The impact of the spectral dimension of hyperspectral datasets on plant disease detection

Precision Agriculture as an information based approach requires explicit spatial information about the within field heterogeneities for site-specific applications. Thus, the usage of cost-intensive agrochemicals and the impact on the environment can be significantly reduced. Spectroscopic approaches are thereby a promising tool for providing fast and precise information on a local to regional level. In this thesis, the impact of hyperspectral near-range and remote sensing data for crop stress detection will be observed since spectroscopic approaches are of great interest for Precision Agriculture. Two greenhouse experiments and three field experiments were conducted with spectroscopic measurements to examine possibilities and limitations of hyperspectral data. The data were acquired using a near-range non-imaging spectrometer (ASD Fieldspec 3) and a near-range imaging spectrometer (ImSpec V10E) in the greenhouse, or were acquired by the airborne sensor systems HyMapTM, ROSIS or AISA for the field experiments. The methodical foci thereby are the improvement of binary detection approaches, discriminating 'vital' and 'infected' wheat stands or parts of wheat stands, and quantification approaches to estimate disease severities at canopy level. This thesis examines the spectral dimension of hyperspectral data for crop stress detection by assessing data redundancy and defining spectral necessities. Different feature selection methods were tested for their suitability in reducing the high amount of spectral data without losing significant information. Conventional classification approaches and recent developments, such as support vector machines for classification (SVM), were thereby tested based on the identified spectral subsets to assess the status of different wheat stands. By focusing on phenomenon-specific spectral bands, stressed wheat stands could successfully be identified with high accuracies. Using optimal band combinations could even increase classification accuracies. The results showed that not the entire spectrum of hyperspectral data is necessary for the detection of fungal infections in wheat. These findings are particularly interesting for future spectral sensor design and remote sensing missions that are aiming at the provision of spatial information for agricultural practice. The ability of hyperspectral data in quantifying the severity of fungal diseases was observed. Site-specific fungicide treatments based on application maps are technically possible and doses can be adjusted if the maps provide information about the health status of the crops. Crop growth anomalies caused by fungal infections were observed, which differed significantly within one field. The derivation of disease severities based on hyperspectral near-range and remote sensing data were examined using classification approaches and support vector machines for regression (SVR). Fungal infections of wheat stands in the greenhouse and wheat stands in the field could be quantified with a high level of certainty. The results are very promising and the findings may be of great interest for agricultural questionnaires and automatic phenotyping approaches, since the presented approaches are fast and non-destructive. Spatial maps with continual disease severity data could be derived, which can be used to generate application maps for agricultural practice. Since the study shows that a reduction of hyperspectral data to a few but specifically selected spectral bands can improve the classification accuracies or regression analyses, a preliminary feature selection should be considered when working with hyperspectral remote sensing data. Agricultural and geographical approaches that are based on spatial-spectral information may thus profit from a faster and more reliable extraction of information. The study shows great advantages of the usage of hyperspectral imaging data but also the necessity of advanced and innovative analyzing methods

Detection, identification, and quantification of fungal diseases of sugar beet leaves using imaging and non-imaging hyperspectral techniques

