scholarly journals An Integrated Use of Topography with RSI in Gully Mapping, Shandong Peninsula, China

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Fuhong He ◽  
Tao Wang ◽  
Lijuan Gu ◽  
Tao Li ◽  
Weiguo Jiang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Accuracy Assessment ◽  
Color Space ◽  
Study Data ◽  
Kappa Coefficient ◽  
New Method ◽  
Positional Accuracy ◽  
Error Matrix ◽  
Remote Sensing Imagery ◽  
Fused Image

Taking the Quickbird optical satellite imagery of the small watershed of Beiyanzigou valley of Qixia city, Shandong province, as the study data, we proposed a new method by using a fused image of topography with remote sensing imagery (RSI) to achieve a high precision interpretation of gully edge lines. The technique first transformed remote sensing imagery into HSV color space from RGB color space. Then the slope threshold values of gully edge line and gully thalweg were gained through field survey and the slope data were segmented using thresholding, respectively. Based on the fused image in combination with gully thalweg thresholding vectors, the gully thalweg thresholding vectors were amended. Lastly, the gully edge line might be interpreted based on the amended gully thalweg vectors, fused image, gully edge line thresholding vectors, and slope data. A testing region was selected in the study area to assess the accuracy. Then accuracy assessment of the gully information interpreted by both interpreting remote sensing imagery only and the fused image was performed using the deviation, kappa coefficient, and overall accuracy of error matrix. Compared with interpreting remote sensing imagery only, the overall accuracy and kappa coefficient are increased by 24.080% and 264.364%, respectively. The average deviations of gully head and gully edge line are reduced by 60.448% and 67.406%, respectively. The test results show the thematic and the positional accuracy of gully interpreted by new method are significantly higher. Finally, the error sources for interpretation accuracy by the two methods were analyzed.

scholarly journals Trends in Remote Sensing Accuracy Assessment Approaches in the Context of Natural Resources

2019 ◽  
Vol 11 (19) ◽  
pp. 2305 ◽  
Author(s):  
Lucia Morales-Barquero ◽  
Mitchell Lyons ◽  
Stuart Phinn ◽  
Chris Roelfsema
Keyword(s):  
Remote Sensing ◽  
Natural Resources ◽  
Sampling Design ◽  
Accuracy Assessment ◽  
Assessment Practices ◽  
Screening Process ◽  
Error Matrix ◽  
Remote Sensing Classification ◽  
Considerable Work ◽  
Land Cover Maps

The utility of land cover maps for natural resources management relies on knowing the uncertainty associated with each map. The continuous advances typical of remote sensing, including the increasing availability of higher spatial and temporal resolution satellite data and data analysis capabilities, have created both opportunities and challenges for improving the application of accuracy assessment. There are well established accuracy assessment methods, but their underlying assumptions have not changed much in the last couple decades. Consequently, revisiting how map error and accuracy have been performed and reported over the last two decades is timely, to highlight areas where there is scope for better utilization of emerging opportunities. We conducted a quantitative literature review on accuracy assessment practices for mapping via remote sensing classification methods, in both terrestrial and marine environments. We performed a structured search for land and benthic cover mapping, limiting our search to journals within the remote sensing field, and papers published between 1998–2017. After an initial screening process, we assembled a database of 282 papers, and extracted and standardized information on various components of their reported accuracy assessments. We discovered that only 56% of the papers explicitly included an error matrix, and a very limited number (14%) reported overall accuracy with confidence intervals. The use of kappa continues to be standard practice, being reported in 50.4% of the literature published on or after 2012. Reference datasets used for validation were collected using a probability sampling design in 54% of the papers. For approximately 11% of the studies, the sampling design used could not be determined. No association was found between classification complexity (i.e. number of classes) and measured accuracy, independent from the size of the study area. Overall, only 32% of papers included an accuracy assessment that could be considered reproducible; that is, they included a probability-based sampling scheme to collect the reference dataset, a complete error matrix, and provided sufficient characterization of the reference datasets and sampling unit. Our findings indicate that considerable work remains to identify and adopt more statistically rigorous accuracy assessment practices to achieve transparent and comparable land and benthic cover maps.

