Master of Science

thesisVegetation phenology results in seasonal changes in spectral reflectance. Phenology is often underutilized in hyperspectral vegetation mapping due to a lack of repeat imagery of the same region over time. Vegetation classification at the species level could benefit from introducing phenological information to spectral libraries. New missions, such as the proposed Hysperspectral Infrared Imager (HyspIRI) mission, could potentially provide easy access to multi-temporal datasets. The availability of these data will require new approaches to building spectral libraries for species classification. This paper explores the use of Iterative Endmember Selection (IES), an automated method for selecting endmembers from an image-derived spectral library, to create single-date and multitemporal endmember libraries. Multiple Endmember Spectral Mixture Analysis (MESMA) was used to classify vegetation species and land cover, applying single-date and multitemporal libraries to Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data acquired on five dates in the same year. Three applications of endmember libraries were tested for their ability to classify single date AVIRIS images: 1) single-date libraries that matched the image date (same-date libraries), 2) single-date libraries that were not matched to the image date (mismatched-date libraries), and 3) a combined multitemporal library containing spectra from all dates applied to all image dates. Results indicate that multitemporal, seasonally-mixed spectral libraries achieved similar overall classification accuracy compared to single-date libraries, and in some cases, resulted in improved classification accuracy. Several species had increased producer's or user accuracy using a multitemporal library, while others had reduced accuracy compared to same-date classifications. The image dates of selected endmembers from the multitemporal library were examined to determine if this information could improve our understanding of phenological spectral differences for specific species. Results demonstrate that multitemporal endmember libraries may provide a more robust alternative to single-date endmember libraries for mapping vegetation species across time and space. Multitemporal endmember libraries could provide a means for mapping species in data where phenology, climatic variability, or spatial gradients are not known in advance or may not be easily accounted for by endmembers from a single date

Arctic shrub expansion revealed by Landsat-derived multitemporal vegetation cover fractions in the Western Canadian Arctic

Warming induced shifts in tundra vegetation composition and structure, including circumpolar expansion of shrubs, modifies ecosystem structure and functioning with potentially global consequences due to feedback mechanisms between vegetation and climate. Satellite-derived vegetation indices indicate widespread greening of the surface, often associated with regional evidence of shrub expansion obtained from long-term ecological monitoring and repeated orthophotos. However, explicitly quantifying shrub expansion across large scales using satellite observations requires characterising the fine-scale mosaic of Arctic vegetation types beyond index-based approaches. Although previous studies have illustrated the potential of estimating fractional cover of various Plant Functional Types (PFTs) from satellite imagery, limited availability of reference data across space and time has constrained deriving fraction cover time series capable of detecting shrub expansion. We applied regression-based unmixing using synthetic training data to build multitemporal machine learning models in order to estimate fractional cover of shrubs and other surface components in the Mackenzie Delta Region for six time intervals between 1984 and 2020. We trained Kernel Ridge Regression (KRR) and Random Forest Regression (RFR) models using Landsat-derived spectral-temporal-metrics and synthetic training data generated from pure class spectra obtained directly from the imagery. Independent validation using very-high-resolution imagery suggested that KRR outperforms RFR, estimating shrub cover with a MAE of 10.6 and remaining surface components with MAEs between 3.0 and 11.2. Canopy-forming shrubs were well modelled across all cover densities, coniferous tree cover tended to be overestimated and differentiating between herbaceous and lichen cover was challenging. Shrub cover expanded by on average + 2.2 per decade for the entire study area and + 4.2 per decade within the low Arctic tundra, while relative changes were strongest in the northernmost regions. In conjunction with shrub expansion, we observed herbaceous plant and lichen cover decline. Our results corroborate the perception of the replacement and homogenisation of Arctic vegetation communities facilitated by the competitive advantage of shrub species under a warming climate. The proposed method allows for multidecadal quantitative estimates of fractional cover at 30 m resolution, initiating new opportunities for mapping past and present fractional cover of tundra PFTs and can help advance our understanding of Arctic shrub expansion within the vast and heterogeneous tundra biome

Combining hyperspectral UAV and mulitspectral FORMOSAT-2 imagery for precision agriculture applications

