Satya Kalluri

The Pathfinder AVHRR land data set: An improved coarse resolution data set for terrestrial monitoring

Abstract A reprocessing of 12 years of global data from the Advanced Very High Resolution Radiometers on board the afternoon-viewing NOAA series satellites (NOAA-7, 9, and 11) is taking place as part of the NASA/NOAA Pathfinder project. A Pathfinder AVHRR land data set is being produced which is composed of global, 8 km NDVI with associated reflectances, brightness temperatures, solar and scan geometry, and cloud estimation. This data set is being processed using the best available methods in order to produce a consistent time series of data of unprecedented quality. Methods used in processing include a cross-satellite calibration, navigation using an orbital model and updated ephemerides, and correction for Rayleigh scattering. The data will be available to the community as both daily and composite data, and analysis of this long time series is expected to provide insight into terrestrial processes, seasonal and annual variability, and methods for handling large volume data sets.

Surveillance of arthropod vector-borne infectious diseases using remote sensing techniques: a review.

Epidemiologists are adopting new remote sensing techniques to study a variety of vector-borne diseases. Associations between satellite-derived environmental variables such as temperature, humidity, and land cover type and vector density are used to identify and characterize vector habitats. The convergence of factors such as the availability of multi-temporal satellite data and georeferenced epidemiological data, collaboration between remote sensing scientists and biologists, and the availability of sophisticated, statistical geographic information system and image processing algorithms in a desktop environment creates a fertile research environment. The use of remote sensing techniques to map vector-borne diseases has evolved significantly over the past 25 years. In this paper, we review the status of remote sensing studies of arthropod vector-borne diseases due to mosquitoes, ticks, blackflies, tsetse flies, and sandflies, which are responsible for the majority of vector-borne diseases in the world. Examples of simple image classification techniques that associate land use and land cover types with vector habitats, as well as complex statistical models that link satellite-derived multi-temporal meteorological observations with vector biology and abundance, are discussed here. Future improvements in remote sensing applications in epidemiology are also discussed.

Beware of per-pixel characterization of land cover

A simulation experiment was carried out to analyse the effects of the modulation transfer function on our ability to estimate the proportions of land cover within a pixel by linear mixture modelling. In the simulated landscape the proportion of each land cover type in every pixel was known exactly. The standard error of the estimate (SEE) between percentages derived from mixture modelling and the actual land cover percentages was 11%. Substantial improvements in estimating the percentages can be obtained simply by deriving estimates for pixels of twice the original dimensions, the SEE dropping to 4.16%, though this is with the obvious consequence of a final product with a coarser spatial resolution. Alternatively by deconvolving the input bands using a linear approximation of the point spread function the SEE can be reduced by almost as much, namely to 5.11%. If we combine the two approaches, by first doconvolving the bands, estimating the percentages and then aggregating resultant pixels to twice their original linear dimensions, the SEE drops to 2.24%.

Impact of sensor's point spread function on land cover characterization: Assessment and deconvolution

Measured and modeled point spread functions (PSF) of sensor systems indicate that a significant portion of the recorded signal of each pixel of a satellite image originates from outside the area represented by that pixel. This hinders the ability to derive surface information from satellite images on a per-pixel basis. In this study, the impact of the PSF of the Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m bands was assessed using four images representing different landscapes. Experimental results showed that though differences between pixels derived with and without PSF effects were small on the average, the PSF generally brightened dark objects and darkened bright objects. This impact of the PSF lowered the performance of a support vector machine (SVM) classifier by 5.4% in overall accuracy and increased the overall root mean square error (RMSE) by 2.4% in estimating subpixel percent land cover. An inversion method based on the known PSF model reduced the signals originating from surrounding areas by as much as 53%. This method differs from traditional PSF inversion deconvolution methods in that the PSF was adjusted with lower weighting factors for signals originating from neighboring pixels than those specified by the PSF model. By using this deconvolution method, the lost classification accuracy due to residual impact of PSF effects was reduced to only 1.66% in overall accuracy. The increase in the RMSE of estimated subpixel land cover proportions due to the residual impact of PSF effects was reduced to 0.64%. Spatial aggregation also effectively reduced the errors in estimated land cover proportion images. About 50% of the estimation errors were removed after applying the deconvolution method and aggregating derived proportion images to twice their dimensional pixel size.

An operational atmospheric correction algorithm for Landsat Thematic Mapper imagery over the land

An operational atmospheric correction algorithm for Thematic Mapper (TM) imagery has been developed for both sequential and parallel computer environments considering both aerosol and molecular scattering and absorption. The aerosol optical depth is estimated from the image itself using the dark object approach on a moving-window basis, and the surface reflectance is then retrieved by searching lookup tables that are created using a numerical radiative transfer code. The dark object pixels are identified and their surface reflectance estimated using TM channel 7 (2.1 μm). A variety of techniques are employed to improve computational efficiency. This method is validated by measured aerosol optical depth and extensive visual evaluations accompanied by statistical analysis. Results indicate that the approach is highly stable and useful for both qualitative imagery interpretation (haze removal) and quantitative analysis. Future research activities are also highlighted. The computer codes are available to the general scientific community.

