An Unsupervised Fuzzy Clustering Approach for the Digital Mapping of Soil Organic Carbon in a Montaneous Region of China

2016 ◽  
pp. 195-203
Author(s):  
Lei Zhu ◽  
Jiandong Sheng ◽  
Hongtao Jia ◽  
Hongqi Wu
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Fuzzy Clustering ◽  
Digital Mapping ◽  
Clustering Approach ◽  
Unsupervised Fuzzy Clustering

scholarly journals An Unsupervised Fuzzy Clustering Approach for Early Screening of COVID-19 from Radiological Images

2021 ◽  
pp. 1-1
Author(s):  
Weiping Ding ◽  
Shouvik Chakraborty ◽  
Kalyani Mali ◽  
Sankhadeep Chatterjee ◽  
Janmenjoy Nayak ◽  
...  
Keyword(s):  
Fuzzy Clustering ◽  
Early Screening ◽  
Radiological Images ◽  
Clustering Approach ◽  
Unsupervised Fuzzy Clustering

Exploring the driving forces and digital mapping of soil organic carbon using remote sensing and soil texture

CATENA ◽  
2019 ◽  
Vol 182 ◽  
pp. 104141 ◽  
Author(s):  
Nikou Hamzehpour ◽  
Hossein Shafizadeh-Moghadam ◽  
Roozbeh Valavi
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Texture ◽  
Driving Forces ◽  
Digital Mapping

scholarly journals Digital mapping of soil organic carbon stocks in Ukraine

2019 ◽  
pp. 5-11
Author(s):  
K.V. Viatkin ◽  
◽  
Y.V. Zalavskyi ◽  
V.V. Lebed ◽  
O.I. Sherstyuk ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Stocks ◽  
Digital Mapping ◽  
Soil Organic Carbon Stocks

scholarly journals Exploring the Influence of Spatial Resolution on the Digital Mapping of Soil Organic Carbon by Airborne Hyperspectral VNIR Imaging

2019 ◽  
Vol 11 (9) ◽  
pp. 1032 ◽  
Author(s):  
Long Guo ◽  
Tiezhu Shi ◽  
Marc Linderman ◽  
Yiyun Chen ◽  
Haitao Zhang ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Autocorrelation ◽  
Spatial Resolution ◽  
Prediction Accuracy ◽  
Hyperspectral Images ◽  
Moran’S I ◽  
Moran's I ◽  
Resampling Methods ◽  
Digital Mapping

Accurate digital mapping of soil organic carbon (SOC) is important in understanding the global carbon cycle and its implications in mitigating climate change. Visible and near-infrared hyperspectral imaging technology provides an alternative for mapping SOC efficiently and accurately, especially at regional and global scales. However, there is a lack of understanding of the impacts of spatial resolution of hyperspectral images and spatial autocorrelation of spectral information on the accuracy of SOC retrievals. In this study, the hyperspectral images (380–1700 nm) with a spatial resolution of 1 m were acquired by Headwall Micro-Hyperspec airborne sensors. Then, hyperspectral images were resampled into three different spatial resolutions of 10 m, 30 m, and 60 m by near neighbor (NN), bilinear interpolation (BI), and cubic convolution (CC) resampling methods. The geographically weighted regression (GWR) model was used to explore the role of spatial autocorrelation in predicting SOC contrast with the partial least squares regression (PLSR) model. Results showed that (1) the hyperspectral images can be used to predict SOC and the spatial autocorrelation can improve the prediction accuracy, as the ratio of performance to interquartile range (RPIQ) values of PLSR and GWR were 1.957 and 2.003; (2) The SOC prediction accuracy decreased with the degradation of spatial resolution, and the RPIQ values of PLSR were from 1.957 to 1.134, and of GWR were from 2.003 to 1.136; (3) Three resampling methods had a much weaker influence than spatial resolution on SOC predictions because the differences of RPIQ values of NN, BI, and CC resampling methods were 0.146, 0.175, and 0.025 in the spatial resolutions of 10 m, 30 m, and 60 m, respectively; (4) Finally, the Global Moran’s I and the Anselin Local Moran’s I proved the existence of the spatial autocorrelation in SOC maps. We hope that this study can offer valuable information for digital soil mapping by satellite hyperspectral images in the near future.

scholarly journals Digital Mapping of Soil Organic Carbon Contents and Stocks in Denmark

PLoS ONE ◽  
2014 ◽  
Vol 9 (8) ◽  
pp. e105519 ◽  
Author(s):  
Kabindra Adhikari ◽  
Alfred E. Hartemink ◽  
Budiman Minasny ◽  
Rania Bou Kheir ◽  
Mette B. Greve ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Digital Mapping
Sign in / Sign up

Export Citation Format

Share Document