Plant diseases influence the optical properties of plants in different ways. Depending on the host pathogen system and disease specific symptoms, different regions of the reflectance spectrum are affected, resulting in specific spectral signatures of diseased plants. The aim of this study was to examine the potential of hyperspectral imaging and non-imaging sensor systems for the detection, differentiation, and quantification of plant diseases. Reflectance spectra of sugar beet leaves infected with the fungal pathogens Cercospora beticola, Erysiphe betae, and Uromyces betae causing Cercospora leaf spot, powdery mildew, and sugar beet rust, respectively, were recorded repeatedly during pathogenesis. Hyperspectral data were analyzed using various methods of data and image analysis and were compared to ground truth data. Several approaches with different sensors on the measuring scales leaf, canopy, and field have been tested and compared. Much attention was paid on the effect of spectral, spatial, and temporal resolution of hyperspectral sensors on disease recording. Another focus of this study was the description of spectral characteristics of disease specific symptoms. Therefore, different data analysis methods have been applied to gain a maximum of information from spectral signatures. Spectral reflectance of sugar beet was affected by each disease in a characteristic way, resulting in disease specific signatures. Reflectance differences, sensitivity, and best correlating spectral bands differed depending on the disease and the developmental stage of the diseases. Compared to non-imaging sensors, the hyperspectral imaging sensor gave extra information related to spatial resolution. The preciseness in detecting pixel-wise spatial and temporal differences was on a high level. Besides characterization of diseased leaves also the assessment of pure disease endmembers as well as of different regions of typical symptoms was realized. Spectral vegetation indices (SVIs) related to physiological parameters were calculated and correlated to the severity of diseases. The SVIs differed in their sensitivity to the different diseases. Combining the information from multiple SVIs in an automatic classification method with Support Vector Machines, high sensitivity and specificity for the detection and differentiation of diseased leaves was reached in an early stage. In addition to the detection and identification, the quantification of diseases was possible with high accuracy by SVIs and Spectral Angle Mapper classification, calculated from hyperspectral images. Knowledge from measurements under controlled condition was carried over to the field scale. Early detection and monitoring of Cercospora leaf spot and powdery mildew was facilitated. The results of this study contribute to a better understanding of plant optical properties during disease development. Methods will further be applicable in precision crop protection, to realize the detection, differentiation, and quantification of plant diseases in early stages.Nachweis, Identifizierung und Quantifizierung pilzlicher Blattkrankheiten der Zuckerrübe mit abbildenden und nicht-abbildenden hyperspektralen Sensoren Pflanzenkrankheiten wirken sich auf die optischen Eigenschaften von Pflanzen in unterschiedlicher Weise aus. Verschiedene Bereiche des Reflektionsspektrums werden in Abhängigkeit von Wirt-Pathogen System und krankheitsspezifischen Symptomen beeinflusst. Hyperspektrale, nicht-invasive Sensoren bieten die Möglichkeit, optische Veränderungen zu einem frühen Zeitpunkt der Krankheitsentwicklung zu detektieren. Ziel dieser Arbeit war es, das Potential hyperspektraler abbildender und nicht abbildender Sensoren für die Erkennung, Identifizierung und Quantifizierung von Pflanzenkrankheiten zu beurteilen. Zuckerrübenblätter wurden mit den pilzlichen Erregern Cercospora beticola, Erysiphe betae bzw. Uromyces betae inokuliert und die Auswirkungen der Entwicklung von Cercospora Blattflecken, Echtem Mehltau bzw. Rübenrost auf die Reflektionseigenschaften erfasst und mit optischen Bonituren verglichen. Auf den Skalenebenen Blatt, Bestand und Feld wurden Messansätze mit unterschiedlichen Sensoren verglichen. Besonders berücksichtigt wurden hierbei Anforderungen an die spektrale, räumliche und zeitliche Auflösung der Sensoren. Ein weiterer Schwerpunkt lag auf der Beschreibung der spektralen Eigenschaften von charakteristischen Symptomen. Verschiedene Auswerteverfahren wurden mit dem Ziel angewendet, einen maximalen Informationsgehalt aus spektralen Signaturen zu gewinnen. Jede Krankheit beeinflusste die spektrale Reflektion von Zuckerrübenblättern auf charakteristische Weise. Differenz der Reflektion, Sensitivität sowie Korrelation der spektralen Bänder zur Befallsstärke variierten in Abhängigkeit von den Krankheiten. Eine höhere Präzision durch die pixelweise Erfassung räumlicher und zeitlicher Unterschiede von befallenem und gesundem Gewebe konnte durch abbildende Sensoren erreicht werden. Spektrale Vegetationsindizes (SVIs), mit Bezug zu pflanzenphysiologischen Parametern wurden aus den Hyperspektraldaten errechnet und mit der Befallsstärke korreliert. Die SVIs unterschieden sich in ihrer Sensitivität gegenüber den drei Krankheiten. Durch den Einsatz von maschinellem Lernen wurde die kombinierte Information der errechneten Vegetationsindizes für eine automatische Klassifizierung genutzt. Eine hohe Sensitivität sowie eine hohe Spezifität bezüglich der Erkennung und Differenzierung von Krankheiten wurden erreicht. Eine Quantifizierung der Krankheiten war neben der Detektion und Identifizierung mittels SVIs bzw. Klassifizierung mit Spektral Angle Mapper an hyperspektralen Bilddaten möglich. Die Ergebnisse dieser Arbeit tragen zu einem besseren Verständnis der optischen Eigenschaften von Pflanzen unter Pathogeneinfluss bei. Die untersuchten Methoden bieten die Möglichkeit in Anwendungen des Präzisionspflanzenschutzes implementiert zu werden, um eine frühzeitige Erkennung, Differenzierung und Quantifizierung von Pflanzenkrankheiten zu ermöglichen

