Definitions of Natural-Language Spatial Relations: Combining Topology and Directions

Because SQL for querying data from spatial databa se s is ineffective, the query based on natural or visual language becomes an attra ctive research field gradually. However, how to define and represent natural lan guages related to spatial data are still gigantic problems. Because existing mod els of direction relations can’t describe by use of some common concepts. First of all, detailed direction relations are proposed to describe the directions re lated to the interior of spatial objects, such as "east part of a region","ea st boundary of a region", and so on. Secondly, by integrating the detailed dire ctions with exterior direction relations and topological relations, several NLSR s are defined, such as "a road goes across the east part of a lake", "a river goes along the east boundary of a province", etc. Finally, based on the NLSRs abovementioned, a natural spatial query language (NSQL) is formed to retrieve da ta from spatial databases.

THE APPLICATION OF SATELLITE POSITIONING TECHNOLOGY AND ITS INDUSTRIALIZATION IN CHINA

Satellite positioning technology has been widely used in all kinds of military and civil land, marine, space and aeronautical target positioning tasks, naviga tion activities and accurate surveying measurements since 90 s in the last cen tury due to its advantage in providing all-weather, real-time, three dimensional and high precision positioning information, as well as speed and accurate timing information. By now, it has already formed a new hi-tech industry basically.This paper briefly reviews the development of the global satellite positioning and navigation technologies including the basic information of China's "Plough navigation system", introduces the history of satellite positioning technology and its major application fields as well as the status quo of this being industri alized trade in China, gives an account of the writers' vision for the application and prospect of the satellite positioning technologies in China, and approaches the tactics and stresses of the satellite positioning technology's application and its industrialization future in China.

Land Use Changes of Mata Lake Using Multi-temporal Satellite Imageries

Land use and protection has become a global hot spot.How to use land resources is an important topic for the future socio-economic sustainable development.This paper analyzes the land use changes of Mata lake of Shandong province in China,from 1985's to 2000's using multi-temporal remotely sensed data including TM in the 1985s,ETM+in the 2000s and ancillary data such as soil use map,water map etc.The remote sensing imageries were calibrated,registered and geo-referenced,then classified by multi-source information data and remote sensing image interpretation expert system based on knowledge base.Five land use types were extracted from remote sensing imageries,that is,water body,agriculture land,rural settlement,bare land and none-use land.The total precision is 80.7% and Kappa index is 0.825.The analysis result of the remote sensing shows that during the past 15 years,water resource dropped off very promptly from 51.77 km2 to 16.65 km2 and bare land reduced greatly more than 60% in Mata lake region.With the development of the economy and agriculture areas,more and more water body and bare land converted to agriculture land use and rural settlement areas.Since last years,the Mata lake has been affected by natural factor,human activity and increasing population.So its land use pattern greatly changed from 1985 to 2000.The information of land use changes provided scientific supports for land planning and environmental protection.

Incorporating DeepLabv3+and object-based image analysis for semantic segmentation of very high resolution remote sensing images

Semantic segmentation of remote sensing images is an important but unsolved problem in the remote sensing society.Advanced image semantic segmentation models,such as DeepLabv3+,have achieved astonishing performance for semantically labeling very high resolution(VHR)remote sensing images.However,it is difficult for these models to capture the precise outlines of ground objects and explore the context information that revealing relationships among image objects for optimizing segmentation results.Consequently,this study proposes a semantic segmentation method for VHR images by incorporating deep learning semantic segmentation model(DeepLabv3+)and objectbased image analysis(OBIA),wherein DSM is employed to provide geometric information to enhance the interpretation of VHR images.The proposed method first obtains two initial probabilistic labeling predictions using a DeepLabv3+network on spectral image and a random forest(RF)classifier on hand-crafted features,respectively.These two predictions are then integrated by Dempster-Shafer(D-S)evidence theory to be fed into an object-constrained higher-order conditional random field(CRF)framework to estimate the final semantic labeling results with the consideration of the spatial contextual information.The proposed method is applied to the ISPRS 2D semantic labeling benchmark,and competitive overall accuracies of 90.6%and 85.0%are achieved for Vaihingen and Potsdam datasets,respectively.

