scholarly journals Surge potential and drainage-basin characteristics in East Greenland

2003 ◽  
Vol 36 ◽  
pp. 142-148 ◽  
Author(s):  
Hester Jiskoot ◽  
Tavi Murray ◽  
Adrian Luckman
Keyword(s):  
Remote Sensing ◽  
Drainage Basin ◽  
Remote Sensing Data ◽  
Environmental Data ◽  
Glacier Inventory ◽  
East Greenland ◽  
Sensing Data ◽  
Logistic Analysis ◽  
Geological Conditions ◽  
Multivariate Logistic Analysis

AbstractWe introduce a new glacier inventory of central East Greenland and use the collected data to test proposed theories on surging. The glacier inventory contains 259 glaciers, of which 10 have observed surges and a further 61 are inferred surge-type. The total glaciated area is 5.5×103 km2. The inventory was created from a combination of remote-sensing data and maps, and some 24 glacial and geological inventory parameters were collected for each glacier. A multivariate logistic analysis is used to test which combination of glacial and environmental data is conducive to surging behaviour in East Greenland. Three different models suggest that glaciers with a large complexity, low slope and oriented in a broad arc from northeast to south are most likely to be of surge type. Geological conditions, and hence substrate character, appeared not to be related to surge potential. On the basis of these results and the surge dynamics in this region, we suggest a hydrologically controlled surge mechanism operates in central East Greenland.

Remote Sensing Prospecting Deposit Model Using Aster Data: A Case Study from Southern Part of Taxkorgan Tajik Autonomous County, China

2015 ◽  
Vol 733 ◽  
pp. 124-129
Author(s):  
Hui Zhi Wu ◽  
Qi Gang Jiang ◽  
Chao Jun Bai
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Mineral Exploration ◽  
Information Source ◽  
Remote Sensing Data ◽  
Screening Methods ◽  
Data Sets ◽  
Sensing Data ◽  
Work Area ◽  
Geological Conditions

This work uses multiple types of remote sensing data to develop a model-based mineral exploration method. Data used include Worldview-2 satellite data as the main information source supplemented by QuickBird satellite data to assist in geological interpretations and ASTER satellite data to extract remote sensing anomalies. We have enhanced the spectral and spatial resolution of the remote sensing data using ENVI software. Human-computer interaction methods have been used to confirm the geological conditions. We have interpreted 24 distinct lithologic units, including various types of metamorphic and sedimentary rocks. A total of 471 remote sensing anomalies were delineated, consisting of 173 hydroxyl anomalies and 298 iron-staining anomalies. Geological background screening methods were applied to identify 98 remote sensing anomalies, of which 29 were recommended for further study. Based on the interpretation of anomalies extracted from the ASTER and other geological remote sensing data sets, we have established a typical-deposit prospecting model. In the model, we delineated remote sensing prospecting targets by considering: remote sensing anomalies, geologic bodies and structures, geophysical anomalies and geochemical anomalies. Using this model, we divided the work area into two zones based on types of mineral generation. Seven prospecting targets (one A class, three B class and three C class) were identified. Trenching and block sorting methods were conducted for field verification, and resulted in the discovery of two copper and two iron occurrences with commercial potential.

scholarly journals Environmental Quality Assessment of Urban Ecology based on Spatial Heterogeneity and Remote Sensing Imagery

2019 ◽  
Author(s):  
Iswari Nur Hidayati ◽  
R Suharyadi ◽  
Projo Danoedoro
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Spatial Heterogeneity ◽  
Urban Ecology ◽  
Vegetation Cover ◽  
Land Use Changes ◽  
Remote Sensing Data ◽  
Environmental Data ◽  
Ecological Index ◽  
Sensing Data

The phenomenon of urban ecology is very comprehensive, for example, rapid land-use changes, decrease in vegetation cover, dynamic urban climate, high population density, and lack of urban green space. Temporal resolution and spatial resolution of remote sensing data are fundamental requirements for spatial heterogeneity research. Remote sensing data is very effective and efficient for measuring, mapping, monitoring, and modeling spatial heterogeneity in urban areas. The advantage of remote sensing data is that it can be processed by visual and digital analysis, index transformation, image enhancement, and digital classification. Therefore, various information related to the quality of urban ecology can be processed quickly and accurately. This study integrates urban ecological, environmental data such as vegetation, built-up land, climate, and soil moisture based on spectral image response. The combination of various indices obtained from spatial data, thematic data, and spatial heterogeneity analysis can provide information related to urban ecological status. The results of this study can measure the pressure of environment caused by human activities such as urbanization, vegetation cover and agriculture land decreases, and urban micro-climate phenomenon. Using the same data source indicators, this method is comparable at different spatiotemporal scales and can avoid the variations or errors in weight definitions caused by individual characteristics. Land use changes can be seen from the results of the ecological index. Change is influenced by human behavior in the environment. In 2002, the ecological index illustrated that regions with low ecology still spread. Whereas in 2017, good and bad ecological indices are clustered.

