地理大数据揭示中国主要城市建成环境物质存量的空间格局

The patterns of material accumulation in buildings and infrastructure accompanied by rapid urbanization offer an important,yet hitherto largely missing stock perspective for facilitating urban system engineering and informing urban resources,waste,and climate strategies.However,our existing knowledge on the patterns of built environment stocks across and particularly within cities is limited,largely owing to the lack of sufficient high spatial resolution data.This study leveraged multi-source big geodata,machine learning,and bottom-up stock accounting to characterize the built environment stocks of 50 cities in China at 500 m fine-grained levels.The per capita built environment stock of many cities(261 tonnes per capita on average)is close to that in western cities,despite considerable disparities across cities owing to their varying socioeconomic,geomorphology,and urban form characteristics.This is mainly owing to the construction boom and the building and infrastructure-driven economy of China in the past decades.China’s urban expansion tends to be more“vertical”(with high-rise buildings)than“horizontal”(with expanded road networks).It trades skylines for space,and reflects a concentration-dispersion-concentration pathway for spatialized built environment stocks development within cities in China.These results shed light on future urbanization in developing cities,inform spatial planning,and support circular and low-carbon transitions in cities.

A Deep Learning Application for Building Damage Assessment Using Ultra-High-Resolution Remote Sensing Imagery in Turkey Earthquake

Rapid building damage assessment following an earthquake is important for humanitarian relief and disaster emergency responses.In February 2023,two magnitude-7.8 earthquakes struck Turkey in quick succession,impacting over 30 major cities across nearly 300 km.A quick and comprehensive understanding of the distribution of building damage is essential for e fficiently deploying rescue forces during critical rescue periods.This article presents the training of a two-stage convolutional neural network called BDANet that integrated image features captured before and after the disaster to evaluate the extent of building damage in Islahiye.Based on high-resolution remote sensing data from WorldView2,BDANet used predisaster imagery to extract building outlines;the image features before and after the disaster were then combined to conduct building damage assessment.We optimized these results to improve the accuracy of building edges and analyzed the damage to each building,and used population distribution information to estimate the population count and urgency of rescue at different disaster levels.The results indicate that the building area in the Islahiye region was 156.92 ha,with an affected area of 26.60 ha.Severely damaged buildings accounted for 15.67%of the total building area in the affected areas.WorldPop population distribution data indicated approximately 253,297,and 1,246 people in the collapsed,severely damaged,and lightly damaged areas,respectively.Accuracy verification showed that the BDANet model exhibited good performance in handling high-resolution images and can be used to directly assess building damage and provide rapid information for rescue operations in future disasters using model weights.

Influence of Range Gate Width on Detection Probability and Ranging Accuracy of Single Photon Laser Altimetry Satellite

The influence of the single photon laser altimeter range-gate width on the detection probability and ranging accuracy is discussed and analyzed,according to the LiDAR equation,single photon detection equation and the Monte Carlo method to simulate the experiment.The simulated results show that the probability of detection is not affected by the range gate,while the probability of false alarm is relative to the gate width.When the gate width is 100 ns,the ranging accuracy can accord with the requirements of satellite laser altimeter.But when the range gate width exceeds 400 ns,ranging accuracy will decline sharply.The noise ratio will be more as long as the range gate to get larger,so the refined filtering algorithm during the data processing is important to extract the useful photons effectively.In order to ensure repeated observation of the same point for 25 times,we deduce the quantitative relation between the footprint size,footprint,and frequency repetition according to the parameters of ICESat-2.The related conclusions can provide some references for the design and the development of the domestic single photon laser altimetry satellite.

The Austrian node of the natural resources satellite remote sensing cloud service platform:examples of Sino-Austrian cooperation

The Austrian node of the Natural Resources Satellite Remote Sensing Cloud Service Platform was established in 2016 through a cooperation agreement between the Land Satellite Remote Sensing Application Center(LASAC),Ministry of Natural Resources of the Peoples Republic of China and the University of Vienna,Austria.Under this agreement panchromatic and multi-spectral data of the Chinese ZY-3 satellite are pushed to the server at the University of Vienna for use in education and research.So far,nearly 500 GB of data have been uploaded to the server.This technical note briefly introduces the ZY-3 system and illustrates the implementation of the agreement by the first China-Sat Workshop and several case studies.Some of them are already completed,others are still ongoing.They include a geometric accuracy validation of ZY-3 data,an animated visualization of image quick views on a spherical display to demonstrate the time series of the image coverage for Austria and Laos,and the use of ZY-3 data to study the spread of bark beetle in the province of Lower Austria.An accuracy study of DTMs from ZY-3 stereo data,as well as a land cover analysis and comparison of Austria with ZY-3 and other sensors are still ongoing.

