Disaster Risk Assessment Based on Heterogeneous Geospatial Information

Kussul, Nataliia; Sokolov, Boris; Zyelyk, Ya. I.; Zelentsov, V. A.; Skakun, Sergii; Shelestov, Andrii

doi:10.1615/jautomatinfscien.v42.i12.40

Cited by 38 publications

(15 citation statements)

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“…However, some other studies show MLP to outperform SVM and DT (Gallego et al, 2012(Gallego et al, , 2014. Though the MLP training phase might be resource and time consuming (but this is becoming less problematic with the use of high-performance computations (Kravchenko et al, 2008;Kussul et al, 2009Kussul et al, , 2010aKussul et al, , 2010bKussul et al, , 2012Shelestov et al, 2006;Shelestov and Kussul, 2008)), and might require experience from the user, it has several advantages over SVM and DT. In particular, MLP is fast at processing new data which can be critical to the processing of large volumes of satellite data, and can produce probabilistic outputs which can be used for indicating reliability of the map.…”

Section: Committee Of Neural Network For Image Classificationmentioning

confidence: 99%

Regional scale crop mapping using multi-temporal satellite imagery

Kussul

Skakun

Shelestov

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:One of the problems in dealing with optical images for large territories (more than 10,000 sq. km) is the presence of clouds and shadows that result in having missing values in data sets. In this paper, a new approach to classification of multi-temporal optical satellite imagery with missing data due to clouds and shadows is proposed. First, self-organizing Kohonen maps (SOMs) are used to restore missing pixel values in a time series of satellite imagery. SOMs are trained for each spectral band separately using nonmissing values. Missing values are restored through a special procedure that substitutes input sample's missing components with neuron's weight coefficients. After missing data restoration, a supervised classification is performed for multi-temporal satellite images. An ensemble of neural networks, in particular multilayer perceptrons (MLPs), is proposed. Ensembling of neural networks is done by the technique of average committee, i.e. to calculate the average class probability over classifiers and select the class with the highest average posterior probability for the given input sample. The proposed approach is applied for regional scale crop classification using multi temporal Landsat-8 images for the JECAM test site in Ukraine in 2013. It is shown that ensemble of MLPs provides better performance than a single neural network in terms of overall classification accuracy, kappa coefficient, and producer's and user's accuracies for separate classes. The overall accuracy more than 85% is achieved. The obtained classification map is also validated through estimated crop areas and comparison to official statistics.

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Section: Committee Of Neural Network For Image Classificationmentioning

confidence: 99%

Regional scale crop mapping using multi-temporal satellite imagery

Kussul

Skakun

Shelestov

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Reliable crop maps can be used for more accurate agriculture statistics estimation (Gallego et al, 2010(Gallego et al, , 2012(Gallego et al, , 2014, stratification purposes (Boryan and Yang, 2013), better crop yield prediction (Kogan et al, 2013a,b;Kolotii et al, 2015), and drought risk assessment (Kussul et al, 2010(Kussul et al, , 2011Skakun et al, 2016b). During the past decades, satellite imagery became the most promising data source for solving such important tasks as LCLU mapping.…”

Section: Introductionmentioning

confidence: 99%

Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping

et al. 2017

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Many applied problems arising in agricultural monitoring and food security require reliable crop maps at national or global scale. Large scale crop mapping requires processing and management of large amount of heterogeneous satellite imagery acquired by various sensors that consequently leads to a "Big Data" problem. The main objective of this study is to explore efficiency of using the Google Earth Engine (GEE) platform when classifying multi-temporal satellite imagery with potential to apply the platform for a larger scale (e.g., country level) and multiple sensors (e.g., Landsat-8 and Sentinel-2). In particular, multiple state-of-the-art classifiers available in the GEE platform are compared to produce a high resolution (30 m) crop classification map for a large territory (∼28,100 km 2 and 1.0 M ha of cropland). Though this study does not involve large volumes of data, it does address efficiency of the GEE platform to effectively execute complex workflows of satellite data processing required with large scale applications such as crop mapping. The study discusses strengths and weaknesses of classifiers, assesses accuracies that can be achieved with different classifiers for the Ukrainian landscape, and compares them to the benchmark classifier using a neural network approach that was developed in our previous studies. The study is carried out for the Joint Experiment of Crop Assessment and Monitoring (JECAM) test site in Ukraine covering the Kyiv region (North of Ukraine) in 2013. We found that GEE provides very good performance in terms of enabling access to the remote sensing products through the cloud platform and providing pre-processing; however, in terms of classification accuracy, the neural network based approach outperformed support vector machine (SVM), decision tree and random forest classifiers available in GEE.

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“…Crop yield forecasting is one of the main components of agriculture monitoring and an extremely important input in enabling food security and sustainable development (Kussul et al, 2011(Kussul et al, , 2010b. Providing timely and reliable crop yield forecasts is equally important at global, national and regional (local) scales.…”

Section: Introductionmentioning

confidence: 99%

Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine

Kolotii

Kussul

Shelestov

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Winter wheat crop yield forecasting at national, regional and local scales is an extremely important task. This paper aims at assessing the efficiency (in terms of prediction error minimization) of satellite and biophysical model based predictors assimilation into winter wheat crop yield forecasting models at different scales (region, county and field) for one of the regions in central part of Ukraine. Vegetation index NDVI, as well as different biophysical parameters (LAI and fAPAR) derived from satellite data and WOFOST crop growth model are considered as predictors of winter wheat crop yield forecasting model. Due to very short time series of reliable statistics (since 2000) we consider single factor linear regression. It is shown that biophysical parameters (fAPAR and LAI) are more preferable to be used as predictors in crop yield forecasting regression models at each scale. Correspondent models possess much better statistical properties and are more reliable than NDVI based model. The most accurate result in current study has been obtained for LAI values derived from SPOT-VGT (at 1 km resolution) on county level. At field level, a regression model based on satellite derived LAI significantly outperforms the one based on LAI simulated with WOFOST.

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Disaster Risk Assessment Based on Heterogeneous Geospatial Information

Cited by 38 publications

References 0 publications

Regional scale crop mapping using multi-temporal satellite imagery

Regional scale crop mapping using multi-temporal satellite imagery

Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping

Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine

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