Precision agriculture requires detailed information regarding the crop status variability within a field. Remote sensing provides an efficient way to obtain such information through observing biophysical parameters, such as canopy nitrogen content, leaf coverage, and plant biomass. However, individual remote sensing sensors often fail to provide information which meets the spatial and temporal resolution required by precision agriculture. The purpose of this study is to investigate methods which can be used to combine imagery from various sensors in order to create a new dataset which comes closer to meeting these requirements. More specifically, this study combined multispectral satellite imagery (Formosat-2) and hyperspectral Unmanned Aerial Vehicle (UAV) imagery of a potato field in the Netherlands. The imagery from both platforms was combined in two ways. Firstly, data fusion methods brought the spatial resolution of the Formosat-2 imagery (8 m) down to the spatial resolution of the UAV imagery (1 m). Two data fusion methods were applied: an unmixing-based algorithm and the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The unmixing-based method produced vegetation indices which were highly correlated to the measured LAI (rs= 0.866) and canopy chlorophyll values (rs=0.884), whereas the STARFM obtained lower correlations. Secondly, a Spectral-Temporal Reflectance Surface (STRS) was constructed to interpolate a daily 101 band reflectance spectra using both sources of imagery. A novel STRS method was presented, which utilizes Bayesian theory to obtain realistic spectra and accounts for sensor uncertainties. The resulting surface obtained a high correlation to LAI (rs=0.858) and canopy chlorophyll (rs=0.788) measurements at field level. The multi-sensor datasets were able to characterize significant differences of crop status due to differing nitrogen fertilization regimes from June to August. Meanwhile, the yield prediction models based purely on the vegetation indices extracted from the unmixing-based fusion dataset explained 52.7% of the yield variation, whereas the STRS dataset was able to explain 72.9% of the yield variability. The results of the current study indicate that the limitations of each individual sensor can be largely surpassed by combining multiple sources of imagery, which is beneficial for agricultural management. Further research could focus on the integration of data fusion and STRS techniques, and the inclusion of imagery from additional sensors.Samenvatting In een wereld waar toekomstige voedselzekerheid bedreigd wordt, biedt precisielandbouw een oplossing die de oogst kan maximaliseren terwijl de economische en ecologische kosten van voedselproductie beperkt worden. Om dit te kunnen doen is gedetailleerde informatie over de staat van het gewas nodig. Remote sensing is een manier om biofysische informatie, waaronder stikstof gehaltes en biomassa, te verkrijgen. De informatie van een individuele sensor is echter vaak niet genoeg om aan de hoge eisen betreft ruimtelijke en temporele resolutie te voldoen. Deze studie combineert daarom de informatie afkomstig van verschillende sensoren, namelijk multispectrale satelliet beelden (Formosat-2) en hyperspectral Unmanned Aerial Vehicle (UAV) beelden van een aardappel veld, in een poging om aan de hoge informatie eisen van precisielandbouw te voldoen. Ten eerste werd gebruik gemaakt van datafusie om de acht Formosat-2 beelden met een resolutie van 8 m te combineren met de vier UAV beelden met een resolutie van 1 m. De resulterende dataset bestaat uit acht beelden met een resolutie van 1 m. Twee methodes werden toegepast, de zogenaamde STARFM methode en een unmixing-based methode. De unmixing-based methode produceerde beelden met een hoge correlatie op de Leaf Area Index (LAI) (rs= 0.866) en chlorofyl gehalte (rs=0.884) gemeten op veldnieveau. De STARFM methode presteerde slechter, met correlaties van respectievelijk rs=0.477 en rs=0.431. Ten tweede werden Spectral-Temporal Reflectance Surfaces (STRSs) ontwikkeld die een dagelijks spectrum weergeven met 101 spectrale banden. Om dit te doen is een nieuwe STRS methode gebaseerd op de Bayesiaanse theorie ontwikkeld. Deze produceert realistische spectra met een overeenkomstige onzekerheid. Deze STRSs vertoonden hoge correlaties met de LAI (rs=0.858) en het chlorofyl gehalte (rs=0.788) gemeten op veldnieveau. De bruikbaarheid van deze twee soorten datasets werd geanalyseerd door middel van de berekening van een aantal vegetatie-indexen. De resultaten tonen dat de multi-sensor datasets capabel zijn om significante verschillen in de groei van gewassen vast te stellen tijdens het groeiseizoen zelf. Bovendien werden regressiemodellen toegepast om de bruikbaarheid van de datasets voor oogst voorspellingen. De unmixing-based datafusie verklaarde 52.7% van de variatie in oogst, terwijl de STRS 72.9% van de variabiliteit verklaarden. De resultaten van het huidige onderzoek tonen aan dat de beperkingen van een individuele sensor grotendeels overtroffen kunnen worden door het gebruik van meerdere sensoren. Het combineren van verschillende sensoren, of het nu Formosat-2 en UAV beelden zijn of andere ruimtelijke informatiebronnen, kan de hoge informatie eisen van de precisielandbouw tegemoet komen.In the context of threatened global food security, precision agriculture is one strategy to maximize yield to meet the increased demands of food, while minimizing both economic and environmental costs of food production. This is done by applying variable management strategies, which means the fertilizer or irrigation rates within a field are adjusted according to the crop needs in that specific part of the field. This implies that accurate crop status information must be available regularly for many different points in the field. Remote sensing can provide this information, but it is difficult to meet the information requirements when using only one sensor. For example, satellites collect imagery regularly and over large areas, but may be blocked by clouds. Unmanned Aerial Vehicles (UAVs), commonly known as drones, are more flexible but have higher operational costs. The purpose of this study was to use fusion methods to combine satellite (Formosat-2) with UAV imagery of a potato field in the Netherlands. Firstly, data fusion was applied. The eight Formosat-2 images with 8 m x 8 m pixels were combined with four UAV images with 1 m x 1 m pixels to obtain a new dataset of eight images with 1 m x 1 m pixels. Unmixing-based data fusion produced images which had a high correlation to field measurements obtained from the potato field during the growing season. The results of a second data fusion method, STARFM, were less reliable in this study. The UAV images were hyperspectral, meaning they contained very detailed information spanning a large part of the electromagnetic spectrum. Much of this information was lost in the data fusion methods because the Formosat-2 images were multispectral, representing a more limited portion of the spectrum. Therefore, a second analysis investigated the use of Spectral-Temporal Reflectance Surfaces (STRS), which allow information from different portions of the electromagnetic spectrum to be combined. These STRS provided daily hyperspectral observations, which were also verified as accurate by comparing them to reference data. Finally, this study demonstrated the ability of both data fusion and STRS to identify which parts of the potato field had lower photosynthetic production during the growing season. Data fusion was capable of explaining 52.7% of the yield variation through regression models, whereas the STRS explained 72.9%. To conclude, this study indicates how to combine crop status information from different sensors to support precision agriculture management decisions

