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  • 1.
    Bhardwaj, Anshuman
    et al.
    Snow and Avalanche Study Establishment, Defence Research and Development Organization (DRDO), Him Parisar, Sector-37A, Chandigarh.
    Joshi, Prakash C.
    Space Applications Centre, ISRO, Ahmedabad, Atmospheric and Oceanic Sciences Group, Space Applications Centre, Indian Space Research Organisation, Ahmedabad.
    Snehmani, Snehmani
    Snow and Avalanche Study Establishment, Defence Research and Development Organization (DRDO), Him Parisar, Sector-37A, Chandigarh.
    Sam, Lydia
    Department of Environmental Science, Sharda University.
    Singh, Mritunjay Kumar
    Snow and Avalanche Study Establishment, Defence Research and Development Organization (DRDO), Him Parisar, Sector-37A, Chandigarh.
    Singh, Shaktiman
    Department of Environmental Science, School of Basic Sciences and Research, Sharda University, Greater Noida.
    Kumar, Ramesh
    Department of Environmental Science, School of Basic Sciences and Research, Sharda University, Greater Noida.
    Applicability of Landsat 8 data for characterizing glacier facies and supraglacial debris2015Inngår i: International Journal of Applied Earth Observation and Geoinformation, ISSN 0303-2434, Vol. 38, s. 51-64Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    present work evaluates the applicability of operational land imager (OLI) and thermal infrared sensor (TIRS) on-board Landsat 8 satellite. We demonstrate an algorithm for automated mapping of glacier facies and supraglacial debris using data collected in blue, near infrared (NIR), short wave infrared (SWIR) and thermal infrared (TIR) bands. The reflectance properties invisible and NIR regions of OLI for various glacier facies are in contrast with those in SWIR region. Based on the premise that different surface types (snow, ice and debris) of a glacier should show distinct thermal regimes, the 'at-satellite brightness temperature' obtained using TIRS was used as a base layer for developing the algorithm. This base layer was enhanced and modified using contrasting reflectance properties of OLI bands. In addition to fades and debris cover characterization, another interesting outcome of this algorithm was extraction of crevasses on the glacier surface which were distinctly visible in output and classified images. The validity of this algorithm was checked using field data along a transect of the glacier acquired during the satellite pass over the study area. With slight scene-dependent threshold adjustments, this work can be replicated for mapping glacier facies and supraglacial debris in any alpine valley glacier

  • 2.
    Bhardwaj, Anshuman
    et al.
    Snow and Avalanche Study Establishment, Defence Research and Development Organization (DRDO), Him Parisar, Sector-37A, Chandigarh.
    Singh, Mritunjay Kumar
    Snow and Avalanche Study Establishment, Defence Research and Development Organization (DRDO), Him Parisar, Sector-37A, Chandigarh.
    Joshi, Prakash C.
    Space Applications Centre, ISRO, Ahmedabad, Atmospheric and Oceanic Sciences Group, Space Applications Centre, Indian Space Research Organisation, Ahmedabad.
    Snehmani, Snehmani
    Snow and Avalanche Study Establishment, Defence Research and Development Organization (DRDO), Him Parisar, Sector-37A, Chandigarh.
    Singh, Shaktiman
    Department of Environmental Science, School of Basic Sciences and Research, Sharda University, Greater Noida.
    Sam, Lydia
    Department of Environmental Science, Sharda University.
    Gupta, R.D.
    Snow and Avalanche Study Establishment, Defence Research and Development Organization (DRDO), Him Parisar, Sector-37A, Chandigarh.
    Kumar, Rajesh
    Department of Environmental Science, School of Basic Sciences and Research, Sharda University, Greater Noida.
    A lake detection algorithm (LDA) using Landsat 8 data: A comparative approach in glacial environment2015Inngår i: International Journal of Applied Earth Observation and Geoinformation, ISSN 0303-2434, Vol. 38, s. 150-163Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Glacial lakes show a wide range of turbidity. Owing to this, the normalized difference water indices (NDWIs) as proposed by many researchers, do not give appropriate results in case of glacial lakes. In addition, the sub-pixel proportion of water and use of different optical band combinations are also reported to produce varying results. In the wake of the changing climate and increasing GLOFs (glacial lake outburst floods), there is a need to utilize wide optical and thermal capabilities of Landsat 8 data for the automated detection of glacial lakes. In the present study, the optical and thermal bandwidths of Landsat 8 data were explored along with the terrain slope parameter derived from Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model Version2 (ASTER GDEM V2), for detecting and mapping glacial lakes. The validation of the algorithm was performed using manually digitized and subsequently field corrected lake boundaries. The pre-existing NDWIs were also evaluated to determine the supremacy and the stability of the proposed algorithm for glacial lake detection. Two new parameters, LDI (lake detection index) and LF (lake fraction) were proposed to comment on the performances of the indices. The lake detection algorithm (LDA) performed best in case of both, mixed lake pixels and pure lake pixels with no false detections (LDI = 0.98) and very less areal underestimation (LF= 0.73). The coefficient of determination (R-2) between areal extents of lake pixels, extracted using the LDA and the actual lake area, was very high (0.99). With understanding of the terrain conditions and slight threshold adjustments, this work can be replicated for any mountainous region of the world.

