Spatial Autocorrelation and Uncertainty Associated with Remotely-Sensed Data

Griffith, Daniel A.; Chun, Yongwan

doi:10.3390/rs8070535

Cited by 39 publications

(27 citation statements)

References 15 publications

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“…Spatial autocorrelation in the reference data has long been known to affect the classification and accuracy assessment [39,40,59,60]. Different sampling strategies for data splitting have already been studied in the literature including spatial [42,55,61,62] and aspatial approaches [61,[63][64][65].…”

Section: Effect Of Spatial Autocorrelation: the Sloo-cv Strategy As Amentioning

confidence: 99%

Statistical Stability and Spatial Instability in Mapping Forest Tree Species by Comparing 9 Years of Satellite Image Time Series

et al. 2019

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Mapping forest composition using multiseasonal optical time series remains a challenge. Highly contrasted results are reported from one study to another suggesting that drivers of classification errors are still under-explored. We evaluated the performances of single-year Formosat-2 time series to discriminate tree species in temperate forests in France and investigated how predictions vary statistically and spatially across multiple years. Our objective was to better estimate the impact of spatial autocorrelation in the validation data on measurement accuracy and to understand which drivers in the time series are responsible for classification errors. The experiments were based on 10 Formosat-2 image time series irregularly acquired during the seasonal vegetation cycle from 2006 to 2014. Due to lot of clouds in the year 2006, an alternative 2006 time series using only cloud-free images has been added. Thirteen tree species were classified in each single-year dataset based on the Support Vector Machine (SVM) algorithm. The performances were assessed using a spatial leave-one-out cross validation (SLOO-CV) strategy, thereby guaranteeing full independence of the validation samples, and compared with standard non-spatial leave-one-out cross-validation (LOO-CV). The results show relatively close statistical performances from one year to the next despite the differences between the annual time series. Good agreements between years were observed in monospecific tree plantations of broadleaf species versus high disparity in other forests composed of different species. A strong positive bias in the accuracy assessment (up to 0.4 of Overall Accuracy (OA)) was also found when spatial dependence in the validation data was not removed. Using the SLOO-CV approach, the average OA values per year ranged from 0.48 for 2006 to 0.60 for 2013, which satisfactorily represents the spatial instability of species prediction between years.

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Section: Effect Of Spatial Autocorrelation: the Sloo-cv Strategy As Amentioning

confidence: 99%

Statistical Stability and Spatial Instability in Mapping Forest Tree Species by Comparing 9 Years of Satellite Image Time Series

et al. 2019

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“…However, the amount of uncertainty in existing LUC mapping information significantly impairs the reliability of classification products [5]. Researchers developing methods of measuring the uncertainty in LUC data focused on quantifying the uncertainty in remote sensing images, determining the classification uncertainty, and assessing the accuracy of LUC products [6][7][8][9][10][11][12]. For example, Griffith and Chun [7] studied the uncertainty in spatial autocorrelation parameters in a spatial autoregressive model associated with remotely sensed images.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Assessment in Multitemporal Land Use/Cover Mapping with Classification System Semantic Heterogeneity

Zhang

Shi

Lv³

2019

Remote Sensing

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Land use/cover (LUC) data are commonly relied on to provide land surface information in a variety of applications. However, the exchange and joint use of LUC information from different datasets can be challenging due to semantic differences between common classification systems (CSs). In this paper, we propose an uncertainty assessment schema to capture the semantic translation uncertainty between heterogeneous LUC CSs and evaluate the data label uncertainty of multitemporal LUC mapping results caused by uncertainty propagation. The semantic translation uncertainty between CSs is investigated using a dynamic semantic reference system (DSRS) model and semantic similarity analysis. An object-based unsupervised change detection algorithm is adopted to determine the probability of changes in land patches, and novel uncertainty metrics are proposed to estimate the patch label uncertainty in LUC maps. The proposed uncertainty assessment schema was validated via experiments on four LUC datasets, and the results confirmed that semantic uncertainty had great impact on data reliability and that the uncertainty metrics could be used in the development of uncertainty controls in multitemporal LUC mapping by referring to uncertainty assessment results. We anticipate our findings will be used to improve the applicability and interoperability of LUC data products.

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“…Spatial autocorrelation prevails in virtually all georeferenced data [24], and it is widely used in Remote Sensing for information extraction [25][26][27], for landslide susceptibility [28] and hazard modeling [29]. The method to measure changes of spatial autocorrelation proposed in this work is based on the analysis of the temporal evolution of the Moran's I index [23], and semivariance measure [25,27] applied to the Log-Ratio (LR) layers obtained coupling consecutive SAR intensity images.…”

Section: Introductionmentioning

confidence: 99%

Measures of Spatial Autocorrelation Changes in Multitemporal SAR Images for Event Landslides Detection

Mondini

2017

Remote Sensing

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Landslides cause damages and affect victims worldwide, but landslide information is lacking. Even large events may not leave records when they happen in remote areas or simply do not impact with vulnerable elements. This paper proposes a procedure to measure spatial autocorrelation changes induced by event landslides in a multi-temporal series of synthetic aperture radar (SAR) intensity Sentinel-1 images. The procedure first measures pixel-based changes between consecutive couples of SAR intensity images using the Log-Ratio index, then it follows the temporal evolution of the spatial autocorrelation inside the Log-Ratio layers using the Moran's I index and the semivariance. When an event occurs, the Moran's I index and the semivariance increase compared to the values measured before and after the event. The spatial autocorrelation growth is due to the local homogenization of the soil response caused by the event landslide. The emerging clusters of autocorrelated pixels generated by the event are localized by a process of optimal segmentation of the log-ratio layers. The procedure was used to intercept an event that occurred in August 2015 in Myanmar, Tozang area, when strong rainfall precipitations triggered a number of landslides. A prognostic use of the method promises to increase the availability of information about the number of events at the regional scale, and to facilitate the production of inventory maps, yielding useful results to study the phenomenon for model tuning, landslide forecast model validation, and the relationship between triggering factors and number of occurred events.

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Spatial Autocorrelation and Uncertainty Associated with Remotely-Sensed Data

Cited by 39 publications

References 15 publications

Statistical Stability and Spatial Instability in Mapping Forest Tree Species by Comparing 9 Years of Satellite Image Time Series

Statistical Stability and Spatial Instability in Mapping Forest Tree Species by Comparing 9 Years of Satellite Image Time Series

Uncertainty Assessment in Multitemporal Land Use/Cover Mapping with Classification System Semantic Heterogeneity

Measures of Spatial Autocorrelation Changes in Multitemporal SAR Images for Event Landslides Detection

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