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To address problems in remote sensing image change detection, this study proposes a method for identifying spurious changes based on an eco-geographical zoning knowledge base and crowdsourced data mining. After preliminary change detection using the super pixel cosegmentation method, eco-geographical zoning is introduced, and the rules of spurious change are collected based on the knowledge of expert interpreters, and from statistics on existing land cover products according to each eco-geographical zone. Uncertain changed patches with a high possibility of spurious change according to the eco-geographical zoning rule were published in the form of a map service on an online platform, and then crowd tagging information on spurious changed patches was collected. The Hyperlink-Induced Topic Search (HITS) algorithm was used to calculate the spurious change degree of changed patches. We selected the northern part of Laos as the experimental area and the Chinese GF-1 Wide Field View (WFV) images for change detection to verify the effectiveness of the method. The results show that the accuracy of change detection improves by 23% after removing the spurious changes. Spurious changes caused by clouds, river water turbidity, spectral differences in cultivated land before and after harvest, and changes in shrubs, grassland, and forest density, can be removed using an eco-geographical zoning knowledge base and crowdsourced data mining methods.
Ling Zhu; Dejun Gao; Tao Jia; Jingyi Zhang. Using Eco-Geographical Zoning Data and Crowdsourcing to Improve the Detection of Spurious Land Cover Changes. Remote Sensing 2021, 13, 3244 .
AMA StyleLing Zhu, Dejun Gao, Tao Jia, Jingyi Zhang. Using Eco-Geographical Zoning Data and Crowdsourcing to Improve the Detection of Spurious Land Cover Changes. Remote Sensing. 2021; 13 (16):3244.
Chicago/Turabian StyleLing Zhu; Dejun Gao; Tao Jia; Jingyi Zhang. 2021. "Using Eco-Geographical Zoning Data and Crowdsourcing to Improve the Detection of Spurious Land Cover Changes." Remote Sensing 13, no. 16: 3244.
Freely available satellite imagery improves the research and production of land-cover products at the global scale or over large areas. The integration of land-cover products is a process of combining the advantages or characteristics of several products to generate new products and meet the demand for special needs. This study presents an ontology-based semantic mapping approach for integration land-cover products using hybrid ontology with EAGLE (EIONET Action Group on Land monitoring in Europe) matrix elements as the shared vocabulary, linking and comparing concepts from multiple local ontologies. Ontology mapping based on term, attribute and instance is combined to obtain the semantic similarity between heterogeneous land-cover products and realise the integration on a schema level. Moreover, through the collection and interpretation of ground verification points, the local accuracy of the source product is evaluated using the index Kriging method. Two integration models are developed that combine semantic similarity and local accuracy. Taking NLCD (National Land Cover Database) and FROM-GLC-Seg (Finer Resolution Observation and Monitoring-Global Land Cover-Segmentation) as source products and the second-level class refinement of GlobeLand30 land-cover product as an example, the forest class is subdivided into broad-leaf, coniferous and mixed forest. Results show that the highest accuracies of the second class are 82.6%, 72.0% and 60.0%, respectively, for broad-leaf, coniferous and mixed forest.
Ling Zhu; Guangshuai Jin; Dejun Gao. Integrating Land-Cover Products Based on Ontologies and Local Accuracy. Information 2021, 12, 236 .
AMA StyleLing Zhu, Guangshuai Jin, Dejun Gao. Integrating Land-Cover Products Based on Ontologies and Local Accuracy. Information. 2021; 12 (6):236.
Chicago/Turabian StyleLing Zhu; Guangshuai Jin; Dejun Gao. 2021. "Integrating Land-Cover Products Based on Ontologies and Local Accuracy." Information 12, no. 6: 236.
The application of cosegmentation in remote sensing image change detection can effectively overcome the salt and pepper phenomenon and generate multitemporal changing objects with consistent boundaries. Cosegmentation considers the image information, such as spectrum and texture, and mines the spatial neighborhood information between pixels. However, each pixel in the minimum cut/maximum flow algorithm for cosegmentation change detection is regarded as a node in the network flow diagram. This condition leads to a direct correlation between computation times and the number of nodes and edges in the diagram. It requires a large amount of computation and consumes excessive time for change detection of large areas. A superpixel segmentation method is combined into cosegmentation to solve this shortcoming. Simple linear iterative clustering is adopted to group pixels by using the similarity of features among pixels. Two-phase superpixels are overlaid to form the multitemporal consistent superpixel segmentation. Each superpixel block is regarded as a node for cosegmentation change detection, so as to reduce the number of nodes in the network flow diagram constructed by minimum cut/maximum flow. In this study, the Chinese GF-1 and Landsat satellite images are taken as examples, the overall accuracy of the change detection results is above 0.80, and the calculation time is only one-fifth of the original.
Ling Zhu; Jingyi Zhang; Yang Sun. Remote Sensing Image Change Detection Using Superpixel Cosegmentation. Information 2021, 12, 94 .
AMA StyleLing Zhu, Jingyi Zhang, Yang Sun. Remote Sensing Image Change Detection Using Superpixel Cosegmentation. Information. 2021; 12 (2):94.