scholarly journals A COMPARATIVE ANALYSIS OF FIVE CROPLAND DATASETS IN AFRICA

2018 ◽  
Vol XLII-3 ◽  
pp. 1863-1870 ◽  
Author(s):  
Y. Wei ◽  
M. Lu ◽  
W. Wu
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Food Security ◽  
Comparative Analysis ◽  
Land Cover ◽  
Spatial Location ◽  
Error Matrix ◽  
Location Accuracy ◽  
Remote Sensing Imagery ◽  
Cropland Area

The food security, particularly in Africa, is a challenge to be resolved. The cropland area and spatial distribution obtained from remote sensing imagery are vital information. In this paper, according to cropland area and spatial location, we compare five global cropland datasets including CCI Land Cover, GlobCover, MODIS Collection 5, GlobeLand30 and Unified Cropland in circa 2010 of Africa in terms of cropland area and spatial location. The accuracy of cropland area calculated from five datasets was analyzed compared with statistic data. Based on validation samples, the accuracies of spatial location for the five cropland products were assessed by error matrix. The results show that GlobeLand30 has the best fitness with the statistics, followed by MODIS Collection 5 and Unified Cropland, GlobCover and CCI Land Cover have the lower accuracies. For the accuracy of spatial location of cropland, GlobeLand30 reaches the highest accuracy, followed by Unified Cropland, MODIS Collection 5 and GlobCover, CCI Land Cover has the lowest accuracy. The spatial location accuracy of five datasets in the Csa with suitable farming condition is generally higher than in the Bsk.

scholarly journals Land Use and Land Cover Mapping of Davangere using Google Earth Engine

2019 ◽  
Vol 8 (3) ◽  
pp. 474-477
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Environmental Changes ◽  
Satellite Image ◽  
Google Earth ◽  
Kappa Coefficient ◽  
Error Matrix ◽  
Sensing Applications ◽  
Google Earth Engine

Ever since the advent of modern geo information systems, tracking environmental changes due to natural and/or manmade causes with the aid of remote sensing applications has been an indispensable tool in numerous fields of geography, most of the earth science disciplines, defence, intelligence, commerce, economics and administrative planning. One among these applications is the construction of land use and land cover maps through image classification process. Land Use / Land Cover (LULC) information is a crucial input in designing efficient strategies for managing natural resources and monitoring environmental changes from time to time. The present study aims to know the extent of land cover and its usage in Davangere region of Karnataka, India. In this study, satellite image of Davangere during October-November 2018 was used for LULC supervised classification with the help of remote sensing tools like QGIS and Google Earth Engine. Six LULC classes were decided to locate on the map and the accuracy assessment was done using theoretical error matrix and Kappa coefficient. The key findings include LULC under Water bodies (8%), Built up Area (15.1%), Vegetation (9%), Horticulture (20.8%), Agriculture (39.3%) and Others (7%) with overall accuracy of 94.8% and Kappa coefficient of 0.866 indicating almost accurate goodness of classification

scholarly journals Coastal Zone Classification Based on Multisource Remote Sensing Imagery Fusion

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Jiahui Li ◽  
Youxin Zhao ◽  
Jiguang Dai ◽  
Hong Zhu
Keyword(s):  
Remote Sensing ◽  
Coastal Zone ◽  
Kappa Coefficient ◽  
Observation System ◽  
High Temporal Resolution ◽  
Fusion Method ◽  
Remote Sensing Imagery ◽  
Multispectral Data ◽  
Earth Observation System ◽  
L Band

