scholarly journals Using spectral mixture analysis of hyperspectral remote sensing data to map lithology of the Sarfartoq carbona tite complex, southern West Greenland

1969 ◽  
Vol 17 ◽  
pp. 69-72 ◽  
Author(s):  
Enton Bedini ◽  
Tapani Tukiainen
Keyword(s):  
Remote Sensing ◽  
Wavelength Region ◽  
Remote Sensing Data ◽  
High Spectral Resolution ◽  
Molecular Absorption ◽  
Landsat Images ◽  
Transitional Metals ◽  
Related Data ◽  
Spectral Mixture ◽  
Absorption Features

Remote sensing is the science of acquiring, processing, and interpreting images and related data acquired from aircraft and satellites that record the interaction between matter and electromagnetic energy (Sabins 1997). The 450–2500 nm wavelength region provides mineralogical information based on analysis of electronic absorption features in transitional metals, especially iron, and of molecular absorption features in carbonate, hydrate and hydroxide minerals (Hunt 1977). Landsat Thematic Mapper satellite im - ages are widely used to interpret structure and geology, but due to their broad spectral bandpasses Landsat images cannot identify specific minerals. However, such details can be achieved by processing and analysing data from hyperspectral sensors. These sensors provide a u nique com bination of high spatial resolution and high spectral resolution imagery of the Earth’s surface unavailable from other sources (Goetz et al. 1985).

scholarly journals Imaging Spectroscopy for Conservation Applications

2021 ◽  
Vol 13 (2) ◽  
pp. 292
Author(s):  
Megan Seeley ◽  
Gregory P. Asner
Keyword(s):  
Remote Sensing ◽  
Decision Making ◽  
Case Studies ◽  
Spatial Scales ◽  
Imaging Spectroscopy ◽  
Remote Sensing Data ◽  
Decision Makers ◽  
High Spectral Resolution ◽  
Conservation Areas ◽  
Broad Array

As humans continue to alter Earth systems, conservationists look to remote sensing to monitor, inventory, and understand ecosystems and ecosystem processes at large spatial scales. Multispectral remote sensing data are commonly integrated into conservation decision-making frameworks, yet imaging spectroscopy, or hyperspectral remote sensing, is underutilized in conservation. The high spectral resolution of imaging spectrometers captures the chemistry of Earth surfaces, whereas multispectral satellites indirectly represent such surfaces through band ratios. Here, we present case studies wherein imaging spectroscopy was used to inform and improve conservation decision-making and discuss potential future applications. These case studies include a broad array of conservation areas, including forest, dryland, and marine ecosystems, as well as urban applications and methane monitoring. Imaging spectroscopy technology is rapidly developing, especially with regard to satellite-based spectrometers. Improving on and expanding existing applications of imaging spectroscopy to conservation, developing imaging spectroscopy data products for use by other researchers and decision-makers, and pioneering novel uses of imaging spectroscopy will greatly expand the toolset for conservation decision-makers.

scholarly journals A snow cover climatology for the Pyrenees from MODIS snow products

2014 ◽  
Vol 11 (11) ◽  
pp. 12531-12571 ◽  
Author(s):  
S. Gascoin ◽  
O. Hagolle ◽  
M. Huc ◽  
L. Jarlan ◽  
J.-F. Dejoux ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Remote Sensing Data ◽  
Landsat Images ◽  
Forested Areas ◽  
Land Cover Map ◽  
Hydropower Production ◽  
Snow Cover Duration

