Effect of the atmosphere on the classification of LANDSAT data

The author has identified the following significant results. In conjunction with Turner's model for the correction of satellite data for atmospheric interference, the LOWTRAN-3 computer was used to calculate the atmospheric interference. Use of the program improved the contrast between different natural targets in the MSS LANDSAT data of Brasilia, Brazil. The classification accuracy of sugar canes was improved by about 9% in the multispectral data of Ribeirao Preto, Sao Paulo

Hyperspectral remote sensing of cyanobacterial pigments as indicators for cell populations and toxins in eutrophic lakes

The growth of mass populations of toxin-producing cyanobacteria is a serious concern for the ecological status of inland waterbodies and for human and animal health. In this study we examined the performance of four semi-analytical algorithms for the retrieval of chlorophyll a (Chl a) and phycocyanin (C-PC) from data acquired by the Compact Airborne Spectrographic Imager-2 (CASI-2) and the Airborne Imaging Spectrometer for Applications (AISA) Eagle sensor. The retrieval accuracies of the semi-analytical models were compared to those returned by optimally calibrated empirical band-ratio algorithms. The best-performing algorithm for the retrieval of Chl a was an empirical band-ratio model based on a quadratic function of the ratio of re!ectance at 710 and 670 nm (R2=0.832; RMSE=29.8%). However, this model only provided a marginally better retrieval than the best semi-analytical algorithm. The best-performing model for the retrieval of C-PC was a semi-analytical nested band-ratio model (R2=0.984; RMSE=3.98 mg m−3). The concentrations of C-PC retrieved using the semi-analytical model were correlated with cyanobacterial cell numbers (R2=0.380) and the particulate and total (particulate plus dissolved) pools of microcystins (R2=0.858 and 0.896 respectively). Importantly, both the empirical and semi-analytical algorithms were able to retrieve the concentration of C-PC at cyanobacterial cell concentrations below current warning thresholds for cyanobacteria in waterbodies. This demonstrates the potential of remote sensing to contribute to early-warning detection and monitoring of cyanobacterial blooms for human health protection at regional and global scales

Basic research planning in mathematical pattern recognition and image analysis

Fundamental problems encountered while attempting to develop automated techniques for applications of remote sensing are discussed under the following categories: (1) geometric and radiometric preprocessing; (2) spatial, spectral, temporal, syntactic, and ancillary digital image representation; (3) image partitioning, proportion estimation, and error models in object scene interference; (4) parallel processing and image data structures; and (5) continuing studies in polarization; computer architectures and parallel processing; and the applicability of "expert systems" to interactive analysis

Data analysis techniques

A large and diverse number of computational techniques are routinely used to process and analyze remotely sensed data. These techniques include: univariate statistics; multivariate statistics; principal component analysis; pattern recognition and classification; other multivariate techniques; geometric correction; registration and resampling; radiometric correction; enhancement; restoration; Fourier analysis; and filtering. Each of these techniques will be considered, in order

Investigation of techniques for inventorying forested regions. Volume 2: Forestry information system requirements and joint use of remotely sensed and ancillary data

The author has identified the following significant results. Effects of terrain topography in mountainous forested regions on LANDSAT signals and classifier training were found to be significant. The aspect of sloping terrain relative to the sun's azimuth was the major cause of variability. A relative insolation factor could be defined which, in a single variable, represents the joint effects of slope and aspect and solar geometry on irradiance. Forest canopy reflectances were bound, both through simulation, and empirically, to have nondiffuse reflectance characteristics. Training procedures could be improved by stratifying in the space of ancillary variables and training in each stratum. Application of the Tasselled-Cap transformation for LANDSAT data acquired over forested terrain could provide a viable technique for data compression and convenient physical interpretations

Integrated approach of remote sensing and micro-sensor technology for estimating evapotranspiration in Cyprus

 Papadavid George1,2, Hadjimitsis Diofantos1(1. Cyprus University of Technology, Cyprus;  2. Agricultural Research Institute, Cyprus) Abstract: The objective of this research project is to describe and apply a procedure for monitoring and improving the performance of on-demand irrigation networks, based on the integration of remote sensing techniques and simulation modeling of irrigation water in Cyprus, which is facing a severe drought in the last five years.  Multi-spectral satellite images are used to infer crop potential evapotranspiration, which is the main input for water balance simulations.  The need for estimating ET in Cyprus is imposed in order to determine the exact quantity of irrigated water needed for each specific crop.  The overuse of water for irrigation has resulted in eliminating the water resources in the whole island.  The determination of ET for irrigation purposes will be used as a vital tool for supporting the decision-making process in the management of water resources, on a technocratic level, and on the other hand will have a positive effect on the rest of water resources of Cyprus.  The integrated method applied, consisting of Remote Sensing techniques and micro-sensor technology, has shown that it can be a useful tool in the hands of agri-policy makers for sustainable irrigation.Keywords: remote sensing, wireless sensors, irrigation management, sustainability Citation: Papadavid George, Hadjimitsis Diofantos.  Integrated approach of remote sensing and micro-sensor technology for estimating evapotranspiration in Cyprus.  Agric Eng Int: CIGR Journal, 2010, 12(3): 1-11.   &nbsp

High Performance Computing Applications in Remote Sensing Studies for Land Cover Dynamics

Global and regional land cover studies require the ability to apply complex models on selected subsets of large amounts 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 such 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 I/O 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 TM scenes, while the second example focuses on image segmentation using a novel hierarchical connected components algorithm. Retrieval of global BRDF (Bidirectional Reflectance Distribution Function) in the red and near infrared wavelengths using four years (1983 to 1986) of Pathfinder AVHRR Land (PAL) data set 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 improvements in computational times can be achieved by using the high performance computing technology

Using Sentinel-3 Satellite Imagery for Cyanobacteria Development Monitoring

Great Lakes basins and inland waterways experience increased harmful algal bloom (HAB) production as a result of eutrophication– excess nutrient loading into waterways from agriculture and urbanization. Muskegon Lake, an estuary located on the eastern coast of Lake Michigan, was classified as a Great Lakes Area of Concern in 1985 due to severe HABs from several impairments including agricultural runoff and industrial waste dumping. Because of the ecosystem and human health threats posed by HABs such as wildlife and human illness and drinking water contamination, monitoring the abundance of HABs in inland waterways is imperative. The goal of this study was to evaluate the application and validity of remote sensing cyanobacteria abundance in Muskegon Lake using Sentinel-3 Ocean and Land Color Instrument (OLCI) satellite imagery from 2016-2021. Results show that the cyanobacteria quantification algorithm calculated high cell densities up to 1.25 million compared to in situ measurements up to 25,000 from a phycocyanin probe on the Muskegon Lake Buoy Observatory. However, remotely sensed cyanobacteria densities follow the same growth and die-off trends as calculated by the Buoy phycocyanin probe. Moreover, the results of this study display that remote sensing cyanobacteria HABs can supplement field data in flagging high HAB days for public health monitoring as well as documenting historical growth trends