Satellite geological and geophysical remote sensing of Iceland: Preliminary results of geologic, hydrologic, oceanographic, and agricultural studies with ERTS-1 imagery

The author has identified the following significant results. The wide variety of geological and geophysical phenomena which can be observed in Iceland, and particularly their very direct relation to the management of the country's natural resources, has provided great impetus to the use of ERTS-1 imagery to measure and map the dynamic natural phenomena in Iceland. MSS imagery is being used to study a large variety of geological and geophysical eruptive products, geologic structure, volcanic geomorphology, hydrologic, oceanographic, and agricultural phenomena of Iceland. Some of the preliminary results from this research projects are: (1) a large number of geological and volcanic features can be studied from ERTS-1 imagery, particularly imagery acquired at low sun angle, which had not previously been recognized; (2) under optimum conditions the ERTS-1 satellite can discern geothermal areas by their snow melt pattern or warm spring discharge into frozen lakes; (3) various maps at scales of 1:1 million and 1:500,000 can be updated and made more accurate with ERTS-1 imagery; (4) the correlation of water reserves with snowcover can improve the basis for planning electrical production in the management of water resources; (5) false-color composites (MSS) permitted the mapping of four types of vegetation: forested; grasslands, reclaimed, and cultivated areas, and the seasonal change of the vegetation, all of high value to rangeland management

A review of remotely sensed satellite image classification

Satellite image classification has a vital role for the extraction and analysis of the useful satellite image information. This paper comprises the study of the satellite images classification and Remote Sensing along with a brief overview of the previous studies that are proposed in this field. In this paper, the existing work has been explained utilizing the classification techniques on satellite images of Alwar region in India that covers decent land cover features like Vegetation, Water, Urban, Barren, and Rocky regions. The post- implementation of the classification algorithms, the classified image is obtained displaying different classes that are represented by different colours. Each feature is represented by a different colour and can be easily perceived from the image obtained after classification. The focus of this study is on enhancing the classification accuracy by using proper classifiers along with the novel feature extraction techniques and pre-processing steps. Work of different authors is being discussed in a tabular form defining the methods and outcomes of the respective studies

Program on Earth Observation Data Management Systems (EODMS)

An assessment was made of the needs of a group of potential users of satellite remotely sensed data (state, regional, and local agencies) involved in natural resources management in five states, and alternative data management systems to satisfy these needs are outlined. Tasks described include: (1) a comprehensive data needs analysis of state and local users; (2) the design of remote sensing-derivable information products that serve priority state and local data needs; (3) a cost and performance analysis of alternative processing centers for producing these products; (4) an assessment of the impacts of policy, regulation and government structure on implementing large-scale use of remote sensing technology in this community of users; and (5) the elaboration of alternative institutional arrangements for operational Earth Observation Data Management Systems (EODMS). It is concluded that an operational EODMS will be of most use to state, regional, and local agencies if it provides a full range of information services -- from raw data acquisition to interpretation and dissemination of final information products

Biogeographic analysis of the Tortugas Ecological Reserve: Examining the refuge effect following reserve establishment

Almost 120 days at sea aboard three NOAA research vessels and one fishing vessel over the past three years have supported biogeographic characterization of Tortugas Ecological Reserve (TER). This work initiated measurement of post-implementation effects of TER as a refuge for exploited species. In Tortugas South, seafloor transect surveys were conducted using divers, towed operated vehicles (TOV), remotely operated vehicles (ROV), various sonar platforms, and the Deepworker manned submersible. ARGOS drifter releases, satellite imagery, ichthyoplankton surveys, sea surface temperature, and diver census were combined to elucidate potential dispersal of fish spawning in this environment. Surveys are being compiled into a GIS to allow resource managers to gauge benthic resource status and distribution. Drifter studies have determined that within the ~ 30 days of larval life stage for fishes spawning at Tortugas South, larvae could reach as far downstream as Tampa Bay on the west Florida coast and Cape Canaveral on the east coast. Together with actual fish surveys and water mass delineation, this work demonstrates that the refuge status of this area endows it with tremendous downstream spillover and larval export potential for Florida reef habitats and promotes the maintenance of their fish communities. In Tortugas North, 30 randomly selected, permanent stations were established. Five stations were assigned to each of the following six areas: within Dry Tortugas National Park, falling north of the prevailing currents (Park North); within Dry Tortugas National Park, falling south of the prevailing currents (Park South); within the Ecological Reserve falling north of the prevailing currents (Reserve North); within the Ecological Reserve falling south of the prevailing currents (Reserve South); within areas immediately adjacent to these two strata, falling north of the prevailing currents (Out North); and within areas immediately adjacent to these two strata, falling south of the prevailing currents (Out South). Intensive characterization of these sites was conducted using multiple sonar techniques, TOV, ROV, diver-based digital video collection, diver-based fish census, towed fish capture, sediment particle-size, benthic chlorophyll analyses, and stable isotope analyses of primary producers, fish, and, shellfish. In order to complement and extend information from studies focused on the coral reef, we have targeted the ecotone between the reef and adjacent, non-reef habitats as these areas are well-known in ecology for indicating changes in trophic relationships at the ecosystem scale. Such trophic changes are hypothesized to occur as top-down control of the system grows with protection of piscivorous fishes. Preliminary isotope data, in conjunction with our prior results from the west Florida shelf, suggest that the shallow water benthic habitats surrounding the coral reefs of TER will prove to be the source of a significant amount of the primary production ultimately fueling fish production throughout TER and downstream throughout the range of larval fish dispersal. Therefore, the status and influence of the previously neglected, non-reef habitat within the refuge (comprising ~70% of TER) appears to be intimately tied to the health of the coral reef community proper. These data, collected in a biogeographic context, employing an integrated Before-After Control Impact design at multiple spatial scales, leave us poised to document and quantify the postimplementation effects of TER. Combined with the work at Tortugas South, this project represents a multi-disciplinary effort of sometimes disparate disciplines (fishery oceanography, benthic ecology, food web analysis, remote sensing/geography/landscape ecology, and resource management) and approaches (physical, biological, ecological). We expect the continuation of this effort to yield critical information for the management of TER and the evaluation of protected areas as a refuge for exploited species. (PDF contains 32 pages.

