Bayesian Method for Building Frequent Landsat-Like NDVI Datasets by Integrating MODIS and Landsat NDVI

Studies related to vegetation dynamics in heterogeneous landscapes often require Normalized Difference Vegetation Index (NDVI) datasets with both high spatial resolution and frequent coverage, which cannot be satisfied by a single sensor due to technical limitations. In this study, we propose a new method called NDVI-Bayesian Spatiotemporal Fusion Model (NDVI-BSFM) for accurately and effectively building frequent high spatial resolution Landsat-like NDVI datasets by integrating Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat NDVI. Experimental comparisons with the results obtained using other popular methods (i.e., the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), and the Flexible Spatiotemporal DAta Fusion (FSDAF) method) showed that our proposed method has the following advantages: (1) it can obtain more accurate estimates; (2) it can retain more spatial detail; (3) its prediction accuracy is less dependent on the quality of the MODIS NDVI on the specific prediction date; and (4) it produces smoother NDVI time series profiles. All of these advantages demonstrate the strengths and the robustness of the proposed NDVI-BSFM in providing reliable high spatial and temporal resolution NDVI datasets to support other land surface process studies

Crop monitoring and yield estimation using polarimetric SAR and optical satellite data in southwestern Ontario

Optical satellite data have been proven as an efficient source to extract crop information and monitor crop growth conditions over large areas. In local- to subfield-scale crop monitoring studies, both high spatial resolution and high temporal resolution of the image data are important. However, the acquisition of optical data is limited by the constant contamination of clouds in cloudy areas. This thesis explores the potential of polarimetric Synthetic Aperture Radar (SAR) satellite data and the spatio-temporal data fusion approach in crop monitoring and yield estimation applications in southwestern Ontario. Firstly, the sensitivity of 16 parameters derived from C-band Radarsat-2 polarimetric SAR data to crop height and fractional vegetation cover (FVC) was investigated. The results show that the SAR backscatters are affected by many factors unrelated to the crop canopy such as the incidence angle and the soil background and the degree of sensitivity varies with the crop types, growing stages, and the polarimetric SAR parameters. Secondly, the Minimum Noise Fraction (MNF) transformation, for the first time, was applied to multitemporal Radarsat-2 polarimetric SAR data in cropland area mapping based on the random forest classifier. An overall classification accuracy of 95.89% was achieved using the MNF transformation of the multi-temporal coherency matrix acquired from July to November. Then, a spatio-temporal data fusion method was developed to generate Normalized Difference Vegetation Index (NDVI) time series with both high spatial and high temporal resolution in heterogeneous regions using Landsat and MODIS imagery. The proposed method outperforms two other widely used methods. Finally, an improved crop phenology detection method was proposed, and the phenology information was then forced into the Simple Algorithm for Yield Estimation (SAFY) model to estimate crop biomass and yield. Compared with the SAFY model without forcing the remotely sensed phenology and a simple light use efficiency (LUE) model, the SAFY incorporating the remotely sensed phenology can improve the accuracy of biomass estimation by about 4% in relative Root Mean Square Error (RRMSE). The studies in this thesis improve the ability to monitor crop growth status and production at subfield scale

Characterizing an Annual Grass Invasion and Its Link to Environmental and Disturbance Factors Using Remote Sensing: New Tools and Applications

The spread of nonnative species across the globe has contributed to biodiversity loss and changes in ecosystem structure and function. Monitoring the introduction, naturalization, and spread of introduced species is critical in abating negative impacts wrought by species invasions. However, providing basic information concerning the presence or spread of many introduced species is often only considered once the invasion is already at an advanced stage, resulting in economic or ecological impacts. To better assess the present and future effects of and risk from introduced species, a clear understanding of invasive species populations' spatial and temporal patterns is needed. In some cases, remote sensing can serve as a useful information source that may be leveraged to characterize and monitor the invasion of nonnative species. This dissertation utilizes remote sensing and other geospatial data sources to better understand a nonnative annual grass (Ventenata dubia) invasion in the northwestern United States. Each research chapter builds a different facet of our understanding of this invasion by connecting land-surface processes, environmental conditions, and landscape disturbances. These three different topics help to describe the current state of the invasion, how it progressed to this state, and what this may mean for the future. The first research-chapter adapts image fusion methods to a cloud-computing environment in an effort to improve the spatial and temporal resolution of estimates of land surface phenology. The research focused on whether these methods would enable the estimation of phenology in heterogeneous landscapes that have historically been difficult to characterize. This chapter showed that high-quality image fusion results are possible with less processing time when image fusion is conducted in a cloud-computing environment. Further, this chapter showed that phenology estimated from these data can capture patterns occurring in grassland, shrubland, and open forest land cover types. The second research-chapter leverages the improved land surface phenology estimates from the first research-chapter to model the present distribution of the invasive annual grass species Ventenata dubia in the Blue Mountains Ecoregion of the interior Pacific Northwest. The results from this chapter suggest that both phenological and environmental information are needed to best detect populations of ventenata. The model based on phenological and environmental information predicted that ventenata was present in 7.8% of the Blue Mountains Ecoregion in 2017. The third research-chapter uses the information gained from the proceeding chapters to examine the change occurring over a decade of invasion by applying the model developed in the second research-chapter to the image archive and examining the invasion progression. Spatial and temporal patterns of the invasion were characterized by their association with the biophysical environment and the effect of wildfire on ventenata occurence was investigated. This analysis revealed that ventenata may have been introduced to lower shrubland ecosystems but has since transitioned to higher elevation dry conifer forests and areas with abundant ecotone. Furthermore, this chapter shows that wildfire occurrence and severity was associated with an increased probability of invasion in some parts of the interior Pacific Northwest. Although this research is focused on a specific annual grass species (Ventenata dubia), insights gained from this investigation are applicable to other invasive annual grasses. This research contributes to the scientific advancement in the study of exotic plant invasion and provides useful baseline ecological information that can be employed to inform both policy and management. Additionally, the methods developed for cloud-computing-based image fusion offer a useful tool to the remote sensing community that has the flexibility to be utilized for many applications

The global tree carrying capacity (keynote)

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