http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Rahul Dev Garg (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
-
Cloud computing for energy requirement and solar potential assessment
Mudit Kapoor,Rahul Dev Garg 대한공간정보학회 2018 Spatial Information Research Vol.26 No.4
The objective of this research is to derive an approach for the assessment of solar potential using cloud computing for a better energy planning. This approach is used to calculate energy requirement and solar potential having precise prediction probability. The energy requirement has been calculated based on the inputs such as the number of fans, tube lights, and electric pump with their wattage and usage hours. The assessment of the solar potential is based on the input parameters such as Global Horizontal Irradiance (GHI), sunshine hours, India Meteorological Department (IMD) data, cloud cover, tilted irradiance, etc. It is executed by software developed in Eclipse IDE (Integrated Development Environment), an open-source toolkit. Perl script has been used to convert the GHI onto the tilted surface which efficiently quantifies the collected solar irradiance. The IMD data are used for predicting the number of cloudy and rainy days for further estimation of solar potential. Cloud computing is used in uploading of the software module on the cloud. Google App Engine is used to deploy project information on the cloud. It has been found that enough solar potential is available to install Solar Photovoltaic (SPV) modules at Meerpur, India using software tool developed.
-
Using multi-source data and decision tree classification in mapping vegetation diversity
Gaurav Shukla,Rahul Dev Garg,Pradeep Kumar,Hari Shanker Srivastava,Pradeep Kumar Garg 대한공간정보학회 2018 Spatial Information Research Vol.26 No.5
This study acknowledges the problem of land cover demarcation in diverse vegetation condition. The Normalized Difference Vegetation Index is used for the preparation of base map. Further identification of mix and incorrect classes was done using ground truth. Radar data in combination with optical indices are used. In different NDVI classes, rRV with additional criteria on Normalized Difference Water Index successfully demarcated waterlogged area, polarization ratio rRV/rRH and backscattering coefficient rRH are found suitable to separate bare land from dry grass land, sparse and dense scrub could be separated by - (rRV ? rRH)/2 and NDVI is efficient to identify dense vegetation. The study area is taken as Keoladeo National Park in Bharatpur, India. Statistical similarity between ground truth and classified class has been assessed using Jaccard coefficient (JC), Jaccard distance (JD), Dice coefficient (DC) and F-score. High similarity values of JC, JD, DC and F-score are achieved for all land cover types except bare land. Although, dry grassland showed low value of F-score; the reason could be low precision of class. The overall accuracy (87.17%), producer’s accuracy (86.39%), user’s accuracy (85.81%) and Kappa Coefficient (0.84) are also utilized to analyze performance of classifier.
-
Sugarcane ratoon discrimination using LANDSAT NDVI temporal data
Sandeep Kumar Singla,Rahul Dev Garg,Om Prakash Dubey 대한공간정보학회 2018 Spatial Information Research Vol.26 No.4
Pre harvest prediction of sugarcane and sugar production is essential for obtaining the objectives of the national food security mission. Traditional field experimentation results are not reliable and are biased. Improvement in the accuracy and timeliness of crop yield estimation by blending of ancillary data and remotely sensed data in the temporal domain is indispensable. Ratoon sugarcane and planted sugarcane are the two prevalent agricultural practices in India. Ratoon sugarcane crop is suitable both from economic and production consideration. Identification of ratoon sugarcane and monitoring of its growth has been poorly studied. The objective of this study is to extract the information related to the ratoon sugarcane using remote sensing data. The present study proposed NDVIT, an index based on temporal values of NDVI data of Landsat 8 for monitoring and discrimination of ratoon sugarcane. This index has been found to provide 91% accuracy when tested on the ground in the Himalayan foothills region of Uttarakhand. Study indicated that the best period for discrimination of ratoon sugarcane crop is during the first week of April and last week of August to the end of September. This matches with the start of tillering stage and during the period of grand growth stage of the sugarcane.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
