1 ABSTRACT The work deals with the use of vegetation indices to study temporal variation in vegetation phenology. The first part was devoted to detailed analysis of domestic and foreign literature, which deals with the work processed in this field. The main research questions were if changed start, end and length of growing period during the analysis period. Other research theme was comparision with ground phenological data. Another objective of this work was search dependencies computed data phenological variables from vegetation indicies with phenological ground data. As a basic data set was used GIMMS set, which distributes the vegetation index NDVI. Other data sets were MERIS MTCI, data MODIS with vegetation indices NDVI, EVI a LAI. The results of analyzes of vegetation phenology show trends in most shifts at the beginning of growing season, where was a shift to an earlier time. Results of the analysis of vegetation remote sensing data with ground-based phenological data ČHMÚ were unfolding always according to the specific forest phenological stations. Interesting results were at the phenological station Svoboda nad Úpou, where the results of trends directives were consistent in almost all data sets. Comparison of process curves vegetation indicies with ground data corresponded most curves at selected...

Bc. Romana Musilová: Využití dat DPZ pro hodnocení aktuálního stavu a vývoje smrkových porostů v Krkonoších Remote sensing for evaluation of state and development of Spruce stands condition in the Giant Mountains Abstract Monitoring the health status of forest areas using remote sensing methods are still under development. This master thesis focuses on the use of SPOT, Landsat, QuickBird and WorldView-2 images to evaluate condition of spruce stands in Giant Mountains National Park. For these purposes were selected vegetation indices available in the lite- rature. First satellite images were preprocessed and subsequently calculated vegetation indices. From the generally known were used Normalized Difference Vegetation Index, leaf area index and Simple Ratio. Than were calculated Green Vegetation Index and Red Green Index based on the monitoring of needles color changes. To evaluate moistu- re conditions were used indices Foliar Moisture Index and wide-band Normalized Diffe- rential Infrared Index. The goal was a comparison of the results of these indices and assessment of their applicability. Map outputs indices were compared with maps of de- foliation and mortality of coniferous stands by Ing. Milan Stoklasa. Keywords: remote sensing, Norway Spruce stands, Giant Mountains, vegetation indi- ces, SPOT,...

Land cover classification using artificial neural networks Abstract This Diploma thesis deals with automatic classification of the satellite high spatial resolution image in the field of land cover. The first half of the work contains the theoretical information about remote sensing and classification methods. The biggest attention is given to the artificial neural networks. In practical part of Diploma thesis are these methods used for the classification of SPOT satellite image. Keywords: remote sensing, image classification, artificial neural networks, SPOT

Recognition and classification of patterned ground polygons from remote sensing data Abstract The main objective of this thesis has been to prove the possibility of using object based image analysis classification for identification of the ice-wedge polygons and to find general method for their classification. The thesis contains a comparison of the object based and pixel based classification of the subject. The three classification rulesets for OBIA were developed on three test sites on Mars captured by HiRISE sensor. As a result, the general classification approach is suggested. The manually collected datasets, which are common in geomorphological research, were used as the reference sample. The OBIA classification provided better results in all three cases, whereas the pixel classification was valid in only one case. Another objective has been the automatization of the process of gaining information about morphometric characteristics of the ice-wedge polygons and the subsequent classification of the polygons. Within the scope of the process were developed methods for creating polygonal network and specified parameters of those methods. Several toolboxes for the ArcGIS software were prepared and they are part of the results of the thesis. Keywords: patterned ground, ice-wedge polygons, remote sensing,...