Abstract. The seasonal snow in the Pyrenees is critical for hydropower production, crop irrigation and tourism in France, Spain and Andorra. Complementary to in situ observations, satellite remote sensing is useful to monitor the effect of climate on the snow dynamics. The MODIS daily snow products (Terra/MOD10A1 and Aqua/MYD10A1) are widely used to generate snow cover climatologies, yet it is preferable to assess their accuracies prior to their use. Here, we use both in situ snow observations and remote sensing data to evaluate the MODIS snow products in the Pyrenees. First, we compare the MODIS products to in situ snow depth (SD) and snow water equivalent (SWE) measurements. We estimate the values of the SWE and SD best detection thresholds to 40 mm water equivalent (we) and 105 mm respectively, for both MOD10A1 and MYD10A1. Kappa coefficients are within 0.74 and 0.92 depending on the product and the variable. Then, a set of Landsat images is used to validate MOD10A1 and MYD10A1 for 157 dates between 2002 and 2010. The resulting accuracies are 97% (κ = 0.85) for MOD10A1 and 96% (κ = 0.81) for MYD10A1, which indicates a good agreement between both datasets. The effect of vegetation on the results is analyzed by filtering the forested areas using a land cover map. As expected, the accuracies decreases over the forests but the agreement remains acceptable (MOD10A1: 96%, κ = 0.77; MYD10A1: 95%, κ = 0.67). We conclude that MODIS snow products have a sufficient accuracy for hydroclimate studies at the scale of the Pyrenees range. Using a gapfilling algorithm we generate a consistent snow cover climatology, which allows us to compute the mean monthly snow cover duration per elevation band. We finally analyze the snow patterns for the atypical winter 2011–2012. Snow cover duration anomalies reveal a deficient snowpack on the Spanish side of the Pyrenees, which seems to have caused a drop in the national hydropower production.

scholarly journals CREATION OF GIS-RESOURCES FOR PHYSIOGRAPHIC ZONING OF THE TRANSBOUNDARY RUSSIAN-MONGOLIAN TERRITORY

2021 ◽  
Vol 26 (2) ◽  
pp. 91-102
Author(s):  
Andrew N. Beshentsev ◽  
◽  
Alexander A. Ayurzhanaev ◽  
Bator V. Sodnomov ◽  
◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Landsat Images ◽  
Statistical Research ◽  
Active Biomass ◽  
Sensing Data ◽  
Digital Elevation ◽  
Geographical Regions ◽  
Elevation Model ◽  
Geographical Characteristics

The article is aimed at the development of methodological foundations for the creation of geoin-formation resources of transboundary territories based on cartographic materials and remote sensing data, as well as physical and geographical zoning of the transboundary Russian-Mongolian territory. The methodological basis of the study is cartographic and statistical research methods, geoinformation technology, as well as processing and analysis of remote sensing data. As a result, the study deter-mines the features of geoinformation resources, presents their characteristics, develops a classification and substantiates their integrating value in making interstate territorial decisions. The article gives the physical and geographical characteristics of the territory, determines the scale of mapping, establishes the basic units of geoinformation mapping and modeling, creates the coverage of the basin division, and proposes a scheme for creating basic geoinformation resources for the physical and geographical zoning of the territory. Based on the analysis of the digital elevation model, the territory was zoned according to the morphometric parameters of the relief. As a result of processing and analysis of Landsat images at different times, the territory was zoned in terms of the amount of photosynthetically active biomass (NDVI). As a result of zoning, 6 physical-geographical regions and 33 physical-geographical areas were identified.

scholarly journals Modeling regional evaporation through ANFIS incorporated solely with remote sensing data

2013 ◽  
Vol 10 (5) ◽  
pp. 6153-6192
Author(s):  
F.-J. Chang ◽  
W. Sun
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Data Allocation ◽  
Remotely Sensed Data ◽  
Estimation Model ◽  
Landsat Images ◽  
Inference System ◽  
Sensing Data

Abstract. The study aims to model regional evaporation that possesses the ability to present the spatial distribution of evaporation across the whole Taiwan by the adaptive network-based fuzzy inference system (ANFIS) based solely on remote sensing data. The remote sensing data used in this study consist of Landsat image products including Enhanced Vegetation Index (EVI) and land surface temperature (LST). The model construction is designed through two types of data allocation (temporal and spatial) driven with the same ten-year data of EVI and LST derived from Landsat images. Evidences indicate the estimation model based solely on remotely sensed data can effectively detect the spatial variation of evaporation and appropriately capture the evaporation trend with acceptable errors of about 1 mm day−1. The results also demonstrate the composite of EVI and LST input to the proposed estimation model improves the accuracy of estimated evaporation values as compared with the model using LST as the only input, which reveals EVI indeed benefits the estimation process. The results suggest Model-T (temporal input allocation) is suitable for making island-wide evaporation estimation while Model-S (spatial input allocation) is suitable for making evaporation estimation at ungauged sites. An island-wide evaporation map for the whole study area (Taiwan Island) is then derived. It concludes the proposed ANFIS model incorporated solely with remote sensing data can reasonably well generate evaporation estimation and is reliable as well as easily applicable for operational estimation of evaporation over large areas where the network of ground-based meteorological gauging stations is not dense enough or readily available.

