基于多源遥感数据提高山地森林识别精度——以祁连山国家公园肃南县段为例

doi:10.11686/cyxb2020496

摘要/Abstract

摘要：

本研究旨在探索一种基于多源遥感数据的提高山地森林识别精度的方法。以祁连山国家公园肃南县段为实验区，结合ICESat/GLAS（geoscience laser altimeter system ）星载激光雷达数据、Landsat OLI影像、Google Earth高分辨率影像、DEM数据以及样地调查数据，综合利用各数据提供的垂直结构、光谱、季相和地形特征探索基于多源遥感数据的山地森林识别精度提升方法。结果表明：1）将经过地形校正后的GLAS数据提取的垂直结构信息与光谱信息结合能够提高山区复杂地形条件下森林范围识别的精度，相比仅依据光谱特征进行分类，依据光谱及垂直结构综合特征分类时其总体分类精度提高了10.67%。2）地形信息的加入能够尽量全面的考虑到不同地形特征上各森林类型的不同光谱特征，从而提升森林类型的识别精度，且就本研究区域而言，加入坡向信息比海拔信息更能够提高森林类型的分类精度。3）多源多时相遥感影像提供的季相特征能够对不同森林类型的分类提供帮助，而不同的波段组合对分类精度几乎没有影响。研究对探索低成本、高时效、操作方便并具有一定精度保证的山地森林识别方法具有一定的借鉴意义。

关键词: 山地森林, 光谱特征, 垂直结构特征, 地形特征, 分类精度

Abstract:

The aim of this study was to develop a method to improve the accuracy of identifying mountain forests from multi-source remote sensing data. The Sunan County section of Qilian Mountains National Park was used as the test area. Data from the ICESat/GLAS （geoscience laser altimeter system） space borne LiDAR， Landsat OLI images， Google Earth high-resolution images， digital elevation model data， and field inventory data were used in the classification process. The vertical structure and the spectral， seasonal， and topographic characteristics identified from these data sources were integrated to extract forest information hierarchically. The results show that， after using a physically based terrain correction model to eliminate the influence of terrain on GLAS waveforms， the vertical structure derived from GLAS data combined with spectral information accurately identified forests at medium spatial resolution from remote sensing images of mountainous areas with complex terrain conditions. Compared with the classification based only on spectral characteristics， the classification incorporating vertical structure characteristics showed significantly improved accuracy， with overall accuracy improved by 10.67%. These results show that the vertical structure information provided by GLAS data can enhance the separability between different land cover types， and make forest range identification more effective. Secondly， for different forest types with similar spectral and vertical structure characteristics， the addition of terrain information reduced the impact of different objects with the same spectrum and the same objects with different spectra caused by terrain shadows， thereby significantly improving the classification accuracy of forest types. The addition of aspect information had a more significant effect than the addition of elevation information on improving the classification accuracy in this region. In addition， seasonal characteristics provided by multi-source and multi-temporal remote sensing images improved the classification of different forest types， while different band combinations had little effect on classification accuracy. The results of this study provide a reference for exploring mountain forest identification methods with low cost， high time efficiency， convenient operation， and a certain guarantee of accuracy.

Key words: mountain forest, spectral characteristic, vertical structure characteristic, topographic characteristic, classification accuracy

宋洁, 刘学录. 基于多源遥感数据提高山地森林识别精度——以祁连山国家公园肃南县段为例[J]. 草业学报, 2021, 30(10): 1-14.

Jie SONG, Xue-lu LIU. Improving the accuracy of forest identification in mountainous areas from multi-source remote sensing data——the Sunan County section of Qilian Mountains National Park as an example[J]. Acta Prataculturae Sinica, 2021, 30(10): 1-14.

图/表 10

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产品号 Product ID	获取时间 Acquisition date （月-日 Month-day）	景中心坐标 Center coordinate	太阳高度角 Sun elevation （°）	太阳方位角 Sun azimuth （°）	云覆盖量 Land cloud cover （%）
132034	7-12	37°28′28″ N 101°56′45″ E	65.39	122.77	1.85
133033	8-21	38°54′16″ N 100°50′15″ E	48.21	151.38	0.78
134033	7-28	38°54′15″ N 99°17′01″ E	45.88	153.65	2.04
135033	7-17	38°54′16″ N 97°44′49″ E	64.15	126.85	2.63

产品号 Product ID	获取时间 Acquisition date （月-日 Month-day）	景中心坐标 Center coordinate	太阳高度角 Sun elevation （°）	太阳方位角 Sun azimuth （°）	云覆盖量 Land cloud cover （%）
132034	7-12	37°28′28″ N 101°56′45″ E	65.39	122.77	1.85
133033	8-21	38°54′16″ N 100°50′15″ E	48.21	151.38	0.78
134033	7-28	38°54′15″ N 99°17′01″ E	45.88	153.65	2.04
135033	7-17	38°54′16″ N 97°44′49″ E	64.15	126.85	2.63

坡度等级 Slope gradient classes	模型决定系数 Coefficient of determination （R²）		均方根误差 Root mean square error （RMSE）（m）
坡度等级 Slope gradient classes	校正前Before correction	校正后After correction	校正前Before correction	校正后After correction
0°~5°	0.62	0.66	2.83	1.84
5°~15°	0.64	0.65	3.32	2.23
15°~25°	0.66	0.67	6.74	3.51
25°~35°	0.63	0.65	8.00	3.91
35°~58°	0.65	0.67	12.59	3.48

坡度等级 Slope gradient classes	模型决定系数 Coefficient of determination （R²）		均方根误差 Root mean square error （RMSE）（m）
坡度等级 Slope gradient classes	校正前Before correction	校正后After correction	校正前Before correction	校正后After correction
0°~5°	0.62	0.66	2.83	1.84
5°~15°	0.64	0.65	3.32	2.23
15°~25°	0.66	0.67	6.74	3.51
25°~35°	0.63	0.65	8.00	3.91
35°~58°	0.65	0.67	12.59	3.48

OLI波段组合 OLI band combination	依据光谱特征分类 Classification based on spectrum		依据光谱和垂直结构综合特征分类 Classification based on spectrum and vertical structure
OLI波段组合 OLI band combination	总体精度 Overall accuracy （%）	Kappa系数 Kappa coefficient	总体精度 Overall accuracy （%）	Kappa系数 Kappa coefficient
近红外波段、红波段、绿波段NIR、Red、Green	82.41	0.79	93.08	0.91
近红外波段、短波红外1、蓝波段NIR、SWIR1、Blue	82.41	0.79	93.08	0.91
短波红外1、近红外波段、红波段SWIR1、NIR、Red	82.41	0.79	93.08	0.91
短波红外1、近红外波段、蓝波段SWIR1、NIR、Blue	82.41	0.79	93.08	0.91
红波段、绿波段、蓝波段Red、Green、Blue	82.41	0.79	93.08	0.91