Multiscale Water Body Extraction in Urban Environments From Satellite Images

2014 ◽  
Vol 7 (10) ◽  
pp. 4301-4312 ◽  
Author(s):  
Ya'nan Zhou ◽  
Jiancheng Luo ◽  
Zhanfeng Shen ◽  
Xiaodong Hu ◽  
Haiping Yang
Keyword(s):  
Water Body ◽  
Satellite Images ◽  
Urban Environments

scholarly journals High-resolution urban air quality monitoring using sentinel satellite images and low-cost ground-based sensor networks

2020 ◽  
Vol 171 ◽  
pp. 02002
Author(s):  
Joseph Gitahi ◽  
Michael Hahn
Keyword(s):  
Satellite Images ◽  
Meteorological Data ◽  
Low Cost ◽  
Correlation Coefficients ◽  
Urban Environments ◽  
Average Correlation ◽  
Second Stage ◽  
Aerosol Distribution ◽  
Level Monitoring ◽  
Sentinel 2

Satellite remote sensing aerosol monitoring products are readily available but limited to regional and global scales due to low spatial resolutions making them unsuitable for city-level monitoring. Freely available satellite images such as Sentinel -2 at relatively high spatial (10m) and temporal (5 days) resolutions offer the chance to map aerosol distribution at local scales. In the first stage of this study, we retrieve Aerosol Optical Depth (AOD) from Sentinel -2 imagery for the Munich region and assess the accuracy against ground AOD measurements obtained from two Aerosol Robotic Network (AERONET) stations. Sen2Cor, iCOR and MAJA algorithms which retrieve AOD using Look-up-Tables (LUT) pre-calculated using radiative transfer (RT) equations and SARA algorithm that applies RT equations directly to satellite images were used in the study. Sen2Cor, iCOR and MAJA retrieved AOD at 550nm show strong consistency with AERONET measurements with average correlation coefficients of 0.91, 0.89 and 0.73 respectively. However, MAJA algorithm gives better and detailed variations of AOD at 10m spatial resolution which is suitable for identifying varying aerosol conditions over urban environments at a local scale. In the second stage, we performed multiple linear regression to estimate surface Particulate Matter (PM2.5) concentrations using the satellite retrieved AOD and meteorological data as independent variables and ground-measured PM2.5 data as the dependent variable. The predicted PM2.5 concentrations exhibited agreement with ground measurements, with an overall coefficient (R2) of 0.59.

scholarly journals Assessment and Predicting of LULC by Kappa Analysis and CA Markov model using RS and GIS Techniques in Udham Singh Nagar District, India

2021 ◽  
Author(s):  
THANGAVELU ARUMUGAM ◽  
RAM LAKHAN YADAV ◽  
SAPNA KINATTINKARA
Keyword(s):  
Markov Model ◽  
Change Detection ◽  
Water Body ◽  
Satellite Images ◽  
Development Planning ◽  
Landsat 8 ◽  
Fallow Land ◽  
River Sand ◽  
Error Matrix ◽  
The Future

Abstract In this study an attempt to generate the LULC maps and investigate change detection analysis over a period of 22 years using Landsat satellite images of 1994, 2000, and 2016 and to predict the LULCC for the year 2016-2032 using CA Markov model in Udham Singh Nagar district, Uttarkhand. Satellite images of Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI sensor of nominal spatial resolution 30m were used. Supervised image classifications with the help of parallel pipe algorithm were used in this study. The validity of the Cellular Automata Markov model were used to predict future (16 years) LULC of 2032. The estimation includes two modules to predict the future land use pattern of the study area such as MARKOV and CA-MARKOV model/modules. Commonly, the accuracy of the classification results is assessed by the error matrix calculation. The result of overall change detection indicates agriculture, forest, water body and fallow land are decreased by 121.75 Km2 (14%), 44.70 Km2 (5%), 38.91 Km2 (4.5%) and 230.71 (26.5%); settlement and river sand are increased by 379.89 Km2 (44%) and 56.18 Km2 (6%). The study has an overall classification accuracy 76.84%, and standard kappa coefficient value (K) of 0.722. The model predicts the future change detection in agriculture 32%, forest 38%, fallow land 5%, settlement 20%, water body 3%, and river sand is 2%. This study is very effective for future LULC prediction that is helpful in urban development planning and the field of management of natural resources.

Outdoor mapping and localization using satellite images

Robotica ◽  
2010 ◽  
Vol 28 (7) ◽  
pp. 1001-1012 ◽  
Author(s):  
C. U. Dogruer ◽  
A. B. Koku ◽  
M. Dolen
Keyword(s):  
Large Scale ◽  
Satellite Images ◽  
Autonomous Robots ◽  
Field Tests ◽  
Urban Environments ◽  
Global Localization ◽  
Standard Equipment ◽  
Sensing Technology ◽  
Outdoor Environments ◽  
Almost All

SUMMARYRecently, satellite images of most urban settings has become available on the internet. In this study, a novel mapping and global localization approach, which uses these images, is proposed for outdoor mobile robots operating in urban environment. The mapping of large-scale outdoor environments is done by employing the satellite images acquired by remote sensing technology, and then a map-based approach, that is, Monte Carlo localization is used for localization. The novelty of proposed method is that it uses standard equipment present on almost all autonomous robots and satellite images thus it acts as an alternative to GPS data in urban environments. Extensive field tests are presented to demonstrate the effectiveness of proposed approach.

scholarly journals Clean and Green Urban Water Bodies Benefit Nocturnal Flying Insects and Their Predators, Insectivorous Bats

2020 ◽  
Vol 12 (7) ◽  
pp. 2634 ◽  
Author(s):  
Tanja M. Straka ◽  
Pia E. Lentini ◽  
Linda F. Lumsden ◽  
Sascha Buchholz ◽  
Brendan A. Wintle ◽  
...  
Keyword(s):  
Water Body ◽  
Urban Environments ◽  
Water Bodies ◽  
Tree Cover ◽  
Urban Water ◽  
Insectivorous Bats ◽  
Insect Order ◽  
Flying Insects ◽  
Riparian Tree ◽  
The Relationship

Nocturnal arthropods form the prey base for many predators and are an integral part of complex food webs. However, there is limited understanding of the mechanisms influencing invertebrates at urban water bodies and the potential flow-on effects to their predators. This study aims to: (i) understand the importance of standing water bodies for nocturnal flying insect orders, including the landscape- and local-scale factors driving these patterns; and (ii) quantify the relationship between insects and insectivorous bats. We investigated nocturnal flying insects and insectivorous bats simultaneously at water bodies (n = 58) and non-water body sites (n = 35) using light traps and acoustic recorders in Melbourne, Australia. At the landscape scale, we found that the presence of water and high levels of surrounding greenness were important predictors for some insect orders. At the water body scale, low levels of sediment pollutants, increased riparian tree cover and water body size supported higher insect order richness and a greater abundance of Coleopterans and Trichopterans, respectively. Most bat species had a positive response to a high abundance of Lepidopterans, confirming the importance of this order in the diet of insectivorous bats. Fostering communities of nocturnal insects in urban environments can provide opportunities for enhancing the prey base of urban nocturnal insectivores.

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