scholarly journals The Impact of Coal Exploitation on Tidal Flat Changes, an Investigation Using Remote Sensing Data in Vietnam

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Thi Lan PHAM ◽  
Si Son TONG ◽  
Thi Thu Ha LE ◽  
Thi Le LE ◽  
Huu Duc HOANG
Keyword(s):  
Remote Sensing ◽  
Coal Mining ◽  
Coal Mine ◽  
Tidal Flat ◽  
Remote Sensing Data ◽  
Tidal Flats ◽  
Mining Activities ◽  
Landsat Images ◽  
Sensing Data ◽  
The Impact

Tidal flat plays a crucial role in socio-economic development and ecological environment.Tidal flats in Ha Long-Cam Pha in Vietnam are impacted by human activities, especially coal miningactivities. Using remote sensing data is able to detect, extract, and monitor the changes of tidal flats andexploited coal mine area with multi-temporal, in various scales, and for a large coverage. This studyaims to investigate the impact of coal mining activities on the changes of tidal flats using remote sensingin Cam Pha, Ha Long, one of the biggest coal basins in Vietnam. Digital Elevation Models (DEMs) oftidal flats constructed by Landsat satellite images acquired in years 1989, 2001, and 2014 are comparedto determine the volume changes. Besides, coal mining activities including coal production, waste rockdump area, and the expansion of open coal mine during the period 1989-2014 are investigated usingcorrespondent Landsat images and the reports from the coal mine companies in the study area. Sedimentsamples in tidal flats are analyzed to determine the origin of the sediments. As the results, organic matterin the tidal flats is dominant with the concentration of 459 g/kg to 607 g/kg, which is evidence for theimpact of coal exploitation on the coastal environment. In addition, the relationship between coal mineactivities and tidal flat variation is well observed in this study.

Quantifying Urban Sprawl with Spatial Autocorrelation Techniques using Multi-Temporal Satellite Data

2014 ◽  
Vol 5 (2) ◽  
pp. 19-37 ◽  
Author(s):  
Gabriele Nolè ◽  
Rosa Lasaponara ◽  
Antonio Lanorte ◽  
Beniamino Murgante
Keyword(s):  
Remote Sensing ◽  
Spatial Autocorrelation ◽  
Urban Areas ◽  
Urban Expansion ◽  
Remote Sensing Data ◽  
Small Towns ◽  
Temporal Data ◽  
Landsat Images ◽  
Multi Temporal ◽  
Soil Consumption

This study deals with the use of satellite TM multi-temporal data coupled with statistical analyses to quantitatively estimate urban expansion and soil consumption for small towns in southern Italy. The investigated area is close to Bari and was selected because highly representative for Italian urban areas. To cope with the fact that small changes have to be captured and extracted from TM multi-temporal data sets, we adopted the use of spectral indices to emphasize occurring changes, and geospatial data analysis to reveal spatial patterns. Analyses have been carried out using global and local spatial autocorrelation, applied to multi-date NASA Landsat images acquired in 1999 and 2009 and available free of charge. Moreover, in this paper each step of data processing has been carried out using free or open source software tools, such as, operating system (Linux Ubuntu), GIS software (GRASS GIS and Quantum GIS) and software for statistical analysis of data (R). This aspect is very important, since it puts no limits and allows everybody to carry out spatial analyses on remote sensing data. This approach can be very useful to assess and map land cover change and soil degradation, even for small urbanized areas, as in the case of Italy, where recently an increasing number of devastating flash floods have been recorded. These events have been mainly linked to urban expansion and soil consumption and have caused loss of human lives along with enormous damages to urban settlements, bridges, roads, agricultural activities, etc. In these cases, remote sensing can provide reliable operational low cost tools to assess, quantify and map risk areas.

