XXII ISPRS Congress 2012: Technical Commission VIII

Shortis, M.; Shimoda, H.; Cho, K.

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Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663822514

Title:: Technical Commission VIII

Scope:: 590 Seiten

Year of publication:: 2014

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663822514

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B8)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Shortis, M.; Shimoda, H.; Cho, K.

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VIII/3: Atmosphere, Climate and Weather]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: OPTIMIZATION OF DECISION-MAKING FOR SPATIAL SAMPLING IN THE NORTH CHINA PLAIN, BASED ON REMOTE-SENSING A PRIORI KNOWLEDGE Jianzhong Feng, Linyan Bai, Shihong Liu, Xiaolu Su, Haiyan Hu

Document type:: Multivolume work

Structure type:: Chapter

A fundamental approach of quantitatively describing spatial correlation makes use of the global and local spatial autocorrelation statistics (Anselin, 1988) that present the spatial dependencies of regionalized proximity objects with their indicator parameter(s)/variable(s) (RIP(s)/RIV(s)) in geographic space, which leads to the corresponding spatial two order effects of the RIP(s)/RIV(s). The former measures an overall spatial dependency of objects in a geographic space and the latter probes into the relational heterogeneous characteristics (variations) of geographic objects. Since the RIP/RIV (ie, percentage of winter wheat planting area in each pixel), extracted from remote sensing images, of spatial sampling in this study was a variable, i.e., function of distances between spatial locations, the three statistics of Moran's I, Geary's C (Cliff and Ord, 1981) and General G (Getis and Ord,1992) were used to serve as available statistics of describing global/local spatial autocorrelation of the geographic RIP/RIV. Given that variability and direction of stability of spatial correlations cannot effectively be explored using global and local autocorrelation measures, semivariance function and semivariogram, being concerned with a technique of exploratory spatial data analysis, are often used to clarify spatial heterogeneous properties, and they can obviously show spatial structure characters of geographic RIP(s)/RIV(s) (i.e., dependencies of which are dominantly described) and are thus very important tools of geographic spatial analysis (Zhuang, 2005; Wang et al, 2005; Ma et al, 2007). Regionalized variation function analysis is implemented under the second order (pseudo-)stationary hypothesis and/or the (pseudo-) intrinsic hypothesis, which easily results in having the experimental semivariance function below: N(h) 3 YO) = ZEN (4) where Z(x,) and z(x +4) are values of variable z(x) at spatial locations x, and x +h , respectively; N(h) is number of pairs of locations separated by a vector 4 (also called lag distance). a theoretical semivariance y(k) can be fitted using a set of values of an experimental semivariance and the relative models such as a linear, spherical, exponential and/or Gaussian model and its associated semivariogram be obtained, a shape of which represents spatial structuring (or correlation properties ) of variable z(x). There are three key parameters of sill (C+C,), rang (a) and nugget (C,) with respect to a y(h) (semivariogram) that is generally a function of lag A. At a large separation distance (a) of h, the semivariogram reaches a plateau si// and the related values of z(x), separated by more than Ah, are considered spatially independent, i.e., uncorrelated. Those are greatly significant for spatial sampling plan (including sample size design and sample-point separated distance determination, etc.) and its optimization because of collecting non-redundant samples being separated with at least the range of correlation apart. A C, is connected with the discontinuity behaviour of a semivariogram near the origin. It reflects the continuity of z(x), which is related to either uncorrelated noise (measurement error) or to spatial structures at spatial scales smaller than the pixel size, and therefore, the pertinent nugget effects in this study were neglected. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 2.2 Spatial Sampling Design Optimization There is a general spatial-sampling paradigm for a regional space (or subspace) that in the first place we should analyze its spatial structure characteristics (ie, the correlation and variation of RIP(s)/RIV(s) of a sampling (sub-)population) as a priori knowledge/information of spatial sampling, and then decide a sample size, allocate sample-point locations, and estimate the total values of and means of and variances of sample and (sub-) population and so on, in order to obtain a optimal sampling solution. Using the RIP(s)/RIV(s) of remotely sensed satellite imagery to carry out gridded spatial sampling, we considered resolution- level based gridding cells as sampling population units. There is a basic principle to find out the unstationary of and influencing ranges of local spatial procedures for the RIP(s)/RIV(s) betaking the local spatial autocorrelation statistics (such as Moran'sI,. Geary's C, and G, or G; ) so as to explore the sample-point sizes (scales) of sampling space (or subspace). Hence, this could avoid the local spatial dependency (correlation) to a certain extent and (approximately) suit the independent principles of sampling objects in classical statistic sampling theory, and then offer a series of selective schemes of designing a minimum sample-point size of spatial sampling plan. Subsequently, we were able to obtain a relatively optimal design of sample-point size by comparing them. A global spatial autocorrelation measure is often founded under the spatial stationary condition, that is, the expectation and variance of the RIP(s)/RIV(s) of spatial objects in a geographic space are constant. Although the stationary of global spatial procedures does not exist in the real world (i.e., unstationary) and even more the hypothesis of spatial stationary is really very impossible, especially when the data of population being very huge (Ord and Getis, 1995; Anselin, 1995), the global spatial autocorrelation can approximatively display the spatial distribution of and trend characteristics of a whole population space (or subpopulation) with the RIP(s)/RIV(s). Thus the regionalized spatial structure characteristics are represented in terms of different perspectives of global spatial autocorrelation and regionalized spatial variation measures, respectively, which present the basis of application for sample-point allocation design of spatial sampling. It is essential for the Kriging methodology (Atkinson et al., 19993; Atkinson et al., 1999b; Journel, 1978) that an unknown value Z,(x,) of a continuous variable is unbiasedly estimated by the known values Z,(x,) (i=1,2,...,n) of a variable in object space using linear model, accuracy of which is determined by the variogram function of Kriging (Hou and Huang, 1990; Wang et al, 1990; Zhuang, 2005). Ordinary Kriging technique is an important type of the Kriging technology, a basic idea of which is to employ Kriging blocks (that is, values of local ranges) to estimate values of a bigger range. So, based on its principles and methods, a set of valid Kriging optimizing models and algorithms of regionalized spatial sampling is easily built (Li et al., 2004). 2.3 Adaptive Analysis and Decision Strategy In this study, using the RIP/RIV (fraction/percentage of winter wheat planting area in each pixel) of remote sensing images and analyzing its spatial structure characteristics (spatial correlation and variation) as a priori knowledge/information, the spatial sampling was performed, depending on the multi-stratified Interr model mei multiplied sampling : sample/por administrat township) helpful rele To estima! regionalize adaptive in algorithms 2010) may of spatial 1 2 X. i=l where 2 is the RIP subarea Z the proxin a unbiase error E[Z RIP/RIV’s draw out t the total obtained Lagrange able to ot technique. space anc RIP/RIV’ merited ar optimizati 3.1 Regi The Nor! being the whole pla 40°N, 11 380,000K and it ca piedmont one of tl From an municipa Hebei, ar in northe more thai al. 20 dominant of crops peanut, s

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