XXII ISPRS Congress 2012: Technical Commission VII

Bibliographic data

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:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

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

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

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:: [VII/4: METHODS FOR LAND COVER CLASSIFICATION]

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

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 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“ * Key Laboratory of Agri-information Service Technology, Ministry of Agriculture, Beijing, 100081, China - fengjzh4680@sina.com ? Center for Earth Observation and Digital Earth Chinese Academy of Sciences, Beijing, 100101, China - lybai@ceode.ac.cn * Institute of Agriculture Information, Chinese Academy of Agricultural Sciences, Beijing 100081, China — fengjzh0338@sina.com Commission VII, WG VII/4 KEY WORDS: RIP(s)/RIV(s), Sampling efficiency ABSTRACT: Agricultural Spatial Sampling, Remote Sensing, a Priori Knowledge, Spatial Structure Characteristics, In this paper, the MODIS remote sensing data, featured with low-cost, high-timely and moderate/low spatial resolutions, in the North China Plain (NCP) as a study region were firstly used to carry out mixed-pixel spectral decomposition to extract an useful regionalized indicator parameter (RIP) (i.e., an available ratio, that is, fraction/percentage, of winter wheat planting area in each pixel as a regionalized indicator variable (RIV) of spatial sampling) from the initial selected indicators. Then, the RIV values were spatially analyzed, and the spatial structure characteristics (i.e., spatial correlation and variation) of the NCP were achieved, which were further processed to obtain the scale- fitting, valid a priori knowledge or information of spatial sampling. Subsequently, founded upon an idea of rationally integrating probability-based and model-based sampling techniques and effectively utilizing the obtained a priori knowledge or information, the spatial sampling models and design schemes and their optimization and optimal selection were developed, as is a scientific basis of improving and optimizing the existing spatial sampling schemes of large-scale cropland remote sensing monitoring. Additionally, by the adaptive analysis and decision strategy the optimal local spatial prediction and gridded system of extrapolation results were able to excellently implement an adaptive report pattern of spatial sampling in accordance with report-covering units in order to satisfy the actual needs of sampling surveys. 1. INTRODUCTION For a long time, cropland area estimate using remote sensing technique is an important research topic of accurately estimating large-area crop yields by remote sensing approaches (Wang et al., 2008). It is very important for macro-economic decision-making departments of governments to timely know the related crop production and make scientific and sound decisions, etc. Spatial sampling technology is used to be able to effectively resolve this problem that a balance is realized between the cropland area accurate estimation and limited investigation budget (Li et al., 2004). It is essential for spatial sampling that the basic principles and methods of statistical sampling are applied to the regionalized attributes of geographical objects. Therefore, there are the probability spatial sampling techniques, based on traditional statistics, (such as simple random, systematic, stratified, and cluster spatial sampling) and model spatial sampling that are tied to spatial variability theory (Stevens Jr. and Olsen, 2004; Li et al., 2004; Dobbie and Henderson, 2008; Jia et al., 2008; Jiang et al., 2009). Due to the complexity of geographical features in a region, the traditional large-scale spatial sampling of resources or environmental investigation (especially on agricultural monitoring with remote sensing) are operated not well to use the a priori knowledge or information of spatial structure characteristics of some research region, but, relied on a certain sampling schema, * Corresponding author. more often to use common probability sampling models and methods, such that they cannot meet actual requirements of spatial sampling investigation because their sampling efficiencies are not very high and ranges of sampling application are not large (Jiao et al., 2002, 2006; Wu et al., 2004). In this paper, founded upon an idea of rationally integrating probability-based and model-based sampling techniques and properly using the relevant sampling models and methods, the valid spatial sampling design was developed in the North China Plain (NCP) as a study region, which is associated with remote sensing a priori knowledge or information, and the existing spatial sampling schemes were suggested to improve and optimize in better order to service the large-scale cropland remote sensing monitoring in the NCP. In short, this study can further effectively enhance the level of spatial sampling survey and decision-making analysis of the related departments of national and local governments. 2. METHODOLOGY 2.1 Models of a priori information acquisition Geographical spatial structure characteristics are represented with the spatially correlated and heterogeneous properties of regionalized geographical elements (of surface things or phenomena) (Feng, 2010).

Access restriction

Copyright

fullscreen: Technical Commission VII (B7)

Multivolume work

Volume

Chapter

Chapter

Table of contents

Cite and reuse

Volume

Chapter

Image

Image fragment

Citation links

Citation recommendation