13 research outputs found
Remote Sensing Data with the Conditional Latin Hypercube Sampling and Geostatistical Approach to Delineate Landscape Changes Induced by Large Chronological Physical Disturbances
This study applies variogram analyses of normalized difference vegetation index (NDVI) images derived from SPOT HRV images obtained before and after the ChiChi earthquake in the Chenyulan watershed, Taiwan, as well as images after four large typhoons, to delineate the spatial patterns, spatial structures and spatial variability of landscapes caused by these large disturbances. The conditional Latin hypercube sampling approach was applied to select samples from multiple NDVI images. Kriging and sequential Gaussian simulation with sufficient samples were then used to generate maps of NDVI images. The variography of NDVI image results demonstrate that spatial patterns of disturbed landscapes were successfully delineated by variogram analysis in study areas. The high-magnitude Chi-Chi earthquake created spatial landscape variations in the study area. After the earthquake, the cumulative impacts of typhoons on landscape patterns depended on the magnitudes and paths of typhoons, but were not always evident in the spatiotemporal variability of landscapes in the study area. The statistics and spatial structures of multiple NDVI images were captured by 3,000 samples from 62,500 grids in the NDVI images. Kriging and sequential Gaussian simulation with the 3,000 samples effectively reproduced spatial patterns of NDVI images. However, the proposed approach, which integrates the conditional Latin hypercube sampling approach, variogram, kriging and sequential Gaussian simulation in remotely sensed images, efficiently monitors, samples and maps the effects of large chronological disturbances on spatial characteristics of landscape changes including spatial variability and heterogeneity
Detecting the Land-Cover Changes Induced by Large-Physical Disturbances Using Landscape Metrics, Spatial Sampling, Simulation and Spatial Analysis
The objectives of the study are to integrate the conditional Latin Hypercube Sampling (cLHS), sequential Gaussian simulation (SGS) and spatial analysis in remotely sensed images, to monitor the effects of large chronological disturbances on spatial characteristics of landscape changes including spatial heterogeneity and variability. The multiple NDVI images demonstrate that spatial patterns of disturbed landscapes were successfully delineated by spatial analysis such as variogram, MoranâI and landscape metrics in the study area. The hybrid method delineates the spatial patterns and spatial variability of landscapes caused by these large disturbances. The cLHS approach is applied to select samples from Normalized Difference Vegetation Index (NDVI) images from SPOT HRV images in the Chenyulan watershed of Taiwan, and then SGS with sufficient samples is used to generate maps of NDVI images. In final, the NDVI simulated maps are verified using indexes such as the correlation coefficient and mean absolute error (MAE). Therefore, the statistics and spatial structures of multiple NDVI images present a very robust behavior, which advocates the use of the index for the quantification of the landscape spatial patterns and land cover change. In addition, the results transferred by Open Geospatial techniques can be accessed from web-based and end-user applications of the watershed management
Evaluating and Mapping of Spatial Air Ion Quality Patterns in a Residential Garden Using a Geostatistic Method
Negative air ions (NAI) produce biochemical reactions that increase the levels of the mood chemical serotonin in the environment. Moreover, they benefit both the psychological well being and the human bodyâs physiological condition. The aim of this research was to estimate and measure the spatial distributions of negative and positive air ions in a residential garden in central Taiwan. Negative and positive air ions were measured at thirty monitoring locations in the study garden from July 2009 to June 2010. Moreover, Kriging was applied to estimate the spatial distribution of negative and positive air ions, as well as the air ion index in the study area. The measurement results showed that the numbers of NAI and PAI differed greatly during the four seasons, the highest and the lowest negative and positive air ion concentrations were found in the summer and winter, respectively. Moreover, temperature was positively affected negative air ions concentration. No matter what temperature is, the ranges of variogram in NAI/PAI were similar during four seasons. It indicated that spatial patterns of NAI/PAI were independent of the seasons and depended on garden elements and configuration, thus the NAP/PAI was a good estimate of the air quality regarding air ions. Kriging maps depicted that the highest negative and positive air ion concentration was next to the waterfall, whereas the lowest air ions areas were next to the exits of the garden. The results reveal that waterscapes are a source of negative and positive air ions, and that plants and green space are a minor source of negative air ions in the study garden. Moreover, temperature and humidity are positively and negatively affected negative air ions concentration, respectively. The proposed monitoring and mapping approach provides a way to effectively assess the patterns of negative and positive air ions in future landscape design projects
