The Potential of SENTINEL-1 Data to Supplement High Resolution Earth Observation Data for Monitoring Green Areas in Cities (original) (raw)
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Remote Sensing
Land-cover (LC) mapping in a morphologically heterogeneous landscape area is a challenging task since various LC classes (e.g., crop types in agricultural areas) are spectrally similar. Most research is still mostly relying on optical satellite imagery for these tasks, whereas synthetic aperture radar (SAR) imagery is often neglected. Therefore, this research assessed the classification accuracy using the recent Sentinel-1 (S1) SAR and Sentinel-2 (S2) time-series data for LC mapping, especially vegetation classes. Additionally, ancillary data, such as texture features, spectral indices from S1 and S2, respectively, as well as digital elevation model (DEM), were used in different classification scenarios. Random Forest (RF) was used for classification tasks using a proposed hybrid reference dataset derived from European Land Use and Coverage Area Frame Survey (LUCAS), CORINE, and Land Parcel Identification Systems (LPIS) LC database. Based on the RF variable selection using Mean Decr...
2018
In recent years, data mining algorithms are increasingly applied to optimise the classification process of remotely sensed imagery. Random Forest algorithms have shown high potential for land cover mapping problems yet have not been sufficiently tested on their ability to process and classify multi-temporal data within one classification process. Additionally, a growing amount of geospatial data is freely available online without having their usability assessed, such as EUROSTAT´s LUCAS land use land cover dataset. This study provides a comparative analysis of two land cover classification approaches using Random Forest on open-access multi-spectral, multi-temporal Sentinel-2A/B data. A classification system composed of six classes (sealed surfaces, non-vegetated unsealed surfaces, water, woody, herbaceous permanent, herbaceous periodic) was designed for this study. Ten images of ten bands plus NDVI each, taken between November 2016 and October 2017 in Central Portugal, were processed in R using a pixel-based approach. Ten maps based on single month data were produced. These were then used as input data for the classifier to create a final map. This map was compared with a map using all 100 bands at once as training for the classifier. This study concluded that the approach using all bands produced maps with 11% higher, yet overall low accuracy of 58%. It was also less time-consuming with about 5 hours to over 15 hours of work for the multi-temporal predictions. The main causes for the low accuracy identified by this thesis are uncertainties with EUROSTAT´s Land Use/Cover Area Statistical Survey (LUCAS) training data and issues with the accompanying nomenclature definition. Additional to the comparison of the classification approaches, the usability of LUCAS (2015) is tested by comparing four different variations of it as training data for the classification based on 100 bands. This research indicates high potential of using Sentinel-2 imagery and multitemporal stacks of bands to achieve an averaged land cover classification of the investigated time span. Moreover, the research points out lower potential of the multimap approach and issues regarding the suitability of using LUCAS open-access data as sole input for training a classifier for this study. Issues include inaccurate surveying and a partially long distance between the marked point and the actual observation point reached by the surveyors of up to 1.5 km. Review of the database, additional sampling and ancillary data appears to be necessary for achieving accurate results. v
Remote Sensing, 2021
The multitemporal acquisition of images from the Sentinel-1 satellites allows continuous monitoring of a forest. This study focuses on the use of multitemporal C-band synthetic aperture radar (SAR) data to assess the results for forest type (FTY), between coniferous and deciduous forest, and tree species (SPP) classification. We also investigated the temporal stability through the use of backscatter from multiple seasons and years of acquisition. SAR acquisitions were pre-processed, histogram-matched, smoothed, and temperature-corrected. The normalized average backscatter was extracted for interpreted plots and used to train Random Forest models. The classification results were then validated with field plots. A principal component analysis was tested to reduce the dimensionality of the explanatory variables, which generally improved the results. Overall, the FTY classifications were promising, with higher accuracies (OA of 0.94 and K = 0.86) than the SPP classification (OA of 0.66 ...
An assessment of the effectiveness of a random forest classifier for land-cover classification
Land cover monitoring using remotely sensed data requires robust classification methods which allow for the accurate mapping of complex land cover and land use categories. Random forest (RF) is a powerful machine learning classifier that is relatively unknown in land remote sensing and has not been evaluated thoroughly by the remote sensing community compared to more conventional pattern recognition techniques. Key advantages of RF include: their non-parametric nature; high classification accuracy; and capability to determine variable importance. However, the split rules for classification are unknown, therefore RF can be considered to be black box type classifier. RF provides an algorithm for estimating missing values; and flexibility to perform several types of data analysis, including regression, classification, survival analysis, and unsupervised learning. In this paper, the performance of the RF classifier for land cover classification of a complex area is explored. Evaluation was based on several criteria: mapping accuracy, sensitivity to data set size and noise. Landsat-5 Thematic Mapper data captured in European spring and summer were used with auxiliary variables derived from a digital terrain model to classify 14 different land categories in the south of Spain. Results show that the RF algorithm yields accurate land cover classifications, with 92% overall accuracy and a Kappa index of 0.92. RF is robust to training data reduction and noise because significant differences in kappa values were only observed for data reduction and noise addition values greater than 50 and 20%, respectively. Additionally, variables that RF identified as most important for classifying land cover coincided with expectations. A McNemar test indicates an overall better performance of the random forest model over a single decision tree at the 0.00001 significance level.