L'objectif de cette thèse est de développer des solutions efficaces pour laclassification interactive des images de télédétection. Cet objectif a étéréalisé en répondant à quatre questions de recherche.La première question porte sur le fait que les descripteursd'images proposées dans la littérature obtiennent de bons résultats dansdiverses applications, mais beaucoup d'entre eux n'ont jamais été utilisés pour la classification des images de télédétection. Nous avons testé douzedescripteurs qui codent les propriétés spectrales et la couleur, ainsi que septdescripteurs de texture. Nous avons également proposé une méthodologie baséesur le classificateur KNN (K plus proches voisins) pour l'évaluation desdescripteurs dans le contexte de la classification. Les descripteurs Joint Auto-Correlogram (JAC),Color Bitmap, Invariant Steerable Pyramid Decomposition (SID) etQuantized Compound Change Histogram (QCCH), ont obtenu les meilleursrésultats dans les expériences de reconnaissance des plantations de café et depâturages.La deuxième question se rapporte au choix del'échelle de segmentation pour la classification d'images baséesur objets.Certaines méthodes récemment proposées exploitent des caractéristiques extraitesdes objets segmentés pour améliorer classification des images hauterésolution. Toutefois, le choix d'une bonne échelle de segmentation est unetâche difficile.Ainsi, nous avons proposé deux approches pour la classification multi-échelles fondées sur le les principes du Boosting, qui permet de combiner desclassifieurs faibles pour former un classifieur fort.La première approche, Multiscale Classifier (MSC), construit unclassifieur fort qui combine des caractéristiques extraites de plusieurséchelles de segmentation. L'autre, Hierarchical Multiscale Classifier(HMSC), exploite la topologie hiérarchique de régions segmentées afind'améliorer l'efficacité des classifications sans perte de précision parrapport au MSC. Les expériences montrent qu'il est préférable d'utiliser des plusieurs échelles plutôt qu'une seul échelle de segmentation. Nous avons également analysé et discuté la corrélation entre lesdescripteurs et des échelles de segmentation.La troisième question concerne la sélection des exemplesd'apprentissage et l'amélioration des résultats de classification basés sur lasegmentation multiéchelle. Nous avons proposé une approche pour laclassification interactive multi-échelles des images de télédétection. Ils'agit d'une stratégie d'apprentissage actif qui permet le raffinement desrésultats de classification par l'utilisateur. Les résultats des expériencesmontrent que la combinaison des échelles produit de meilleurs résultats que leschaque échelle isolément dans un processus de retour de pertinence. Par ailleurs,la méthode interactive permet d'obtenir de bons résultats avec peud'interactions de l'utilisateur. Il n'a besoin que d'une faible partie del'ensemble d'apprentissage pour construire des classificateurs qui sont aussiforts que ceux générés par une méthode supervisée qui utilise l'ensembled'apprentissage complet.La quatrième question se réfère au problème de l'extraction descaractéristiques d'un hiérarchie des régions pour la classificationmulti-échelles. Nous avons proposé une stratégie qui exploite les relationsexistantes entre les régions dans une hiérarchie. Cette approche, appelée BoW-Propagation, exploite le modèle de bag-of-visual-word pour propagerles caractéristiques entre les échelles de la hiérarchie. Nous avons égalementétendu cette idée pour propager des descripteurs globaux basés sur leshistogrammes, l'approche H-Propagation. Ces approches accélèrent leprocessus d'extraction et donnent de bons résultats par rapport à l'extractionde descripteurs globaux. / A huge effort has been made in the development of image classification systemswith the objective of creating high-quality thematic maps and to establishprecise inventories about land cover use. The peculiarities of Remote SensingImages (RSIs) combined with the traditional image classification challengesmake RSI classification a hard task. Many of the problems are related to therepresentation scale of the data, and to both the size and therepresentativeness of used training set.In this work, we addressed four research issues in order to develop effectivesolutions for interactive classification of remote sensing images.The first research issue concerns the fact that image descriptorsproposed in the literature achieve good results in various applications, butmany of them have never been used in remote sensing classification tasks.We have tested twelve descriptors that encodespectral/color properties and seven texture descriptors. We have also proposeda methodology based on the K-Nearest Neighbor (KNN) classifier for evaluationof descriptors in classification context. Experiments demonstrate that JointAuto-Correlogram (JAC), Color Bitmap, Invariant Steerable Pyramid Decomposition(SID), and Quantized Compound Change Histogram (QCCH) yield the best results incoffee and pasture recognition tasks.The second research issue refers to the problem of selecting the scaleof segmentation for object-based remote sensing classification. Recentlyproposed methods exploit features extracted from segmented objects to improvehigh-resolution image classification. However, the definition of the scale ofsegmentation is a challenging task. We have proposedtwo multiscale classification approaches based on boosting of weak classifiers.The first approach, Multiscale Classifier (MSC), builds a strongclassifier that combines features extracted from multiple scales ofsegmentation. The other, Hierarchical Multiscale Classifier (HMSC), exploits thehierarchical topology of segmented regions to improve training efficiencywithout accuracy loss when compared to the MSC. Experiments show that it isbetter to use multiple scales than use only one segmentation scale result. Wehave also analyzed and discussed about the correlation among the useddescriptors and the scales of segmentation.The third research issue concerns the selection of training examples and therefinement of classification results through multiscale segmentation. We have proposed an approach forinteractive multiscale classification of remote sensing images.It is an active learning strategy that allows the classification resultrefinement by the user along iterations. Experimentalresults show that the combination of scales produces better results thanisolated scales in a relevance feedback process. Furthermore, the interactivemethod achieves good results with few user interactions. The proposed methodneeds only a small portion of the training set to build classifiers that are asstrong as the ones generated by a supervised method that uses the whole availabletraining set.The fourth research issue refers to the problem of extracting features of ahierarchy of regions for multiscale classification. We have proposed a strategythat exploits the existing relationships among regions in a hierarchy. Thisapproach, called BoW-Propagation, exploits the bag-of-visual-word model topropagate features along multiple scales. We also extend this idea topropagate histogram-based global descriptors, the H-Propagation method. The proposedmethods speed up the feature extraction process and yield good results when compared with globallow-level extraction approaches.

