The Use of Remotely Sensed Imagery as A Pedagogic Tool for Natural Hazards Education

Abstract

Remotely sensed imagery from space-borne platforms is a valuable tool for illustrating the spatial scale and temporal changes associated with a variety of natural hazards. Imagery in numerous formats is freely available from US government websites. This article illustrates the use of such imagery as a pedagogic tool in a university-level Natural Hazards class. Illustrated examples include such hazards as volcanic eruptions, forest fires, dust storms, and hurricanes.     

Application of remote sensing for investigating mining geological hazards

Abstract

To investigate geological mining hazards using digital techniques such as high-resolution remote sensing, a semi-automatically geological mining hazards extraction method is proposed based on the case of the Shijiaying coal mine, located in Fangshan District, Beijing, China. In the method, the vegetation is first removed using the normalized difference vegetation index (NDVI) on the GeoEye-1 data. Then, geological mining hazards interpretation features are determined after color enhancement using principal component analysis (PCA) transformation. Bitmaps mainly covered by geological mining hazards are isolated by masking operation in the environment for visualizing images software. Next, each bitmap is classified into a two-valued imagery using support vector machine algorithm. In the two-valued imagery, 1 denotes the geological mining hazards, while 0 denotes none. Afterwards, the two-valued imagery is converted into a vector graph by corresponding functions in the ArcGIS software and no geological mining hazards regions in the vector graph are deleted manually. Finally, the correlation between factors (such as mining activity, lithology, geological structure, and slope) and geological mining hazards is analyzed using a logistic regression and a hazardous-area forecasting model is built. The results of field verification show that the accuracy of the geological mining hazards extraction method is 98.1% and the results of the hazardous-area forecasting indicate that the logistic regression is an effective model in assessing geological hazard risks and that mining activity is the main contributing factor to the hazards, while geological structure, slope, lithology, roughness of the surface, and aspect are the secondary.     

“Mission Geography” and the Use of Satellite Imagery in K‐12 Geographic Education — A NASA ‐ GENIP Partnership

Abstract

Mission Geography is a collaboration between the United States' National Aeronautic and Space Administration (NASA) and the American “Geography Education National Implementation Project” (GENIP), created to provide for and develop curriculum supplementary material for teachers of grades Kindergarten through High School (grade twelve). Mission Geography uses NASA imagery and scientific inquiries to teach about Earth. This paper provides an example of how geographic concepts concerning volcanoes and volcanic hazards can be taught at the American middle school level (grades 5‐8) using NASA resources     

Use of Very High Spatial Resolution Remotely Sensed Imagery for Assessing Land‐Cover Changes in Shrub Habitat Preserves of Southern California

Abstract

The goal of this research was to explore the utility of very high spatial resolution, digital remotely sensed imagery for monitoring land‐cover changes in habitat preserves within southern California coastal shrublands. Changes were assessed for Los Penasquitos Canyon Preserve, a large open space in San Diego County, over the 1996 to 1999 period for which imagery was available.

Multispectral, digital camera imagery from two summer dates, three years apart, was acquired using the Airborne Data Acquisition and Registration (ADAR) digital‐camera system. These very high resolution (VHR) image data (1m), composed of three visible and one near‐infrared wavebands (V/NIR), were the primary image input for assessing land cover change. Image‐derived datasets generated from georeferenced and registered ADAR imagery included multitemporal overlays and multitemporal band differencing with threshold selection. Two different multitemporal image classifications were generated from these datasets and compared. Single‐date imagery was analyzed interactively with image‐derived datasets and with information from field observations in an effort to discern change types. A ground sampling survey conducted soon after the 1999 image acquisition provided concurrent ground reference data.

Most changes occurring within the three‐year interval were associated with transitional phenological states and differential precipitation effects on herbaceous cover. Variations in air temperatures and timing of rainfall contributed to differences that the seven‐week image acquisition offset may have caused. Disturbance factors of mechanical clearing, erosion, potentially invasive plants, and fire were evident and their influence on the presence, absence, and type of vegetation cover were likely sources of change signals.

