Application of spectral and spatial indices for specific class identification in Airborne Prism EXperiment (APEX) imaging spectrometer data for improved land cover classification

Hyperspectral remote sensing's ability to capture spectral information of targets in very narrow bandwidths gives rise to many intrinsic applications. However, the major limiting disadvantage to its applicability is its dimensionality, known as the Hughes Phenomenon. Traditional classification and image processing approaches fail to process data along many contiguous bands due to inadequate training samples. Another challenge of successful classification is to deal with the real world scenario of mixed pixels i.e. presence of more than one class within a single pixel. An attempt has been made to deal with the problems of dimensionality and mixed pixels, with an objective to improve the accuracy of class identification. In this paper, we discuss the application of indices to cope with the disadvantage of the dimensionality of the Airborne Prism EXperiment (APEX) hyperspectral Open Science Dataset (OSD) and to improve the classification accuracy using the Possibilistic c–Means (PCM) algorithm. This was used for the formulation of spectral and spatial indices to describe the information in the dataset in a lesser dimensionality. This reduced dimensionality is used for classification, attempting to improve the accuracy of determination of specific classes. Spectral indices are compiled from the spectral signatures of the target and spatial indices have been defined using texture analysis over defined neighbourhoods. The classification of 20 classes of varying spatial distributions was considered in order to evaluate the applicability of spectral and spatial indices in the extraction of specific class information. The classification of the dataset was performed in two stages; spectral and a combination of spectral and spatial indices individually as input for the PCM classifier. In addition to the reduction of entropy, while considering a spectral-spatial indices approach, an overall classification accuracy of 80.50% was achieved, against 65% (spectral indices only) and 59.50% (optimally determined principal component

Comparison of Three Operative Models for Estimating the Surface Water Deficit Using ASTER Reflective and Thermal Data

24 pages, 4 figures, 4 tables.-- Special Issue "Remote Sensing of Natural Resources and the Environment".Three operative models with minimum input data requirements for estimating the partition of available surface energy into sensible and latent heat flux using ASTER data have been evaluated in a semiarid area in SE Spain. The non-evaporative fraction (NEF) is proposed as an indicator of the surface water deficit. The best results were achieved with NEF estimated using the "Simplified relationship" for unstable conditions (NEF_Seguin) and with the S-SEBI (Simplified Surface Energy Balance Index) model corrected for atmospheric conditions (NEF_S-SEBIt,) which both produced equivalent results. However, results with a third model, NEF_Carlson, that estimates the exchange coefficient for sensible heat transfer from NDVI, were unrealistic for sites with scarce vegetation cover. These results are very promising for an operative monitoring of the surface water deficit, as validation with field data shows reasonable errors, within those reported in the literature (RMSE were 0.18 and 0.11 for the NEF, and 29.12 Wm-2 and 25.97 Wm-2 for sensible heat flux, with the Seguin and S-SEBIt models, respectively).This study received financial support from several different research projects: the integrated EU project, DeSurvey (A Surveillance System for Assessing and Monitoring of Desertification) (ref.: FP6- 00.950, Contract nº. 003950), the PROBASE (ref.: CGL2006-11619/HID) and CANOA (ref.: CGL2004-04919-C02-01/HID) projects funded by the Spanish Ministry of Education and Science; and the BACAEMA ('Balance de carbono y de agua en ecosistemas de matorral mediterráneo en Andalucía: Efecto del cambio climático', RNM-332) and CAMBIO ('Efectos del cambio global sobre la biodiversidad y el funcionamiento ecosistémico mediante la identificación de áreas sensibles y de referencia en el SE ibérico', RNM 1280) projects funded by the Junta de Andalucía (Andalusian Regional Government).Peer reviewe

Earth Resources: a continuing bibliography with indexes

This bibliography lists 337 reports, articles, and other documents introduced into the NASA scientific and technical information system between July 31, 1980 and September 30, 1980. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Implications of spectral and spatial features to improve the identification of specific classes

Dimensionality is one of the greatest challenges when deciphering hyperspectral imaging data. Although the multiband nature of the data is beneficial, algorithms are faced with a high computational load and statistical incompatibility due to the insufficient number of training samples. This is a hurdle to downstream applications. The combination of dimensionality and the real-world scenario of mixed pixels makes the identification and classification of imaging data challenging. Here, we address the complications of dimensionality using specific spectral indices from band combinations and spatial indices from texture measures for classification to better identify the classes. We classified spectral and combined spatial–spectral data and calculated measures of accuracy and entropy. A reduction in entropy and an overall accuracy of 80.50% was achieved when using the spectral–spatial input, compared with 65% for the spectral indices alone and 59.50% for the optimally determined principal components

