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.     

Imagery Scale and Type for Natural Hazards Analysis: Classroom Examples Using Forest Fires and Snow Avalanches

Abstract

The type of remotely sensed imagery best suited for natural hazards analysis is largely dependent on the geographic size and discreteness of the hazard in question. Some forms of imagery that are appropriate for analyses of spatially fixed, relatively fine‐scale hazards are inappropriate for use with geographically widespread and transient hazards, and vice versa. In this paper we utilize one study area, Waterton‐Glacier International Peace Park (Canada and USA), and two natural hazard types (snow avalanches and forest fires), to illustrate how these remote sensing principles can be presented to students of natural hazards. We also address the issue of familiarity, or lack thereof, that students have with different images from a variety of platforms.     

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.     

“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     

Landcover Classification in the Taieri River Catchment,New Zealand: A Focus on the Riparian Zone

Abstract

Riparian vegetation has a fundamental influence on the biological, chemical and physical nature of rivers. The quantification of riparian landcover is now recognised as being essential to the holistic study of the ecosystem characteristics of rivers. Medium resolution satellite imagery is now commonly used as an efficient and cost effective method for mapping vegetation cover; however such data often lack the resolution to provide accurate information about vegetation cover within riparian corridors. To assess this, we measure the accuracy of SPOT multispectral satellite imagery for classification of riparian vegetation along the Taieri River in New Zealand. In this paper, we discuss different sampling strategies for the classification of riparian zones. We conclude that SPOT multispectral imagery requires considerable interpretative analysis before being adequate to produce sufficiently detailed maps of riparian vegetation required for use in stream ecological research.     

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.     

A Cartographer,Shaped by Context and Challenged by Classics

Abstract

Using examples from the governmental, commercial and educational sectors, the development of photo maps and mapping using satellite imagery is outlined. The emphasis of this paper is on the design possibilities and limitations which are evident in these products. Contrast and legibility problems and image clutter are considered, but it is concluded that the institutional impediments to the collaboration of specialists in researching and designing such maps may be of most importance in determining progress in the use of such maps.     

ANALYSIS OF REMOTE SENSING IMAGERY IN ENGINEERING GEOMORPHOLOGY

An image interpretation technique known as “indicational analysis” is applied to engineering geomorphology–i.e., image characteristics visible on remote sensing imagery are used to infer (indicate) the presence of features that are not directly visible. In this case the identification of particular relief forms provides a basis for inferring the level of risk posed by geomorphological hazards to major construction projects such as railroads, pipelines, and highways. A key aspect of the procedure is a multiscalar approach, in which different components of the overall store of information brought to bear on a problem are obtained at different levels of interpretation. Translated from: Geomorfologiya, 1987, No. 2, pp. 35–42.     

Earth observations during space shuttle mission STS‐45 mission to planet earth March 24—April 2, 1992

Abstract

The impact of animals upon the landscape, through such actions as trampling, feeding, burrowing, mounding, and dam building, is a topic of interest in university geography classrooms. However, the relatively fine‐scale nature of most such impacts precludes its examination via remote sensing. Nevertheless, remote sensing of many types of animal impact is possible, and in this paper I illustrate several such examples, primarily drawn from free‐access sites on the World Wide Web, using a variety of remote sensing platforms. Instructors of physical geography or resource/land use management courses can thus introduce a variety of remote sensing concepts and imagery types in their classrooms while at the same time examining the role of animals on the landscape.     

Potential of Landsat-8 and Sentinel-2A composite for land use land cover analysis

Abstract

This study proposes the development of a multi-sensor, multi-spectral composite from Landsat-8 and Sentinel-2A imagery referred to as ‘LSC’ for land use land cover (LULC) characterisation and compared with respect to the hyperspectral imagery of the EO1: Hyperion sensor. A three-stage evaluation was implemented based on the similarity observed in the spectral response, supervised classification results and endmember abundance information obtained using linear spectral unmixing. The study was conducted for two areas located around Dhundi and Rohtak in Himachal Pradesh and Haryana, respectively. According to the analysis of the spectral reflectance curves, the spectral response of the LSC is capable of identifying major LULC classes. The kappa accuracy of 0.85 and 0.66 was observed for the classification results from LSC and Hyperion data for Dhundi and Rohtak datasets, respectively. The coefficient of determination was found to be above 0.9 for the LULC classes in both the datasets as compared to Hyperion, indicating a good agreement. Thus, these three-stage results indicated the significant potential of a composite derived from freely available multi-sensor multi-spectral imagery as an alternative to hyperspectral imagery for LULC studies.     