Chicago/Turabian StyleLing Zhu; Jingyi Zhang; Yang Sun. 2021. "Remote Sensing Image Change Detection Using Superpixel Cosegmentation." Information 12, no. 2: 94.
Land cover products obtained from remote sensing image classification inevitably contain a large number of false classification or uncertain pixels because of spectral confusion, image resolution limitation, and ground object complexity. The confusion matrix used to evaluate the classification accuracy cannot reflect the spatial variation. The information provided to users of land cover products is incomplete and uncertain. In this study, a method is presented to evaluate and improve the accuracy of land cover classification products by coupling Geo-Eco zoning and Markov chain geoscience statistical simulation. Validation points collected from various sources are used in the model calculation and accuracy verification of results. The pre-classified image that needs to be improved and Geo-Eco zoning attribute data are used as auxiliary data for co-simulation. Results show that the accuracy of Globeland30 data can be improved by more than 10% by coupling Geo-Eco zoning and Markov chain geostatistical simulation.
Ling Zhu; Jing Li; Yixuan La; Tao Jia. Improving the Accuracy of Remote Sensing Land Cover Classification by GEO-ECO Zoning Coupled with Geostatistical Simulation. Applied Sciences 2021, 11, 553 .
AMA StyleLing Zhu, Jing Li, Yixuan La, Tao Jia. Improving the Accuracy of Remote Sensing Land Cover Classification by GEO-ECO Zoning Coupled with Geostatistical Simulation. Applied Sciences. 2021; 11 (2):553.
Chicago/Turabian StyleLing Zhu; Jing Li; Yixuan La; Tao Jia. 2021. "Improving the Accuracy of Remote Sensing Land Cover Classification by GEO-ECO Zoning Coupled with Geostatistical Simulation." Applied Sciences 11, no. 2: 553.
Highly accurate and detailed information on land cover products is crucial in studying global climate change and sustainable development. GlobeLand30 is the first global 30 m resolution Land cover (LC) product based on remote sensing data developed by Chinese scientists. GlobeLand30 has 10 first-level classes of products. However, no second-level classification products have been released. This study presents an integration method based on fuzzy theory and combines three 30 m resolution LC products, namely, National Land Cover Database 2011 (NLCD 2011), Fine Resolution Observation and Monitoring of Global Land Cover Segmentation 2010 (FROM-GLC-Seg) and global forest cover data (treecover2010) products, using the European Environment Information and Observation Network Action Group on Land Monitoring in the European (EAGLE) system of semantic translation. The conterminous United States region is adopted as the research area, and the GlobeLand30 (2010) forest class is subdivided into coniferous, broadleaf and mixed forests. Three different weighted voting methods are applied. The difference is whether the user or local accuracy of the source product is considered. The result shows that the respective accuracies of broadleaf, coniferous, and mixed forests are approximately 65.8%, 57.0%, and 40.9% of the weighted voting method without considering any product’s accuracy; 79.3%, 65.9%, and 58.4% of the weighted voting method considering the user accuracy; and 79.9%, 69.9%, and 59.3% of the weighted voting method considering the local accuracy of each product, respectively. The proposed method for refining the GlobeLand30 first class forest can be applied to other classes and land cover products.
Ling Zhu; Guangshuai Jin; Xiaohong Zhang; Ruoming Shi; Yixuan La; Cunwen Li. Integrating global land cover products to refine GlobeLand30 forest types: a case study of conterminous United States (CONUS). International Journal of Remote Sensing 2020, 42, 2105 -2130.
AMA StyleLing Zhu, Guangshuai Jin, Xiaohong Zhang, Ruoming Shi, Yixuan La, Cunwen Li. Integrating global land cover products to refine GlobeLand30 forest types: a case study of conterminous United States (CONUS). International Journal of Remote Sensing. 2020; 42 (6):2105-2130.
Chicago/Turabian StyleLing Zhu; Guangshuai Jin; Xiaohong Zhang; Ruoming Shi; Yixuan La; Cunwen Li. 2020. "Integrating global land cover products to refine GlobeLand30 forest types: a case study of conterminous United States (CONUS)." International Journal of Remote Sensing 42, no. 6: 2105-2130.
Forests have potential economic value and play a significant role in maintaining ecological balance. Considering its outdated and incomplete forest statistics, the Kyrgyzstan Republic urgently needs a forest cover map for assessing its current forest resources and assisting national policies on improving rural livelihood and sustainability. This study adopted a hybrid fusion strategy to develop a forest cover map for the Kyrgyzstan Republic with improved accuracy. The fusion strategy uses the merits of the GlobeLand30 in 2010 and the USGS TreeCover2010, the benefits of auxiliary geographic information, and the advantages of the stacking learning method in classification. Additionally, we explored the influence of different forest definitions, based on the tree cover percentage value in the USGS TreeCover2010, on the accuracy of forest cover. Results suggested that the accuracy of our model can be improved significantly by including auxiliary geographic features and feeding the optimal size of training samples. Thereafter, using our model, forest cover maps were derived at different tree cover threshold values in the USGS TreeCover2010. Importantly, the forest cover map at the tree cover threshold value of 40% was determined as the most accurate one with the kappa value of 0.89, whose spatial extent constitutes about 2.4% of the entire territory. This estimated forest cover percentage suggests a low estimation of forest resources based on rigorous definition, which can be valuable for reviewing and amending the current national forest policies.
