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Reconstruction of historical land uses helps to understand patterns, drivers, and impacts of land-use change, and is essential for finding solutions to land-use sustainability. In order to analyze the relationship between land-use change and urban flooding, this study used the Classification and Regression Tree (CART) method to extract modern (2017) land-use data based on remote sensing images. Then, the Paleo-Land-Use Reconstruction (PLUR) program was used to reconstruct the land-use maps of Xi'an during the Ming (1582) and Qing (1766) dynasties by consulting and collecting records of land-use change in historical documents. Finally, the Flo-2D model was used to simulate urban flooding under different land-use scenarios. Over the past 435 years (1582–2017), the urban construction land area showed a trend of increasing, while the unused land area and water bodies were continuously decreasing. The increase in urban green space and buildings was 20.49% and 19.85% respectively, and the unused land area changed from 0.32 km2 to 0. Urban flooding in the modern land-use scenario is the most serious. In addition to the increase in impervious areas, the increase in building density and the decrease in water areas are also important factors that aggravate urban flooding. This study can provide a reference for future land-use planning and urban flooding control policy formulation and revision in the study area. [ABSTRACT FROM AUTHOR]

Floods and droughts cause catastrophic damage in paddy fields, and farmers need to be compensated for their loss. Mobile applications have allowed farmers to claim losses by providing mobile photos and polygons of their land plots drawn on satellite base maps. This paper studies diverse methods to verify those claims at a parcel level by employing (i) Normalized Difference Vegetation Index (NDVI) and (ii) Normalized Difference Water Index (NDWI) on Sentinel-2A images, (iii) Classification and Regression Tree (CART) on Sentinel-1 SAR GRD images, and (iv) a convolutional neural network (CNN) on mobile photos. To address the disturbance from clouds, we study the combination of multi-modal methods—NDVI+CNN and NDWI+CNN—that allow 86.21% and 83.79% accuracy in flood detection and 73.40% and 81.91% in drought detection, respectively. The SAR-based method outperforms the other methods in terms of accuracy in flood (98.77%) and drought (99.44%) detection, data acquisition, parcel coverage, cloud disturbance, and observing the area proportion of disasters in the field. The experiments conclude that the method of CART on SAR images is the most reliable to verify farmers' claims for compensation. In addition, the CNN-based method's performance on mobile photos is adequate, providing an alternative for the CART method in the case of data unavailability while using SAR images. [ABSTRACT FROM AUTHOR]

Floods and droughts cause catastrophic damage in paddy fields, and farmers need to be compensated for their loss. Mobile applications have allowed farmers to claim losses by providing mobile photos and polygons of their land plots drawn on satellite base maps. This paper studies diverse methods to verify those claims at a parcel level by employing (i) Normalized Difference Vegetation Index (NDVI) and (ii) Normalized Difference Water Index (NDWI) on Sentinel-2A images, (iii) Classification and Regression Tree (CART) on Sentinel-1 SAR GRD images, and (iv) a convolutional neural network (CNN) on mobile photos. To address the disturbance from clouds, we study the combination of multi-modal methods—NDVI+CNN and NDWI+CNN—that allow 86.21% and 83.79% accuracy in flood detection and 73.40% and 81.91% in drought detection, respectively. The SAR-based method outperforms the other methods in terms of accuracy in flood (98.77%) and drought (99.44%) detection, data acquisition, parcel coverage, cloud disturbance, and observing the area proportion of disasters in the field. The experiments conclude that the method of CART on SAR images is the most reliable to verify farmers’ claims for compensation. In addition, the CNN-based method’s performance on mobile photos is adequate, providing an alternative for the CART method in the case of data unavailability while using SAR images.

Reconstruction of historical land uses helps to understand patterns, drivers, and impacts of land-use change, and is essential for finding solutions to land-use sustainability. In order to analyze the relationship between land-use change and urban flooding, this study used the Classification and Regression Tree (CART) method to extract modern (2017) land-use data based on remote sensing images. Then, the Paleo-Land-Use Reconstruction (PLUR) program was used to reconstruct the land-use maps of Xi’an during the Ming (1582) and Qing (1766) dynasties by consulting and collecting records of land-use change in historical documents. Finally, the Flo-2D model was used to simulate urban flooding under different land-use scenarios. Over the past 435 years (1582–2017), the urban construction land area showed a trend of increasing, while the unused land area and water bodies were continuously decreasing. The increase in urban green space and buildings was 20.49% and 19.85% respectively, and the unused land area changed from 0.32 km2 to 0. Urban flooding in the modern land-use scenario is the most serious. In addition to the increase in impervious areas, the increase in building density and the decrease in water areas are also important factors that aggravate urban flooding. This study can provide a reference for future land-use planning and urban flooding control policy formulation and revision in the study area.

