Impacts Of Market Forces And Agricultural Practices On Land Surfaces Related To Scientific Modeling And Remote Sensing Applications

Due to the rotational needs of crops, homogenous crop fields, and external influences such as market and policy changes, crop production generates significant changes to the landscape on annual and semi-annual basis. In this study we looked at two aspects of this change. In the first aspect of the study, we attempt to account for market and policy driven producer’s decision making through a new model constructed by pairing an economics model with the ALMANAC crop simulation model via a two-way coupling. This coupled model approach integrated farmer’s land-use choices based on relative economic returns and produced dynamic land use probabilities for ALMANAC simulations through a feedback loop. The coupled model approach was inter-compared with static crop modeling through a historic acreage approach, and comparable accuracies were found from both modeling efforts for the 2014 growing season. Furthermore, as a proof-concept effort, the method was applied to evaluate the impact of two scenarios on crop simulations: major crops (maize, soybean, and wheat) intensification through price increases (e.g. market change), as well as incentivized grassland conservation (e.g. policy change). The results of this sensitivity study suggest that the coupled system has the capability of integrating economic factors into traditional crop simulation, allowing for insight into the impacts of changes in markets and policies on agricultural landscapes and crop yields. In the second aspect of this study, changes to surface albedo driven by these landscape changes are investigated. Using collocated Moderate Resolution Imaging Spectroradiometer (MODIS) derived Bidirectional Reflectance Distribution Function (BRDF) with the Cropland Data Layer (CDL), we computed the daily albedo of homogenous agricultural fields across the United States for 55 crop types by wavelength, sky-type, day of year, crop, and hardiness zone over a four-year period (2015-2018). This study suggests that cropland spectral albedo is complicated by large variations over the course of the growing season, which can result in changes in reflectivity up to a factor of 2 at most wavelengths. This change was found to be unique per crop type, but predictable year-to-year for individual crops within specific regions, so generating a lookup table that incorporates these factors for use in remote sensing and atmospheric modeling applications is viable for albedo estimation. Additionally, impacts of crop types on broadband albedo were studied and found to be less conspicuous than the individual wavelength counterpart, but still significant over cropland. The results were used to evaluate the accuracy of a common method of albedo estimation, where NDVI is used as a proxy for albedo over cropland, and the NDVI method was found to have some significant limitations dependent on wavelength and day of year. Finally, a database of surface albedo variations as a function of growing period is constructed for 55 crops common to croplands across the United States. The constructed database can be used to aid both satellite remote sensing applications and long-term weather modeling efforts by providing a method for parameter adjustments based on crop driven albedo changes, including changes in cropland composition related to commodity markets and other external factors

Techniques for the extraction of spatial and spectral information in the supervised classification of hyperspectral imagery for land-cover applications

The objective of this PhD thesis is the development of spatialspectral information extraction techniques for supervised classification tasks, both by means of classical models and those based on deep learning, to be used in the classification of land use or land cover (LULC) multi- and hyper-spectral images obtained by remote sensing. The main goal is the efficient application of these techniques, so that they are able to obtain satisfactory classification results with a low use of computational resources and low execution time

Multitemporal assessment of crop parameters using multisensorial flying platforms

UAV sensors suitable for precision farming (Sony NEX-5n RGB camera; Canon Powershot modified to infrared sensitivity; MCA6 Tetracam; UAV spectrometer) were compared over differently treated grassland. The high resolution infrared and RGB camera allows spatial analysis of vegetation cover while the UAV spectrometer enables detailed analysis of spectral reflectance at single points. The high spatial and six-band spectral resolution of the MCA6 combines the opportunities of spatial and spectral analysis, but requires huge calibration efforts to acquire reliable data. All investigated systems were able to provide useful information in different distinct research areas of interest in the spatial or spectral domain. The UAV spectrometer was further used to assess multiangular reflectance patterns of wheat. By flying the UAV in a hemispherical path and directing the spectrometer towards the center of this hemisphere, the system acts like a large goniometer. Other than ground based goniometers, this novel method allows huge diameters without any need for infrastructures on the ground. Our experimental results shows good agreement with models and other goniometers, proving the approach valid. UAVs are capable of providing airborne data with a high spatial and temporal resolution due to their flexible and easy use. This was demonstrated in a two year survey. A high resolution RGB camera was flown every week over experimental plots of barley. From the RGB imagery a time series of the barley development was created using the color values. From this analysis we could track differences in the growth of multiple seeding densities and identify events of plant development such as ear pushing. These results lead towards promising practical applications that could be used in breeding for the phenotyping of crop varieties or in the scope of precision farming. With the advent of high endurance UAVs such as airships and the development of better light weight sensors, an exciting future for remote sensing from UAV in agriculture is expected

Vertex unique labelled subgraph mining

This thesis proposes the novel concept of Vertex Unique Labelled Subgraph (VULS) mining with respect to the field of graph-based knowledge discovery (or graph mining). The objective of the research is to investigate the benefits that the concept of VULS can offer in the context of vertex classification. A VULS is a subgraph with a particular structure and edge labelling that has a unique vertex labelling associated with it within a given (set of) host graph(s). VULS can describe highly discriminative and significant local geometries each with a particular associated vertex label pattern. This knowledge can then be used to predict vertex labels in ``unseen" graphs (graphs with edge labels, but without vertex labels). Thus this research is directed at identifying (mining) VULS, of various forms, that ``best" serve to both capture effectively graph information, while at the same time allowing for the generation of effective vertex label predictors (classifiers). To this end, four VULS classifiers are proposed, directed at mining four different kinds of VULS: (i) complete, (ii) minimal, (iii) frequent and (iv) minimal frequent. The thesis describes and discusses each of these in detail including, in each case, the theoretical definition and algorithms with respect to VULS identification and prediction. A full evaluation of each of the VULS categories is also presented. VULS has wide applicability in areas where the domain of interest can be represented in the form of some sort of a graph. The evaluation was primarily directed at predicting a form of deformation, known as springback, that occurs in the Asymmetric Incremental Sheet Forming (AISF) manufacturing process. For the evaluation two flat-topped, square-based, pyramid shapes were used. Each pyramid had been manufactured twice using Steel and twice using Titanium. The utilisation of VULS was also explored by applying the VULS concept to the field of satellite image interpretation. Satellite data describing two villages located in a rural part of the Ethiopian hinterland were used for this purpose. In each case the ground surface was represented in a similar manner to the way that AISF sheet metal surfaces were represented, with the $z$ dimension describing the grey scale value. The idea here was to predict vertex labels describing ground type. As will become apparent, from the work presented in this thesis, the VULS concept is well suited to the task of 3D surface classification with respect to AISF and satellite imagery. The thesis demonstrates that the use of frequent VULS (rather than the other forms of VULS considered) produces more efficient results in the AISF sheet metal forming application domain, whilst the use of minimal VULS provided promising results in the context of the satellite image interpretation domain. The reported evaluation also indicates that a sound foundation has been established for future work on more general VULS based vertex classification

On the separability of urban land-use categories in fine spatial scale land-cover data using structural pattern recognition

It has been widely asserted that the morphology of urban areas is a result of the interactions of urban function and urban form. This has led a number of studies to postulate, either explicitly or implicitly, that a mapping exists between the physical form (land cover) of the urban fabric and its corresponding function and activity (land use), although relatively little quantitative evidence has been presented to support this assertion. This paper presents the results of an investigation into the relationship between urban form and urban function using fine spatial scale digital map data. These are used to derive quantitative information on the morphological properties and spatial structure of the buildings present in a series of urban land-use categories identified in two urban areas (Cardiff and Orpington) in the United Kingdom. A statistical separability analysis of these land-use samples suggests that a mapping exists between urban form and function, which, if replicated for other urban areas, would allow urban land use to be inferred from an analysis of the spatial disposition of land-cover parcels, particularly buildings.

The Moderate Resolution Imaging Spectroradiometer (MODIS): Land Remote Sensing for Global Change Research

The first Moderate Resolution Imaging Spectroradiometer (MODIS) instrument is planned for launch by NASA in 1998. This instrument will provide a new and improved capability for terrestrial satellite remote sensing aimed at meeting the needs of global change research. The MODIS standard products will provide new and improved tools for moderate resolution land surface monitoring. These higher order data products have been designed to remove the burden of certain common types of data processing from the user community and meet the more general needs of global-to-regional monitoring, modeling, and assessment. The near-daily coverage of moderate resolution data from MODIS, coupled with the planned increase in high-resolution sampling from Landsat 7, will provide a powerful combination of observations. The full potential of MODIS will be realized once a stable and well-calibrated time-series of multispectral data has been established. In this paper the proposed MODIS standard products for land applications are described along with the current plans for data quality assessment and product validation