Preeti Mali

Use and analysis of temporal map algebra for vegetation index compositing

Temporal image cubes are created using co-registered temporal image data sets as ordered stacks of bands within a multi-band image. These may be manipulated and analyzed using new temporal map algebra (TMA) functions that extend normal raster map algebra from operating on a single raster band to operating on one, many, or all bands within the temporal image cube. Temporal image cubes can be constructed to encode attribute information such as image quality, scan angle, or other attribute per each pixel. Multiple cubes may be utilized to manipulate image data and generate model-specific results. Low resolution imagery such as NOAA-AVHRR and MODIS require the use maximum value compositing (MVC) that consider local pixel values in time series multi-temporal NDVI image cube. Using temporal map algebra multiple criteria may be imposed on attribute cubes to create masks cubes that can select from temporal image cubes only those specific pixels that meet scan angle, quality, or other user-defined criteria. After reducing the image data to only the desired pixels, local and focal functions may be employed to create custom composites for specific temporal intervals.

Multi-Sensor & Temporal Data Fusion for Cloud-Free Vegetation Index Composites

Remotely sensed data from satellite sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) provide almost daily global coverage. Satellite sensor data are used to create scientific data products that include surface reflectance, land surface temperature, sea-surface temperature and many others, as well as ancillary metadata like satellite viewing angle and data quality information. Vegetation indices, like Normalized Difference Vegetation Index (NDVI) (Jensen, 2000), derived from reflectance products of satellite sensors, are generally used as indicators of relative abundance and activity of green vegetation, often including leaf-area index, percentage green cover, chlorophyll content, green biomass, and absorbed photosynthetically active radiation. Frequently reflectance data products needed to create vegetation indices include undesired cloud, water vapour, aerosols, or other poor quality pixels. Continuous monitoring of occurrences such as droughts, frosts, floods, major fires, forest stress, or natural disasters are just a few of the circumstances when daily cloud-free vegetation index composites data are of high utility. The traditional approach to creating a single synthetic cloud-free image that includes ideal values selected from a temporal set of possibly cloudy satellite images collected over a continuous time period of interest is called multi-temporal compositing (MTC). MTC compositing is generally used to create vegetation indices images from data products with high temporal and low spatial resolution such as those produced by the National Oceanic and Atmospheric Administration’s (NOAA) AVHRR sensor or NASA’s MODIS (Justice, 1998). Various methods of MTC have been utilized to produce scientific data products including Maximum Value Compositing (MVC), Constrained View Maximum Value Compositing (CV-MVC) (Cihlar et. al., 1994, Heute et. al., 1999), and CVMVC which incorporates sensor data quality information. The motivation for investigating multi-sensor and temporal fusion for creating hightemporal frequency composites is to overcome the limitations of single-sensor MTC methods and deliver continuous monitoring capabilities that exceed the temporal frequency of currently available 8-day, 10-day, 14-day, and 16-day composite vegetation index data products. Currently available composite products do not provide sufficient frequency and temporal detail to capture and quantify important events, do not deliver data for continuous environmental monitoring, and provide temporally sparse inputs precluding effective agricultural productivity modelling. O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

Data Fusion, De-noising, and Filtering to Produce Cloud-Free High Quality Temporal Composites Employing Parallel Temporal Map Algebra

Remotely sensed images from satellite sensors such as MODIS Aqua and Terra provide high temporal resolution and wide area coverage. Unfortunately, these images frequently include undesired cloud and water cover. Areas of cloud or water cover preclude analysis and interpretation of terrestrial land cover, vegetation vigor, and/or analysis of change. Cross platform multi-temporal image compositing techniques may be employed to create daily synthetic cloud free images using fused images from Aqua and Terra MODIS satellite images, and then creating a composite that includes representative values derived from a set of possibly cloudy satellite images collected during a given longer time period of interest. Spatio-temporal analytical processing methods that utilize moderate spatial resolution satellite imagery with high temporal resolution to create multi-temporal composites are data intensive and computationally intensive. Therefore, a study of the strategies using high performance parallel solutions is required. This research focuses on analyzing the fusion, de-noising, filtering, and compositing strategies for vegetation indices using parallel temporal map algebra. The report provides objective findings on methods and the relative benefits observed from various analysis methods and parallelization strategies.

Detecting and managing change in spatial data-land use and infrastructure change analysis and detection

In an effort to improve the accuracy, currency, and maintainability of spatial databases, technologies are needed to provide efficient, cost-effective land use change detection and feature extraction capabilities. A hybrid change detection methodology was explored as an initial process in a systematic multiresolution approach that separates areas of change from areas of no change. Change detection methods employed in the exploration of a hybrid change detection methodology were univariate image differencing, image ratioing, tasseled cap analysis, vegetation indexing, change vector analysis and post-classification thematic change detection. Textural analysis was then employed as a method for further refinement of change detection outputs to eliminate unwanted errors of commission. The results of the proposed hybrid change detection method show promise, and continued research is proposed to improve results

Using an Interoperable Geoprocessing System for Hydrological Simulation

Calculation of land use and topographical parameters for hydrological models is usually performed using GIS software. Current approaches, however, are limited by the intensive user dependency on fixed routines, commands, sequences, etc., specific to the software been used. Therefore, current modeling approaches for geoprocessing suffer from a lack of interoperability. Algorithms that translate/summarize geographical information have been tightly designed to the needs and characteristics of specific models and GIS systems, and are not reusable. This means that each connection between a specific pairing of an environmental model and a GIS system requires a unique translation algorithm, which, in turn, requires new resources to repeatedly solve the same conceptual problem. This paper proposes the use of the Geospatial Object Library for Environmental Modeling (GEOLEM) as an alternative or complementary tool for calculating land use and topographical parameters. Although this research focuses on the calculati...