Digital forestry research in China

Tang, Shouzheng; Tang, Lina; Shao, Guofan; Dai, Limin

doi:10.1007/s11431-006-8101-5

Cited by 11 publications

(5 citation statements)

References 5 publications

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“…This process managed by the operating system, the advantage of this API is that it allows applications to associate and interact with other applications. Programming language used Google Maps consists of HTML, Javascript, AJAX, and XML which can display Google Maps on other sites [11]. Google also provides Google Maps API services, and developers can integrate Google Maps into their website by adding their data points alone.…”

Section: Google Maps Apimentioning

confidence: 99%

Android Based Forest Fire Monitoring System

Andrea¹,

Wahyuni²,

Safitri³

et al. 2022

IJIEEB

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Forest fires are burning areas of forest or land in large or small areas. Forest fires are often uncontrollable and when this happens, the fire will burn anything nearby for two reasons, one of which is burning naturally or burning caused by humans. One of the man-made fires deliberate by humans is the burning used by the community around the forest to open or clear agricultural or plantation land. The community feels that clearing land with fire does not require a long time and is more economical, if the use of fire is not used properly it can cause the forest to burn. Forest fires in Riau area are still classified as minimal or fires rarely occur. However, the community is at least aware of the forest fires in Riau due to a lack of media information about where the fire hotspots occurred. With this application, it can help the public to better know where the hotspots have been burned. This application consists of 2 levels of access, namely: admin and user. For admins and users, the manufacturing process uses the Android Studio application with Java as the programming language, this application uses Firebase as its database to find out the location of fire hotspots using the Google Maps API. Testing on the application is created to test whether the application has run as desired. Test results using white box testing method.

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Section: Google Maps Apimentioning

confidence: 99%

Android Based Forest Fire Monitoring System

Andrea¹,

Wahyuni²,

Safitri³

et al. 2022

IJIEEB

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“…The relationship between compression yield strength, pulling yield strength, MOE and density of sheet can be obtained from the assumption 1 [4,5].…”

Section: Relations Of Compression Yield Strength Tensile Yield Streng...mentioning

confidence: 99%

“…As the load-deflection graph shown in figure 5, the bending process of fiberboard is divided into two periods, namely, elastic bending period and elastic-plastic bending period. And section stress distribution presents the following characteristics in process [4,6,7]. ① In the elastic bending period, load has a linear dependence on deflection.…”

Section: Relations Of Compression Yield Strength Tensile Yield Streng...mentioning

confidence: 99%

“…Fiberboard can be divided into infinite homogeneous sheets with tiny thickness. Elasticity-plasticity theory and lamella inter-bedded theory are adopted to focus on bending strength, and the basic hypothesis are as below [3,4,5]: Ⅰ.With similar wood fibers, glue content and moisture content, it shows isotropy horizontally while the mechanical properties of fiberboard in thickness direction are decided by density;…”

Section: Introductionmentioning

confidence: 99%

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Predicted Model of Section Stress Distribution and Bending Strength of Fiberboard Based on Vertical Density Profile

Zhang

2011

MSF

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To study the impact of VDP on the bending process of fiberboard, this paper deeply researched into the dynamic changes of section stress distribution of fiberboard during the process of loading and bending and built a static bending strength predicting model, which is based on the piecewise function by simulating fiberboard VDP, theory of elasticity and plasticity, lamella inter-bedded theory and VDP model. The results show: The bending process of fiberboard can be divided into two stages which are elasticity period and elasticity-plasticity period. The latter includes both elasticity region and plasticity region, and compression region comes to elasticity bending before pulling region. The curve of bending section stress distribution is nonlinear and affected by loads and VDP. Critical section stress distribution of bending breakage and breakage load can be predicted by VDP with other condition unchanged. The value of static bending strength predicted by model is basically consistent with testing data. And the static bending strength is closely related to qualification factors of VDP. Fiberboard with high average density doesn’t always contain high static bending strength. VDP is a significant physical parameter which has impact on the bending process and performance of fiberboard, so it must be optimized and controlled in production according to for specific purpose. Key words: fiberboard, vertical density profile, section stress distribution, bending strength, predicting model

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“…Detailing a specific forest object by displaying its certain subject-true properties is one of the elements of improving the quality of information display. Chinese scientists introduced the idea of digital forestry [1], and the concept of the synergistic application of computationally demanding quantitative methodologies and information technology was proposed.…”

Section: Introductionmentioning

confidence: 99%

Visual Digital Forest Model Based on a Remote Sensing Data and Forest Inventory Data

et al. 2021

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This article discusses the process of creating a digital forest model based on remote sensing data, three-dimensional modeling, and forest inventory data. Remote sensing data of the Earth provide a fundamental tool for integrating subsequent objects into a digital forest model, enabling the creation of an accurate digital model of a selected forest quarter by using forest inventory data in educational and experimental forestry, and providing a valuable and extensive database of forest characteristics. The formalization and compilation of technologies for connecting forest inventory databases and remote sensing data with the construction of three-dimensional tree models for a dynamic display of changes in forests provide an additional source of data for obtaining new knowledge. The quality of forest resource management can be improved by obtaining the most accurate details of the current state of forests. Using machine learning and regression analysis methods as part of a digital model, it is possible to visually assess the course of planting growth, changes in species composition, and other morphological characteristics of forests. The goal of digital, interactive forest modeling is to create virtual simulations of the future status of forests using a combination of predictive forest inventory models and machine learning technology. The research findings provide a basic idea and technique for developing local digital forest models based on remote sensing and data integration technologies.

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Digital forestry research in China

Cited by 11 publications

References 5 publications

Android Based Forest Fire Monitoring System

Android Based Forest Fire Monitoring System

Predicted Model of Section Stress Distribution and Bending Strength of Fiberboard Based on Vertical Density Profile

Visual Digital Forest Model Based on a Remote Sensing Data and Forest Inventory Data

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