This paper introduces a methodology for predicting and mapping surface motion beneath road pavement structures caused by environmental factors. Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) measurements, geospatial analyses, and Machine Learning Algorithms (MLAs) are employed for achieving the purpose. Two single learners, i.e., Regression Tree (RT) and Support Vector Machine (SVM), and two ensemble learners, i.e., Boosted Regression Trees (BRT) and Random Forest (RF) are utilized for estimating the surface motion ratio in terms of mm/year over the Province of Pistoia (Tuscany Region, central Italy, 964 km2), in which strong subsidence phenomena have occurred. The interferometric process of 210 Sentinel-1 images from 2014 to 2019 allows exploiting the average displacements of 52,257 Persistent Scatterers as output targets to predict. A set of 29 environmental-related factors are preprocessed by SAGA-GIS, version 2.3.2, and ESRI ArcGIS, version 10.5, and employed as input features. Once the dataset has been prepared, three wrapper feature selection approaches (backward, forward, and bi-directional) are used for recognizing the set of most relevant features to be used in the modeling. A random splitting of the dataset in 70% and 30% is implemented to identify the training and test set. Through a Bayesian Optimization Algorithm (BOA) and a 10-Fold Cross-Validation (CV), the algorithms are trained and validated. Therefore, the Predictive Performance of MLAs is evaluated and compared by plotting the Taylor Diagram. Outcomes show that SVM and BRT are the most suitable algorithms; in the test phase, BRT has the highest Correlation Coefficient (0.96) and the lowest Root Mean Square Error (0.44 mm/year), while the SVM has the lowest difference between the standard deviation of its predictions (2.05 mm/year) and that of the reference samples (2.09 mm/year). Finally, algorithms are used for mapping surface motion over the study area. We propose three case studies on critical stretches of two-lane rural roads for evaluating the reliability of the procedure. Road authorities could consider the proposed methodology for their monitoring, management, and planning activities.

Recent studies have suggested that due to climate change, the number of wildfires across the globe have been increasing and continue to grow even more. The recent massive wildfires, which hit Australia during the 2019–2020 summer season, raised questions to what extent the risk of wildfires can be linked to various climate, environmental, topographical, and social factors and how to predict fire occurrences to take preventive measures. Hence, the main objective of this study was to develop an automatized and cloud-based workflow for generating a training dataset of fire events at a continental level using freely available remote sensing data with a reasonable computational expense for injecting into machine learning models. As a result, a data-driven model was set up in Google Earth Engine platform, which is publicly accessible and open for further adjustments. The training dataset was applied to different machine learning algorithms, i.e., Random Forest, Naïve Bayes, and Classification and Regression Tree. The findings show that Random Forest outperformed other algorithms and hence it was used further to explore the driving factors using variable importance analysis. The study indicates the probability of fire occurrences across Australia as well as identifies the potential driving factors of Australian wildfires for the 2019–2020 summer season. The methodical approach and achieved results and drawn conclusions can be of great importance to policymakers, environmentalists, and climate change researchers, among others. [ABSTRACT FROM AUTHOR]

This paper introduces a methodology for predicting and mapping surface motion beneath road pavement structures caused by environmental factors. Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) measurements, geospatial analyses, and Machine Learning Algorithms (MLAs) are employed for achieving the purpose. Two single learners, i.e., Regression Tree (RT) and Support Vector Machine (SVM), and two ensemble learners, i.e., Boosted Regression Trees (BRT) and Random Forest (RF) are utilized for estimating the surface motion ratio in terms of mm/year over the Province of Pistoia (Tuscany Region, central Italy, 964 km2), in which strong subsidence phenomena have occurred. The interferometric process of 210 Sentinel-1 images from 2014 to 2019 allows exploiting the average displacements of 52,257 Persistent Scatterers as output targets to predict. A set of 29 environmental-related factors are preprocessed by SAGA-GIS, version 2.3.2, and ESRI ArcGIS, version 10.5, and employed as input features. Once the dataset has been prepared, three wrapper feature selection approaches (backward, forward, and bi-directional) are used for recognizing the set of most relevant features to be used in the modeling. A random splitting of the dataset in 70% and 30% is implemented to identify the training and test set. Through a Bayesian Optimization Algorithm (BOA) and a 10-Fold Cross-Validation (CV), the algorithms are trained and validated. Therefore, the Predictive Performance of MLAs is evaluated and compared by plotting the Taylor Diagram. Outcomes show that SVM and BRT are the most suitable algorithms; in the test phase, BRT has the highest Correlation Coefficient (0.96) and the lowest Root Mean Square Error (0.44 mm/year), while the SVM has the lowest difference between the standard deviation of its predictions (2.05 mm/year) and that of the reference samples (2.09 mm/year). Finally, algorithms are used for mapping surface motion over the study area. We propose three case studies on critical stretches of two-lane rural roads for evaluating the reliability of the procedure. Road authorities could consider the proposed methodology for their monitoring, management, and planning activities. [ABSTRACT FROM AUTHOR]