Mapping of forest tree distribution and estimation of forest biodiversity using Sentinel-2 imagery in the University Research Forest Taxiarchis in Chalkidiki,Greece

Abstract

The aim of this study is to investigate the potential of Sentinel-2 imagery for the identification and determination of forest patches of particular interest, with respect to ecosystem integrity and biodiversity and to produce a relevant biodiversity map, based on Simpson’s diversity index in Taxiarchis university research forest, Chalkidiki, North Greece. The research is based on OBIA being developed on to bi-temporal summer and winter Sentinel-2 imagery. Fuzzy rules, which are based on topographic factors, such as terrain elevation and slope for the distribution of each tree species, derived from expert knowledge and field observations, were used to improve the accuracy of tree species classification. Finally, Simpson’s diversity index for forest tree species, was calculated and mapped, constituting a relative indicator for biodiversity for forest ecosystem organisms (fungi, insects, birds, reptiles, mammals) and carrying implications for the identification of patches prone to disturbance or that should be prioritized for conservation.     

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.     

Upscaling in situ albedo for validation of coarse scale albedo product over heterogeneous surfaces

ABSTRACT

One of the essential steps for satellite albedo validation is upscaling in situ measurements to corresponding pixel scale over relatively heterogeneous land surfaces. Although the multi-scale validation strategy is applicable for heterogeneous surfaces, the calibration of the high-resolution imagery during upscaling process is never perfect, and thus the upscaling results suffer from errors. The regression-kriging (RK) technique can compensate the calibration part by applying kriging to upscale residuals and produce more accurate upscaling results. In this paper, in situ measurements and high spatial resolution albedo imagery combined with RK technique was proposed. This method is illustrated by upscaling surface albedo from in situ measurements scale to the coarse pixel scale in the core experimental area of HiWATER, where 17 WSN nodes were deployed at heterogeneous area. The upscaling results of this method were compared with the upscaling results from multi-scale strategy. The results show that the upscaling method based on in situ measurements and high-resolution imagery combined with RK technique can capture the spatial characteristics of surface albedo better. Further, an attempt was made to expand this method in time series. Finally, a preliminary validation of the Moderate Resolution Imaging Spectroradiometer albedo product was performed as the tentative application.     

Parallelizing maximum likelihood classification on computer cluster and graphics processing unit for supervised image classification

ABSTRACT

Supervised image classification has been widely utilized in a variety of remote sensing applications. When large volume of satellite imagery data and aerial photos are increasingly available, high-performance image processing solutions are required to handle large scale of data. This paper introduces how maximum likelihood classification approach is parallelized for implementation on a computer cluster and a graphics processing unit to achieve high performance when processing big imagery data. The solution is scalable and satisfies the need of change detection, object identification, and exploratory analysis on large-scale high-resolution imagery data in remote sensing applications.     

A Test of Central Place Theory Using Shuttle Imaging Radar (SIR‐A) of China's North Central Plain

Abstract

This study utilizes radar imagery to test the application of Walter Christaller's central‐place theory on China's North Central Plain. Examination of the spatial order and layout of settlements reveal that central place theory's transportation principle, k=4 exists. The study distinguishes four hierarchical settlement sizes and shows how effective rural societies are in evolving settlement structures whose spatial order and regularity avoids spatial inefficiencies. The advent of space based imaging systems such as SIR‐A radar imagery has permitted tests of new sites where the restrictive conditions of central place theory exist. These studies allow parallels to be drawn between the postulates of this theory of settlement structure and whether the theory is matched in reality.

This work is needed because of such scarcities. It allows the theory to undergo further subjections, a scrutiny needed for the validation, or otherwise of its postulates. The work adds to a large body of literature on this topic whose limitation has been the scarcity of actual test sites where theory and reality can be compared. The study presents positive indications that central place theory exists in reality, a compliment to the remarkable perceptions of Walter Christaller.     

