scholarly journals Flood Risk Mapping by Remote Sensing Data and Random Forest Technique

Water ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 3115
Author(s):  
Hadi Farhadi ◽  
Mohammad Najafzadeh
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Flood Risk ◽  
Google Earth ◽  
Risk Mapping ◽  
Geographical Information ◽  
Slope Aspect ◽  
Landsat 8 ◽  
Risk Indices ◽  
Flood Risk Mapping

Detecting effective parameters in flood occurrence is one of the most important issues that has drawn more attention in recent years. Remote Sensing (RS) and Geographical Information System (GIS) are two efficient ways to spatially predict Flood Risk Mapping (FRM). In this study, a web-based platform called the Google Earth Engine (GEE) (Google Company, Mountain View, CA, USA) was used to obtain flood risk indices for the Galikesh River basin, Northern Iran. With the aid of Landsat 8 satellite imagery and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), 11 risk indices (Elevation (El), Slope (Sl), Slope Aspect (SA), Land Use (LU), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Topographic Wetness Index (TWI), River Distance (RD), Waterway and River Density (WRD), Soil Texture (ST]), and Maximum One-Day Precipitation (M1DP)) were provided. In the next step, all of these indices were imported into ArcMap 10.8 (Esri, West Redlands, CA, USA) software for index normalization and to better visualize the graphical output. Afterward, an intelligent learning machine (Random Forest (RF)), which is a robust data mining technique, was used to compute the importance degree of each index and to obtain the flood hazard map. According to the results, the indices of WRD, RD, M1DP, and El accounted for about 68.27 percent of the total flood risk. Among these indices, the WRD index containing about 23.8 percent of the total risk has the greatest impact on floods. According to FRM mapping, about 21 and 18 percent of the total areas stood at the higher and highest risk areas, respectively.

scholarly journals IRANIAN LAND COVER MAPPING USING LANDSAT-8 IMAGERY AND RANDOM FOREST ALGORITHM

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 77-81
Author(s):  
M. Amani ◽  
A. Ghorbanian ◽  
S. Mahdavi ◽  
A. Mohammadzadeh
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Land Cover ◽  
Large Scale ◽  
Accuracy Assessment ◽  
Land Cover Classification ◽  
Google Earth ◽  
Machine Learning Techniques ◽  
Land Cover Mapping ◽  
Landsat 8

Abstract. Land cover classification is important for various environmental assessments. The opportunity of imaging the Earth’s surface makes remote sensing techniques efficient approaches for land cover classification. The only country-wide land cover map of Iran was produced by the Iranian Space Agency (ISA) using low spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and a basic classification method. Thus, it is necessary to produce a more accurate map using advanced remote sensing and machine learning techniques. In this study, multi-temporal Landsat-8 data (1,321 images) were inserted into a Random Forest (RF) algorithm to classify the land cover of the entire country into 13 categories. To this end, all steps, including pre-processing, classification, and accuracy assessment were implemented in the Google Earth Engine (GEE) platform. The overall classification accuracy and Kappa Coefficient obtained from the Iran-wide map were 74% and 0.71, respectively, indicating the high potential of the proposed method for large-scale land cover mapping.

Spatial Characteristics of the Southern Al Jabal Al Akhdar Watersheds: Remote Sensing Approach

2020 ◽  
Vol 3 (1) ◽  
pp. 37-48
Author(s):  
Salah Hamad
Keyword(s):  
Remote Sensing ◽  
Global Climate ◽  
Geographical Information ◽  
Slope Aspect ◽  
Landsat 8 ◽  
The South ◽  
Stream Length ◽  
Spatial Characteristics ◽  
Gis And Remote Sensing ◽  
The North

The present study is to evaluate the spatial characteristics of the watersheds located in Northeast Libya, which is vital since the area is promising for surface water investment in rain-fed agriculture and pastoral activities. The study conducted using Geographical Information System (GIS) and Remote Sensing (RS) data sets: SRTM elevation data, Landsat 8 (OLI) imagery and Global Climate Monitor (GCM) data. SRTM data processed in ArcGIS, where elevations show a progressive decrease towards the South and eleven watersheds delineated (17721km2). Moreover, the perimeter, slope, aspect, and stream length of the watersheds also calculated. The hydrologic divide bounds the watersheds in the North delineated; it extends from the Southwest to the East with a length of 470km. Also, the outlets of the watersheds, which are mostly temporary lakes, those locally known as Balat assessed spatially. Landsat 8 imagery classified by Quantum GIS (QGIS), where five classes identified (alluvial plains, spreading zones, forest, grassland and bare rocks). Furthermore, precipitation and temperature data from the GCM was mapped, where the precipitation shows the highest rates in the North and gradual decrease to the South, unlike the temperature values indicate an increase towards the South and drop in the North.

scholarly journals Next Generation Mapping: Combining Deep Learning, Cloud Computing, and Big Remote Sensing Data

2019 ◽  
Vol 11 (23) ◽  
pp. 2881 ◽  
Author(s):  
Leandro Parente ◽  
Evandro Taquary ◽  
Ana Silva ◽  
Carlos Souza ◽  
Laerte Ferreira
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Deep Learning ◽  
Random Forest ◽  
Short Term Memory ◽  
Learning Algorithms ◽  
Google Earth ◽  
Landsat 8 ◽  
Learning Approaches ◽  
Validation Data

