scholarly journals Temporal-spatial distribution of euphotic depth and its influencing factors in Lake Chenghai, Yunnan Province, China

2019 ◽  
Vol 31 (1) ◽  
pp. 256-267
Author(s):  
LI Kaidi ◽  
◽  
ZHOU Yuanyang ◽  
ZHOU Qichao ◽  
DONG Yunxian ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Influencing Factors ◽  
Yunnan Province ◽  
Euphotic Depth ◽  
Lake Chenghai

scholarly journals Mapping Panax Notoginseng Plantations by Using an Integrated Pixel- and Object-Based (IPOB) Approach and ZY-3 Imagery

2021 ◽  
Vol 13 (11) ◽  
pp. 2184
Author(s):  
Zhiqi Yang ◽  
Jinwei Dong ◽  
Weili Kou ◽  
Yuanwei Qin ◽  
Xiangming Xiao
Keyword(s):  
Spatial Distribution ◽  
Yunnan Province ◽  
Vascular Diseases ◽  
Panax Notoginseng ◽  
Traditional Herbal Medicine ◽  
Target Class ◽  
Object Based ◽  
The Government ◽  
Level Scale ◽  
Iteration Analysis

Plantations of Panax notoginseng (PN), traditional herbal medicine for the prevention and treatment of vascular diseases, are expanding rapidly in China, especially in the Yunnan province of China, due to its increasing demands and prices and causing dramatic environmental concerns. However, existing information on its planting area and spatial distribution are limited. Here, we mapped the PN planting area by using a new integrated pixel- and object-based (IPOB) approach, the Random Forest (RF) classifier, and the high-resolution ZiYuan-3 (ZY-3) imagery. We improved the procedures of classification in three aspects: (1) a new spectral index—Normalized Difference PN Index (NDPI)—was proposed, (2) the efficiency and scale of segmentation were optimized by using the Bi-level Scale-sets Model (BSM), and (3) feature variables were selected through an iteration analysis from 99 feature variables (spectral, textural, geometric, and geographic). Compared with the pixel- and the object-based methods, the IPOB has the highest F1 score of 0.98 and also has high robustness in terms of user and producer accuracies (97% and 99%, respectively), following by the object-based method (F1 = 0.94) and the pixel-based method (F1 = 0.93). The high accuracy was expected since the target class has very distinctive spectral and textural characteristics. Although all three approaches showed reasonably high accuracies due to the application of the NDPI and optimized procedures, the result showed the outperformance of the proposed IPOB approach. The framework established in this study expects to apply for regional or national PN surveys extensively. The information on the area and spatial distribution of PN can guide the government on policy making for the planting and exporting of traditional Chinese medicine resources.

Spatial Distribution and Influencing Factors of Crop Loss Caused by Wildlife

2021 ◽  
Vol 36 (1-2) ◽  
pp. 103-123
Author(s):  
Wenhui Chen ◽  
Sha Li ◽  
Xi Chenqi
Keyword(s):  
Spatial Distribution ◽  
Influencing Factors ◽  
Crop Loss

Spatial distribution of tuberculosis and its socioeconomic influencing factors in mainland China 2013–2016

2019 ◽  
Vol 24 (9) ◽  
pp. 1104-1113
Author(s):  
Qi Wang ◽  
Liang Guo ◽  
Jing Wang ◽  
Leijie Zhang ◽  
Wanqi Zhu ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Influencing Factors ◽  
Mainland China
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