Mapping of Urban Green Spaces Using SENTINEL-2 a Data : Methodical Aspects
2016
Urban green spaces (UGS) such as parks, forests, green roofs, streams, and community gardens provide a broad range of ecosystem services (urban heat mitigation, stormwater infiltration, food security, physical recreation, and psychological well-being of residents). Proper evaluation and inter-city comparison of UGS, therefore, requires not only information on its relative quantity, but also a closer examination of UGS in terms of quality and related ecosystem services, which can be derived from its land cover composition and spatial structure. Here we present an approach to UGS extraction from newly available Sentinel-2A satellite imagery, provided in the frame of European Copernicus program. The multispectral imagery includes 13 spectral bands (from visible, through near-infrared to short wave infrared bands) with high spatial resolution (from 10 m to 60 m) and frequent revisit time (currently around ten days). Supervised maximum likelihood classification was used to identify UGS p...
Machine Learning Methods for Classification of the Green Infrastructure in City Areas
ISPRS International Journal of Geo-Information
Rapid urbanization in cities can result in a decrease in green urban areas. Reductions in green urban infrastructure pose a threat to the sustainability of cities. Up-to-date maps are important for the effective planning of urban development and the maintenance of green urban infrastructure. There are many possible ways to map vegetation; however, the most effective way is to apply machine learning methods to satellite imagery. In this study, we analyze four machine learning methods (support vector machine, random forest, artificial neural network, and the naïve Bayes classifier) for mapping green urban areas using satellite imagery from the Sentinel-2 multispectral instrument. The methods are tested on two cities in Croatia (Varaždin and Osijek). Support vector machines outperform random forest, artificial neural networks, and the naïve Bayes classifier in terms of classification accuracy (a Kappa value of 0.87 for Varaždin and 0.89 for Osijek) and performance time.
Remote Sensing, 2013
Mapping and monitoring forest extent is a common requirement of regional forest inventories and public land natural resource management, including in Australia. The state of Victoria, Australia, has approximately 7.2 million hectares of mostly forested public land, comprising ecosystems that present a diverse range of forest structures, composition and condition. In this paper, we evaluate the performance of the Random Forest (RF) classifier, an ensemble learning algorithm that has recently shown promise using multi-spectral satellite sensor imagery for large area feature classification. The RF algorithm was applied using selected Landsat Thematic Mapper (TM) imagery metrics and auxiliary terrain and climatic variables, while the reference data was manually extracted from systematically distributed plots of sample aerial photography and used for training (75%) and accuracy (25%) assessment. The RF algorithm yielded an overall accuracy of 96% and a Kappa statistic of 0.91 (confidence interval (CI) 0.909-0.919) for the forest/non-forest classification model, given a Kappa maximised binary threshold value of 0.5. The area under the receiver operating characteristic plot produced a score of 0.91, also indicating high model performance. The framework described in this study contributes to the operational deployment of a robust, but affordable, program, able to collate and process OPEN ACCESS Remote Sens. 2013, 5 2839 large volumes of multi-sourced data using open-source software for the production of consistent and accurate forest cover maps across the full spectrum of Victorian sclerophyll forest types.
ISPRS International Journal of Geo-Information
Forest canopy cover (FCC) is one of the most important forest inventory parameters and plays a critical role in evaluating forest functions. This study examines the potential of integrating Sentinel-1 (S-1) and Sentinel-2 (S-2) data to map FCC in the heterogeneous Mediterranean oak forests of western Iran in different data densities (one-year datasets vs. three-year datasets). This study used very high-resolution satellite images from Google Earth, gridded points, and field inventory plots to generate a reference dataset. Based on it, four FCC classes were defined, namely non-forest, sparse forest (FCC = 1–30%), medium-density forest (FCC = 31–60%), and dense forest (FCC > 60%). In this study, three machine learning (ML) models, including Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART), were used in the Google Earth Engine and their performance was compared for classification. Results showed that the SVM produced the highest accurac...
Reviewing the Pertinence of Sentinel-1 SAR for Urban Land Use Land Cover Classification
IJRES, 2020
Conventional approaches for urban land use land cover classification and quantification of land use changes have often relied on the ground surveys and urban censuses of urban surface properties. Advent of Remote Sensing technology supporting metric to centimetric spatial resolutions with simultaneous wide coverage, significantly reduced huge operational costs previously encountered using ground surveys. Weather, sensor's spatial resolution and the complex compositions of urban areas comprising concrete, metallic, water, bare-and vegetation-covers, limits Remote Sensing ability to accurately discriminate urban features. The launch of Sentinel-1 Synthetic Aperture Radar, which operates at metric resolution and microwave frequencies evades the weather limitations and has been reported to accurately quantify urban compositions. This paper assessed the feasibility of Sentinel-1 SAR data for urban land use land cover classification by reviewing research papers that utilised these data. The review found that since 2014, 11 studies have specifically utilised the datasets. The reviewed studies demonstrated that, features representing urban topography such as morphology and texture can easily and accurately be extracted from Sentinel-1 SAR and subjected to state-of-the-art classification algorithms such as Support Vector Machine and ensemble Decision Trees for accurate urban land use land cover classification. Development of robust algorithms to deal with the complexities of SAR imagery is still an active research area. Furthermore, augmentation of SAR with optical imagery is required especially for classification accuracy assessments.