This study advances understanding of how the changes in ecosystem structure and function associated with woody shrub encroachment in semi-arid grasslands alter ecosystem carbon (C) dynamics. In terms of both magnitude and dynamism, dryland ecosystems represent a major component of the global C cycle. Woody shrub encroachment is a widespread phenomenon globally, which is known to substantially alter ecosystem structure and function, with resultant impacts on C dynamics. A series of focal sites were studied at the Sevilleta National Wildlife Refuge in central New Mexico, USA. A space-for-time analogue was used to identify how landscape structure and function change at four stages over a grassland to shrubland transition. The research had three key threads: 1. Soil-associated carbon: Stocks of organic and inorganic C in the near-surface soil, and the redistribution of these C stocks by erosion during high-intensity rainfall events were quantified using hillslope-scale monitoring plots. Coarse (>2 mm) clasts were found to account for a substantial proportion of the organic and inorganic C in these calcareous soils, and the erosional effluxes of both inorganic and organic C increased substantially across the vegetation ecotone. Eroded sediment was found to be significantly enriched in organic C relative to the contributing soil with systematic changes in OC enrichment across the vegetation transition. The OC enrichment dynamics observed were inconsistent with existing understanding (derived largely from reductionist, laboratory-based experiments) that OC enrichment is largely insignificant in the erosional redistribution of C. 2. Plant biomass: Cutting-edge proximal remote sensing approaches, using a remotely piloted lightweight multirotor drone combined with structure-from-motion (SfM) photogrammetry were developed and used to quantify biomass carbon stocks at the focal field sites. In such spatially heterogeneous and temporally dynamic ecosystems existing measurement techniques (e.g. on-the-ground observations or satellite- or aircraft-based remote sensing) struggle to capture the complexity of fine-grained vegetation structure, which is crucial for accurately estimating biomass. The data products available from the novel SfM approach developed for this research quantified plants just 15 mm high, achieving a fidelity nearly two orders of magnitude finer than previous implementations of the method. The approach developed here will revolutionise the study of biomass dynamics in short-sward ecogeomorphic systems. 3. Ecohydrological modelling: Understanding the effects of water-mediated degradation processes on ecosystem carbon dynamics over greater than observable spatio-temporal scales is complicated by significant scale-dependencies and thus requires detailed mechanistic understanding. A process-based, spatially-explicit ecohydrological modelling approach (MAHLERAN - Model for Assessing Hillslope to Landscape Erosion, Runoff and Nutrients) was therefore comprehensively evaluated against a large assemblage of rainfall runoff events. This evaluation highlighted both areas of strength in the current model structure, and also areas of weakness for further development. The research has improved understanding of ecosystem degradation processes in semi-arid rangelands, and demonstrates that woody shrub encroachment may lead to a long-term reduction in ecosystem C storage, which is contrary to the widely promulgated view that woody shrub encroachment increases C storage in terrestrial ecosystems.