The multitemporal VHR, V/NIR image data enabled relatively fine‐scale land cover changes to be detected and identified. Band differencing followed by multitemporal classification provided an effective means for detecting vegetation increase or decrease. Detailed information on short‐term disturbance effects and long‐term vegetation type conversions can be extracted if image acquisitions are carefully planned and geometric and radiometric processing steps are implemented.     

Customized Visualization of Natural Hazards Assessment Results and Associated Uncertainties through Interactive Functionality

Communication of natural hazard assessment results is crucial to protect people and infrastructure from devastating impacts of extreme events. While hazard maps provide important information on potential impacts, their interpretation and the general knowledge exchange between stakeholders is often difficult. Web-based information systems contain the potential to support hazard management tasks by fast distribution and customization of hazard visualizations through interactive functionality. Cartographic principles are, however, often ignored in existing web-based visualizations which leads to poor graphical results and consequently to an impairment of the information flow. While these issues need to be solved, a new task is already waiting: the integration of uncertainty information into hazard visualizations. Since many hazard management activities rely on hazard assessment results, communication of associated uncertainties among experts is vital.

The challenge of this research is to overcome these existing shortcomings by combining high quality cartographic visualizations of natural hazard data as well as associated uncertainties with interactive functionality. The resulting web-based cartographic information system will convene the needs of natural hazard specialists by offering a high level of customization: the suggested visualizations include various cartographic techniques such as the application of textures, bars, and interpolated surfaces. The possibility to interactively select particular data sets, customize colors, choose dimensions, query attribute data, and include uncertainty information facilitates the interpretation of complex data and finally the communication among natural hazard specialists.

In this article we summarize requirements that have to be considered, suggest functionalities necessary to perform natural hazards management tasks, and present a prototype of an expert system for the visualization and exploration of natural hazards assessments results and associated uncertainties.     

Using a Combination of Digital Aerial Photography and Satellite Remote Sensing to Assist Contemporary Restructuring in an Urban Area of South Africa

Abstract

The transition and restructuring process of urban South Africa are currently in the phase of identifying land development objectives. These objectives aim to integrate previously segregated areas through integrated development plans. This research aims firstly to identify and describe the historical development of the spatial form and structure of the secondary city and capital of the Northern Province, Pietersburg and its dispersed peripheral towns. Supervised classification of SPOT HRV multispectral imagery is used to support the theoretical explanation. Images from an airborne digital Kodak DCS 420 camera are used to provide training sites in the pre‐classification stages, and also provide field data to the process of post‐classification accuracy assessment. Secondly, SPOT HRV imagery is applied to identify the stark contrast in urban development between the city of Pietersburg and its surrounding former homeland towns. Both built and natural environmental aspects are investigated. In conclusion benefits and problems of assessing urban morphology and development in a developing country by means of a combination of satellite imagery and digital aerial photography are discussed.     

The 13th Asian conference on remote sensing,Mongolia

Abstract

Three spatial resolutions of airborne remote sensing imagery (60 cm, 1 m, and 2 m) collected over multi‐layer aspen, pine, spruce, and mixedwood forest stands in Alberta on July 18^th, 1998 were tested for their ability to provide a statistical stand discrimination based on spatial co‐occurrence texture analysis. As spatial resolution increased, classification accuracies increased. The highest classification accuracy of 86.7% was obtained using the highest image spatial resolution data (60 cm), with spatial co‐occurrence texture and spectral signatures combined, and a thirteen‐class multi‐layer stand stratification. The texture of the highest spatial resolution imagery (60 cm pixel resolution) was interpreted to contain information on the crown architecture of individual trees. In larger windows, the texture was interpreted to contain information on stand structure. Texture of lower spatial resolution imagery (1 m and 2 m pixel resolution) could not detect individual tree crown architecture and was determined to be related primarily to stand structure characteristics. The use of texture channels improved the per‐plot classification accuracies by 15.7%, compared to the use of the spectral data alone.     

Fuzzy Modelling of African Ecoregions and Ecotones Using AVHRR NDVI Temporal Imagery

Abstract

Conventional methods of deriving global or continental vegetation maps from the National Oceanic and Atmospheric Administration's (NOAA) Advanced Very High Resolution Radiometer (AVHRR) time series data are based on two‐value Boolean logic, which cannot properly model the so‐called ecotone, the transition zone between adjacent ecosystems. New methods and data models that have been developed on the basis of fuzzy logic to address the “mixed pixel” issue in multi‐spectral imagery can also be used with multi‐temporal imagery to handle the mixture of vegetation types within an ecotone. This study introduces the concept of semantic space and its transformation from spectral feature space, which utilizes a fuzzy logic approach to characterize the continuum of vegetation communities in the African continent from AVHRR multi‐temporal (12 months for three years from 1986 to 1988) NDVI data. The fuzzy procedure was based on the Fuzzy c‐Means (FCM) algorithm with significant modifications to improve processing speed for handling large volumes of data. A second‐order mapping approach was also devised to explicitly represent subdominant vegetative coverage in ecotones and other heterogeneous regions. Comparisons between a Sub‐Saharan African Vegetation Map compiled by the International Union for Conservation of Nature (IUCN) in 1986 and the maps derived from this study demonstrated that fuzzy modeling and classification might provide a better and more realistic representation of the vegetative characteristics of the region.     

Remote Sensing of Spatial and Temporal Vegetation Dynamics in Hispaniola: A Comparison of Haiti and the Dominican Republic

Abstract

Spatial and temporal vegetation contrasts between the nations of Haiti and the Dominican are analyzed using NDVI data derived from 30m resolution Landsat imagery and 8km resolution AVHRR imagery from the NOAA / NASA Pathfinder database. Analysis of vegetation dynamics in the Hispaniola border region indicates denser vegetation cover and a stronger correlation between elevation, slope, and NDVI on the Dominican side of the frontier. Temporal patterns of NDVI dynamics along the frontier suggest that changes in biomass are both more homogeneous and more extreme on the Haitian side. Analysis of 17 years of 8km resolution AVHRR imagery for the entire island of Hispaniola reveals consistently higher NDVI values for the Dominican Republic and a distinct intra‐annual pattern of mean monthly NDVI deviations that have important implications for future studies of vegetation dynamics in the region.     

FORSAT: a 3D forest monitoring system for cover mapping and volumetric 3D change detection

ABSTRACT

A 3D forest monitoring system, called FORSAT (a satellite very high resolution image processing platform for forest assessment), was developed for the extraction of 3D geometric forest information from very high resolution (VHR) satellite imagery and the automatic 3D change detection. FORSAT is composed of two complementary tasks: (1) the geometric and radiometric processing of satellite optical imagery and digital surface model (DSM) reconstruction by using a precise and robust image matching approach specially designed for VHR satellite imagery, (2) 3D surface comparison for change detection. It allows the users to import DSMs, align them using an advanced 3D surface matching approach and calculate the 3D differences and volume changes (together with precision values) between epochs. FORSAT is a single source and flexible forest information solution, allowing expert and non-expert remote sensing users to monitor forests in three and four (time) dimensions. The geometric resolution and thematic content of VHR optical imagery are sufficient for many forest information needs such as deforestation, clear-cut and fire severity mapping. The capacity and benefits of FORSAT, as a forest information system contributing to the sustainable forest management, have been tested and validated in case studies located in Austria, Switzerland and Spain.     

我国灾害遥感的作用、现状及发展

本文从现代遥感技术系统的构成和特点出发,论述了遥感在减灾活动,包括预警、监测、评价和辅助决策中的重要作用。并在回顾我国灾害遥感现状的基础上,提出了今后发展的设想。作者认为:建立实用性的重大自然灾害遥感监测评价技术系统,是今后我国灾害遥感发展的中心环节和主要任务。该系统应包括航天遥感宏观监测系统,航空遥感全天候、实时监测系统,地面灾害及背景监测系统,灾害及背景数据库系统,灾害分析、评价与辅助决策系统以及灾害防治救援通讯指挥系统。     

Exploring natural and anthropogenic risk for cultural heritage in Cyprus using remote sensing and GIS