Gravity anomalies determined from mean sea surface model data over the Gulf of Mexico

With the improvements in the density and quality of satellite altimetry data,a high-precision and high-resolution mean sea surface model containing abundant information regarding a marine gravity field can be calculated from long-time series multi-satellite altimeter data.Therefore,in this study,a method was proposed for determining marine gravity anomalies from a mean sea surface model.Taking the Gulf of Mexico(15°–32°N,80°–100°W)as the study area and using a removal-recovery method,the residual gridded deflections of the vertical(DOVs)are calculated by combining the mean sea surface,mean dynamic topography,and XGM2019e_2159 geoid,and then using the inverse Vening-Meinesz method to determine the residual marine gravity anomalies from the residual gridded DOVs.Finally,residual gravity anomalies are added to the XGM2019e_2159 gravity anomalies to derive marine gravity anomaly models.In this study,the marine gravity anomalies were estimated with mean sea surface models CNES_CLS15MSS,DTU21MSS,and SDUST2020MSS and the mean dynamic topography models CNES_CLS18MDT and DTU22MDT.The accuracy of the marine gravity anomalies derived by the mean sea surface model was assessed based on ship-borne gravity data.The results show that the difference between the gravity anomalies derived by DTU21MSS and CNES_CLS18MDT and those of the ship-borne gravity data is optimal.With an increase in the distance from the coast,the difference between the gravity anomalies derived by mean sea surface models and ship-borne gravity data gradually decreases.The accuracy of the difference between the gravity anomalies derived by mean sea surface models and those from ship-borne gravity data are optimal at a depth of 3–4 km.The accuracy of the gravity anomalies derived by the mean sea surface model is high.

RepDDNet:a fast and accurate deforestation detection model with high-resolution remote sensing image

Forest is the largest carbon reservoir and carbon absorber on earth.Thus,mapping forest cover change accurately is of great significance to achieving the global carbon neutrality goal.Accurate forest change information could be acquired by deep learning methods using high-resolution remote sensing images.However,deforestation detection based on deep learning on a large-scale region with high-resolution images required huge computational resources.Therefore,there was an urgent need for a fast and accurate deforestation detection model.In this study,we proposed an interesting but effective re-parameterization deforestation detection model,named RepDDNet.Unlike other existing models designed for deforestation detection,the main feature of RepDDNet was its decoupling feature,which means that it allowed the multi-branch structure in the training stages to be converted into a plain structure in the inference stage,thus the computation efficiency can be significantly improved in the inference stage while maintaining the accuracy unchanged.A large-scale experiment was carried out in Ankang city with 2-meter high-resolution remote sensing images(the total area of it was over 20,000 square kilometers),and the result indicated that the model computation efficiency could be improved by nearly 30%compared with the model without re-parameterization.Additionally,compared with other lightweight models,RepDDNet also displayed a trade-off between accuracy and computation efficiency.

High-resolution urban land-cover mapping and landscape analysis of the 42 major cities in China using ZY-3 satellite images

Detailed and precise urban land-cover maps are crucial for urban-related studies. However, there are limited ways of mapping high-resolution urban land cover over large areas. In this paper, we propose an operational framework to map urban land cover on the basis of Ziyuan-3 satellite images. Based on this framework, we produced the first high-resolution(2 m) urban land-cover map(Hi-ULCM) covering the 42 major cities of China. The overall accuracy of the Hi-ULCM dataset is 88.55%, of which 14 cities have an overall accuracy of over 90%. Most of the producer’s accuracies and user’s accuracies of the land-cover classes exceed 85%. We further conducted a landscape pattern analysis in the 42 cities based on Hi-ULCM. In terms of the comparison between the 42 cities in China, we found that the difference in the land-cover composition of urban areas is related to the climatic characteristics and urbanization levels, e.g., cities with warm climates generally have higher proportions of green spaces. It is also interesting to find that cities with higher urbanization levels are more habitable, in general. From the landscape viewpoint, the geometric complexity of the landscape increases with the urbanization level.Compared with the existing medium-resolution land-cover/use datasets(at a 30-m resolution), HiULCM represents a significant advance in accurately depicting the detailed land-cover footprint within the urban areas of China, and will be of great use for studies of urban ecosystems.

