scholarly journals Mapping the Tso Kar basin in Ladakh

2012 ◽  
Vol 8 (2) ◽  
Author(s):  
Shashank Srinivasan
Keyword(s):  
Remote Sensing ◽  
High Altitude ◽  
Human Activities ◽  
Spatial Information ◽  
Remote Sensing Data ◽  
Climatic Changes ◽  
Pastoral Community ◽  
Sensing Data ◽  
Management Plans ◽  
Basin Wetlands

High-altitude wetlands are critical ecosystems at risk from global climatic changes and local human activities. Management plans for the conservation of these wetlands require spatial information, from remote sensing data and from local human communities. I describe my research aims and methodology working with the Changpa, a nomadic pastoral community who inhabit the high-altitude regions around the Tso Kar basin wetlands in Ladakh, India.

scholarly journals The predictability of reported drought events and impacts in the Ebro Basin using six different remote sensing data sets

2017 ◽  
Vol 21 (9) ◽  
pp. 4747-4765 ◽  
Author(s):  
Clara Linés ◽  
Micha Werner ◽  
Wim Bastiaanssen
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Data Sets ◽  
Drought Management ◽  
Ebro Basin ◽  
Sensing Data ◽  
Drought Impacts ◽  
Management Plans ◽  
Drought Indicators ◽  
Selection Of

Abstract. The implementation of drought management plans contributes to reduce the wide range of adverse impacts caused by water shortage. A crucial element of the development of drought management plans is the selection of appropriate indicators and their associated thresholds to detect drought events and monitor the evolution. Drought indicators should be able to detect emerging drought processes that will lead to impacts with sufficient anticipation to allow measures to be undertaken effectively. However, in the selection of appropriate drought indicators, the connection to the final impacts is often disregarded. This paper explores the utility of remotely sensed data sets to detect early stages of drought at the river basin scale and determine how much time can be gained to inform operational land and water management practices. Six different remote sensing data sets with different spectral origins and measurement frequencies are considered, complemented by a group of classical in situ hydrologic indicators. Their predictive power to detect past drought events is tested in the Ebro Basin. Qualitative (binary information based on media records) and quantitative (crop yields) data of drought events and impacts spanning a period of 12 years are used as a benchmark in the analysis. Results show that early signs of drought impacts can be detected up to 6 months before impacts are reported in newspapers, with the best correlation–anticipation relationships for the standard precipitation index (SPI), the normalised difference vegetation index (NDVI) and evapotranspiration (ET). Soil moisture (SM) and land surface temperature (LST) offer also good anticipation but with weaker correlations, while gross primary production (GPP) presents moderate positive correlations only for some of the rain-fed areas. Although classical hydrological information from water levels and water flows provided better anticipation than remote sensing indicators in most of the areas, correlations were found to be weaker. The indicators show a consistent behaviour with respect to the different levels of crop yield in rain-fed areas among the analysed years, with SPI, NDVI and ET providing again the stronger correlations. Overall, the results confirm remote sensing products' ability to anticipate reported drought impacts and therefore appear as a useful source of information to support drought management decisions.

scholarly journals High Accuracy Geochemical Map Generation Method by a Spatial Autocorrelation-Based Mixture Interpolation Using Remote Sensing Data

2020 ◽  
Vol 12 (12) ◽  
pp. 1991
Author(s):  
Chenhui Huang ◽  
Akinobu Shibuya
Keyword(s):  
Remote Sensing ◽  
Spatial Autocorrelation ◽  
Data Augmentation ◽  
Prediction Models ◽  
Spatial Information ◽  
Thermal Emission ◽  
Remote Sensing Data ◽  
Regression Problem ◽  
Sensing Data ◽  
Multivariate Prediction

Generating a high-resolution whole-pixel geochemical contents map from a map with sparse distribution is a regression problem. Currently, multivariate prediction models like machine learning (ML) are constructed to raise the geoscience mapping resolution. Methods coupling the spatial autocorrelation into the ML model have been proposed for raising ML prediction accuracy. Previously proposed methods are needed for complicated modification in ML models. In this research, we propose a new algorithm called spatial autocorrelation-based mixture interpolation (SABAMIN), with which it is easier to merge spatial autocorrelation into a ML model only using a data augmentation strategy. To test the feasibility of this concept, remote sensing data including those from the advanced spaceborne thermal emission and reflection radiometer (ASTER), digital elevation model (DEM), and geophysics (geomagnetic) data were used for the feasibility study, along with copper geochemical and copper mine data from Arizona, USA. We explained why spatial information can be coupled into an ML model only by data augmentation, and introduced how to operate data augmentation in our case. Four tests—(i) cross-validation of measured data, (ii) the blind test, (iii) the temporal stability test, and (iv) the predictor importance test—were conducted to evaluate the model. As the results, the model’s accuracy was improved compared with a traditional ML model, and the reliability of the algorithm was confirmed. In summary, combining the univariate interpolation method with multivariate prediction with data augmentation proved effective for geological studies.

scholarly journals Land use/cover classification in the Brazilian Amazon using satellite images

