An automatic approach for land-change detection and land updates based on integrated NDVI timing analysis and the CVAPS method with GEE support

Hu, Yuanman; Dong, Yu; Batunacun,

doi:10.1016/j.isprsjprs.2018.10.008

Cited by 110 publications

(58 citation statements)

References 33 publications

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“…The technologies applied in the recent Industry 4.0 concept to create a Smart Factory are more interconnected, more communicative and more intelligent than traditional manufacturing (Hu et al 2018). Instead of traditional supply chains, a digital global supply chain network is needed in the Industry 4.0 concept, one that can adapt flexibly to the changing unique customers' demands, to the activity of the supply chain members and to the changing market environment.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Instead of traditional supply chains, a digital global supply chain network is needed in the Industry 4.0 concept, one that can adapt flexibly to the changing unique customers' demands, to the activity of the supply chain members and to the changing market environment. The technologies applied in the recent Industry 4.0 concept to create a Smart Factory are more interconnected, more communicative and more intelligent than traditional manufacturing (Hu et al 2018). Instead of traditional supply chains, a digital global supply chain network is needed in the Industry 4.0 concept, one that can adapt flexibly to the changing unique customers' demands, to the activity of the supply chain members and to the changing market environment.…”

Section: Literature Reviewmentioning

confidence: 99%

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Economic, Social Impacts and Operation of Smart Factories in Industry 4.0 Focusing on Simulation and Artificial Intelligence of Collaborating Robots

2019

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Smart Factory is a complex system that integrates the main elements of the Industry 4.0 concept (e.g., autonomous robots, Internet of Things, and Big data). In Smart Factories intelligent robots, tools, and smart workpieces communicate and collaborate with each other continuously, which results in self-organizing and self-optimizing production. The significance of Smart Factories is to make production more competitive, efficient, flexible and sustainable. The purpose of the study is not only the introduction of the concept and operation of the Smart Factories, but at the same time to show the application of Simulation and Artificial Intelligence (AI) methods in practice. The significance of the study is that the economic and social operational requirements and impacts of Smart Factories are summarized and the characteristics of the traditional factory and the Smart Factory are compared. The most significant added value of the research is that a real case study is introduced for Simulation of the operation of two collaborating robots applying AI. Quantitative research methods are used, such as numerical and graphical modeling and Simulation, 3D design, furthermore executing Tabu Search in the space of trajectories, but in some aspects the work included fundamental methods, like suggesting an original whip-lashing analog for designing robot trajectories. The conclusion of the case study is that—due to using Simulation and AI methods—the motion path of the robot arm is improved, resulting in more than five percent time-savings, which leads to a significant improvement in productivity. It can be concluded that the establishment of Smart Factories will be essential in the future and the application of Simulation and AI methods for collaborating robots are needed for efficient and optimal operation of production processes.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Economic, Social Impacts and Operation of Smart Factories in Industry 4.0 Focusing on Simulation and Artificial Intelligence of Collaborating Robots

2019

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“…Seven municipalities in the Upper Tiber Valley, a public authority managing an area of 67 km 2 with a high proportion of forested areas (more than 60%) located in eastern Tuscany (Central Italy), represent the area of interest (AOI) ( Figure 1). carried out, errors from both are summed, resulting in cumulative errors that reduce the change detection accuracy [6]. Such errors are unacceptable, especially if the products serve as reference in decision-making over wide areas.…”

Section: Study Area and Inventory Datamentioning

confidence: 99%

“…Examples of traditional algorithms used to solve this issue are maximum likelihood, support vector machine classification, classification and regression trees, and random forests [5]. The main operational drawback with such approaches is that when two standalone image classification processes are carried out, errors Land 2019, 8, 58 2 of 11 from both are summed, resulting in cumulative errors that reduce the change detection accuracy [6]. Such errors are unacceptable, especially if the products serve as reference in decision-making over wide areas.…”

Section: Introductionmentioning

confidence: 99%

Towards a Tool for Early Detection and Estimation of Forest Cuttings by Remotely Sensed Data