Abstract

On site observation is the most common way of monitoring cultural heritage sites and monuments in Cyprus. However, this procedure that includes data collection, periodical observations, and multivariate risk assessment analysis is difficult to accomplish with the traditional practices and methods since it is time consuming and expensive. Furthermore, many archaeological sites and monuments are located at inaccessible areas, far away from the main road network and urban areas. Satellite remote sensing and Geographical Information Systems (GIS) can successfully confront this problem by providing the scientists with integrated monitoring of the study areas and the unique advantage to store and manipulate a large amount of spatial and attribute data simultaneously. Actually the monitoring and identification of several natural and anthropogenic hazards in the vicinity of the cultural heritage sites in Cyprus, seems to be one of the main priorities of its governmental and municipal authorities. This study aims to integrate both satellite remote sensing techniques and GIS in a multidisciplinary approach, for monitoring anthropogenic and natural hazards with the use of archived and up-to-date multitemporal remotely sensed images in the study area, namely in areas nearby cultural heritage sites and monuments in Cyprus. In this study anthropogenic hazards include urbanisation and extended land use changes in the surroundings of archaeological sites and natural hazards concern seismicity and sea erosion.     

Mt. Pinatubo Volcano/Typhoon Yunya

Abstract

Lineaments have proved to have greater control over groundwater but their depth persistence and their control over groundwater flow have remain unexplored for many years. In the present study an attempt has been made to unearth the depth persistence of lineaments by integrating data collected from resistivity soundings. The lineaments are interpreted from IRS 1A imagery and revealed NE‐SW, NNW‐SSE, NNE‐SSW, N‐S, E‐W and NW‐SE orientations. The integration of resistivity data shows that N‐S and NW‐SE trending lineaments have depth persistence and also have greater control over groundwater movement.     

COMMENTS ON K.A. SALISHCHEV'S PAPER

A method is described for the mapping of exogenous geomorphologic hazards (mass movements, frost heaving, suffosion, melting of permafrost) in the Lake Baykal area using space imagery. Black-and-white photography from the Salyut-4 space station (1:2,100,000 scale) was used as a base for the preparation of a series of maps depicting natural conditions (morphotectonic and physical geographic regions, faults, climate, dominant exogenous processes) used as a base for compiling a map of planning and engineering measures for the prevention and amelioration of geomorphologic hazards. Translated from: Geografiya i prirodnyye resursy, 1985, No. 2, pp. 59-67.     

Evaluation of machine learning models for rice dry biomass estimation and mapping using quad-source optical imagery

ABSTRACT

Several machine learning regression models have been advanced for the estimation of crop biophysical parameters with optical satellite imagery. However, literature on the comparative performances of such models is still limited in range and scope, especially under multiple data sources, despite the potential of multi-source imagery to improving crop monitoring in cloudy areas. To fill in this knowledge gap, this study explored the synergistic use of Landsat-8, Sentinel-2A, China’s environment and disaster monitoring and forecasting satellites (HJ-1 A and B) and Gaofen-1 (GF-1) data to evaluate four machine learning regression models that include Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and Gradient Boosting Decision Tree (GBDT), for rice dry biomass estimation and mapping. Taking a major rice cultivation area in southeast China as case study during the 2016 and 2017 growing seasons, a cross-calibrated time series of the Enhanced Vegetation Index (EVI) was obtained from the quad-source optical imagery and on which the aforementioned models were applied, respectively. Results indicate that in the before rice heading scenario, the most accurate dry biomass estimates were obtained by the GBDT model (R² of 0.82 and RMSE of 191.8 g/m²) followed by the RF model (R² of 0.79 and RMSE of 197.8 g/m²). After heading, the k-NN model performed best (R² of 0.43 and RMSE of 452.1 g/m²) followed by the RF model (R² of 0.42 and RMSE of 464.7 g/m²). Whist the k-NN model performed least in the before heading scenario, SVM performed least in the after heading scenario. These findings may suggest that machine learning regression models based on an ensemble of decision trees (RF and GBDT) are more suitable for the estimation of rice dry biomass, at least with optical satellite imagery. Studies that would extend the evaluation of these machine learning models, to other parameters like leaf area index, and to microwave imagery, are hereby recommended.     

