scholarly journals Thematic quality assessment of land surface geospatial data based on confusion matrices: A matrix set for research on measures and procedures

2021 ◽  
Author(s):  
Francisco J. Ariza‐López ◽  
José L. García‐Balboa ◽  
María V. Alba‐Fernández ◽  
José Rodríguez‐Avi
Keyword(s):  
Quality Assessment ◽  
Land Surface ◽  
Geospatial Data ◽  
Confusion Matrices

Improving the Quality Assessment band in Landsat cloud images for the application of cloud removal  

2021 ◽  
Author(s):  
Boli Yang ◽  
Yan Feng ◽  
Ruyin Cao
Keyword(s):  
Quality Assessment ◽  
Land Surface ◽  
Missing Values ◽  
Similarity Index ◽  
Visual Assessment ◽  
New Method ◽  
Landsat Data ◽  
Cloud Detection ◽  
Cloud Removal ◽  
Ultimate Objective

<p>Cloud contamination is a serious obstacle for the application of Landsat data. Thick clouds can completely block land surface information and lead to missing values. The reconstruction of missing values in a Landsat cloud image requires the cloud and cloud shadow mask. In this study, we raised the issue that the quality of the quality assessment (QA) band in current Landsat products cannot meet the requirement of thick-cloud removal. To address this issue, we developed a new method (called Auto-PCP) to preprocess the original QA band, with the ultimate objective to improve the performance of cloud removal on Landsat cloud images. We tested the new method at four test sites and compared cloud-removed images generated by using three different QA bands, including the original QA band, the modified QA band by a dilation of two pixels around cloud and cloud shadow edges, and the QA band processed by Auto-PCP (“QA_Auto-PCP”). Experimental results, from both actual and simulated Landsat cloud images, show that QA_Auto-PCP achieved the best visual assessment for the cloud-removed images, and had the smallest RMSE values and the largest Structure SIMilarity index (SSIM) values. The improvement for the performance of cloud removal by QA_Auto-PCP is because the new method substantially decreases omission errors of clouds and shadows in the original QA band, but meanwhile does not increase commission errors. Moreover, Auto-PCP is easy to implement and uses the same data as cloud removal without additional image collections. We expect that Auto-PCP can further popularize cloud removal and advance the application of Landsat data.     </p><p><strong> </strong></p><p><strong>Keywords: </strong>Cloud detection, Cloud shadows, Cloud simulation, Cloud removal, MODTRAN</p>

Developing a Predictive Model for Plasmodium knowlesi–Susceptible Areas in Malaysia Using Geospatial Data and Artificial Neural Networks

2021 ◽  
pp. 101053952110486
Author(s):  
Rozita Hod ◽  
Siti Aisah Mokhtar ◽  
Farrah Melissa Muharam ◽  
Ummi Kalthom Shamsudin ◽  
Jamal Hisham Hashim
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Environmental Factors ◽  
Predictive Model ◽  
Land Surface ◽  
Plasmodium Knowlesi ◽  
Geospatial Data ◽  
Susceptibility Index ◽  
Malaria Susceptibility ◽  
Artificial Neural

Plasmodium knowlesi is an emerging species for malaria in Malaysia, particularly in East Malaysia. This infection contributes to almost half of all malaria cases and deaths in Malaysia and poses a challenge in eradicating malaria. The aim of this study was to develop a predictive model for P. knowlesi susceptibility areas in Sabah, Malaysia, using geospatial data and artificial neural networks (ANNs). Weekly malaria cases from 2013 to 2014 were used to identify the malaria hotspot areas. The association of malaria cases with environmental factors (elevation, water bodies, and population density, and satellite images providing rainfall, land surface temperature, and normalized difference vegetation indices) were statistically determined. The significant environmental factors were used as input for the ANN analysis to predict malaria cases. Finally, the malaria susceptibility index and zones were mapped out. The results suggested integrating geospatial data and ANNs to predict malaria cases, with overall correlation coefficient of 0.70 and overall accuracy of 91.04%. From the malaria susceptibility index and zoning analyses, it was found that areas located along the Crocker Range of Sabah and the East part of Sabah were highly susceptible to P. knowlesi infections. Following this analysis, targetted entomological mapping and malaria control programs can be initiated.

scholarly journals Quality Assessment of S-NPP VIIRS Land Surface Temperature Product

