Increasing the Accuracy of Mapping Urban Forest Carbon Density by Combining Spatial Modeling and Spectral Unmixing Analysis
Accurately mapping urban vegetation carbon density is challenging because of complex landscapes and mixed pixels. In this study, a novel methodology was proposed that combines a linear spectral unmixing analysis (LSUA) with a linear stepwise regression (LSR), a logistic model-based stepwise regressi...
Main Authors: | Hua Sun, Guangping Qie, Guangxing Wang, Yifan Tan, Jiping Li, Yougui Peng, Zhonggang Ma, Chaoqin Luo |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2015-11-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | http://www.mdpi.com/2072-4292/7/11/15114 |
Similar Items
-
Density Based Clustering using Mutual K-Nearest Neighbors
by: Dixit, Siddharth
Published: (2015) -
Experimenting the design-based k-NN approach for mapping and estimation under forest management planning
by: Mattioli W, et al.
Published: (2012-02-01) -
MAPPING AND UNCERTAINTY ANALYSIS OF URBAN VEGETATION CARBON DENSITY BY COMBINING SPATIAL MODELING, DE-SHADOW & SPECTRAL UNMIXING ANALYSIS
by: Qie, Guangping
Published: (2019) -
Mutual k Nearest Neighbor based Classifier
by: Gupta, Nidhi
Published: (2010) -
Density Peaks Clustering Based on Weighted Local Density Sequence and Nearest Neighbor Assignment
by: Donghua Yu, et al.
Published: (2019-01-01)