Tao Jia; Yuqian Li; Wenzhong Shi; Ling Zhu. Deriving a Forest Cover Map in Kyrgyzstan Using a Hybrid Fusion Strategy. Remote Sensing 2019, 11, 2325 .
AMA StyleTao Jia, Yuqian Li, Wenzhong Shi, Ling Zhu. Deriving a Forest Cover Map in Kyrgyzstan Using a Hybrid Fusion Strategy. Remote Sensing. 2019; 11 (19):2325.
Chicago/Turabian StyleTao Jia; Yuqian Li; Wenzhong Shi; Ling Zhu. 2019. "Deriving a Forest Cover Map in Kyrgyzstan Using a Hybrid Fusion Strategy." Remote Sensing 11, no. 19: 2325.
Ling Zhu; Yang Sun; Ruoming Shi; Yixuan La; Shu Peng. Exploiting Cosegmentation and Geo-Eco Zoning for Land Cover Product Updating. Photogrammetric Engineering & Remote Sensing 2019, 85, 597 -611.
AMA StyleLing Zhu, Yang Sun, Ruoming Shi, Yixuan La, Shu Peng. Exploiting Cosegmentation and Geo-Eco Zoning for Land Cover Product Updating. Photogrammetric Engineering & Remote Sensing. 2019; 85 (8):597-611.
Chicago/Turabian StyleLing Zhu; Yang Sun; Ruoming Shi; Yixuan La; Shu Peng. 2019. "Exploiting Cosegmentation and Geo-Eco Zoning for Land Cover Product Updating." Photogrammetric Engineering & Remote Sensing 85, no. 8: 597-611.
Change detection method is an efficient way in the aim of land cover product updating on the basis of the existing products, and at the same time saving lots of cost and time. Considering the object-oriented change detection method for 30m resolution Landsat image, analysis of effect of different segmentation scales on the method of the object-oriented is firstly carried out. On the other hand, for analysing the effectiveness and availability of pixel-based change method, the two indices which complement each other are the differenced Normalized Difference Vegetation Index (dNDVI), the Change Vector (CV) were used. To demonstrate the performance of pixel-based and object-oriented, accuracy assessment of these two change detection results will be conducted by four indicators which include overall accuracy, omission error, commission error and Kappa coefficient.
Zhenlei Xie; Ruoming Shi; Ling Zhu; Shu Peng; Xu Chen. COMPARISON OF PIXEL-BASED AND OBJECT-ORIENTED LAND COVER CHANGE DETECTION METHODS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2016, XLI-B7, 579 -583.
AMA StyleZhenlei Xie, Ruoming Shi, Ling Zhu, Shu Peng, Xu Chen. COMPARISON OF PIXEL-BASED AND OBJECT-ORIENTED LAND COVER CHANGE DETECTION METHODS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2016; XLI-B7 ():579-583.
Chicago/Turabian StyleZhenlei Xie; Ruoming Shi; Ling Zhu; Shu Peng; Xu Chen. 2016. "COMPARISON OF PIXEL-BASED AND OBJECT-ORIENTED LAND COVER CHANGE DETECTION METHODS." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7, no. : 579-583.
Light detection and ranging (LiDAR) point cloud data can contain millions of point returns from a diverse range of surface features, and directly reconstructing buildings from these data is challenging. Trees and other vegetation pose a particular problem in many built environments. This paper investigates several efficient procedures for detecting buildings and excluding vegetation using LiDAR and imagery data. Two general approaches for identifying and filtering out returns from vegetation are investigated: the first uses a normalized difference vegetation index (NDVI) image, while the second uses height differences. The utility of an entropy filter for improving NDVI filter performance as well as two distinct approaches for height-difference modeling are also evaluated. All methods use efficient raster-based algorithms for filtering while retaining the high spatial precision of the vector LiDAR point returns. Following removal of nonbuilding points, remaining points are segmented into distinct building features. In addition, we place particular emphasis on the analysis of processing challenges and special cases as well as the accuracy of these different methods on a large-volume LiDAR dataset covering a challenging build environment.
Ling Zhu; Ashton M. Shortridge; David Lusch. Conflating LiDAR data and multispectral imagery for efficient building detection. Journal of Applied Remote Sensing 2012, 6, 063602 -063602.
AMA StyleLing Zhu, Ashton M. Shortridge, David Lusch. Conflating LiDAR data and multispectral imagery for efficient building detection. Journal of Applied Remote Sensing. 2012; 6 (1):063602-063602.
Chicago/Turabian StyleLing Zhu; Ashton M. Shortridge; David Lusch. 2012. "Conflating LiDAR data and multispectral imagery for efficient building detection." Journal of Applied Remote Sensing 6, no. 1: 063602-063602.