2015 ◽  
Vol 7 (9) ◽  
pp. 12215-12241 ◽  
Author(s):  
Yuling Liu ◽  
Yunyue Yu ◽  
Peng Yu ◽  
Frank Göttsche ◽  
Isabel Trigo
Keyword(s):  
Surface Temperature ◽  
Quality Assessment ◽  
Land Surface Temperature ◽  
Land Surface

scholarly journals A Supplementary Module to Improve Accuracy of the Quality Assessment Band in Landsat Cloud Images

2021 ◽  
Vol 13 (23) ◽  
pp. 4947
Author(s):  
Ruyin Cao ◽  
Yan Feng ◽  
Jin Chen ◽  
Ji Zhou
Keyword(s):  
Quality Assessment ◽  
Land Surface ◽  
Temporal Change ◽  
New Method ◽  
Validation Dataset ◽  
Landsat 8 ◽  
Landsat Data ◽  
Multi Temporal ◽  
Reference Images ◽  
Cloud Removal

Cloud contamination is a serious obstacle for the application of Landsat data. To popularize the applications of Landsat data, each Landsat image includes the corresponding Quality Assessment (QA) band, in which cloud and cloud shadow pixels have been flagged. However, previous studies suggested that Landsat QA band still needs to be modified to fulfill the requirement of Landsat data applications. In this study, we developed a Supplementary Module to improve the original QA band (called QA_SM). On one hand, QA_SM extracts spectral and geometrical features in the target Landsat cloud image from the original QA band. On the other, QA_SM incorporates the temporal change characteristics of clouds and cloud shadows between the target and reference images. We tested the new method at four local sites with different land covers and the Landsat-8 cloud cover validation dataset (“L8_Biome”). The experimental results show that QA_SM performs better than the original QA band and the multi-temporal method ATSA (Automatic Time-Series Analyses). QA_SM decreases omission errors of clouds and shadows in the original QA band effectively but meanwhile does not increase commission errors. Besides, the better performance of QA_SM is less affected by the selections of reference images because QA_SM considers the temporal change of land surface reflectance that is not caused by cloud contamination. By further designing a quantitative assessment experiment, we found that the QA band generated by QA_SM improves cloud-removal performance on Landsat cloud images, suggesting the benefits of the new method to advance the applications of Landsat data.

scholarly journals Formation of Fused Images of the Land Surface from Radar and Optical Images in Spatially Distributed On-Board Operational Monitoring Systems

2021 ◽  
Vol 7 (12) ◽  
pp. 251
Author(s):  
Vadim A. Nenashev ◽  
Igor G. Khanykov
Keyword(s):  
Quality Assessment ◽  
Information Content ◽  
Land Surface ◽  
Clustering Algorithm ◽  
Reference Points ◽  
Operational Monitoring ◽  
Functional Transformation ◽  
Location System ◽  
Fused Image ◽  
Spatially Distributed

This paper considers the issues of image fusion in a spatially distributed small-size on-board location system for operational monitoring. The purpose of this research is to develop a new method for the formation of fused images of the land surface based on data obtained from optical and radar devices operated from two-position spatially distributed systems of small aircraft, including unmanned aerial vehicles. The advantages of the method for integrating information from radar and optical information-measuring systems are justified. The combined approach allows removing the limitations of each separate system. The practicality of choosing the integration of information from several widely used variants of heterogeneous sources is shown. An iterative approach is used in the method for combining multi-angle location images. This approach improves the quality of synthesis and increases the accuracy of integration, as well as improves the information content and reliability of the final fused image by using the pixel clustering algorithm, which produces many partitions into clusters. The search for reference points on isolated contours is carried out on a pair of left and right images of the docked image from the selected partition. For these reference points, a functional transformation is determined. Having applied it to the original multi-angle heterogeneous images, the degree of correlation of the fused image is assessed. Both the position of the reference points of the contour and the desired functional transformation itself are refined until the quality assessment of the fusion becomes acceptable. The type of functional transformation is selected based on clustered images and then applied to the original multi-angle heterogeneous images. This process is repeated for clustered images with greater granularity in case if quality assessment of the fusion is considered to be poor. At each iteration, there is a search for pairs of points of the contour of the isolated areas. Areas are isolated with the use of two image segmentation methods. Experiments on the formation of fused images are presented. The result of the research is the proposed method for integrating information obtained from a two-position airborne small-sized radar system and an optical location system. The implemented method can improve the information content, quality, and reliability of the finally established fused image of the land surface.

