Ground Truth Simulation for Deep Learning Classification of Mid-Resolution Venus Images Via Unmixing of High-Resolution Hyperspectral Fenix Data

Training a deep neural network for classification constitutes a major problem in remote sensing due to the lack of adequate field data. Acquiring high-resolution ground truth (GT) by human interpretation is both cost-ineffective and inconsistent. We propose, instead, to utilize high-resolution, hyperspectral images for solving this problem, by unmixing these images to obtain reliable GT for training a deep network. Specifically, we simulate GT from high-resolution, hyperspectral FENIX images, and use it for training a convolutional neural network (CNN) for pixel-based classification. We show how the model can be transferred successfully to classify new mid-resolution VENuS imagery

Remote sensing grass quantity under different grassland management treatments practised in the Southern African rangelands.

Doctor of Philosophy in Environmental Science. University of KwaZulu-Natal, Pietermaritzburg 2016.Abstract available in PDF file

Agro-hydrology and multi temporal high resolution remote sensing: toward an explicit spatial processes calibration

The recent and forthcoming availability of high resolution satellite image series offers new opportunities in agro-hydrological research and modeling. We investigated the perspective offered by improving the crop growth dynamic simulation using the distributed agro-hydrological model, Topography based Nitrogen transfer and Transforma­ tion (TNT2), using LAI map series derived from 105 Formosat-2 (F2) images during the period 2006-2010. The TNT2 model (Beaujouan et al., 2002), calibrated with dis­ charge and in-stream nitrate fluxes for the period 1985-2001, was tested on the 2006-201O dataset (climate, land use, agricultural practices, discharge and nitrate fluxes at the outlet). A priori agricultural practices obtained from an extensive field survey such as seeding date, crop cultivar,and fertilizer amount were used as input variables.Con­tinuous values of LAI as a function of cumulative daily temperature were obtained at the crop field level by fitting a double logistic equation against discrete satellite-derived LAI. Model predictions of LAI dynamics with a priori input parameters showed an temporal shift with observed LAI profiles irregularly distributed in space (between field crops) and time (between years). By re-setting seeding date at the crop field level, we proposed an optimization method to minimize efficiently this temporal shift and better fit the crop growth against the spatial observations as well as crop production. This optimization of simulated LAI has a negligible impact on water budget at the catchment scale (1 mm yr-1 in average) but a noticeable impact on in-stream nitrogen fluxes(around 12%) which is of interest considering nitrate stream contamination issues and TNT2 model objectives. This study demonstrates the contribution of forthcoming high spatial and temporal resolution products of Sentinel-2 satellite mission in improving agro-hydrological modeling by constraining the spatial representation of crop productivity

An Analysis of Temperate Deciduous Shrub Phenology in Downer Woods, University of Wisconsin-Milwaukee, Wisconsin, USA

Shrub species, both native and non-native, are an important component of temperate deciduous forest ecosystems but are an often-overlooked and under-studied functional group. Shrubs tend to leaf-out earlier than trees in spring and retain their leaves later in autumn thus extending the overall growing season and the carbon uptake period of the forest ecosystem. In this study, a range of 5- native and 3- non-native shrub species were identified in a deciduous urban woodlot, and the phenology was monitored over a 3-year period on the University of Wisconsin-Milwaukee campus. The aim of this work was to determine any variation in the timing (DOY) and duration (days) of key spring (bud-open, leaf-out, full-leaf unfolded) and autumn (leaf color, leaf fall) phenophases between native and non-native species. Preliminary results revealed interesting findings with buckthorn Rhamnus cathartica (an alien invasive/non-native species) consistently leafing out later than most native species and taking longer to reach full-leaf unfolded. Additionally, non-native species such as European privet Lingustrum vulgare have a longer growing season than native species ranging from 14 days to 35 days longer in non-native species than native species across the three-year period. This shows how non-native species can lengthen the fall season compared to native species. These results could add to the understanding of how non-native shrub species may gain a competitive advantage over native shrubs and may help inform future conservation management plans

