Sentinel-Based Adaptation of the Local Climate Zones Framework to a South African Context (original) (raw)
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Remote Sensing, 2021
Local climate zone (LCZ) maps have been used widely to study urban structures and urban heat islands. Because remote sensing data enable automated LCZ mapping on a large scale, there is a need to evaluate how well remote sensing resources can produce fine LCZ maps to assess urban thermal environments. In this study, we combined Sentinel-2 multispectral imagery and dual-polarized (HH + HV) PALSAR-2 data to generate LCZ maps of Nanchang, China using a random forest classifier and a grid-cell-based method. We then used the classifier to evaluate the importance scores of different input features (Sentinel-2 bands, PALSAR-2 channels, and textural features) for the classification model and their contribution to each LCZ class. Finally, we investigated the relationship between LCZs and land surface temperatures (LSTs) derived from summer nighttime ASTER thermal imagery by spatial statistical analysis. The highest classification accuracy was 89.96% when all features were used, which highlig...
ISPRS International Journal of Geo-Information
Atmospheric correction is one of the key parts of remote sensing preprocessing because it can influence and change the final classification result. This research examines the impact of five different atmospheric correction processing on land cover classification accuracy using Sentinel-2 satellite imagery. Those are surface reflectance (SREF), standardized surface reflectance (STDSREF), Sentinel-2 atmospheric correction (S2AC), image correction for atmospheric effects (iCOR), dark object subtraction (DOS) and top of the atmosphere (TOA) reflectance without any atmospheric correction. Sentinel-2 images corrected with stated atmospheric corrections were classified using four different machine learning classification techniques namely extreme gradient boosting (XGB), random forests (RF), support vector machine (SVM) and catboost (CB). For classification, five different classes were used: bare land, low vegetation, high vegetation, water and built-up area. SVM classification provided th...
Exploring How Street-Level Images Help Enhance Remote-Sensing-Based Local Climate Zone Mapping
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
The local climate zone (LCZ) classification scheme is effective for climatic studies, and thus, timely and accurate LCZ mapping becomes critical for scientific climate research. Remote sensing images can efficiently capture the information of large-scale landscapes overhead, while street-level images can supplement the ground-level information, thus helping improve the LCZ mapping. Previous study has proven the usefulness of street-level images in enhancing LCZ mapping results; however, how they help to improve the results still remains unexplored. To unveil the underlying mechanism and fill the gap, in this study, the feature importance analysis is performed on classification experiments using different data sources to reveal the contributions of different components, while feature correlation analysis is adopted to find the relationship between street view images and key LCZ indicators. The results show that fusing street view images can help improve the classification performance considerably, especially for compact urban types such as compact highrise and compact midrise. In addition, the results further show that the building and sky information embedded in the street view images contribute the most. The feature correlation analysis further demonstrates their strong correlations with key LCZ indicators, which define the LCZ scheme. The findings of the study can help us better understand how street-level images can contribute to LCZ mapping and facilitate future urban climate studies.
Computational Urban Science
The Local Climate Zone (LCZ) classification is already widely used in urban heat island and other climate studies. The current classification method does not incorporate crucial urban auxiliary GIS data on building height and imperviousness that could significantly improve urban-type LCZ classification utility as well as accuracy. This study utilized a hybrid GIS- and remote sensing imagery-based framework to systematically compare and evaluate different machine and deep learning methods. The Convolution Neural Network (CNN) classifier outperforms in terms of accuracy, but it requires multi-pixel input, which reduces the output’s spatial resolution and creates a tradeoff between accuracy and spatial resolution. The Random Forest (RF) classifier performs best among the single-pixel classifiers. This study also shows that incorporating building height dataset improves the accuracy of the high- and mid-rise classes in the RF classifiers, whereas an imperviousness dataset improves the l...
