Application of RS Vegetation Index in Forest Cutting-area Detection for“Three Total”Inspection in State-owned Forest Regions——A Case Study in Heli Forestry Bureau

FOREST RESOURCES WANAGEMENT ›› 2009›› Issue (2): 100-105.

• Science and Technology • Previous Articles Next Articles

Application of RS Vegetation Index in Forest Cutting-area Detection for“Three Total”Inspection in State-owned Forest Regions——A Case Study in Heli Forestry Bureau

HAN Aihui^1,2, BAI Jianwei³, ZHI Chuanggui²

1. The Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing 100083, China;
2. Academy of Forestry Inventory and Planning, Beijing 100714, China;
3. Office of Forest Resources Supervision and Administration, SFA, Beijing 100714, China

Received:2009-01-12 Revised:2009-04-01 Online:2009-04-28 Published:2020-12-16

Abstract

Abstract: Using the results of TM image interpretation and field sampling investigation of “Three Total” inspection in 2007 in Heli ForestryBureau, the paper analyses NDVI changes of TM data in forest cutting-area and analyses the changes relationship between different cutting modes and different cutting intensities based on TM data of the same year or different years, which provides a useful study on the application of remote sense images in “Three Total” inspection.

Key words: cutting-area, vegetation index, remote sensing, “Three Total” inspection, state-owned forest regions

CLC Number:

TP79
S757.4

HAN Aihui, BAI Jianwei, ZHI Chuanggui. Application of RS Vegetation Index in Forest Cutting-area Detection for“Three Total”Inspection in State-owned Forest Regions——A Case Study in Heli Forestry Bureau[J]. FOREST RESOURCES WANAGEMENT, 2009, (2): 100-105.

[1]	YANG Chen, LI Yichen, ZHANG Maobin, LI Xin, SHI Wenjie, ZE Sangzi, MA Yunqiang. Research progress on remote sensing of pine wilt disease based on phased prevention and control [J]. Forest and Grassland Resources Research, 2025, 0(5): 121-128.
[2]	HE Ziwei, LIU Zhijun. Spatiotemporal variation analysis of grassland vegetation coverage in the Three-River-Source National Park (Tangbei Area)in 2000-2023 [J]. Forest and Grassland Resources Research, 2025, 0(5): 25-31.
[3]	GUO Liming, LIU Baojun, TIAN Naling, LI Qingbo, LI Wanying. Analysis of ecological environment change and driving factors of enclosure-conservation area for desertified land in Ningxia [J]. Forest and Grassland Resources Research, 2025, 0(3): 9-18.
[4]	ZHANG Jiao, CHEN Lingxiu, DAI Hongxia, LIU Shenglong, CHEN Sitao, XU Mengjie, XIE Huili, LIU Xingzhao. Vegetation change and its drivers in the Minjiang River Basin in 2000—2020 [J]. Forest and Grassland Resources Research, 2025, 0(1): 104-113.
[5]	CAI Yingli, ZHU Hongge. Research on Forest Loss Assessment Based on Multi-Source Datasets [J]. Forest and Grassland Resources Research, 2024, 0(6): 27-34.
[6]	ZENG Shuangbei, ZHANG Dawei, DU Zongyi. Analysis of Changes in Land Use Landscape Pattern in Wenchuan County [J]. Forest and Grassland Resources Research, 2024, 0(4): 70-77.
[7]	XIONG Ke, XING Yuanjun, HE Xiaofeng, TANG Lin, LU Hongwang. Remote Sensing Estimation of Forest AGB Based on KNN Algorithm [J]. Forest and Grassland Resources Research, 2024, 0(3): 106-112.
[8]	AN Da, WANG Haibing, BAI Yunfei, LI Siqi, ZHANG Xue, XU Guangfu. Spatial Distribution Patterns and Landscape Characteristics of Caragana korshinskii Groves in the Kubuqi Desert [J]. Forest and Grassland Resources Research, 2024, 0(2): 43-50.
[9]	LUO Yaopei, LI Heping, YANG Guangbin, CEN Gang, LI Man, CAO Qianyang, WANG Renru, CHEN Panfang. Volume Estimation Method of High Canopy Density Pinus massoniana Forest Based on UAV Image [J]. Forest and Grassland Resources Research, 2024, 0(2): 68-79.
[10]	HAO Jun, LYU Kangting, HU Tianqi, WANG Yunge, XU Gang. Remote Sensing Inversion of Mangrove Biomass Based on Machine Learning [J]. Forest and Grassland Resources Research, 2024, 0(1): 65-72.
[11]	ZHU Zan, WANG Yongjun, WANG Jianqi, XU Yulan, QIU Xinqi, WAN Xi. Construction of a Carbon Storage Measurement Model for Eucalyptus Canopy in Guangxi Based on Drone Oblique Photography [J]. Forest and Grassland Resources Research, 2024, 0(1): 88-94.
[12]	REN Xiaoqi, HOU Peng, CHEN Yan. Advances in Remote Sensing Retrieval of Forest Aboveground Biomass [J]. Forest and Grassland Resources Research, 2023, 0(6): 146-158.
[13]	JIAO Quanjun, ZHENG Yanfeng, HUANG Wenjiang, ZHANG Bing, ZHANG Heyi, SHI Yimeng, WU Fayun, FU Anmin. Detection of Discolored Trees Caused by Pine Wilt Disease Based on Vegetation Index Method Using Terrestrial Ecosystem Carbon Inventory Satellite Data [J]. FOREST RESOURCES WANAGEMENT, 2023, 0(4): 123-131.
[14]	ZOU Weimin, CHEN Chao, HUANG Lei, SONG Meixuan, LI Xuejian, DU Huaqiang. Geographic Weighted Regression Model Combined with Remote Sensing for Estimating Forest Aboveground Carbon Storage of Songyang County [J]. FOREST RESOURCES WANAGEMENT, 2023, 0(4): 132-140.
[15]	LIANG Licheng, FU Xiaoqiang, ZHANG Bin, CHENG Guxun, LI Zuohui. Dynamic Monitoring of Spartina alterniflora in Xiangshan Harbor Based on GEE and Random Forest [J]. FOREST RESOURCES WANAGEMENT, 2023, 0(3): 38-45.

Application of RS Vegetation Index in Forest Cutting-area Detection for“Three Total”Inspection in State-owned Forest Regions——A Case Study in Heli Forestry Bureau

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments