Flood Mapping Based on Multiple Endmember Spectral Mixture Analysis and Random Forest Classifier - The Case of Yuyao, China

Remote sensing is recognized as a valuable tool for flood mapping due to its synoptic view and continuous coverage of the flooding event. This paper proposed a hybrid approach based on multiple endmember spectral analysis (MESMA) and Random Forest classifier to extract inundated areas in Yuyao City in China using medium resolution optical imagery. MESMA was adopted to tackle the mixing pixel problem induced by medium resolution data. Specifically, 35 optimal endmembers were selected to construct a total of 3111 models in the MESMA procedure to derive accurate fraction information. A multi-dimensional feature space was constructed including the normalized difference water index (NDWI), topographical parameters of height, slope, and aspect together with the fraction maps. A Random Forest classifier consisting of 200 decision trees was adopted to classify the post-flood image based on the above multi-features. Experimental results indicated that the proposed method can extract the inundated areas precisely with a classification accuracy of 94% and a Kappa index of 0.88. The inclusion of fraction information can help improve the mapping accuracy with an increase of 2.5%. Moreover, the proposed method also outperformed the maximum likelihood classifier and the NDWI thresholding method. This research provided a useful reference for flood mapping using medium resolution optical remote sensing imagery.

• Publication year

  2015

• Venue

  Remote Sensing

• Publication date

  2015-09-23

• Fields of study

  Computer Science, Environmental Science

• Identifiers
  DOI 10.3390/rs70912539
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Flood monitoring and damage assessment using water indices: A case study of Pakistan flood-2012

  2015cited by this paper
• Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China

  2015cited by this paper
• UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis

  2015cited by this paper
• Destriping remotely sensed data using anisotropic diffusion in wavelet domain

  2014cited by this paper
• Real Time Estimation of the Calgary Floods Using Limited Remote Sensing Data

  2014cited by this paper
• High-resolution landcover classification using Random Forest

  2014cited by this paper
• Comparative Analysis of Normalised Difference Spectral Indices Derived from MODIS for Detecting Surface Water in Flooded Rice Cropping Systems

  2014cited by this paper
• Monitoring Urban Greenness Dynamics Using Multiple Endmember Spectral Mixture Analysis

  2014cited by this paper
• A comparative study of different classification techniques for marine oil spill identification using RADARSAT-1 imagery

  2014cited by this paper
• Mapping vegetation morphology types in a dry savanna ecosystem: integrating hierarchical object-based image analysis with Random Forest

  2014cited by this paper
• Random forest classification of urban landscape using Landsat archive and ancillary data: Combining seasonal maps with decision level fusion

  2014cited by this paper
• The Strengths and Limitations in Using the Daily MODIS Open Water Likelihood Algorithm for Identifying Flood Events

  2014cited by this paper
• Mapping vegetation in an urban area with stratified classification and multiple endmember spectral mixture analysis

  2013influential reference
• Varying Scale and Capability of Envisat ASAR-WSM, TerraSAR-X Scansar and TerraSAR-X Stripmap Data to Assess Urban Flood Situations: A Case Study of the Mekong Delta in Can Tho Province

  2013cited by this paper
• bject-oriented mapping of urban trees using Random Forest lassifiers

  2013cited by this paper
• Multiple Endmember Spectral Mixture Analysis (MESMA) to map burn severity levels from Landsat images in Mediterranean countries

  2013influential reference
• Land cover and impervious surface extraction using parametric and non-parametric algorithms from the open-source software R: an application to sustainable urban planning in Sicily

  2013cited by this paper
• Flood Mapping and Flood Dynamics of the Mekong Delta: ENVISAT-ASAR-WSM Based Time Series Analyses

  2013cited by this paper
• Multi-sensoral and automated derivation of inundated areas using TerraSAR-X and ENVISAT ASAR data

  2012cited by this paper
• Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data

  2012cited by this paper
• Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture

  2012influential reference
• Monitoring two decades of urbanization in the Poyang Lake area, China through spectral unmixing

  2012cited by this paper
• Development of a sub-pixel analysis method applied to dynamic monitoring of floods

  2012cited by this paper
• Mapping Hurricane Katrina's widespread destruction in New Orleans using multisensor data and the normalized difference change detection (NDCD) technique

  2011cited by this paper
• Coastal wetland vegetation classification with a Landsat Thematic Mapper image

  2011cited by this paper
• An object-based approach for flood area delineation in a transboundary area using ENVISAT ASAR and LANDSAT TM data

  2011influential reference
• A method for generating floodplain maps using IKONOS images and DEMs

  2010influential reference
• A Hierarchical Spatio-Temporal Markov Model for Improved Flood Mapping Using Multi-Temporal X-Band SAR Data

  2010cited by this paper
• Analysis of Dynamic Thresholds for the Normalized Difference Water Index

  2009cited by this paper
• Available online at:

  2009cited by this paper
• Extracting urban vegetation characteristics using spectral mixture analysis and decision tree classifications.