Quantitatively determine the dominant driving factors of the spatial–temporal changes of vegetation NPP in the Hengduan Mountain area during 2000-2015

The Hengduan mountain area,located in the upper reaches of the Yangtze River of China,is an important ecological barrier that significantly impacts the climate and ecological environment of the surrounding region and western China as a whole.This paper introduces the gravity center model used to analyze the spatial-temporal variation patterns of vegetation Net Primary Productivity(NPP)from 2000 to 2015,which were determined by the use of MOD17 A3 NPP products.Additionally,the dominant driving factors of the spatial–temporal changes of vegetation NPP of the Hengduan Mountain area were quantitatively determined with a geographical detector over 2000-2015.The results revealed that:(1)From 2000 to 2015,there was an increasing trend of vegetation NPP in the Hengduan mountain area.Throughout the whole study region,the vegetation NPP with a mean value of 611.37 gC·m^(-2)·a^(-1) indicated a decreasing trend from southeast to northwest in terms of spatial distribution.(2)The gravity centers of vegetation NPP in 2000-2015 were mainly concentrated in Zhongdian County.During the study period,the gravity center of vegetation NPP moved northward,which indicated that the increment and increasing rate of vegetation NPP in the northern parts were greater than that of the southern areas.(3)The vegetation NPP showed a moderately positive correlation with temperature,accumulated temperature(>10℃),and sunshine,while there was an overall negative relationship between NPP and precipitation.(4)The dominant factors and interactive dominant factors changed in different subregions over different segments of the study period.The dominant factors of most sub-regions in Hengduan mountain were natural factors,and the climate change factors played an increasingly greater role over the 16 years of the study period.

Calibration for GOCE Gradiometer Data Based on the Prior Gravity Models

The determination of the calibration parameters of the gravity gradiometer play an important role in the GOCE gravity gradient data processing.In this paper,the temporal signals and outliers in the GOCE gravity gradient observations are analyzed.Based on the different global gravity field models,the scale factors and biases are determined in all the components of GOCE gravity gradients.And then the accuracy of the calibration results is validated.The results indicated that the effect of the ocean tide is at mE magnitude in the measurement band,which is equivalent to the precision of the gravity gradiometer,while the effect of the non-tide temporal signals,such as terrestrial water is in the order of 10-4 E,is slightly less than that of the ocean tide.The outliers in all the gravity gradient components are larger than 0.2%.And after the calibration using global gravity field models except EGM96,the stability of scale factors in the V xx、V yy、V zz、V yz components reaches 10-4 magnitude,and the V xz component reaches 10-5 while that of the V xy component is about 10-2,which are in accordance with the accuracy differences of the gradient components.

Research on Greenway Route Selection of Traditional Villages Based on GIS: A Case Study of Xingtai County, China

Greenway is a green linear corridor connecting scenic spots, residential areas, nature reserves, road traffic, etc. Rational greenway route selection can not only attract tourists to increase the income of local villages, but also arouse people’s awareness of the protection of traditional villages. This paper took Xingtai County, Hebei Province, China as an example to practice greenway route selection in the county territory. Eight factors were selected for greenway route planning, namely elevation, water body, mountain, ecological protection redline, cultural relics protection units, population density, road traffic network, and important transportation hub. These eight factors were assigned in the GIS platform, and the suitability evaluation system of each factor was constructed. The analytic hierarchy process (AHP) and entropy weight method were combined to calculate the weight of these eight factors. The raster calculator was used to weight and overlay the suitability evaluation system of each factor, and the comprehensive suitability evaluation map of greenway route selection was obtained. Based on an important transport hub, along with the water body and the road traffic, the final selection greenway route of traditional villages in Xingtai county was formed. The results of the research can provide a certain reference for the greenway route selection at the county scale.