Training and validation dataset optimization for Earth observation classification accuracy improvement
DiplomovĂĄ prĂĄce se zabĂœvĂĄ optimalizacĂ trĂ©novacĂho a validaÄnĂho datasetu pro ĆĂzenou klasifikaci dat v DPZ. V rĂĄmci ĆeĆĄenĂ prĂĄce jsou v ĂșzemĂ lesnÄ-luÄnĂ krajiny v PodkrkonoĆĄĂ provĂĄdÄny pro dva klasifikaÄnĂ algoritmy (Maximum Likelihood - MLC a Support Vector Machine - SVM) experimenty s trĂ©novacĂmi a validaÄnĂmi daty. PrĂĄce vychĂĄzĂ z pĆedpokladu, ĆŸe pro dosaĆŸenĂ maximĂĄlnĂ pĆesnosti klasifikace je ideĂĄlnĂ podĂl 1/3 trĂ©novacĂch a 2/3 validaÄnĂch dat (Foody, 2009). DalĆĄĂ hypotĂ©zou prĂĄce byl pĆedpoklad, ĆŸe v pĆĂpadÄ klasifikace pomocĂ algoritmu SVM je pro dosaĆŸenĂ stejnĂ©/podobnĂ© pĆesnosti klasifikace potĆeba niĆŸĆĄĂ poÄet trĂ©novacĂch bodĆŻ neĆŸ v pĆĂpadÄ klasifikaÄnĂho algoritmu Maximum Likelihood (Foody, 2004). CĂlem prĂĄce bylo testovat vliv podĂlu/mnoĆŸstvĂ trĂ©novacĂch a validaÄnĂch dat na pĆesnost klasifikace multispektrĂĄlnĂch dat senzoru Sentinel-2A s vyuĆŸitĂm algoritmu Maximum Likelihood. NejvyĆĄĆĄĂ celkovĂ© pĆesnosti pĆi vyuĆŸitĂ klasifikaÄnĂho algoritmu Maximum Likelihood bylo dosaĆŸeno pro podĂl 375 trĂ©novacĂch a 625 validaÄnĂch bodĆŻ. CelkovĂĄ pĆesnost pro tento podĂl byla 72,88 %. Teorie Foodyho (2009), ĆŸe pro dosaĆŸenĂ nejvyĆĄĆĄĂ pĆesnosti klasifikace je ideĂĄlnĂ podĂl 1/3 trĂ©novacĂch a 2/3 validaÄnĂch dat potvrzujĂ vĂœsledky hodnocenĂ celkovĂ© pĆesnosti a Kappa koeficientu pro Maximum Likelihood. AvĆĄak...This thesis deals with training dataset and validation dataset for Earth observation classification accuracy improvement. Experiments with training data and validation data for two classification algorithms (Maximum Likelihood - MLC and Support Vector Machine - SVM) are carried out from the forest-meadow landscape located in the foothill of the Giant Mountains (PodkrkonoĆĄĂ). The thesis is base on the assumption that 1/3 of training data and 2/3 of validation data is an ideal ratio to achieve maximal classification accuracy (Foody, 2009). Another hypothesis was that in a case of SVM classification, a lower number of training point is required to achieve the same or similar accuracy of classification, as in the case of the MLC algorithm (Foody, 2004). The main goal of the thesis was to test the influence of proportion / amount of training and validation data on the classification accuracy of Sentinel - 2A multispectral data using the MLC algorithm. The highest overal accuracy using the MLC classification algorithm was achieved for 375 training and 625 validation points. The overal accuracy for this ratio was 72,88 %. The theory of Foody (2009) that 1/3 of training data and 2/3 of validation data is an ideal ratio to achieve the highest classification accuracy, was confirmed by the overal accuracy and...Katedra aplikovanĂ© geoinformatiky a kartografieDepartment of Applied Geoinformatics and CartographyPĆĂrodovÄdeckĂĄ fakultaFaculty of Scienc
Geomorphometry 2020. Conference Proceedings
Geomorphometry is the science of quantitative land surface analysis. It gathers various mathematical, statistical and image processing techniques to quantify morphological, hydrological, ecological and other aspects of a land surface. Common synonyms for geomorphometry are geomorphological analysis, terrain morphometry or terrain analysis and land surface analysis. The typical input to geomorphometric analysis is a square-grid representation of the land surface: a digital elevation (or land surface) model.
The first Geomorphometry conference dates back to 2009 and it took place in ZĂŒrich, Switzerland. Subsequent events were in Redlands (California), NĂĄnjÄ«ng (China), Poznan (Poland) and Boulder (Colorado), at about two years intervals. The International Society for Geomorphometry (ISG) and the Organizing Committee scheduled the sixth Geomorphometry conference in Perugia, Italy, June 2020. Worldwide safety measures dictated the event could not be held in presence, and we excluded the possibility to hold the conference remotely. Thus, we postponed the event by one year - it will be organized in June 2021, in Perugia, hosted by the Research Institute for Geo-Hydrological Protection of the Italian National Research Council (CNR IRPI) and the Department of Physics and Geology of the University of Perugia.
One of the reasons why we postponed the conference, instead of canceling, was the encouraging number of submitted abstracts. Abstracts are actually short papers consisting of four pages, including figures and references, and they were peer-reviewed by the Scientific Committee of the conference. This book is a collection of the contributions revised by the authors after peer review. We grouped them in seven classes, as follows:
âą Data and methods (13 abstracts)
âą Geoheritage (6 abstracts)
âą Glacial processes (4 abstracts)
âą LIDAR and high resolution data (8 abstracts)
âą Morphotectonics (8 abstracts)
âą Natural hazards (12 abstracts)
âą Soil erosion and fluvial processes (16 abstracts)
The 67 abstracts represent 80% of the initial contributions. The remaining ones were either not accepted after peer review or withdrawn by their Authors. Most of the contributions contain original material, and an extended version of a subset of them will be included in a special issue of a regular journal publication
Handbook of Mathematical Geosciences
This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences