Mapping an Urban Boundary Based on Multi-Temporal Sentinel-2 and POI Data: A Case Study of Zhengzhou City

Accurately identifying and delineating urban boundaries are the premise for and foundation of the control of disorderly urban sprawl, which is helpful for us to accurately grasp the scale and form of cities, optimize the internal spatial structure and pattern of cities, and guide the expansion of urban spaces in the future. At present, the concept and delineation of urban boundaries do not follow a unified method or standard. However, many scholars have made use of multi-source remote sensing images of various scales and social auxiliary data such as point of interest (POI) data to achieve large-scale, high-resolution, and high-precision land cover mapping and impermeable water surface mapping. The accuracy of small- and medium-scale urban boundary mapping has not been improved to an obvious extent. This study uses multi-temporal Sentinel-2 high-resolution images and POI data that can reflect detailed features of human activities to extract multi-dimensional features and use random forests and mathematical morphology to map the urban boundaries of the city of Zhengzhou. The research results show that: (1) the urban construction land extraction model established with multi-dimensional features has a great improvement in accuracy; (2) when the training sample accounts for 65% of the sample data set, the urban construction land extraction model has the highest accuracy, reaching 96.25%, and the Kappa coefficient is 0.93; (3) the optimized boundary of structural elements with a size of 13 × 13 is selected, which is in good agreement in terms of scope and location with the boundary of FROM-GLC10 (Zhengzhou) and visual interpretations. The results from the urban boundary delineation in this paper can be used as an important database for detailed basic land use mapping within cities. Moreover, the method in this paper has some reference value for other cities in terms of delineating urban boundaries.

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Continuous high-resolution midlatitude-belt simulations for July–August 2013 with WRF

Geoscientific Model Development ◽

10.5194/gmd-10-2031-2017 ◽

2017 ◽

Vol 10 (5) ◽

pp. 2031-2055 ◽

Cited By ~ 14

Author(s):

Thomas Schwitalla ◽

Hans-Stefan Bauer ◽

Volker Wulfmeyer ◽

Kirsten Warrach-Sagi

Keyword(s):

High Resolution ◽

Land Surface ◽

Large Scale ◽

Temperature Fields ◽

Surface Model ◽

Limited Area ◽

Data Set ◽

Latitude Belt ◽

Simulated Precipitation ◽

The Impact

Abstract. Increasing computational resources and the demands of impact modelers, stake holders, and society envision seasonal and climate simulations with the convection-permitting resolution. So far such a resolution is only achieved with a limited-area model whose results are impacted by zonal and meridional boundaries. Here, we present the setup of a latitude-belt domain that reduces disturbances originating from the western and eastern boundaries and therefore allows for studying the impact of model resolution and physical parameterization. The Weather Research and Forecasting (WRF) model coupled to the NOAH land–surface model was operated during July and August 2013 at two different horizontal resolutions, namely 0.03 (HIRES) and 0.12° (LOWRES). Both simulations were forced by the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis data at the northern and southern domain boundaries, and the high-resolution Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) data at the sea surface.The simulations are compared to the operational ECMWF analysis for the representation of large-scale features. To analyze the simulated precipitation, the operational ECMWF forecast, the CPC MORPHing (CMORPH), and the ENSEMBLES gridded observation precipitation data set (E-OBS) were used as references.Analyzing pressure, geopotential height, wind, and temperature fields as well as precipitation revealed (1) a benefit from the higher resolution concerning the reduction of monthly biases, root mean square error, and an improved Pearson skill score, and (2) deficiencies in the physical parameterizations leading to notable biases in distinct regions like the polar Atlantic for the LOWRES simulation, the North Pacific, and Inner Mongolia for both resolutions.In summary, the application of a latitude belt on a convection-permitting resolution shows promising results that are beneficial for future seasonal forecasting.

