Post-Disaster Recovery Monitoring with Google Earth Engine

Post-disaster recovery is a complex process in terms of measuring its progress after a disaster and understanding its components and influencing factors. During this process, disaster planners and governments need reliable information to make decisions towards building the affected region back to normal (pre-disaster), or even improved, conditions. Hence, it is essential to use methods to understand the dynamics/variables of the post-disaster recovery process, and rapid and cost-effective data and tools to monitor the process. Google Earth Engine (GEE) provides free access to vast amounts of remote sensing (RS) data and a powerful computing environment in a cloud platform, making it an attractive tool to analyze earth surface data. In this study we assessed the suitability of GEE to analyze and track recovery. To do so, we employed GEE to assess the recovery process over a three-year period after Typhoon Haiyan, which struck Leyte island, in the Philippines, in 2013. We developed an approach to (i) generate cloud and shadow-free image composites from Landsat 7 and 8 satellite imagery and produce land cover classification data using the Random Forest method, and (ii) generate damage and recovery maps based on post-classification change analysis. The method produced land cover maps with accuracies >88%. We used the model to produce damage and three time-step recovery maps for 62 municipalities on Leyte island. The results showed that most of the municipalities had recovered after three years in terms of returning to the pre-disaster situation based on the selected land cover change analysis. However, more analysis (e.g., functional assessment) based on detailed data (e.g., land use maps) is needed to evaluate the more complex and subtle socio-economic aspects of the recovery. The study showed that GEE has good potential for monitoring the recovery process for extensive regions. However, the most important limitation is the lack of very-high-resolution RS data that are critical to assess the process in detail, in particular in complex urban environments.

[1]  Andrew Molthan,et al.  Synthetic Aperture Radar and Optical Remote Sensing of Crop Damage Attributed to Severe Weather in the Central United States , 2019, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium.

[2]  Patrick Hostert,et al.  A Pixel-Based Landsat Compositing Algorithm for Large Area Land Cover Mapping , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[3]  Meng Zhao,et al.  Regional Mapping of Plantation Extent Using Multisensor Imagery , 2016, Remote. Sens..

[4]  Prasanna H. Gowda,et al.  Using Thematic Mapper Data to Identify Contrasting Soil Plains and Tillage Practices , 1997 .

[5]  Cheng-Chien Liu,et al.  Flood Prevention and Emergency Response System Powered by Google Earth Engine , 2018, Remote. Sens..

[6]  André Stumpf,et al.  Object-oriented mapping of landslides using Random Forests , 2011 .

[7]  Saman Ghaffarian,et al.  AUTOMATIC BUILDING DETECTION BASED ON SUPERVISED CLASSIFICATION USING HIGH RESOLUTION GOOGLE EARTH IMAGES , 2014 .

[8]  Gregory S. Okin,et al.  Leveraging Google Earth Engine (GEE) and machine learning algorithms to incorporate in situ measurement from different times for rangelands monitoring , 2020 .

[9]  Norman Kerle,et al.  Structural Building Damage Detection with Deep Learning: Assessment of a State-of-the-Art CNN in Operational Conditions , 2019, Remote. Sens..

[10]  George Vosselman,et al.  Multi-Resolution Feature Fusion for Image Classification of Building Damages with Convolutional Neural Networks , 2018, Remote. Sens..

[11]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[12]  Alfred Stein,et al.  Urban social vulnerability assessment with physical proxies and spatial metrics derived from air- and spaceborne imagery and GIS data , 2009 .

[13]  Stephen Platt,et al.  Disaster Recovery Indicators: guidelines for monitoring and evaluation , 2010 .

[14]  Senén Barro,et al.  Do we need hundreds of classifiers to solve real world classification problems? , 2014, J. Mach. Learn. Res..

[15]  Saman Ghaffarian,et al.  Automatic building detection based on Purposive FastICA (PFICA) algorithm using monocular high resolution Google Earth images , 2014 .

[16]  Joydeep Ghosh,et al.  Investigation of the random forest framework for classification of hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[17]  A. Vetrivel,et al.  Towards automated satellite image segmentation and classification for assessing disaster damage using data-specific features with incremental learning , 2016 .

[18]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[19]  Edoardo Pasolli,et al.  Post-Disaster Building Database Updating Using Automated Deep Learning: An Integration of Pre-Disaster OpenStreetMap and Multi-Temporal Satellite Data , 2019, Remote. Sens..

[20]  Francesco Carlo Nex,et al.  UAV-Based Structural Damage Mapping: A Review , 2019, ISPRS Int. J. Geo Inf..

[21]  Russell G. Congalton,et al.  Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using a random forest classifier on the Google Earth Engine Cloud , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[22]  Yifang Ban,et al.  Dimensionality Reduction and Feature Selection for Object-Based Land Cover Classification based on Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine , 2019, Remote. Sens..

[23]  R. Congalton,et al.  Automated cropland mapping of continental Africa using Google Earth Engine cloud computing , 2017 .

[24]  Norman Kerle,et al.  Utility of geo - informatics for disaster risk management : linking structural damage assessment, recovery and resilience , 2014 .

[25]  Norman Kerle,et al.  Evaluating Resilience-Centered Development Interventions with Remote Sensing , 2019, Remote. Sens..

[26]  A. Huete,et al.  A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[27]  Saeid Homayouni,et al.  The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform , 2018, Remote. Sens..

[28]  Eve McDonald-Madden,et al.  Using Landsat observations (1988–2017) and Google Earth Engine to detect vegetation cover changes in rangelands - A first step towards identifying degraded lands for conservation , 2019, Remote Sensing of Environment.