The utility of very-high resolution unmanned aerial vehicles (UAV) imagery in monitoring the spatial and temporal variations in leaf moisture content of smallholder maize farming systems.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Maize moisture stress, resulting from rainfall variability, is a primary challenge in the production of rain-fed maize farming, especially in water-scarce regions such as southern Africa. Quantifying maize moisture variations throughout the growing season can support agricultural decision-making and prompt the rapid and robust detection of smallholder maize moisture stress. Unmanned Aerial Vehicles (UAVs), equipped with light-weight multispectral sensors, provide spatially explicit near real-time information for determining maize moisture content at farm scale. Therefore, this study evaluated the utility of UAV derived multispectral imagery in estimating maize leaf moisture content indicators on smallholder farming systems throughout the maize growing season. The first objective of the study was to conduct a comparative analysis in order to evaluate the performance of five regression techniques (support vector regression, random forest regression, decision trees regression, artificial neural network regression and the partial least squares regression) in predicting maize water content indicators (i.e. equivalent water thickness (EWT), fuel moisture content (FMC) and specific leaf area (SLA)), and determine the most suitable indicator of smallholder maize water content variability based on multispectral UAV data. The results illustrated that both NIR and red-edge derived spectral variables were critical in characterising maize moisture indicators on smallholder farms. Furthermore, the best models for estimating EWT, FMC and SLA were derived from the random forest regression algorithm with a relative root mean square error (rRMSE) of 3.13%, 1% and 3.48 %, respectively. Additionally, EWT and FMC yielded the highest predictive performance of maize leaf moisture and demonstrated the best correlation with remotely sensed data. The study’s second objective was to evaluate the utility of UAVderived multispectral imagery in estimating the temporal variability of smallholder maize moisture content across the maize growing season using the optimal maize moisture indicators. The findings illustrated that the NIR and red-edge wavelengths were influential in characterising maize moisture variability with the best models for estimating maize EWT and FMC resulting in a rRMSE of 2.27 % and 1%, respectively. Furthermore, the early reproductive stage was the most optimal for accurately estimating maize EWT and FMC using UAVproximal remote sensing. The findings of this study demonstrate the prospects of UAV- derived multispectral data for deriving insightful information on maize moisture availability and overall health conditions. This study serves as fundamental step towards the creation of an early maize moisture stress detection and warning systems, and contributes towards climate change adaptation and resilience of smallholder maize farming

Sentinel-2 Image Scene Classification: A Comparison between Sen2Cor and a Machine Learning Approach

Given the continuous increase in the global population, the food manufacturers are advocated to either intensify the use of cropland or expand the farmland, making land cover and land usage dynamics mapping vital in the area of remote sensing. In this regard, identifying and classifying a high-resolution satellite imagery scene is a prime challenge. Several approaches have been proposed either by using static rule-based thresholds (with limitation of diversity) or neural network (with data-dependent limitations). This paper adopts the inductive approach to learning from surface reflectances. A manually labeled Sentinel-2 dataset was used to build a Machine Learning (ML) model for scene classification, distinguishing six classes (Water, Shadow, Cirrus, Cloud, Snow, and Other). This models was accessed and further compared to the European Space Agency (ESA) Sen2Cor package. The proposed ML model presents a Micro-F1 value of 0.84, a considerable improvement when compared to the Sen2Cor corresponding performance of 0.59. Focusing on the problem of optical satellite image scene classification, the main research contributions of this paper are: (a) an extended manually labeled Sentinel-2 database adding surface reflectance values to an existing dataset; (b) an ensemble-based and a Neural-Network-based ML models; (c) an evaluation of model sensitivity, biasness, and diverse ability in classifying multiple classes over different geographic Sentinel-2 imagery, and finally, (d) the benchmarking of the ML approach against the Sen2Cor package

Automated and robust geometric and spectral fusion of multi-sensor, multi-spectral satellite images