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Modelling above-ground carbon dynamics using multi-temporal airborne lidar: insights from a Mediterranean woodland

Biogeosciences ◽

10.5194/bg-13-961-2016 ◽

2016 ◽

Vol 13 (4) ◽

pp. 961-973 ◽

Cited By ~ 17

Author(s):

W. Simonson ◽

P. Ruiz-Benito ◽

F. Valladares ◽

D. Coomes

Keyword(s):

Climate Change ◽

Large Scale ◽

Critical Role ◽

Carbon Fluxes ◽

Carbon Dynamics ◽

Airborne Lidar ◽

Lidar Data ◽

Low Carbon ◽

Data Set ◽

Multi Temporal

Abstract. Woodlands represent highly significant carbon sinks globally, though could lose this function under future climatic change. Effective large-scale monitoring of these woodlands has a critical role to play in mitigating for, and adapting to, climate change. Mediterranean woodlands have low carbon densities, but represent important global carbon stocks due to their extensiveness and are particularly vulnerable because the region is predicted to become much hotter and drier over the coming century. Airborne lidar is already recognized as an excellent approach for high-fidelity carbon mapping, but few studies have used multi-temporal lidar surveys to measure carbon fluxes in forests and none have worked with Mediterranean woodlands. We use a multi-temporal (5-year interval) airborne lidar data set for a region of central Spain to estimate above-ground biomass (AGB) and carbon dynamics in typical mixed broadleaved and/or coniferous Mediterranean woodlands. Field calibration of the lidar data enabled the generation of grid-based maps of AGB for 2006 and 2011, and the resulting AGB change was estimated. There was a close agreement between the lidar-based AGB growth estimate (1.22 Mg ha−1 yr−1) and those derived from two independent sources: the Spanish National Forest Inventory, and a tree-ring based analysis (1.19 and 1.13 Mg ha−1 yr−1, respectively). We parameterised a simple simulator of forest dynamics using the lidar carbon flux measurements, and used it to explore four scenarios of fire occurrence. Under undisturbed conditions (no fire) an accelerating accumulation of biomass and carbon is evident over the next 100 years with an average carbon sequestration rate of 1.95 Mg C ha−1 yr−1. This rate reduces by almost a third when fire probability is increased to 0.01 (fire return rate of 100 years), as has been predicted under climate change. Our work shows the power of multi-temporal lidar surveying to map woodland carbon fluxes and provide parameters for carbon dynamics models. Space deployment of lidar instruments in the near future could open the way for rolling out wide-scale forest carbon stock monitoring to inform management and governance responses to future environmental change.

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Melt Pond Retrieval Based on the LinearPolar Algorithm Using Landsat Data

Remote Sensing ◽

10.3390/rs13224674 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4674

Author(s):

Yuqing Qin ◽

Jie Su ◽

Mingfeng Wang

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Relative Error ◽

Large Scale ◽

The Arctic ◽

Landsat Data ◽

Melt Pond ◽

Melt Ponds ◽

Very High ◽

Sentinel 2

The formation and distribution of melt ponds have an important influence on the Arctic climate. Therefore, it is necessary to obtain more accurate information on melt ponds on Arctic sea ice by remote sensing. The present large-scale melt pond products, especially the melt pond fraction (MPF), still require verification, and using very high resolution optical satellite remote sensing data is a good way to verify the large-scale retrieval of MPF products. Unlike most MPF algorithms using very high resolution data, the LinearPolar algorithm using Sentinel-2 data considers the albedo of melt ponds unfixed. In this paper, by selecting the best band combination, we applied this algorithm to Landsat 8 (L8) data. Moreover, Sentinel-2 data, as well as support vector machine (SVM) and iterative self-organizing data analysis technique (ISODATA) algorithms, are used as the comparison and verification data. The results show that the recognition accuracy of the LinearPolar algorithm for melt ponds is higher than that of previous algorithms. The overall accuracy and kappa coefficient results achieved by using the LinearPolar algorithm with L8 and Sentinel-2A (S2), the SVM algorithm, and the ISODATA algorithm are 95.38% and 0.88, 94.73% and 0.86, and 92.40%and 0.80, respectively, which are much higher than those of principal component analysis (PCA) and Markus algorithms. The mean MPF (10.0%) obtained from 80 cases from L8 data based on the LinearPolar algorithm is much closer to Sentinel-2 (10.9%) than the Markus (5.0%) and PCA algorithms (4.2%), with a mean MPF difference of only 0.9%, and the correlation coefficients of the two MPFs are as high as 0.95. The overall relative error of the LinearPolar algorithm is 53.5% and 46.4% lower than that of the Markus and PCA algorithms, respectively, and the root mean square error (RMSE) is 30.9% and 27.4% lower than that of the Markus and PCA algorithms, respectively. In the cases without obvious melt ponds, the relative error is reduced more than that of those with obvious melt ponds because the LinearPolar algorithm can identify 100% of dark melt ponds and relatively small melt ponds, and the latter contributes more to the reduction in the relative error of MPF retrieval. With a wider range and longer time series, the MPF from Landsat data are more efficient than those from Sentinel-2 for verifying large-scale MPF products or obtaining long-term monitoring of a fixed area.

