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Analysis of Forest Cover Changes and Driving Forces in the Loess Plateau (Gansu Region) Based on Multisensor Remote Sensing Images
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Bo LIU1, 2, Quan-fu NIU1, 2, 3, *, Gang WANG1, 2, Ming-zhi LIU1, 2, Hao WANG1, 2, Jiao-jiao LEI1, 2
Science Technology and Engineering | 2025, 25(1) : 54 - 66
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Science Technology and Engineering | 2025, 25(1): 54-66
Papers·Astronomy and Geosciences
Analysis of Forest Cover Changes and Driving Forces in the Loess Plateau (Gansu Region) Based on Multisensor Remote Sensing Images
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Bo LIU1, 2, Quan-fu NIU1, 2, 3, *, Gang WANG1, 2, Ming-zhi LIU1, 2, Hao WANG1, 2, Jiao-jiao LEI1, 2
Affiliations
  • 1. School of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, China
  • 2. Emergency Mapping Engineering Research Center of Gansu Province, Lanzhou 730050, China
  • 3. Academician Expert Workstation of Gansu Dayu Jiuzhou Space Information Technology Co., Ltd., Lanzhou 730000, China
Published: 2025-01-08 doi: 10.12404/j.issn.1671-1815.2402147
Outline
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The Loess Plateau, as a natural ecological barrier in the western region of China, has made positive contributions to the sustainable development of the nation. The governance and restoration of the ecological environment on the Loess Plateau (Gansu region) plays a critical role in the implementation of China’s ecological civilization construction strategy. To monitor the changes in forest resources on the Loess Plateau (Gansu region) from 2008 to 2018, based on cloud platform, Landsat, PALSAR, and terrain data were integrated to explore the advantages of spectral index, backscatter, texture, and terrain features in obtaining forest resource information. The random forest feature selection algorithm was utilized to obtain the spatiotemporal distribution of forest cover in the study area for 10 years, and factor detection was conducted using geographic detectors. The results indicate that the random forest feature selection algorithm can effectively screen important feature information, with an overall accuracy of 91.88% and a Kappa coefficient of 0.91. The experimental scheme that integrates Landsat, PALSAR, and terrain data presents significantly higher accuracy compared to the forest classification results using a single data source. The overall accuracy of the four classification results is 86.65%, 88.23%, 90.15%, and 89.86% respectively. Over the past 10 years, the net increase in forest area in the study area is 0.60×104 km2. The areas with increased forests are primarily distributed in the central and eastern parts of Qingyang City, Pingliang City, Tianshui City, and the western region of Linxia Hui Autonomous Prefecture, while forest degradation primarily occurs in the southwestern part of Dingxi City and the central and eastern areas of Linxia Hui Autonomous Prefecture. In single-factor detection, land use type is the dominant factor in forest cover change, and the spatial distribution of suitable soil type and the auxiliary effect of rainfall provide favorable natural conditions for the survival rate of afforestation and the healthy growth of forests.

Loess Plateau (Gansu region)  /  feature selection  /  remote sensing monitoring  /  geographic detector
Bo LIU, Quan-fu NIU, Gang WANG, Ming-zhi LIU, Hao WANG, Jiao-jiao LEI. Analysis of Forest Cover Changes and Driving Forces in the Loess Plateau (Gansu Region) Based on Multisensor Remote Sensing Images[J]. Science Technology and Engineering, 2025 , 25 (1) : 54 -66 . DOI: 10.12404/j.issn.1671-1815.2402147
Year 2025 volume 25 Issue 1
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Article Info
doi: 10.12404/j.issn.1671-1815.2402147
  • Receive Date:2024-03-26
  • Online Date:2025-07-29
  • Published:2025-01-08
Article Data
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History
  • Received:2024-03-26
  • Revised:2024-10-11
Funding
Affiliations
    1. School of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, China
    2. Emergency Mapping Engineering Research Center of Gansu Province, Lanzhou 730050, China
    3. Academician Expert Workstation of Gansu Dayu Jiuzhou Space Information Technology Co., Ltd., Lanzhou 730000, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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