composition. Therefore, this thesis contributes the evaluation of an approach to compensate for changes in illumination and obtain reflectance that is less labour intensive than traditional empirical methods. Convenient field protocols are produced that only require a representative set of illumination and reflectance spectral samples. In addition, a method for determining a line scanning camera's rigid 6 degree of freedom (DOF) offset and uncertainty with respect to a navigation system is developed, enabling accurate georegistration and sensor fusion. The thesis then applies the data captured from the platform to two different agricultural applications. The first is a self-supervised weed detection framework that allows training of a per-pixel classifier using hyperspectral data without manual labelling. The experiments support the effectiveness of the framework, rivalling classifiers trained on hand labelled training data. Then the thesis demonstrates the mapping of mango maturity using hyperspectral data on an orchard wide scale using efficient image scanning techniques, which is a world first result. A novel classification, regression and mapping pipeline is proposed to generate per tree mango maturity averages. The results confirm that maturity prediction in mango orchards is possible in natural daylight using a hyperspectral camera, despite complex micro-illumination-climates under the canopy

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

Remote sensing of crop biophysical parameters for site-specific agriculture

xiv, 194 leaves : ill. (some col.) ; 29 cm.Support for sustainable agriculture by farmers and consumers is increasing as environmental and socio-economic issues rise due to more intensive farm practices. Site-specific crop management is an important component of sutainable agriculture, within which remote sensing can play an integral role. Field and image data were acquired over a farm in Saskatchewan as part of a national research project to demonstrate the advantages of site-specific agriculture for farmers. This research involved the estimation of crop biophysical parameters from airborne hyperspectral imagery using Spectral Mixture Analysis (SMA), a relatively new sub-pixel scale image processing method that derives the fraction of sunlit canopy, soil and shadow that is contributing to a pixel's relectance. SMA of three crop types (peas, wheat and canola) performed slightly better than conventional vegetation indices in predicting leaf area index (LAI) and biomass using Probe-1 imagery acquired early in the growing season. Other potential advantages for SMA were also indentified, and it was conclude that future research is warranted to assess the full potential of SMA in a multi-temporal sense throughout the growing season