Analyzing the Adoption, Cropping Rotation, and Impact of Winter Cover Crops in the Mississippi Alluvial Plain (MAP) Region through Remote Sensing Technologies

This dissertation explores the application of remote sensing technologies in conservation agriculture, specifically focusing on identifying and mapping winter cover crops and assessing voluntary cover crop adoption and cropping patterns in the Arkansas portion of the Mississippi Alluvial Plain (MAP). In the first chapter, a systematic review using the PRISMA methodology examines the last 30 years of thematic research, development, and trends in remote sensing applied to conservation agriculture from a global perspective. The review uncovers a growing interest in remote sensing-based research in conservation agriculture and emphasizes the necessity for further studies dedicated to conservation practices. Among the 68 articles examined, 94% of studies utilized a pixel-based classification method, while only 6% employed an object-based approach. The analysis also revealed a thematic shift over time, with tillage practices being extensively studied before 2005, followed by a focus on crop residue from 2004 to 2012. From 2012 to 2020, there was a renewed emphasis on cover crops research. These findings highlight the evolving research landscape and provide insights into the trends within remote sensing-based conservation agriculture studies. The second chapter presents a methodological framework for identifying and mapping winter cover crops. The framework utilizes the Google Earth Engine (GEE) and a Random Forest (RF) classifier with time series data from Landsat 8 satellite. Results demonstrate a high classification accuracy (97.7%) and a significant increase (34%) in model-predicted cover crop adoption over the study period between 2013 and 2019. Additionally, the study showcases the use of multi-year datasets to efficiently map the growing season\u27s length and cover crops\u27 phenological characteristics. The third chapter assesses the voluntary adoption of winter cover crops and cropping patterns in the MAP region. Remote sensing technologies, USDA-NRCS government cover crop data sources, and the USDA Cropland Data Layer (CDL) are employed to identify cover crop locations, analyze county-wide voluntary adoption, and cropping rotations. The result showed a 5.33% increase in the overall voluntary adoption of cover crops in the study region between 2013 and 2019. The findings also indicate a growing trend in cover crop adoption, with soybean-cover crop rotations being prominent. This dissertation enhances our understanding of the role of remote sensing in conservation agriculture with a particular focus on winter cover crops. These insights are valuable for policymakers, stakeholders, and researchers seeking to promote sustainable agricultural practices and increased cover crop adoption. The study also underscores the significance of integrating remote sensing technologies into agricultural decision-making processes and highlights the importance of collaboration among policymakers, researchers, and producers. By leveraging the capabilities of remote sensing, it will enhance conservation agriculture contribution to long-term environmental sustainability and agricultural resilience. Keywords: Remote sensing technologies, Conservation agriculture, Winter cover crops, Voluntary adoption, Cropping patterns, Sustainable agricultural practice

