Large-scale estimates of gross primary production on the Qinghai-Tibet plateau based on remote sensing data

2017 ◽  
Vol 11 (11) ◽  
pp. 1166-1183 ◽  
Author(s):  
Minna Ma ◽  
Wenping Yuan ◽  
Jie Dong ◽  
Fawei Zhang ◽  
Wenwen Cai ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Large Scale ◽  
Gross Primary Production ◽  
Remote Sensing Data ◽  
Tibet Plateau ◽  
Sensing Data ◽  
Qinghai Tibet Plateau

scholarly journals Aplikasi Penghitungan Gross Primary Production dari Data Penginderaan Jauh

2016 ◽  
pp. 31
Author(s):  
Komang Gede Kurniadi ◽  
I Putu Agung Bayupati ◽  
I Dewa Nyoman Nurweda Putra
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Gross Primary Production ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Automatic Data ◽  
Sensing Data ◽  
Metadata File ◽  
Production Calculation

Calculation of Gross Primary Production that utilize remote sensing data is can be done on commercial remote sensing software by manual method. The commercial remote sensing software does not provides a specific feature that allow the user to do the Gross Primary Production calculation. This research is aimed to to build a remote sensing software that can be specifically used to do the Gross Primary Production calculation for Denpasar area. This software accepts remote sensing data as an input, such as satellite image from Landsat 8 OLI and TIRS and metadata file. The formulas and supporting data that required on the Gross Primary Production calculation are implemented on software in order to make an automatic image processing software. There also some additional feature on this software such as automatic data parsing from metadata file, cropping, masking and zoom that could help user to do the Gross Primary Production calculation. The developed software is able to produce information such as Gross Primary Production  value that depicted by a figure with color segmentation, area of the segments and mean, minimum and maximum value of the Gross Primary Production.  

scholarly journals ESTIMATION OF GROSS PRIMARY PRODUCTION USING SATELLITE DATA AND GIS IN URBAN AREA, DENPASAR

2011 ◽  
Vol 7 (1) ◽  
Author(s):  
A.R. As-syakur ◽  
T. Osawa ◽  
IW.S. Adnyana
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Primary Production ◽  
Spatial Resolution ◽  
Satellite Data ◽  
High Spatial Resolution ◽  
Gross Primary Production ◽  
Remote Sensing Data ◽  
Sensing Data

Remote sensing data with high spatial resolution is very useful to provideinformation about Gross Primary Production (GPP) especially over spatial coverage in theurban area. Most models of ecosystem carbon exchange based on remote sensing data usedlight use efficiency (LUE) model. The aim of this research was to analyze the distributionof annual GPP urban area of Denpasar. Two main satellite data used in this study wereALOS/AVNIR-2 and Aster satellite data. Result showed that annual value of GPP usingALOS/AVNIR-2 varied from 0.130 gC m-2 yr-1 to 2586.181 gC m-2 yr-1. Meanwhile, usingAster the value varied from 0.144 gC m-2 yr-1 to 2595.264 gC m-2 yr-1. The annual value ofGPP ALOS was lower than the value of Aster, because ALOS have high spatial resolutionand smaller interval of spectral resolution compared to Aster. Different land use couldeffect the value of GPP, because the different land use has different vegetation type,distribution, and different photosynthetic pathway type. The high spatial resolution of theremote sensing data is crucial to discriminate different land cover types in urban region.With heterogeneous land cover surface, maximum value of GPP using ALOS/AVNIR-2was smaller than that of Aster, however, the annual mean of GPP value usingALOS/AVNIR-2 was higher than that of Aster.

scholarly journals Spatio-Temporal Changes of Snow Cover and Its Relationship With Meteorological Factors on the Qinghai-Tibet Plateau, China

2021 ◽  
Author(s):  
Haoyu Jin ◽  
xiaohong chen
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Snow Cover ◽  
Remote Sensing Data ◽  
Downward Trend ◽  
Tibet Plateau ◽  
Important Indicator ◽  
Sensing Data ◽  
Spatio Temporal ◽  
Qinghai Tibet Plateau

Abstract The Qinghai-Tibet Plateau (TP) is one of the most sensitive areas to climate change, and its ecological environment changes directly or indirectly reflect the global climate change trend. The snow cover ratio (SCR) is an important indicator reflecting the climate and environmental changes of the TP. The daily remote sensing data of snow cover on the TP from 2003 to 2014 were used to study the spatio-temporal distribution of snow cover on the TP. The results have shown that the average snowmelt day on the TP starts on the 103rd day and ends on the 223rd day of a year, and the snowmelt duration has a downward trend. Snow is mainly distributed in the Nyainqentanglha Mountains, Karakoram Mountains and Himalayas. The SCR in summer has a downward trend, while in autumn has a rising trend. This shows that the difference in SCR during the year has enlarged, increasing the risk of snowmelt floods. The SCR is highly correlated with temperature, but weakly correlated with precipitation. Using the long-term remote sensing data of snow cover, the distribution of glacier coverage on the TP can be extracted, in which glaciers on the TP account for about 1%. This research provides an important reference for in-depth understanding of the snow cover changes on the TP and their impact on the environment.

scholarly journals Permafrost Presence/Absence Mapping of the Qinghai-Tibet Plateau Based on Multi-Source Remote Sensing Data

2018 ◽  
Vol 10 (2) ◽  
pp. 309 ◽  
Author(s):  
Yaya Shi ◽  
Fujun Niu ◽  
Chengsong Yang ◽  
Tao Che ◽  
Zhanju Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Tibet Plateau ◽  
Sensing Data ◽  
Qinghai Tibet Plateau
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