Spatiotemporal subpixel mapping of time-series images

Land cover/land use (LCLU) information extraction from multitemporal sequences of remote sensing imagery is becoming increasingly important. Mixed pixels are a common problem in Landsat and MODIS images that are used widely for LCLU monitoring. Recently developed subpixel mapping (SPM) techniques can extract LCLU information at the subpixel level by dividing mixed pixels into subpixels to which hard classes are then allocated. However, SPM has rarely been studied for time-series images (TSIs). In this paper, a spatiotemporal SPM approach was proposed for SPM of TSIs. In contrast to conventional spatial dependence-based SPM methods, the proposed approach considers simultaneously spatial and temporal dependences, with the former considering the correlation of subpixel classes within each image and the latter considering the correlation of subpixel classes between images in a temporal sequence. The proposed approach was developed assuming the availability of one fine spatial resolution map which exists among the TSIs. The SPM of TSIs is formulated as a constrained optimization problem. Under the coherence constraint imposed by the coarse LCLU proportions, the objective is to maximize the spatiotemporal dependence, which is defined by blending both spatial and temporal dependences. Experiments on three data sets showed that the proposed approach can provide more accurate subpixel resolution TSIs than conventional SPM methods. The SPM results obtained from the TSIs provide an excellent opportunity for LCLU dynamic monitoring and change detection at a finer spatial resolution than the available coarse spatial resolution TSIs

LANDSCAPE HETEROGENEITY AND SPATIO-TEMPORAL RESOLUTION CONSIDERATIONS FOR MAPPING LAND COVER CHANGES

Land cover is changing dramatically worldwide from both anthropogenic and natural drivers. In the United States, the rates and types of land cover change have varied temporally due to government policy, environmental regulation, global and national economic conditions, and regional weather and climate variability. Land cover changes can cause environmental degradation that affects long-term sustainability of human societies. Therefore, balancing the human need and environmental degradation requires explicit knowledge about environmental changes over multiple scales and perspectives. Remote sensing has been used as an effective tool to assess land changes across broad scales with multiple resolutions. However, the extraction of information from remotely sensed images is still challenged by the complex interaction between land cover heterogeneity and spatial as well as temporal resolutions. This dissertation aims at exploring such interaction in data classification and data fusion to better extract useful information about land cover. To achieve such goal, this dissertation first analyzes the impact of land cover heterogeneity in per-pixel and subpixel classification. Furthermore, this study also analyzes and proposes a data fusion method to better detect forest disturbances with high spatial and temporal resolutions. Using a high spatio-temporal resolution map of forest disturbances, this study suggests the use of temporal characteristics of disturbances to identify disturbance types. This study uses the South-Central United States as a case study for all experiments