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On the isolatitude property of the rHEALPix Discrete Global Grid System

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Big Earth Data 2019年第4期3卷 362-377页

作者： David Bowater Emmanuel Stefanakis department of Geodesy and geomatics Engineering University of New BrunswickFrederictonCanada department of geomatics Engineering University of CalgaryCalgaryCanada

Digital Earth frameworks provide a way to integrate,analyze,and visualize large volumes of geospatial data,and the foundation of such frameworks is the Discrete Global Grid System(DGGS).One approach in particular,the rHEALPix DGGS,has the rare property of distribution of cell nuclei along rings of constant latitude(or isolatitude rings).However,this property is yet to be *** this paper,we extend existing work on the rHEALPix DGGS by proposing a method to determine the isolatitude ring on which the nucleus of a given cell falls by converting a cell identifier to isolatitude ring without recourse to geodetic *** addition,we present an efficient method to calculate the geodetic latitude of a cell’s nucleus via its associated isolatitude ***,we use the proposed methods to demonstrate how the isolatitude property of the rHEALPix DGGS can be utilized to facilitate latitudinal data analysis at multiple resolutions.

关键词： rHEALPix Discrete Global Grid System isolatitude property latitudinal data analysis Digital Earth geospatial big data

Carbon stock estimation by dual-polarized synthetic aperture radar (SAR) and forest inventory data in a Mediterranean forest landscape

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Journal of Forestry Research 2022年第3期33卷 827-838页

作者： Can Vatandaşlar Saygin Abdikan department of Forest Engineering Artvin Coruh University08100ArtvinTurkey department of geomatics Engineering Hacettepe University06800AnkaraTurkey

Forest ecosystems play a crucial role in mitigating global climate change by forming massive carbon sinks. Their carbon stocks and stock changes need to be quantified for carbon budget balancing and international reporting schemes. However, direct sampling and biomass weighing may not always be possible for quantification studies conducted in large forests. In these cases, indirect methods that use forest inventory information combined with remote sensing data can be beneficial. Synthetic aperture radar (SAR) images offer numerous opportunities to researchers as freely distributed remote sensing data. This study aims to estimate the amount of total carbon stock (TCS) in forested lands of the Kizildag Forest Enterprise. To this end, the actual storage capacities of five carbon pools, i.e. above- and below-ground, deadwood, litter, and soil, were calculated using the indirect method based on ground measurements of 264 forest inventory plots. They were then associated with the backscattered values from Sentinel-1 and ALOS-2 PALSAR-2 data in a Geographical information System (GIS). Finally, TCS was separately modelled and mapped. The best regression model was developed using the HH polarization of ALOS-2 PALSAR-2 with an adjusted R^(2) of 0.78 (p systems could be estimated using appropriate SAR images at acceptable accuracy levels for forestry purposes. The use of additional ancillary data may provide more delicate and reliable estimations in the future. Given the implications of this study, the spatiotemporal dynamics of carbon can be effectively controlled

关键词： Carbon storage Aboveground carbon Soil-bound carbon Forest biomass Synthetic aperture radar(SAR)

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An automatic procedure for generating burn severity maps from the satellite images-derived spectral indices

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International Journal of Digital Earth 2021年第11期14卷 1659-1673页

作者： Saeid Gholinejad Elahe Khesali department of geomatics Engineering Faculty of Civil Engineering and TransportationUniversity of IsfahanIsfahanIran Remote Sensing&Photogrametry department Faculty of Geodesy and Geomatics EngineeringK.N.Toosi University of TechnologyTehranIran

Fire,especially wildfire,which can be considered as one of the main threats to vegetation cover and animals’life,has attracted lots of attention from environmental *** better manage the fire crisis and take the necessary measures to compensate for its damages,it is essential to have detailed information about the burn severity ***,satellite images and their spectral indices have been widely considered in the literature as powerful tools in producing burn severity *** the efficiency of the previously proposed methods,the necessity of ground reference data for their thresholding step faces them with serious *** address this problem,in this study,an automatic procedure based on the change-point analysis is presented for thresholding differenced normalized burn ratio(dNBR)and its another version,*** this procedure,a mean-shift based change-point analysis is performed on the dNBR and dNBR2 images for classifying them into burn severity ***,conducted on some parts of Alaska and California in the United States,illustrated the high efficiency of the proposed ***,as an applied experiment,the severity of the fires,occurred in 2020 in the Khaeiz protected area in Iran,was estimated and compared with local reports.

