Assessing the Accuracy of Remotely Sensed Data

Congalton, Russell G.; Green, Kass

doi:10.1201/9781420055139

Cited by 1,830 publications

(1,792 citation statements)

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“…The 165 statistical criteria were based on the confusion matrix created from the comparison 166 between the classification and the verification data. Generated from this confusion 167 matrix, the percentage of Pixels Correctly Classified (PCC) and the kappa coefficient of 168 agreement (κ) (Congalton, 1991) were analysed to assess the discrimination of classes. 169…”

Section: Classification Process 126mentioning

confidence: 99%

Land cover mapping of wetland areas in an agricultural landscape using SAR and Landsat imagery

Álamo

Ducrot

2009

Journal of Environmental Management

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9Saline wetlands in the Monegros Desert, NE Spain, are situated in an 10 agricultural landscape which is undergoing significant changes. Agricultural 11 intensification in recent decades and current installation of new irrigation systems 12 threaten these valuable habitats, set to be included in the Natura2000 network. Their 13 preservation and successful management depend on the information available regarding 14 the transformation of surrounding areas. When soil and vegetation maps at adequate 15 scale are not available, remote sensing is an alternative means to obtain needed data. We 16 have used SAR data, taking advantage of the soil surface characteristics perceived in 17 SAR images. The objective of this work is to explore the capability of multitemporal 18 SAR data to characterize the land covers of these wetlands and their environment. We 19 have developed specific contextual classifications which take into account the statistical 20 properties of the radar distribution. Moreover, we tested the contribution of radar in 21Landsat classification. 22 2

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Section: Classification Process 126mentioning

confidence: 99%

Land cover mapping of wetland areas in an agricultural landscape using SAR and Landsat imagery

Álamo

Ducrot

2009

Journal of Environmental Management

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“…The Kappa coefficient is used to attest the quality of predictive mappings (Giasson et al, 2011). The error matrix for calculation of the Kappa coefficient was established according to Congalton (1991).…”

Section: Quality Of Models and Mapsmentioning

confidence: 99%

Digital soil mapping: strategy for data pre-processing

Caten

Dalmolin

Ruiz

2012

Rev. Bras. Ciênc. Solo

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SUMMARYThe region of greatest variability on soil maps is along the edge of their polygons, causing disagreement among pedologists about the appropriate description of soil classes at these locations. The objective of this work was to propose a strategy for data pre-processing applied to digital soil mapping (DSM). Soil polygons on a training map were shrunk by 100 and 160 m. This strategy prevented the use of covariates located near the edge of the soil classes for the Decision Tree (DT) models. Three DT models derived from eight predictive covariates, related to relief and organism factors sampled on the original polygons of a soil map and on polygons shrunk by 100 and 160 m were used to predict soil classes. The DT model derived from observations 160 m away from the edge of the polygons on the original map is less complex and has a better predictive performance.

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“…The model output of these algorithms can be seen as thematic maps with the two discrete classes PRA and NoPRA. Hence, for validation an error matrix (Figure 2) is calculated as it is known for the accuracy assessment of thematic mapping in remote sensing (Congalton and Green, 1999). To produce an error matrix, a reference dataset is needed.…”

Section: Validation Methodsmentioning

confidence: 99%

Automated snow avalanche release area delineation – validation of existing algorithms and proposition of a new object-based approach for large scale hazard indication mapping

Bühler¹,

Rickenbach²,

Stoffel³

et al. 2018

Preprint

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Abstract. Snow avalanche hazard is threatening people and infrastructure in all alpine regions with seasonal or permanent snow cover around the globe. Coping with this hazard is a big challenge and during the past centuries, different strategies were 10 developed. Today, in Switzerland, experienced avalanche engineers produce hazard maps with a very high reliability based on avalanche cadastre information, terrain analysis, climatological datasets and numerical modelling of the flow dynamics for selected avalanche tracks that might affect settlements. However, for regions outside the considered settlement areas such areawide hazard maps are not available mainly because of the too high cost, in Switzerland and in most mountain regions around the world. Therefore, hazard indication maps, even though they are less reliable and less detailed, are often the only spatial 15 planning tool available. To produce meaningful and cost-effective avalanche hazard indication maps over large regions (regional to national scale), automated release area delineation has to be combined with volume estimations and state-of-theart numerical avalanche simulations.In this paper we validate existing potential release area (PRA) delineation algorithms, published in peer-reviewed journals, 20 that are based on digital terrain models and their derivatives such as slope angle, aspect, roughness and curvature. For validation, we apply avalanche cadastre data from three different ski resorts in the vicinity of Davos, Switzerland, where experienced ski-patrol staff mapped most avalanches in detail since many years. After calculating the best fit input parameters for every tested algorithm, we compare their performance based on the reference datasets. Because all tested algorithms do not provide meaningful delineation between individual potential release areas (PRA), we propose a new algorithm based on object-25 based image analysis (OBIA). In combination with an automatic procedure to estimate the average release depth (d0), defining the avalanche release volume, this algorithm enables the numerical simulation of thousands of avalanches over large regions applying the well-established avalanche dynamics model RAMMS. We demonstrate this for the region of Davos for two hazard scenarios, frequent (10 -30 years return period) and extreme (100 -300 years return period). This approach opens the door for large scale avalanche hazard indication mapping in all regions where high quality and resolution digital terrain models and 30 snow data are available.Nat. Hazards Earth Syst. Sci. Discuss., https://doi

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Assessing the Accuracy of Remotely Sensed Data

Cited by 1,830 publications

References 0 publications

Land cover mapping of wetland areas in an agricultural landscape using SAR and Landsat imagery

Land cover mapping of wetland areas in an agricultural landscape using SAR and Landsat imagery

Digital soil mapping: strategy for data pre-processing

Automated snow avalanche release area delineation – validation of existing algorithms and proposition of a new object-based approach for large scale hazard indication mapping

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