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Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

Remote Sensing ◽

10.3390/rs11070820 ◽

2019 ◽

Vol 11 (7) ◽

pp. 820 ◽

Cited By ~ 8

Author(s):

Haifeng Tian ◽

Ni Huang ◽

Zheng Niu ◽

Yuchu Qin ◽

Jie Pei ◽

...

Keyword(s):

Time Series ◽

Large Scale ◽

Time Windows ◽

Vegetation Indices ◽

Optical Images ◽

Landsat 7 ◽

Multi Temporal ◽

Winter Crop ◽

Winter Crops ◽

Sentinel 2

Timely and accurate mapping of winter crop planting areas in China is important for food security assessment at a national level. Time-series of vegetation indices, such as the normalized difference vegetation index (NDVI), are widely used for crop mapping, as they can characterize the growth cycle of crops. However, with the moderate spatial resolution optical imagery acquired by Landsat and Sentinel-2, it is difficult to obtain complete time-series curves for vegetation indices due to the influence of the revisit cycle of the satellite and weather conditions. Therefore, in this study, we propose a method for compositing the multi-temporal NDVI, in order to map winter crop planting areas with the Landsat-7 and -8 and Sentinel-2 optical images. The algorithm composites the multi-temporal NDVI into three key values, according to two time-windows—a period of low NDVI values and a period of high NDVI values—for the winter crops. First, we identify the two time-windows, according to the time-series of the NDVI obtained from daily Moderate Resolution Imaging Spectroradiometer observations. Second, the 30 m spatial resolution multi-temporal NDVI curve, derived from the Landsat-7 and -8 and Sentinel-2 optical images, is composited by selecting the maximal value in the high NDVI value period, and the minimal and median values in the low NDVI value period, using an algorithm of the Google Earth Engine. Third, a decision tree classification method is utilized to perform the winter crop classification at a pixel level. The results indicate that this method is effective for the large-scale mapping of winter crops. In the study area, the area of winter crops in 2018 was determined to be 207,641 km2, with an overall accuracy of 96.22% and a kappa coefficient of 0.93. The method proposed in this paper is expected to contribute to the rapid and accurate mapping of winter crops in large-scale applications and analyses.

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EMO-5: Copernicus pan-European high-resolution meteorological data set for large-scale hydrological modelling

10.5194/egusphere-egu2020-21551 ◽

2020 ◽

Author(s):

Vera Thiemig ◽

Peter Salamon ◽

Goncalo N. Gomes ◽

Jon O. Skøien ◽

Markus Ziese ◽

...

Keyword(s):

High Resolution ◽

Real Time ◽

Large Scale ◽

Interpolation Method ◽

Meteorological Data ◽

Hydrological Modelling ◽

Data Set ◽

Average Temperature ◽

Time Period ◽

Meteorological Observations

We present EMO-5, a Pan-European high-resolution (5 km), (sub-)daily, multi-variable meteorological data set especially developed to the needs of an operational, pan-European hydrological service (EFAS; European Flood Awareness System). The data set is built on historic and real-time observations coming from 18,964 meteorological in-situ stations, collected from 24 data providers, and 10,632 virtual stations from four high-resolution regional observational grids (CombiPrecip, ZAMG - INCA, EURO4M-APGD and CarpatClim) as well as one global reanalysis product (ERA-Interim-land). This multi-variable data set covers precipitation, temperature (average, min and max), wind speed, solar radiation and vapor pressure; all at daily resolution and in addition 6-hourly resolution for precipitation and average temperature. The original observations were thoroughly quality controlled before we used the Spheremap interpolation method to estimate the variable values for each of the 5 x 5 km grid cells and their affiliated uncertainty. EMO-5 v1 grids covering the time period from 1990 till 2019 will be released as a free and open Copernicus product mid-2020 (with a near real-time release of the latest gridded observations in future). We would like to present the great potential EMO-5 holds for the hydrological modelling community.&#160;footnote: EMO = European Meteorological Observations