关键词： Burn severity mapping spectral indices changepoint analysis normalized burn ratio(NBR)

How the physical inactivity is affected by social-, economic- and physical-environmental factors: an exploratory study using the machine learning approach

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International Journal of Digital Earth 2023年第1期16卷 2503-2521页

作者： Kangjae Lee Jue Wang Joon Heo department of Location-Based information System Kyungpook National UniversitySangju-siGyeongsangbukdoRepublic of Korea department of Geography Geomatics and EnvironmentUniversity of Toronto MississaugaMississaugaCanada department of Civil and Environmental Engineering Yonsei UniversitySeoulRepublic of Korea

Previous studies have utilized regression models to investigate the impact of environmental factors on physical ***,such approaches are inadequate for data-driven analysis seeking to identify robust associations from the intricate and multi-variable interactions between physical activity and environmental *** the emergence of the concept of the exposome,which encompasses the totality of exposures,this paper explores machine learning models for predicting the percentage of physical inactivity in ***,while considering 28 social-,economic-,and physical-environmental *** aim of this study is to address the research gap and gain insight into the complex associations between environmental exposures and physical *** machine learning models were tested,and the performances were compared to select the best classifier for further *** study used data from the Behavioral Risk Factor Surveillance System(BRFSS)of the Centers for Disease Control and *** mean population of all counties was 102,841,and the mean percentage of population below 18 years was 22.3%.The partial dependence plot analysis indicated that only one feature–bachelor’s degree–exhibited a close-to-linear relationship with physical ***-vehicle crash death rate and mean temperature showed nonlinear and non-monotonic relationships with the predicted percentage of physical inactivity.

关键词： Physical inactivity environmental effects machine learning GIS

GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms

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Journal of Mountain Science 2019年第3期16卷 595-618页

作者： Alireza ARABAMERI Biswajeet PRADHAN Khalil REZAE Masoud SOHRABI Zahra KALANTARI department of Geomorphology Tarbiat Modares University Centre for Advanced Modelling and geospatial information systems (CAMGIS) Faculty of Engineering and ITUniversity of Technology Sydney department of Energy and Mineral Resources Engineering Choongmu-gwanSejong University Faculty of Earth Sciences Kharazmi University department of Civil Engineering- geotechnics Islamic Azad university of Urmia Stockholm University Department of Physical Geography and Bolin Centre for Climate Research

In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar remote sensing data and geographic information system(GIS), for landslide susceptibility mapping(LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory(70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party(ILWP), Forestry, Rangeland and Watershed Organisation(FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve(AUC), frequency ratio(FR) and seed cell area index(SCAI).Normalised difference vegetation index, land use/land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models(AUC = 0.912(91.2%) and 0.907(90.7%), respectively) had high predictive accuracy than the LNRF model alone(AUC = 0.855(85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.

关键词： Landslide susceptibility GIS Remote sensing Bivariate model Multivariate model Machine learning model

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Estimation of Tree Height and Forest Biomass Using Airborne LiDAR Data: A Case Study of Londiani Forest Block in the Mau Complex, Kenya

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Open Journal of Forestry 2017年第2期7卷 255-269页

作者： Faith Kagwiria Mutwiri Patroba Achola Odera Mwangi James Kinyanjui department of Geomatic Engineering and geospatial information systems Jomo Kenyatta University of Science and Technology Nairobi Kenya Division of geomatics School of Architecture Planning and Geomatics University of Cape Town Cape Town South Africa School of Natural Resources and Environmental Studies Karatina University Karatina Kenya

Tactical decisions on natural resource management require accurate and up to date spatial information for sustainable forest management. Remote sensing devices by the use of multispectral data obtained from satellites or airborne sensors, allow substantial data acquisition that reduce cost of data collection and satisfy demands for continuous precise data. Forest height and Diameter at Breast Height (DBH) are crucial variables to predict volume and biomass. Traditional methods for estimation of tree heights and biomass are time consuming and labour intensive making it difficult for countries to carry out periodic National forest inventories to support forest management and REDD+ activities. This study assessed the applicability of LiDAR data in estimating tree height and biomass in a variety of forest conditions in Londiani Forest Block. The target forests were natural forest, plantation forests and other scattered forests analysed in a variety of topographic conditions. LiDAR data were collected by an aircraft flying at an elevation of 1550 m. The LIDAR pulses hitting the forest were used to estimate the forest height and the density of the vegetation, which implied biomass. LiDAR data were collected in 78 sampling plots of 15 m radius. The LiDAR data were ground truthed to compare its accuracy for above ground biomass (AGB) and height estimation. The correlation coefficients for heights between LiDAR and field data were 0.92 for the pooled data, 0.79 in natural forest, 0.95 in plantation forest and 0.92 in other scattered forest. AGB estimated from LiDAR and ground truthed data had a correlation coefficient of 0.86 for the pooled data, 0.78 in natural forest, 0.84 in plantation forest and 0.51 in other scattered forests. This implied 62%, 84% and 89% accuracy of AGB estimation in natural forests, other scattered forests and plantation forests respectively. The even aged conditions of plantation forests might have resulted to better estimates of height and AGB as c