scholarly journals Bio-optical provinces in the eastern Atlantic Ocean and their biogeographical relevance

2011 ◽  
Vol 8 (12) ◽  
pp. 3609-3629 ◽  
Author(s):  
B. B. Taylor ◽  
E. Torrecilla ◽  
A. Bernhardt ◽  
M. H. Taylor ◽  
I. Peeken ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Atlantic Ocean ◽  
Large Scale ◽  
Chlorophyll Concentration ◽  
Remote Sensing Data ◽  
In Situ Measurements ◽  
High Spectral Resolution ◽  
Phytoplankton Assemblages ◽  
Eastern Atlantic Ocean

Abstract. The relationship between phytoplankton assemblages and the associated optical properties of the water body is important for the further development of algorithms for large-scale remote sensing of phytoplankton biomass and the identification of phytoplankton functional types (PFTs), which are often representative for different biogeochemical export scenarios. Optical in-situ measurements aid in the identification of phytoplankton groups with differing pigment compositions and are widely used to validate remote sensing data. In this study we present results from an interdisciplinary cruise aboard the RV Polarstern along a north-to-south transect in the eastern Atlantic Ocean in November 2008. Phytoplankton community composition was identified using a broad set of in-situ measurements. Water samples from the surface and the depth of maximum chlorophyll concentration were analyzed by high performance liquid chromatography (HPLC), flow cytometry, spectrophotometry and microscopy. Simultaneously, the above- and underwater light field was measured by a set of high spectral resolution (hyperspectral) radiometers. An unsupervised cluster algorithm applied to the measured parameters allowed us to define bio-optical provinces, which we compared to ecological provinces proposed elsewhere in the literature. As could be expected, picophytoplankton was responsible for most of the variability of PFTs in the eastern Atlantic Ocean. Our bio-optical clusters agreed well with established provinces and thus can be used to classify areas of similar biogeography. This method has the potential to become an automated approach where satellite data could be used to identify shifting boundaries of established ecological provinces or to track exceptions from the rule to improve our understanding of the biogeochemical cycles in the ocean.

scholarly journals Potencial de las imágenes UAV como datos de verdad terreno para la clasificación de la severidad de quema de imágenes Landsat: aproximaciones a un producto útil para la gestión post incendio

2017 ◽  
pp. 91 ◽  
Author(s):  
M. Pla ◽  
A. Duane ◽  
L. Brotons
Keyword(s):  
Remote Sensing ◽  
Decision Making ◽  
Forest Fires ◽  
Fire Severity ◽  
Remote Sensing Data ◽  
Ground Truth ◽  
Linear Regression Models ◽  
Kappa Index ◽  
Landsat Images ◽  
Uav Images

<p>Mapping fire severity is determinant to understand landscape evolution after a wildfire and provides useful information for decision making during post fire management. Quantitative fire severity mapping from relative changes in Normalized Burn Ratio index (RdNBR) is not actually being incorporated into decision making processes, being more useful the categorization in severity levels (high, moderate and low). However, the most common mapping severity methodologies based on the definition of RdNBR thresholds from field information are not always possible due to lack of field data or because the published thresholds are unsatisfactory in new regions. The boom in the use of UAVs (Unmanned Aerial Vehicle) has raised these platforms as potential tools for validation of remote sensing data. This paper presents the potential of UAVs as ground truth information in forest fires. From the photointerpretation of high resolution RGB images, the Aerial Severity Proportion Index (ASPI) has been created. Non-linear regression models between RdNBR and ASPI allows to delimitate of thresholds for the classification of Landsat images and to obtain qualitative severity maps. Validation with random points presents a kappa index of 0,5 and a relative accuracy of 70,8%. Therefore, UAV images become a very useful tool for wildfire severity mapping and for fill the gap between remote sensing information and expensive field ground campaigns</p>

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