scholarly journals Geographic Distribution of Desert Locusts in Africa, Asia and Europe Using Multiple Sources of Remote-Sensing Data

2020 ◽  
Vol 12 (21) ◽  
pp. 3593
Author(s):  
Chaoliang Chen ◽  
Jing Qian ◽  
Xi Chen ◽  
Zengyun Hu ◽  
Jiayu Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Logistic Regression ◽  
Regression Model ◽  
Geographic Distribution ◽  
Logistic Regression Model ◽  
Remote Sensing Data ◽  
Environmental Data ◽  
Desert Locust ◽  
Sensing Data ◽  
Potential Habitats

In history, every occurrence of a desert locust plague has brought a devastating blow to local agriculture. Analyses of the potential geographic distribution and migration paths of desert locusts can be used to better monitor and provide early warnings about desert locust outbreaks. By using environmental data from multiple remote-sensing data sources, we simulate the potential habitats of desert locusts in Africa, Asia and Europe in this study using a logistic regression model that was developed based on desert locust monitoring records. The logistic regression model showed high accuracy, with an average training area under the curve (AUC) value of 0.84 and a kappa coefficient of 0.75. Our analysis indicated that the temperature and leaf area index (LAI) play important roles in shaping the spatial distribution of desert locusts. A model analysis based on data for six environmental variables over the past 15 years predicted that the potential habitats of desert locust present a periodic movement pattern between 40°N and 30°S latitude. The area of the potential desert locust habitat reached a maximum in July, with a suitable area exceeding 2.77 × 107 km2 and located entirely between 0°N and 40°N in Asia-Europe and Africa. In December, the potential distribution of desert locusts reached its minimum area at 0.68 × 107 km2 and was located between 30°N and 30°S in Asia and Africa. According to the model estimates, desert locust-prone areas are distributed in northern Ethiopia, South Sudan, northwestern Kenya, the southern Arabian Peninsula, the border area between India and Pakistan, and the southern Indian Peninsula. In addition, desert locusts were predicted to migrate from east to west between these areas and in Africa between 10°N and 17°N. Countries in these areas should closely monitor desert locust populations and respond rapidly.

scholarly journals Roads and Intersections Extraction from High-Resolution Remote Sensing Imagery Based on Tensor Voting under Big Data Environment

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Ke Sun ◽  
Junping Zhang ◽  
Yingying Zhang
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Environmental Data ◽  
Tensor Voting ◽  
Remote Sensing Imagery ◽  
Sensing Data ◽  
The Road ◽  
Road Intersections

Currently, big data is a new and hot object of research. In particular, the development of the Internet of things (IoT) results in a sharp increase in data. Enormous amounts of networking sensors are constantly collecting and transmitting data for storage and processing in the cloud including remote sensing data, environmental data, geographical data, etc. Road information extraction from remote sensing data is mainly researched in this paper. Roads are typical man-made objects. Extracting roads from remote sensing imagery has great significance in various applications such as GIS data updating, urban planning, navigation, and military. In this paper a multistage and multifeature method to extract roads and detect road intersections from high-resolution remotely sensed imagery based on tensor voting is presented. Firstly, the input remote sensing image is segmented into two groups including road candidate regions and nonroad regions using template matching; then we can obtain preliminary road map. Secondly, nonroad regions are removed by geometric characteristics of road (large area and long strip). Thirdly, tensor voting is used to overcome the broken roads and discontinuities caused by the different disturbing factors and then delete the nonroad areas that are mixed into the road areas due to mis-segmentation, improving the completeness of extracted roads. And then, all the road intersections are extracted by using tensor voting. The experiments are conducted on different remote sensing images to test the effectiveness of our method. The experimental results show that our method can get more complete and accurate extracted results than the state-of-the-art methods.

scholarly journals Application of Copernicus Data for Climate-Relevant Urban Planning Using the Example of Water, Heat, and Vegetation

2021 ◽  
Vol 13 (18) ◽  
pp. 3634
Author(s):  
Michael Max Bühler ◽  
Christoph Sebald ◽  
Diana Rechid ◽  
Eberhard Baier ◽  
Alexander Michalski ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Urban Planning ◽  
Local Governments ◽  
Climate Adaptation ◽  
Remote Sensing Data ◽  
Environmental Data ◽  
Local Data ◽  
Vegetation Monitoring ◽  
Adaptation Measures ◽  
Sensing Data