Puletti

Bascietto²

2019

Land

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Knowing the extent and frequency of forest cuttings over large areas is crucial for forest inventories and monitoring. Remote sensing has amply proved its ability to detect land cover changes, particularly in forested areas. Among various strategies, those focusing on mapping using classification approaches of remotely sensed time series are the most frequently used. The main limit of such approaches stems from the difficulty in perfectly and unambiguously classifying each pixel, especially over wide areas. The same procedure is of course simpler if performed over a single pixel. An automated method for identifying forest cuttings over a predefined network of sampling points (IUTI) using multitemporal Sentinel 2 imagery is described. The method employs normalized difference vegetation index (NDVI) growth trajectories to identify the presence of disturbances caused by forest cuttings using a large set of points (i.e., 1580 "forest" points). We applied the method using a total of 51 S2 images extracted from the Google Earth Engine over two years (2016 and 2017) in an area of about 70 km 2 in Tuscany, central Italy. Author Contributions: Conceptualization and Methodology N.P.; Formal Analysis N.P. and M.B.; Writing-Original Draft Preparation, Review and Editing, N.P. and M.B. Funding: This research received no external funding. Conflicts of Interest:The authors declare no conflicts of interest.

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“…The former method directly compares and detects the differences between two remote sensing images, such as the ratio method, the difference method, and the variation vector method. For example, Hu et al [26] developed a change-vector analysis in posterior probability space (CVAPS) change monitoring method that the coupled normalized difference vegetation index (NDVI) time-series characteristics during the year and achieved good results in land use mapping in western China in 2009 and 2014. PCC first classifies the satellite images of two specific phases and then extracts the change map based on the land classification map.…”

Section: Introductionmentioning

confidence: 99%

Detecting Forest Disturbance and Recovery in Primorsky Krai, Russia, Using Annual Landsat Time Series and Multi–Source Land Cover Products

Yang

2020

Remote Sensing

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The spatial distribution and dynamic changes of the forests in Primorsky Krai, Russia, are of great significance for regional ecological security and sustainable economic and societal development. With the support of the Google Earth Engine cloud computing platform, we first synthesized yearly Landsat surface reflectance images of the best quality of the research area and then used the random forest method to calculate the forest classification probability of the study area year by year from 1998 to 2015. Furthermore, we used a time–series segmentation algorithm to perform temporal trajectory segmentation for forest classification probability estimation, and determined the spatial and temporal distribution characteristics and change laws of the forest. We extended the existing algorithms and parameters of forest classification probability trajectory analysis, achieving a high overall accuracy (86.2%) in forest change detection in the study area. The extended method can accurately capture the time node information of the changes. In the present research we observed: (1) that from 1998 to 2015, the forest area of the whole district showed a net loss state, with a loss area of 0.56 × 106 ha, of which the cumulative forest disturbance area reached 1.12 × 106 ha, and the cumulative forest recovery area reached 0.55 × 106 ha; and (2) that more than 90% of the forest change occurred in areas with a slope of less than 18°, at a distance of less than 20 km from settlements, and at a distance of less than 10 km from roads. The forest disturbance monitoring results are consistent with the changes in official statistical results over time, but there was a 20% overestimation. The technical method we extended in this study can be used as a reference for large–scale and high–precision dynamic monitoring of the forests in Russia’s Far East and other regions of the world; it also provides a basis for estimating illegal timber harvesting and determining the appropriate amount of forest harvested.

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An automatic approach for land-change detection and land updates based on integrated NDVI timing analysis and the CVAPS method with GEE support

Cited by 110 publications

References 33 publications

Economic, Social Impacts and Operation of Smart Factories in Industry 4.0 Focusing on Simulation and Artificial Intelligence of Collaborating Robots

Economic, Social Impacts and Operation of Smart Factories in Industry 4.0 Focusing on Simulation and Artificial Intelligence of Collaborating Robots

Towards a Tool for Early Detection and Estimation of Forest Cuttings by Remotely Sensed Data

Detecting Forest Disturbance and Recovery in Primorsky Krai, Russia, Using Annual Landsat Time Series and Multi–Source Land Cover Products

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