scholarly journals Simulation of the Grazing Effects on Grassland Aboveground Net Primary Production Using DNDC Model Combined with Time-Series Remote Sensing Data—A Case Study in Zoige Plateau, China

2016 ◽  
Vol 8 (3) ◽  
pp. 168 ◽  
Author(s):  
Jiyan Wang ◽  
Ainong Li ◽  
Jinhu Bian
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Primary Production ◽  
Net Primary Production ◽  
Remote Sensing Data ◽  
Aboveground Net Primary Production ◽  
Dndc Model ◽  
Sensing Data ◽  
Grazing Effects

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.

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