scholarly journals Natural Resource Monitoring ofRheum tanguticumby Multilevel Remote Sensing

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Caixiang Xie ◽  
Jingyuan Song ◽  
Fengmei Suo ◽  
Xiwen Li ◽  
Ying Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Medicinal Plants ◽  
Remote Sensing Image ◽  
Vegetation Coverage ◽  
Resource Monitoring ◽  
Satellite Imaging ◽  
Ground Investigation ◽  
Natural Resource Monitoring ◽  
Low Altitude ◽  
Rheum Tanguticum

Remote sensing has been extensively applied in agriculture for its objectiveness and promptness. However, few applications are available for monitoring natural medicinal plants. In the paper, a multilevel monitoring system, which includes satellite and aerial remote sensing, as well as ground investigation, was initially proposed to monitor naturalRheum tanguticumresource in Baihe Pasture, Zoige County, Sichuan Province. The amount ofR. tanguticumfrom images isM=S*ρandSis vegetation coverage obtained by satellite imaging, whereasρisR. tanguticumdensity obtained by low-altitude imaging. Only theR. tanguticumwhich coverages exceeded 1 m2could be recognized from the remote sensing image because of the 0.1 m resolution of the remote sensing image (called effective resource at that moment), and the results of ground investigation represented the amounts ofR. tanguticumresource in all sizes (called the future resource). The data in paper showed that the present available amount ofR. tanguticumaccounted for 4% to 5% of the total quantity. The quantity information and the population structure ofR. tanguticumin the Baihe Pasture were initially confirmed by this system. It is feasible to monitor the quantitative distribution for natural medicinal plants with scattered distribution.

Acquisitions of Vegetation Coverage and Cultivated Land Occupation Ratio of Taiyuan Valley Plain for Example Using CBERS-02B CCD Image

2012 ◽  
Vol 518-523 ◽  
pp. 5663-5667
Author(s):  
Shi Wei Li ◽  
Ji Long Zhang ◽  
Jian Sheng Yang
Keyword(s):  
Remote Sensing ◽  
Water Conservation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Image ◽  
Google Earth ◽  
Vegetation Coverage ◽  
Cultivated Land ◽  
Land Occupation ◽  
Coverage Ratio

Vegetation covering situation is very important for the quality of air quality, soil and water conservation ability and soil forming in an area. By using the remote sensing image of Taiyuan Valley Plain, the application of Normalized Difference Vegetation Index (NDVI) and unsupervised classification, the vegetation coverage map which includes non-cultivated land disposition and cultivated land disposition was obtained using ERDAS Imagine software. To evaluate the accuracy of the results, 200 points were sampled randomly, the high spatial resolution remote sensing image from Google Earth was used as the reference. The overall classification accuracy is 82%, with the Kappa statistic of 0.81. By counting the totally pixel acreage, it was gotten that the vegetation coverage was 46% and the cultivated land coverage ratio was 31% in the study area.

Climate Engine: Cloud Computing and Visualization of Climate and Remote Sensing Data for Advanced Natural Resource Monitoring and Process Understanding

2017 ◽  
Vol 98 (11) ◽  
pp. 2397-2410 ◽  
Author(s):  
Justin L. Huntington ◽  
Katherine C. Hegewisch ◽  
Britta Daudert ◽  
Charles G. Morton ◽  
John T. Abatzoglou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Big Data ◽  
Natural Resource ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Resource Monitoring ◽  
Sensing Data ◽  
Process Understanding ◽  
Natural Resource Monitoring

Abstract The paucity of long-term observations, particularly in regions with heterogeneous climate and land cover, can hinder incorporating climate data at appropriate spatial scales for decision-making and scientific research. Numerous gridded climate, weather, and remote sensing products have been developed to address the needs of both land managers and scientists, in turn enhancing scientific knowledge and strengthening early-warning systems. However, these data remain largely inaccessible for a broader segment of users given the computational demands of big data. Climate Engine (http://ClimateEngine.org) is a web-based application that overcomes many computational barriers that users face by employing Google’s parallel cloud-computing platform, Google Earth Engine, to process, visualize, download, and share climate and remote sensing datasets in real time. The software application development and design of Climate Engine is briefly outlined to illustrate the potential for high-performance processing of big data using cloud computing. Second, several examples are presented to highlight a range of climate research and applications related to drought, fire, ecology, and agriculture that can be rapidly generated using Climate Engine. The ability to access climate and remote sensing data archives with on-demand parallel cloud computing has created vast opportunities for advanced natural resource monitoring and process understanding.

Satellite remote sensing in the policy sphere

2019 ◽  
pp. 133-147
Author(s):  
Nathalie Pettorelli
Keyword(s):  
Remote Sensing ◽  
Natural Resources ◽  
Satellite Remote Sensing ◽  
Natural Capital ◽  
Remote Sensing Data ◽  
Resource Monitoring ◽  
Biodiversity Indicators ◽  
Sensing Technology ◽  
Essential Biodiversity Variables ◽  
Natural Resource Monitoring

This chapter focuses on the interface between satellite remote sensing and policy relevant to the management of natural resources, looking at ways for this technology to support decision making at the national to international scale. First, it briefly introduces (1) the main international conventions that are relevant to the management of natural resources and that could easily benefit from an increased consideration for satellite remote sensing technology, and (2) the main platforms facilitating the integration of satellite remote sensing data at the convention level. Second, it introduces the most popular conceptual frameworks that are being considered to help coordinate and structure natural resource monitoring efforts worldwide, namely the essential biodiversity variables framework, the biodiversity indicators framework, the ecosystem services framework, and the natural capital accounting framework. The final part highlights current challenges and lists a series of possible ways forward.

Analysis on Terrain Effects to Vegetation Coverage by Quantitative Remote Sensing

2012 ◽  
Vol 518-523 ◽  
pp. 5673-5677 ◽  
Author(s):  
Zheng Zheng Yu ◽  
Jun Ge Zhu ◽  
Yue Lei Qian
Keyword(s):  
Remote Sensing ◽  
High Altitude ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Coverage ◽  
Mountain Area ◽  
Binary Model ◽  
Quantitative Remote Sensing ◽  
Terrain Effects ◽  
Low Altitude

In this paper,we use SPOT VEGETATION dataset of west mountain area of Henan provience in year 1998,2003 and 2008, calculated the vagetation coverage based on the normalized difference vegetation index and improved pixel binary model. And then, combine with the DEM, we quantitatively analysed the terrain effects to vegetation coverage,the result shows that: The vegetation coverage in high altitude areas was steady- going. But in the low altitude areas, the change of vegetation coverage is very sharp, and the ecosystem is fragile in the heavy gradient area. In the small slope areas, the probability of degradation and resoration both relative high; In a same phases,the vegetation coverage in south and north aspect is relatively high but lower in east and west aspect.The vegetation degradation in southwest aspect was more seriously than in southeast aspect.

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