  • 3.
    Bhardwaj, Anshuman
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Rymdteknik. Department of Environmental Science, Sharda University.
    Singh, Shaktiman
    Department of Environmental Science, Sharda University,.
    Sam, Lydia
    Department of Environmental Science, Sharda University,.
    Joshi, PK
    School of Environmental Sciences, Jawaharlal Nehru University, New Delhi.
    Bhardwaj, Akanksha
    Banaras Hindu University.
    Martín-Torres, Javier F.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Rymdteknik. Instituto Andaluz de Ciencias de la Tierra (CSIC-UGR).
    Kumar, Rajesh
    Department of Environmental Science, Sharda University.
    A review on remotely sensed land surface temperature anomaly as an earthquake precursor2017Inngår i: International Journal of Applied Earth Observation and Geoinformation, ISSN 0303-2434, Vol. 63, s. 158-166Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The low predictability of earthquakes and the high uncertainty associated with their forecasts make earthquakes one of the worst natural calamities, capable of causing instant loss of life and property. Here, we discuss the studies reporting the observed anomalies in the satellite-derived Land Surface Temperature (LST) before an earthquake. We compile the conclusions of these studies and evaluate the use of remotely sensed LST anomalies as precursors of earthquakes. The arrival times and the amplitudes of the anomalies vary widely, thus making it difficult to consider them as universal markers to issue earthquake warnings. Based on the randomness in the observations of these precursors, we support employing a global-scale monitoring system to detect statistically robust anomalous geophysical signals prior to earthquakes before considering them as definite precursors.

  • 4.
    Haas, Jan
    et al.
    KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Samhällsplanering och miljö, Geodesi och geoinformatik.
    Furberg, Dorothy
    KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Samhällsplanering och miljö, Geodesi och geoinformatik.
    Ban, Yifang
    KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Samhällsplanering och miljö, Geodesi och geoinformatik.
    Satellite monitoring of urbanization and environmental impacts: A comparison of Stockholm and Shanghai2015Inngår i: International Journal of Applied Earth Observation and Geoinformation, ISSN 0303-2434, Vol. 38, s. 138-149Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This study investigates urbanization and its potential environmental consequences in Shanghai andStockholm metropolitan areas over two decades. Changes in land use/land cover are estimated fromsupport vector machine classifications of Landsat mosaics with grey-level co-occurrence matrix fea-tures. Landscape metrics are used to investigate changes in landscape composition and configurationand to draw preliminary conclusions about environmental impacts. Speed and magnitude of urbaniza-tion is calculated by urbanization indices and the resulting impacts on the environment are quantified byecosystem services. Growth of urban areas and urban green spaces occurred at the expense of croplandin both regions. Alongside a decrease in natural land cover, urban areas increased by approximately 120%in Shanghai, nearly ten times as much as in Stockholm, where the most significant land cover changewas a 12% urban expansion that mostly replaced agricultural areas. From the landscape metrics results,it appears that fragmentation in both study regions occurred mainly due to the growth of high densitybuilt-up areas in previously more natural/agricultural environments, while the expansion of low densitybuilt-up areas was for the most part in conjunction with pre-existing patches. Urban growth resulted inecosystem service value losses of approximately 445 million US dollars in Shanghai, mostly due to thedecrease in natural coastal wetlands while in Stockholm the value of ecosystem services changed very lit-tle. Total urban growth in Shanghai was 1768 km2and 100 km2in Stockholm. The developed methodologyis considered a straight-forward low-cost globally applicable approach to quantitatively and qualitativelyevaluate urban growth patterns that could help to address spatial, economic and ecological questions inurban and regional planning.