The rapid growth of satellites orbiting the planet is generating massive amounts of data for Earth science applications. Concurrently, state-of-the-art deep-learning-based algorithms and cloud computing infrastructure have become available with a great potential to revolutionize the image processing of satellite remote sensing. Within this context, this study evaluated, based on thousands of PlanetScope images obtained over a 12-month period, the performance of three machine learning approaches (random forest, long short-term memory-LSTM, and U-Net). We applied these approaches to mapped pasturelands in a Central Brazil region. The deep learning algorithms were implemented using TensorFlow, while the random forest utilized the Google Earth Engine platform. The accuracy assessment presented F1 scores for U-Net, LSTM, and random forest of, respectively, 96.94%, 98.83%, and 95.53% in the validation data, and 94.06%, 87.97%, and 82.57% in the test data, indicating a better classification efficiency using the deep learning approaches. Although the use of deep learning algorithms depends on a high investment in calibration samples and the generalization of these methods requires further investigations, our results suggest that the neural network architectures developed in this study can be used to map large geographic regions that consider a wide variety of satellite data (e.g., PlanetScope, Sentinel-2, Landsat-8).

A GIS Multi-Criteria Analysis and Remote Sensing for Flood Risk Management of Bou Salem City, Tunisia

2021 ◽  
pp. 1-19
Author(s):  
Salwa Saidi ◽  
Anis Ghattassi ◽  
Samar Zaggouri ◽  
Ahmed Ezzine
Keyword(s):  
Remote Sensing ◽  
Flood Risk ◽  
Low Cost ◽  
Multicriteria Analysis ◽  
Geographical Information ◽  
Gis And Remote Sensing ◽  
Risk Levels ◽  
North East ◽  
Flood Risk Mapping ◽  
High Flood

In the context of global warming, it is very critical to delineate areas of high flood vulnerability and risk. Climate and hydrologic surveying using traditional methods is not always available and depends on external factors. So, the use of geographical information system and remote sensing is of high importance as a decision support system. This approach is of low cost and can cover a long period for surveying. This study aims to provide decision makers a framework of GIS based on multicriteria analysis for flood risk mapping. Classified remote sensing image layers are used to complete GIS-multicriteria results. Results show that the high to very high-risk levels affect the majority of the study area, particularly the south-west and north-east zones. The comparison between GIS and remote sensing approaches shows the same areas of risk and reveals that it is a reliable methodology that greatly enhances decision making.

scholarly journals Development of a global 30 m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform

2020 ◽  
Vol 12 (3) ◽  
pp. 1625-1648 ◽  
Author(s):  
Xiao Zhang ◽  
Liangyun Liu ◽  
Changshan Wu ◽  
Xidong Chen ◽  
Yuan Gao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Human Activity ◽  
Impervious Surface ◽  
Google Earth ◽  
Landsat 8 ◽  
Impervious Surfaces ◽  
Sar Images ◽  
Training Samples ◽  
Google Earth Engine

Abstract. The amount of impervious surface is an important indicator in the monitoring of the intensity of human activity and environmental change. The use of remote sensing techniques is the only means of accurately carrying out global mapping of impervious surfaces covering large areas. Optical imagery can capture surface reflectance characteristics, while synthetic-aperture radar (SAR) images can be used to provide information on the structure and dielectric properties of surface materials. In addition, nighttime light (NTL) imagery can detect the intensity of human activity and thus provide important a priori probabilities of the occurrence of impervious surfaces. In this study, we aimed to generate an accurate global impervious surface map at a resolution of 30 m for 2015 by combining Landsat 8 Operational Land Image (OLI) optical images, Sentinel-1 SAR images and Visible Infrared Imaging Radiometer Suite (VIIRS) NTL images based on the Google Earth Engine (GEE) platform. First, the global impervious and nonimpervious training samples were automatically derived by combining the GlobeLand30 land-cover product with VIIRS NTL and MODIS enhanced vegetation index (EVI) imagery. Then, the local adaptive random forest classifiers, allowing for a regional adjustment of the classification parameters to take into account the regional characteristics, were trained and used to generate regional impervious surface maps for each 5∘×5∘ geographical grid using local training samples and multisource and multitemporal imagery. Finally, a global impervious surface map, produced by mosaicking numerous 5∘×5∘ regional maps, was validated by interpretation samples and then compared with five existing impervious products (GlobeLand30, FROM-GLC, NUACI, HBASE and GHSL). The results indicated that the global impervious surface map produced using the proposed multisource, multitemporal random forest classification (MSMT_RF) method was the most accurate of the maps, having an overall accuracy of 95.1 % and kappa coefficient (one of the most commonly used statistics to test interrater reliability; Olofsson et al., 2014) of 0.898 as against 85.6 % and 0.695 for NUACI, 89.6 % and 0.780 for FROM-GLC, 90.3 % and 0.794 for GHSL, 88.4 % and 0.753 for GlobeLand30, and 88.0 % and 0.745 for HBASE using all 15 regional validation data. Therefore, it is concluded that a global 30 m impervious surface map can accurately and efficiently be generated by the proposed MSMT_RF method based on the GEE platform. The global impervious surface map generated in this paper is available at https://doi.org/10.5281/zenodo.3505079 (Zhang and Liu, 2019).

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Flood risk mapping and scaling

2012 ◽  
Author(s):  
Nathalie Saint-Geours ◽  
Jean-Stéphane Bailly ◽  
Frédéric Grelot ◽  
Christian Lavergne
Keyword(s):  
Flood Risk ◽  
Risk Mapping ◽  
Flood Risk Mapping
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