Using Statistical Learning Algorithms in Regional Landslide Susceptibility Zonation with Limited Landslide Field Data

Regional Landslide Susceptibility Zonation(LSZ) is always challenged by the available amount of field data, especially in southwestern China where large mountainous areas and limited field information coincide. Statistical learning algorithms are believed to be superior to traditional statistical algorithms for their data adaptability. The aim of the paper is to evaluate how statistical learning algorithms perform on regional LSZ with limited field data. The focus is on three statistical learning algorithms, Logistic Regression(LR), Artificial Neural Networks(ANN) and Support Vector Machine(SVM). Hanzhong city, a landslide prone area in southwestern China is taken as a study case. Nine environmental factors are selected as inputs. The accuracies of the resulting LSZ maps are evaluated through landslide density analysis(LDA), receiver operating characteristic(ROC) curves and Kappa index statistics. The dependence of the algorithm on the size of field samples is examined by varying the sizes of the training set. The SVM has proven to be the most accurate and the most stable algorithm at small training set sizes and on all known landslide sizes. The accuracy of SVM shows a steadilyincreasing trend and reaches a high level at a small size of the training set, while accuracies of LR and ANN algorithms show distinct fluctuations. The geomorphological interpretations confirm the strength of SVM on all landslide sizes. Our results show that the strengths of SVM in generalization capability and model robustness make it an appropriate and efficient tool for regional LSZ with limited landslide field samples.

The spatial scaling effect of continuous canopy Leaves Area Index retrieved by remote sensing

Leave Area Index (LAI) is one of the most basic parameters to describe the geometric structure of plant canopies. It is also important input data for climatic model and interaction model between Earth surface and atmosphere, and some other things. The spatial scaling of retrieved LAI has been widely studied in recent years. Based on the new canopy reflectance model, the mechanism of the scaling effect of con- tinuous canopy Leaf Area Index is studied, and the scaling transform formula among different scales is found. Both the numerical simulation and the field validation show that the scale transform formula is reliable.

The spatial scaling effect of the discrete-canopy effective leaf area index retrieved by remote sensing

The leaf area index(LAI) is a critical biophysical variable that describes canopy geometric structures and growth conditions.It is also an important input parameter for climate,energy and carbon cycle models.The scaling effect of the LAI has always been of concern.Considering the effects of the clumping indices on the BRDF models of discrete canopies,an effective LAI is defined.The effective LAI has the same function of describing the leaf density as does the traditional LAI.Therefore,our study was based on the effective LAI.The spatial scaling effect of discrete canopies significantly differed from that of continuous canopies.Based on the directional second-derivative method of effective LAI retrieval,the mechanism responsible for the spatial scaling effect of the discrete-canopy LAI is discussed and a scaling transformation formula for the effective LAI is suggested in this paper.Theoretical analysis shows that the mean values of effective LAIs retrieved from high-resolution pixels were always equal to or larger than the effective LAIs retrieved from corresponding coarse-resolution pixels.Both the conclusions and the scaling transformation formula were validated with airborne hyperspectral remote sensing imagery obtained in Huailai County,Zhangjiakou,Hebei Province,China.The scaling transformation formula agreed well with the effective LAI retrieved from hyperspectral remote sensing imagery.

Enhancing building pattern recognition through multi-scale data and knowledge graph:a case study of C-shaped patterns

Building pattern recognition is important for understanding urban forms,automating map generalization,and visualizing 3D city models.However,current approaches based on object-independent methods have limitations in capturing all visually aware patterns due to the part-based nature of human vision.Moreover,these approaches also suffer from inefficiencies when applying proximity graph models.To address these limitations,we propose a framework that leverages multi-scale data and a knowledge graph,focusing on recognizing C-shaped building patterns.We first employ a specialized knowledge graph to represent the relationships between buildings within and across various scales.Subsequently,we convert the rules for C-shaped pattern recognition and enhancement into query conditions,where the enhancement refers to using patterns recognized at one scale to enhance pattern recognition at other scales.Finally,rule-based reasoning is applied within the constructed knowledge graph to recognize and enrich C-shaped building patterns.We verify the effectiveness of our method using multi-scale data with three levels of detail(LODs)collected from AMap,and our method achieves a higher recall rate of 26.4%for LOD1,20.0%for LOD2,and 9.1%for LOD3 compared to existing methods with similar precisionrates.We,also achieve recognition efficiency improvements of 0.91,1.37,and 9.35 times,respectively.