Groundwater monitoring in regional scales using conventional methods is challenging since it requires a dense network monitoring well system and regular measurements. Satellite measurement of time-variable gravity from the Gravity Recovery and Climate Experiment (GRACE) mission since 2002 provided an exceptional opportunity to observe the variations in Terrestrial Water Storage (TWS) from space. This study has been divided into 3 parts: First different satellite and hydrological model data have been used to validate the TSW measurements derived from GRACE in High Plains Aquifer (HPA). Terrestrial Water Storage derived from GRACE was compared to TWS derived from a water budget whose inputs determined from independent datasets. The results were similar to each other both in magnitude and timing with a correlation coefficient of 0.55. The seasonal groundwater storage changes are also estimated using GRACE and auxiliary data for the period of 2004 to 2009, and results are compared to the local in situ measurements to test the capability of GRACE in detecting groundwater changes in this region. The results from comparing seasonal groundwater changes from GRACE and in situ measurements indicated a good agreement both in magnitude and seasonality with a correlation coefficient of 0.71. This finding reveals the worthiness of GRACE satellite data in detecting the groundwater level anomalies and the benefits of using its data in regional hydrological modelling. In the second part of the study the feasibility of the GRACE TWS for predicting groundwater level changes is investigated in different locations of the High Plains Aquifer. The Artificial Neural Networks (ANNs) are used to predict the monthly groundwater level changes. The input data employed in the ANN include monthly gridded GRACE TWS based on Release-05 of GRACE Level-3, precipitation, minimum and maximum temperature which are estimated from Parameter elevation Regression on Independent Slopes Model (PRISM), and the soil moisture estimations derived from Noah Land Surface Model for the period of January 2004 to December 2009. All the values for mentioned datasets are extracted at the location of 21 selected wells for the study period. The input data is divided into 3 parts which 60% is dedicated to training, 20% to validation, and 20% to testing. The output to the developed ANNs is the groundwater level change which is compared to the US Geological Survey's National Water Information well data. Results from statistical downscaling of GRACE data leaded to a significant improvement in predicting groundwater level changes, and the trained ensemble multi-layer perceptron shows a "good" to a "very good" performance based on the obtained Nash-Sutcliff Efficiency which demonstrates the capability of these data for downscaling. In the third part of this study the soil moisture from 4 different Land Surface models (NOAH, VIC, MOSAIC, and CLM land surface models) which are accessible through NASA Global Land Data Assimilation System (GLDAS) is included in developing the ANNs and the results are compared to each other to quantify the effect of soil moisture in the downscaling process of GRACE. The relative importance of each predictor was estimated using connection weight technique and it was found that the GRACE TWS is a significant parameter in the performance of Artificial Neural Network ensembles, and based on the Root Mean Squared (RMSE) and the correlation coefficients associated to the models in which the soil moisture from Noah and CLM Land Surface Models are used, it is found that using these datasets in process of downscaling GRACE delivers a higher correlated simulation values to the observed values.

Doctor of Philosophy / Department of Horticulture, Forestry, and Recreation Resources / Dale J. Bremer / Jack Fry / Increasing water scarcity may result in greater irrigation restrictions for turfgrass. Drought tolerance and recovery of Kentucky bluegrasses (Poa. pratensis L.) (KBG) were evaluated during and after 88 and 60 day dry downs in 2010 and 2011, respectively, under a rainout shelter. Changes in green coverage were evaluated with digital images. Green coverage declined slowest during dry downs and increased fastest during recoveries in the cultivar ‘Apollo’, indicating it had superior drought tolerance. Electrolyte leakage, photosynthesis, and leaf water potential were evaluated in 7 KBG cultivars during and after the dry downs. Soil moisture at 5 and 20 cm was measured. There were generally no differences in physiological parameters among cultivars during or after dry down. The highest reduction in soil moisture at 5 and 20 cm was in Apollo, suggesting it had a better developed root system for mining water from the profile during drought. Weed prevention and turfgrass establishment of ‘Legacy’ buffalograss (Buchloe dactyloides [Nutt.] Engelm.) and ‘Chisholm’ zoysiagrass (Zoysia japonica Steud.) grown on turf reinforcement mats (TRM) was evaluated. ‘Chisholm’ zoysiagrass stolons grew under the TRM; as such, use of TRM for this cultivar is not practical. Buffalograss had 90% or greater coverage when established on TRM in 2010 and 65% or greater coverage in 2011; coverage was similar to that in oxadiazon-treated plots at the end of each year. ‘Legacy’ buffalograss plugs were established on TRM over plastic for 3 weeks, stored in TRM under tree shade for 7, 14, or 21 days, and evaluated for establishment after storage. In 2010, plugs on mats stored for 7 days had similar coverage to the control, but in 2011 displayed similar coverage to plugs stored on TRM for 14 or 21 day treatments. Green leaf are index (LAI) is an important indicator of turfgrass performance, but its measurement is time consuming and destructive. Measurements using hyperspectral radiometry were compared with destructive measurements of LAI. Results suggest spectral radiometry has potential to accurately predict LAI. The robustness of prediction models varied over the growing season. Finding one model to predict LAI across and entire growing season still seems unrealistic.