The main objective of this paper was to assess the capability of multisource remote sensing imagery fusion for coastal zone classification. Five scenes of Gaofen- (GF-) 1 optic imagery and four scenes of synthetic aperture radar (SAR) (C-band Sentinel-1 and L-band ALOS-2) imagery were collected and matched. Note that GF-1 is the first satellite of the China high-resolution earth observation system, which acquires multispectral data with decametric spatial resolution, high temporal resolution, and wide coverage. The results showed that based on the comparison of C- and L-band SAR for coastal coverage, it is verified that C band is superior to L band and parameter subsets of σvv0, σvh0, and Dcross can be effectively used for coastal classification. A new fusion method based on the wavelet transform (WT) was also proposed and used for imagery fusion. Statistical values for the mean, entropy, gradient, and correlation coefficient of the proposed method were 67.526, 7.321, 6.440, and 0.955, respectively. We therefore conclude that the result of our proposed method is superior to GF-1 imagery and traditional HIS fusion results. Finally, the classification output was determined along with an assessment of classification accuracy and kappa coefficient. The kappa coefficient and overall accuracy of the classification were 0.8236 and 85.9774%, respectively, so the proposed fusion method had a satisfying performance for coastal coverage mapping.

scholarly journals Mapping vegetation communities of the Karkonosze National Park using APEX hyperspectral data and Support Vector Machines

2014 ◽  
Vol 18 (2) ◽  
pp. 23-29 ◽  
Author(s):  
Adriana Marcinkowska ◽  
Bogdan Zagajewski ◽  
Adrian Ochtyra ◽  
Anna Jarocińska ◽  
Edwin Raczko ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Support Vector Machines ◽  
National Park ◽  
Kappa Coefficient ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Error Matrix ◽  
Vegetation Communities ◽  
Vector Machines

Abstract This research aims to discover the potential of hyperspectral remote sensing data for mapping mountain vegetation ecosystems. First, the importance of mountain ecosystems to the global system should be stressed due to mountainous ecosystems forming a very sensitive indicator of global climate change. Furthermore, a variety of biotic and abiotic factors influence the spatial distribution of vegetation in the mountains, producing a diverse mosaic leading to high biodiversity. The research area covers the Szrenica Mount region on the border between Poland and the Czech Republic - the most important part of the Western Karkonosze and one of the main areas in the Karkonosze National Park (M&B Reserve of the UNESCO). The APEX hyperspectral data that was classified in this study was acquired on 10th September 2012 by the German Aerospace Center (DLR) in the framework of the EUFAR HyMountEcos project. This airborne scanner is a 288-channel imaging spectrometer operating in the wavelength range 0.4-2.5 μm. For reference patterns of forest and non-forest vegetation, maps (provided by the Polish Karkonosze National Park) were chosen. Terrain recognition was based on field walks with a Trimble GeoXT GPS receiver. It allowed test and validation dominant polygons of 15 classes of vegetation communities to be selected, which were used in the Support Vector Machines (SVM) classification. The SVM classifier is a type of machine used for pattern recognition. The result is a post classification map with statistics (total, user, producer accuracies, kappa coefficient and error matrix). Assessment of the statistics shows that almost all the classes were properly recognised, excluding the fern community. The overall classification accuracy is 79.13% and the kappa coefficient is 0.77. This shows that hyperspectral images and remote sensing methods can be support tools for the identification of the dominant plant communities of mountain areas.

scholarly journals Improvement of Forest Canopy Density Mapping of Spare Forests Using a Novel RS-GIS Based Classification Method

2021 ◽  
Author(s):  
Mohammad Hassan Naseri ◽  
Shaban Shataee
Keyword(s):  
Remote Sensing ◽  
High Performance ◽  
Forest Canopy ◽  
Direct Method ◽  
Accuracy Assessment ◽  
Canopy Cover ◽  
Remote Sensing Data ◽  
Kappa Coefficient ◽  
Density Mapping ◽  
Cover Density