Preliminary Calibration of GPS Signals and Its Effects on Soil Moisture Estimation

In recent years, Global Navigation Satellite Systems Reflectometry （GNSS-R） is developed to estimate soil moisture content （SMC） as a new remote sensing tool. Signal error of Global Positioning System （GPS） bistatic radar is an important factor that affects the accuracy of SMC estimation. In this paper, two methods of GPS signal calibration involving both the direct and reflected signals are introduced, and a detailed explanation of the theoretical basis for such methods is given. An improved SMC estimation model utilizing calibrated GPS L-band signals is proposed, and the estimation accuracy is validated using the airborne GPS data from the Soil Moisture Experiment in 2002 （SMEX02）. We choose 21 sites with soybean and corn in the Walnut Creek region of the US for validation. The sites are divided into three categories according to their vegetation cover： bare soil, mid-vegetation cover （Mid-Veg）, and high-vegetation cover （High-Veg）. The accuracy of SMC estimation .is 11.17% for bare soil and 8.12% for Mid-Veg sites, much better than that of the traditional model. For High-Veg sites, the effect of signal attenuation due to vegetation cover is preliminarily taken into consideration and a linear model related to Normalized Difference Vegetation Indices （NDVI） is adopted to obtain a factor for rectifying the ＂over-calibration＂, and the error for High-Veg sites is finally reduced to 3.81%.

Evaluation of the Visible and Shortwave Infrared Drought Index in China

In this article, the performance of the Visible and Shortwave infrared Drought Index(VSDI), a drought index recently developed and validated in Oklahoma, United States, is further explored and validated in China. The in-situ measured soil moisture from 585 weather stations across China are used as ground-truth data, and five commonly used drought indices are compared with VSDI for surface drought monitoring. The results reveal that VSDI is robust and reliable in the estimation of surface dryness—it has the highest correlation with soil moisture among the six indices when computed using both the original and cloud removed data. All six indices show the highest correlation with soil moisture at the 10 cm layer and the averaged 10–50 cm layer. The spatiotemporal patterns of surface moisture indicated by the MODIS-based VSDI are further compared with the precipitation-based drought maps and the Global Land Data Assimilation System(GLDAS) simulated surface soil moisture maps over five provinces located in the Middle-Lower Yangtze Plain of China. The results indicate that despite the difference between the spatial and temporal resolutions of the three products, the VSDI maps still show good agreement with the other two drought products through the rapidly alternating drought and flood events in 2011 in this region. Therefore, VSDI can be used as an effective surface wetness indicator at both the provincial and the national scales in China.

Satellite detection of increases in global land surface evapotranspiration during 1984-2007

As a key component of digital earth,remotely sensed data provides the compelling evidence that the amount of water vapour transferred from the entire global surface to the atmosphere increased from 1984 to 2007.The validation results from the earlier evapotranspiration(ET)estimation algorithm based on net radiation(Rn),Normalised Difference Vegetation Index(NDVI),air temperature and diurnal air temperature range(DTaR)showed good agreement between estimated monthly ET and ground-measured ET from 20 flux towers.Our analysis indicates that the estimated actual ET has increased on average over the entire global land surface except for Antarctica during 1984
2007.However,this increasing trend disappears after 2000 and the reason may be that the decline in net radiation and NDVI during this period depleted surface soil moisture.Moreover,the good correspondence between the precipitation trend and the change in ET in arid and semi-arid regions indicated that surface moisture linked to precipitation affects ET.The input parameters Rn,Tair,NDVI and DTaR show substantial spatio-temporal variability that is almost consistent with that of actual ET from 1984 to 2007 and contribute most significantly to the variation in actual ET.

A bi-directional gap model for simulating the directional thermal radiance of row crops

Row crops are a kind of typical vegetation canopy between discrete canopy and continuous canopy. Kimes et al. studied the directional thermal radiation of row crops using the geometrical optical model, which simplified row structure as 'box' and neglected the gap among foliage and did not consider the emissivity effects. In this work we take account of the gaps along illumination and viewing directions and propose a bi-direction gap model on the basis of the idea of gap probability of discrete vegetation canopy introduced by 'Li-Strahler' and inter-correlation of continuous vegetation developed by Kuusk. It can be used to explain 'hot spot' effects in thermal infrared region. The gap model has been validated by field experiment on winter wheat planted in shape of rows and results show that the gap model is better than Kimes' model in describing the directionality of thermal infrared emission for row crops.

