Report from field reconnaissance at Gebelein, Khozam and el-Rizeiqat

A field reconnaissance in the region of Gebelein, Khozam and el-Rizeiqat in 2013 was aimed at obtaining information on site topography and state of preservation, even as it tested mobile GIS devices and remote sensing analysis to improve usage procedures in field prospection. Archival maps and satellite imaging were used to locate archaeological features, analyze changes of landscape and modern expansion of the cultivation zone from the natural alluvial plain into the low desert area.

Download Full-text

Modeling and Simulation of Mission Planning Problem for Remote Sensing Satellite Imaging

MATEC Web of Conferences ◽

10.1051/matecconf/201817903024 ◽

2018 ◽

Vol 179 ◽

pp. 03024 ◽

Cited By ~ 1

Author(s):

Yao Pan ◽

Zhong Ming Chi ◽

Qi Long Rao ◽

Kai Peng Sun ◽

Yi Nan Liu

Keyword(s):

Remote Sensing ◽

Time Window ◽

Constraint Satisfaction Problem ◽

Optimal Path ◽

Time Constraint ◽

Mission Planning ◽

Planning Problem ◽

Satellite Imaging ◽

Problem Model ◽

Remote Sensing Satellite

Mission planning problem for remote sensing satellite imaging is studied. Firstly, the time constraint satisfaction problem model is presented after analyzing the characteristic of time constraint. Then, An optimal path searching algorithm based on the discrete time window is proposed according to the non-uniqueness for satellite to mission in the visible time window. Simulation results verify the efficiency of the model and algorithm.

Download Full-text

Remote Sensing in Oil Spill Handling in Offshore North West Java

10.2118/205607-ms ◽

2021 ◽

Author(s):

Audra Ligafinza ◽

Farasdaq Muchibbus Sajjad ◽

Mohammad Abdul Jabbar ◽

Anggia Fatmawati ◽

Alvin Derry Wirawan ◽

...

Keyword(s):

Remote Sensing ◽

Real Time ◽

Oil Spill ◽

Environmental Damage ◽

Satellite Imaging ◽

North West ◽

Novel Technologies ◽

Oil Boom ◽

Remediation Strategies ◽

Restoration Strategies

Abstract During the blowout event, it is critical to track the oil spill to minimize environmental damage and optimize restoration cost. In this paper, we deliver our success story in handling oil spill from recent experiences. We utilize remote sensing technologies to establish our analysis and plan the remediation strategies. We also comprehensively discuss the techniques to analyze big data from the satellites, to utilize the downloaded data for forecasting, and to align the satellite information with restoration strategies. PHE relies on its principle to maintain minimum damage and ensures safety by dividing the steps into several aspects of monitoring, response (offshore and onshore), shoreline management and waste management. PHE utilizes latest development in survey by using satellite imaging, survey boat, chopper and UAV drone. Spill containment is done using several layers of oil boom to recover oil spill, complemented with skimmers and storage tanks. PHE encourages shoreline remediation using nets and manual recovery for capturing oil sludge. Using this combination of technologies, PHE is able to model and anticipate oil spill movement from the source up until the farthest shoreline. This enables real time monitoring and handling, therefore minimum environmental damage is ensured. PHE also employs prudent engineering design based on real time field condition in order to ensure the equipment are highly suited for the condition, as well as ensuring good supply chain of the material availability. This publication addresses the first offshore blowout mitigation and handling in Indonesia that uses novel technologies such as static oil boom, satellite imaging and integrated effort in handling shoreline damage. It is hoped that the experience can be replicated for other offshore operating contractors in Indonesia in designing blowout remediation.

Download Full-text

Delineation of groundwater potential zone for sustainable development: A case study from Ganga Alluvial Plain covering Hooghly district of India using remote sensing, geographic information system and analytic hierarchy process

Journal of Cleaner Production ◽

10.1016/j.jclepro.2017.11.161 ◽

2018 ◽

Vol 172 ◽

pp. 2485-2502 ◽

Cited By ~ 44

Author(s):

Suman Patra ◽

Pulak Mishra ◽

Subhash Chandra Mahapatra

Keyword(s):

Remote Sensing ◽

Information System ◽

Groundwater Potential ◽

Alluvial Plain ◽

Groundwater Potential Zone ◽

Potential Zone ◽

Analytic Hierarchy ◽

Ganga Alluvial Plain ◽

Hierarchy Process

Download Full-text

Identification of Leveled Archeological Mounds (Höyük) in the Alluvial Plain of the Ceyhan River (Southern Turkey) by Satellite Remote-Sensing Analyses

Remote Sensing ◽

10.3390/rs10020241 ◽

2018 ◽

Vol 10 (2) ◽

pp. 241 ◽

Cited By ~ 8

Author(s):

Monica Bini ◽

Ilaria Isola ◽

Giovanni Zanchetta ◽

Adriano Ribolini ◽

Andrea Ciampalini ◽

...

Keyword(s):

Remote Sensing ◽

Satellite Remote Sensing ◽

Alluvial Plain ◽

Southern Turkey

Download Full-text

The Perspective from Space Unlocks the Amazon Water Cycle

Eos ◽

10.1029/2021eo210659 ◽

2021 ◽

Vol 102 ◽

Author(s):

Aaron Sidder

Keyword(s):

Remote Sensing ◽

Water Cycle ◽

Satellite Imaging

Satellite imaging and remote sensing offer unique insights into the Amazon’s complex hydrology. A new review summarizes decades of findings and charts a path forward for new remote sensing missions.

Download Full-text

Development and testing of a remote sensing-based model for estimating groundwater levels in aeolian desert areas of China

Canadian Journal of Soil Science ◽

10.4141/cjss10044 ◽

2011 ◽

Vol 91 (1) ◽

pp. 29-37 ◽

Cited By ~ 9

Author(s):

Aidi Huo ◽

Xunhong Chen ◽

Huike Li ◽

Ming Hou ◽

Xiaojing Hou

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Groundwater Level ◽

Remote Sensing Data ◽

Water Levels ◽

Desert Area ◽

Groundwater Levels ◽

Sensing Data ◽

Desert Areas ◽

Regional Groundwater

Huo, A., Chen, X., Li, H., Hou, M. and Hou, X. 2011. Development and testing of a remote sensing-based model for estimating groundwater levels in aeolian desert areas of China. Can. J. Soil Sci. 91: 29–37. Regional groundwater level is an important data set for understanding the relationships between groundwater resources and regional ecological environments. The decline in water table levels leads to vegetation degradation and thus affects the ecological environment. Such a negative effect is especially apparent in the desertification areas. In this study, a remote-sensing based method was proposed to predict the distribution of the regional groundwater level in an aeolian desert area in northern China. The study used the Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data and field investigations. Based on field investigation of groundwater level, soil moisture, and other supporting information in the aeolian desert area, as well as the soil moisture distribution derived from the MODIS images, empirical equations describing the relationship between the soil moisture and groundwater level were obtained. The groundwater levels derived using the MODIS image data were verified by groundwater levels measured from 58 wells. The results show that the correlation coefficient between the measured groundwater levels and the remote sensing-based estimated water levels was 0.868, indicating that the error is small and the predictions closely reflect the real water levels. This model can be used to predict groundwater levels in aeolian desert areas based on remote sensing data sets.

Download Full-text