Comparison of Classification Algorithms for Detecting Typical Coastal Reclamation in Guangdong Province with Landsat 8 and Sentinel 2 Images

Coastal reclamation in Guangdong Province is highly concentrated and is growing rapidly. However, intensive reclamation use has resulted in serious influence on the coastal ecosystem, directly and indirectly. The current conditions and spatial distribution of reclamations must be detected for coastal preservation and management using efficient technology. This study aims to find a suitable method and data to map reclamations accurately at a large scale. Pixel-based and object-oriented classification methods were applied in extracting the three typical types of coastal reclamation, namely, ports, aquaculture ponds, and salt pans, in Guangdong Province from Landsat 8 and Sentinel 2 images. The algorithms of a support vector machine, random forest, decision tree, and rule-based algorithm were performed. Classification results were compared with statistical measures to assess the performance of different algorithms. The results indicated that all of the algorithms could obtain classification results with high accuracy, whereas the object-oriented algorithm showed less efficiency than other algorithms in classifying ports with complicated features. High-resolution data were not always superior to lower-resolution data in the reclamation classification. Generally speaking, applying the rule-based object-oriented algorithm in Sentinel 2A MSI images is relatively efficient at detecting the reclamation use in coastal Guangdong considering its actual situation. The mapping of reclamations in the whole of coastal Guangdong shows that they present obvious agglomeration characteristics in the space. The aquaculture ponds are mainly distributed in the coastal zones of western Guangdong and eastern Guangdong, with the largest area of 77,963 ha. The other types of ports are mainly distributed in the coastal zones of the Pearl River Delta, with an area of 8146 ha, while salt pans are mainly distributed in the coastal zones of Jiangmen, Zhuhai, and Zhongshan, with a total area of 4072 ha. The results can provide key supporting data for decision making in coastal management and preservation.

2000 years of annual ice core data from Law Dome, East Antarctica

Ice core records from Law Dome in East Antarctica, collected over the the last three decades, provide high resolution data for studies of the climate of Antarctica, Australia and the Southern and Indo-Pacific Oceans. Here we present a set of annually dated records of trace chemistry, stable water isotopes and snow accumulation from Law Dome covering over the period from −11 to 2017 CE (1961 to −66 BP 1950), as well as the level 1 chemistry data from which the annual chemistry records are derived. This dataset provides an update and extensions both forward and back in time of previously published subsets of the data, bringing them together into a coherent set with improved dating. The data are available for download from the Australian Antarctic Data Centre at https://doi.org/10.26179/5zm0-v192.

EMO-5: A high-resolution multi-variable gridded meteorological data set for Europe

In this paper we present EMO-51, a European high-resolution, (sub-)daily, multi-variable meteorological data set built on historical and real-time observations obtained by integrating data from 18,964 ground weather stations, four high-resolution regional observational grids (i.e. CombiPrecip, ZAMG - INCA, EURO4M-APGD and CarpatClim) as well as one global reanalysis (ERA-Interim/Land). EMO-5 includes at daily resolution: total precipitation, temperatures (mean, minimum and maximum), wind speed, solar radiation and water vapour pressure. In addition, EMO-5 also makes available 6-hourly precipitation and mean temperature. The raw observations from the ground weather stations underwent a set of quality controls, before SPHEREMAP and Yamamoto interpolation methods were applied in order to estimate for each 5 x 5 km grid cell the variable value and its affiliated uncertainty, respectively. The quality of the EMO-5 precipitation data was evaluated through (1) comparison with two regional high resolution data sets (i.e. seNorge2 and seNorge2018), (2) analysis of 15 heavy precipitation events, and (3) examination of the interpolation uncertainty. Results show that EMO-5 successfully captured 80 % of the heavy precipitation events, and that it is of comparable quality to a regional high resolution data set. The availability of the uncertainty fields increases the transparency of the data set and hence the possible usage. EMO-5 (release 1) covers the time period from 1990 to 2019, with a near real-time release of the latest gridded observations foreseen soon. As a product of Copernicus, the EU's Earth observation programme, EMO-5 dataset is free and open, and can be accessed at https://doi.org/10.2905/0BD84BE4-CEC8-4180-97A6-8B3ADAAC4D26 (Thiemig et al., 2021).1 EMO stands for “European Meteorological Observations”, whereas the 5 denotes the spatial resolution of 5 km.

Territorial impact of the COVID-19 pandemic in Galicia (Spain): a geographical approach

The coronavirus pandemic is causing a huge impact around the world. Its real magnitude presents very important regional differences, which are appreciable in the number of infected and victims in the different countries. The outbreak of the pandemic and the ignorance of the virus mean that, even today, there are many unknowns about essential aspects related to it. In this sense, geographic knowledge can help answer many questions from the territorial analysis of the data. The objective of this article will be to analyze the behavior of the coronavirus pandemic within the Spanish region of Galicia. The authors of this study propose a multiscale analysis that allows deciphering the most common propagation patterns. For this, we have high spatial resolution data that has been provided by the competent authority under confidentiality. The results of this work allow us to represent and interpret the territorial impact of the pandemic, understanding its behavior as far as possible, allowing future dynamics to be predicted.

Modifying Signal Retiming Procedures and Policies by Utilizing High-Fidelity Modeling with Medium-Resolution Traffic Data

Current signal retiming policies are deficient in recognizing the potential of emerging traffic datasets and simulation tools to improve signal timings. Consequently, current practice advocates the use of periodically collected (low-resolution) traffic datasets and deterministic (low-fidelity) simulation tools. When deployed in the field, such signal timings require excessive fine-tuning. The most recent trends promote the use of high-resolution data collected at 10 Hz frequency. While such an approach shows promise, the process heavily relies on specific data sets that are neither widely available nor clearly integrated into the existing signal retiming practices and procedures. Interestingly, data collected in an ongoing fashion and aggregated in several-minute bins (referred to here as medium-resolution) have not received much attention in the traditional retiming procedures. This study examines traditional signal retiming practices to provide a contextual framework for the other retiming alternatives. The authors define and classify different resolutions of various traffic data used in the signal retiming process and propose a signal retiming procedure based on widely available medium-resolution data and high-fidelity simulation modeling. The authors apply the traditional (low-resolution and low-fidelity) and a proposed (medium-resolution and high-fidelity) approach to a 28-intersection corridor in southeastern Florida. Signal timing plans developed from the proposed approach outperformed current plans from field and those plans developed in the traditional approach by reducing the average delay anywhere between 6.5 and 26%. With regard to the number of stops, changes for the traditional and proposed approaches were of much lower significance when compared with the field signal timings. The traveling speeds have been increased by 4.1%–18% by the proposed signal timings and delay was not transferred onto the neighboring streets, as was the case for plans developed by the traditional approach. Development, calibration, and validation of models within the proposed approach are more time-consuming and challenging than the modeling needs of the traditional approach. One direction of future research should address the automation of calibration and validation procedures. The other direction for future research should be related to the field evaluation of proposed signal timing plans.