A study of Minnesota land and water resources using remote sensing

A pilot study of 60 lakes in Minnesota shows that LANDSAT data correlate very well with the Carlson trophic state index which is derived from measurements in the field. Nimbus satellite data reveal improvement in water quality in Lake Superior since the dumping of taconite tailings stopped in 1980. A feasibility study of using color infrared photography as a near real time tool for soil and crop management in corn and soybean areas of the state generated strong interest from farmers and agribusiness firms. The state geological survey had success in the use and applications of LANDSAT images. Subtleties of changes in vegetation, soil, and topography are such that ground water presence and depth to water table are nearly always impossible to qualify except for broad scale applications. Bedrock and structural differences as shown in lineaments offer great potential for resolution of some kinds of geologic studies. A synergistic concept is to be used to search for mineral resources in the northeastern part of the state

Texture Extraction Techniques for the Classification of Vegetation Species in Hyperspectral Imagery: Bag of Words Approach Based on Superpixels

Texture information allows characterizing the regions of interest in a scene. It refers to the spatial organization of the fundamental microstructures in natural images. Texture extraction has been a challenging problem in the field of image processing for decades. In this paper, different techniques based on the classic Bag of Words (BoW) approach for solving the texture extraction problem in the case of hyperspectral images of the Earth surface are proposed. In all cases the texture extraction is performed inside regions of the scene called superpixels and the algorithms profit from the information available in all the bands of the image. The main contribution is the use of superpixel segmentation to obtain irregular patches from the images prior to texture extraction. Texture descriptors are extracted from each superpixel. Three schemes for texture extraction are proposed: codebook-based, descriptor-based, and spectral-enhanced descriptor-based. The first one is based on a codebook generator algorithm, while the other two include additional stages of keypoint detection and description. The evaluation is performed by analyzing the results of a supervised classification using Support Vector Machines (SVM), Random Forest (RF), and Extreme Learning Machines (ELM) after the texture extraction. The results show that the extraction of textures inside superpixels increases the accuracy of the obtained classification map. The proposed techniques are analyzed over different multi and hyperspectral datasets focusing on vegetation species identification. The best classification results for each image in terms of Overall Accuracy (OA) range from 81.07% to 93.77% for images taken at a river area in Galicia (Spain), and from 79.63% to 95.79% for a vast rural region in China with reasonable computation timesThis work was supported in part by the Civil Program UAVs Initiative, promoted by the Xunta de Galicia and developed in partnership with the Babcock Company to promote the use of unmanned technologies in civil services. We also have to acknowledge the support by Ministerio de Ciencia e Innovación, Government of Spain (grant number PID2019-104834GB-I00), and Consellería de Educación, Universidade e Formación Profesional (ED431C 2018/19, and accreditation 2019-2022 ED431G-2019/04). All are cofunded by the European Regional Development Fund (ERDF)S

Utilizing Skylab data in on-going resources management programs in the state of Ohio

The author has identified the following significant results. The use of Skylab imagery for total area woodland surveys was found to be more accurate and cheaper than conventional surveys using aerial photo-plot techniques. Machine-aided (primarily density slicing) analyses of Skylab 190A and 190B color and infrared color photography demonstrated the feasibility of using such data for differentiating major timber classes including pines, hardwoods, mixed, cut, and brushland providing such analyses are made at scales of 1:24,000 and larger. Manual and machine-assisted image analysis indicated that spectral and spatial capabilities of Skylab EREP photography are adequate to distinguish most parameters of current, coal surface mining concern associated with: (1) active mining, (2) orphan lands, (3) reclaimed lands, and (4) active reclamation. Excellent results were achieved when comparing Skylab and aerial photographic interpretations of detailed surface mining features. Skylab photographs when combined with other data bases (e.g., census, agricultural land productivity, and transportation networks), provide a comprehensive, meaningful, and integrated view of major elements involved in the urbanization/encroachment process

THE EFFECTS OF TRAINING SET SIZE ON THE ACCURACY OF MAXIMUM LIKELIHOOD, NEURAL NETWORK AND SUPPORT VECTOR MACHINE CLASSIFICATION

In this paper, we assess the accuracy of maximum likelihood, neural network and support vector machine classification with changing training set size. The data come from Landsat-5 TM satellite covering the area of Klang, located in Selangor, Malaysia. Initially, single or multiple region of interest (ROI) are drawn on each of the land cover classes identified in order to extract the training sets. The size of the training pixels are then varied from 10% to 90% by resampling the pixels within the ROI using stratified random sampling technique, where nine training sets are generated. Landsat bands 1, 2, 3, 4, 5 and 7 are then used as the input for the maximum likelihood, neural network and support vector machine classification by making use all the nine training sets. The accuracy of the classifications are then assessed by comparing the classifications with a reference set using a confusion matrix. The result reveals that support vector machine classification has a more stable increase in accuracy than maximum likelihood but neural network shows a decreasing trend as the size of training set increases

SciTech News Volume 71, No. 1 (2017)

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