Crop Disease Detection Using Remote Sensing Image Analysis

Pest and crop disease threats are often estimated by complex changes in crops and the applied agricultural practices that result mainly from the increasing food demand and climate change at global level. In an attempt to explore high-end and sustainable solutions for both pest and crop disease management, remote sensing technologies have been employed, taking advantages of possible changes deriving from relative alterations in the metabolic activity of infected crops which in turn are highly associated to crop spectral reflectance properties. Recent developments applied to high resolution data acquired with remote sensing tools, offer an additional tool which is the opportunity of mapping the infected field areas in the form of patchy land areas or those areas that are susceptible to diseases. This makes easier the discrimination between healthy and diseased crops, providing an additional tool to crop monitoring. The current book brings together recent research work comprising of innovative applications that involve novel remote sensing approaches and their applications oriented to crop disease detection. The book provides an in-depth view of the developments in remote sensing and explores its potential to assess health status in crops

Monitoring the Severity of Pantana phyllostachysae Chao Infestation in Moso Bamboo Forests Based on UAV Multi-Spectral Remote Sensing Feature Selection

In recent years, the rapid development of unmanned aerial vehicle (UAV) remote sensing technology has provided a new means to efficiently monitor forest resources and effectively prevent and control pests and diseases. This study aims to develop a detection model to study the damage caused to Moso bamboo forests by Pantana phyllostachysae Chao (PPC), a major leaf-eating pest, at 5 cm resolution. Damage sensitive features were extracted from multispectral images acquired by UAVs and used to train detection models based on support vector machines (SVM), random forests (RF), and extreme gradient boosting tree (XGBoost) machine learning algorithms. The overall detection accuracy (OA) and Kappa coefficient of SVM, RF, and XGBoost were 81.95%, 0.733, 85.71%, 0.805, and 86.47%, 0.811, respectively. Meanwhile, the detection accuracies of SVM, RF, and XGBoost were 78.26%, 76.19%, and 80.95% for healthy, 75.00%, 83.87%, and 79.17% for mild damage, 83.33%, 86.49%, and 85.00% for moderate damage, and 82.5%, 90.91%, and 93.75% for severe damage Moso bamboo, respectively. Overall, XGBoost exhibited the best detection performance, followed by RF and SVM. Thus, the study findings provide a technical reference for the regional monitoring and control of PPC in Moso bamboo

Integrating Growth and Environmental Parameters to Discriminate Powdery Mildew and Aphid of Winter Wheat Using Bi-Temporal Landsat-8 Imagery

Monitoring and discriminating co-epidemic diseases and pests at regional scales are of practical importance in guiding differential treatment. A combination of vegetation and environmental parameters could improve the accuracy for discriminating crop diseases and pests. Different diseases and pests could cause similar stresses and symptoms during the same crop growth period, so combining growth period information can be useful for discerning different changes in crop diseases and pests. Additionally, problems associated with imbalanced data often have detrimental effects on the performance of image classification. In this study, we developed an approach for discriminating crop diseases and pests based on bi-temporal Landsat-8 satellite imagery integrating both crop growth and environmental parameters. As a case study, the approach was applied to data during a period of typical co-epidemic outbreak of winter wheat powdery mildew and aphids in the Shijiazhuang area of Hebei Province, China. Firstly, bi-temporal remotely sensed features characterizing growth indices and environmental factors were calculated based on two Landsat-8 images. The synthetic minority oversampling technique (SMOTE) algorithm was used to resample the imbalanced training data set before model construction. Then, a back propagation neural network (BPNN) based on a new training data set balanced by the SMOTE approach (SMOTE-BPNN) was developed to generate the regional wheat disease and pest distribution maps. The original training data set-based BPNN and support vector machine (SVM) methods were used for comparison and testing of the initial results. Our findings suggest that the proposed approach incorporating both growth and environmental parameters of different crop periods could distinguish wheat powdery mildew and aphids at the regional scale. The bi-temporal growth indices and environmental factors-based SMOTE-BPNN, BPNN, and SVM models all had an overall accuracy high than 80%. Meanwhile, the SMOTE-BPNN method had the highest G-means among the three methods. These results revealed that the combination of bi-temporal crop growth and environmental parameters is essential for improving the accuracy of the crop disease and pest discriminating models. The combination of SMOTE and BPNN could effectively improve the discrimination accuracy of the minor disease or pest

Remote Sensing of Biophysical Parameters

Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security)

The potential for using remote sensing to quantify stress in and predict yield of sugarcane (Saccharum spp. hybrid)

Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2010

Remote Sensing in Agriculture: State-of-the-Art

The Special Issue on “Remote Sensing in Agriculture: State-of-the-Art” gives an exhaustive overview of the ongoing remote sensing technology transfer into the agricultural sector. It consists of 10 high-quality papers focusing on a wide range of remote sensing models and techniques to forecast crop production and yield, to map agricultural landscape and to evaluate plant and soil biophysical features. Satellite, RPAS, and SAR data were involved. This preface describes shortly each contribution published in such Special Issue

Examining Ecosystem Drought Responses Using Remote Sensing and Flux Tower Observations

Indiana University-Purdue University Indianapolis (IUPUI)Water is fundamental for plant growth, and vegetation response to water availability influences water, carbon, and energy exchanges between land and atmosphere. Vegetation plays the most active role in water and carbon cycle of various ecosystems. Therefore, comprehensive evaluation of drought impact on vegetation productivity will play a critical role for better understanding the global water cycle under future climate conditions. In-situ meteorological measurements and the eddy covariance flux tower network, which provide meteorological data, and estimates of ecosystem productivity and respiration are remarkable tools to assess the impacts of drought on ecosystem carbon and water cycles. In regions with limited in-situ observations, remote sensing can be a very useful tool to monitor ecosystem drought status since it provides continuous observations of relevant variables linked to ecosystem function and the hydrologic cycle. However, the detailed understanding of ecosystem responses to drought is still lacking and it is challenging to quantify the impacts of drought on ecosystem carbon balance and several factors hinder our explicit understanding of the complex drought impacts. This dissertation addressed drought monitoring, ecosystem drought responses, trends of vegetation water constraint based on in-situ metrological observations, flux tower and multi-sensor remote sensing observations. This dissertation first developed a new integrated drought index applicable across diverse climate regions based on in-situ meteorological observations and multi-sensor remote sensing data, and another integrated drought index applicable across diverse climate regions only based on multi-sensor remote sensing data. The dissertation also evaluated the applicability of new satellite dataset (e.g., solar induced fluorescence, SIF) for responding to meteorological drought. Results show that satellite SIF data could have the potential to reflect meteorological drought, but the application should be limited to dry regions. The work in this dissertation also accessed changes in water constraint on global vegetation productivity, and quantified different drought dimensions on ecosystem productivity and respiration. Results indicate that a significant increase in vegetation water constraint over the last 30 years. The results highlighted the need for a more explicit consideration of the influence of water constraints on regional and global vegetation under a warming climate

Spatial-temporal responses of Louisiana forests to climate change and hurricane disturbance

This dissertation research focused on three questions: (1) what is the current carbon stock in Louisiana’s forest ecosystems? (2) how will the biomass carbon stock respond to future climate change? and (3) how vulnerable are the coastal forest resources to natural disturbances, such as hurricanes? The research utilized a geographic information system, remote sensing techniques, ecosystem modeling, and statistical approaches with existing data and in-situ measurements. Future climate changes were adapted from predictions by the Community Climate System Model on the basis of low (B1), moderate (A1B), and high (A2) greenhouse gas emission scenarios. The study on forest carbon assessment found that Louisiana’s forests currently store 219.2 Tg of biomass carbon, 90% of which is stored in wetland and evergreen forests. Spatial variation of the carbon storage was mainly affected by forest biomass distribution. No correlation was identified between carbon storage in watersheds with the average watershed slope and drainage density. The modeling study on growth response to future climate found that forest net primary productivity (NPP) would decline from 2000 to 2050 under scenario B1, but may increase under scenarios A1B and A2 due primarily to minimum temperature and precipitation changes. Uncertainties of the NPP prediction were apparent, owing to spatial resolution of the climate variables. The remote sensing study on hurricane disturbance to coastal forests found that increases in the intensity of severe weather in the future would likely increase the turn-over rate of coastal forest carbon stock. Forest attributes and site conditions had a variety of effects on the vulnerability of forests to hurricane disturbance and thereby, spatial patterns of disturbed landscape. Soil groups and stand factors, including forest types, forest coverage, and stand density contributed to 85% of accuracy in the modeling probability of Hurricane Katrina disturbance to forests. In conclusion, this research demonstrated that quantification of forest biomass carbon, using geo-referenced datasets and GIS techniques, provides a credible approach to increase accuracy and constrain the uncertainty of large-scale carbon assessment. A combination of ecosystem modeling and GIS/Remote Sensing techniques can provide insight into future climate change effects on forest carbon change at the landscape scale