  • 5.
    Karlson, Martin
    et al.
    Linköpings universitet, Institutionen för tema, Tema Miljöförändring. Linköpings universitet, Filosofiska fakulteten.
    Ostwald, Madelene
    Linköpings universitet, Institutionen för tema, Tema Miljöförändring. Linköpings universitet, Filosofiska fakulteten. Linköpings universitet, Institutionen för tema, Centrum för klimatpolitisk forskning. University of Gothenburg, Sweden; Chalmers, Sweden.
    Reese, Heather
    Swedish University of Agriculture Science, Sweden.
    Romeo Bazie, Hugues
    University of Ouagadougou, Burkina Faso.
    Tankoano, Boalidioa
    Polytech University of Bobo Dioulasso, Burkina Faso.
    Assessing the potential of multi-seasonal WorldView-2 imagery for mapping West African agroforestry tree species2016Inngår i: International Journal of Applied Earth Observation and Geoinformation, ISSN 0303-2434, Vol. 50, s. 80-88Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    High resolution satellite systems enable efficient and detailed mapping of tree cover, with high potential to support both natural resource monitoring and ecological research. This study investigates the capability of multi-seasonal WorldView-2 imagery to map five dominant tree species at the individual tree crown level in a parkland landscape in central Burkina Faso. The Random Forest algorithm is used for object based tree species classification and for assessing the relative importance of WorldView-2 predictors. The classification accuracies from using wet season, dry season and multi-seasonal datasets are compared to gain insights about the optimal timing for image acquisition. The multi-seasonal dataset produced the most accurate classifications, with an overall accuracy (OA) of 83.4%. For classifications based on single date imagery, the dry season (OA=78.4%) proved to be more suitable than the wet season (OA=68.1%). The predictors that contributed most to the classification success were based on the red edge band and visible wavelengths, in particular green and yellow. It was therefore conchided that WorldView-2, with its unique band configuration, represents a suitable data source for tree species mapping in West African parklands. These results are particularly promising when considering the recently launched WorldView-3, which provides data both at higher spatial and spectral resolution, including shortwave infrared bands. (C) 2016 Elsevier B.V. All rights reserved.

  • 6. Pitkänen, Timo P.
    et al.
    Skånes, Helle
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för naturgeografi.
    Käyhkö, Niina
    Detecting subpixel deciduous components to complement traditional land cover classifications in Southwest Finland2015Inngår i: International Journal of Applied Earth Observation and Geoinformation, ISSN 0303-2434, Vol. 42, s. 97-105Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    To ensure successful conservation of ecological and cultural landscape values, detailed and up-to-datespatial information of existing habitat patterns is essential. However, traditional satellite-based and rasterclassifications rely on pixels that are assigned to a single category and often generalized. For many frag-mented key habitats, such a strategy is too coarse and complementary data is needed. In this paper,we aim at detecting pixel-wise fractional coverage of broadleaved woodland and grassland componentsin a hemiboreal landscape. This approach targets ecologically relevant deciduous fractions and com-plements traditional crisp land cover classifications. We modeled fractional components using a k-NNapproach, which was based on multispectral satellite data, assisted by a digital elevation model and acontemporary map database. The modeled components were then analyzed based on landscape struc-ture indicators, and evaluated in conjunction with CORINE classification. The results indicate that bothbroadleaved forest and grassland components are widely distributed in the study area, principally orga-nized as transition zones and small patches. Landscape structure indicators show a substantial variationbased on the fractional threshold, pinpointing their dependency on the classification scheme and grain.The modeled components, on the other hand, suggest high internal variation for most CORINE classes,indicating their heterogeneous appearance and showing that the presence of deciduous components inthe landscape are not properly captured in a coarse land cover classification. To gain a realistic perceptionof the landscape, and use this information for the needs of spatial planning, both fractional results andexisting land cover classifications are needed. This is because they mutually contribute to an improvedunderstanding of habitat patterns and structures, and should be used to complement each other.

