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2022 ◽  
Vol 14 (1) ◽  
pp. 545
Author(s):  
Hiroki Amano ◽  
Yoichiro Iwasaki

Agricultural fields, grasslands, and forests are very important areas for groundwater recharge. However, these types of land cover in the Kumamoto area, Japan, were damaged by the Kumamoto earthquake and heavy rains in 2016. In this region, where groundwater provides almost 100% of the domestic water supply for a population of about 1 million, quantitative evaluation of changes in groundwater recharge due to land cover changes induced by natural disasters is important for the sustainable use of groundwater in the future. The objective of this study was to create a land cover map and estimate the groundwater recharge in 2016. Geographic information system (GIS) data and SPOT 6/7 satellite images were used to classify the Kumamoto area into nine categories. The maximum likelihood classifier of supervised classification was applied in ENVI 5.6. Eventually, the map was cleaned up with a 21 × 21 kernel filter, which is larger than the common size of 3 × 3. The created land cover map showed good performance of the larger filter size and sufficient validity, with overall accuracy of 91.7% and a kappa coefficient of 0.88. The estimated total groundwater recharge amount reached 757.56 million m3. However, if areas of paddy field, grassland, and forest had not been reduced due to the natural disasters, it is estimated that the total groundwater recharge amount would have been 759.86 million m3, meaning a decrease of 2.30 million m3 in total. The decrease of 2.13 million m3 in the paddy fields is temporary, because the paddy fields and irrigation channels have been improved and the recharge amount will recover. On the other hand, since the topsoil on the landslide scars will not recover easily in natural conditions, it is expected to take at least 100 years for the groundwater recharge to return to its original state. The recharge amount was estimated to decrease by 0.17 million m3 due to landslides. This amount is quite small compared to the total recharge amount. However, since the reduced recharge amount accounts for the annual water consumption for 1362 people, and 12.1% of the recharge decrease of 1.41 million m3 each year to fiscal year 2024 is expected by municipalities, we conclude that efforts should be made to compensate for the reduced amount due to the disasters.


Author(s):  
Yajuan Li ◽  
Toru Matsumoto ◽  
Atsushi Fujiyama

The “Regional Circular and Ecological Sphere” takes advantage of the SDGs’ concept of integrated solutions to numerous concerns, complementing and supporting resources based on the region’s features while maximizing the utilization of local resources. This research makes a comprehensive evaluation of the three aspects of the environment, economy, and society. First, formulate the evaluation indicators of the regional circulation symbiosis zone. Then, choose the cutting conditions of trees according to geographical factors, use the thinning forecasting system and forest GIS data to evaluate the supply potential of thinned wood in the area, and calculate the heat and power generation of wood biomass. According to the above analysis and calculation, 12,000 tons of unused wood chips can be supplied per year for 36 years from 2016 to 2051. From the economic point of view, the purchase of wood chips of 146 million yen due to the local circulation of wood fuel is expected to save about 50 million yen in intermediate input. And it is estimated that if 12,000 tons of unused wood chips can be supplied in the city per year, and about 98.4 million yen can be saved annually. Finally, from a social perspective point of view, biomass power generation of unused thinned timber using materials worth about 146 million yen is expected to create about 20 jobs.


2021 ◽  
Vol 6 (24) ◽  
pp. 278-289
Author(s):  
Wan Nor Fa’aizah Wan Abdul Basir ◽  
Uznir Ujang ◽  
Zulkepli Majid

Building Information Modeling (BIM) is a technology that focusing on the building element properties to the construction components which cover the interior and exterior building, while Geographic Information System (GIS) describe to the technology that can provide the large-scale information which cover inside and outside buildings (spaces and areas). In construction project application, BIM technology already been used as a worldwide tool while GIS rarely been applied. Each technology contains their own advantages that can be utilized in the construction project application. To bring the best effective approach in construction project, the integration between BIM and GIS technology can be considered. This paper presented an attempt in integrating BIM and GIS by using FME as a data integration platform to solve the limitation of BIM in construction project by using advantages of GIS. Through this research, an investigation of the data exchange during integration process between BIM and GIS will be look up. By using this approach, it is possible to store the BIM and GIS data in one environment. The end results for this paper will cover the method of the data exchange between BIM to GIS and GIS to BIM. Besides that, this paper highlight how GIS can solve the limitation in BIM in construction project.