Cartografía de severidad de incendios forestales a partir de la combinación del modelo de mezclas espectrales y la clasificación basada en objetos

This study shows an accurate and fast methodology in order to evaluate fire severity classes of large forest fires. A single Landsat Enhanced Thematic Mapper multispectral image was utilized in this study with the aim of mapping fire severity classes (high, moderate and low) using a combined-approach based in an spectral mixing model and object-based image analysis. A large wildfire in the Northwest of Spain is used to test the model. Fraction images obtained by Landsat unmixing were used as input data in the object-based image analysis. A multilevel segmentation and a classification were carried out by using membership functions. This method was compared with other simplest ones in order to evaluate the suitability to distinguish between the three fire severity classes above mentioned. McNemar’s test was used to evaluate the statistical significance of the difference between approaches tested in this study. The combined approach achieved the highest accuracy reaching 97.32% and kappa index of agreement of 95.96% and improving accuracy of individual classes.Este estudio presenta una metodología rápida y precisa para la evaluación de los niveles de severidad que afectan a grandes incendios forestales. El trabajo combina un modelo de mezclas espectrales y un análisis de imágenes basado en objetos con el objetivo de cartografiar distintos niveles de severidad (alto, moderado y bajo) empleando una imagen multiespectral Landsat Enhanced Thematic Mapper. Este modelo es testado en un gran incendio forestal ocurrido en el noroeste de España. Las imágenes fracción obtenidas tras aplicar el modelo de mezclas a la imagen Landsat fueron utilizadas como datos de entrada en el análisis basado en objetos. En este se llevó a cabo una segmentación multinivel y una posterior clasificación usando funciones de pertenencia. Esta metodología fue comparada con otras más simples con el fin de evaluar su conveniencia a al hora de distinguir entre los tres niveles de severidad anteriormente mencionados. El test de McNemar fue empleado para evaluar la significancia estadística de la diferencia entre los métodos testados en el estudio. El método combinado alcanzó la más alta precisión con un 97,32% y un índice Kappa del 95,96%, además de mejorar la precisión de los niveles individualmente

Analyzing the Adoption, Cropping Rotation, and Impact of Winter Cover Crops in the Mississippi Alluvial Plain (MAP) Region through Remote Sensing Technologies

