Power line monitoring using optical satellite data

2016 ◽  
Author(s):  
L. Hame ◽  
J. Norppa ◽  
P. Salovaara ◽  
J. Pylvanainen
Keyword(s):  
Satellite Data ◽  
Power Line ◽  
Optical Satellite Data

scholarly journals Combining optical satellite data and airborne laser scanner data for vegetation classification

2011 ◽  
Vol 3 (5) ◽  
pp. 393-401 ◽  
Author(s):  
Karin Nordkvist ◽  
Ann-Helen Granholm ◽  
Johan Holmgren ◽  
Håkan Olsson ◽  
Mats Nilsson
Keyword(s):  
Satellite Data ◽  
Laser Scanner ◽  
Vegetation Classification ◽  
Scanner Data ◽  
Airborne Laser Scanner ◽  
Airborne Laser ◽  
Optical Satellite Data

scholarly journals Integration of synthetic aperture radar and optical satellite data for corn biomass estimation

MethodsX ◽  
2020 ◽  
Vol 7 ◽  
pp. 100857
Author(s):  
Mehdi Hosseini ◽  
Heather McNairn ◽  
Scott Mitchell ◽  
Laura Dingle Robertson ◽  
Andrew Davidson ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Satellite Data ◽  
Synthetic Aperture ◽  
Biomass Estimation ◽  
Optical Satellite Data ◽  
Aperture Radar

scholarly journals Synergistic Use of Radar and Optical Satellite Data for Improved Monsoon Cropland Mapping in India

2020 ◽  
Vol 12 (3) ◽  
pp. 522 ◽  
Author(s):  
Abdul Qadir ◽  
Pinki Mondal
Keyword(s):  
Satellite Data ◽  
Critical Role ◽  
Mixed Forest ◽  
Google Earth ◽  
Optical Data ◽  
Growth Stages ◽  
Global Agriculture ◽  
Optical Satellite Data ◽  
Sentinel 2 ◽  
Cropland Mapping

Monsoon crops play a critical role in Indian agriculture, hence, monitoring these crops is vital for supporting economic growth and food security for the country. However, monitoring these crops is challenging due to limited availability of optical satellite data due to cloud cover during crop growth stages, landscape heterogeneity, and small field sizes. In this paper, our objective is to develop a robust methodology for high-resolution (10 m) monsoon cropland mapping appropriate for different agro-ecological regions (AER) in India. We adapted a synergistic approach of combining Sentinel-1 Synthetic Aperture Radar (SAR) data with Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 optical data using the Google Earth Engine platform. We developed a new technique, Radar Optical cross Masking (ROM), for separating cropland from non-cropland by masking out forest, plantation, and other non-dynamic features. The methodology was tested for five different AERs in India, representing a wide diversity in agriculture, soil, and climatic variations. Our findings indicate that the overall accuracy obtained by using the SAR-only approach is 90%, whereas that of the combined approach is 93%. Our proposed methodology is particularly effective in regions with cropland mixed with tree plantation/mixed forest, typical of smallholder dominated tropical countries. The proposed agriculture mask, ROM, has high potential to support the global agriculture monitoring missions of Geo Global Agriculture Monitoring (GEOGLAM) and Sentinel-2 for Agriculture (S2Agri) project for constructing a dynamic monsoon cropland mask.

scholarly journals Agreement Index for Burned Area Mapping: Integration of Multiple Spectral Indices Using Sentinel-2 Satellite Images

2020 ◽  
Vol 12 (11) ◽  
pp. 1862 ◽  
Author(s):  
Daniela Smiraglia ◽  
Federico Filipponi ◽  
Stefania Mandrone ◽  
Antonella Tornato ◽  
Andrea Taramelli
Keyword(s):  
Decision Making ◽  
Satellite Data ◽  
Burned Area ◽  
Decision Making Process ◽  
Large Set ◽  
Spectral Indices ◽  
Commission Errors ◽  
Burned Area Mapping ◽  
Optical Satellite Data ◽  
Sentinel 2

Identifying fire-affected areas is of key importance to support post-fire management strategies and account for the environmental impact of fires. The availability of high spatial and temporal resolution optical satellite data enables the development of procedures for detailed and prompt post-fire mapping. This study proposes a novel approach for integrating multiple spectral indices to generate more accurate burned area maps by exploiting Sentinel-2 images. This approach aims to develop a procedure to combine multiple spectral indices using an adaptive thresholding method and proposes an agreement index to map the burned areas by optimizing omission and commission errors. The approach has been tested for the burned area classification of four study areas in Italy. The proposed agreement index combines multiple spectral indices to select the actual burned pixels, to balance the omission and commission errors, and to optimize the overall accuracy. The results showed the spectral indices singularly performed differently in the four study areas and that high levels of commission errors were achieved, especially for wildfires which occurred during the fall season (up to 0.93) Furthermore, the agreement index showed a good level of accuracy (minimum 0.65, maximum 0.96) for all the study areas, improving the performance compared to assessing the indices individually. This suggests the possibility of testing the methodology on a large set of wildfire cases in different environmental conditions to support the decision-making process. Exploiting the high resolution of optical satellite data, this work contributes to improving the production of detailed burned area maps, which could be integrated into operational services based on the use of Earth Observation products for burned area mapping to support the decision-making process.

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