2021 ◽  
Author(s):  
Anthony Fuentes ◽  
Michelle Michaels ◽  
Sally Shoop

The challenge of autonomous off-road operations necessitates a robust understanding of the relationships between remotely sensed terrain data and vehicle performance. The implementation of statistical analyses on large geospatial datasets often requires the transition between multiple software packages that may not be open-source. The lack of a single, modular, and open-source analysis environment can reduce the speed and reliability of an analysis due to an increased number of processing steps. Here we present the capabilities of a workflow, developed in R, to perform a series of spatial and statistical analyses on vehicle and terrain datasets to quantify the relationship between sensor data and vehicle performance in winter conditions. We implemented the R-based workflow on datasets from a large, coordinated field campaign aimed at quantifying the response of military vehicles on snow-covered terrains. This script greatly reduces processing times of these datasets by combining the GIS, data-assimilation and statistical analyses steps into one efficient and modular interface.


2021 ◽  
Vol 13 (22) ◽  
pp. 4682
Author(s):  
Yahao Zhang ◽  
Yuanyong Dian ◽  
Jingjing Zhou ◽  
Shoulian Peng ◽  
Yue Hu ◽  
...  

Pine wood nematode (PWN), Bursaphelenchus xyophilus, originating from North America, has caused great ecological and economic hazards to pine trees worldwide, especially affecting the coniferous forests and mixed forests of masson pine in subtropical regions of China. In order to prevent PWN disease expansion, the risk level and susceptivity of PWN outbreaks need to be predicted in advance. For this purpose, we established a prediction model to estimate the susceptibility and risk level of PWN with vegetation condition variables, anthropogenic activity variables, and topographic feature variables across a large-scale district. The study was conducted in Dangyang City, Hubei Province in China, which was located in a subtropical zone. Based on the location of PWN points derived from airborne imagery and ground survey in 2018, the predictor variables were conducted with remote sensing and geographical information system (GIS) data, which contained vegetation indices including normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), normalized burn ratio (NBR), and normalized red edge index (NDRE) from Sentinel-2 imagery in the previous year (2107), the distance to different level roads which indicated anthropogenic activity, topographic variables in including elevation, slope, and aspect. We compared the fitting effects of different machine learning algorithms such as random forest (RF), K-neighborhood (KNN), support vector machines (SVM), and artificial neural networks (ANN) and predicted the probability of the presence of PWN disease in the region. In addition, we classified PWN points to different risk levels based on the density distribution of PWN sites and built a PWN risk level model to predict the risk levels of PWN outbreaks in the region. The results showed that: (1) the best model for the predictive probability of PWN presence is the RF classification algorithm. For the presence prediction of the dead trees caused by PWN, the detection rate (DR) was 96.42%, the false alarm rate (FAR) was 27.65%, the false detection rate (FDR) was 4.16%, and the area under the receiver operating characteristic curve (AUC) was equal to 0.96; (2) anthropogenic activity variables had the greatest effect on PWN occurrence, while the effects of slope and aspect were relatively weak, and the maximum, minimum, and median values of remote sensing indices were more correlated with PWN occurrence; (3) modeling analysis of different risk levels of PWN outbreak indicated that high-risk level areas were the easiest to monitor and identify, while lower incidence areas were identified with relatively low accuracy. The overall accuracy of the risk level of the PWN outbreak was identified with an AUC value of 0.94. From the research findings, remote sensing data combined with GIS data can accurately predict the probability distribution of the occurrence of PWN disease. The accuracy of identification of high-risk areas is higher than other risk levels, and the results of the study may improve control of PWN disease spread.


2021 ◽  
pp. 87-103
Author(s):  
Kakoli Saha ◽  
Yngve K. Frøyen
Keyword(s):  

2021 ◽  
Author(s):  
Yoshiki Ogawa ◽  
Takuya Oki ◽  
Shenglong Chen ◽  
Yoshihide Sekimoto

2021 ◽  
Vol 2083 (3) ◽  
pp. 032023
Author(s):  
Le Zhang

Abstract Traditional data collection and review methods in power grid planning have always had the problems of time-consuming, poor real-time performance, and cumbersome processes. The application of mobile GIS solves the problems of data collection and review methods and makes more efficient use of mobile GIS terminal collection. The data of the mobile GIS solve the urgent problems that need to be solved since the popularization and application of mobile GIS. This system implements functions such as storage, transmission, and review based on mobile GIS data, which will greatly improve the efficiency of data collection by mobile terminals and reduce the cost of data collection. Realize the planning simulation of the power grid under the intelligent cycle of the whole scene.


2021 ◽  
pp. 111706
Author(s):  
SeyedehRabeeh HosseiniHaghighi ◽  
Pilar Monsalvete Álvarez de Uribarri ◽  
Rushikesh Padsala ◽  
Ursula Eicker

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