Accuracy of remote sensing data versus other sources of information for estimating net primary production inEucalyptus globulusLabill. andPinus pinasterAit. ecosystems in Portugal

2009 ◽  
Vol 35 (1) ◽  
pp. 37-53 ◽  
Author(s):  
Domingos M Lopes ◽  
José T Aranha ◽  
Nigel Walford ◽  
James O'Brien ◽  
Neil Lucas
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Net Primary Production ◽  
Remote Sensing Data ◽  
Sources Of Information ◽  
Sensing Data

The Research of Livestock Carrying Capacity of Rangeland Ecosystem in HulunBuir

2011 ◽  
Vol 365 ◽  
pp. 110-114 ◽  
Author(s):  
Rui Jie Wang ◽  
Lian Wei Yang
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Carrying Capacity ◽  
Net Primary Production ◽  
Remote Sensing Data ◽  
Population Pressure ◽  
Grassland Ecosystem ◽  
Sensing Data ◽  
Ecological Degradation

Due to population pressure and over-grazing, ecological degradation of the rangeland in HulunBuir becomes more and more serious in past decades. To balance pasture grazing activities and ecosystem sustainability, monitoring grass productivity of the rangelands and livestock carrying capacity are very necessary. Grassland yield is the basis of developing livestock, net primary production (NPP) is an important parameters in determining carrying capacity. Using MODIS remote sensing data, we estimated the NPP of grassland ecosystem of HulunBuir in 2006. The total NPP of grassland ecosystem in HulunBuir was 2.9×1013 gC/a in 2006, with an average of 261.01gC/m2•a. Then we based on the estimated NPP to calculate the optimal carrying capacity which was 765.41 ten thousands sheep units. The actual carrying capacity was 1838.45 ten thousands sheep units, total overloading was 1073.04 ten thousands sheep units. The serious regions of overloading were Arong Banner with 325.45 ten thousands sheep units, Zlantun City with 270.72 ten thousands sheep units, Molidawadahanr Autonomous Banner with 254.71 ten thousands sheep units. The carrying capacity of Oroqin Autonomous Banner, Genhe City and Erguna City were scarcity, there were great potential to excavate in livestock.

scholarly journals Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

2021 ◽  
Vol 13 (6) ◽  
pp. 1131
Author(s):  
Tao Yu ◽  
Pengju Liu ◽  
Qiang Zhang ◽  
Yi Ren ◽  
Jingning Yao
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Production ◽  
Remote Sensing Data ◽  
Forest Degradation ◽  
Observation Data ◽  
Area Index ◽  
Sensing Data ◽  
Multi Scale

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

scholarly journals Monitoring of glaciers in the Polar Urals using remote sensing Data

2020 ◽  
Vol 217 ◽  
pp. 04003
Author(s):  
Natalia Martynova ◽  
Valentina Budarova ◽  
Artem Sheremetinsky ◽  
Nikita Mezentsev
Keyword(s):  
Remote Sensing ◽  
Technological Progress ◽  
Satellite Images ◽  
Research Question ◽  
Remote Sensing Data ◽  
Field Work ◽  
Sources Of Information ◽  
Sensing Data ◽  
Polar Urals ◽  
The Polar Urals

The development of technological progress provides more opportunities for indirect monitoring of changes in the environment. Remote sensing is one of The most accessible and reliable sources of information. In this work, we used satellite images from the Landsat family. The theoretical justification of the research question is given. The research methodology was developed. Collection and processing of satellite images for various time periods. A series of schematic maps based on remote sensing Data has been created. As a result of digitization of satellite images, 9 glacier contours were obtained by year. We determined the area of the Romantics glacier and found that it lost at least 60% of its original area. These studies were used to build a series of cartographic schemes that clearly show the reduction of the glacier area. It is concluded that the use of remote sensing allows you to solve problems, monitoring the object. The use of this method allows not only to save time for field work, but also material costs for expedition equipment and various equipment. This method can be tested on any objects.

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.  

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