REMOTELY MAPPING SURFACE ROUGHNESS ON ALLUVIAL FANS: AN APPROACH FOR UNDERSTANDING DEPOSITIONAL PROCESSES

A technique using multiple images from a single year to find surface roughness-based differences in directional radiance across sparsely-vegetated surfaces has been developed to help efficiently map and understand depositional processes on active, alluvial fan surfaces in Death Valley, CA. Surface roughness on the scales of grain size and topography on alluvial fan surfaces is expected to vary with depositional processes, including fluvial and mass movement events, as well as surface runoff and eolian processes. The Bidirectional Reflectance Distribution Function (BRDF) describes changes in reflectance based on changes in the angle of irradiance and radiation-scattering effects of a surface. Using Landsat 7 satellite imagery, the changes in observed surface reflectance, resulting from seasonal changes in the angle of incoming, solar radiation, can be classified and interpreted to show differences in surface roughness. Observations of grain size and topography, and other variables that affect reflectance (e.g. vegetation, composition) from field sites on eastern, alluvial fan surfaces in Death Valley show that seasonal changes in surface radiation are related to surface shadowing that result from grain size primarily, but also topography. Statistical tests show that the total amount of sand found on the land surface is the most correlated variable with the remote sensing method. Spatial relationships of surface features provide further interpretation of depositional process in addition to surface roughness. Airborne Laser Swath Mapping (ALSM) data was also used to map surface roughness, and shows positive trends with the Landsat imagery analyses. Mapping surface roughness over large areas and in remote settings using multi-spectral, satellite imagery has the potential to be a powerful tool for studying the geomorphology of both Earth and Mars

Multiple stable states and catastrophic shifts in coastal wetlands: Progress, challenges, and opportunities in validating theory using remote sensing and other methods

open5siThe analysis by K.B. Moffett was partially supported by National Science Foundation grant EAR-1013843 to Stanford University. Any opinions, findings, and onclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. The analysis by W. Nardin was partially supported by Office of Naval Research Award N00014-14-1-0114 to Boston University. The analysis by C. Wang was partially supported by National Natural Science Funds of China (41376120 and 41401413). The analysis by C. Wang and S. Temmerman was also partially supported by the European Union Programme Erasmus Mundus External Cooperation Window (EMECW)-Lot 14-China. K.B. Moffett thanks B.C. Smith for the analysis for the Wax Lake Delta example of Section 4.2 and S.M. Gorelick for the funding leading to the San Francisco Bay example of Section 4.3. W. Nardin thanks S. Fagherazzi and C. Woodcock for the funding leading to the Mekong River Delta example of Section 4.1. S. Silvestri thanks M. Marani for inspiring ideas and research on coastal wetland processes.Multiple stable states are established in coastal tidal wetlands (marshes, mangroves, deltas, seagrasses) by ecological, hydrological, and geomorphological feedbacks. Catastrophic shifts between states can be induced by gradual environmental change or by disturbance events. These feedbacks and outcomes are key to the sustainability and resilience of vegetated coastlines, especially as modulated by human activity, sea level rise, and climate change. Whereas multiple stable state theory has been invoked to model salt marsh responses to sediment supply and sea level change, there has been comparatively little empirical verification of the theory for salt marshes or other coastal wetlands. Especially lacking is long-term evidence documenting if or how stable states are established and maintained at ecosystem scales. Laboratory and field-plot studies are informative, but of necessarily limited spatial and temporal scope. For the purposes of long-term, coastal-scale monitoring, remote sensing is the best viable option. This review summarizes the above topics and highlights the emerging promise and challenges of using remote sensing-based analyses to validate coastal wetland dynamic state theories. This significant opportunity is further framed by a proposed list of scientific advances needed to more thoroughly develop the field.openMoffett K.B.; Nardin W.; Silvestri S.; Wang C.; Temmerman S.Moffett K.B.; Nardin W.; Silvestri S.; Wang C.; Temmerman S

Earth Resources: A continuing bibliography with indexes, issue 33

This bibliography list 436 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution sytems, instrumentation and sensors, and economic analysis

Using Unmanned Aerial Vehicles to assess the rehabilitation performance of open cut coal mines

Mine sites are routinely required to rehabilitate their post-mining landforms with a safe, stable and sustainable land-cover. To assess these post-mining landforms, traditional on-ground field monitoring is generally undertaken. However, these labour intensive and time-consuming measurements are generally insufficient to catalogue land rehabilitation efforts across the large scales typical of mining sites (>100 ha). As an alternative, information derived from Unmanned Aerial Vehicles (UAV) can be used to map rehabilitation success and provide evidence of achieving rehabilitation site requirements across a range of scales. UAV based sensors have the capacity to collect information on rehabilitation sites with extensive spatial coverage in a repeatable, flexible and cost-effective manner. Here, we present an approach to automatically map indicators of safety, stability and sustainability of rehabilitation efforts, and demonstrate this framework across three coalmine sites. Using multi-spectral UAV imagery together with geographic object-based image analysis, an empirical classification system is proposed to convert these indicators into a status category based on a number of criteria related to land-cover, landform, erosion, and vegetation structure. For this study, these criteria include: mapping tall trees (Eucalyptus species); vegetation extent; senescent vegetation; extent of bare ground; and steep slopes. Converting these land-cover indicators into appropriate mapping categories on a polygon basis indicated the level of rehabilitation success and how these varied across sites and age of the rehabilitation activity. This work presents a framework and workflow for undertaking a UAV based assessment of safety, stability and sustainability of mine rehabilitation and also provides a set of recommendations for future rehabilitation assessment efforts