scholarly journals Analysis of the Impact of Positional Accuracy When Using a Single Pixel for Thematic Accuracy Assessment

2020 ◽  
Vol 12 (24) ◽  
pp. 4093
Author(s):  
Jianyu Gu ◽  
Russell G. Congalton

The primary goal of thematic accuracy assessment is to measure the quality of land cover products and it has become an essential component in global or regional land cover mapping. However, there are many uncertainties introduced in the validation process which could propagate into the derived accuracy measures and therefore impact the decisions made with these maps. Choosing the appropriate reference data sample unit is one of the most important decisions in this process. The majority of researchers have used a single pixel as the assessment unit for thematic accuracy assessment, while others have claimed that a single pixel is not appropriate. The research reported here shows the results of a simulation analysis from the perspective of positional errors. Factors including landscape characteristics, the classification scheme, the spatial scale, and the labeling threshold were also examined. The thematic errors caused by positional errors were analyzed using the current level of geo-registration accuracy achieved by several global land cover mapping projects. The primary results demonstrate that using a single-pixel as an assessment unit introduces a significant amount of thematic error. In addition, the coarser the spatial scale, the greater the impact on positional errors as most pixels in the image become mixed. A classification scheme with more classes and a more heterogeneous landscape increased the positional effect. Using a higher labeling threshold decreased the positional impact but greatly increased the number of abandoned units in the sample. This research showed that remote sensing applications should not employ a single-pixel as an assessment unit in the thematic accuracy assessment.

Geographies ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 143-165
Author(s):  
Jianyu Gu ◽  
Russell G. Congalton

Pixels, blocks (i.e., grouping of pixels), and polygons are the fundamental choices for use as assessment units for validating per-pixel image classification. Previous research conducted by the authors of this paper focused on the analysis of the impact of positional accuracy when using a single pixel for thematic accuracy assessment. The research described here provided a similar analysis, but the blocks of contiguous pixels were chosen as the assessment unit for thematic validation. The goal of this analysis was to assess the impact of positional errors on the thematic assessment. Factors including the size of a block, labeling threshold, landscape characteristics, spatial scale, and classification schemes were also considered. The results demonstrated that using blocks as an assessment unit reduced the thematic errors caused by positional errors to under 10% for most global land-cover mapping projects and most remote-sensing applications achieving a half-pixel registration. The larger the block size, the more the positional error was reduced. However, there are practical limitations to the size of the block. More classes in a classification scheme and higher heterogeneity increased the positional effect. The choice of labeling threshold depends on the spatial scale and landscape characteristics to balance the number of abandoned units and positional impact. This research suggests using the block of pixels as an assessment unit in the thematic accuracy assessment in future applications.


2021 ◽  
Vol 257 ◽  
pp. 112357
Author(s):  
James Wickham ◽  
Stephen V. Stehman ◽  
Daniel G. Sorenson ◽  
Leila Gass ◽  
Jon A. Dewitz

2021 ◽  
pp. 1-16
Author(s):  
Katawut Waiyasusri

Krabi Estuary Wetland (KEW) is an outstanding wetland with an estuary environment. At present, the tourism industry has rapidly grown, resulting in the impact of land cover changes. This research aims to assess the changes that have occurred in the KEW from 1999 to 2020 using NDVI and NDBI for monitoring changes in mangrove areas and urbanization in Krabi Province, Thailand. Landsat satellite images in years 1999, 2009 and 2020 were classified by using a band ratio to create land cover maps. The results show that NDVI between 0.41–1.00 clearly shows the mangrove forest area, while NDBI between 0.01–0.40 shows urban and built-up land, and 0.41–1.00 appears as bare land. The NDVI overall accuracy assessment is 82.88%, 97.46% and 88.25% with Kappa values of 0.64, 0.92, and 0.85 for year 1999, 2009 and 2020, respectively. The NDBI overall accuracy assessment is 92.81%, 77.11% and 64% with Kappa values of 0.93, 0.77, and 0.63 for year 1999, 2009 and 2020, respectively. In addition, areas that are sensitive to land-cover change appear around the Chi rat River, Pak Nam Krabi River, and Yuan River, which are tourist areas close to the Krabi and Ao Nang communities. Therefore, it is necessary to speed up the problem solving and find measures to prevent mangrove forest degradation in these 3 mangrove forest areas so that the mangrove forest areas will not decrease rapidly in the future. This research can be valuable for land-cover management in the KEW by policy and decision makers.


2017 ◽  
Vol 191 ◽  
pp. 328-341 ◽  
Author(s):  
James Wickham ◽  
Stephen V. Stehman ◽  
Leila Gass ◽  
Jon A. Dewitz ◽  
Daniel G. Sorenson ◽  
...  

Author(s):  
A. Jamali ◽  
A. Abdul Rahman

Abstract. Environmental change monitoring in earth sciences needs land use land cover change (LULCC) modeling to investigate the impact of climate change phenomena such as droughts and floods on earth surface land cover. As land cover has a direct impact on Land Surface Temperature (LST), the Land cover mapping is an essential part of climate change modeling. In this paper, for land use land cover mapping (LULCM), image classification of Sentinel-1A Synthetic Aperture Radar (SAR) Ground Range Detected (GRD) data using two machine learning algorithms including Support Vector Machine (SVM) and Random Forest (RF) are implemented in R programming language and compared in terms of overall accuracy for image classification. Considering eight different scenarios defined in this research, RF and SVM classification methods show their best performance with overall accuracies of 90.81 and 92.09 percent respectively.


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