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Large-Scale High-Resolution Coastal Mangrove Forests Mapping Across West Africa With Machine Learning Ensemble and Satellite Big Data

Frontiers in Earth Science ◽

10.3389/feart.2020.560933 ◽

2021 ◽

Vol 8 ◽

Author(s):

Xue Liu ◽

Temilola E. Fatoyinbo ◽

Nathan M. Thomas ◽

Weihe Wendy Guan ◽

Yanni Zhan ◽

...

Keyword(s):

Machine Learning ◽

High Resolution ◽

West Africa ◽

Spatial Resolution ◽

Mangrove Forest ◽

Large Scale ◽

Google Earth ◽

Mangrove Forests ◽

Data Products ◽

Sentinel 2

Coastal mangrove forests provide important ecosystem goods and services, including carbon sequestration, biodiversity conservation, and hazard mitigation. However, they are being destroyed at an alarming rate by human activities. To characterize mangrove forest changes, evaluate their impacts, and support relevant protection and restoration decision making, accurate and up-to-date mangrove extent mapping at large spatial scales is essential. Available large-scale mangrove extent data products use a single machine learning method commonly with 30 m Landsat imagery, and significant inconsistencies remain among these data products. With huge amounts of satellite data involved and the heterogeneity of land surface characteristics across large geographic areas, finding the most suitable method for large-scale high-resolution mangrove mapping is a challenge. The objective of this study is to evaluate the performance of a machine learning ensemble for mangrove forest mapping at 20 m spatial resolution across West Africa using Sentinel-2 (optical) and Sentinel-1 (radar) imagery. The machine learning ensemble integrates three commonly used machine learning methods in land cover and land use mapping, including Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Network (NN). The cloud-based big geospatial data processing platform Google Earth Engine (GEE) was used for pre-processing Sentinel-2 and Sentinel-1 data. Extensive validation has demonstrated that the machine learning ensemble can generate mangrove extent maps at high accuracies for all study regions in West Africa (92%–99% Producer’s Accuracy, 98%–100% User’s Accuracy, 95%–99% Overall Accuracy). This is the first-time that mangrove extent has been mapped at a 20 m spatial resolution across West Africa. The machine learning ensemble has the potential to be applied to other regions of the world and is therefore capable of producing high-resolution mangrove extent maps at global scales periodically.

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Lateral variation of crustal properties from aerogeophysical data in northern Brazil

Geophysics ◽

10.1190/geo2016-0206.1 ◽

2017 ◽

Vol 82 (5) ◽

pp. J39-J60

Author(s):

João Willy Corrêa Rosa ◽

José Wilson Corrêa Rosa ◽

Götz Bokelmann

Keyword(s):

High Resolution ◽

Radon Transform ◽

Large Scale ◽

Amazon Basin ◽

Seismic Anisotropy ◽

Sedimentary Cover ◽

Azimuthal Anisotropy ◽

Lateral Variation ◽

Data Set ◽

Northern Brazil

A new method is proposed for the study of crustal azimuthal anisotropy, and the lateral variation of other crustal aerogeophysical parameters, using a quantitative approach. The processing of a large, newly acquired, high-resolution set of aerogeophysical data is considered using the existing Radon transform and the geostatistical analysis approaches. The data set includes an area of the Guyana shield, in northern Brazil, which was not included in previous surveys. The area is covered by dense rain forest vegetation and thick soil layers. Parameterization was performed considering the possible anisotropic character of the geophysical 2D data. Application of the newly proposed geostatistical data processing yielded high-resolution images of the lateral variation of quantitative geophysical parameters, which indicate good correlation with previously determined seismic anisotropy in the area. Average anisotropy as measured by the Radon transform and variogram analysis is scale dependent. At scales greater than [Formula: see text], the results seem to match those of the previous seismic studies. Images of the derived quantitative parameters from magnetic and radiometric data in the shield area indicate sharp changes that follow the known geologic changes observable in the survey area. Observed large-scale anisotropy in the area seems to be autocorrelated with two different geochronological provinces of the Amazonian craton. Application of the new technique to two other lower resolution aeromagnetic surveys located on the Amazon basin has produced two different results, including one successful data treatment of the aeromagnetic survey. The results obtained for this area indicate that the sedimentary cover seems to play almost no role in the observed magnetic anomaly anisotropy in the middle Amazon basin area. Most of the observed magnetic anomalies appear to be controlled by the continuing geologic features of the cratonic basement.

