Suggestions on the Selection of Satellite Imagery for Future Remote Sensing-Based Humanitarian Applications

Satellite imagery is an important information source for research on remote sensing (RS)based humanitarian applications. The selection of satellite imagery is one of the most important steps for such research. This paper firstly shows the selection of satellite imagery in past research from 2015 to 2021. It can be found that most research on land cover and land use (LCLU) change caused by conflicts or refugees/internally displaced persons (IDPs) chose medium spatial resolution (MSR) imagery. Most research on dwelling detection of refugee/IDP camps applied high or very high spatial resolution (HSR/VHSR) imagery. There is much research that applied multiple types of satellite imagery for humanitarian applications. Then, the paper presents general characteristics of commonly available optical satellite imagery. Next, with the development of sensors, this paper suggests that data fusion of SPOT5 and Sentinel-2 may be helpful in LCLU change detection caused by refugees/IDPs or conflicts. Smallsat imagery may be promising for research on humanitarian applications that require a high temporal frequency of imagery.

[1]  Christopher D. Elvidge,et al.  Dark Times: nighttime satellite imagery as a detector of regional disparity and the geography of conflict , 2017 .

[2]  Deren Li,et al.  Intercalibration between DMSP/OLS and VIIRS night-time light images to evaluate city light dynamics of Syria’s major human settlement during Syrian Civil War , 2017 .

[3]  Adnan Firoze,et al.  Machine learning for predicting landslide risk of Rohingya refugee camp infrastructure , 2020, J. Inf. Telecommun..

[4]  Clement Atzberger,et al.  A Combined Satellite-Derived Drought Indicator to Support Humanitarian Aid Organizations , 2016, Remote Sensing.

[5]  John A. Quinn,et al.  Humanitarian applications of machine learning with remote-sensing data: review and case study in refugee settlement mapping , 2018, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[6]  F. Rembold,et al.  Mapping land enclosures and vegetation cover changes in the surroundings of Kenya's Dadaab refugee camps with very high resolution satellite imagery , 2018, Land Degradation & Development.

[7]  Yanhua Xie,et al.  Using satellite data and machine learning to study conflict-induced environmental and socioeconomic destruction in data-poor conflict areas: The case of the Rakhine conflict , 2021 .

[8]  Petra Füreder,et al.  Earth Observation for Humanitarian Operations , 2018 .

[9]  The Impact Of The Syrian Civil War On Land Use / Land Cover In Al-Yarmouk Basin During 2010–2018 , 2020 .

[10]  Edzer Pebesma,et al.  Detecting dwelling destruction in Darfur through object-based change analysis of very high-resolution imagery , 2017 .

[11]  Edith Rogenhofer,et al.  Monitoring of Refugee and Camps for Internally Displaced Persons Using Sentinel-2 Imagery – A Feasibility Study , 2017 .

[12]  M. Krupiński,et al.  Aplicability of Multifractal Features as Descriptors of the Complex Terrain Situation in IDP/Refugee Camps , 2019, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium.

[13]  A. Braun,et al.  Elevation change of Bhasan Char measured by persistent scatterer interferometry using Sentinel-1 data in a humanitarian context , 2020 .

[14]  Aliihsan Sekertekin,et al.  Pixel-Based Classification Analysis of Land Use Land Cover Using SENTINEL-2 and LANDSAT-8 Data , 2017 .

[15]  H. Abu Qdais,et al.  Mapping urban land surface temperature using remote sensing techniques and artificial neural network modelling , 2019, International Journal of Remote Sensing.

[16]  S. Voigt,et al.  Earth observation-based multi-scale impact assessment of internally displaced person (IDP) camps on wood resources in Zalingei, Darfur , 2016 .

[17]  Andreas Braun Braun.  Spaceborne radar imagery – An under-utilized source of information for humanitarian relief , 2020 .

[18]  A. Paul,et al.  Spatiotemporal changes of vegetation and land surface temperature in the refugee camps and its surrounding areas of Bangladesh after the Rohingya influx from Myanmar , 2020, Environment, Development and Sustainability.

[19]  V. Mueller,et al.  Vegetation changes attributable to refugees in Africa coincide with agricultural deforestation , 2020, Environmental research letters : ERL [Web site].

[20]  Marta Segal Block.  Mapping the Land , 2008 .

[21]  Dirk Tiede,et al.  Stratified Template Matching to Support Refugee Camp Analysis in OBIA Workflows , 2017, Remote. Sens..

[22]  Munshi Khaledur Rahman,et al.  Rohingya Refugee Crisis and Forest Cover Change in Teknaf, Bangladesh , 2018, Remote. Sens..

[23]  Omid Ghorbanzadeh,et al.  Transferable instance segmentation of dwellings in a refugee camp - integrating CNN and OBIA , 2020, European Journal of Remote Sensing.

[24]  Satellite analysis of the environmental impacts of armed-conflict in Rakhine, Myanmar , 2021 .

[25]  Christopher X. Ren,et al.  On the Detectability of Conflict: a Remote Sensing Study of the Rohingya Conflict , 2020, 2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI).

[26]  Guojin He,et al.  Ongoing Conflict Makes Yemen Dark: From the Perspective of Nighttime Light , 2017, Remote. Sens..

[27]  F. Fakhri,et al.  Integration of Sentinel-1 and Sentinel-2 data for change detection: A case study in a war conflict area of Mosul city , 2021, Remote Sensing Applications: Society and Environment.

[28]  A. Trémeau,et al.  International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives , 2013 .

[29]  Volker Hochschild,et al.  Refugee Camp Monitoring and Environmental Change Assessment of Kutupalong, Bangladesh, Based on Radar Imagery of Sentinel-1 and ALOS-2 , 2019, Remote. Sens..

[30]  S. Lang,et al.  Impact of refugee camps on their environment. A case study using multi-temporal SAR data , 2016 .

[31]  Stefan Voigt,et al.  Towards semi-automated satellite mapping for humanitarian situational awareness , 2014, IEEE Global Humanitarian Technology Conference (GHTC 2014).

[32]  Barasa Bernard,et al.  The impact of refugee settlements on land use changes and vegetation degradation in West Nile Sub-region, Uganda , 2020, Geocarto International.

[33]  Patrick Aravena Pelizari,et al.  Object-Based Analysis and Fusion of Optical and SAR Satellite Data for Dwelling Detection in Refugee Camps , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[34]  F. Witmer.  Remote sensing of violent conflict: eyes from above , 2015 .

[35]  Saad M. Alayyash,et al.  Rainfall Floods as a Result of Land Use Alteration in a Syrian Refugee Camp in Jordan , 2017 .

[36]  Volker Hochschild,et al.  A SAR-Based Index for Landscape Changes in African Savannas , 2017, Remote. Sens..

[37]  Samuel N. Goward,et al.  Remote Sensing in Human Rights and International Humanitarian Law Monitoring: Concepts and Methods† , 2013 .

[38]  John C. Trinder.  Editorial for Special Issue "Applications of Synthetic Aperture Radar (SAR) for Land Cover Analysis" , 2020, Remote. Sens..

[39]  Nijad Al-Najdawi,et al.  Infrastructure Evolution Analysis via Remote Sensing in an Urban Refugee Camp – Evidence from Za’atari , 2016 .

[40]  Chiman Kwan,et al.  Deep Learning for Effective Refugee Tent Extraction Near Syria–Jordan Border , 2020, IEEE Geoscience and Remote Sensing Letters.