This dissertation explores the application of remote sensing technologies in conservation agriculture, specifically focusing on identifying and mapping winter cover crops and assessing voluntary cover crop adoption and cropping patterns in the Arkansas portion of the Mississippi Alluvial Plain (MAP). In the first chapter, a systematic review using the PRISMA methodology examines the last 30 years of thematic research, development, and trends in remote sensing applied to conservation agriculture from a global perspective. The review uncovers a growing interest in remote sensing-based research in conservation agriculture and emphasizes the necessity for further studies dedicated to conservation practices. Among the 68 articles examined, 94% of studies utilized a pixel-based classification method, while only 6% employed an object-based approach. The analysis also revealed a thematic shift over time, with tillage practices being extensively studied before 2005, followed by a focus on crop residue from 2004 to 2012. From 2012 to 2020, there was a renewed emphasis on cover crops research. These findings highlight the evolving research landscape and provide insights into the trends within remote sensing-based conservation agriculture studies. The second chapter presents a methodological framework for identifying and mapping winter cover crops. The framework utilizes the Google Earth Engine (GEE) and a Random Forest (RF) classifier with time series data from Landsat 8 satellite. Results demonstrate a high classification accuracy (97.7%) and a significant increase (34%) in model-predicted cover crop adoption over the study period between 2013 and 2019. Additionally, the study showcases the use of multi-year datasets to efficiently map the growing season\u27s length and cover crops\u27 phenological characteristics. The third chapter assesses the voluntary adoption of winter cover crops and cropping patterns in the MAP region. Remote sensing technologies, USDA-NRCS government cover crop data sources, and the USDA Cropland Data Layer (CDL) are employed to identify cover crop locations, analyze county-wide voluntary adoption, and cropping rotations. The result showed a 5.33% increase in the overall voluntary adoption of cover crops in the study region between 2013 and 2019. The findings also indicate a growing trend in cover crop adoption, with soybean-cover crop rotations being prominent. This dissertation enhances our understanding of the role of remote sensing in conservation agriculture with a particular focus on winter cover crops. These insights are valuable for policymakers, stakeholders, and researchers seeking to promote sustainable agricultural practices and increased cover crop adoption. The study also underscores the significance of integrating remote sensing technologies into agricultural decision-making processes and highlights the importance of collaboration among policymakers, researchers, and producers. By leveraging the capabilities of remote sensing, it will enhance conservation agriculture contribution to long-term environmental sustainability and agricultural resilience. Keywords: Remote sensing technologies, Conservation agriculture, Winter cover crops, Voluntary adoption, Cropping patterns, Sustainable agricultural practice

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

Monitoring the Sustainable Intensification of Arable Agriculture:the Potential Role of Earth Observation

Sustainable intensification (SI) has been proposed as a possible solution to the conflicting problems of meeting projected increases in food demand and preserving environmental quality. SI would provide necessary production increases while simultaneously reducing or eliminating environmental degradation, without taking land from competing demands. An important component of achieving these aims is the development of suitable methods for assessing the temporal variability of both the intensification and sustainability of agriculture. Current assessments rely on traditional data collection methods that produce data of limited spatial and temporal resolution. Earth Observation (EO) provides a readily accessible, long-term dataset with global coverage at various spatial and temporal resolutions. In this paper we demonstrate how EO could significantly contribute to SI assessments, providing opportunities to quantify agricultural intensity and environmental sustainability. We review an extensive body of research on EO-based methods to assess multiple indicators of both agricultural intensity and environmental sustainability. To date these techniques have not been combined to assess SI; here we identify the opportunities and initial steps required to achieve this. In this context, we propose the development of a set of essential sustainable intensification variables (ESIVs) that could be derived from EO data

Remote Sensing Methods and Applications for Detecting Change in Forest Ecosystems

Forest ecosystems are being altered by climate change, invasive species, and additional stressors. Our ability to detect these changes and quantify their impacts relies on detailed data across spatial and temporal scales. This dissertation expands the ecological utility of long-term satellite imagery by developing high quality forest mapping products and examining spatiotemporal changes in tree species abundance and phenology across the northeastern United States (US; the ‘Northeast’). Species/genus-level forest composition maps were developed by integrating field data and Landsat images to model abundance at a sub-pixel scale. These abundance maps were then used to 1) produce a more detailed, accurate forest classification compared to similar products and 2) construct a 30-year time-series of abundance for eight common species/genera. Analyzing the time-series data revealed significant abundance trends in notable species, including increases in American beech (Fagus grandifolia) at the expense of sugar maple (Acer saccharum). Climate was the dominant predictor of abundance trends, indicating climate change may be altering competitive relationships. Spatiotemporal trends in deciduous forest phenology – start and end of the growing season (SOS/EOS) – were examined based on MODIS imagery from 2001-2015. SOS exhibited a slight advancing trend across the Northeast, but with a distinct spatial pattern: eastern ecoregions showed advance and western ecoregions delay. EOS trended substantially later almost everywhere. SOS trends were linked to winter-spring temperature and precipitation trends; areas with higher elevation and fall precipitation anomalies had negative associations with EOS trends. Together, this work demonstrates the value of remote sensing in furthering our understanding of long-term forest responses to changing environmental conditions. By highlighting potential changes in forest composition and function, the research presented here can be used to develop forest conservation and management strategies in the Northeast