Specific climate adaptation and resilience measures can be efficiently designed and implemented at regional and local levels. Climate and environmental databases are critical for achieving the sustainable development goals (SDGs) and for efficiently planning and implementing appropriate adaptation measures. Available federated and distributed databases can serve as necessary starting points for municipalities to identify needs, prioritize resources, and allocate investments, taking into account often tight budget constraints. High-quality geospatial, climate, and environmental data are now broadly available and remote sensing data, e.g., Copernicus services, will be critical. There are forward-looking approaches to use these datasets to derive forecasts for optimizing urban planning processes for local governments. On the municipal level, however, the existing data have only been used to a limited extent. There are no adequate tools for urban planning with which remote sensing data can be merged and meaningfully combined with local data and further processed and applied in municipal planning and decision-making. Therefore, our project CoKLIMAx aims at the development of new digital products, advanced urban services, and procedures, such as the development of practical technical tools that capture different remote sensing and in-situ data sets for validation and further processing. CoKLIMAx will be used to develop a scalable toolbox for urban planning to increase climate resilience. Focus areas of the project will be water (e.g., soil sealing, stormwater drainage, retention, and flood protection), urban (micro)climate (e.g., heat islands and air flows), and vegetation (e.g., greening strategy, vegetation monitoring/vitality). To this end, new digital process structures will be embedded in local government to enable better policy decisions for the future.

scholarly journals Assessment of Phytoecological Variability by Red-Edge Spectral Indices and Soil-Landscape Relationships

2019 ◽  
Vol 11 (20) ◽  
pp. 2448 ◽  
Author(s):  
Helena S. K. Pinheiro ◽  
Theresa P. R. Barbosa ◽  
Mauro A. H. Antunes ◽  
Daniel Costa de Carvalho ◽  
Alexis R. Nummer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Remote Sensing Data ◽  
Parent Material ◽  
Mountainous Area ◽  
Kappa Index ◽  
Sensing Data ◽  
Taxonomic Level ◽  
Red Edge ◽  
Geological Conditions

There is a relation of vegetation physiognomies with soil and geological conditions that can be represented spatially with the support of remote sensing data. The goal of this research was to map vegetation physiognomies in a mountainous area by using Sentinel-2 Multispectral Instrument (MSI) data and morphometrical covariates through data mining techniques. The research was based on red-edge (RE) bands, and indices, to classify phytophysiognomies at two taxonomic levels. The input data was pixel sampled based on field sample sites. Data mining procedures comprised covariate selection and supervised classification through the Random Forest model. Results showed the potential of bands 3, 5, and 6 to map phytophysiognomies for both seasons, as well as Green Chlorophyll (CLg) and SAVI indices. NDVI indices were important, particularly those calculated with bands 6, 7, 8, and 8A, which were placed at the RE position. The model performance showed reasonable success to Kappa index 0.72 and 0.56 for the first and fifth taxonomic level, respectively. The model presented confusion between Broadleaved dwarf-forest, Parkland Savanna, and Bushy grassland. Savanna formations occurred variably in the area while Bushy grasslands strictly occur in certain landscape positions. Broadleaved forests presented the best performance (first taxonomic level), and among its variation (fifth level) the model could precisely capture the pattern for those on deep soils from gneiss parent material. The approach was thus useful to capture intrinsic soil-plant relationships and its relation with remote sensing data, showing potential to map phytophysiognomies in two distinct taxonomic levels in poorly accessible areas.

scholarly journals WATER BALANCE BASED ON REMOTE SENSING DATA IN PANTANAL

2019 ◽  
Vol 46 (3) ◽  
pp. 33
Author(s):  
Hudson De Azevedo Macedo ◽  
José Cândido Stevaux ◽  
Aguinaldo Silva ◽  
Ivan Bergier
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Drainage Basin ◽  
Temperature Anomaly ◽  
Remote Sensing Data ◽  
Water Security ◽  
Tropical Rainfall Measuring Mission ◽  
Water Parameters ◽  
Sensing Data ◽  
Annual Basis

Water storage in a drainage basin determines its water security. The quantity of water retained in the watershed can be measured by means of the water balance calculation. This balance can be defined by the input of water subtracted from the outputs. However, for the Pantanal, the measurements of water inlet and outlet are expensive, which makes the use of remote sensing data a high impact tool with clear socioeconomic advantages. Studies of water availability with orbital sensors are relatively scarce in the Upper Paraguay Basin (BAP). This work is an attempt to estimate the BAP water balance using rainfall and evapotranspiration remote sensing data from the Tropical Rainfall Measuring Mission (TRMM) and the MODIS Global Evapotranspiration Project (MOD16), respectively. The results indicate that BAP had an annual surplus of water between 2000 and 2014, though water parameters seem weakly correlated at annual basis. However, there may be atmospheric-climatic phenomena that maximize the correlation between the hydrological parameters and the temperature anomaly with delays of 2 to 5 years, suggesting lagged teleconnections with QBO and ENSO.

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