Characterizing land surface anisotropy from AVHRR data at a global scale using high performance computing

We used the multi-temporal ten-day composite data from the Advanced Very High Resolution Radiometer (AVHRR) for the years 1983 to 1986 to retrieve the Bidirectional Ree ectance Distribution Function (BRDF) using high performance computing techniques. Three di Verent models are used: a simple linear model, a semi-empirical iterative model and a temporal model. The objectives of this study were to compare the performance of di Verent BRDF models at a global scale, assess the computational requirements and optimize the algorithm implementation using high performance computational techniques, and to determine ifthere is any coherent spatial structure in the coe Ycients ofdiVerent BRDF models corresponding to di Verent land cover types. The standard error between model computed ree ectances and the input data was used to quantify the performance ofthe models.Even though the iterative model is computationally more expensive (158 minutes) than either the simple linear model (15 minutes) or the temporal model (16 minutes), the results from all the three models were very similar when the BRDF was estimated at discrete time periods. If the BRDF models were applied without dividing the input data into discrete time intervals, then the temporal model gave better results than the other two. All the models were run on an IBM SP2 parallel machine with 16 CPUs. Most of the mountain- ous and snow covered areas in high latitudes had null values since the cloud screening algorithm used in the Pathe nder processing performed poorly in distin- guishing between snow and clouds. The BRDF coe Ycients of the iterative model and the Fourier coe Ycients of the temporal model showed a strong spatial structure corresponding to known variations in land cover.

High performance computing algorithms for land cover dynamics using remote sensing data.

Global and regional land cover studies need to apply complex models on selected subsets of large volumes of multi-sensor and multi-temporal data sets that have been derived from raw instrument measurements using widely accepted pre-processing algorithms. The computational and storage requirements of most of these studies far exceed what is possible on a single workstation environment. We have been pursuing a new approach that couples scalable and open distributed heterogeneous hardware with the development of high performance software for processing, indexing and organizing remotely sensed data. Hierarchical data management tools are used to ingest raw data, create metadata and organize the archived data so as to automatically achieve computational load balancing among the available nodes and minimize input/output overheads. We illustrate our approach with four specific examples. The first is the development of the first fast operational scheme for the atmospheric correction of Landsat Thematic Mapper scenes, while the second example focuses on image segmentation using a novel hierarchical connected components algorithm. Retrieval of the global Bidirectional Reflectance Distribution Function in the red and near-infrared wavelengths using four years (1983 to 1986) of Pathfinder Advanced Very High Resolution Radiometer (AVHRR) Land data is the focus of our third example. The fourth example is the development of a hierarchical data organization scheme that allows on-demand processing and retrieval of regional and global AVHRR data sets. Our results show that substantial reductions in computational times can be achieved by the high performance computing technology.

A simple single layer model to estimate transpiration from vegetation using multi-spectral and meteorological data

Abstract A methodology is developed here to model evapotranspiration (λEc ) from the canopy layer over large areas by combining satellite and ground measurements of biophysical and meteorological variables. The model developed here follows the energy balance approach, where λEc is estimated as a residual when the net radiation (Rn), sensible heat flux (H) and ground flux (G) are known. Multi-spectral measurements from the NOAA Advanced Very High Resolution Radiometer (AVHRR) were used along with routine meteorological measurements made on the ground to estimate components of the energy balance. The upwelling long wave radiation, and H from the canopy layer were modelled using the canopy temperature, obtained from a linear relation between the Normalized Difference Vegetation Index (NDVI) and surface temperature. This method separates flux measurements from the canopy and bare soil without the need for a complex two layer model. From theoretical analysis of canopy reflectance, leaf area, and canopy resista...

Historical satellite data used to map Pan‐Amazon forest cover

Deforestation in the Brazilian Amazon is well documented and the contributions of Brazilian deforestation to global change have been extensively discussed in both scientific and popular literature [e.g., Skole and Tucker, 1993]. However, deforestation within the non-Brazilian tropics of South America has received much less attention. The Pan-Amazon region covering Venezuela, Colombia, Ecuador, Peru, and Bolivia comprises ˜2 million km2 of tropical forest that is under increasing pressure from logging and development. Wall-to-wall high-resolution forest cover maps are needed to properly document the complex distribution patterns of deforestation in the Pan-Amazon [Tucker and Townshend, 2000]. The Deforestation Mapping Group at the University of Marylands Global Land Cover Facility is using Landsat data to generate tropical forest cover maps in this region (Figure l). The study shows that while rates of forest loss are generally lower than those in Brazil, there are hot spots where deforestation rates run as high as 2,200 km2 yr1.

A hierarchical data archiving and processing system to generate custom tailored products from AVHRR data

A novel indexing scheme is described to catalogue satellite data on a pixel basis. The objective of this research is to develop an efficient methodology to archive, retrieve and process satellite data, so that data products can be generated to meet the specific needs of individual scientists. When requesting data, users can specify the spatial and temporal resolution, geographic projection, choice of atmospheric correction, and the data selection methodology. The data processing is done in two stages. Satellite data is calibrated, navigated and quality flags are appended in the initial processing. This processed data is then indexed and stored. Secondary processing such as atmospheric correction and projection are done after a user requests the data to create custom made products. By dividing the processing in to two stages saves time, since the basic processing tasks such as navigation and calibration which are common to all requests are not repeated when different users request satellite data. The indexing scheme described can be extended to allow fusion of data sets from different sensors.