Master of Arts / Department of Geography / J. M. Shawn Hutchinson / Time series of normalized difference vegetation index (NDVI) data from satellite spectral measurements can be used to characterize and quantify changes in vegetation phenology and explore the role of natural and anthropogenic activities in causing those changes. Several programs and methods exist to process phenometric data from remotely-sensed imagery, including TIMESAT, which extracts seasonality parameters from time-series image data by fitting a smooth function to the series. This smoothing function, however, is dependent upon user-defined input parameter settings which have an unknown amount of influence in shaping the final phenometric estimates. To test this, a sensitivity analysis was conducted using MODIS maximum value composite NDVI time-series data acquired for Fort Riley, Kansas during the period 2001-2012. The phenometric data generated from the different input setting files were compared against that from a base scenario using Pearson and Lin’s Concordance Correlation Analyses. Findings show that small changes to parameter settings results in insignificant differences in phenometric estimates, with the exception of end of season data and growing season length. Next, a time-series analysis of the same MODIS NDVI data for Fort Riley and nearby Konza Prairie Biological Station (KPBS) was conducted to determine if significant differences existed in selected vegetation phenometrics. Phenometrics of interest were estimated using TIMESAT and based on a Savitzky-Golay filter with parameter settings found optimal in the previous study. The phenometrics start of season, end of season, length of season, maximum value, and small seasonal integral were compared using Kolmogorov-Smirnov (K-S) and showed significant differences existed for all phenometrics in the comparison of Fort Riley training areas and KPBS, as well as low- versus high-training intensity areas within Fort Riley. Fort Riley and high-intensity training areas have earlier dates for the start and end of the growing season, shorter growing season lengths, lower maximum NDVI values, and lower small seasonal integrals compared to KPBS and low-intensity training areas, respectively. Evidence was found that establishes a link between military land uses and/or land management practices and observed phenometric differences.

Doctor of Philosophy / Department of Geography / Douglas G. Goodin / Jesse A. Poland / Kevin Price / Wheat breeders are in a race for genetic gain to secure the future nutritional needs of a growing population. Multiple barriers exist in the acceleration of crop improvement. Emerging technologies are reducing these obstacles. Advances in genotyping technologies have significantly decreased the cost of characterizing the genetic make-up of candidate breeding lines. However, this is just part of the equation. Field-based phenotyping informs a breeder’s decision as to which lines move forward in the breeding cycle. This has long been the most expensive and time-consuming, though most critical, aspect of breeding. The grand challenge remains in connecting genetic variants to observed phenotypes followed by predicting phenotypes based on the genetic composition of lines or cultivars. In this context, the current study was undertaken to investigate the utility of UAS in assessment field trials in wheat breeding programs. The major objective was to integrate remotely sensed data with geospatial analysis for high throughput phenotyping of large wheat breeding nurseries. The initial step was to develop and validate a semi-automated high-throughput phenotyping pipeline using a low-cost UAS and NIR camera, image processing, and radiometric calibration to build orthomosaic imagery and 3D models. The relationship between plot-level data (vegetation indices and height) extracted from UAS imagery and manual measurements were examined and found to have a high correlation. Data derived from UAS imagery performed as well as manual measurements while exponentially increasing the amount of data available. The high-resolution, high-temporal HTP data extracted from this pipeline offered the opportunity to develop a within season grain yield prediction model. Due to the variety in genotypes and environmental conditions, breeding trials are inherently spatial in nature and vary non-randomly across the field. This makes geographically weighted regression models a good choice as a geospatial prediction model. Finally, with the addition of georeferenced and spatial data integral in HTP and imagery, we were able to reduce the environmental effect from the data and increase the accuracy of UAS plot-level data. The models developed through this research, when combined with genotyping technologies, increase the volume, accuracy, and reliability of phenotypic data to better inform breeder selections. This increased accuracy with evaluating and predicting grain yield will help breeders to rapidly identify and advance the most promising candidate wheat varieties.