A high resolution multispectral video system

The design, operation, and testing of a high resolution multispectral video system (HRMVS) is described. The system uses state‐of‐the‐art video technology. It incorporates three black‐and‐white (B & W) visible/near‐infrared (NIR) (0.4–1.1 μm) light sensitive solid‐state cameras equipped with band‐pass filters and provides two kinds of simultaneously synchronized video images: (1) color‐infrared (CIR) composite imagery and (2) its three‐band B & W image components. Only CIR composite imagery is presented here with its B & W components (yellow‐green, red, and NIR bands), but any false color combination can be generated by the encoder. Images are recorded on high resolution (400 horizontal lines) Super (S)‐VHS recorders. An independent solid‐state conventional color (0.4–0.7 μm) camcorder (S‐VHS) was optional to this system. It was set up to acquire imagery at approximately the same field‐of‐view as the three‐camera synchronized system. Examples of imagery of various natural resource characteristics are given. Color‐infrared composite imagery had similar color tonal renditions to that of CIR film. The high resolution multispectral Black & White B & W images showed that some terrain features could be discriminated better in certain bands. For example, the yellow‐green (543–0.552 μm) band was best for distinguishing chlorosis in grain sorghum (Sorghum bicolor Moench), whereas the NIR band was optimum for separating biomass levels in alfalfa (Medicago sativa L.). The color and Black & White B & W multispectral image results showed this system to be a valuable and versatile tool for a variety of remote sensing applications.     

Mapping Wildfire Burn Severity in Southern California Forests and Shrublands Using Enhanced Thematic Mapper Imagery

Abstract

Wildfire is a major disturbance agent in Mediterranean Type Ecosystems (MTEs). Providing reliable, quantitative information on the area of burns and the level of damage caused is therefore important both for guiding resource management and global change monitoring. Previous studies have successfully mapped burn severity using remote sensing, but reliable accuracy has yet to be gained using standard methods over different vegetation types. The objective of this research was to classify burn severity across several vegetation types using Landsat ETM imagery in two areas affected by wildfire in southern California in June 1999. Spectral mixture analysis (SMA) using four reference endmembers (vegetation, soil, shade, non‐photosynthetic vegetation) and a single (charcoal‐ash) image endmember were used to enhance imagery prior to burn severity classification using decision trees. SMA provided a robust technique for enhancing fire‐affected areas due to its ability to extract sub‐pixel information and minimize the effects of topography on single date satellite data. Overall kappa classification accuracy results were high (0.71 and 0.85, respectively) for the burned areas, using five canopy consumption classes. Individual severity class accuracies ranged from 0.5 to 0.94.     

REMOTE SENSING IMAGERY IN THE STUDY OF SETTLEMENT ON ALLUVIAL FANS

The authors describe how remote sensing imagery may be employed in the zonation and mapping of the alluvial fans of the Fergana Basin, USSR. Settlement patterns depicted on these maps can then be analyzed by planners and civil defense personnel with a view toward minimizing risks posed by natural hazards endemic to alluvial fans: mudflows and other seismically triggered mass movements. Translated from: Geografiya i prirodnyye resursy, 1987, No. 3, pp. 50-55.     

Ocean Internal Wave Observations Using Space Shuttle and Satellite Imagery

Abstract

Space shuttle photographs and satellite radar (SAR) images provide an excellent view of high‐contrast ocean features such as internal waves, fronts, eddies, oil slicks, and cloud patterns which contain the signatures of atmospheric processes. Since ocean internal waves generate local currents which modulate surface wavelets and slicks, we have been able to detect packets of internal waves in space shuttle photographs and radar imagery of the Atlantic, Pacific, and Indian Oceans. A global database on internal waves has been developed at our center with support from ONR and NASA, and is accessible on the Internet. The database includes visible and radar imagery. To test the database, digitally orthorectified images were used for dynamic and statistical analysis of internal waves. In the deep ocean we found the wavelength distribution to be Gaussian while in the coastal ocean it is Rayleigh. Results have also been applied to non‐linear evolution studies of ocean internal waves, atmospheric solitary waves and to estimate ocean currents.