scholarly journals Mapping Above-Ground Biomass of Winter Oilseed Rape Using High Spatial Resolution Satellite Data at Parcel Scale under Waterlogging Conditions

2017 ◽  
Vol 9 (3) ◽  
pp. 238 ◽  
Author(s):  
Jiahui Han ◽  
Chuanwen Wei ◽  
Yaoliang Chen ◽  
Weiwei Liu ◽  
Peilin Song ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Oilseed Rape ◽  
Satellite Data ◽  
High Spatial Resolution ◽  
Above Ground Biomass ◽  
Winter Oilseed Rape ◽  
Ground Biomass

scholarly journals Estimation and Mapping of Winter Oilseed Rape LAI from High Spatial Resolution Satellite Data Based on a Hybrid Method

2017 ◽  
Vol 9 (5) ◽  
pp. 488 ◽  
Author(s):  
Chuanwen Wei ◽  
Jingfeng Huang ◽  
Lamin Mansaray ◽  
Zhenhai Li ◽  
Weiwei Liu ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Oilseed Rape ◽  
Satellite Data ◽  
Hybrid Method ◽  
High Spatial Resolution ◽  
Winter Oilseed Rape

Modeling Above-Ground Biomass in Tallgrass Prairie Using Ultra-High Spatial Resolution sUAS Imagery

2014 ◽  
Vol 80 (12) ◽  
pp. 1151-1159 ◽  
Author(s):  
Huan Wang ◽  
Chuyuan Wang ◽  
Kevin P. Price ◽  
Deon van der Merwe ◽  
Nan An
Keyword(s):  
Spatial Resolution ◽  
Tallgrass Prairie ◽  
High Spatial Resolution ◽  
Above Ground Biomass ◽  
Ground Biomass

scholarly journals Above-ground biomass estimation for Quercus rotundifolia using vegetation indices derived from high spatial resolution satellite images

2018 ◽  
Vol 51 (1) ◽  
pp. 932-944 ◽  
Author(s):  
Fabrício L. Macedo ◽  
Adélia M. O. Sousa ◽  
Ana Cristina Gonçalves ◽  
José R. Marques da Silva ◽  
Paulo A. Mesquita ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Satellite Images ◽  
High Spatial Resolution ◽  
Vegetation Indices ◽  
Biomass Estimation ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Quercus Rotundifolia

Improvement in Estimation of Aboveground Biomass of Albizia lebbeck using Fraction Reflectance of Landsat TM Data

2015 ◽  
Vol 1 (01) ◽  
pp. 99-105
Author(s):  
O. P. Tripathi ◽  
J. Deka ◽  
J. Y. Yumnam ◽  
P. Mahanta
Keyword(s):  
Satellite Data ◽  
Vegetation Index ◽  
Linear Regression Analysis ◽  
Landsat Tm ◽  
Surface Reflectance ◽  
Above Ground Biomass ◽  
Mixed Pixel ◽  
Ground Biomass ◽  
Medium Resolution ◽  
Reflectance Data

The study emphasizes on the ability of spectral mixture analysis (SMA) to improve the estimate of above ground biomass from spectral reflectance of satellite data. Digital number (DN) values of satellite data were converted to surface reflectance and fraction reflectance data. Linear regression analysis was performed between above ground biomass data with both total surface reflectance and SMA produced fraction reflectance values separately. The analysis revealed that the best positive correlation was observed between AGB and the fraction reflectance values of FR_TM 5/3 with R2 = 0.865 whereas with the total reflectance data, atmospherically resistant vegetation index (ARVI) and TM5/3 resulted moderate correlation R2= 0.452 and - 0.248, respectively. Pixel level AGB ranges between 0.001t to 13.53t. The total AGB of the study area (275.85 ha) was estimated to be 14249t. Thus separation of endmembers from the mixed pixel from a course to medium resolution satellite data can play an important role in improving AGB estimation performance, especially for those sites with multiple cover features. The study demonstrates the potentiality of Landsat TM data in concatenate with SMA in improving the estimation of AGB which can be further improved by identifying all possible endmembers and highly configured methodology.

Sign in / Sign up

Export Citation Format

Share Document