ISPRS, 2019
Environmental change monitoring in earth sciences needs land use land cover change (LULCC) modeling to investigate the impact of climate change phenomena such as droughts and floods on earth surface land cover. As land cover has a direct impact on Land Surface Temperature (LST), the Land cover mapping is an essential part of climate change modeling. In this paper, for land use land cover mapping (LULCM), image classification of Sentinel-1A Synthetic Aperture Radar (SAR) Ground Range Detected (GRD) data using two machine learning algorithms including Support Vector Machine (SVM) and Random Forest (RF) are implemented in R programming language and compared in terms of overall accuracy for image classification. Considering eight different scenarios defined in this research, RF and SVM classification methods show their best performance with overall accuracies of 90.81 and 92.09 percent respectively.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Green areas play an important role within urban agglomerations due to their impact on local climate and their recreation function. For detailed monitoring, frameworks like the flora fauna habitat (FFH) classification scheme of the European Union's Habitat Directive are broadly used. By date, FFH classifications are mostly expert-based. Within this study, a data-driven approach for FFH classification is tested. For two test areas in the municipality of Vienna, ALS point cloud data are used to derive predictor variables like terrain features, vegetation structure and potential insulation as well as reflection properties from full waveform analysis on a 1 m grid. In addition, Sentinel-1 C-Band time series data are used to increase the temporal resolution of the predicting features and to add phenological characteristics. For two 1.3 × 1.3 km test tiles, random forest classifiers are trained using different combinations (ALS, SAR, ALS+SAR) of input features. For all model test runs, the combination of ALS and SAR input features lead to best prediction accuracies when applied on test data.
2019
Sentinel-2 land monitoring constellation mission aims to generate products similar with the Landsat-8 images, the world’s longest continuously acquired collection of space-based land earth observation data. Though both sensors share similar spectral characteristics, their Relative Spectral Response Filters (RSRFs) are not identical. It is consequently important to assess whether and to what extent endproducts, such as land use maps, may vary between these two sensors. For this purpose, the random forest classifier was applied over a semi-arid environment in the Eastern Mediterranean (Cyprus). Initially the Sentinel-2 image was sampled to the Landsat-8 spatial resolution. Then, two different classification strategies have been followed: the first one using an equal (balance) training sample between the 11 land use classes, while the second classification was based on a random training sample. In addition, land use maps were also generated based on maximum likelihood, mahalanobis dist...
Remote Sensing, 2021
Multi-temporal imagery classification using spectral information and indices with random forest allows improving accuracy in land use and cover classification in semiarid Mediterranean areas, where the high fragmentation of the landscape caused by multiple factors complicates the task. Hence, since data come from different dates, atmospheric correction is needed to retrieve surface reflectivity values. The Sen2Cor, MAJA and ACOLITE algorithms have proven their good performances in these areas in different comparative studies, and DOS is a basic method that is widely used. The aim in this study was to test the feasibility of its application to the data set to improve the values of accuracy in classification and the performance in properly labelling different classes. Additionally, we tried to correct accuracy and separability mixing predictors with different algorithms. The results showed that, using a single algorithm, the general accuracy and kappa index from ACOLITE were the highe...
Advances of Local Climate Zone Mapping and Its Practice Using Object-Based Image Analysis
Atmosphere
In the context of climate change and urban heat islands, the concept of local climate zones (LCZ) aims for consistent and comparable mapping of urban surface structure and cover across cities. This study provides a timely survey of remote sensing-based applications of LCZ mapping considering the recent increase in publications. We analyze and evaluate several aspects that affect the performance of LCZ mapping, including mapping units/scale, transferability, sample dataset, low accuracy, and classification schemes. Since current LCZ analysis and mapping are based on per-pixel approaches, this study implements an object-based image analysis (OBIA) method and tests it for two cities in Germany using Sentinel 2 data. A comparison with a per-pixel method yields promising results. This study shall serve as a blueprint for future object-based remotely sensed LCZ mapping approaches.