Die in den letzten Jahrzehnten aufgenommenen Satellitenbilder zur Erdbeobachtung bieten eine ideale Grundlage für eine genaue Langzeitüberwachung und Kartierung der Erdoberfläche und Atmosphäre. Unterschiedliche Sensoreigenschaften verhindern jedoch oft eine synergetische Nutzung. Daher besteht ein dringender Bedarf heterogene Multisensordaten zu kombinieren und als geometrisch und spektral harmonisierte Zeitreihen nutzbar zu machen. Diese Dissertation liefert einen vorwiegend methodischen Beitrag und stellt zwei neu entwickelte Open-Source-Algorithmen zur Sensorfusion vor, die gründlich evaluiert, getestet und validiert werden. AROSICS, ein neuer Algorithmus zur Co-Registrierung und geometrischen Harmonisierung von Multisensor-Daten, ermöglicht eine robuste und automatische Erkennung und Korrektur von Lageverschiebungen und richtet die Daten an einem gemeinsamen Koordinatengitter aus. Der zweite Algorithmus, SpecHomo, wurde entwickelt, um unterschiedliche spektrale Sensorcharakteristika zu vereinheitlichen. Auf Basis von materialspezifischen Regressoren für verschiedene Landbedeckungsklassen ermöglicht er nicht nur höhere Transformationsgenauigkeiten, sondern auch die Abschätzung einseitig fehlender Spektralbänder. Darauf aufbauend wurde in einer dritten Studie untersucht, inwieweit sich die Abschätzung von Brandschäden aus Landsat mittels synthetischer Red-Edge-Bänder und der Verwendung dichter Zeitreihen, ermöglicht durch Sensorfusion, verbessern lässt. Die Ergebnisse zeigen die Effektivität der entwickelten Algorithmen zur Verringerung von Inkonsistenzen bei Multisensor- und Multitemporaldaten sowie den Mehrwert einer geometrischen und spektralen Harmonisierung für nachfolgende Produkte. Synthetische Red-Edge-Bänder erwiesen sich als wertvoll bei der Abschätzung vegetationsbezogener Parameter wie z. B. Brandschweregraden. Zudem zeigt die Arbeit das große Potenzial zur genaueren Überwachung und Kartierung von sich schnell entwickelnden Umweltprozessen, das sich aus einer Sensorfusion ergibt.Earth observation satellite data acquired in recent years and decades provide an ideal data basis for accurate long-term monitoring and mapping of the Earth's surface and atmosphere. However, the vast diversity of different sensor characteristics often prevents synergetic use. Hence, there is an urgent need to combine heterogeneous multi-sensor data to generate geometrically and spectrally harmonized time series of analysis-ready satellite data. This dissertation provides a mainly methodical contribution by presenting two newly developed, open-source algorithms for sensor fusion, which are both thoroughly evaluated as well as tested and validated in practical applications. AROSICS, a novel algorithm for multi-sensor image co-registration and geometric harmonization, provides a robust and automated detection and correction of positional shifts and aligns the data to a common coordinate grid. The second algorithm, SpecHomo, was developed to unify differing spectral sensor characteristics. It relies on separate material-specific regressors for different land cover classes enabling higher transformation accuracies and the estimation of unilaterally missing spectral bands. Based on these algorithms, a third study investigated the added value of synthesized red edge bands and the use of dense time series, enabled by sensor fusion, for the estimation of burn severity and mapping of fire damage from Landsat. The results illustrate the effectiveness of the developed algorithms to reduce multi-sensor, multi-temporal data inconsistencies and demonstrate the added value of geometric and spectral harmonization for subsequent products. Synthesized red edge information has proven valuable when retrieving vegetation-related parameters such as burn severity. Moreover, using sensor fusion for combining multi-sensor time series was shown to offer great potential for more accurate monitoring and mapping of quickly evolving environmental processes

Estimation spatialisée de la biomasse et des besoins en eau des cultures à l'aide de données satellitales à hautes résolutions spatiale et temporelle : application aux agrosystèmes du sud-ouest de la France

Il existe un lien étroit entre les agrosystèmes et les cycles du carbone (processus de séquestration du carbone dans les sols) et de l'eau (systèmes de production par irrigation). Cette thèse contribue à l'analyse et la validation des méthodes de quantification, sur de grandes surfaces, de la biomasse (cycle du carbone) et des besoins en eau (cycle de l'eau) des agrosystèmes. Pour répondre à cet objectif, des données de télédétection sont assimilées dans un modèle de cultures, SAFY (Simple Algorithm For Yield Estimate), au travers d'une variable biophysique clés, le GAI (Green area index). Des méthodes d'estimation in situ (par proxy-détection) et spatialisées (par inversion de modèles de transfert radiatif) du GAI sont, tout d'abord, étudiées et validées. Les séries temporelles de GAI déterminées à partir des données de télédétection sont ensuite utilisées pour étalonner le modèle SAFY, conduisant à des estimations spatialisées de biomasse et des besoins en eau des cultures. Ces estimations sont validées par confrontation à un dispositif expérimental mis en place entre 2006 et 2010 et situé dans le sud-ouest de la France. Les cultures étudiées sont des cultures d'été non irriguées (tournesol) et irriguées (maïs, soja). Les données de télédétection utilisées pour estimer les séries temporelles de GAI sont issues du capteur Formosat-2. Ces données sont particulièrement pertinentes car elles combinent une haute résolution spatiale (8 m) et une haute fréquence temporelle (1 jour), indispensables pour le suivi des surfaces agricoles.There is a close relationship between agrosystems (or agroecosystems) and carbon (soil carbon sequestration process) and water (irrigation management systems) cycles. This PhD thesis contributes to the analysis and the validation of methods for quantification of agrosystems biomass (carbon cycle) and water needs (water cycle) over large land surfaces. To this end, remote sensing data are assimilated within a crop model, SAFY (Simple Algorithm For Yield Estimate), through a key biophysical variable, the GAI (Green area index). GAI in situ (proxy-detection) and spatialized (inversion of radiative transfer models) estimation methods are first assessed. Secondly, remote sensed time series of GAI are used for the calibration of the SAFY crop model in order to deliver spatial estimates of crop biomass and water needs. These estimations are validated, through direct comparison with an experimental system which is located in the southwest of France and run from 2006 to 2010. Studied crops are maize and soybean, which are irrigated, and also sunflower, which is non-irrigated. Remote sensing data used to estimate the time series of GAI are taken from Formosat-2 sensors. Such data are particularly relevant for the crop monitoring because they combine high spatial resolution (8 m) and high temporal frequency (1 day)