Validation methods of LAI products on the basis of scaling effect

Validation is one of the most important processes used to evaluate whether remotely sensed products can accurately reflect land surface configuration. Leaf Area Index( LAI) is a key parameter that represents vegetation canopy structures and growth conditions. Accurate evaluation of LAI products is the basis for applying them to land surface models. In this study,validation methods of coarse resolution MODIS and GLASS LAI products for heterogeneous pixels are established on the basis of the scaling effect and the scaling transformation. Considering spatial heterogeneity and growth difference,we transformed LAI from field measurements into a 1 km resolution scale with the aid of middle resolution images. We used average LAI and apparent LAI separately to validate the algorithms and products of MODIS and GLASS LAI. Two study areas,Hebi City and the Yingke Oasis,were selected for validation. Both MODIS and GLASS LAI products underestimate the true LAI in crop area. However,this result cannot be completely attributed to their algorithms. Instead,the primary reason is the heterogeneity and nonuniformity of the coarse pixels.Underestimation is evident in the Yingke Oasis,where heterogeneity is significant. Given that GLASS LAI product is the fusion of multiple LAI products,the mean value of this product is closer to the real situation,but the dynamic range is narrower than that of MODIS LAI product.

Suggestion of EFS-small satellite system for impending earthquake forecast

In the IAF Congress ’92 a multiple small satellite Earth observation system was put forward with sensors of visible and infrared spectrums. The system could shorten the revisiting period so that any place on the world could be observed twice a day Now we extend the idea to the microwave remote sensing satellite system. The main purpose of the system is the impending forecast of earthquakes. According to the theory and long-time concrete practice of Qiang Zuji through the observation of temperature increase of the low layer of atmosphere and its moving trend caused by some sorts of radiation and gases released from Earth interior, an impending strong earthquake could be predicted in time. As the temperature increase is detected by thermo-infrared spectrum sensors on the meteorological satellites. the observation may be sometimes obstructed by cloud or rain. In the suggested system, mm-wave radiometers are used and those obstructions could be generally overcome. Besides, radiometers of some other microwave frequencies are also included so as to make the small satellite system useful for observation of atmosphere. soil and crops. The plattorm construction and the altitude control system of the satellites suitable to the sweeping radiometer antennas are stressfully implemented. The orbit of the satellites in the system is well designed so that any place in the world could be observed twice a day in accordance with the optical small satellite system.

Taking the pulse of COVID-19:a spatiotemporal perspective

The sudden outbreak of the Coronavirus disease(COVID-19)swept across the world in early 2020,triggering the lockdowns of several billion people across many countries,including China,Spain,India,the U.K.,Italy,France,Germany,Brazil,Russia,and the U.S.The transmission of the virus accelerated rapidly with the most confirmed cases in the U.S.,India,Russia,and Brazil.In response to this national and global emergency,the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implementation strategies to rapidly respond to this crisis,for supporting research,saving lives,and protecting the health of global citizens.This perspective paper presents our collective view on the global health emergency and our effort in collecting,analyzing,and sharing relevant data on global policy and government responses,human mobility,environmental impact,socioeconomical impact;in developing research capabilities and mitigation measures with global scientists,promoting collaborative research on outbreak dynamics,and reflecting on the dynamic responses from human societies.