2012 ◽  
Vol 47 (9) ◽  
pp. 1185-1208 ◽  
Author(s):  
Dengsheng Lu ◽  
Mateus Batistella ◽  
Guiying Li ◽  
Emilio Moran ◽  
Scott Hetrick ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Data Fusion ◽  
Spatial Information ◽  
Classification Tree ◽  
Remote Sensing Data ◽  
Brazilian Amazon ◽  
Classification Performance ◽  
Sensor Data ◽  
Sensing Data

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation‑based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi‑resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Among the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, have the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical‑based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

scholarly journals UAV STRATEGIES VALIDATION AND REMOTE SENSING DATA FOR DAMAGE ASSESSMENT IN POST-DISASTER SCENARIOS

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 121-128 ◽  
Author(s):  
A. Calantropio ◽  
F. Chiabrando ◽  
G. Sammartano ◽  
A. Spanò ◽  
L. Teppati Losè
Keyword(s):  
Remote Sensing ◽  
Emergency Response ◽  
Spatial Information ◽  
Central Italy ◽  
Remote Sensing Data ◽  
3D Models ◽  
Point Of View ◽  
Sensing Data ◽  
Rapid Mapping ◽  
Fundamental Information

<p><strong>Abstract.</strong> The recent seismic swarms, occurred in Italy since August 2016, outlined the importance of deepen Geomatics researches for the validation of new strategies aimed at rapid-mapping and documenting differently accessible and complex environments, as in urban contexts and damaged built heritage. In the emergency response, the crucial exploitation of technological advances should obtain and efficiently organize high-scale reliable geospatial data for the early warning, impact, and recovery phases. Fulfilling these issues, among others, the Copernicus EMS, has played by now an important role in immediate and extensive damage reconnaissance, as in the case of Centre Italy. Nevertheless, the use of remote sensing data is still affected by a problem of point-of-view, scale and detectable detail. Nadir images, airborne or satellite, in fact, strongly limited the confidence level of these products. The subjectivity of the operator involvement is still an open issue, both in the first fieldwork assessment, and in the following operational approach of interpretative damage detection and rapid mapping production. To overcome these limits, the introduction of UAV platforms for photogrammetric purposes, has proven to be a sustainable approach in terms of time savings, operators’ safety, reliability and accuracy of results: the nadir and oblique integration can provide large multiscale models, with the fundamental information related to the façades conditions. The presented research, conducted within the Central Italy earthquakes events, will focus on potentialities and limits of UAV photogrammetry in the two documented sites: Pescara del Tronto and Accumoli. Here, the aim is not limited to describe a series of strategies for georeferencing, blocks orientation and multitemporal co-registration solutions, but also to validate the implemented pipelines as a workflow that could be integrated in the operative intervention for emergency response in early impact activities. Thus, it would be possible to use this 3D metric products as a reference-data for significative improvements of reliability in typical visual inspection and mapping, flanking the traditional nadir airborne- or satellite-based products. The UAV acquisitions performed in two damaged villages are displayed, in order to underline the implication of the spatial information embedded in DSM reconstruction and 3D models, supporting more reliable damage assessments.</p>

scholarly journals The predictability of reported drought events and impacts in the Ebro Basin using six different remote sensing data sets

2017 ◽  
Author(s):  
Clara Linés ◽  
Micha Werner ◽  
Wim Bastiaanssen
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Data Sets ◽  
Drought Management ◽  
Ebro Basin ◽  
Sensing Data ◽  
Drought Impacts ◽  
Management Plans ◽  
Drought Indicators ◽  
Selection Of

Abstract. The implementation of drought management plans contributes to reduce the wide range of adverse impacts caused by water shortage. A crucial element of the development of drought management plans is the selection of appropriate indicators and their associated thresholds to detect drought events and monitor their evolution. Drought indicators should be able to detect emerging drought processes that will lead to impacts with sufficient anticipation to allow measures to be undertaken effectively. However, in the selection of appropriate drought indicators the connection to the final impacts is often disregarded. This paper explores the utility of remotely sensed data sets to detect early stages of drought at the river basin scale, and how much time can be gained to inform operational land and water management practices. Six different remote sensing data sets with different spectral origin and measurement frequency are considered, complemented by a group of classical in situ hydrologic indicators. Their predictive power to detect past drought events is tested in the Ebro basin. Qualitative (binary information based on media records) and quantitative (crop yields) data of drought events and impacts spanning a period of 12 years are used as a benchmark in the analysis. Results show that early signs of drought impacts can be detected up to some 6 months before impacts are reported in newspapers, with the best correlation-anticipation relationships for the Standard Precipitation Index (SPI), the Normalized Difference Vegetation Index (NDVI) and Evapotranspiration (ET). Soil Moisture (SM) and Land Surface Temperature (LST) offer also good anticipation, but with weaker correlations, while Gross Primary Production (GPP) presents moderate positive correlations only for some of the rainfed areas. Although classical hydrological information from water levels and water flows provided better anticipation than remote sensing indicators in most of the areas, correlations were found to be weaker. The indicators show a consistent behaviour with respect to the different levels of crop yield in rainfed areas among the analysed years, with SPI, NDVI and ET providing again the stronger correlations. Overall, the results confirm remote sensing products’ ability to anticipate reported drought impacts and therefore appear as a useful source of information to support drought management decisions.

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