Monitoring Wind Farms Occupying Grasslands Based on Remote-Sensing Data from China’s GF-2 HD Satellite—a Case Study of Jiuquan City, Gansu Province, China
Resources Conservation and Recycling（2017）
Key Laboratory of Agri-informatics of the Ministry of Agriculture
Abstract
Wind power is a clean and renewable resource, and it is rapidly becoming an important component of sustainable development and resource transfer. However, the construction of wind farms impacts the environment and has been the subject of considerable research. In this study, we verified whether China's GF-2 HD satellite (GF-2) could be used to monitor the 10 million kilowatt wind power grassland construction area in Jiuquan City, Gansu Province. Monitoring was performed by comparing the imaging results from the Landsat 8 OLI and China's GF-1 HD satellite (GF-1). We performed an interactive interpretation of the remote sensing images and verified the accuracy of these interpretations using measured field data. We evaluated 354 pieces of wind turbine equipment with an average construction density of 0.31 km(2) per device. The construction of a single wind turbine was found to damage nearly 3000 m(2) of grassland. The average area of grassland damaged by 3 MW and 1.5 MW turbines was 5757 m(2) and 2496 m(2), respectively. Approximately 2.44 km(2) of farmland was occupied by wind power construction and accounted for approximately 2.2% of the study area. Roads covered 60.6% of the farmland occupied by wind power construction. The average difference between the measured and calculated GF-2 image data was 0.09, and the overall interpretation accuracy was approximately 84%. Therefore, the use of comprehensive imaging analyses and GF-2 image data are feasible for monitoring grasslands under construction for wind power. In addition, the impacts of wind farm construction on vegetation destruction and soil erosion are discussed. In this study, grassland wind farms are explored using remote sensing tools to guide decision making with regards to the rational use of grassland resources and their sustainable development. (C) 2016 Elsevier B.V. All rights reserved.
MoreTranslated text
Key words
Wind farm,Grassland monitoring,China’s GF-2 HD satellite,Gansu province
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话