王金亮导师研究小组的论文在SCI期刊Journal of the Indian Society of Remote Sensing发表

近日，以实验室2017级硕士研究生玉院和为第一作者、王金亮教授为通讯作者撰写的“Forest Leaf Area Index Inversion Based on Landsat OLI Data in the Shangri-La City”论文在SCI期刊Journal of the Indian Society of Remote Sensing上发表，论文利用Landsat 8 OLI影像单波段和多波段提取的15种植被指数与CI-110植被冠层数字图像仪实测LAI数据相结合，建立了香格里拉LAI估算模型。该论文得到了王金亮教授主持的国家自然科学基金和云南省哲学社会科学规划办公室重点项目的资助。

标题：Forest Leaf Area Index Inversion Based on Landsat OLI Data in the Shangri-La City

作者：Yuanhe Yu，Jinliang Wang*，Guangjie Liu，Feng Cheng

通讯作者： Jinliang Wang，wang_jinliang@hotmail.com

作者单位：College of Tourism and Geographic Sciences, Yunnan Normal University, Kunming, Yunnan 650500, China；Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan, Kunming, Yunnan 650500, China；Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming, Yunnan 650500, China.

出版物：Journal of the Indian Society of Remote Sensing

摘要：LAI (Leaf Area Index) is an important index that reflects the growth status of forest vegetation and land surface processes. It is of important practical significance to quantitatively and accurately estimate leaf area index. We used the Landsat-8 OLI (Operational Land Imager) single-band images and 15 vegetation indices that were extracted from the multi-band were combined with the LAI data measured from the CI-110 canopy digital imager to establish the LAI estimation model. Through the LOOCV (Leave-one-out cross-validation) method, the accuracy of various model estimation results was verified and compared, and the optimal estimation model was obtained to generate the LAI distribution map of Shangri-La City. The results show that: (1) The multivariable model method is better than the single variable model method when estimating LAI, and its determination coefficient is the highest (R² = 0.7903). (2) The full-sample dataset is divided into Alpine Pine forest, Oak forest, Spruce-fir forest and Yunnan Pine forest for analysis. The coefficient of determination of the model simulation is improved to varying degrees, and the highest R² increased by 0.1652, 0.1040, 0.1264, and 0.0079, respectively, over the full-sample. The corresponding best models are LAI-DVI (Difference Vegetation Index), LAI-NNIR (Normalized Near-infrared), LAI-NMDI (Normalized Multi-band Drought Index), and LAI-RVI (Ratio Vegetation Index). (3) The LAI values in Shangri-La City ranged from 0.9654 to 5.5145 and are mainly concentrated in high vegetation coverage areas; and the higher the vegetation coverage level, the higher the LAI value.

关键字：Leaf area index； Vegetation index； Landsat OLI； Shangri-La City； Forest