  • 7. Reese, Heather
    et al.
    Nyström, Mattias
    Nordkvist, Karin
    Olsson, Håkan
    Combining airborne laser scanning data and optical satellite data for classification of alpine vegetation2014Inngår i: International Journal of Applied Earth Observation and Geoinformation, ISSN 0303-2434, Vol. 27, nr Part A, s. 81-90Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Climate change and outdated vegetation maps are among the reasons for renewed interest in mapping sensitive alpine and subalpine vegetation. Satellite data combined with elevation derivatives have been shown to be useful for mapping alpine vegetation, however, there is room for improvement. The inclusion of airborne laser scanning data metrics has not been widely investigated for alpine vegetation. This study has combined SPOT 5 satellite data, elevation derivatives, and laser data metrics for a 25km x 31km study area in Abisko, Sweden. Nine detailed vegetation classes defined by height, density and species composition in addition to snow/ice, water, and bare rock were classified using a supervised Random Forest classifier. Several of the classes consisted of shrub and grass species with a maximum height of 0.4m or less. Laser data metrics were calculated from the nDSM based on a 10m x 10m grid, and after variable selection, the metrics used in the classification were the 95th and 99th height percentiles, a vertical canopy density metric, the mean and standard deviation of height, a vegetation ratio based on the raw laser data point cloud with a variable height threshold (from 0.1 to 1.0m with 0.1m intervals), and standard deviation of these vegetation ratios. The satellite data used in classification was all SPOT bands plus NDVI and NDII, while the elevation derivatives consisted of elevation, slope and the Saga Wetness Index. Overall accuracy when using the combination of laser data metrics, elevation derivatives and SPOT 5 data increased by 6% as compared to classification of SPOT and elevation derivatives only, and increased by 14.2% compared to SPOT 5 data alone. The classes which benefitted most from inclusion of laser data metrics were mountain birch and alpine willow. The producer’s accuracy for willow increased from 18% (SPOT alone) to 41% (SPOT+elevation derivatives) and then to 55% (SPOT+elevation derivatives+laser data) when laser data were included, with the 95th height percentile and Saga Wetness Index contributing most to willow’s improved classification. Addition of laser data metrics did not increase the classification accuracy of spectrally similar dry heath (<0.3m height) and mesic heath (0.3-1.0m height), which may have been a result of laser data penetration of sparse shrub canopy or laser data processing choices. The final results show that laser data metrics combined with satellite data and elevation derivatives contributed overall to a better classification of alpine and subalpine vegetation.

  • 8.
    Wästfelt, Anders
    et al.
    Section of Agrarian History, Department of Economics, Swedish University of Agricultural Science, Uppsala.
    Arnberg, Wolter
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för naturgeografi och kvartärgeologi (INK).
    Local spatial context measurements used to explore the relationship between land cover and land use functions2013Inngår i: International Journal of Applied Earth Observation and Geoinformation, ISSN 0303-2434, Vol. 23, s. 234-244Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Research making use of satellite data for land change science has developed in the last decades. However, analysis of land use has not developed with the same speed as development of new satellite sensors and available land cover data. Improvement of land use analysis is possible, but more advanced methods are needed which make it possible to link image data to analysis of land use functions. To make this linking possible, variable which affect farmer's long term decisions must be taken into account in analysis as well as the relative importance of the landscape itself. A GIS-based tool for the measurement of local spatial context in satellite data is presented in this paper and used to explore the relationship between land covers present in satellite data and land use represented in official databases. By the use of the developed tool, a land configuration image (LCI) over the Siljan area in northern Sweden was produced and used for analysis. The results are twofold. First, the produced LCI holds new information about variables that are relevant for the interpretation of land use. Second, the comparison with statistics of agricultural production shows that production in the study area varies depending on the relative land configuration. Villages consisting of relatively large-scale arable fields and less diverse landscape are less diverse in production than villages which consist of smaller-scale and more heterogonous landscapes. The result is especially relevant for land use studies and policymakers working on environmental and agricultural policies. We conclude that local spatial context is an endogenous variable in the relation between landscape configuration and agricultural land use.

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