On-orbit geometric calibration of satellite laser altimeters using infrared detectors and corner-cube retroreflectors

After being launched into orbit,the geometric calibration of a satellite laser altimeter will reduce errors in laser pointing and ranging caused by satellite vibrations during launch,environmental changes,and thermal effects during long-term operation,which guarantees the accuracy of measurement data.In this study,a satellite laser geometric calibration method combining infrared detectors and corner-cube retroreflectors(CCRs)is proposed.First,a CCR-based laser ranging error calibration method was established,and then a laser pointing error calibration model was derived based on a single infrared detector array.Taking GaoFen-7(GF-7)satellite laser beam 2 as the experimental object,laser geometric calibration was realized using an infrared detector and CCR-measured data.Then,the accuracy of the proposed method was compared with that of other calibration methods,the CMLID and the CMSPR.The results show that the accuracy of the proposed calibration method is equivalent to that of the CMLID and higher than that of the CMSPR.Among them,the accuracy of the laser pointing after calibration using the proposed method is better than 0.8 arcsec,and the elevation accuracy of the laser on flat,sloping,and mountainous terrains is better than 0.11 m,0.30 m,and 1.80 m,respectively.

Preliminary Pointing Bias Calibration of ZY3-03 Laser Altimeter

ZiYuan3-03(ZY3-03)satellite was launched on July 25,2020,equipped with China’s second-generation laser altimeter for earth observation.In order to preliminarily evaluate the in-orbit performance of the ZY3-03 laser altimeter,the pointing bias calibration based on terrain matching method was adopted.Three tracks of laser data were employed for the ZY3-03 laser altimeter calibration test.Three groups of pointing parameters were obtained respectively,and the mean value of pointing is considered as the optimal calibration result.After calibration,ZY3-03 laser pointing accuracy is greatly improved by the method,and its pointing accuracy is approximately 12.7 arcsec.The first-track laser data on the Black Sea surface is used to evaluate the relative elevation accuracy of ZY3-03 laser altimeter after pointing bias calibration,which is improved from 0.33 m to 0.19 m after calibration.Meanwhile,the absolute elevation accuracy of ZY3-03 laser altimeter after pointing bias calibration is evaluated by the Ground Control Points(GCPs)measured by RTK(Real-Time Kinematic),which is better than 0.5 m in the flat terrain.

Marine aquaculture mapping using GF-1 WFV satellite images and full resolution cascade convolutional neural network

Growing demand for seafood and reduced fishery harvests have raised intensive farming of marine aquaculture in coastal regions,which may cause severe coastal water problems without adequate environmental management.Effective mapping of mariculture areas is essential for the protection of coastal environments.However,due to the limited spatial coverage and complex structures,it is still challenging for traditional methods to accurately extract mariculture areas from medium spatial resolution(MSR)images.To solve this problem,we propose to use the full resolution cascade convolutional neural network(FRCNet),which maintains effective features over the whole training process,to identify mariculture areas from MSR images.Specifically,the FRCNet uses a sequential full resolution neural network as the first-level subnetwork,and gradually aggregates higher-level subnetworks in a cascade way.Meanwhile,we perform a repeated fusion strategy so that features can receive information from different subnetworks simultaneously,leading to rich and representative features.As a result,FRCNet can effectively recognize different kinds of mariculture areas from MSR images.Results show that FRCNet obtained better performance than other classical and recently proposed methods.Our developed methods can provide valuable datasets for large-scale and intelligent modeling of the marine aquaculture management and coastal zone planning.

Kinematic inventory of rock glaciers in the Nyainqentanglha Range using the MT-InSAR method

Rock glaciers are typical periglacial landforms with tongue or lobate morphological shapes and characterized by the distinct front,lateral margins,and often by ridge-and-furrow surface topography textures as well as kinematic characteristics,widely distributed in alpine environments.Multitemporal Synthetic aperture radar interferometry(MT-InSAR),is a remote sensing technique with demonstrated effectiveness for detecting landform kinematics.However,its application to rock glaciers is challenged by temporal decorrelation and atmospheric phase noises due to complex topography and snow cover.We designed a quadtree segmentation and parallel computing-based MT-InSAR method to improve the quality and efficiency of deformation measurement of rock glaciers.We applied the method to a rock glacier inventory of the Nyainqentanglha Range,China,derived from high-resolution Gaofen-2 images,to quantify the activity rate of each rock glacier.Results showed that 32.1%(6,389)of the identified rock glaciers exhibited slope-parallel deformation rates exceeding 100 mm/y.The activities of the rock glaciers exhibited strong correlations with their distance to glaciers,precipitation,freeze-thaw magnitude,and permafrost occurrence probability.The results demonstrate the effectiveness of the developed segmentation-parallel MT-InSAR method for monitoring rock glacier deformation over a large region.