[29]  Onisimo Mutanga,et al.  Google Earth Engine Applications Since Inception: Usage, Trends, and Potential , 2018, Remote. Sens..

[30]  Johannes R. Sveinsson,et al.  Random Forests for land cover classification , 2006, Pattern Recognit. Lett..

[31]  Patrick Oswald,et al.  Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes , 2018, Remote. Sens..

[32]  Mariana Belgiu,et al.  Random forest in remote sensing: A review of applications and future directions , 2016 .

[33]  George Vosselman,et al.  Identification of Structurally Damaged Areas in Airborne Oblique Images Using a Visual-Bag-of-Words Approach , 2016, Remote. Sens..

[34]  Thomas Blaschke,et al.  Monitoring recovery after earthquakes through the integration of remote sensing, GIS, and ground observations: the case of L’Aquila (Italy) , 2016 .

[35]  Sabine Vanhuysse,et al.  Very High Resolution Object-Based Land Use–Land Cover Urban Classification Using Extreme Gradient Boosting , 2018, IEEE Geoscience and Remote Sensing Letters.

[36]  Edzer Pebesma,et al.  Using Google Earth Engine to detect land cover change: Singapore as a use case , 2018 .

[37]  Norman Kerle,et al.  TOWARDS POST-DISASTER DEBRIS IDENTIFICATION FOR PRECISE DAMAGE AND RECOVERY ASSESSMENTS FROM UAV AND SATELLITE IMAGES , 2019, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

[38]  M. Wulder,et al.  Generating intra-year metrics of wildfire progression using multiple open-access satellite data streams , 2019, Remote Sensing of Environment.

[39]  P. Sutton,et al.  Estimation of Gross Domestic Product at Sub-National Scales using Nighttime Satellite Imagery , 2007 .

[40]  Carsten Jürgens,et al.  The modified normalized difference vegetation index (mNDVI) a new index to determine frost damages in agriculture based on Landsat TM data , 1997 .

[41]  Thomas R. Loveland,et al.  A review of large area monitoring of land cover change using Landsat data , 2012 .

[42]  Diogo Duarte,et al.  Towards Real-Time Building Damage Mapping with Low-Cost UAV Solutions , 2019, Remote. Sens..

[43]  B. Gao.  NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[44]  Monika Kuffer,et al.  Post-Disaster Recovery Assessment with Machine Learning-Derived Land Cover and Land Use Information , 2019, Remote. Sens..

[45]  J. Qi,et al.  Remote Sensing for Grassland Management in the Arid Southwest , 2006 .

[46]  Jerry T. Mitchell,et al.  Evaluating post-Katrina recovery in Mississippi using repeat photography. , 2011, Disasters.

[47]  Fabio Dell'Acqua,et al.  Phisical Vulnerability Proxies from Remotes Sensing: Reviewing, Implementing and Disseminating Selected Techniques , 2015, IEEE Geoscience and Remote Sensing Magazine.

[48]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[49]  Norman Kerle,et al.  Post-disaster recovery assessment using multi-temporal satellite images with a deep learning approach , 2019 .

[50]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[51]  M. Babel,et al.  APPLICATIONS OF SENTINEL-1 SYNTHETIC APERTURE RADAR IMAGERY FOR FLOODS DAMAGE ASSESSMENT: A CASE STUDY OF NAKHON SI THAMMARAT, THAILAND , 2019, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

[52]  F. Yamazaki,et al.  Post-Disaster Urban Recovery Monitoring in Pisco After the 2007 Peru Earthquake Using Satellite Image , 2014 .

[53]  Joanne C. White,et al.  Pixel-Based Image Compositing for Large-Area Dense Time Series Applications and Science , 2014 .

[54]  Knut Conradsen,et al.  Statistical Analysis of Changes in Sentinel-1 Time Series on the Google Earth Engine , 2019, Remote. Sens..

[55]  Norman Kerle,et al.  Disasters : risk assessment, management, and post - disaster studies using remote sensing , 2015 .

[56]  Alexei Novikov,et al.  Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping , 2017, Front. Earth Sci..

[57]  Michael A. Wulder,et al.  Opening the archive: How free data has enabled the science and monitoring promise of Landsat , 2012 .

[58]  Norman Kerle,et al.  Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review , 2018, Remote. Sens..

[59]  Z. Ren,et al.  Mapping annual land use changes in China's poverty-stricken areas from 2013 to 2018 , 2019, Remote Sensing of Environment.

[60]  Iliana Mladenova,et al.  Leveraging Google Earth Engine for Drought Assessment using Global Soil Moisture Data , 2018, Remote. Sens..

[61]  Chris Bacon,et al.  Using Google Earth Engine to Map Complex Shade-Grown Coffee Landscapes in Northern Nicaragua , 2018, Remote. Sens..

[62]  A. Huete,et al.  A comparison of vegetation indices over a global set of TM images for EOS-MODIS , 1997 .

[63]  Phan Anh,et al.  Rapid Assessment of Flood Inundation and Damaged Rice Area in Red River Delta from Sentinel 1A Imagery , 2019, Remote. Sens..

[64]  Abdulhakim M. Abdi,et al.  Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data , 2019, GIScience & Remote Sensing.

[65]  Qiusheng Wu,et al.  Integrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine. , 2019, Remote sensing of environment.

[66]  A. Huete,et al.  A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[67]  Meng Liu,et al.  The use of remotely sensed data and ground survey tools to assess damage and monitor early recovery following the 12.5.2008 Wenchuan earthquake in China , 2012, Bulletin of Earthquake Engineering.

[68]  Xiangming Xiao,et al.  Landscape-scale characterization of cropland in China using Vegetation and Landsat TM images , 2002 .