关键词： LiDAR Height Biomass Relationship Correlation

THREE-IMAGE MATCHING FOR 3-D LINEAR OBJECT TRACKING

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Geo-Spatial information Science 2000年第2期3卷 13-18,40页

作者： SHAO Juliang Clive Fraser department of geomatics The University of Melboume Parkville Australla

This paper will discuss strategies for trinocular image rectification and matching for linear object *** is well known that a pair of stereo images generates two epipolar *** overlapped images can yield six epipolar images in situations where any two are required to be rectified for the purpose of image *** this case,the search for feature correspondences is computationally intensive and matching complexity increases.A special epipolar image rectification for three stereo images,which simplifies the image matching process,is therefore *** method generates only three rectified images,with the result that the search for matching features becomes more *** the three rectified images,a particular line_segment_based correspondence strategy is *** primary characteristics of the feature correspondence strategy include application of specific epipolar geometric constraints and reference to three_ray triangulation residuals in object space.

关键词： three-image matching 3-D linear object tracking

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Qualitative spatial reasoning on topological relations by combining the semantic web and constraint satisfaction

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Geo-Spatial information Science 2018年第2期21卷 80-92页

作者： Yandong Wang Mengling Qiao Hui Liu Xinyue Ye State Key Laboratory of information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina Collaborative Innovation Center of geospatial Technology Wuhan UniversityWuhanChina Wuhan Land Resource and Planning and information Center WuhanChina department of Geography Kent State UniversityKentOHUSA

Qualitative spatial reasoning on topological relations can extract hidden spatial knowledge from qualitatively described topological information,which is of significant importance for decisionmaking and query optimization in spatial *** reasoning on spatial topological information based on semantic knowledge and reasoning rules is an efficient means of reducing both the known relations and the corresponding rules,which can result in enhanced reasoning *** paper proposes a qualitative reasoning method for spatial topological relations based on the semantic description of reasoning rules and constraint *** with knowledge from the Semantic Web,the proposed method can easily extract potential spatial results consistent with both unique and non-unique *** Constraint-Satisfactionbased approach,describing constraint set with semantic expressions,is then used together with an improved path consistency algorithm to verify the consistency of the unique-rules-based and non-unique-rules-based reasoning *** verification can eliminate certain reasoning results to ensure the reliability of the final ***,the task of qualitative spatial reasoning on topological relations is completed.

关键词： Qualitative spatial reasoning spatial rules constraint satisfaction topological relations

Performance investigation of LAMBDA and bootstrapping methods for PPP narrow-lane ambiguity resolution

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Geo-Spatial information Science 2021年第4期24卷 604-614页

作者： Omer Faruk Atiz Sermet Ogutcu Salih Alcay Pan Li Ilkay Bugdayci department of geomatics Engineering Necmettin Erbakan UniversityKonyaTurkey department of Geodesy and Remote Sensing German Research Centre for GeosciencesPotsdamGermany

Precise point positioning with ambiguity resolution(PPP-AR)is a powerful tool for geodetic and time-constrained applications that require high *** performance of PPP-AR highly depends on the reliability of the correct integer carrier-phase ambiguity *** this study,the performance of narrow-lane ambiguity resolution of PPP using the Least-squares AMBiguity Decorrelation(LAMBDA)and bootstrapping methods is extensively investigated using real data from 55 IGS stations over one-month in *** PPP with 24-,12-,8-,4-,2-,1-and½-h sessions using two different cutoff angles(7°and 30°)was conducted with three PPP modes:***-float and two kinds of ambiguity-fixed PPP using the LAMBDA and bootstrapping methods for narrow-lane AR,*** results show that the LAMBDA method can produce more reliable results for 2 hour and shorter observation sessions com-pared with the bootstrapping method using a 7°cutoff *** a 30°cutoff angle,the LAMBDA method outperforms the bootstrapping method for observation sessions of 4 h and *** long observation times,the bootstrapping method produced much more accurate coordinates compared with the LAMBDA method without considering the wrong fixes *** results also show that occurrences of fixing the wrong integer ambiguities using the bootstrapping method are higher than that of the LAMBDA method.

关键词： Ambiguity resolution narrow-lane Precise point positioning(PPP) LAMBDA bootstrapping