TLS-bridged co-prediction of tree-level multifarious stem structure variables from worldview-2 panchromatic imagery: a case study of the boreal forest

In forest ecosystem studies,tree stem structure variables(SSVs)proved to be an essential kind of parameters,and now simultaneously deriving SSVs of as many kinds as possible at large scales is preferred for enhancing the frontier studies on marcoecosystem ecology and global carbon cycle.For this newly emerging task,satellite imagery such as WorldView-2 panchromatic images(WPIs)is used as a potential solution for co-prediction of tree-level multifarious SSVs,with static terrestrial laser scanning(TLS)assumed as a‘bridge’.The specific operation is to pursue the allometric relationships between TLS-derived SSVs and WPI-derived feature parameters,and regression analyses with one or multiple explanatory variables are applied to deduce the prediction models(termed as Model1s and Model2s).In the case of Picea abies,Pinus sylvestris,Populus tremul and Quercus robur in a boreal forest,tests showed that Model1s and Model2s for different tree species can be derived(e.g.the maximum R^(2)=0.574 for Q.robur).Overall,this study basically validated the algorithm proposed for co-prediction of multifarious SSVs,and the contribution is equivalent to developing a viable solution for SSV-estimation upscaling,which is useful for large-scale investigations of forest understory,macroecosystem ecology,global vegetation dynamics and global carbon cycle.

A method for canopy water content estimation for highly vegetated surfaces-shortwave infrared perpendicular water stress index

In this paper, a new method for canopy water content (FMC) estimation for highly vegetated surfaces- shortwave infrared perpendicular water stress index (SPSI) is developed using NIR, SWIR wavelengths of Enhanced Thematic Mapper Plus (ETM+) on the basis of spectral features and distribution of surface targets with different water conditions in NIR-SWIR spectral space. The developed method is further explored with radiative transfer simulations using PROSPECT, Lillesaeter, SailH and 6S. It is evident from the results of validation derived from satellite synchronous field measurements that SPSI is highly correlated with FMC, coefficient of determination (R squared) and root mean square error are 0.79 and 26.41%. The paper concludes that SPSI has a potential in vegetation water content estimation in terms of FMC.

NIR-red spectral space based new method for soil moisture monitoring

Drought is a complex natural disaster that occurs frequently. Soil moisture has been the main issue in remote monitoring of drought events as the most direct and important variable describing the drought. Spatio-temporal distribution and variation of soil moisture evidently affect surface evapotranspiration, agricultural water demand, etc. In this paper, a new simple method for soil moisture monitoring is de- veloped using near-infrared versus red (NIR-red) spectral reflectance space. First, NIR-red spectral reflectance space is established using atmospheric and geometric corrected ETM+ data, which is manifested by a triangle shape, in which different surface covers have similar spatial distribution rules. Next, the model of soil moisture monitoring by remote sensing (SMMRS) is developed on the basis of the distribution characteristics of soil moisture in the NIR-red spectral reflectance space. Then, the SMMRS model is validated by comparison with field measured soil moisture data at different depths. The results showed that satellite estimated soil moisture by SMMRS is highly accordant with field measured data at 5 cm soil depth and average soil moisture at 0―20 cm soil depths, correlation coef- ficients are 0.80 and 0.87, respectively. This paper concludes that, being simple and effective, the SMMRS model has great potential to estimate surface moisture conditions.

Passive Super-Low Frequency electromagnetic prospecting technique

The Super-Low Frequency （SLF） electromag- netic prospecting technique, adopted as a non-imaging remote sensing tool for depth sounding, is systematically proposed for subsurface geological survey. In this paper, we propose and theoretically illustrate natural source magnetic amplitudes as SLF responses for the first step. In order to directly calculate multi-dimensional theoretical SLF responses, modeling algorithms were developed and evaluated using the finite difference method. The theore- tical results of three-dimensional （3-D） models show that the average normalized SLF magnetic amplitude responses were numerically stable and appropriate for practical interpretation. To explore the depth resolution, three-layer models were configured. The modeling results prove that the SLF technique is more sensitive to conductive objective layers than high resistive ones, with the SLF responses of conductive objective layers obviously show- ing uprising amplitudes in the low frequency range. Afterwards, we proposed an improved Frequency-Depth transformation based on Bostick inversion to realize the depth sounding by empirically adjusting two parameters. The SLF technique has already been successfully applied in geothermal exploration and coalbed methane （CBM） reservoir interpretation, which demonstrates that the proposed methodology is effective in revealing low resistive distributions. Furthermore, it siginificantly contributes to reservoir identification with electromagnetic radiation anomaly extraction. Meanwhile, the SLF inter- pretation results are in accordance with dynamic production status of CBM reservoirs, which means it could provide an economical, convenient and promising method for exploring and monitoring subsurface geo-objects.