  2009cited by this paper
• Modelling land‐cover types using multiple endmember spectral mixture analysis in a desert city

  2009cited by this paper
• Hierarchical Multiple Endmember Spectral Mixture Analysis (MESMA) of hyperspectral imagery for urban environments

  2009cited by this paper
• Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil

  2007cited by this paper
• Using Landsat 7 TM data acquired days after a flood event to delineate the maximum flood extent on a coastal floodplain

  2004cited by this paper
• Spectral Mixture Analysis of the Urban Landscape in Indianapolis with Landsat ETM+ Imagery

  2004cited by this paper
• Application of spectral mixture analysis to Amazonian land-use and land-cover classification

  2004cited by this paper
• Reducing signature variability in unmixing coastal marsh Thematic Mapper scenes using spectral indices

  2004cited by this paper
• THE EFFECT OF TRAINING STRATEGIES ON SUPERVISED CLASSIFICATION AT DIFFERENT SPATIAL RESOLUTIONS

  2002influential reference
• Integration of remote sensing data and GIS for accurate mapping of flooded areas

  2002influential reference
• An efficient method for mapping flood extent in a coastal floodplain using Landsat TM and DEM data

  2002cited by this paper
• Random Forests

  2001cited by this paper
• Estimation of sub-pixel land cover composition in the presence of untrained classes

  2000cited by this paper
• Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models

  1998cited by this paper
• Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview

  1997cited by this paper
• The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features

  1996cited by this paper
• Classification of multispectral images based on fractions of endmembers: Application to land-cover change in the Brazilian Amazon

  1995influential reference
• Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification

  1992cited by this paper

• Integrating geospatial intelligence and machine learning for flood susceptibility mapping.

  2026cites this paper
• Machine Learning for Mapping Glacier Surface Facies in Svalbard

  2025cites this paper
• Global flood extent monitoring using SAR satellite and hydrological data: A multi-scale and multi-source approach

  2025cites this paper
• Dynamic surface water fraction (DSWF): Global surface water fraction mapping at 10-meter spatial resolution with Sentinel-2 imagery in Google Earth Engine

  2025cites this paper
• Land-cover classification in Addo Elephant National Park: Analyzing the impact of variables, classifiers, and object-based approach

  2025cites this paper
• Assessing Machine Learning Models on Temporal and Multi‐Sensor Data for Mapping Flooded Areas

  2025cites this paper
• Mapping Vegetation-Covered Water Areas Using Sentinel-2 and RadarSat-2 Data: A Case Study of the Caohai Wetland in Guizhou Province

  2025cites this paper
• Evaluation of agriculture land transformations with socio-economic influences on wheat demand and supply for food sustainability

  2025cites this paper
• DeepSARFlood: Rapid and Automated SAR-based flood inundation mapping using Vision Transformer-based Deep Ensembles with uncertainty estimates

  2025cites this paper
• Extraction of Surface Water Bodies using Optical Remote Sensing Images: A Review

  2024cites this paper
• Surface water mapping in rice paddies and wetlands using SENTINEL-1 data, random forests, and texture features

  2024cites this paper
• Enhancing Urban Ecological Risk Assessment by Integrating Spatial Modeling and Machine Learning for Resilient Environmental Management in UNESCO World Heritage Cities

  2024cites this paper
• Classifying Plant Communities in the North American Coastal Plain With PRISMA Spaceborne Hyperspectral Imagery and the Spectral Mixture Residual

  2024cites this paper
• Environmental Monitoring of Land Use/ Land Cover by Integrating Remote Sensing and Machine Learning Algorithms

  2024cites this paper
• Leveraging Sentinel-2 and Geographical Information Systems in Mapping Flooded Regions around the Sesia River, Piedmont, Italy

  2024cites this paper
• Assessment of machine learning algorithms and new hybrid multi-criteria analysis for flood hazard and mapping

  2024cites this paper
• Residual wave vision U-Net for flood mapping using dual polarization Sentinel-1 SAR imagery

  2024cites this paper
• A NEW APPROACH FOR MAPPING LAND USE / LAND COVER USING GOOGLE EARTH ENGINE: A COMPARISON OF COMPOSITION IMAGES

  2023cites this paper
• Flood-Prone Zones of Meandering Rivers: Machine Learning Approach and Considering the Role of Morphology (Kashkan River, Western Iran)

  2023cites this paper
• Navigating nature’s toll: Assessing the ecological impact of the refugee crisis in Cox’s Bazar, Bangladesh

  2023cites this paper
• Machine learning approach for modeling daily pluvial flood dynamics in agricultural landscapes