Validation and Quality Assessment of the ECOSTRESS Level-2 Land Surface Temperature and Emissivity Product

2021 ◽  
pp. 1-23
Author(s):  
Glynn C. Hulley ◽  
Frank M. Gottsche ◽  
Gerardo Rivera ◽  
Simon J. Hook ◽  
Robert J. Freepartner ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Quality Assessment ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Level 2

scholarly journals ArcGIS-mapping to sustainable wastewater management in Greece: siting artificial wetlands systems in a biosystem

2021 ◽  
Keyword(s):  
Spatial Data ◽  
Land Surface ◽  
Surface Characteristics ◽  
Geospatial Data ◽  
Wastewater Management ◽  
Management Problem ◽  
Northern Greece ◽  
Artificial Wetlands ◽  
Site Assessment ◽  
Adaptive Representation

<p>In this work, the ArcGIS technology combines analogue and digital geospatial data to derive multiple resolution meshes with a triangulated irregular networks (TINs) approach that serves to integrate the geospatial data such as surface topography, hydro graphic features and land surface characteristics into an adaptive representation of a basin biosystem. The ArcGIS model that has been developed is applied at the municipal level to a small remote settlement with less than 2000 people in Northern Greece. The aim was a site assessment for constructing an artificial wetland (ATW) system as a viable solution to the wastewater management problem and protection of biosystems. This study demonstrates that there are discrepancies in Greece between the existing open geospatial data and on the basis of the results from our study we can conclude that this combination of local maps and geographic information in ArcGIS with a TIN approach increases our knowledge of the physical terrain. It accordingly facilitates the analysis and implementation of action plans by selecting suitable sites for construction of ATW systems in small remote settlements. We moreover discuss problems regarding spatial data quality and scale and provide suggestions for improvement while the desktop classification steps can be easily reproduced for other data-similar countries.</p>

scholarly journals Methods of Rapid Quality Assessment for National-Scale Land Surface Change Monitoring

2020 ◽  
Vol 12 (16) ◽  
pp. 2524
Author(s):  
Qiang Zhou ◽  
Christopher Barber ◽  
George Xian
Keyword(s):  
United States ◽  
Land Cover ◽  
Quality Assessment ◽  
Land Surface ◽  
Simulated Data ◽  
The United States ◽  
Surface Change ◽  
Tile Basis ◽  
Local Outlier ◽  
Change Monitoring

Providing rapid access to land surface change data and information is a goal of the U.S. Geological Survey. Through the Land Change Monitoring, Assessment, and Projection (LCMAP) initiative, we have initiated a monitoring capability that involves generating a suite of 10 annual land cover and land surface change datasets across the United States at a 30-m spatial resolution. During the LCMAP automated production, on a tile-by-tile basis, erroneous data can occasionally be generated due to hardware or software failure. While crucial to assure the quality of the data, rapid evaluation of results at the pixel level during production is a substantial challenge because of the massive data volumes. Traditionally, product quality relies on the validation after production, which is inefficient to reproduce the whole product when an error occurs. This paper presents a method for automatically evaluating LCMAP results during the production phase based on 14 indices to quickly find and flag erroneous tiles in the LCMAP products. The methods involved two types of comparisons: comparing LCMAP values across the temporal record to measure internal consistency and calculating the agreement with multiple intervals of the National Land Cover Database (NLCD) data to measure the consistency with existing products. We developed indices on a tile-by-tile basis in order to quickly find and flag potential erroneous tiles by comparing with surrounding tiles using local outlier factor analysis. The analysis integrates all indices into a local outlier score (LOS) to detect erroneous tiles that are distinct from neighboring tiles. Our analysis showed that the methods were sensitive to partially erroneous tiles in the simulated data with a LOS higher than 2. The rapid quality assessment methods also successfully identified erroneous tiles during the LCMAP production, in which land surface change results were not properly saved to the products. The LOS map and indices for rapid quality assessment also point to directions for further investigations. A map of all LOS values by tile for the published LCMAP shows all LOS values are below 2. We also investigated tiles with high LOS to ensure the distinction with neighboring tiles was reasonable. An index in this study shows the overall agreement between LCMAP and NLCD on a tile basis is above 71.5% and has an average at 89.1% across the 422 tiles in the conterminous United States. The workflow is suitable for other studies with a large volume of image products.

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