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Large Interstellar Polarisation Survey

Astronomy and Astrophysics ◽

10.1051/0004-6361/201731814 ◽

2018 ◽

Vol 611 ◽

pp. A5 ◽

Cited By ~ 5

Author(s):

R. Siebenmorgen ◽

N. V. Voshchinnikov ◽

S. Bagnulo ◽

N. L. J. Cox ◽

J. Cami ◽

...

Keyword(s):

High Resolution ◽

Interstellar Medium ◽

Southern Hemisphere ◽

Large Scale ◽

Small Scale ◽

Data Set ◽

Carbon Particles ◽

Prolate Shape ◽

Multiple Clouds ◽

H 20

It is well known that the dust properties of the diffuse interstellar medium exhibit variations towards different sight-lines on a large scale. We have investigated the variability of the dust characteristics on a small scale, and from cloud-to-cloud. We use low-resolution spectro-polarimetric data obtained in the context of the Large Interstellar Polarisation Survey (LIPS) towards 59 sight-lines in the Southern Hemisphere, and we fit these data using a dust model composed of silicate and carbon particles with sizes from the molecular to the sub-micrometre domain. Large (≥6 nm) silicates of prolate shape account for the observed polarisation. For 32 sight-lines we complement our data set with UVES archive high-resolution spectra, which enable us to establish the presence of single-cloud or multiple-clouds towards individual sight-lines. We find that the majority of these 35 sight-lines intersect two or more clouds, while eight of them are dominated by a single absorbing cloud. We confirm several correlations between extinction and parameters of the Serkowski law with dust parameters, but we also find previously undetected correlations between these parameters that are valid only in single-cloud sight-lines. We find that interstellar polarisation from multiple-clouds is smaller than from single-cloud sight-lines, showing that the presence of a second or more clouds depolarises the incoming radiation. We find large variations of the dust characteristics from cloud-to-cloud. However, when we average a sufficiently large number of clouds in single-cloud or multiple-cloud sight-lines, we always retrieve similar mean dust parameters. The typical dust abundances of the single-cloud cases are [C]/[H] = 92 ppm and [Si]/[H] = 20 ppm.

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Trajectory Segmentation Map-Matching Approach for Large-Scale, High-Resolution GPS Data

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2645-08 ◽

2017 ◽

Vol 2645 (1) ◽

pp. 67-75 ◽

Cited By ~ 11

Author(s):

Lei Zhu ◽

Jacob R. Holden ◽

Jeffrey D. Gonder

Keyword(s):

High Resolution ◽

Large Scale ◽

Longest Common Subsequence ◽

Gps Data ◽

Map Matching ◽

Trajectory Data ◽

Data Set ◽

Matching Algorithm ◽

Gps Trajectory ◽

Trajectory Segmentation

With the development of smartphones and portable GPS devices, large-scale, high-resolution GPS data can be collected. Map matching is a critical step in studying vehicle driving activity and recognizing network traffic conditions from the data. A new trajectory segmentation map-matching algorithm is proposed to deal accurately and efficiently with large-scale, high-resolution GPS trajectory data. The new algorithm separated the GPS trajectory into segments. It found the shortest path for each segment in a scientific manner and ultimately generated a best-matched path for the entire trajectory. The similarity of a trajectory segment and its matched path is described by a similarity score system based on the longest common subsequence. The numerical experiment indicated that the proposed map-matching algorithm was very promising in relation to accuracy and computational efficiency. Large-scale data set applications verified that the proposed method is robust and capable of dealing with real-world, large-scale GPS data in a computationally efficient and accurate manner.

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