  2023cites this paper
• INTEGRAÇÃO DA INCERTEZA NA AMOSTRAGEM E CLASSIFICAÇÃO RANDOM FOREST UTILIZANDO BANDAS E ÍNDICES ESPECTRAIS PARA O MAPEAMENTO DE INUNDAÇÃO

  2023cites this paper
• Regression-based surface water fraction mapping using a synthetic spectral library for monitoring small water bodies

  2023cites this paper
• Challenges of Retrieving LULC Information in Rural-Forest Mosaic Landscapes Using Random Forest Technique

  2023cites this paper
• On the Importance of Feature Representation for Flood Mapping using Classical Machine Learning Approaches

  2023cites this paper
• Nowcasting from space: tropical cyclones’ impacts on Fiji’s agriculture

  2023cites this paper
• Comparing Machine Learning Algorithms in Land Use Land Cover Classification of Landsat 8 (OLI) Imagery

  2022cites this paper
• Spectral index-driven FCN model training for water extraction from multispectral imagery

  2022cites this paper
• COMPARATIVE ASSESSMENT OF MACHINE LEARNING ALGORITHMS FOR LAND USE AND LAND COVER CLASSIFICATION USING MULTISPECTRAL REMOTE SENSING IMAGE

  2022cites this paper
• Integrated Analytical Hierarchical Process and Geographical Information System for Allocation of Compatible Land Uses along Uluguru Mountain Slopes

  2022cites this paper
• Multi-Temporal Inundated Areas Monitoring Made Easy: The Case of Kerkini Lake in Greece

  2021cites this paper
• Automatic water detection from multidimensional hierarchical clustering for Sentinel-2 images and a comparison with Level 2A processors

  2021cites this paper
• Assessment of spatio-temporal direction of impervious surface area surface temperature in Pretoria, South Africa

  2021cites this paper
• Domain Adaptation using NDWI index for Water-land Semantic Segmentation

  2021cites this paper
• Inundation Mapping of Kerala Flood Event in 2018 using ALOS-2 and Temporal Sentinel-1 SAR Images

  2021cites this paper
• Wetland‐Inundated Area Modeling and Monitoring Using Supervised and Machine Learning Classifiers

  2021cites this paper
• TOWARDS AN AUTOMATED FLOOD AREA EXTRACTION FROM HIGH RESOLUTION SATELLITE IMAGES

  2020cites this paper
• Flood extent mapping of Kerala flood event in 2018 using ALOS-2 and temporal Sentinel-1 SAR images

  2020cites this paper
• Sensoriamento remoto de parâmetros hidráulicos geométricos para estimativas da vazão de um rio de médio porte

  2020cites this paper
• An integrated approach of GIS and hybrid intelligence techniques applied for flood risk modeling

  2020cites this paper
• Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations - A Review

  2020cites this paper
• Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water

  2019cites this paper
• Application of Machine Learning to Model Wetland Inundation Patterns Across a Large Semiarid Floodplain

  2019cites this paper
• Flood Mapping with Convolutional Neural Networks Using Spatio-Contextual Pixel Information

  2019cites this paper
• Multi-Index Image Differencing Method (MINDED) for Flood Extent Estimations

  2019cites this paper
• Fusion of Sentinel-1 data with Sentinel-2 products to overcome non-favourable atmospheric conditions for the delineation of inundation maps

  2019cites this paper
• Assessment of Building Damage Risk by Natural Disasters in South Korea Using Decision Tree Analysis

  2018cites this paper
• Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data

  2018cites this paper
• Flood Mapping in a Complex Environment Using Bistatic TanDEM-X/TerraSAR-X InSAR Coherence

  2018cites this paper
• Dynamic analysis of Robinia pseudoacacia forest health levels from 1995 to 2013 in the Yellow River Delta, China using multitemporal Landsat imagery

  2018cites this paper
• Direct tangible damage assessment for regional snowmelt flood disasters with HJ-1 and HR satellite images: a case study of the Altay region, northern Xinjiang, China

  2018cites this paper
• On water surface delineation in rivers using Landsat-8, Sentinel-1 and Sentinel-2 data

  2018cites this paper
• Flood Risk Assessment in Urban Areas Based on Spatial Analytics and Social Factors

  2017cites this paper
• Integrating rapid assessment of flood proneness into urban planning under data constraints: a fuzzy logic and bricolage approach

  2017cites this paper
• Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea

  2017cites this paper
• Application of Sentinel-2 and EnMAP new satellite data to the mapping of alpine vegetation of the Karkonosze Mountains

  2017cites this paper
• Application of Sentinel-2 and EnMAP new satellite data to the mapping of alpine vegetation of the Karkonosze Mountains

  2017cites this paper
• Dynamics of Hierarchical Urban Green Space Patches and Implications for Management Policy

  2017cites this paper
• Land cover classification using random forest with genetic algorithm-based parameter optimization

  2016cites this paper
• Preface: Remote Sensing in Flood Monitoring and Management

  2015cites this paper