A scalable cyberinfrastructure and cloud computing platform for forest aboveground biomass estimation based on the Google Earth Engine

ABSTRACT

Earth observation (EO) data, such as high-resolution satellite imagery or LiDAR, has become one primary source for forests Aboveground Biomass (AGB) mapping and estimation. However, managing and analyzing the large amount of globally or locally available EO data remains a great challenge. The Google Earth Engine (GEE), which leverages cloud-computing services to provide powerful capabilities on the management and rapid analysis of various types of EO data, has appeared as an inestimable tool to address this challenge. In this paper, we present a scalable cyberinfrastructure for on-the-fly AGB estimation, statistics, and visualization over a large spatial extent. This cyberinfrastructure integrates state-of-the-art cloud computing applications, including GEE, Fusion Tables, and the Google Cloud Platform (GCP), to establish a scalable, highly extendable, and high-performance analysis environment. Two experiments were designed to demonstrate its superiority in performance over the traditional desktop environment and its scalability in processing complex workflows. In addition, a web portal was developed to integrate the cyberinfrastructure with some visualization tools (e.g. Google Maps, Highcharts) to provide a Graphical User Interfaces (GUI) and online visualization for both general public and geospatial researchers.     

Employing airborne multispectral digital imagery to map Brazilian pepper infestation in South Texas

A study was conducted in south Texas to determine the feasibility of using airborne multispectral digital imagery for differentiating the invasive plant Brazilian pepper (Schinus terebinthifolius) from other cover types. Imagery obtained in the visible, near-infrared, and mid-infrared regions of the light spectrum and a supervised classification approach were employed to develop thematic maps of two areas infested with Brazilian pepper. Map accuracies ranged from 84.2 to 100% for the Brazilian pepper class. Findings support using airborne multispectral digital imagery as a tool for separating Brazilian pepper from associated land cover types and further encourage exploration of airborne multispectral digital imagery and image processing techniques for developing maps of Brazilian pepper infestation in Texas and abroad.     

Monitoring Detailed Land‐Cover Changes in Shrubland Habitat Reserves Using Multi‐temporal IKONOS Data

Abstract

The objective of this study was to explore the utility of multi‐temporal, multi‐spectral image data acquired by the IKONOS satellite system for monitoring detailed land cover changes within shrubland habitat reserves. Sub‐pixel accuracy in date‐to‐date registration was achieved, in spite of the irregular relief of the study area and the high spatial resolution of the imagery. Change vector classification enabled features ranging in size from tens of square meters to several hectares to be detected and six general land cover change classes to be identified. Interpretation of the change vector classification product in conjunction with visual inspection of the multi‐temporal imagery enabled identification of specific change types such as: vegetation disturbance and associated increase in soil exposure, shrub removal, urban edge vegetation clearing and fire maintenance, increase in vegetation cover, spread of invasive plant species, fire scars and subsequent recovery, erosional scouring, trail and road development, and expansion of bicycle disturbances.     

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.     

An improved approach for the production of satellite-based geospatial reference imagery

Abstract

An innovative and practical satellite image product is described that is ideal for applications in Northern Canada because of its wide area coverage and mapping-quality features. This product is generated from a new procedure developed at the Canada Centre for Remote Sensing (CCRS) for processing Landsat 7 imagery, and by extension, imagery from other Earth Observation satellites. By working with multiple satellite passes, each containing the equivalent of multiple scenes, the new procedure could dramatically reduce the turn-around time for generating georeferenced image products, and also increase their geometric and radiometric accuracy compared to those produced by the current methods. The objective of the process has been to generate satellite image mosaics covering large areas (e.g. >500 000 km²) with uniformly distributed errors at sub-pixel resolution. The paper discusses the theoretical basis of a photogrammetric adjustment for satellite imagery and the results obtained from several tests. The process is generic, involving a sensor model, a satellite orbit model and ground control information; thus it may be easily adapted to any satellite that allows for repeat coverage with overlapping paths. By performing an adjustment to correct the satellite position and attitude data prior to the production of orthoimage products, it is possible to create a mosaic with a single resampling process which minimises both the radiometric and geometric resampling artifacts. The results from three separate tests are presented, along with a discussion of the procedures that were followed in each case. All three tests have successfully demonstrated that sub-pixel sample size errors may be consistently obtained over large areas. A by-product process developed to support the measurement of ground control point coordinates for the satellite adjustment was the automatic matching of geographic features such as lakes and islands in vector data format. This has been a significant development in that it has eliminated manual intervention in the measurement of these features in the imagery, allowing the ground control for entire passes containing several scenes to be obtained in minutes instead of hours.