Monitoring and identification of spatiotemporal landscape changes in multiple remote sensing images by using a stratified conditional Latin hypercube sampling approach and geostatistical simulation

Lin, Yu‐Pin; Chu, Hsin; Huang, Yu Long; Tang, Chia Hsi; Rouhani, Shahrokh

doi:10.1007/s10661-010-1639-5

Cited by 15 publications

(8 citation statements)

References 29 publications

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“…For mapping of non‐stationary target variables, some efforts have been made in sampling design to improve the precision. Lin et al () combined the cLHS method and variance quad‐tree to sample geographical and feature space at the same time. Similarly Simbahan and Dobermann () proposed to firstly stratify using ancillary data and then combine MMSD+WM (Warrick‐Myers criterion) optimization to draw a sample for variogram estimation and interpolation in one time.…”

Section: Related Workmentioning

confidence: 99%

A Spatial Conditioned Latin Hypercube Sampling Method for Mapping Using Ancillary Data

Gao

Pan

Chen

et al. 2016

Transactions in GIS

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For obtaining maps of good precision by the spatial inference method, the distribution of sampling sites in geographical and feature space is very important. For a regional variable with trends, the predicting error comes from trend estimation, variogram estimation and spatial interpolation. Based on the cLHS (conditioned Latin hypercube Sampling) method, a sampling method called scLHS (spatial cLHS) considering all these three aspects with the help of ancillary data is proposed in this article. Its advantage lies in simultaneously improving trend estimation, variogram estimation and spatial interpolation. MODIS data and simulated data were used as sampling fields to draw sample sets using scLHS, cLHS, cLHS with x and y coordinates as covariates, simple random and spatial even sampling methods, and the distribution and prediction errors of sample sets from different methods were evaluated. The results showed that scLHS performed well in balancing spreading in geographic and feature space, and can generate points pairs with small distances, and the sample sets drawn by scLHS produced smaller mapping error, especially when there were trends in the target variable.

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Section: Related Workmentioning

confidence: 99%

A Spatial Conditioned Latin Hypercube Sampling Method for Mapping Using Ancillary Data

Gao

Pan

Chen

et al. 2016

Transactions in GIS

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“…The availability of multi-source remote sensing data makes it imperative to combine information from different sources to produce land cover maps [38][39][40][41]. However, multisource images generally have different band compositions and different land cover ranges.…”

Section: Voting Scenariomentioning

confidence: 99%

Improving Seasonal Land Cover Maps of Poyang Lake Area in China by Taking into Account Logical Transitions

Guang

Fang

Dian

et al. 2016

IJGI

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Land cover maps are fundamental materials for resource management and change detection. Remote sensing technology is crucial for fast mapping with low cost. However, besides the inherent classification errors in the land cover products, numerous illogical transitions exist between the neighboring time points. In this study, we introduce a series of logical codes for all the land cover types according to the ecological rules in the study area. The codes represent the transformational logicality of species between different seasons. The classification performance and the codes for all the seasons are imposed on the initial land cover maps which have been produced independently by the conventional hierarchical strategy. We exploit the proposed modified hierarchical mapping strategy to map the land cover of Poyang Lake Basin area, Middle China. The illogical transitions between neighboring seasons and the accuracies based on the labeled samples are calculated for both the initial and modified strategies. The number of illogical pixels have been reduced by 13%-35% for different seasons and the average accuracy has been improved by 9.7% for the specific land cover maps. The accuracy of land cover changes has also presented great improvement of the proposed strategy. The experimental results have suggested the scheme is effective.

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“…Geographically stratified simple random sampling can divide the study area into various strata and then draw samples from each stratum. Lin et al (2011) sampled the geographical and feature spaces by combining the CLHS method and variance quad-tree. B.…”

mentioning

confidence: 99%

A soil sampling design for arable land quality observation by using SPCOSA–CLHS hybrid approach

et al. 2021

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Arable land quality has been evaluated through weighted average of indicators related to soil properties and tillage technics to present the most basic function of cropland: food production potential. A hybrid sampling method spatial coverage sampling and random sampling-conditioned Latin hypercube sampling (SPCOSA-CLHS) was designed for arable land quality observation network in this paper. The SPCOSA-CLHS integrates the uniform spatial partitioning results generated by the spatial coverage sampling and random sampling (SPCOSA) into the conditioned Latin hypercube sampling (CLHS) method along with other arable land quality indicators such as field slope, soil bulk density, organic matter content, thickness of plough layer and irrigation. Then, SPCOSA, CLHS, SPCOSA-CLHS, CLHS with x and y coordinates as covariates (XY-CLHS), random sampling method (RSM) were compared using the example of Heilongjiang Province. The sample population covers 12,147,008 grid cells and 17 arable land quality indicators. Five parameters: information entropy, Kullback-Leibler divergence, similarity distance, expression ability to local spatial heterogeneity of arable land quality and distribution homogeneity of sampling results were used to compare the applicability of these methods for overall arable land quality. The SPCOSA-CLHS can better trade off sampling result's representative ability to the population and spatial heterogeneity of arable land quality, as well as its samples have advantages of spatially uniform distribution. When the sample size is between 5000 and 20,000, all methods show good applicability. When the sample size is below 5000, however, the differences among these methods become significant. SPCOSA and random sampling method offset most dramatically. Based on this detailed comparison of the five sampling strategy approaches, we strongly recommend to use SPCOSA-CLHS to design arable land quality observation.

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Monitoring and identification of spatiotemporal landscape changes in multiple remote sensing images by using a stratified conditional Latin hypercube sampling approach and geostatistical simulation

Cited by 15 publications

References 29 publications

A Spatial Conditioned Latin Hypercube Sampling Method for Mapping Using Ancillary Data

A Spatial Conditioned Latin Hypercube Sampling Method for Mapping Using Ancillary Data

Improving Seasonal Land Cover Maps of Poyang Lake Area in China by Taking into Account Logical Transitions

A soil sampling design for arable land quality observation by using SPCOSA–CLHS hybrid approach

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