Abstract Background: Accurate mapping and monitoring canopy cover using remote sensing data as an alternative way for field surveys are very important for forest managers, particularly in the spare and low dense forests. Due to being area-based of canopy cover density and mixing spectral responses of tree crowns and soil in the thin and semi-dense forests, finding the high-performance method of classification is a challenge particularly on high-resolution imagery. In this study, we compared produced maps of canopy cover using direct remote sensing and indirect (RS-GIS-based) methods in two forest sites on the Quickbird and WorldView-2 images using the Artificial Neural Network (ANN) algorithm. Also, the optimal plot area was examined by different plot areas.Results: In the direct method and based on the obtained results, in the Dashte Barm using Quickbird image, the best classification was for plots of 7500 m2 with an overall accuracy of 56.57% and kappa coefficient of 0.32. In the Ilam site and on the WorldView-2 image, the best result is obtained by the plots of 5,000 m2 area with an overall accuracy of 45.71% and the kappa coefficient of 0.263. The results of accuracy assessment of maps of indirect method in the Dashte Barm site for grids with different areas showed that the best classifications obtained from sample plot areas of 10000 m2 with overall accuracy of 82.69% and Kappa coefficient of 0.744; but in the Ilam sites the best result was obtained using sample area of 1000 m2 with overall accuracy of 74.27% and the Kappa coefficient of 0.690. Conclusions: The results exposed that use of the RS-GIS based method could considerably improve the results compare to direct classification. Also, the results showed concerning the conditions of canopy cover density of forest stands, plots with different areas can be used to map of forest canopy cover density; however, for direct classification the use of plots with areas of 5000 m2 and more are suitable in sparse forests. For RS-GIS based method, the plot areas of 1000 m2 are optimal due to time and cost saving.

New method for assessment of geometry correction of remote sensing imagery

2007 ◽  
Author(s):  
Huaguo Zhang ◽  
Weigen Huang ◽  
Dongling Li ◽  
Changbao Zhou
Keyword(s):  
Remote Sensing ◽  
New Method ◽  
Remote Sensing Imagery ◽  
Geometry Correction

scholarly journals THE NEW METHOD FOR ACCURACY ASSESSMENT OF IMAGE CLASSIFICATION OBTAINED USING UNMANNED AERIAL VEHICLES BASED ON A WEIGHTED CONFUSION MATRIX AND ITS ACCURACY COEFFICIENTS

2021 ◽  
pp. 82-88
Author(s):  
Sofiia Alpert
Keyword(s):  
Remote Sensing ◽  
Environmental Monitoring ◽  
Image Classification ◽  
Oil And Gas ◽  
Accuracy Assessment ◽  
Confusion Matrix ◽  
Weighted Average ◽  
New Method ◽  
Thematic Maps ◽  
Forest Classification

The proposed new method for accuracy assessment of image classification in UAV-based Remote Sensing can be applied in solution of different ecological and practical tasks. Nowadays thematic maps play an important role in solution of different remote sensing tasks. Thematic maps are applied for forest classification, determing of soil types and properties, environmental monitoring, exploring of oil and gas. That’s why the accuracy assessment is necessary to evaluate the quality of thematic maps. It is important to know the accuracy of thematic maps before they are used for further scientific investigations. Users and producers of maps compare several maps to see which is best, or to check how well they agree. It was proposed to use Weighted confusion matrix for accuracy assessment of thematic maps. Proposed Weighted confusion matrix was considered with Confusion matrix. It was noted, that Confusion matrix needs in large samples and can not take into account the “seriousness” of errors. It also were shown main advantages of Weighted confusion matrix. It was noted, that Weighted confusion matrix gives different weights for different mistakes of classification. Proposed Weighted confusion matrix gives a partial credit for classification results. This property of the Weighted confusion matrix is very important, when not all mistakes are equally serious and rough for user. Proposed method uses the Weights matrix for Confusion matrix that contains weights for each element in the Confusion matrix. Accuracy coefficient of the Weighted confusion matrix, such as: Overall accuracy, User’s accuracy, Producer’s accuracy and Weighted average of the weights for each class and their main properties were described in this work too. It was also considered a numerical example of calculation of accuracy coefficients of Weighted confusion matrix. This proposed new method for accuracy assessment of image classification can be applied in land-cover classification, environmental monitoring, exploring for minerals, numerous agricultural tasks.

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